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Differences in working memory as a function of physical activity in children.

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Exercise and Children's Intelligence, Cognition, and Academic Achievement

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https://www.ncbi.nlm.nih.gov/pubmed/?term=Exercise+makes+you+smarter

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I figured it out!
https://pahl-primo.hosted.exlibrisgroup.com/permalink/f/i4khkh/UWS215233970001864

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https://www.sciencedirect.com/science/article/pii/S0025619619305221?via%3Dihub

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Evaluating Weaknesses of "Perceptual-Cognitive Training" and "Brain Training" Methods in Sport: An Ecological Dynamics Critique.

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https://www.ncbi.nlm.nih.gov/pubmed/?term=Exercise+makes+you+smarter

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https://www.ncbi.nlm.nih.gov/pubmed/31918406

Mental health

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Active lifestyles moderate clinical outcomes in autosomal dominant frontotemporal degeneration.

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Citation

Title:

Authors:

Harvey SB; From King's College London, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, London; the School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Sydney, Australia; the Norwegian Institute of Public Health, Oslo, Norway; the University of Bergen, Norway; the South London and Maudsley NHS Foundation Trust, London; the Centre for Work and Mental Health, Nordland Hospital Trust, Bodø, Norway; and the Arctic University of Norway, Tromsø, Norway.
Øverland S; From King's College London, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, London; the School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Sydney, Australia; the Norwegian Institute of Public Health, Oslo, Norway; the University of Bergen, Norway; the South London and Maudsley NHS Foundation Trust, London; the Centre for Work and Mental Health, Nordland Hospital Trust, Bodø, Norway; and the Arctic University of Norway, Tromsø, Norway.
Hatch SL; From King's College London, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, London; the School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Sydney, Australia; the Norwegian Institute of Public Health, Oslo, Norway; the University of Bergen, Norway; the South London and Maudsley NHS Foundation Trust, London; the Centre for Work and Mental Health, Nordland Hospital Trust, Bodø, Norway; and the Arctic University of Norway, Tromsø, Norway.
Wessely S; From King's College London, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, London; the School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Sydney, Australia; the Norwegian Institute of Public Health, Oslo, Norway; the University of Bergen, Norway; the South London and Maudsley NHS Foundation Trust, London; the Centre for Work and Mental Health, Nordland Hospital Trust, Bodø, Norway; and the Arctic University of Norway, Tromsø, Norway.
Mykletun A; From King's College London, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, London; the School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Sydney, Australia; the Norwegian Institute of Public Health, Oslo, Norway; the University of Bergen, Norway; the South London and Maudsley NHS Foundation Trust, London; the Centre for Work and Mental Health, Nordland Hospital Trust, Bodø, Norway; and the Arctic University of Norway, Tromsø, Norway.
Hotopf M; From King's College London, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, London; the School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Sydney, Australia; the Norwegian Institute of Public Health, Oslo, Norway; the University of Bergen, Norway; the South London and Maudsley NHS Foundation Trust, London; the Centre for Work and Mental Health, Nordland Hospital Trust, Bodø, Norway; and the Arctic University of Norway, Tromsø, Norway.

Source:

The American Journal Of Psychiatry [Am J Psychiatry] 2018 Jan 01; Vol. 175 (1), pp. 28-36. Date of Electronic Publication: 2017 Oct 03.

Publication Type:

Journal Article; Research Support, Non-U.S. Gov't

Language:

English

Journal Info:

Publisher: American Psychiatric Association Country of Publication: United States NLM ID: 0370512 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1535-7228 (Electronic) Linking ISSN: 0002953X NLM ISO Abbreviation: Am J Psychiatry Subsets: Core Clinical (AIM); MEDLINE

Imprint Name(s):

Original Publication: Arlington, VA : American Psychiatric Association

MeSH Terms:

Anxiety*/diagnosis
Anxiety*/physiopathology
Anxiety*/prevention & control
Anxiety*/psychology
Depression*/diagnosis
Depression*/physiopathology
Depression*/prevention & control
Depression*/psychology
Exercise/*psychology
Adult ; Cohort Studies ; Female ; Health Behavior/physiology ; Health Status ; Humans ; Leisure Activities/psychology ; Male ; Middle Aged ; Norway/epidemiology ; Outcome Assessment (Health Care) ; Protective Factors ; Psychological Tests ; Socioeconomic Factors

Abstract:

Objective: The purpose of the present study was to address 1) whether exercise provides protection against new-onset depression and anxiety and 2) if so, the intensity and amount of exercise required to gain protection and, lastly, 3) the mechanisms that underlie any association.
Method: A "healthy" cohort of 33,908 adults, selected on the basis of having no symptoms of common mental disorder or limiting physical health conditions, was prospectively followed for 11 years. Validated measures of exercise, depression, anxiety, and a range of potential confounding and mediating factors were collected.
Results: Undertaking regular leisure-time exercise was associated with reduced incidence of future depression but not anxiety. The majority of this protective effect occurred at low levels of exercise and was observed regardless of intensity. After adjustment for confounders, the population attributable fraction suggests that, assuming the relationship is causal, 12% of future cases of depression could have been prevented if all participants had engaged in at least 1 hour of physical activity each week. The social and physical health benefits of exercise explained a small proportion of the protective effect. Previously proposed biological mechanisms, such as alterations in parasympathetic vagal tone, did not appear to have a role in explaining the protection against depression.
Conclusions: Regular leisure-time exercise of any intensity provides protection against future depression but not anxiety. Relatively modest changes in population levels of exercise may have important public mental health benefits and prevent a substantial number of new cases of depression.

Comments:

Comment in: Am J Psychiatry. 2018 Jan 1;175(1):2-3. (PMID: 29301422)

Contributed Indexing:

Keywords: Anxiety*; Depression*; Exercise*; Mental disorders*; Prevention*

Entry Date(s):

Date Created: 20171004 Date Completed: 20180207 Latest Revision: 20180207

Update Code:

20181211

DOI:

10.1176/appi.ajp.2017.16111223

PMID:

28969440

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Exercise and the Prevention of Depression: Results of the HUNT Cohort Study

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Title: Authors: Harvey SB; From King's College London, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, London; the School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Sydney, Australia; the Norwegian Institute of Public Health, Oslo, Norway; the University of Bergen, Norway; the South

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Isaias Z

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Summary

Better management by individuals of their long‐term conditions is promoted to improve health and reduce healthcare expenditure. However, there is limited evidence on the determinants and consequences of self‐management activity. We investigate the determinants of two forms of self‐management, exercise and relaxation, and their impact on the health and wellbeing of 3472 individuals with long‐term health conditions over a 1‐year period. We use simultaneous recursive trivariate models to estimate the effects of these two inputs on three health and wellbeing outcomes: the EuroQol five‐dimensional (EQ‐5D) score, self‐assessed health and happiness. We reflect the opportunity cost of time and knowledge with employment status and education and find that employment reduces relaxation and education increases exercise. We find that neither exercise nor relaxation affects the EuroQol five‐dimensional score, but exercise increases self‐assessed health and relaxation increases happiness. Our findings show that individuals tailor their self‐management activities to their economic constraints, with effects on different aspects of their utility. Interventions to encourage self‐management should take account of heterogeneous effects and constraints. © 2017 The Authors. Health Economics Published by John Wiley & Sons, Ltd.

Keywords: self‐management, long‐term conditions, panel data, simultaneous recursive trivariate regression

1. Introduction

Healthcare systems in Organisation for Economic Co‐operation and Development (OECD) countries are endeavouring to rise to the challenges emerging from ageing populations, growing levels of chronic illness, advancing technical possibilities and rising public expectations. These challenges have resulted in healthcare expenditure growing more rapidly than GDP in all OECD countries over the past 30 years (Pammolli et al., 2011). The downturn in the world economy has added to this and raised concerns regarding the sustainability of expenditure on healthcare systems (Tooke, 2011). In the UK, spending on the National Health Service (NHS) as a proportion of GDP is expected to fall from 8% to just over 6% by 2021 (Appleby et al., 2014). This could result in a funding gap of approximately £30 billion by 2020/21 ( Illmann).

One way to reduce pressure on formal health services is to reduce demand, especially amongst people with long‐term conditions. According to Roberts et al. ( 2012), pressure on NHS costs from people with long‐term conditions is at least equal to, if not more than, that from the expanding and ageing population. It is estimated that there are more than 15 million people in the UK with at least one long‐term condition, and the number of people with multiple long‐term conditions has been predicted to rise by over a third over the next 10 years (Department of Health, 2012). There is a similar trend across OECD countries; in 1990, average spending on long‐term conditions was 0.7% of GDP, and this had risen to 1.5% of GDP by 2012 (Gershlick, 2015).

A sustainable way to manage demand on services used by people with long‐term conditions is to encourage people to engage in their own health and healthcare. This was described by Wanless ( 2002) as the 'fully engaged scenario'. This vision has resulted in heightened policy emphasis on encouraging patients with long‐term conditions towards 'self‐management' through schemes such as the Expert Patients Programme (Department of Health, 2001). Self‐management has been defined by the Department of Health as '[t]he care taken by individuals towards their own health and wellbeing: it comprises of the actions they take to lead a healthy lifestyle; to meet their social, emotional and psychological needs; to take care of their long‐term condition; and to prevent further illness or accidents' (Department of Health, 2005). There has been increasing advocacy of self‐management in the UK and internationally over the last two decades (Rogers et al., 2009).

Recently, Naylor et al. ( 2015) have argued that active self‐management support is one of the key priorities for healthcare commissioners to focus on helping to sustain the NHS. Self‐management support aims to increase 'the capacity, confidence and efficacy of the individual' to manage their condition (Kennedy et al., 2013). There are two core models of self‐management support: a provider‐based model with support embedded in clinical practice and a patient‐based model, which enables patients through individual or group‐based education, telehealth or telecare (Kennedy et al., 2013). Despite the growing policy emphasis on the importance of self‐management support, there is modest evidence of the effects on health and healthcare utilization (Griffiths et al., 2007; Kennedy et al., 2013; Richardson et al., 2008). More notably, evaluations of interventions to increase self‐management activity focus on self‐efficacy (the confidence of the individual to manage their condition through self‐management) and changes in health and healthcare utilization in the short term. Little attention has been paid to the self‐management activities per se, which are treated as intervening variables. In this paper, we aim to bridge the gap between self‐management literature and economic literature to enhance understanding of self‐management behaviour.

Previous studies have shown that the allocation of time to a health input such as self‐management depends on its associated opportunity cost. Brown and Roberts ( 2011) analysed the frequency of participation in physical activity by using six waves of data from Household Income and Labour Dynamics of Australia. They measured opportunity cost through employment status and found that full‐time employment had a significant negative impact on exercise for women. Mullahy and Robert ( 2010) hypothesized that the opportunity cost of time differs by level of human capital endowment (measured through education). Using data from the American Time Use Survey, they found that human capital endowments affect the way participants use their time to produce different health outcomes. For example, more educated individuals were more likely to participate in physical exercise. These studies indicate that differences in the cost of time can change the way that people choose to invest in self‐management.

Brown and Roberts ( 2011) and Mullahy and Robert ( 2010) both used general population data. One study that focuses on people with long‐term conditions is by Ettner et al. ( 2009). Unlike Mullahy and Robert ( 2010), Ettner et al. found that education reduced the amount of time spent on self‐management activities in a sample of 11 927 patients with at least one long‐term condition. This implies that time constraints may affect the choice of health inputs differently amongst people with long‐term conditions.

Changes in health inputs as a result of time constraints can also affect health outcomes (Balia and Jones, 2008; Contoyannis and Jones, 2004). Despite the clear link between inputs to health and health outcomes, we have found only one paper that investigates the link between opportunity cost and health through the choice of health inputs. Kuvaja‐Köllner et al. ( 2013) used 2 years of data from a randomized controlled trial in Finland to link the effect of the opportunity cost of time spent exercising with the effect of exercise on health. They assume physical exercise to be endogenous and so conduct a two‐stage least squares analysis, using motivation to exercise as the instrumental variable for exercise. Kuvuja‐Köllner et al. find that a lower opportunity cost of time (measured through labour market status) has a significant effect on the amount of time spent exercising. This increase in time spent on physical exercise was found to have a positive effect on the short form 6D.

The Kuvaja‐Köllner et al. ( 2013) paper shows that the opportunity cost of time affects health inputs and health outcomes. However, their instrumental variable is a short‐term decision that could be correlated with the outcome. It could be that enjoyment of physical exercise can change and therefore is not a suitable instrument for exercise. A further limitation of Kuvaja‐Köllner et al.'s ( 2013) analysis is that lagged measures of health are not included in the model. Previous endowments of health are likely to affect people's allocation to time spent on exercise as well as people's future health. Therefore, the results could be affected by omitted variable bias. In addition, the input (exercise) and output (health) equations were not modelled simultaneously, even though it is likely that the errors between these equations could be correlated.

Kuvaja‐Köllner et al. ( 2013) paved the way for linking a health input to a health output within the same dataset, but they did not explain how the opportunity cost of time could affect the specific self‐management activities that policy is aiming to encourage. In this paper, we describe how the opportunity cost of time (measured through employment and education) can impact on the mix of self‐management activities and estimate the effects of self‐management activities on future health and wellbeing. Our study is distinct because we study a population of individuals with long‐term conditions, precisely those for whom self‐management potential is greatest. As we use data from a clinical trial, we have rich data on health and wellbeing. We improve on Kuvaja‐Köllner et al.'s ( 2013) study by modelling inputs and outputs simultaneously and by using more plausible identifying restrictions. We estimate exercise, relaxation and health as part of a simultaneous model and allow for the possibility that there is correlation between these equations.

2. Methods

2.1. Theoretical motivation

We motivate our empirical estimation of the relationship between the opportunity cost of time and self‐management (measured through exercise and relaxation) with a modified time allocation framework. The time allocation framework is based upon Becker's ( 1965) theory of allocation of time and the Cawley ( 2004) 'SLOTH' (sleep, leisure, occupation, transportation and home‐based activities) framework. We can think of patients who demand health for two reasons: (i) as a consumption commodity, because it directly enters their preference functions, and (ii) as an investment commodity, because it determines the amount of time available for other activities. Individuals choose how to allocate their time amongst work, exercise, relaxation and other activities to maximize their utility subject to a budget and time constraint. The budget constraint includes a time price of 'self‐management' which is dependent on wages and educational qualifications. The time price indicates the opportunity cost of participating in exercise and relaxation. The budget constraint includes the money price of exercise (e.g. gym membership), income and the time price of working. Health output is determined by two inputs, exercise and relaxation. If the time price of exercise increases due to the individual entering employment, then the opportunity cost of time spent exercising increases. But as the individual becomes richer, he or she is able to afford the money price of exercise. The overall effect of a change in the time price of exercise on its demand is ambiguous and depends on the relative size of the income and substitution effects.

2.2. Data

We use data collected in the trial of Whole System Informing Self‐Management Engagement, a provider‐based model of self‐management support (Bower et al., 2012; Kennedy et al., 2014, 2013). The trial was based in the north‐west of England between the years of 2009 and 2012. The population in the local area was predominantly white and socioeconomically deprived. The objective of the trial was to determine the effectiveness of a provider‐based model of self‐management support. No statistically significant differences were found between the patients receiving the provider‐based intervention and those in the control group. However, the trial highlighted the need to elicit and understand the active components required for effective self‐management support (Kennedy et al., 2014).

Patients were eligible for the trial if they had any of three long‐term conditions: diabetes, chronic obstructive pulmonary disease and irritable bowel syndrome. These were selected as exemplar conditions that could be influenced by self‐management interventions and where there was also published evidence of effectiveness (Deakin et al., 2005; Richardson et al., 2006). Patients were identified from electronic health records and checked for any exclusion criteria (under 18 years of age, insufficient English language, receiving palliative care or insufficient capacity to give written consent) before being sent a postal invitation to take part in the trial. Forty‐four general practices were recruited.

2.3. Empirical strategy

Our empirical strategy aims to investigate the determinants of self‐management activities and the effect of these health inputs on health outputs. Self‐management activities such as exercise and relaxation have the potential to be endogenous if there is an unobserved variable that is correlated with both these inputs and health outputs or there could be unobserved individual characteristics that drive both the extent of self‐management activity and health. This potential for endogeneity raises the concern that if we estimate these equations separately, then there would be unobserved correlation between the error terms. Therefore, we adopt a simultaneous recursive trivariate model that controls for the potential endogeneity of exercise and relaxation and for unobserved heterogeneity between the equations (Maddala, 1983). Our model simultaneously estimates three equations, one output equation (health) and two input equations (exercise and relaxation):

Health at baseline and 6 months is denoted by h ₀ and h t − 1, and x denotes sociodemographic variables. Equations 2 and 3 show that we exclude employment ( w ₀) and education ( ed ₀) from the health equation. Thus, we assume that, conditional on baseline and lagged health and the other covariates, employment and education affect health indirectly through their influence on exercise and relaxation.

The variance-covariance matrix Ω can be written as follows:

where ρ jk with j, k = 1, 2,3 ∀ j ≠  k is the correlation between the error terms. Unobserved heterogeneity can be allowed with the potentially non‐zero values of the off‐diagonal elements of the matrix. We use Roodman's ( 2011) conditional recursive mixed process model to estimate the system of equations (1-3) in stata and report them in Table 4.

There has been much discussion of the best estimation technique for EQ‐5D given its bimodal distribution. Hernández Alava et al. (2012) argue that estimation of EQ‐5D should take into account its bimodal distribution by using an adjusted limited dependent variable mixture model. Other researchers have also used two‐part Tobit models (Acaster et al., 2015). However, numerous studies find that when EQ‐5D is treated as continuous, the model performs just as efficiently as any other estimation strategy, if not better (Kim et al., 2014).

To check for robustness of our results, we also run our analysis treating SAH and happiness as ordered probits to reflect the ordinal nature of these dependent variables. We present average marginal effects for each possible response category (none of the time, a little of the time, etc.).

Exercise and relaxation are ordinal variables on a three‐point scale. We include these variables in two different ways. When we model the health/wellbeing variables using linear models, we also treat the self‐management inputs as continuous. When we model SAH and happiness as ordinal variables, we include the self‐management input as binary variables, separating those who reported 'very true' from those who reported 'partly true' or 'not true'.

We also test the independence of the equations and the validity of the over‐identifying restrictions. We test the independence of the equations with a likelihood ratio test that compares an unrestricted equation with all correlations between equations and a restricted one with correlations set to zero. We conduct an F ‐test for over‐identification by setting the coefficients of the instruments equal to 0, excluding lagged exercise for exercise and lagged relaxation for relaxation.

3. Results

3.1. Descriptive statistics

At baseline, the average EQ‐5D score reported by the respondents was 0.655, ranging from −0.426 to 1 (Table ). Also at baseline, 40% of the respondents reported their SAH as 'poor' or 'fair', and 43% stated that they were happy 'none of the time', 'a little bit of the time' or 'some of the time'.

The average age of the participants was 63 years (ranging from 18 to 102 years). The sample was predominantly from a White ethnic group (97%), slightly higher than the 95% across England as a whole for the population aged 65 years (see Office for National Statistics, 2009) and reflecting the population composition of Salford. A large proportion of the sample (71%) owned their own accommodation. Diabetes is the most common long‐term condition of the participants, with 50% suffering the condition. Arthritis and heart conditions were the next most frequent at 46% and 45% respectively.

At 12 months, 30.2% of the respondents said that it was 'not true' that they used exercise to manage their condition, 46.6% said that it was 'partly true', and 23.2% said that it was 'very true' (Table ). Of the respondents, 9.5% said that it was 'not true' that they rested and relaxed, 47.6% said that it was 'partly true', and 42.3% said that it was 'very true'. The descriptive statistics for the 10 other self‐management activities are also provided in Table . We investigated the correlations among the 12 self‐management activities and found that exercise was most strongly correlated with diet ( ρ = 0.24) and relaxation was most strongly correlated with stress management ( ρ = 0.39) and medication ( ρ = 0.20). Exercise and relaxation were not highly correlated with each other ( ρ  = 0.07).

3.2. Simultaneous trivariate recursive regression results

Being in full‐time employment decreases the probability that the respondent uses relaxation to manage their condition by 11 percentage points (Table ). Table also shows that having a degree or other professional qualification increases the probability that the individual will use exercise to manage their condition by 5 percentage points respectively.

Exercise and relaxation are found to have no statistically significant effect on EQ‐5D. A one‐unit increase in exercise, which represents a change from 'not true' to 'partly true' or 'partly true' to 'very true' that this form of self‐management was used by the respondent, increases SAH by 0.08 units on a five‐point scale. The same change in the use of rest and relaxation increases happiness by 0.22 units on a six‐point scale.

The value for ρ ₂₃ is positive and significant (Table ). This implies that those who have a higher propensity to exercise also have a higher propensity to relax (and vice versa).

3.3. Robustness checks

The results of the simultaneous recursive trivariate models where SAH and happiness are treated as ordinal variables are presented in Table . Exercise increases the probability that SAH is good, very good or excellent and reduces the probability that it is either poor or fair. Relaxation instead decreases the probability that SAH is good, very good or excellent and increases the probability that it is either poor or fair. Relaxation significantly increases the probability of being happy a good bit of the time, most or all of the time.

3.4. Test statistics

The test statistics in Table show that the equations are not independent. The p‐values from a likelihood ratio test imply that we should reject the null hypothesis and conduct a joint analysis. The results of an F ‐test for over‐identification indicate that we can reject the null hypothesis; the model is not over‐identified.

4. Discussion

In this paper, we have examined the determinants and consequences of two self‐management activities, relaxation and exercise. We have motivated our empirical specification with a time allocation framework where a change in the opportunity cost of time has an ambiguous effect on the choice of these health inputs because of the relative size of the income and substitution effects. As a result, the effect of exercise and relaxation on health is ambiguous as well. We apply this framework to a unique longitudinal trial of 3472 individuals observed at three points in time. This group is representative of populations with long‐term conditions, exactly those at whom self‐management support schemes are targeted.

We use education and employment status as measures of the opportunity cost of time of health investments. If the substitution effect is dominant, then increases in the opportunity cost of time (being in full‐time employment or having a degree) should reduce the amount of time participating in these activities. An increase in the opportunity cost of time also means an increase in income (if you are working or have higher education, then you are likely to have a higher income). Individuals are more likely to trade off labour for leisure as real income increases as long as leisure is a normal good.

Our results indicate that those who work full time are less likely to spend time resting and relaxing as a form of self‐management compared with those who do not work full time. This implies that the substitution effect is dominating. People are substituting self‐management for working as the opportunity cost of self‐management is increasing. This finding coincides with Humphrey and Ruseski's ( 2011) paper.

Education increases the probability of exercising. This implies that the income effect is dominating (when the shadow price of time is higher, the respondents have higher income and therefore do not need to spend as much time working). This finding is consistent with research on exercise within the general population (Eberth and Smith, 2010).

Time preference has been suggested as a channel through which health and education are correlated (Grossman, 2006). Van der Pol ( 2011) found that the relationship between education and health can be partly explained through time preference. Our finding that education increases exercise and decreases relaxation could also be partly explained by time preference. People with higher education could have a lower time preference and therefore are more willing to engage in an activity that benefits health in the long term, whereas people with lower education might prefer to engage in a self‐management that has a more instantaneous payoff.

Employment status and education change the way that people allocate their time to health investments. This finding supports similar findings in the literature (Brown and Roberts, 2011; Kuvaja‐Köllner et al., 2013; Mullahy and Robert, 2010). We find that the way in which people decide to allocate their time to self‐management behaviours affects their health. For example, as found by Kuvaja‐Köllner et al. ( 2013), exercise has a positive effect on SAH. Our findings could explain why researchers have found self‐management support schemes to affect efficacy but to only have modest effects on health and patient outcomes. One way to encourage people to engage in their health could be to attempt to make health‐promoting behaviours less costly to the individual.

One of our key findings is that the effect of exercise and relaxation on health and wellbeing depended on which measures of health and wellbeing we used. We found that both self‐management activities had no significant effect on EQ‐5D score, whereas exercise had a positive effect on SAH and relaxation had a positive effect on happiness. This is likely to be because different measures capture different aspects of health and wellbeing. For example, Sun et al. ( 2016) found that the anxiety/depression domain of the EQ‐5D was most strongly associated with subjective wellbeing. Böckerman et al. ( 2011) found that psychiatric disorders had a large effect on wellbeing even after controlling for the EQ‐5D (Böckerman et al., 2011). This could explain the stronger relationship shown between relaxation and happiness, due to its conceivably greater effect on mental health.

This may also be the explanation for the positive effect of exercise on SAH, which may be more focused on physical health. Exercise may positively affect physical health more than any other aspect, and hence show up most significantly in SAH. The lack of significant effect of either exercise or relaxation on EQ‐5D scores over time, however, may be explained by its insensitivity to changes over a short period of time. Clearly, the potential differences among EQ‐5D, SAH and happiness could have important ramifications for policy and allocation of resources depending on what measure we take to best capture utility.

Our test statistics indicate that there is enough correlation between the errors to justify modelling the equations simultaneously. Through modelling the equations simultaneously, we are also controlling for two sources of unobserved heterogeneity. First, we control for the possibility that exercise and relaxation are endogenous. We also control for the possibility that there is unobservable heterogeneity between the equations in our model. Through conducting a joint analysis, we found that the propensity to participate in relaxation is positively correlated with the propensity to participate in exercise. This could be picking up a measure of unobserved motivation to engage in health‐promoting behaviour.

To our knowledge, this is the first study in the UK that links the effect of employment and education on health inputs and health. We are also the first study to our knowledge to model exercise, relaxation and health simultaneously. However, there are several limitations to this study. First, there is a relatively short follow‐up. We look at the effect that self‐management has on health after just 6 months. One explanation for our modest findings for the effect of self‐management on EQ‐5D could be that 6 months is too short to pick up most of the effects of self‐management behaviour. Another potential limitation of the study is that we assume that exercise and relaxation are separate inputs with additive effects. We also note that our results are confined to a group of older people with an ethnic composition that is not representative of the younger population in England.

A further limitation relates to the available measures of exercise and relaxation. A change from 'partly true' to 'very true' is not an easily transferable measure of health input. However, further analysis (Table ) shows that our results do not differ when using binary variables for whether the respondents reported 'very true' versus 'partly true' or 'not true'.

Overall, our study demonstrates that constraints on time can change the way individuals engage in self‐management, with effects on health and wellbeing. We find no effect of exercise and relaxation on EQ‐5D, but we find significant positive effects of exercise on SAH and relaxation on happiness. These results are relevant for the National Institute for Health and Clinical Excellence guidelines on best practice management of long‐term conditions (NICE Guidelines, 2009; NICE Guidelines, 2015). These guidelines promote a combination of drug treatment and behaviour change interventions targeting exercise but do not mention the importance of relaxation. Our findings indicate that initiatives designed to encourage self‐management should take account of heterogeneous constraints on an individual's potential to use different inputs and the potential for different effects on alternative aspects of their health and wellbeing. These results support the provision of individualized care and shared decisions between doctors and patients on the best behaviour change interventions depending on the individual's time constraints (NICE Guidelines, 2015).

Acknowledgements

This is an independent research supported by the National Institute for Health Research (Research Methods Fellowship MET 12-22) and the Medical Research Council (Early Career Fellowship in Economics of Health MR/K021583/1). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

Notes

Forbes H., Fichera E., Rogers A., and Sutton M. (2017) The Effects of Exercise and Relaxation on Health and Wellbeing. Health Econ., 26: e67-e80. doi: [ 10.1002/hec.3477. [ PubMed] Google Scholar]

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# data[].attributes.attachment_link.title
The Effects of Exercise and Relaxation on Health and Wellbeing

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Better management by individuals of their long‐term conditions is promoted to improve health and reduce healthcare expenditure. However, there is limited evidence on the determinants and consequences of self‐management activity. We investigate the determinants of two forms of self‐management, exercise and relaxation, and their impact on the health and wellbeing of 3472 individuals with long‐term health conditions over a 1‐year period.

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https://pubmed.ncbi.nlm.nih.gov/26223831-benefits-of-exercise-training-in-multiple-sclerosis/?from_term=Exercise+benefits&from_pos=3

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http://www.joyfulboogie.com/uploads/2/1/6/1/21611390/dancing_makes_you_smarter.pdf

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Massive hematothorax after thoracic spinal manipulation for acute thoracolumbar pain.

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https://www.health.harvard.edu/blog/regular-exercise-changes-brain-improve-memory-thinking-skills-201404097110

# data[].attributes.headline
Preoperative exercise and recovery after heart surgery 

# data[].attributes.subject
Preoperative exercise and recovery after heart surgery 

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http://eds.a.ebscohost.com/eds/detail/detail?vid=1&sid=d4d40cba-4f5c-425e-99a3-62c3dba67f03%40sessionmgr4007&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=2018-41152-001&db=pdh

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https://www.ncbi.nlm.nih.gov/pubmed/31915624

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http://web.a.ebscohost.com/ehost/detail/detail?vid=3&sid=430ba5b7-36e5-4d3b-9fff-2efcf0a64d26%40sessionmgr4006&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#AN=141077312&db=aph

# data[].attributes.attachment_link.readable_text
Objectives. We assessed children's potential differences in academic achievement based on aerobic fitness over a 2-year period.

Methods. The longitudinal study sample included 3 cohorts of students (n = 1725; 50.1% male) enrolled in a West Virginia public school system. Students received baseline fitness and academic assessments as fifth graders and at a 2-year follow-up assessment. We used FitnessGram to assess fitness in aerobic capacity and WESTEST, a criterion-based assessment, for academic performance.

Results. Students who stayed in the healthy fitness zone (HFZ) had significantly higher WESTEST scores than did students who stayed in the needs improvement zone (NIZ). Students who moved into or out of the HFZ occasionally had significantly higher WESTEST scores than did students who stayed in the NIZ, but they were rarely significantly lower than those of students who stayed in the HFZ.

Conclusions. Students' aerobic capacity is associated with greater academic achievement as defined by standardized test scores. This advantage appears to be maintained over time, especially if the student stays in the HFZ. (Am J Public Health. 2012;102:2303-2307.)

RESEARCH AND PRACTICE

Multiple studies have examined the variety of benefits that physical activity and physical fitness offer youths. The health benefits of physical activity for children of any age (e.g., physical, physiological, emotional) are well documented.1-11 A growing body of research has built an argument for positive associations between physical activity, physical fitness, and academic achievement.

In 2003, a meta-analysis by Sibley and Etnier12 established that more physically active students perform better on a variety of tasks than students who are not as physically active. The investigators particularly noted that this level of performance was also found among physically active students who had experienced fewer hours of academic instruction time. A 2005 literature review by Taras13 documented evidence of short-term cognitive benefits of physical activity.

More recently, studies in California,14 Massachusetts,15 and West Virginia16 have demonstrated a significant positive association between the number of FitnessGram tests passed and academic achievement test performance. These assessments measure 3 health-related fitness areas: ( 1) aerobic capacity, ( 2) body composition, and ( 3) muscular strength, endurance, and flexibility; they are scored by criterion-referenced standards.17 Other studies have found that academic achievement is most associated with aerobic fitness, generally the FitnessGram mile run or the Progressive Aerobic Cardiovascular Endurance Run (PACER), a back-and-forth run across a 20-meter space at a pace defined on a beep-only or music audiotape.18-20 Additional research has provided further evidence that time away from the desk for physical education does not appear to affect academic achievement.21,22 However, further work is needed to understand the extent to which physical activity affects student performance as measured by academic achievement.

Longitudinal fitness effects on student academics are limited. In one study, Aberg et al. used compulsory screening results from all Swedish men born from 1950 to 1976. They found that aerobic fitness levels from ages 15 to 18 predicted cognitive performance at age 18.23 Examining similar outcomes among young children and in reference to their physical and academic performance could have significant clinical and policy implications. Accordingly, we explored the effects of aerobic fitness over a 2-year period on student academic achievement test scores. Sufficient evidence exists to predict differences in academic achievement between children in the healthy fitness zone (HFZ) and children in the needs improvement zone (NIZ) for aerobic fitness. However, greater evidence is needed to decipher the impact of children's fitness on their academic achievement over time. We therefore used a longitudinal examination to assess potential differences in academic achievement among children on the basis of their aerobic fitness FitnessGram category in the 2-year period.

The study sample consisted of 3 cohorts of students (n= 1725) enrolled in the 19 elementary school (fifth-grade) and 6 middle school (seventh-grade) sites of a West Virginia public school system set within a relatively rural, primarily White area of West Virginia. During the 2009-2010 school year, 51% of the students met the financial requirements to receive their meals at a reduced rate or for free. Students were in fifth grade during the 2005-2006, 2006-2007, and 2007-2008 school years and in seventh grade during the 2007-2008, 2008-2009, and 2009-2010 school years. Students received baseline fitness and academic assessments as fifth graders. Follow-up assessments were conducted 2 years later. About half (50.1%) of the sample were male. Students participated through passive school-based consent. Fewer than 2% of students opted out of the study by parent or guardian request each year.

Instruments

FitnessGram, a health-related fitness assessment, is used in West Virginia schools to assess health-related fitness levels in grades 4 through 8 and in 1 high school year. Assessment areas include aerobic capacity, upper body strength and endurance, body composition, abdominal strength and endurance, flexibility, and trunk extensor strength and flexibility. Aerobic capacity is measured by either the mile run or the PACER. The score for the mile run is the time it takes the child to run or walk 1 mile. With the PACER assessment, the student is expected to run back and forth across a 20-meter space at a pace defined on a beep-only or music audiotape, which gets faster each minute. A student who gets to the other side before the pacing beep must wait until the beep to run back. A student is stopped if they do not reach the line the second time before the beep.

Choice of the PACER versus the mile run was each individual physical education teacher's decision, based on available resources and teacher preference. It was possible that students could have different tests in the different grades (for example, the PACER in the fifth grade and the mile run in the seventh). The validity or reliability of each assessment and the rationale behind the determination of the standards is explained in the FitnessGram/ActivityGram Reference Guide.24 Trainers from Physical Best (a health-related fitness education program) trained physical education teachers in FitnessGram administration in October 2005 and conducted follow-up discussion and review sessions in February and May 2006. To standardize assessment procedures, we provided Physical Best and FitnessGram books and PACER compact discs.

We measured student aerobic capacity on the basis of each student's performance on either the PACER or mile run of the FitnessGram. Students performed in the HFZ if they met or exceeded the fitness target or in the NIZ if they failed to meet the fitness target. These designations were originally based on the number of circuits or miles completed by the student but were available for analyses only in categorical form.

WESTEST (West Virginia Educational Standards Test) is the West Virginia criterion-based reference for academic performance in grades 3 through 8 and 10. It is used to identify areas in need of additional instruction in 4 test areas-mathematics, science, social studies, and reading and language arts. Student results in each area are classified as ( 1) novice, ( 2) partial mastery, ( 3) mastery, ( 4) above mastery, and ( 5) distinguished. An acceptable range of mastery in a given subject is a score of 3 or higher (raw scores are not available). The criterion for the subscale scores across the 2-year assessment changed slightly. To correct for criterion differences, we standardized the mean scores for each of the 4 test areas.

Procedure

Fifth-grade students take physical education over the course of the school year, with a minimum of three 30-minute classes per week required; fifth-grade aerobic capacity assessment is completed in the spring of the academic year. Seventh-grade students are required to take 1 semester (2 terms) of physical education; seventh-grade FitnessGram assessments are completed in January, March, or May (second, third, or fourth term) depending on student physical education schedules.

WESTEST was conducted in compliance with state guidelines in the spring of each academic year. FitnessGram results, entered into a Web-based application, were later merged with WESTEST scores and other student information maintained in the West Virginia Education Information System (a computer-based data collection system) into a de-identified database for this study. Prior to statistical analysis, all descriptors, such as school identification number, student identification number, and student birth date, were removed.

Data Analysis

Using analysis of variance, we compared the standardized WESTEST scores in fifth and seventh grades of ( 1) children who stayed in the HFZ over the 2 years (880 students), ( 2) children who were in the HFZ in the fifth grade but fell into the NIZ in the seventh (280 students), ( 3) children who were in the NIZ in the fifth grade and moved into the HFZ in the seventh (221 students), and ( 4) children who stayed in the NIZ in both grades (344 students). We conducted all analyses using SPSS 18.0 (SPSS Inc, Chicago, IL).

We included all students who had complete information from both assessment periods (fifth and seventh grade) in these analyses. Of the total available (n = 2844) in the 3 cohorts, 681 did not have matching records between the fifth and seventh grades for a variety of reasons (moving into or out of a school or district, home schooling, prolonged absence), leaving 2163 available. Of the remainder, some students were missing aerobic fitness data for the fifth grade (n = 272) or seventh grade (n = 90), and others were missing 1 or more WESTEST scores for the fifth grade (n = 46) or seventh grade (n = 87). Fifth- and seventh-grade data were often missing for the same student In all, 438 students were eliminated from the cohorts because of missing data, leaving 1725 records in the database.

Students who stayed in the HFZ in the fifth and seventh grades had significantly higher WESTEST scores than students who stayed in the NIZ in both grades. The students who moved into the HFZ or fell out of it between the fifth and seventh grades had similar WESTEST scores in both fifth and seventh grades. Compared with students who stayed in the NIZ, students who moved into or out of the HFZ had WESTEST scores closer to those of students who stayed in the HFZ. There was only one occasion, for fifth-grade reading, when scores were significantly lower for children in the NIZ who later moved into the HFZ than for students who stayed in the HFZ. On the other hand, there were multiple times when students who moved into or out of the HFZ scored significantly higher on WESTEST than students who stayed in the NIZ (Table 1). Figure 1 illustrates the often sizable difference between the scores of the children who stayed in the NIZ and the scores of children in the other 3 categories. For each of the 4 categories (HFZ to HFZ, HFZ to NIZ, NIZ to HFZ, and NIZ to NIZ), there were no significant differences in WESTEST scores for fifth versus seventh grades.

TABLE: TABLE 1-Association Between Average Standardized Student Academic Achievement Scores and Student Fitness in Fifth and Seventh Grades: Wood County, West Virginia, 2005-2010

Fitness Zone Cohort Math Score, Mean Reading Score, Mean Science Score, Mean Social Studies Score, Mean HFZ-HFZ (n = 880) 5th grade 0.1333 0.148ab 0.088a 0.084a 7th grade 0.127"1 0.133"1 0.101"1 0.087m HFZ-NIZ (n = 280) 5th grade -0.033 -0.014c -0.005 0.055" 7th grade -0.029 -0.056 -0.018 -0.030 NIZ-HFZ (n = 221) 5th grade -0.053 -0.078b -0.052 -0.058 7th grade -0.030n -0.006n -0.004 -0.048 NIZ-NIZ (n = 344) 5th grade -0.276a -0.311a,c -0.181a -0.209a,b 7th grade -0.284m,n -0.298m,n -0.244m -0.164m Note. HFZ - healthy fitness zone; NIZ = needs improvement zone. Students were assigned cohorts based on whether they stayed in the same or changed fitness zone. For standardized test scores, overall mean = 0, standard deviation= 1. For superscripts "a" and "m," P < .001; for superscripts "b," "c," and "n," P < .01. Superscripts "a," "b," and "c" always refer to fifth grade and "m" and "n" always to seventh grade. Comparisons are always within the same category. For example, HFZ-HFZ fifth-grade math is significantly different from NIZ-NIZ fifth-grade math at the .001 level, as designated by the "a" superscript. Likewise, NIZ-NIZ reading in seventh grade is significantly different at the .001 level from HFZ-HFZ reading as designated by the "m" superscript and significantly different at the .01 level from NIZ-HFZ reading as designated by the "n" superscript.

The results of this study expand on existing knowledge about the association between student fitness-particularly aerobic capacity-and academic achievement by illustrating potential changes that could occur over time. Specifically, students who maintained a healthy fitness zone in terms of their aerobic capacity had the highest mean scores in academic achievement tests. Those who had the lowest mean scores were students who were not in the healthy zone for aerobic capacity in either grade (fifth or seventh). These results suggest that reaching and maintaining a healthy fitness level in this particular area could be fruitful in terms of one's performance on standardized tests.

It is interesting that children who gained fitness between the fifth and the seventh grades did not increase their academic mean scores to equal the academic mean scores of children who stayed in the HFZ. Likewise, students who fell out of the HFZ did not experience a drop in their academic achievement scores.

There are several factors that affect a child's ability to score in the HFZ for aerobic capacity. The child's level of fitness is obviously one of them. Also important to performance are considerations such as a child's health status, self-confidence, attitude, and appropriate clothing and shoes. The child's age is also a factor. For instance, a 10- or 11-year-old boy needs only to complete 23 circuits on the PACER test to score in the HFZ, but 32 circuits is required for a 12-year-old boy and 41 for a 13-year-old boy. If the child took the aerobic fitness test the week before he turned 13, he would be much more likely to pass than a child who turned 13 the week before the tests. Nevertheless, FitnessGram has been extensively tested for validity and reliability.24

Academic achievement is also influenced by many factors, especially a family's socioeconomic conditions20 and, undoubtedly, genetics. Other factors are gender,20,25 health, and sleep. One of the few factors that schools seem able to influence significantly is how much a child is able to move. Encouraging movement and fitness is likely to improve grades.12,13,15-16,18-23

Another possible way to look at the data presented here is that the children who "gained" or "lost" fitness during the 2 years between fifth and seventh grades were actually children whose fitness levels allowed them to score near the FitnessGram aerobic fitness target. Because FitnessGram is categorical (HFZ vs NIZ), it does not distinguish students who have moderate fitness; it is meant to distinguish between fit children and those who need improvement in their fitness. Children with moderate fitness may be those who come close to achieving the HFZ or barely make the HFZ, and their performance on any given day (or year) could fluctuate between the 2 categories.

Wittberg et al. identified aerobic fitness thresholds in a group of fifth-grade students that confer the greatest benefit to academic achievement.25 In the current study, students' aerobic capacity over time was associated with more effective academic achievement. Children who maintained aerobic fitness scored significantly higher in every WESTEST subject than did children who were in the NIZ both years. Children who changed aerobic fitness zones between the fifth and seventh grades scored higher in WESTEST than did those in the NIZ both years but lower than did those in the HFZ both years. Because the academic scores of children who changed fitness zones were closer to those of children who stayed in the HFZ than to those of children who stayed in the NIZ, it is possible that aerobic fitness levels near the FitnessGram aerobic fitness target will confer much of the benefit of consistently exceeding the FitnessGram aerobic fitness target. These findings have significant implications for the role of school-based physical activity and fitness programming in promoting each child's optimal academic achievement. Interventions designed to increase aerobic capacity should have an impact on increased cognitive performance.

A recent review by the Centers for Disease Control and Prevention presents collective evidence supporting the association between physical activity and cognitive performance across diverse research designs.26 Limited studies indicate improvement in cognitive function associated with physical activity interventions. For example, Davis et al. randomized healthy but overweight children into 20 minutes of aerobic exercise a day, 40 minutes of aerobic exercise a day, or a control group that met for 15 weeks.27 The high- exercise group showed improved planning scores on a cognitive performance assessment. A study by Hillman et al. documented improved cognitive performance in children with a single session of moderately intense aerobic exercise.28

FitnessGram tests were administered by different physical education teachers, who, despite training and review, may have varied administration technique. Although FitnessGram standards were based on the best available research at the time, they have been revised because of issues such as classification discrepancies between the PACER and the mile run, and the high prevalence of passing PACER scores among young girls, which may indicate that the bar for this test is set too low.29 Both FitnessGram and WESTEST results were reported categorically, and actual scores were not available. Furthermore, student health status the day of assessment as well as issues such as athletic ability, self-confidence, and attitude could affect results. There was also a substantial number of students who had missing data because they missed one of the many assessments or moved in or out of the school district. It should be noted that the results may not translate to more ethnically diverse areas because these students were primarily White and representative of an average West Virginia school district. Many factors, such as socioeconomic status, parental education, gender, sleep, health, and genetics, influence academic achievement, and none of these were examined in this study. Finally, there is some evidence that there may be gender differences associated with fitness testing, physical activity, and academic achievement.25,30

Much work remains to better understand the relationship between physical activity, physical fitness, and academic achievement. Interventions designed to increase aerobic capacity should be assessed in terms of their impact on increased cognitive performance. Additional work to better understand the dose of physical activity (frequency, length, strength) required to affect academic achievement is also needed. If effective, these interventions may create a paradigm shift in school-based support strategies for optimal academic achievement.

Furthermore, improving child fitness levels through regular physical activity may address 2 seemingly unrelated goals: ( 1) improving academic achievement and ( 2) reducing rates of childhood obesity and associated health risk factors. Fitness has been shown to be a better predictor of academic achievement than body mass index (BMI),16,31 and fit individuals generally have healthier BMIs.32,33 Therefore, intervening in 1 domain has a positive effect on many aspects of children's functioning. By promoting fun physical activity of a proper dose during the school day, children may not only optimize academic potential but learn and practice healthier lifestyle habits, reduce health risks associated with overweight (sedentary lifestyle), and develop a foundation of knowledge and practice that will benefit them through adulthood.

This article was accepted October 23, 2011.

R. A. Wittberg developed the initial conceptual draft, contributed to the analyses, and was the primary writer of all drafts. K. L. Northrup contributed to the conceptual design, oversaw all data collection and database development issues, and contributed to the written drafts. L. A. Cottrell contributed to the conceptual design of the study, designed and conducted the statistical analyses, developed related tables and figures, and contributed to the written drafts.

Support for this study was provided in part by the Sisters of Saint Joseph Charitable Fund (grant map-pro-07s2007) and the Claude Worthington Benedum Foundation (grant 20060393).

We thank the West Virginia Office of Information and Technology for their detailed focus and assistance with the data used in this research. We especially appreciate Jim Gilbert's efforts in developing the Web-based application and ongoing management of the database.

Study procedures were implemented at the county level and approved by the county superintendent of schools and the West Virginia University internal review board.

FIGURE 1-Standardized mean WESTEST subtests compared across all assessment periods: Wood County, West Virginia, 2005-2010.
References 14. California Dept of Education. A study of the relationship between physical fitness and academic achievement in California using 2004 test results. 2005. Available at: http://www.cde.ca.gov/ta/tg/pf/documents/2004pftresults.doc. Accessed May 26, 2009. 21. Trudeau F, Shephard RJ. Physical education, school physical activity, school sports and academic performance. Int J Behav Nutr Phys Act. 2008;5:l 0. Available at: http://www.ijbnpa.org/content/pdf/1479-5868-510.pdf. Accessed December 2, 2010. 29. FitnessGram: Healthy Fitness Zone Standards. 2011. Available at: http://www.fitnessgram.net/newstandards/#whynew. Accessed October 11, 2011. 31. Roberts CK, Freed B, McCarthy WJ. Low aerobic fitness and obesity are associated with lower standardized test scores in children. J Pediatr. 2010;l 56(5):711-718.
~~~~~~~~

By Richard A. Wittberg, PhD; Karen L. Northrup, RN, MS and Lesley A. Cottrell, PhD

Richard A. Wittberg is with the Mid-Ohio Valley Health Department, Parkersburg, WV. Karen L. Northrup is with Wood County Schools, Parkersburg, WV. Lesley A. Cottrell is with the Department of Pediatrics, West Virginia University, Morgantown. Correspondence should be sent to Richard A. Wittberg, Executive Director, Mid-Ohio Valley Health Department, 211 6th St, Parkersburg, WV 26101 (e-mail: richard.a.wittberg@wv.gov). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link.

Copyright of American Journal of Public Health is the property of American Public Health Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

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Children's Aerobic Fitness and Academic Achievement: A Longitudinal Examina...: Discovery Service for University of Western States

# data[].attributes.attachment_link.description
Objectives. We assessed children's potential differences in academic achievement based on aerobic fitness over a 2-year period. Methods. The longitudinal study sample included 3 cohorts of students (n = 1725; 50.1% male) enrolled in a West Virginia public school system. Students received baseline fitness and academic assessments as fifth graders and at a 2-year follow-up assessment.

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Amber Adolf 🙈

# data[].attributes.attachment_link.title
Physical activity is associated with better global cognition and frontal function in overweight/obese older adults with metabolic syndrome.

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Citation

Title:

Authors:

Paravidino VB; Department of Epidemiology, Institute of Social Medicine, State University of Rio de Janeiro, Rio de Janeiro, Brazil. vparavidino@gmail.com.; Department of Physical Education and Sports, Naval Academy - Brazilian Navy, Rio de Janeiro, Rio de Janeiro, Brazil. vparavidino@gmail.com.
Mediano MFF; Evandro Chagas National Institute of Infectious Disease, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil.
Silva ICM; Post-graduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil.
Wendt A; Post-graduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil.
Del Vecchio FB; Post-Graduate Program in Physical Education, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil.
Neves FA; Department of Physiology Science, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
Terra BS; Research Laboratory of Exercise Sciences, Physical Education Center Admiral Adalberto Nunes, Brazilian Navy, Rio de Janeiro, Rio de Janeiro, Brazil.
Gomes EAC; Laboratory of Exercise Sciences, Fluminense Federal University, Niterói, Rio de Janeiro, Brazil.
Moura AS; Department of Physiology Science, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
Sichieri R; Department of Epidemiology, Institute of Social Medicine, State University of Rio de Janeiro, Rio de Janeiro, Brazil.

Source:

Trials [Trials] 2018 Mar 07; Vol. 19 (1), pp. 167. Date of Electronic Publication: 2018 Mar 07.

Publication Type:

Clinical Trial Protocol; Journal Article

Language:

English

Journal Info:

Publisher: BioMed Central Country of Publication: England NLM ID: 101263253 Publication Model: Electronic Cited Medium: Internet ISSN: 1745-6215 (Electronic) Linking ISSN: 17456215 NLM ISO Abbreviation: Trials Subsets: MEDLINE

Imprint Name(s):

Original Publication: [London] : BioMed Central, 2006-

MeSH Terms:

Abstract:

Background: Physical exercise interventions have been extensively advocated for the treatment of obesity; however, clinical trials evaluating the effectiveness of exercise interventions on weight control show controversial results. Compensatory mechanisms through a decrease in energy expenditure and/or an increase in caloric consumption is a possible explanation. Several physiological mechanisms involved in the energy balance could explain compensatory mechanisms, but the influences of physical exercise on these adjustments are still unclear. Therefore, the present trial aims to evaluate the effects of exercise on non-exercise physical activity energy expenditure, energy intake and appetite sensations among active overweight/obese adults, as well as, to investigate hormonal changes associated with physical exercise.
Methods: This study is a randomized controlled trial with parallel, three-group experimental arms. Eighty-one overweight/obese adults will be randomly allocated (1:1:1 ratio) to a vigorous exercise group, moderate exercise group or control group. The trial will be conducted at a military institution and the intervention groups will be submitted to exercise sessions in the evening, three times a week for 65 min, during a 2-week period. The primary outcome will be total spontaneous physical activity energy expenditure during a 2-week period. Secondary outcomes will be caloric intake, appetite sensations and laboratorial biomarkers. Intention-to-treat analysis will be performed using linear mixed-effects models to evaluate the effect of treatment-by-time interaction on primary and secondary outcomes. Data analysis will be performed using SAS 9.3 and statistical significance will be set at p < 0.05.
Discussion: The results of the present study will help to understand the effect of physical exercise training on subsequent non-exercise physical activity, appetite and energy intake as well as understand the physiological mechanisms underlying a possible compensatory phenomenon, supporting the development of more effective interventions for prevention and treatment of obesity.
Trial Registration: Physical Exercise and Energy Balance trial registry, trial registration number: NCT 03138187 . Registered on 30 April 2017.

References:

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Contributed Indexing:

Keywords: Compensatory effect; Energy intake; Exercise training; Obesity; Physical activity energy expenditure

Molecular Sequence:

ClinicalTrials.gov NCT03138187

Entry Date(s):

Date Created: 20180309 Date Completed: 20190314 Latest Revision: 20190314

Update Code:

20190315

DOI:

10.1186/s13063-018-2445-6

PMID:

29514690

Database:

MEDLINE Complete

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Effect of physical exercise on spontaneous physical activity energy expendi...: Discovery Service for University of Western States

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Title: Authors: Paravidino VB; Department of Epidemiology, Institute of Social Medicine, State University of Rio de Janeiro, Rio de Janeiro, Brazil. vparavidino@gmail.com.; Department of Physical Education and Sports, Naval Academy - Brazilian Navy, Rio de Janeiro, Rio de Janeiro, Brazil. vparavidino@gmail.com.

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Summary

Better management by individuals of their long‐term conditions is promoted to improve health and reduce healthcare expenditure. However, there is limited evidence on the determinants and consequences of self‐management activity. We investigate the determinants of two forms of self‐management, exercise and relaxation, and their impact on the health and wellbeing of 3472 individuals with long‐term health conditions over a 1‐year period. We use simultaneous recursive trivariate models to estimate the effects of these two inputs on three health and wellbeing outcomes: the EuroQol five‐dimensional (EQ‐5D) score, self‐assessed health and happiness. We reflect the opportunity cost of time and knowledge with employment status and education and find that employment reduces relaxation and education increases exercise. We find that neither exercise nor relaxation affects the EuroQol five‐dimensional score, but exercise increases self‐assessed health and relaxation increases happiness. Our findings show that individuals tailor their self‐management activities to their economic constraints, with effects on different aspects of their utility. Interventions to encourage self‐management should take account of heterogeneous effects and constraints. © 2017 The Authors. Health Economics Published by John Wiley & Sons, Ltd.

Keywords: self‐management, long‐term conditions, panel data, simultaneous recursive trivariate regression

1. Introduction

Healthcare systems in Organisation for Economic Co‐operation and Development (OECD) countries are endeavouring to rise to the challenges emerging from ageing populations, growing levels of chronic illness, advancing technical possibilities and rising public expectations. These challenges have resulted in healthcare expenditure growing more rapidly than GDP in all OECD countries over the past 30 years (Pammolli et al., 2011). The downturn in the world economy has added to this and raised concerns regarding the sustainability of expenditure on healthcare systems (Tooke, 2011). In the UK, spending on the National Health Service (NHS) as a proportion of GDP is expected to fall from 8% to just over 6% by 2021 (Appleby et al., 2014). This could result in a funding gap of approximately £30 billion by 2020/21 ( Illmann).

One way to reduce pressure on formal health services is to reduce demand, especially amongst people with long‐term conditions. According to Roberts et al. ( 2012), pressure on NHS costs from people with long‐term conditions is at least equal to, if not more than, that from the expanding and ageing population. It is estimated that there are more than 15 million people in the UK with at least one long‐term condition, and the number of people with multiple long‐term conditions has been predicted to rise by over a third over the next 10 years (Department of Health, 2012). There is a similar trend across OECD countries; in 1990, average spending on long‐term conditions was 0.7% of GDP, and this had risen to 1.5% of GDP by 2012 (Gershlick, 2015).

A sustainable way to manage demand on services used by people with long‐term conditions is to encourage people to engage in their own health and healthcare. This was described by Wanless ( 2002) as the 'fully engaged scenario'. This vision has resulted in heightened policy emphasis on encouraging patients with long‐term conditions towards 'self‐management' through schemes such as the Expert Patients Programme (Department of Health, 2001). Self‐management has been defined by the Department of Health as '[t]he care taken by individuals towards their own health and wellbeing: it comprises of the actions they take to lead a healthy lifestyle; to meet their social, emotional and psychological needs; to take care of their long‐term condition; and to prevent further illness or accidents' (Department of Health, 2005). There has been increasing advocacy of self‐management in the UK and internationally over the last two decades (Rogers et al., 2009).

Recently, Naylor et al. ( 2015) have argued that active self‐management support is one of the key priorities for healthcare commissioners to focus on helping to sustain the NHS. Self‐management support aims to increase 'the capacity, confidence and efficacy of the individual' to manage their condition (Kennedy et al., 2013). There are two core models of self‐management support: a provider‐based model with support embedded in clinical practice and a patient‐based model, which enables patients through individual or group‐based education, telehealth or telecare (Kennedy et al., 2013). Despite the growing policy emphasis on the importance of self‐management support, there is modest evidence of the effects on health and healthcare utilization (Griffiths et al., 2007; Kennedy et al., 2013; Richardson et al., 2008). More notably, evaluations of interventions to increase self‐management activity focus on self‐efficacy (the confidence of the individual to manage their condition through self‐management) and changes in health and healthcare utilization in the short term. Little attention has been paid to the self‐management activities per se, which are treated as intervening variables. In this paper, we aim to bridge the gap between self‐management literature and economic literature to enhance understanding of self‐management behaviour.

Previous studies have shown that the allocation of time to a health input such as self‐management depends on its associated opportunity cost. Brown and Roberts ( 2011) analysed the frequency of participation in physical activity by using six waves of data from Household Income and Labour Dynamics of Australia. They measured opportunity cost through employment status and found that full‐time employment had a significant negative impact on exercise for women. Mullahy and Robert ( 2010) hypothesized that the opportunity cost of time differs by level of human capital endowment (measured through education). Using data from the American Time Use Survey, they found that human capital endowments affect the way participants use their time to produce different health outcomes. For example, more educated individuals were more likely to participate in physical exercise. These studies indicate that differences in the cost of time can change the way that people choose to invest in self‐management.

Brown and Roberts ( 2011) and Mullahy and Robert ( 2010) both used general population data. One study that focuses on people with long‐term conditions is by Ettner et al. ( 2009). Unlike Mullahy and Robert ( 2010), Ettner et al. found that education reduced the amount of time spent on self‐management activities in a sample of 11 927 patients with at least one long‐term condition. This implies that time constraints may affect the choice of health inputs differently amongst people with long‐term conditions.

Changes in health inputs as a result of time constraints can also affect health outcomes (Balia and Jones, 2008; Contoyannis and Jones, 2004). Despite the clear link between inputs to health and health outcomes, we have found only one paper that investigates the link between opportunity cost and health through the choice of health inputs. Kuvaja‐Köllner et al. ( 2013) used 2 years of data from a randomized controlled trial in Finland to link the effect of the opportunity cost of time spent exercising with the effect of exercise on health. They assume physical exercise to be endogenous and so conduct a two‐stage least squares analysis, using motivation to exercise as the instrumental variable for exercise. Kuvuja‐Köllner et al. find that a lower opportunity cost of time (measured through labour market status) has a significant effect on the amount of time spent exercising. This increase in time spent on physical exercise was found to have a positive effect on the short form 6D.

The Kuvaja‐Köllner et al. ( 2013) paper shows that the opportunity cost of time affects health inputs and health outcomes. However, their instrumental variable is a short‐term decision that could be correlated with the outcome. It could be that enjoyment of physical exercise can change and therefore is not a suitable instrument for exercise. A further limitation of Kuvaja‐Köllner et al.'s ( 2013) analysis is that lagged measures of health are not included in the model. Previous endowments of health are likely to affect people's allocation to time spent on exercise as well as people's future health. Therefore, the results could be affected by omitted variable bias. In addition, the input (exercise) and output (health) equations were not modelled simultaneously, even though it is likely that the errors between these equations could be correlated.

Kuvaja‐Köllner et al. ( 2013) paved the way for linking a health input to a health output within the same dataset, but they did not explain how the opportunity cost of time could affect the specific self‐management activities that policy is aiming to encourage. In this paper, we describe how the opportunity cost of time (measured through employment and education) can impact on the mix of self‐management activities and estimate the effects of self‐management activities on future health and wellbeing. Our study is distinct because we study a population of individuals with long‐term conditions, precisely those for whom self‐management potential is greatest. As we use data from a clinical trial, we have rich data on health and wellbeing. We improve on Kuvaja‐Köllner et al.'s ( 2013) study by modelling inputs and outputs simultaneously and by using more plausible identifying restrictions. We estimate exercise, relaxation and health as part of a simultaneous model and allow for the possibility that there is correlation between these equations.

2. Methods

2.1. Theoretical motivation

We motivate our empirical estimation of the relationship between the opportunity cost of time and self‐management (measured through exercise and relaxation) with a modified time allocation framework. The time allocation framework is based upon Becker's ( 1965) theory of allocation of time and the Cawley ( 2004) 'SLOTH' (sleep, leisure, occupation, transportation and home‐based activities) framework. We can think of patients who demand health for two reasons: (i) as a consumption commodity, because it directly enters their preference functions, and (ii) as an investment commodity, because it determines the amount of time available for other activities. Individuals choose how to allocate their time amongst work, exercise, relaxation and other activities to maximize their utility subject to a budget and time constraint. The budget constraint includes a time price of 'self‐management' which is dependent on wages and educational qualifications. The time price indicates the opportunity cost of participating in exercise and relaxation. The budget constraint includes the money price of exercise (e.g. gym membership), income and the time price of working. Health output is determined by two inputs, exercise and relaxation. If the time price of exercise increases due to the individual entering employment, then the opportunity cost of time spent exercising increases. But as the individual becomes richer, he or she is able to afford the money price of exercise. The overall effect of a change in the time price of exercise on its demand is ambiguous and depends on the relative size of the income and substitution effects.

2.2. Data

We use data collected in the trial of Whole System Informing Self‐Management Engagement, a provider‐based model of self‐management support (Bower et al., 2012; Kennedy et al., 2014, 2013). The trial was based in the north‐west of England between the years of 2009 and 2012. The population in the local area was predominantly white and socioeconomically deprived. The objective of the trial was to determine the effectiveness of a provider‐based model of self‐management support. No statistically significant differences were found between the patients receiving the provider‐based intervention and those in the control group. However, the trial highlighted the need to elicit and understand the active components required for effective self‐management support (Kennedy et al., 2014).

Patients were eligible for the trial if they had any of three long‐term conditions: diabetes, chronic obstructive pulmonary disease and irritable bowel syndrome. These were selected as exemplar conditions that could be influenced by self‐management interventions and where there was also published evidence of effectiveness (Deakin et al., 2005; Richardson et al., 2006). Patients were identified from electronic health records and checked for any exclusion criteria (under 18 years of age, insufficient English language, receiving palliative care or insufficient capacity to give written consent) before being sent a postal invitation to take part in the trial. Forty‐four general practices were recruited.

2.3. Empirical strategy

Our empirical strategy aims to investigate the determinants of self‐management activities and the effect of these health inputs on health outputs. Self‐management activities such as exercise and relaxation have the potential to be endogenous if there is an unobserved variable that is correlated with both these inputs and health outputs or there could be unobserved individual characteristics that drive both the extent of self‐management activity and health. This potential for endogeneity raises the concern that if we estimate these equations separately, then there would be unobserved correlation between the error terms. Therefore, we adopt a simultaneous recursive trivariate model that controls for the potential endogeneity of exercise and relaxation and for unobserved heterogeneity between the equations (Maddala, 1983). Our model simultaneously estimates three equations, one output equation (health) and two input equations (exercise and relaxation):

Health at baseline and 6 months is denoted by h ₀ and h t − 1, and x denotes sociodemographic variables. Equations 2 and 3 show that we exclude employment ( w ₀) and education ( ed ₀) from the health equation. Thus, we assume that, conditional on baseline and lagged health and the other covariates, employment and education affect health indirectly through their influence on exercise and relaxation.

The variance-covariance matrix Ω can be written as follows:

where ρ jk with j, k = 1, 2,3 ∀ j ≠  k is the correlation between the error terms. Unobserved heterogeneity can be allowed with the potentially non‐zero values of the off‐diagonal elements of the matrix. We use Roodman's ( 2011) conditional recursive mixed process model to estimate the system of equations (1-3) in stata and report them in Table 4.

There has been much discussion of the best estimation technique for EQ‐5D given its bimodal distribution. Hernández Alava et al. (2012) argue that estimation of EQ‐5D should take into account its bimodal distribution by using an adjusted limited dependent variable mixture model. Other researchers have also used two‐part Tobit models (Acaster et al., 2015). However, numerous studies find that when EQ‐5D is treated as continuous, the model performs just as efficiently as any other estimation strategy, if not better (Kim et al., 2014).

To check for robustness of our results, we also run our analysis treating SAH and happiness as ordered probits to reflect the ordinal nature of these dependent variables. We present average marginal effects for each possible response category (none of the time, a little of the time, etc.).

Exercise and relaxation are ordinal variables on a three‐point scale. We include these variables in two different ways. When we model the health/wellbeing variables using linear models, we also treat the self‐management inputs as continuous. When we model SAH and happiness as ordinal variables, we include the self‐management input as binary variables, separating those who reported 'very true' from those who reported 'partly true' or 'not true'.

We also test the independence of the equations and the validity of the over‐identifying restrictions. We test the independence of the equations with a likelihood ratio test that compares an unrestricted equation with all correlations between equations and a restricted one with correlations set to zero. We conduct an F ‐test for over‐identification by setting the coefficients of the instruments equal to 0, excluding lagged exercise for exercise and lagged relaxation for relaxation.

3. Results

3.1. Descriptive statistics

At baseline, the average EQ‐5D score reported by the respondents was 0.655, ranging from −0.426 to 1 (Table ). Also at baseline, 40% of the respondents reported their SAH as 'poor' or 'fair', and 43% stated that they were happy 'none of the time', 'a little bit of the time' or 'some of the time'.

The average age of the participants was 63 years (ranging from 18 to 102 years). The sample was predominantly from a White ethnic group (97%), slightly higher than the 95% across England as a whole for the population aged 65 years (see Office for National Statistics, 2009) and reflecting the population composition of Salford. A large proportion of the sample (71%) owned their own accommodation. Diabetes is the most common long‐term condition of the participants, with 50% suffering the condition. Arthritis and heart conditions were the next most frequent at 46% and 45% respectively.

At 12 months, 30.2% of the respondents said that it was 'not true' that they used exercise to manage their condition, 46.6% said that it was 'partly true', and 23.2% said that it was 'very true' (Table ). Of the respondents, 9.5% said that it was 'not true' that they rested and relaxed, 47.6% said that it was 'partly true', and 42.3% said that it was 'very true'. The descriptive statistics for the 10 other self‐management activities are also provided in Table . We investigated the correlations among the 12 self‐management activities and found that exercise was most strongly correlated with diet ( ρ = 0.24) and relaxation was most strongly correlated with stress management ( ρ = 0.39) and medication ( ρ = 0.20). Exercise and relaxation were not highly correlated with each other ( ρ  = 0.07).

3.2. Simultaneous trivariate recursive regression results

Being in full‐time employment decreases the probability that the respondent uses relaxation to manage their condition by 11 percentage points (Table ). Table also shows that having a degree or other professional qualification increases the probability that the individual will use exercise to manage their condition by 5 percentage points respectively.

Exercise and relaxation are found to have no statistically significant effect on EQ‐5D. A one‐unit increase in exercise, which represents a change from 'not true' to 'partly true' or 'partly true' to 'very true' that this form of self‐management was used by the respondent, increases SAH by 0.08 units on a five‐point scale. The same change in the use of rest and relaxation increases happiness by 0.22 units on a six‐point scale.

The value for ρ ₂₃ is positive and significant (Table ). This implies that those who have a higher propensity to exercise also have a higher propensity to relax (and vice versa).

3.3. Robustness checks

The results of the simultaneous recursive trivariate models where SAH and happiness are treated as ordinal variables are presented in Table . Exercise increases the probability that SAH is good, very good or excellent and reduces the probability that it is either poor or fair. Relaxation instead decreases the probability that SAH is good, very good or excellent and increases the probability that it is either poor or fair. Relaxation significantly increases the probability of being happy a good bit of the time, most or all of the time.

3.4. Test statistics

The test statistics in Table show that the equations are not independent. The p‐values from a likelihood ratio test imply that we should reject the null hypothesis and conduct a joint analysis. The results of an F ‐test for over‐identification indicate that we can reject the null hypothesis; the model is not over‐identified.

4. Discussion

In this paper, we have examined the determinants and consequences of two self‐management activities, relaxation and exercise. We have motivated our empirical specification with a time allocation framework where a change in the opportunity cost of time has an ambiguous effect on the choice of these health inputs because of the relative size of the income and substitution effects. As a result, the effect of exercise and relaxation on health is ambiguous as well. We apply this framework to a unique longitudinal trial of 3472 individuals observed at three points in time. This group is representative of populations with long‐term conditions, exactly those at whom self‐management support schemes are targeted.

We use education and employment status as measures of the opportunity cost of time of health investments. If the substitution effect is dominant, then increases in the opportunity cost of time (being in full‐time employment or having a degree) should reduce the amount of time participating in these activities. An increase in the opportunity cost of time also means an increase in income (if you are working or have higher education, then you are likely to have a higher income). Individuals are more likely to trade off labour for leisure as real income increases as long as leisure is a normal good.

Our results indicate that those who work full time are less likely to spend time resting and relaxing as a form of self‐management compared with those who do not work full time. This implies that the substitution effect is dominating. People are substituting self‐management for working as the opportunity cost of self‐management is increasing. This finding coincides with Humphrey and Ruseski's ( 2011) paper.

Education increases the probability of exercising. This implies that the income effect is dominating (when the shadow price of time is higher, the respondents have higher income and therefore do not need to spend as much time working). This finding is consistent with research on exercise within the general population (Eberth and Smith, 2010).

Time preference has been suggested as a channel through which health and education are correlated (Grossman, 2006). Van der Pol ( 2011) found that the relationship between education and health can be partly explained through time preference. Our finding that education increases exercise and decreases relaxation could also be partly explained by time preference. People with higher education could have a lower time preference and therefore are more willing to engage in an activity that benefits health in the long term, whereas people with lower education might prefer to engage in a self‐management that has a more instantaneous payoff.

Employment status and education change the way that people allocate their time to health investments. This finding supports similar findings in the literature (Brown and Roberts, 2011; Kuvaja‐Köllner et al., 2013; Mullahy and Robert, 2010). We find that the way in which people decide to allocate their time to self‐management behaviours affects their health. For example, as found by Kuvaja‐Köllner et al. ( 2013), exercise has a positive effect on SAH. Our findings could explain why researchers have found self‐management support schemes to affect efficacy but to only have modest effects on health and patient outcomes. One way to encourage people to engage in their health could be to attempt to make health‐promoting behaviours less costly to the individual.

One of our key findings is that the effect of exercise and relaxation on health and wellbeing depended on which measures of health and wellbeing we used. We found that both self‐management activities had no significant effect on EQ‐5D score, whereas exercise had a positive effect on SAH and relaxation had a positive effect on happiness. This is likely to be because different measures capture different aspects of health and wellbeing. For example, Sun et al. ( 2016) found that the anxiety/depression domain of the EQ‐5D was most strongly associated with subjective wellbeing. Böckerman et al. ( 2011) found that psychiatric disorders had a large effect on wellbeing even after controlling for the EQ‐5D (Böckerman et al., 2011). This could explain the stronger relationship shown between relaxation and happiness, due to its conceivably greater effect on mental health.

This may also be the explanation for the positive effect of exercise on SAH, which may be more focused on physical health. Exercise may positively affect physical health more than any other aspect, and hence show up most significantly in SAH. The lack of significant effect of either exercise or relaxation on EQ‐5D scores over time, however, may be explained by its insensitivity to changes over a short period of time. Clearly, the potential differences among EQ‐5D, SAH and happiness could have important ramifications for policy and allocation of resources depending on what measure we take to best capture utility.

Our test statistics indicate that there is enough correlation between the errors to justify modelling the equations simultaneously. Through modelling the equations simultaneously, we are also controlling for two sources of unobserved heterogeneity. First, we control for the possibility that exercise and relaxation are endogenous. We also control for the possibility that there is unobservable heterogeneity between the equations in our model. Through conducting a joint analysis, we found that the propensity to participate in relaxation is positively correlated with the propensity to participate in exercise. This could be picking up a measure of unobserved motivation to engage in health‐promoting behaviour.

To our knowledge, this is the first study in the UK that links the effect of employment and education on health inputs and health. We are also the first study to our knowledge to model exercise, relaxation and health simultaneously. However, there are several limitations to this study. First, there is a relatively short follow‐up. We look at the effect that self‐management has on health after just 6 months. One explanation for our modest findings for the effect of self‐management on EQ‐5D could be that 6 months is too short to pick up most of the effects of self‐management behaviour. Another potential limitation of the study is that we assume that exercise and relaxation are separate inputs with additive effects. We also note that our results are confined to a group of older people with an ethnic composition that is not representative of the younger population in England.

A further limitation relates to the available measures of exercise and relaxation. A change from 'partly true' to 'very true' is not an easily transferable measure of health input. However, further analysis (Table ) shows that our results do not differ when using binary variables for whether the respondents reported 'very true' versus 'partly true' or 'not true'.

Overall, our study demonstrates that constraints on time can change the way individuals engage in self‐management, with effects on health and wellbeing. We find no effect of exercise and relaxation on EQ‐5D, but we find significant positive effects of exercise on SAH and relaxation on happiness. These results are relevant for the National Institute for Health and Clinical Excellence guidelines on best practice management of long‐term conditions (NICE Guidelines, 2009; NICE Guidelines, 2015). These guidelines promote a combination of drug treatment and behaviour change interventions targeting exercise but do not mention the importance of relaxation. Our findings indicate that initiatives designed to encourage self‐management should take account of heterogeneous constraints on an individual's potential to use different inputs and the potential for different effects on alternative aspects of their health and wellbeing. These results support the provision of individualized care and shared decisions between doctors and patients on the best behaviour change interventions depending on the individual's time constraints (NICE Guidelines, 2015).

Acknowledgements

This is an independent research supported by the National Institute for Health Research (Research Methods Fellowship MET 12-22) and the Medical Research Council (Early Career Fellowship in Economics of Health MR/K021583/1). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

Notes

Forbes H., Fichera E., Rogers A., and Sutton M. (2017) The Effects of Exercise and Relaxation on Health and Wellbeing. Health Econ., 26: e67-e80. doi: [ 10.1002/hec.3477. [ PubMed] Google Scholar]

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The Effects of Exercise and Relaxation on Health and Wellbeing

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Better management by individuals of their long‐term conditions is promoted to improve health and reduce healthcare expenditure. However, there is limited evidence on the determinants and consequences of self‐management activity. We investigate the determinants of two forms of self‐management, exercise and relaxation, and their impact on the health and wellbeing of 3472 individuals with long‐term health conditions over a 1‐year period.

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Beth Anderson

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Background: Practice of yoga has been linked to emotional awareness and positive moods as characteristics of happiness. There is a need to investigate whether a customized yoga programme enhances emotional awareness and happiness in children.Objective: This article reports a two-year multi-city study examining the effects of a customized Chinmaya Mission anchored Bala Vihar yoga programme on the emotional awareness and happiness of children.Method: For this repeated measures waitlist control design study, participants comprised 1589 children across 20 global cities and an equal number in the comparison group. Bala Vihar yoga programme participants had greater emotional awareness and scored higher on the happiness measure at phase 2, vis-à-vis the comparison group.Results: Participant children from American cities, girls, middle class children, those whose mothers were their primary caregivers, who lived in standard family arrangements, single children, who scored lower on the clinician-rated pediatric anxiety screening measure at phase 2, who attended most of the Bala Vihar yoga lessons and regularly self-practiced, were more emotionally aware and happier than their counterparts. Self-practice regularity was the strongest predictor of phase 2 scores.Conclusions: Results emphasize the positive emotions promoting potential of yoga for children, cross-culturally, with some modifications for certain subgroups of children. Though this study was primarily with Hindu children, some components such as postures and meditation could also be replicated with a wider cross-section of children.

Keywords: Yoga; Emotional awareness; Happiness; Bala Vihars; Repeated measures design

Within the domain of positive child psychology, which lays emphasis on positive behavior and emotion learning and reinforcement, yoga as a spiritual technique is fast gaining importance as having beneficial effects for children. Benefits include enabling improvement in coping abilities from self-perceived stress (Smith-Vogtmann 2009) and improving executive functioning (Beattie 2014) including enhancing children's verbal and spatial memories (Manjunath and Telles 2004). Yoga is also a therapeutic technique impactful for children with attention disorders (Abadi et al. 2008) and those dealing and coping with loss and trauma (Kaley-Isley et al. 2010).

Yoga is an ancient Indian technique of mind-body discipline, the rigorous development of which was later attributed to a group of scholars known under the name Patanjali. There are primarily eight limbs of yoga: yama (ethical relationships with self and others), niyama (discipline), asana (postures), pranayama (breathing), prathyahara (maintaining the balance), dhyana (meditation), dharana (consecration and concentration) and Samadhi (transcendental stage) (see Khalsa et al. 2016). The focus is on breath, low impact exercises, physical relaxation, quieting the mind and maintaining balance and is noncompetitive and focused on the individual child, which is why it has been used with children in fitness programs (Toscano and Clemente 2008). There is further evidence from randomized controlled trials that yoga improves memory and attention span in children (Khalsa et al. 2012), enables stress and aggression reduction (Stuck and Gloeckner 2005) and hence has therapeutic potential for children (Birdee et al. 2009).

Apart from being noncompetitive and individual focused, a distinctive feature of the impact of yoga as a scientific technique is that it is contingent on the child's biographical and psychosocial realities, such as gender and parental support (Rich 2011) as well as mental state such as persistent anxieties (Vohra and McClafferty 2016), and his/her own engagement with the yoga program through practice (Prakash et al. 2010).

Practice of yoga has also been linked to emotional awareness (Leite et al. 2010) and positive moods and disposition as characteristics of happiness (Amaranath et al. 2016). Emotional awareness and happiness coexist and signify reduced existential anxieties (Riley and Park 2015). The theoretical support for this comes from Evans et al. (2009) who have explained the benefits of yoga through a bio-psychosocial model. They propose that there are physical health benefits that could be similar to any form of exercise, but unlike more competitive physical endeavors, yoga helps "quiet the body." The non-competitive nature, combined with the mindfulness practice, may foster self-efficacy, coping, social support, and positive mood. Evans et al. (2009) also describe the spiritual benefits of yoga, including compassionate understanding, acceptance, and mindful and emotional awareness. This emotional awareness promotes a connection with self and others, and thereby clients are able to engage in healthy thoughts, choices and behaviors, and take actions to facilitate wellbeing, as well as a happy state of mind. While yoga is seen as effective for school going children (Khalsa and Butzer 2016), whether a customized yoga programme enhances emotional awareness and happiness in children is not yet explored, which is the focus of the present study.

In terms of teaching yoga, one widely prevalent phenomenon is that of guru-led new religious movements and their customized yoga programs for their clientele across cultures and age groups. Modern yoga gurus have become prominent charismatic figures influencing lifestyle choices of their followers (Copeman and Ikegame 2012). These yoga gurus offer a customized and innovative self-reflective technology to their followers and adherents (Singleton and Byrne 2008). The 'self' is at the centre, and they draw on the notion of a 'cosmic consciousness' or universe as the main source of energy for beings, to explain how the individual self may have powers and potential beyond its 'this-worldly' or tangible constraints. This permeates to the realms of emotions, emotional balance and happiness, the outcome contingent on followers' seriousness (Hunt 2010). However, a political dimension of these movements is also the permeation of Hindu hegemony while performing service. This includes reported instances of abuses, involvement in mainstream politics, corruption and lack of democratic accountability by some gurus (Copeman and Ikegame 2012). The fact, however, remains that these gurus and their institutions have plural forms, and have emerged as effective institutions in the South Asian contexts playing multifaceted roles.

One such popular guru-led movement originated in India but now having a massive global presence is the Chinmaya Mission. Based on the teachings of the charismatic guru Swami Chinmayananda, the Mission was started by a group of followers in Madras, India in 1953. Currently headquartered in a retreat structure at suburban Mumbai, India, and headed by Swami Swarupananda, the main philosophy is to spread the ancient ideals of yoga and Vedanta for modern day living. Vedanta is the school of Indian philosophy that concerns discourses on soul and the divine through arguments of duality, non-duality and unity. Chinmaya Mission philosophy focuses on soul discourses and human soul as the core reality of existence. There is an emphasis on holistic development of the body, mind and intellect or BMI as it is popularly known. Chinmaya Mission has followers across age groups and an important institution therein in Bala Vihar (children's group) functioning in practically every centre. The aim of the Bala Vihar programme is to socialize children aged 6-14-years into yoga and Vedanta ideals. Bala Vihar instructors are Chinmaya followers who undergo the training prior to commencing classes, which are usually held once a week for an hour with groups of children (see also Locklin and Lauwers 2009). The content of the Bala Vihar lessons range from moral education, Vedanta and yoga lessons. Primarily the focus is on religious teaching, however, the Bala Vihars as platforms of non-formal education for children, are also open to experimenting. Yoga lessons, gaining popularity worldwide, are a recent inclusion in the repertoire of Bala Vihars too. Within that frame, the question that then needs to be explored is that whether children who participate in this customized yoga programme of the Bala Vihars are more emotionally aware and happy compared to their counterparts, who are also Bala Vihar students, but do only the routine religious lessons.

This article reports on a multi-city two-year repeated measures waitlist control design study examining the effects of a customized Bala Vihar yoga programme on the emotional awareness and happiness of its participants vis-à-vis a comparison group of children who did not participate in the customized programme. The treatment group comprised 1589 Bala Vihar participants across 20 cities (Hindu children in the diaspora) who underwent the customized yoga programme and an equal number were assigned to the comparison group. The comparison group did not undergo the customized Bala Vihar yoga programme but attended the classes once a week comprising chanting, stories and religious education exercises during the two-year study period. Apart from biographical and demographic details, an anxiety screening measure was used: Paediatric Anxiety Rating Scale (PARS). Two scales were used to measure the outcomes-emotional awareness and happiness: Emotion Awareness Questionnaire-30 (EAQ-30) and Smiley Faces Assessment Scale (SFAS).

Drawing from the evidence on the potential of yoga to impact emotions (e.g. Amaranath et al. 2016; Leite et al. 2010) and differentials in yoga impact by demographic profile and psychosocial makeup of participants (e.g. Rich 2011; Vohra and McClafferty 2016) and the importance of self-engagement in yoga practice (e.g. Prakash et al. 2010), the following are the initial hypotheses of the study:

Bala Vihar customized yoga programme participants would have greater emotional awareness and score higher on the happiness scale at phase 2 vis-à-vis the comparison group.

The phase 2 emotional awareness and happiness scores of the Bala Vihar customized yoga programme participants would be contingent on biographical and demographic variables (such as city, age, gender, class, primary caregiver, primary caregiver education, primary caregiver occupation, living arrangement and siblings), psychological screening markers such as anxiety state, and intervention moderators such as Bala Vihar yoga classes attended and self-practice.

The strongest predictor of phase 2 emotional awareness and happiness scores of the customized yoga programme participants would be self-practice.

The repeated measures waitlist control design was used, with children assigned to the participant and comparison groups respectively. Table 1 depicts the profile of the Bala Vihar yoga programme participants and the comparison group.

Recruitment was done stage-wise, wherein at the first stage, 20 Bala Vihars were identified in 20 cities, where there was a contact with the Bala Vihar instructors as well as clinical psychologists who agreed to administer the pre- and post-test assessments for the study. Across the 20 Bala Vihars, at stage 2, Bala Vihar instructors provided a list of 3408 children as potential study participants. These were Hindu children in the diaspora, their parents/guardians were associated with the Chinmaya Mission, and the said children had joined the Bala Vihar classes for a year. Of the 3408 children across 20 Bala Vihars, 3178 children and their parents agreed to participate in the two-year study and experiment and 230 of them declined. The researcher did the randomization using computer generated random number tables at the third stage, through which 1589 children were assigned to the participant group and an equal number to the comparison group. Both the groups were comparable since at the time of recruitment and randomization, all the children had undergone Bala Vihar regular weekly lessons for a period of one-year.

The experimental conditions were masked from the participants and the comparison group. Participant group children underwent the customized yoga programme. Comparison group children were also Bala Vihar members, however, they did not undergo the customized yoga programme. They participated in the routine classes comprising religious lessons, chanting and stories. Participant and comparison group children were offered Amazon gift vouchers at the end of the study period.

Informed consent was sought from the participant and comparison group children and their primary caregivers. All procedures followed were in accordance with the ethical standards of research, conforming to the norms prescribed by the independent ethics committee of the University of Mumbai, India, adheres to all legal and ethical requirements of the study sites, and is in compliance with the Helsinki Declaration of 1975, as revised in 2000.

There are no conflicts of interest to report for this submission. The author takes responsibility for the integrity of the data and the accuracy of the data analysis. There is no registered funder to report for this submission. Acknowledgments are due to all the Bala Vihar instructors of the 20 Bala Vihar classes for conducting the lessons. Acknowledgements are also due to the clinical psychologists who administered PARS and trained investigators in each of the cities who administered the schedules and collected the data. Bala Vihar instructors conducted the lessons on an honorary basis and clinical psychologists and trained investigators were offered Flipkart gift vouchers.

Interview schedule was administered to the Bala Vihar yoga programme participants and the comparison group children at phase 1 and 2. Basic biographical and demographic details were sought: city, age, gender, class, primary caregiver, primary caregiver education, primary caregiver occupation, living arrangements, siblings. The PARS scale, administered by the clinical psychologists, was used to screen for anxiety states, details given below. At phase 2, additional details from the Bala Vihar yoga programme participants were recorded: lessons attended and self-practiced. Self-practice was recorded through a check-card provided to the primary caregivers, who maintained a record of their respective wards' weekly practice session.

The Pediatric Anxiety Rating Scale (PARS) developed by the Research Unit on Pediatric Psychopharmacology Anxiety Study Group, Riddle and Greenhill (2002) is a clinician-rated instrument for assessing the severity of anxiety symptoms associated with common DSM-IV anxiety disorders (social phobia, separation anxiety disorder, and generalized anxiety disorder) in children aged 6-17 years. The PARS has two sections: the symptom checklist and the severity items. The symptom checklist is used to determine the child's repertoire of symptoms during the past week. The 7-severity item is used to determine the severity of symptoms and the PARS total score. The total score for the PARS is the total of 7 severity items. The total score ranges from 0 to 35, with higher scores indicating greater symptom severity. Few previously published studies have validated the PARS in different cultural contexts (such as Japan) as well as with cross-section of children's groups (including those with symptoms, developmental disabilities as well as non-anxious groups), and concluded acceptable psychometric properties including internal and external construct validity, reliability and convergent validity (e.g. Ginsburg et al. 2011; Ito et al. 2012; Russo-Ponsaran et al. 2014; Storch et al. 2012). For the present study: Cronbach α = .93; item-scale intercorrelation = .91; Pearson's r = .91.

Two scales have been used to assess the outcome measures: Emotion Awareness Questionnaire and Smiley Faces Assessment Scale. The Emotion Awareness Questionnaire (EAQ-30) (Rieffe et al. 2007, 2008) aims to identify how children and adolescents feel and think about their feelings. EAQ-30 is designed with a six-factor structure describing six aspects of emotional functioning: ( 1) Differentiating Emotions, ( 2) Verbal Sharing of Emotions, ( 3) Not Hiding Emotions, ( 4) Bodily Awareness of Emotions, ( 5) Attending to Others' Emotions, and ( 6) Analyses of Emotions. The aspect of differentiating emotions has 7 statements of which all are reversed scored (e.g. It is difficult to know whether I feel sad or angry or something else). The aspect of verbal sharing of emotions has 3 statements of which two are reversed scored (e.g. I find it difficult to explain to a friend how I feel). The aspect of not hiding emotions has 4 statements of which all are reversed scored (When I am angry or upset, I try to hide this). The aspect of bodily awareness of emotions has 5 statements of which four are reversed scored (When I am sad, my body feels weak). The aspect of attending to others' emotions has 5 statements of which two are reversed scored (I don' t care about how my friends are feeling inside) and the aspect of analyses of emotions has 6 statements. Of the 30 statements, 20 items are conversely formulated with respect to the trait. Respondents are asked to rate the degree to which each item is true about them on a three-point scale (1 = not true, 2 = sometimes true, 3 = often true).

Few previously published studies have validated the EAQ-30 in different cultural contexts (such as Netherlands, France, Spain, Japan, Pakistan) as well as with cross-section of children's groups (such as those with anxiety, and somatic complaints and non anxious populations), and concluded acceptable psychometrics, including internal and external construct validity, reliability and convergent validity (e.g. Camodeca and Rieffe 2013; Ishizu and Shimoda 2013; Lahaye et al. 2011; Mihalca 2015). Scoring can be done subscale wise or summative, as used in the present study. Cumulative score ranges from 50 to 70, with 50 being the ideal score for heightened emotional self-awareness, and higher scores indicating greater preference for conversely formulated statements and hence lower emotional awareness. For the present study: Cronbach α = .93; item-scale intercorrelation = .91; Pearson's r = .92.

The Smiley Faces Assessment Scale (SFAS) is a Likert-type self-report assessment instrument with a pictorial response system. SFAS has five-point (Henerson et al. 1987) and seven-point versions (Victorine et al. 2004). For this study, the seven point version is used where to the question on happiness feelings of children in the past two-weeks, seven animated faces/emoticons were presented with the following rating: 1 = very unhappy, 2 = unhappy, 3 = moderately unhappy, 4 = neutral, 5 = moderately happy, 6 = happy and 7 = very happy. Few previously published studies have validated the SFAS in different cultural contexts (such as Malayasia) as well as with cross-section of children's groups (such as hospitalized children, sick children, as well as well-adjusted and healthy children), and concluded acceptable psychometrics, including internal and external construct validity, reliability and convergent validity (e.g. Hall et al. 2016; van der Sluis et al. 2012). Scores ranged from 1 to 7, with higher scores indicating greater happiness. For the present study: Cronbach α = .91; item-scale intercorrelation = .93; Pearson's r = .89.

Phase 1 data was collected by investigators (clinical psychology student interns working with the clinical psychologists in each of the cities) at the beginning of the study period (January-February 2015). Phase 2 data was collected at the end of the two-years period (January-February 2017). The schedules were administered in English language and the data was collected on tablets and then transferred to the mainframe of the researcher. Self-practice checkcards were collected by the Bala Vihar instructors and dispatched to the researcher.

Members of the monastic order of the Mission who were also master trainers of the Bala Vihar classes were consulted to identify the suitable yoga programme package to promote emotional awareness and happiness among young children. Through the deliberations, the lesson plan was developed comprising asanas (postures), pranayama (breathing techniques) and dhyana (meditation). Child-oriented simple postures, breathing techniques and meditation procedures, which focused on watching the breath and becoming aware of ones thoughts, feelings and emotions, were identified for the programme. At the end the need for a balance and harmony between the body, mind and intellect was emphasized, which is the core of Chinmaya Mission's philosophy.

Bala Vihar classes were held once a week for an hour at the Chinmaya Mission centres' premises. The weekly lessons had some core tenets, covered through asanas, pranayama and dhyana, such as ( 1) appreciating the self ( 2) appreciating others including parents, family and friends ( 3) counting one's blessings and ( 4) happiness as a learnt behaviour. The repertoire of each one-hour guided lesson was as follows:

Prayer and slow breathing and focusing on the breath

Instant relaxation technique through isometric contraction of muscles

Tree posture and slow breathing and focusing on the breath in tree posture

Half-wheel posture and slowing breathing and focusing on the breath in half-wheel posture

Forward-bending posture and slow breathing and focusing on the breath in forward-bending posture

Backward-bending posture and slow breathing and focusing on the breath in backward-bending posture

Focusing on one's thoughts and emotions in the present and being aware of them

Trying to focus on the positive in most situations

Focusing on being happy

Deep relaxation technique in supine position.

Self-practice of the postures and breathing exercises once at home prior to the next lesson was recommended and to be maintained as a record by the primary caregiver. On an average, each centre had about 100 lessons in a period of two-years ( SD = 3.78).

The Bala Vihar instructors (n = 20) domiciled in each of the cities and affiliated to the Mission, conducted the lessons. They were trained by the master trainers of the monastic order and a webinar was organized to orient them to the study aims and objectives. They liaised with the clinical psychologists and the investigators for phase 1 and 2 data collection. A Google-group was created for continuous online support. The profile of the yoga instructors was as follows: age ( M = 44.78, SD = 5.04); gender (all women); education (bachelor's degree = 12, higher and professional qualifications = 8); occupation (paid employment = 7, homemakers = 13); duration of association with the Mission ( M = 21.23 years, SD = 6.08 years); and, experience in conducting Bala Vihar classes ( M = 16.78, SD = 4.86). The lessons were delivered in the city-wise centre premises with batches of participants (mean batch size = 32.67, SD = 3.46) and in English language.

Phase 1 and 2 outcome measure scores of the Bala Vihar participants and the comparison group were analysed through one-way analyses of variance and compared using paired t-tests. Multivariate analysis has been used to analyse phase 2 outcome measure scores. To determine the independent, pairwise and combined effects of independent socio-demographic predictor variables, anxiety screening scores and programme related moderators such as Bala Vihar lessons attended and self-practice on the emotional awareness and happiness outcome measures, Kruskal-Wallis non-parametric tests, Bonferroni's adjusted alpha levels and Pillai's trace statistics were used. The strongest predictor of phase 2 outcome measure scores was identified through hierarchical regression models.

Primary analyses comprise 1589 Bala Vihar yoga programme participants and 1589 children in the comparison group. The Bala Vihar yoga programme was delivered as intended in terms of average 100 one-hour lessons (once a week over a period of two years) in each of the cities. Through the two-year study period, across the 20 Bala Vihars, 74.64% of the participants attended majority (75-100) lessons and 66.21% of the participants regularly self-practiced. Table 2 depicts the said details. Figure 1 explains the flow of participants through each stage of the study.Flow of participants through each stage of the study

PHOTO (COLOR)

Bala Vihar yoga programme participation details

Bala Vihar yoga programme lessons attended in the study period

Reasons cited for more than 75% of classes missed (N = 403)

Self-practice

The intent-to-treat analyses compare the phase 1 and 2 outcome measure scores of the yoga programme participants and the comparison group. Phase 2 scores of the participant group have been further analysed to examine the significant independent predictors and moderators and the strongest predictor.

The average phase 1 EAQ-30 score of the comparison group children was 62.76 ( SD = 4.89). The average phase 1 SFAS score of comparison group children was 3.22 ( SD = .43). The effects of gender, class and primary caregiver were significant. Post hoc analyses using Scheffe's post hoc criterion for significance indicated that within the comparison group, phase 1 EAQ-30 cumulative scores were lower and SFAS scores were higher for girls vis-à-vis boys, for the middle class children vis-à-vis affluent children and for those whose mothers were their primary caregivers vis-à-vis fathers and grandparents/kin.

The average phase 2 EAQ-30 score of the comparison group children was 61.08 ( SD = 5.62). The average phase 2 SFAS score of the comparison group children was 3.31 ( SD = .52).The effects of gender, class and phase 2 PARS scores were significant. Post hoc analyses using Scheffe's post hoc criterion for significance indicated that within the comparison group, phase 2 EAQ-30 cumulative scores were lower and SFAS scores were higher for girls vis-à-vis boys, for middle class children vis-à-vis affluent children and for those who had lower phase 2 PARS scores vis-à-vis higher.

The average phase 1 EAQ-30 score of the Bala Vihar yoga programme participants was 61.22 ( SD = 4.78). The average phase 1 SFAS score of the Bala Vihar yoga programme participants was 3.29 ( SD = .48).The effects of gender, class and primary caregiver were significant. Post hoc analyses using Scheffe's post hoc criterion for significance indicated that within the Bala Vihar participant group, phase 1 EAQ-30 cumulative scores were lower and SFAS scores were higher for girls vis-à-vis boys, for middle class children vis-à-vis affluent children and for those whose mothers were their primary caregivers vis-à-vis fathers and grandparents/kin.

The average phase 2 EAQ-30 score of the Bala Vihar yoga programme participants was 51.65 ( SD = 1.41). The average phase 2 SFAS score of the Bala Vihar yoga programme participants was 5.38 ( SD = .96).The effects of city, gender, class, primary caregiver, living arrangement, siblings, phase 2 PARS scores, Bala Vihar yoga lessons attended and self-practice were significant. Post hoc analyses using Scheffe's post hoc criterion for significance indicated that within the Bala Vihar participant group, at phase 2, EAQ-30 cumulative scores were lower and SFAS scores were higher and for Bala Vihar yoga programme participants of American cities vis-à-vis Asian, African, Canadian and Australian cities, for girls vis-à-vis boys, for middle class participants vis-à-vis affluent participants, for those whose mothers were their primary caregivers vis-à-vis fathers/grandparents/kin, for those who lived with both parents vis-à-vis in a single parent household or with extended family, those who were single children vis-à-vis those who had siblings, for those who had lower phase 2 PARS scores vis-à-vis higher, for those who attended more than 75% of the Bala Vihar yoga lessons vis-à-vis fewer and for those who regularly self-practiced vis-à-vis those who did so occasionally.

Table 3 compares the phase 1 and 2 EAQ-30 and SFAS scores of the comparison group and Bala Vihar yoga programme participants. The scores on EAQ-30 and SFAS of the comparison group and Bala Vihar yoga programme participants were equal at baseline (phase 1) [Cohen's d = .21 (EAQ-30) and Cohen's d = .22 (SFAS)]. There was no significant change in the phase 2 scores of the comparison group [Cohen's d = .22 (EAQ-30) and Cohen's d = .24 (SFAS)]. Phase 2 SFAS scores of the Bala Vihar yoga programme participants were higher and EAQ-30 scores were lower than their own phase 1 scores [Cohen's d = .52 (EAQ-30) and Cohen's d = .54 (SFAS)] as well as the phase 2 scores of the comparison group[Cohen's d = .48 (EAQ-30) and Cohen's d = .53 (SFAS)].

The phase 2 EAQ-30 and SFAS were further subjected to a MANOVA with nine independent variables: city, gender, class, primary caregiver, living arrangement, siblings, phase 2 PARS scores, Bala Vihar yoga lessons attended and self-practice. Significant associations were examined further by non-parametric testing (Kruskal-Wallis). All the variables independently influenced the outcome measures. The combined effects of the following pairs of independent predictors and moderator variables (biographical variables and Bala Vihar participation variables) were significant: city, gender, class, primary caregiver, living arrangement, siblings, phase 2 PARS scores and Bala Vihar yoga lessons attended; city, gender, class, primary caregiver, living arrangements, siblings, phase 2 PARS scores and self-practice; and, Bala Vihar yoga lessons attended and self-practice.

Tests were further conducted using Bonferroni adjusted alpha levels of.0056 per test (.05/9). In addition to the independent and pair-wise effects of significant predictor variables, the combined effects were found significant. The average phase 2 EAQ-30 scores were lower under all the more significant independent predictor and moderator variables combined (i.e. for Bala Vihar yoga programme participants from American cities, girls, middle class children, those whose mothers were their primary caregivers, those who lived with both parents, single children, those who had lower phase 2 PARS scores, who attended at least 75% of the Bala Vihar yoga lessons and who regularly self-practiced) ( M = 50.82, SD = .53) vis-à-vis under all the less significant independent predictor and moderator variables (i.e. for Bala Vihar yoga programme participants from Asian, Australian, Canadian and African cities, boys, affluent children, those whose fathers or grandparents/kin were their primary caregivers, those who lived in single parent households or with extended family, children with siblings, who had higher phase 2 PARS scores, who attended fewer Bala Vihar yoga lessons and who occasionally self-practiced) ( M = 57.02, SD = 1.87), F (228, 1581) = 29.83, p = .001. Similarly, the average phase 2 SFAS scores were higher under all the more significant independent predictor and moderator variables combined ( M = 6.09, SD = .35) vis-à-vis under all the less significant independent predictor and moderator variables combined ( M = 4.32, SD = .61), F (228, 1581) = 31.29, p = .001. Table 4 depicts the significant multivariate effects.

Tables 5 and 6 depict the five-step hierarchical regression model of the variables predicting the phase 2 EAQ-30 and SFAS scores of the Bala Vihar yoga programme participants. At step 1, domicile city of the Bala Vihar yoga programme participants contributed significantly to the regression model F (19, 1588) = 17.31, p = .01, and accounted for 4.3% of the variation in the phase 2 EAQ-30 scores of the Bala Vihar yoga programme participants. Introducing the variables gender, class and primary caregiver explained an additional 15% of the variation in the phase 2 EAQ-30 scores of the Bala Vihar yoga programme participants and this change in RÇ was significant F (38, 1585) = 29.32, p = .02. Adding living arrangement, siblings and phase 2 PARS scores to the regression model further explained an additional 24.1% of the variation in the regression model and this change in RÇ was significant F (76, 1582) = 33.41, p = .03. At step 4, adding the moderator variable viz. Bala Vihar yoga lessons attended explained 13% of the variation in the phase 2 EAQ-30 scores of the participants and this change in RÇ was also significant F (228, 1581) = 22.89, p = .01. At step 5, adding the variable self-practice explained an additional 17% of the variation in the phase 2 EAQ-30 scores of the participants and this change in RÇ ² was significant F (228, 1580) = 6.08, p = .001. When all the variables were included in step 5 of the regression model, self-practice emerged as the most important predictor, which uniquely explained 17% of the variation in the phase 2 EAQ-30 scores. Together all the nine independent predictor and moderator variables accounted for 73.4% variation in the phase 2 EAQ-30 scores of the Bala Vihar yoga programme participants.

Table 6 depicts the five-step hierarchical regression model of the variables predicting the phase 2 SFAS scores of the Bala Vihar yoga programme participants. At step 1, domicile city of the Bala Vihar yoga programme participants contributed significantly to the regression model F (19, 1588) = 27.31, p = .02, and accounted for 5.1% of the variation in the phase 2 SFAS scores of the Bala Vihar yoga programme participants. Introducing the variables gender, class and primary caregiver explained an additional 17.2% of the variation in the phase 2 SFAS scores of the Bala Vihar yoga programme participants and this change in RÇ was significant F (38, 1585) = 32.98, p = .03. Adding living arrangement, siblings and phase 2 PARS scores to the regression model further explained an additional 26.2% of the variation in the regression model and this change in RÇ was significant F (76, 1582) = 41.09, p = .04. At step 4, adding the moderator variable viz. Bala Vihar yoga lessons attended explained 12% of the variation in the phase 2 SFAS scores of the participants and this change in RÇ was also significant F (228, 1581) = 18.28, p = .01. At step 5, adding the variable self-practice explained an additional 18% of the variation in the phase 2 SFAS scores of the participants and this change in RÇ ² was significant F (228, 1580) = 4.02, p = .001. When all the variables were included in step 5 of the regression model, self-practice emerged as the most important predictor, which uniquely explained 18% of the variation in the phase 2 SFAS scores. Together all the nine independent predictor and moderator variables accounted for 78.3% variation in the phase 2 SFAS scores of the Bala Vihar yoga programme participants.

Results support the initial hypotheses. Bala Vihar yoga programme participants had greater emotional awareness and scored higher on the happiness scale at phase 2 vis-à-vis the comparison group. The phase 2 emotional awareness and happiness scores of the Bala Vihar yoga programme participants was contingent on certain biographical and demographic variables, psychological screening markers such as anxiety state, and intervention moderators such as Bala Vihar yoga lessons attended and self-practice. The strongest predictor of phase 2 emotional awareness and happiness scores of the Bala Vihar yoga programme participants was self-practice.

The scores on emotional awareness and happiness measures of the comparison group and Bala Vihar yoga programme participants were equal at baseline. There was no significant change in the phase 2 scores of the comparison group. Within the comparison group, girls, middle class children, those whose mother were their primary caregivers and who were less anxious, had greater emotional awareness and scored higher on the smiley faces scale vis-à-vis their counterparts. This was also so for Bala Vihar yoga programme participants at phase 1. Phase 2 SFAS scores of the Bala Vihar yoga programme participants were higher and EAQ-30 scores were lower than their own phase 1 scores as well as the phase 2 scores of the comparison group. Hence Bala Vihar yoga programme participants were more emotionally aware and happier after undergoing the Bala Vihar yoga lessons. Within the participant group, at phase 2, children from American cities, girls, middle class children, those whose mothers were their primary caregivers, who lived in standard family arrangements (i.e. with both parents), who were single children (had no siblings), who were less anxious (had lower phase 2 PARS scores), who attended more than 75% of the Bala Vihar yoga lessons and who regularly self-practiced, were more emotionally aware and happier than their counterparts.

Multivariate analyses showed that participants from American cities, girls, middle class children, those who mothers were their primary caregivers, those who lived in standard family arrangements, single children and those who were less anxious, were more likely to attend more yoga lessons and also regularly self-practice. The strongest predictor of phase 2 outcome measure scores of the participant group was self-practice of the yoga lessons.

Overall the results support the effectiveness of the Bala Vihar yoga programme lessons in enhancing emotional awareness and promoting happiness among its participants. Greater responsiveness to the programme by children from American cities vis-à-vis Asian, Australian, Canadian, and African cities, could be attributed to the differential impact of diaspora or cultural issues, though a more focused research is required to propose this. There was a clear gender difference, with girls being more emotionally aware and happier in the pre-programme phase, and responding more positively to the programme. This corroborates the literature on emotional awareness, which emphasises girls being more emotionally aware than boys (e.g. Brody and Hall 2000; Fernández-Berrocal et al. 2012; Naghavi and Redzuan 2011) and also points to a need for a more focused and intensive yoga programme for boys. This specialized training, specifically for boys, may include through positive modelling, spaces to connect with each other and have experiences that provide them opportunities to feel a greater range of emotions and be comfortable with the same.

Middle class children were at a pre-programme advantage in terms of being more emotionally aware and happier as well as more responsive to the yoga programme vis-à-vis their affluent counterparts. There is some research discussing affluent/upper class children experiencing greater emotional difficulties, thereby making them less emotionally aware (e.g. Luthar and Becker 2002; Luthar and Latendresse 2005), to which the present investigation adds that customized programmes aiming at enhancing emotional awareness may need more intensive inputs for affluent children as compared to middle class children. Having an involved mother, living in a standard family set-up (with both parents/caregivers) and being an only child, were robust predictors of being more responsive to the yoga programme. While overall the participant and comparison group sample was essentially a non-anxious one (i.e. there were no persistent and pathological symptoms of anxiety disorders), those who significantly scored lower on the anxiety screening marker (PARS) during phase 2, were more emotionally aware and happier vis-à-vis their counterparts who were more anxious. There is ample evidence on the link between being less anxious and more emotionally aware (e.g. Suveg and Zeman 2004; Mathews et al. 2016), and this investigation highlights that the yoga programme would need to be modified to specifically address child participant population experiencing chronic anxiety disorders. Further, self-involvement in the yoga programme by attending all lessons and regular self-practice were strong predictors of programme effectiveness, which substantiates the yoga technique literature that emphasises its efficacy as contingent on participants' self-engagement (e.g. Prakash et al. 2010). The robust independent predictors (participant children from American cities, girls, middle class participants, those whose mothers were their primary caregivers, who lived in standard family set-ups, and single children), gave further impetus to programme related moderators (Bala Vihar yoga lessons attendance and self-practice), which in turn determined programme effectiveness.

The study has some of the following limitations. There is no qualitative data capturing the experiences of Bala Vihar yoga programme participants on their emotional awareness and happiness states upon undergoing the programme. These nuances would need to be explored in a further investigation. Secondly, the pre- and post-test data was collected in this repeated measures design at two specific phases only. To examine iterative changes in emotional awareness and happiness, frequent and periodic measurement of the outcomes would be required. Thirdly, the PARS measure was used as a screening instrument, with the results affirming that the participant and comparison group comprised a generally non-anxious population. An investigation would be required with anxiety as a moderating variable impacting emotional awareness and happiness. Fourthly, both emotional awareness and happiness are dependent outcomes in the present study. As emotional awareness is theoretically a precursor to happiness states, a mediation analysis would be required. Finally, some other intervening variables such as significant life course events, formal school environments, and other siblings related variables such as sibling biographies and birth order, which have not been considered or controlled for in the present study, highlights the need for further research.

Results have the following practical applications. The Bala Vihar yoga programme is effective in promoting emotional awareness and happiness among children across cultural settings. The customized programme would need further modifications and intensification for boys, affluent children, children cared for by fathers/kin and living in non-standard family set-ups, and those clinically diagnosed with anxiety disorders. For the diaspora Hindu children from Asian, African, Australian and Canadian cities, the customized yoga programme may need to incorporate culturally relevant exercises or instructions to facilitate greater effectiveness. The scope of the programme outreach is restricted to Hindu children, however, some components such as postures and meditation could also be replicated with a wider cross-section of children.

Acknowledgments are due to all the Bala Vihar instructors of the 20 Bala Vihar classes for conducting the lessons. Acknowledgements are also due to the clinical psychologists who administered PARS and trained investigators in each of the cities who administered the schedules and collected the data. Bala Vihar instructors conducted the lessons on an honorary basis and clinical psychologists and trained investigators were offered a small honorarium.

There is no conflict of interest to report for this submission.

All procedures followed were in accordance with the ethical standards of research, conforming to the norms prescribed by the independent ethics committee of the University of Mumbai, India, and in compliance with the Helsinki Declaration of 1975, as revised in 2000.

Informed consent was sought from the participant and comparison group children and their primary caregivers.
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By Samta P. Pandya

Child & Youth Care Forum is a copyright of Springer, 2018. All Rights Reserved.

# data[].attributes.attachment_link.title
Yoga, Emotional Awareness and Happiness in Children: A Multi-City Study of ...: Discovery Service for University of Western States

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Background: Practice of yoga has been linked to emotional awareness and positive moods as characteristics of happiness.

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https://www.ncbi.nlm.nih.gov/pubmed/30547722

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Brittany Santeford

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Detailed Record

Title:

Authors:

Wu CH; Graduate Institute of Athletics and Coaching Science, National Taiwan Sport University, Taoyuan County, Taiwan, Republic of China.
Karageorghis CI; Department of Life Sciences, Brunel University London, Middlesex, UK.
Wang CC; Department of Physical Education, National Taiwan Normal University, Taipei, Taiwan, Republic of China.
Chu CH; Department of Physical Education, National Taiwan Normal University, Taipei, Taiwan, Republic of China.
Kao SC; Department of Health and Kinesiology, Purdue University, IN, USA.
Hung TM; Department of Physical Education, National Taiwan Normal University, Taipei, Taiwan, Republic of China; Institute for Research Excellence in Learning Science, National Taiwan Normal University, Taipei, Taiwan, Republic of China.
Chang YK; Department of Physical Education, National Taiwan Normal University, Taipei, Taiwan, Republic of China; Institute for Research Excellence in Learning Science, National Taiwan Normal University, Taipei, Taiwan, Republic of China. Electronic address: yukaichangnew@gmail.com.

Source:

Journal Of Science And Medicine In Sport [J Sci Med Sport] 2019 Dec; Vol. 22 (12), pp. 1367-1372. Date of Electronic Publication: 2019 Jul 24.

Publication Type:

Journal Article

Language:

English

Journal Info:

Publisher: Elsevier Australia Country of Publication: Australia NLM ID: 9812598 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-1861 (Electronic) Linking ISSN: 18781861 NLM ISO Abbreviation: J Sci Med Sport Subsets: In Process; MEDLINE

Imprint Name(s):

Publication: Victoria : Elsevier Australia
Original Publication: Belconnen : Sports Medicine Australia, [1998?-

Abstract:

Objectives: This study addressed the effects of acute, moderate-intensity aerobic and resistance exercise on the shifting aspect of cognition following a 30-min recovery period. It also explored the neuro-electrical activation that underlies the relationship between acute exercise and cognitive function through the examination of P3b and N1 components of event-related potentials.
Design: A counterbalanced, repeated-measures experimental design.
Methods: Thirty-five volunteer young adults completed two experimental sessions (i.e., acute aerobic exercise (AE) and resistance exercise (RE), matched in terms of intensity, and one reading session (control). The AE entailed cycling at 60-70% of maximal heart rate reserve for 30min. In the RE session, participants performed seven exercises with two sets of 8-12 repetitions at 70% of 10-repetition maximum. Each participant's neuro-electrical activation was recorded 30min after each session while s/he completed the task-switching test.
Results: After the 30-min recovery period, both AE and RE elicited shorter response times in global switching (ηp2=0.24) and local switching (ηp2=0.16) were observed when compared to control. Additionally, larger P3b amplitudes (but not N1 amplitudes) were evident in global switching (ηp2=0.15) and local switching (ηp2=0.16), regardless of exercise modality.
Conclusions: The present findings suggest that acute exercise has positive effects on cognitive function. Exercise-induced alterations during the later stages of mental processing might result in superior performance. There were significant selective benefits in terms of brain function regardless of exercise modality.
(Copyright © 2019 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Contributed Indexing:

Keywords: Brain function; EEG; Executive function; Physical activity; Shifting

Entry Date(s):

Date Created: 20190826 Latest Revision: 20191102

Update Code:

20191102

DOI:

10.1016/j.jsams.2019.07.009

PMID:

31445953

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Effects of acute aerobic and resistance exercise on executive function: An ...: EBSCOhost

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Title: Authors: Wu CH; Graduate Institute of Athletics and Coaching Science, National Taiwan Sport University, Taoyuan County, Taiwan, Republic of China. Karageorghis CI; Department of Life Sciences, Brunel University London, Middlesex, UK. Wang CC; Department of Physical Education, National Taiwan Normal University, Taipei, Taiwan, Republic of China.

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Hi

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http://web.a.ebscohost.com/ehost/detail/detail?vid=3&sid=430ba5b7-36e5-4d3b-9fff-2efcf0a64d26%40sessionmgr4006&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#AN=141077312&db=aph

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http://eds.a.ebscohost.com/eds/detail/detail?vid=2&sid=2e00d1f6-7edd-4973-9da2-70e613a869bf%40sdc-v-sessmgr03&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=28185806&db=mdc

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https://www.ncbi.nlm.nih.gov/pubmed/31901896

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Hyman, Mark. The Ultramind Solution : Fix Your Broken Brain by Healing Your Body First. London: Simon & Schuster, 2010. Print.

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http://eds.b.ebscohost.com/eds/viewarticle/render?data=dGJyMPPp44rp2%2fdV0%2bnjisfk5Ie46bdIrqqzTbOk63nn5Kx94um%2bSa2nrkewprBInqevUq6ouEyulr9lpOrweezp33vy3%2b2G59q7SbOosUm1prBJrpzqeezdu33snOJ6u%2bPie6Tq33%2b7t8w%2b3%2bS7SravtFGyqq4%2b5OXwhd%2fqu37z4uqM4%2b7y&vid=0&sid=5941ff76-be0c-4525-94f3-ae2c591472c7@sessionmgr103

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C1 adjustment is all you need

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https://www.geriatric.theclinics.com/article/S0749-0690(09)00090-1/abstract

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http://eds.a.ebscohost.com/eds/detail/detail?vid=7&sid=3a866291-1864-4a3b-b26b-0b028d509252%40sessionmgr4007&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=128837244&db=s3h

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Moderate-to-vigorous physical activity, but not sedentary time, predicts changes in cardiometabolic risk factors in 10-y-old children: the Active Smarter Kids Study.

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http://web.b.ebscohost.com/ehost/detail/detail?vid=6&sid=d35d5781-250c-4626-b522-75ba5f7f493a%40sessionmgr102&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#AN=31445953&db=mdc

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http://eds.a.ebscohost.com/eds/detail/detail?vid=2&sid=75af916a-8f68-4bbe-b179-d5cc44c13810%40sdc-v-sessmgr01&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=120928205&db=pbh

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Aerobic exercise to improve cognitive function in older people without known cognitive impairment

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Learn and Burn
https://link.gale.com/apps/doc/A173711543/PPSM?u=s9004555&sid=PPSM&xid=78afd0ee

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The role of glucose homeostasis on immune function in response to exercise: The impact of low or higher energetic conditions.

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The role of glucose homeostasis on immune function in response to exercise: The impact of low or higher energetic conditions.

# data[].attributes.body
http://eds.a.ebscohost.com/eds/detail/detail?vid=3&sid=75bf1ad7-6fc6-4bfb-b858-feeebef19d62%40sdc-v-sessmgr03&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=83522178&db=pbh

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OBJECTIVES:

The aim of the study was to compare the hippocampus sizes of healthy medical faculty students, who were exposed to an intense data input and who underwent serious learning activity and those of healthy sport faculty students who did sports regularly by using MR images and to examine the relationship between a hippocampus size and intelligence.

METHODS:

We made the study with 58 healthy young males (27 sport sciences faculty students and 31 medical faculty students). R. B. Cattell 3A Culture Fair Intelligence Test was administered to the volunteers. Following this, we got MR images of our volunteers.

RESULTS:

We could not find a statistically significant difference between medical faculty students and sport sciences faculty students in terms of hippocampus volumes. We could not find a correlation between IQ values and hippocampus volumes. Also, we could not find a significant difference between a right and left hippocampus.

CONCLUSION:

While doing sport, blood flow increases in the hippocampus, as in all areas of the body. This increased blood flow creates a stimulating effect on neurogenesis. Neurons, which develop as the result of neurogenesis, mean an increase in volume (Tab. 4, Ref. 54).

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Exercise is good also for a healthy hippocampus.

# data[].attributes.attachment_link.description
OBJECTIVES: The aim of the study was to compare the hippocampus sizes of healthy medical faculty students, who were exposed to an intense data input and who underwent serious learning activity and those of healthy sport faculty students who did sports regularly by using MR images and to examine the relationship between a hippocampus size and intelligence.

# data[].attributes.body
http://eds.a.ebscohost.com/eds/viewarticle/render?data=dGJyMPPp44rp2%2fdV0%2bnjisfk5Ie46bdIrqqzTbOk63nn5Kx94um%2bSa2nrkewprBInqeuUrKquEi3lr9lpOrweezp33vy3%2b2G59q7Tramt1GxqLBJpOLfhuWz44ak2uBV4d%2fsSq6c8nnls79mpNfsVa%2bqrki2r7VIt5zkh%2fDj34y73POE6urjkPIA&vid=0&sid=c924fa13-f34b-41ef-ae74-0a2cf94e0008@sdc-v-sessmgr01

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Intensity and workload related dose-response effects of acute resistance exercise on domain-specific cognitive function and affective response – A four-armed randomized controlled crossover trial.

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Simon Bruckner - Exercise Makes You Smarter

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Simon Bruckner - Exercise Makes You Smarter

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Hello

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Effect of exercise on mental health for pregnant women

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Physical Activity and Intelligence: A Causal Exploration

http://eds.b.ebscohost.com/eds/detail/detail?vid=4&sid=d8125e36-cc66-4e2b-a35b-30d7aeaba34d%40pdc-v-sessmgr05&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=73951903&db=s3h

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Carson turner Preventing cognitive decline

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Carson turner Preventing cognitive decline

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Rates of age- and amyloid β-associated cortical atrophy in older adults with superior memory performance.

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Effects of exercise on brain function: role of free radicals
https://pdfs.semanticscholar.org/d205/385f65afabec64c77e9fe4984025ddeb96ac.pdf

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Sharpen your edge scientists find more evidence that exercise makes you smarter

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Sharpen your edge scientists find more evidence that exercise makes you smarter

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BACKGROUND:

Food consumption and nutritional status affect an individual's health throughout their life-course and an unhealthy diet is a major risk factor for the current global burden of chronic disease. The promotion of health and good nutrition through healthy eating requires the active involvement of all health professionals including chiropractors. This paper reports findings from the first nationally representative examination of the use of nutritional guidance within chiropractic patient management in Australia.

METHODS:

A sample of 1000 practising chiropractors was randomly selected from the Australian Chiropractic Research Network (ACORN) practice-based research network database for a cross-sectional study and 33% participated in the online survey in November 2016. The questionnaire, based on previous designs used in similar surveys and nutrition resources developed by the National Health and Medical Research Council, was pretested prior to the survey. Pearson's Chi square and bivariate logistic regression were undertaken to explore relationships with variables of interest.

RESULTS:

The demographic details of the respondents are similar to those of the chiropractic workforce registered in Australia. Most chiropractors provided nutritional advice as part of their patient care and around a quarter provided specific dietary advice to their patients, including the use of nutrition supplements. Nutrition-related conditions most commonly encountered by the chiropractors were musculoskeletal, usually inflammatory in origin. Common nutritional assessment methods used included questioning patients to assess their nutritional and health status and physical appearance. Most of the participants provided nutritional resources to their patients in their clinics. However, the Australian Dietary Guidelines and the accompanying Australian Guide to Healthy Eating were not well utilised by the respondents. Australian chiropractors often referred patients with nutrition issues to qualified dietitians and other health professionals when deemed necessary.

CONCLUSIONS:

Australian chiropractors regularly provide nutritional advice and appear to acknowledge the importance of nutrition in their clinical practice especially for patients presenting with chronic disease. If chiropractors are to fulfil their potential in providing such wider public health and preventative health advice to patients, further research examining the utilisation of evidence-based nutritionresources within chiropractic patient management is recommended.

KEYWORDS:

Chiropractors; Diet; Dietary supplements; Health promotion; Nutrition; Nutritional assessment; Nutritional guidance

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What y’all doin

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https://pubmed.ncbi.nlm.nih.gov/27856411-personalising-exercise-recommendations-for-brain-health-considerations-and-future-directions/?from_term=Exercise+AND+brain&from_size=10&from_pos=4

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Hi

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https://www.ncbi.nlm.nih.gov/pubmed/31909172

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Active Smarter Kids (ASK)

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Citation

Title:

Authors:

Resaland GK; Faculty of Teacher Education and Sports, Sogn og Fjordane University College, Sogndal, Norway. geirkr@hisf.no.
Moe VF; Faculty of Teacher Education and Sports, Sogn og Fjordane University College, Sogndal, Norway. vegard.fusche.moe@hisf.no.
Aadland E; Faculty of Teacher Education and Sports, Sogn og Fjordane University College, Sogndal, Norway. eivind.aadland@hisf.no.
Steene-Johannessen J; Faculty of Teacher Education and Sports, Sogn og Fjordane University College, Sogndal, Norway. jostein.steene-johannessen@nih.no.
Glosvik Ø; Faculty of Teacher Education and Sports, Sogn og Fjordane University College, Sogndal, Norway. oyvind.glosvik@hisf.no.
Andersen JR; Faculty of Health Studies, Sogn og Fjordane University College, Førde, Norway. john.andersen@hisf.no.; Centre of Health Research, Førde Central Hospital, Førde, Norway. john.andersen@hisf.no.
Kvalheim OM; Faculty of Health Studies, Sogn og Fjordane University College, Førde, Norway. olav.kvalheim@uib.no.; Department of Chemistry, University of Bergen, Bergen, Norway. olav.kvalheim@uib.no.
McKay HA; Department of Family Practice, Faculty of Medicine, The University of British Columbia, Vancouver, Canada. heather.mckay@ubc.ca.
Anderssen SA; Faculty of Teacher Education and Sports, Sogn og Fjordane University College, Sogndal, Norway. s.a.anderssen@nih.no.; Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway. s.a.anderssen@nih.no.

Corporate Authors:

ASK study group

Source:

BMC Public Health [BMC Public Health] 2015 Jul 28; Vol. 15, pp. 709. Date of Electronic Publication: 2015 Jul 28.

Publication Type:

Journal Article; Randomized Controlled Trial

Language:

English

Journal Info:

Publisher: BioMed Central Country of Publication: England NLM ID: 100968562 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2458 (Electronic) Linking ISSN: 14712458 NLM ISO Abbreviation: BMC Public Health Subsets: MEDLINE

Imprint Name(s):

Original Publication: London : BioMed Central, [2001-

MeSH Terms:

Achievement*
Health Status*
Exercise/*psychology
Health Promotion/*methods
School Health Services/*statistics & numerical data
Child ; Cluster Analysis ; Female ; Health Promotion/statistics & numerical data ; Humans ; Male ; Norway ; Obesity/prevention & control ; Physical Education and Training ; Primary Prevention ; Quality of Life ; Risk Factors

Abstract:

Background: Evidence is emerging from school-based studies that physical activity might favorably affect children's academic performance. However, there is a need for high-quality studies to support this. Therefore, the main objective of the Active Smarter Kids (ASK) study is to investigate the effect of daily physical activity on children's academic performance. Because of the complexity of the relation between physical activity and academic performance it is important to identify mediating and moderating variables such as cognitive function, fitness, adiposity, motor skills and quality of life (QoL). Further, there are global concerns regarding the high prevalence of lifestyle-related non-communicable diseases (NCDs). The best means to address this challenge could be through primary prevention. Physical activity is known to play a key role in preventing a host of NCDs. Therefore, we investigated as a secondary objective the effect of the intervention on risk factors related to NCDs. The purpose of this paper is to describe the design of the ASK study, the ASK intervention as well as the scope and details of the methods we adopted to evaluate the effect of the ASK intervention on 5 (th) grade children.
Methods& Design: The ASK study is a cluster randomized controlled trial that includes 1145 fifth graders (aged 10 years) from 57 schools (28 intervention schools; 29 control schools) in Sogn and Fjordane County, Norway. This represents 95.3 % of total possible recruitment. Children in all 57 participating schools took part in a curriculum-prescribed physical activity intervention (90 min/week of physical education (PE) and 45 min/week physical activity, in total; 135 min/week). In addition, children from intervention schools also participated in the ASK intervention model (165 min/week), i.e. a total of 300 min/week of physical activity/PE. The ASK study was implemented over 7 months, from November 2014 to June 2015. We assessed academic performance in reading, numeracy and English using Norwegian National tests delivered by The Norwegian Directorate for Education and Training. We assessed physical activity objectively at baseline, midpoint and at the end of the intervention. All other variables were measured at baseline and post-intervention. In addition, we used qualitative methodologies to obtain an in-depth understanding of children's embodied experiences and pedagogical processes taking place during the intervention.
Discussion: If successful, ASK could provide strong evidence of a relation between physical activity and academic performance that could potentially inform the process of learning in elementary schools. Schools might also be identified as effective settings for large scale public health initiatives for the prevention of NCDs.
Trial Registration: Clinicaltrials.gov ID nr: NCT02132494 . Date of registration, 6(th) of May, 2014.

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Molecular Sequence:

ClinicalTrials.gov NCT02132494

Entry Date(s):

Date Created: 20150729 Date Completed: 20160210 Latest Revision: 20181202

Update Code:

20181211

DOI:

10.1186/s12889-015-2049-y

PMID:

26215478

Database:

MEDLINE Complete

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Active Smarter Kids (ASK): Rationale and design of a cluster-randomized con...: Discovery Service for University of Western States

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Title: Authors: Resaland GK; Faculty of Teacher Education and Sports, Sogn og Fjordane University College, Sogndal, Norway. geirkr@hisf.no. Moe VF; Faculty of Teacher Education and Sports, Sogn og Fjordane University College, Sogndal, Norway. vegard.fusche.moe@hisf.no. Aadland E; Faculty of Teacher Education and Sports, Sogn og Fjordane University College, Sogndal, Norway. eivind.aadland@hisf.no.

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https://www.ncbi.nlm.nih.gov/pubmed/28912076

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Hi

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The effectiveness of exercise intervention for academic achievement, cognitive function, and physical health among children in Mongolia: a cluster RCT study protocol

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The effectiveness of exercise intervention for academic achievement, cognitive function, and physical health among children in Mongolia: a cluster RCT study protocol

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Exercise as a Positive Modulator of Brain Function

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https://uws.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=s3h&AN=73951903&site=eds-live&scope=site

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Exercise

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Exercise as Positive Modulator of Brain Function

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Exercise as Positive Modulator of Brain Function

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