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Health Behavior and Health Education | Part Three, Chapter Eight: Key Constructs

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For more information: Winett RA, Anderson ES, Whiteley JA, Wojcik JR, Rovniak LS, Graves KD, Galper DI, Winett SG. Church-based health behavior programs: Using Social Cognitive Theory to formulate interventions for at-risk populations. Applied & Preventive Psychology 1999; 8:129-142.

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How parents are having to bribe distracted children to read: Sweets, later bedtimes and trips to the zoo being used to encourage youngsters to pick up a book 

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How parents are having to bribe distracted children to read: Sweets, later bedtimes and trips to the zoo being used to encourage youngsters to pick up a book 

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How parents are having to bribe distracted children to read: Sweets, later bedtimes and trips to the zoo being used to encourage youngsters to pick up a book

Almost two-thirds of parents admit to treating youngsters to incentives

Stickers and star charts most popular incentive to get children to read

Late bedtimes and money also popular ways of encouraging habit

Parents are bribing their children to read with sweets, money and trips to theme parks amid the rise of 'digital distractions', a report published today reveals.

Almost two-thirds admit to giving youngsters kickbacks such as visits to the zoo and chocolates if they pick up a book.

A survey of over 500 families with children aged three to eight found that 60 per cent admitted 'bribing' their children to encourage reading.

Rewards included stickers and star charts (32 per cent); late bedtimes (23 per cent); extra screen time (14 per cent); money (eight per cent); trips to theme parks and zoos (seven per cent) and sweets and chocolates (five per cent).

The study was carried out by education publisher, Rising Stars, to mark the launch of its new reading scheme, Reading Planet.

Maria Casemore, 44, a mother-of-three, from south London, said: 'My children do like reading but by the end of the day when they've had a full on day at school, probably an after school club, then when they have to do homework and practise their spellings for the next day, getting them to read the school reading book can be a bit of a struggle.

'I tend to dangle an incentive when I know they are tired and don't want to do it. This may include the promise of an ice-cream on Friday after school, a comic at the weekend or the chance to go and choose a book of their own from our local bookshop.'

Marie Jones, a mother-of-three children from Belfast, added: 'I remember reading somewhere that the gift of learning is reading. I've always been a bit strict about getting my kids to read but it hasn't been easy.

'I have to bribe them to read and for my youngest right now I would bribe her with a nice experience, so going to see a show, going to the theatre, going to a country house or afternoon tea.

'Once a month a good bribe works wonders and we choose the bribe at the beginning of the book and it's a book a bribe.'

Helen Parker, publisher at Rising Stars, said: 'In an era when there are so many digital distractions, parents are having to incentivise their children to pick up a book with promises of late bedtimes, trips out and even money and sweets.

'We need books that are as up-to-date, exciting and attractive as possible so that children want to spend time reading.'

A separate survey of 250 primary school teachers commissioned by Rising Stars found that staff are also using incentives.

As well as giving stickers (75 per cent), more than 30 per cent promise creative activities such as drawing or painting in return for extra reading.

Over a quarter give pupils additional playtime.

The teachers also attempted to quantify the impact of parents regularly reading with their young children after school and at weekends.

More than 80 per cent of staff estimated that reading at home most nights put children months ahead.

Nearly a quarter (22 per cent) said the advantage in their reading age was over six months and up to a year.

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How parents are having to bribe distracted children to read

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Almost two-thirds of parents admit to treating youngsters to incentives Stickers and star charts most popular incentive to get children to read Late bedtimes and money also popular ways of encouraging habit Parents are bribing their children to read with sweets, money and trips to theme parks amid the rise of 'digital distractions', a report published today reveals.

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Pew Research Center: Internet, Science & Tech

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Mothers are especially likely to give and receive support on social media

Social media networks have become vital channels for Americans' daily interactions. Users rely on these platforms to keep in touch with family and friends, gather information and share what is important to them. This report explores how parents - 75% of whom use social media - turn to social media for parenting-related information and social support.

Mothers are heavily engaged on social media, both giving and receiving a high level of support via their networks.

Social media networks are host to a wide range of human experiences; they help connect people with one another in both good times and bad. Parents - in this study defined as those with children under 18 - are especially likely to try to respond to the good news others post, answer others' questions or receive support via online networks. This is true for all kinds of personal matters they encounter - not just parenting posts. While somewhat less common, a majority of parents agree that they try to respond to bad news as well. Mothers are more likely than fathers to engage their networks across a variety of these outreach and support examples:

81% of parents who use social media try to respond to good news others share in their networks, including 45% of social-media-using parents who "strongly agree" that they do so. Some 53% of mothers say they "strongly agree," compared with 33% of fathers who say that.

74% of parents who use social media get support from their friends there. Digging into the data, 35% of social-media-using parents "strongly agree" that they get support from friends on social media. Fully 45% of mothers who use social media "strongly agree" that they get support from friends on social media, compared with just 22% of fathers.

71% of all parents on social media try to respond if they know the answer to a question posed by someone in their online network. About a third (32%) of parents who use social media "strongly agree" that they try to respond to questions. Mothers and fathers are relatively similar in their responsiveness to questions on social media; 35% of mothers say they "strongly agree" that they make an effort to respond to questions, compared with 28% of fathers.

58% of parents who use social media try to respond when a friend or acquaintance shares bad news online. Mothers are particularly likely to "strongly agree" that they try to do this - 31% say so, compared with 21% of fathers.

Social media is broadly viewed as a source of useful information and as one parenting tool among a collection of options. Mothers use it as a parenting resource slightly more often than fathers.

While a large share of parents find value in social media as a general information resource, fewer say they come across useful parenting information while using social media. At the same time, one-in-four say they get support from their networks for parenting issues, and mothers who use social media are more than three times as likely as fathers to say they get support.

79% of parents who use social media agree that they get useful information via their networks. One-in-three (32%) "strongly agree" that they get useful information. Mothers are just as likely as fathers to "strongly agree" that they find useful information through their social media networks (35% vs. 27%).

59% of social-media-using parents indicate that they have come across useful information specifically about parenting in the last 30 days while looking at other social media content. Mothers are particularly likely to encounter helpful parenting information - 66% have done so in the last 30 days, compared with 48% of fathers.

42% of these parents have received social or emotional support from their online networks about a parenting issue in the last 30 days. This includes 50% of mothers, compared with 28% of fathers on social media.

31% of parents who use social media have posed parenting questions to their online networks in the last 30 days. Mothers and fathers are equally likely to do so.

Few parents say they have felt uncomfortable when information about their children is shared by other family members or caregivers on social media.

Most parents have not felt uneasy about the content posted about their children by other family members or caregivers on social media.

12% of all parents of children under 18 say they have ever felt uncomfortable about something posted about their child on social media by a spouse, family member or friend. Fully 88% say they have not felt this way.

11% of all parents have ever asked for content about their child posted by a family member, caregiver or friend to be removed from social media.

Parents, like non-parents, use a variety of social media platforms.

This survey also took a broad look at the social media habits of parents. Among internet-using parents, social media use across a variety of platforms is common. Parents look a lot like non-parents in this regard, though there are differences between mothers and fathers:

Three-quarters of online parents use Facebook, as do 70% of non-parents. Mothers are more likely to use Facebook than fathers, with 81% of moms and 66% of dads using the platform.

28% of online parents use Pinterest, including 40% of mothers and 15% of fathers.

27% of online parents use LinkedIn. Mothers and fathers are equally likely to use LinkedIn.

25% of online parents are Instagram users. Mothers are more likely than fathers to use the platform, 30% vs. 19%. Younger parents (those under 40) also are more likely to use Instagram than older parents, 33% vs. 18%.

23% of online parents use Twitter. Moms and dads are equally as likely to use Twitter.

Parents are particularly active on Facebook and LinkedIn, while non-parents use Instagram more frequently.

Parents on Facebook are especially avid users: 75% log on daily, including 51% who do so several times a day. This is a statistically significant difference when compared with non-parents, of whom 67% log on to Facebook daily, including 42% who do so several times a day. Mothers on Facebook are more likely to check the platform several times a day compared with fathers, 56% vs. 43%.

Parents who use LinkedIn are more likely than non-parents to use the site daily (19% vs. 10% of non-parents).

Parents who use Instagram are not as active as non-parent users. Some 54% of non-parents who use Instagram say they use the site daily, compared with 39% of parents.

Among Twitter and Pinterest users, there are few statistically significant differences in how frequently parents and non-parents use the platforms.

How parents use Facebook

Given Facebook's status as the most popular social media platform, this report takes a deeper look into the network composition and habits of parents on the network: Three-quarters (74%) of parents use Facebook.

Parents, particularly mothers, interact with their networks frequently.

Fully 94% of Facebook-using parents share, post or comment on Facebook (as opposed to simply reading or viewing content), with 70% of parents doing so "frequently" or "sometimes." This compares to a statistically similar 93% of non-parents who say they ever post content to the platform.

Overall, mothers and fathers who use Facebook are equally likely to say they ever share, post, or comment on Facebook. However, mothers are more likely to do this with greater frequency. Some 76% of mothers on Facebook say they do so "frequently" or "sometimes," compared with 61% of fathers who say the same.

A typical parent has 150 Facebook friends. One-third of them are "actual" friends.

Among parents, the median number of Facebook friends is 150, while the typical non-parent Facebook user has a median of 200 friends. The largest group of parents on Facebook (42%) have between 0 and 100 Facebook friends on the network. Another 20% of parents say they have between 101 and 250 friends, while 23% have between 251 and 500 friends. Non-parents are more likely than parents to have very large Facebook networks - 16% of non-parent Facebook users have more than 500 Facebook friends, compared with 11% of parents.

Facebook networks are composed of a variety of people, not all of whom are necessarily considered close. Parents who use Facebook were asked how many of their friends on the platform they considered "actual friends." A majority, 76%, said they considered between 0 and 100 of their Facebook friends "actual friends." The typical parent reports a median of 50 "actual" Facebook friends, while the typical non-parent counts 40 of their Facebook friends as "actual" friends. As noted above, parents report having a median of 150 Facebook friends, and non-parents report having a median of 200 Facebook friends. Thus, a typical parent's Facebook network is about one-third "actual" friends, while non-parents typically report that less than a quarter of their network is made up of actual friends.

Family and friends make up the bulk of Facebook networks for parents and non-parents alike.

Family and friends, both new and old, are the most common types of connections in users' Facebook networks.

93% of parents who use Facebook are friends with family members other than their parents or children on the network.

88% are Facebook friends with their current friends.

88% are connected with friends from the past, such as high school or college classmates.

60% are friends with their work colleagues.

53% are friends with their parents.

47% are friends with their children on Facebook.

41% are connected with people they have never met in person.

41% are friends with their neighbors.

Parents are more likely to be Facebook friends with their parents than non-parents, 53% vs. 40%. They also are more likely to be friends with their neighbors on the network, 41% vs. 34% of non-parents.

Age is a major factor in who makes up the bulk of parents' Facebook networks. Some 93% of younger parents (those under age 40) who use Facebook are connected with friends from the past. This is significantly more than the 83% of older parents (those ages 40 and older) who say the same. Younger parents also are more likely to be Facebook friends with their own parents (71% vs. 35%).

Older parents, those ages 40 and above, are more likely to be friends with their children on Facebook. Some 65% said so, compared with 30% of younger parents. These findings likely reflect the fact that Facebook requires users to be ages 13 and older.

The data reported here were collected in two telephone surveys conducted by PSRAI and fielded from Sept. 11 to 14, 2014, and Sept. 18 to 21, 2014. The surveys interviewed a nationally representative sample of 2,003 American adults ages 18 and older. Telephone interviews were conducted by landline (1,002) and cell phone (1,001, including 594 without a landline phone). Interviews were conducted in English and Spanish by Princeton Data Source from Sept. 11 to 14, 2014, and Sept. 18 to 21, 2014. Statistical results are weighted to correct known demographic discrepancies. The margin of sampling error for the complete set of weighted data is ± 2.5 percentage points.

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Mothers are especially likely to give and receive support on social media Social media networks have become vital channels for Americans' daily interactions. Users rely on these platforms to keep in touch with family and friends, gather information and share what is important to them.

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Don’t panic! Here’s how to make screens a positive in family life

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Don’t panic! Here’s how to make screens a positive in family life

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Don't panic! Here's how to make screens a positive in family life

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Yes, too much digital isolation can be a problem... but parents who integrate new media more wisely will reap rewards

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Campaign for a Commercial-Free Childhood

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Changing children's screen habits can be a challenge for both kids and parents. That's why we created "7 Parent-Tested Tips to Unplug and Play," strategies for getting young kids to spend less time with screens from real parents who have done it and noticed a world of difference.

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What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour—and are they the same?

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What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour—and are they the same?

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Abstract

Introduction

Self-efficacy is defined as 'the belief in one's capabilities to organize and execute the courses of action required to produce given attainments' [ 1] and is a key construct within several theories popular within health psychology, e.g. social cognitive theory [ 2], protection motivation theory [ 3] and theory of planned behaviour (TPB) [ 4]. It has been consistently shown to be a predictor of the adoption and maintenance of physical activity behaviour in healthy adults [ 5-7]. Experimental evidence has further demonstrated self-efficacy to be a mediator of the effects of interventions on objectively measured physical activity behaviour [ 8-10].

It therefore seems reasonable to target self-efficacy in order to change physical activity behaviour. It is thus imperative that effective techniques for changing self-efficacy are identified for inclusion in physical activity interventions. However, there is limited evidence for 'how' to do this [11]. Without this evidence, intervention developers may develop ineffective physical activity interventions due to the techniques they employ not changing self-efficacy.

A recent systematic review with meta-analysis, synthesized evidence of the intervention techniques most effective for changing self-efficacy in physical activity interventions for healthy adults [ 12]. The review was the first to identify which specific behaviour change techniques based on social cognitive theory [ 1] are associated with an improvement or deterioration in self-efficacy for physical activity. The techniques identified in the review were elaborated from the four sources of self-efficacy behaviour proposed by Bandura [ 2]; enactive mastery experience, vicarious experience, verbal persuasion and physiological or affective states. The meta-analysis found that interventions that included feedback on past performance or feedback in comparison to others' performance, and vicarious experience, produced the highest levels of self-efficacy. In contrast, interventions that included persuasion, graded mastery or barrier identification were associated with lower levels of self-efficacy compared with those that did not include these techniques [ 12]. Some techniques used most commonly were found to be the least effective, e.g. persuasion, and some used rarely were those found to be the most effective, e.g. vicarious experience. This finding highlights the importance of re-evaluating the effectiveness of the intervention techniques that are commonly used by intervention developers.

The Ashford et al. [ 12] review focused on which techniques were associated with change in self-efficacy, not their association with physical activity behaviour change. Consequently, their findings should only be used to inform the development of future physical activity interventions if these techniques also affect physical activity behaviour. Further, evidence is therefore required concerning which of these techniques are associated with change in physical activity behaviour. The potential impact of gathering such evidence is 2-fold. Firstly, the data would provide an evidence base for future intervention developers to draw upon, thus enhancing the effectiveness of interventions to promote physical activity. Secondly, if a comparison of the techniques associated with self-efficacy and physical activity shows the same techniques to be effective in changing both self-efficacy and physical activity, this would provide further evidence of a causal pathway between the psychological construct and behaviour.

Two previous systematic reviews have examined which specific behaviour change techniques are associated with the effectiveness of physical activity interventions in altering physical activity behaviour [ 13, 14]. Both reviews used a taxonomy considered to be comprehensive for physical activity and diet behaviours [ 15] to reliably classify behaviour change techniques and aimed to assess which specific behaviour change techniques were associated with intervention effectiveness. Univariate meta-regression in the Michie et al. [ 13] review identified only one behaviour change technique that was significantly associated with physical activity or healthy eating outcomes; 'prompt self-monitoring of behaviour' [ 13]. Furthermore, this review found interventions that included self-monitoring plus one of five self-regulation techniques derived from control theory [ 16, 17] were significantly more effective than those not including these techniques, according to meta-regression [ 13]. Similarly, Dombrowski et al. [ 14] found no significant effects of behaviour change techniques on physical activity behaviour in their review.

The focus of each review differs from that of the present review in terms of the target population and behavioural outcome investigated. In the study by Michie et al. [ 13], the univariate meta-regression analyses were completed for physical activity or healthy eating outcomes together, consequently, the contribution of each specific intervention technique to physical activity behaviour change in isolation was not assessed. Specifically, it is not possible to tease out which intervention strategies were aimed at physical activity and which were aimed at dietary behaviours in the studies included in this review. The interventions included in the review by Dombrowski et al. [ 14] were weight management interventions for adult obesity. Thus, the findings of the two reviews do not allow us to establish which behaviour change techniques are effective for changing lifestyle physical inactivity behaviour in a non- clinical, 'healthy' population. This is a notable omission considering that physical inactivity is a leading cause of mortality, responsible for an estimated 16.6% of total deaths in the United States [ 18].

Self-efficacy outcomes were not measured in either systematic review, consequently on the basis of these reviews, we are unable to make inferences regarding the effects of specific intervention techniques on both self-efficacy and physical activity in the same studies.

The present study reports additional analyses on the studies identified in the Ashford et al. [ 12] systematic review, with two main aims:

(i) Assess what specific intervention techniques, included in an updated version of a taxonomy of behaviour change techniques [19], are included in the least or most successful interventions to change physical activity behaviour.

(ii) Compare the contribution of specific intervention techniques to changes in both self-efficacy and physical activity behaviour outcomes.

Methods

Brief details of inclusion and exclusion criteria, and search methods, are provided below. Further details are published elsewhere [12].

Inclusion criteria

Types of studies

Published randomized experimental, non-randomized experimental, quasi-experimental or pre- and post-intervention studies. Studies assessing self-efficacy only as a predictor of physical activity, qualitative studies or surveys were excluded. English language-only papers were included for pragmatic reasons.

Type of participants

Studies that included participants on the basis of pre-existing medical conditions or based on clinically defined populations including obese individuals were excluded. Studies including children and student populations were not included; studies that recruited older adults only over 60 years or where the mean age was over 60 years were also excluded.

Type of intervention

Lifestyle and recreational physical activity interventions that aimed to increase physical activity self-efficacy. Sport or laboratory-based studies focused on competitive sports or fitness were excluded, as were papers if the intervention targeted more than one behaviour (e.g. physical activity and diet).

Type of outcome measures

Information needed to calculate effect sizes must have been made available for changes in both self-efficacy and physical activity. Both outcomes should have been measured pre- and post-intervention in single-group studies. In studies with intervention and comparison groups, self-efficacy and physical activity should have been measured at least once following the end of the intervention. Self-report or objective measures (e.g. accelerometer readings) of physical activity were acceptable.

Search methods for identification of studies

The electronic databases Web of Science (1966-2007), PsycInfo (1966-2007), SPORTDiscus (1966-2007) and the Cochrane Library were searched using a comprehensive search strategy including self-efficacy, physical activity and trial search terms. The review was subsequently updated. Each database search was updated, using the same search strategy, from January 2008 to December 2009. The full search strategy is published elsewhere or is available from the first author on request [12].

Searches were completed, and eligibility of each study was determined, by the first author. Abstracts were cross-checked against the inclusion criteria and where necessary, the full text was retrieved. Where the first author was unsure of relevance, decisions regarding inclusion and exclusion were resolved by discussion with the second author.

Data extraction

Relevant papers were automatically entered into reference software (Endnote version 5). Effect size data were extracted for physical activity self-efficacy and physical activity behaviour.

Intervention content was coded by the first author using an updated version [ 19] of a comprehensive taxonomy for behaviour change techniques [ 15]. This taxonomy is in contrast to the coding frame used by the Ashford et al. [ 12] review, which was a theory-specific coding frame elaborated from the four sources of self-efficacy behaviour proposed by Bandura [ 2]. The taxonomy used in the present review can be considered all encompassing in terms of the theoretical basis of intervention techniques. The use of the updated Abraham and Michie taxonomy [ 15, 19] in the present review also facilitates comparisons with other similar reviews [ 13, 14].

The original taxonomy has previously been found to be reliable for coding behaviour change techniques in intervention descriptions [ 15]. The updated version used in this study [ 19] consisted of 39 behaviour change techniques (See Table III for the full list of behaviour change techniques). It was developed on the basis of disentangling conceptual and empirical overlap between categories, in the original taxonomy. The revised version of the taxonomy is available from the authors upon request.

The first author coded all intervention descriptions using the most recent version of the taxonomy, coding was validated by a second coder following detailed instructions. Any disagreements were resolved by discussion. Interrater reliability was also found to be acceptable (all kappas between 0.83 and 1). Coding of the additional studies included following the extended database searches was conducted by the first author only.

Data analysis

The effect size estimate employed was Cohen's d [ 20], the standardized mean difference. Effect sizes were calculated for self-efficacy and physical activity behaviour independently. Meta-analytic calculations were conducted using Schwarzer's [ 21] Meta computer program, using a random-effects model. Meta analyses were conducted separately for physical activity self-efficacy and physical activity behaviour

Where there were two experimental groups within one study, control group data were compared with each experimental group separately to yield two effect size estimates. In the case of multiple measurement time-points, we took the first measurement following the end of the intervention, as this is when the largest change attributable to the intervention should have occurred.

Heterogeneity was assessed using the Q coefficient. Moderator analyses were conducted to explore causes of heterogeneity, by comparing the mean variability in self-efficacy or physical activity effect size estimates of two groups of studies characterized by the presence or not of particular study features, e.g. intervention techniques. Pairwise Z tests were used to determine which intervention techniques accounted for significantly different effect size estimates.

A one-way Spearman's Rho correlation coefficient was conducted to assess the extent to which change in self-efficacy, as a consequence of particular intervention characteristics, was associated with change in physical activity behaviour. The z-score for each intervention technique was assigned as positive if interventions that included the technique produced a higher effect size than those interventions that did not. Likewise, when interventions that did not include a particular technique produced a higher effect size than those that did, the z-score was considered negative for the purposes of this analysis. Paired Z-scores for each intervention technique were then correlated to assess the association between changes in self-efficacy effect size estimates and changes in physical activity effect size estimates, for each intervention technique.

Results

The search strategy identified a total of 2958 potentially relevant papers; we extracted full text publications for 296 of these papers for further examination. A total of 36 intervention groups from 27 unique studies provided self-efficacy and physical activity data and were included in this study.

Study characteristics

The mean number of participants included in each study was 199, range 33-874 (see Table I). There were ten randomized experiments, four pre- and post-intervention studies, one non-randomized experiment and one quasi-experimental study. The mean age of participants was 43.17 (SD = 7.7), average number of females included in each study was 132 and the average number of males was 60. There were 139 white participants on average in each study. However, not all studies provided full demographic information.

Intervention characteristics

The majority of the intervention studies focused on lifestyle physical activity, e.g. walking and gardening ( n = 24), a further three studies targeted recreational physical activity, e.g. aerobics class, gym sessions. There were 19 individual interventions, 8 interventions were delivered in group setting. Modes of delivery included training sessions, discussion groups, telephone and mass media. Workplace, primary care, media and university settings were most often utilized. Most commonly, a researcher or health professional was assigned the role of intervention deliverer (See Table II for details).

A theoretical rationale was explicitly mentioned in 24 of the included studies, although 3 studies mentioned no theoretical rationale in their study description.

See Table III for the frequency of behaviour change techniques included in the intervention studies.

Meta-analysis results

There were a total of 36 experimental groups that reported changes in self-efficacy and physical activity behaviour. There were small but significant effects of interventions on physical activity self-efficacy [ d = 0.16, 95% confidence interval (CI): 0.08-0.24, P < 0.001), and on physical activity behaviour ( d = 0.21, 95% CI 0.11-0.31, P < 0.001).

Moderator analyses

Greater variability in effect size estimates existed than that explained by random sampling error alone (self-efficacy: Q = 57.57, P < 0.01; physical activity: Q = 116.47, P < 0.001). Therefore, moderator analyses were conducted to search for the sources of heterogeneity separately for self-efficacy and physical activity, to allow us to examine the differences and similarities in the moderators associated with change in these variables. We conducted 20 moderator analyses using the techniques included in the revised taxonomy of behaviour change as grouping variable [ 19] (see Table IV). It was not sensible to conduct moderator analyses on the remaining 19 techniques as they were used by one or zero intervention groups. Refer to Table V for details on the intervention techniques used by individual studies.

Intervention techniques associated with changes in physical activity behaviour

Six techniques were significantly associated with higher physical activity behaviour effect sizes; 'provide information on consequences of the behaviour in general' (included d = 0.27; not included d = 0.08, P = P < 0.001), 'action planning' (included d = 0.38; not included d = 0.16, P = 0.009), 'reinforcing effort or progress towards behaviour' (included d = 0.33; not included d = 0.16, P = 0.011), 'provide instruction' (included d = 0.26; not included d = 0.12, P = 0.004), 'facilitate social comparison' (included d = 0.46; not included d = 0.18, P = 0.022) and 'time management' (included d = 0.33; not included d = 0.17, P = 0.039).

Three intervention techniques were significantly associated with lower physical activity effect sizes; 'set graded tasks' (included d = −0.01, not included d = 0.21, P = 0.001), 'use of follow-up prompts' (included d = 0.02, not included d = 0.21, P = 0.021) and 'relapse prevention' (included d = 0.01, not included d = 0.26, P < 0.001).

The remaining 11 techniques included in the moderator analysis were not associated with significant differences in physical activity effect size estimates between studies that included those techniques and studies that did not.

Intervention techniques associated with changes in self-efficacy

Three intervention techniques were significantly associated with higher self-efficacy effect sizes; 'action planning' (included d = 0.49; not included d = 0.11, P < 0.001), 'reinforcing effort or progress towards behaviour' (included d = 0.31; not included d = 0.11, P = 0.003) and 'provide instruction' (included d = 0.21; not included d = 0.11, P = 0.017).

Five intervention techniques were significantly associated with lower self-efficacy effect sizes, 'set graded tasks' (included d = −0.52; not included d = 0.20, P = 0.01), 'prompt self-monitoring of behaviour' (included d = 0.06, not included d = 0.19, P = 0.004), 'prompt practice' (included d = 0.13, not included d = 0.22, P = 0.026), 'plan social support/social change' (included d = 0.06; not included d = 0.20, P = 0.010) and 'relapse prevention' (included d = 0.05; not included d = 0.22, P < 0.001).

A further 12 techniques included in the moderator analysis were not associated with significant differences in effect size estimates between studies that included those techniques and those that did.

Comparison of techniques associated with self-efficacy and physical activity

There was a significant moderate to large positive relationship between the change in self-efficacy and the change in physical activity for the 20 intervention techniques examined (Spearman's Rho = 0.690, P (one-tailed) < 0.001).

Of the 20 techniques in the moderator analyses, 13 of these were congruent, i.e. when a technique was associated with increases in effect sizes for self-efficacy, it was also associated with an increase in effect size for physical activity and vice versa. Five techniques were found to be significantly associated with self-efficacy and physical activity and in the same direction. These were 'action planning', 'set graded tasks', reinforcing effort or progress towards behaviour', provide instruction' and 'relapse prevention'. Interventions that included 'action planning', 'reinforcing effort or progress towards behaviour' and 'provide instruction' produced a significantly higher self-efficacy effect sizes as well as physical activity compared with interventions that did not include this technique. By contrast, interventions that included 'set graded tasks' and 'relapse prevention' techniques produced significantly lower self-efficacy and physical activity effect sizes compared with interventions that did not include these techniques. The remaining eight techniques associated with changes in self-efficacy and physical activity effect sizes in the same direction produced non-significant effect sizes ( P > 0.05) in at least one of these two constructs. There were seven non-congruent techniques [goal setting (behaviour)', 'barrier identification', 'prompt review of behavioural goals', 'prompt practice', 'plan social support/social change', 'provide feedback on performance', 'time management'], i.e. their impact on effect size estimates was in opposing directions for self-efficacy and physical activity. However, changes in self-efficacy and physical activity behaviour effect size estimates were non-significant for three of these techniques ['goal setting (behaviour)', 'barrier identification' and 'prompt review of behavioural goals', 'provide feedback on performance']. Two of the non-congruent techniques ('prompt practice', 'plan social support/social change') produced significant changes in effect size for self-efficacy but non-significant change in physical activity behaviour. One non-congruent technique produced significant changes in effect size for physical activity but non-significant change in self-efficacy ('time management').

Discussion

This meta-analysis of physical activity interventions for 'healthy' adults found a small but significant effect of interventions on self-efficacy ( d = 0.16) and physical activity ( d = 0.21). The moderator analyses identified that significantly higher physical activity effect sizes were produced when interventions included 'provide information on consequences of behaviour in general', 'action planning', 'reinforcing effort or progress towards behaviour', 'provide instruction', 'facilitate social comparison' and 'time management' techniques. In contrast, significantly lower physical activity effect size estimates were found when each of the following three techniques were included; 'set graded tasks', 'use of follow-up prompts' and 'relapse prevention'. A significant large association between changes in self-efficacy and changes in physical activity was found ( r s = 0.690).

Intervention techniques associated with changes in self-efficacy and physical activity

Intervention studies that included 'action planning', defined in this study as specific detailed planning of when, where and how the specific behaviour is going to be performed produced significantly higher self-efficacy and physical activity behaviour scores. This is contrast to a previous meta-analysis which showed that implementation intentions, in which if-then plans that also specify when, where and how one with achieve a goal, did not produce significant positive effects on self-efficacy [ 63]. However, the studies included in the Webb and Sheeran [ 63] meta-analysis targeted numerous behaviours and thus the findings for physical activity interventions only is unclear. Furthermore, the Webb and Sheeran [ 63] review contained several studies of simple implementation intention interventions, which are based on laboratory experiments and tend to be provided by experimenters. These might have limited effectiveness in applied settings and for interventions targeting health behaviours, in which more complex elaborate planning is required and is more effective, as demonstrated in this study [ 64].

There are a number of potential explanations for this effect. As theories of goal setting [ 65] suggest, setting specific difficult goals are likely to lead to better performance than non-specific goals, for example 'do more physical activity'. This is a consequence of a reduction in the ambiguity of what is to be attained offered with specific goals [ 66]. Greater specification of how goals are to be achieved may increase self-efficacy by making clear exactly what the individual needs to do [ 2]. Likewise, successful mastery performance of the behaviour as a consequence of a specific goal or plan will lead to improved self-efficacy. People with high self-efficacy are also likely to be more committed to assigned goals and use better strategies to attain the goals than those with lower self efficacy [ 65] so are more likely to successfully perform the behaviour. Finally, specific goal setting, or rather action planning, is likely to have an impact at the volitional phase of behaviour change, i.e. by translating intentions into behaviour, explaining its subsequent impact on physical activity behaviour.

The provision of specific instructions on where, when and how to perform the behaviour, e.g. by providing information on local places people can access for exercise, was also associated with an increase in both self-efficacy and physical activity (both P < 0.01). It is likely that the same mechanism is responsible for the effects of this technique as is responsible for the effects of action planning. When, where and how to perform a behaviour are important components of effective action plans, so providing explicit direction about this allows the participant to identify how they are able to achieve the target behaviour which thus supports effective implementation of behaviour. Although action plans may be more effective when self-generated and are therefore tailored to that individual, it possible that some participant generated action plans include insufficient or inappropriate detail. Thus, providing participants with specific instructions on how, where and when they might become physically active should offer specific, tangible and appropriate options for inclusion in an action plan.

Praising or rewarding participants for their 'attempts' at achieving a behavioural goal was associated with significantly higher self-efficacy and physical activity effect sizes ( P < 0.05). This finding that providing positive feedback enhances self-efficacy, focusing on small successes and progress towards a behavioural goal rather than actual achievement of final target behaviour, supports Bandura's view that personal performance successes enhance perceived self-efficacy [ 1]. This is particularly pertinent at the early initial stages of behaviour change, as measured in this review, where the participant has not quite achieved mastery of the behaviour and is therefore not fully confident in their own abilities to be able to do this. Thus, focusing on small success might serve to enhance this initial self-efficacy and subsequently impact physical activity behaviour. Providing rewards and reinforcement contingent on successful behaviour change and achievement of a specific goal might serve to maintain self-efficacy and physical activity in the longer term, once initial mastery has been achieved.

Intervention studies that included 'set graded tasks', in which the target behaviour or increments towards target behaviour become increasingly difficult, were associated with significantly lower physical activity self-efficacy and physical activity behaviour effect sizes. Previous studies in laboratory settings have found that breaking down a distal goal into achievable sub goals that are approached in a hierarchical manner increases self-efficacy [67]. While this is in opposition to the findings of the present review, it is possible that continuously increasing the level of difficulty in physical activity tasks might compromise initial self-efficacy and physical activity levels. This might be the case particularly if participants are encouraged to increase the difficulty of physical activity (e.g. increased duration) before they have successfully mastered their previous task or met their previous physical activity target. Thus, it is possible that this technique might lead to low self-efficacy and physical activity behaviour initially, as reflected in the results of the present review but might be more helpful in the longer term once initial successful mastery of the behaviour has been achieved. However, we cannot confirm this as self-efficacy and physical activity were measured only at one time point, i.e. immediately post-intervention in this review.

Significantly, lower self-efficacy and physical activity behaviour effect sizes were produced when 'relapse prevention' was used in intervention studies. Relapse prevention, as defined in this study, involves planning how to maintain a behaviour that has already been changed via the identification of potential barriers to maintenance in advance of such situations arising. The technique also involves developing strategies to avoid or cope with that given situation.

An explicit exploration of the reasons why the individual 'cannot' perform the behaviour might undermine self-efficacy possibly due to the fact that it will provide further evidence of the barriers, and thus their inability to perform the behaviour [12]. Given that self-efficacy is a predictor of physical activity, the reduction in self-efficacy owing to this technique will likely have a subsequent impact on physical activity behaviour itself. Alternatively given that this technique is explicitly concerned with the maintenance of behaviour, it may be helpful in the longer term but not in the short-term as assessed in the present meta-analysis. It is also possible that relapse prevention techniques may not be suitable for interventions aimed at changing physical activity behaviour. Due to the nature of physical activity, interventions are aimed at initiation and activation, whereas other behaviours, e.g. addictive behaviours, in which there is a need for coping strategies to prevent giving in to urges, may be more amenable to the use of relapse prevention techniques. Furthermore, it may be that the way in which the technique is implemented which hinders its effectiveness rather than the technique itself. Thus, it is possible that it was the ineffective execution of the technique in the face of an adverse situation, rather than the technique itself that is ineffective, that might have led to lower self-efficacy and thus physical activity effect sizes.

In addition to the five techniques that were significantly associated with self-efficacy and physical activity in the same direction as previously discussed, there were another eight congruent techniques, i.e. the impact on self-efficacy and physical activity change was in the same direction. There were seven non-congruent techniques, i.e. their impact was in opposing directions for self-efficacy and physical activity. Therefore, in most instances, when a technique was associated with improvements in effect sizes for self-efficacy, it also tended to be associated with an improvement in effect size for physical activity. This review thus lends further support for the targeting of self-efficacy in physical activity interventions in order to induce physical activity behaviour change, and in doing so further endorses the applicability of social cognitive theory [1] to behaviour change interventions.

Additional intervention techniques associated with physical activity

Providing information on the consequences of performing physical activity, e.g. the benefits and costs was associated with higher physical activity effect size estimates. This supports the view that an individual is more likely to initiate successful behaviour change if they perceive that there are positive favourable outcomes associated with it [ 68]. This is particularly pertinent for the initiation of behaviour, as assessed in the current review, yet is likely to be less effective for maintaining physical activity. In this case, decision to maintain behaviour is a consequence of satisfaction with such outcomes, not just the presence of the favourable outcomes [ 68]. We also found that interventions that included 'facilitate social comparison', which involves explicitly drawing an individual's attention to others' performance to elicit comparisons, produced significantly larger effect size estimates of physical activity than those that did not, though this technique was rarely used. The impact of this technique on self-efficacy was positive, albeit non-significant. This supports the view that one route to enhancing efficacy beliefs, and thus performance, is by observing similar others successfully perform the target behaviour and comparing one's own performance with that of others [ 2]. Similarly, 'time management', referring to techniques designed to teach the person how to manage their time in order to make time to perform the behaviour, was used by only five intervention groups. However, the inclusion of this technique produced significantly higher physical activity effect sizes.

In the opposite direction, we found 'follow-up prompts' to be an ineffective technique, with interventions that included this techniques producing significantly lower physical activity effect sizes.

Strengths and limitations

This is the first study to demonstrate, via meta-analysis with moderator analyses, which behaviour change techniques [ 12] are associated with changes in self-efficacy, and physical activity behaviour change. An understanding of how, and the mechanisms by which, interventions work is required in order to progress the science of behaviour change [ 69]. The present review has contributed to the growing theoretical understanding of the mechanisms by which physical activity behaviour change occurs. The present review not only provides guidance on which techniques are likely to be most suitable for changing this behaviour but has also provided hypotheses for future experimental studies to test.

However, the findings presented in this paper must be interpreted with caution due to a number of limitations. Significant heterogeneity was still present in the meta-analysis even after moderator analyses were conducted, so caution should be exercised when interpreting the pooled effect sizes for self-efficacy and physical activity. It must also be highlighted that given the number of independent moderators assessed in this review, and therefore the number of comparisons conducted, it is plausible that the significant effects produced by some moderators are a result of chance alone. Potential confounders, including study, population and intervention characteristics of included studies were not statistically controlled for; likewise differing combinations of intervention techniques were not analysed for their impact on self-efficacy and physical activity behaviour. Finally, where there was more than one experimental group within one study, each group was compared with the comparison group separately. Therefore the effect size estimates produced by these comparisons may be somewhat unreliable. However, it should be noted that aim of the present review is to generate hypotheses on the basis of a small number of eligible studies, so we aimed to include as many intervention groups as possible

The intervention studies included in this meta-analysis were not described particularly well, specifically, the reporting of intervention techniques were inadequate. It is therefore possible that there are differences in the definitions of specific behaviour change techniques used by the authors of the included studies, and the definitions included in the behaviour change taxonomy [ 15] used to code these. We are therefore limited by the descriptions of the interventions included in this review. Intervention developers should be encouraged to follow current guidelines for the reporting of study and intervention components [ 70-72] to allow for subsequent replication of successful interventions and to enhance our understanding of which techniques are responsible for behaviour change.

Finally, the focus of the present review was physical activity interventions for 'healthy' adults. The authors therefore acknowledge that the findings of the present review cannot be generalized to other health behaviours or populations.

Implications and future research

A number of techniques found to be significantly associated with physical activity behaviour change were also found to be associated with self-efficacy change, and in the same direction. On the basis of this review, the authors would therefore encourage the targeting of self-efficacy in physical activity interventions and the assessment of self-efficacy as a likely mediator of such interventions. The authors also provide tentative recommendations for the inclusion of particular intervention techniques in physical activity interventions and suggest techniques which future research might investigate to optimize efficacy.

It was found that setting a specific detailed plan of when, where and how to perform the behaviour and also providing instruction on these same categories of information was effective at bringing about a positive change in self-efficacy and physical activity behaviour. This finding supports previous literature regarding the importance of goal specificity [65]. We therefore suggest that future physical activity interventions encourage participants to make specific, detailed plans of how they are going to bring about behaviour change, rather than encouraging participants to set a general goal or make a general intention.

Furthermore, we suggest that intervention deliverers provide specific recommendations on when, where and how a participant might become physically active. We also recommend that intervention deliverers provide positive feedback and reinforce participants' efforts in attempting to become more physically active, independent of achievement of a specific physical activity goal.

As a result of our finding that relapse prevention techniques had a deleterious impact on physical activity, we would not recommend the use of such techniques in future interventions aimed at increasing physical activity by changing self-efficacy. However, this recommendation is limited to social cognitive interventions to increase physical activity, as relapse prevention techniques might have been used effectively in interventions that were not eligible for inclusion in this review and for which we have no data on. Yet, for physical activity interventions based on social cognitive theory [2], we would recommend that those delivering such an intervention facilitate a discussion of the reasons why the participant 'can' perform the behaviour and avoid emphasising the reasons why they 'cannot'.

We would also recommend caution in using graded tasks at the early stages of behaviour change. Instead, we recommend that this is used once initial mastery of the behaviour has been achieved and recommend that any incremental increases should be decided in consultation with the participant to ensure that they remain confident in their abilities to achieve such increases.

Likewise, we recommend that intervention developers draw participant's attention towards the performance of others in order to induce physical activity change. We would also suggest that caution be used when using 'follow-up prompts' as strategies to enhance both self-efficacy and physical activity behaviour.

While the findings of the present review are informative, they are not considered to be definitive. It is possible that certain techniques are ineffective as a consequence of poor implementation rather than the result of the technique itself. Furthermore, the present review findings are based on self-efficacy and physical activity measurements immediately post-intervention, therefore it is possible that some techniques are helpful at maintaining self-efficacy and physical activity but are ineffective at initiating this change.

The authors would like to thank F.J., B.M., Y.M., R.S., K.A.-N. and C.M. for providing additional data; and C.T. for her helpful comments on an earlier draft of this paper.

References

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What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour-and are they the same?

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Self-efficacy is defined as 'the belief in one's capabilities to organize and execute the courses of action required to produce given attainments' [ 1] and is a key construct within several theories popular within health psychology, e.g. social cognitive theory [ 2], protection motivation theory [ 3] and theory of planned behaviour (TPB) [ 4].

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Strategies to increase physical activity

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Strategies to increase physical activity

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Strategies to Increase Physical Activity

Phillip Tuso, MD, FACP, FASN Perm J 2015 Fall; 19(4):84-88 [Full Citation]
https://doi.org/10.7812/TPP/14-242

ABSTRACT

The benefits of physical activity are well known and well publicized. Healthy People 2020 has determined that physical activity is one of their key interventions to improve health in America. Despite wide acceptance that physical activity is a low-cost alternative to disease treatment and prevention, most Americans still do not exercise the recommended minimum of 150 minutes per week. Underpinning such recommendations is the growing concern that unless we change our behavior around active living, health care costs to treat preventable disease will become unsustainable and have a substantial impact on the financial health of the US. For this reason, physicians, health care executives, and community leaders are working together to improve total health for all Americans. One key intervention to prevent preventable diseases and to make health care more affordable is to increase the percentage of Americans who are physically active. No single intervention will increase activity rates, but a group of interventions working together in synergy may be the stimulus needed to get Americans moving. The five strategies discussed in this paper include 1) measure physical activity as a vital sign; 2) encourage patients to be physically active at least 150 minutes per week; 3) create healthy environments by making it easier for patients to be physically active where they live, learn, work, play, and pray; 4) monitor disease incidence of patients who are physically active vs those who are not physically active; and 5) spread best practices.

INTRODUCTION

The benefits of physical activity are well known and well publicized. Despite wide acceptance that physical activity is a low-cost alternative to disease treatment and prevention, most Americans still do not exercise the recommended minimum of 150 minutes per week. During the past several years organizations like Kaiser Permanente, Intermountain Health, and Mayo Clinic have started to measure physical activity as a vital sign. In addition, many community groups and organizations have joined together in the Every Body Walk! campaign in an attempt to get America walking and overcome behavior change and social barriers that keep people from being physically active. During the past decade, the American College of Sports Medicine, the Centers for Disease Control and Prevention, and the National Institutes of Health have all recommended regular physical activity as an option for those who are inactive. In addition, Healthy People 2020 now has physical activity as one of their key interventions to improve health in America.

Underpinning such recommendations is the growing concern that unless we change our behavior around active living, health care costs to treat preventable disease will become unsustainable and have a substantial impact on the financial health of the US. For this reason, physicians, health care executives, and community leaders are working together to improve total health for all Americans. One key intervention to prevent preventable diseases and to make health care more affordable is to increase the percentage of Americans who are physically active.

Since the publication in 1996 of Physical Activity and Health: A Report of the Surgeon General and the 2008 Physical Activity Guidelines for Americans, there has been a widespread call for action to prevent preventable disease and to improve the care provided to individuals with chronic disease through physical activity. Strong evidence exists that both children and adults benefit from being physically active. Benefits of physical activity include lower risk of chronic diseases such as obesity, heart disease, diabetes, cancer, and depression. Exercise may even help improve academic performance and help us live longer.

Physical activity is widely recognized as a means for the primary prevention of chronic diseases. Moreover, activity has beneficial effects on an individual's health and well-being. Despite the benefits of regular physical activity, the percentage of physically inactive adults in the world is high. Environmental and policy approaches that aim to increase physical activity require evidence from studies investigating disease mechanisms as well as controlled clinical trials. A meta-analysis reported in 2007 that physical activity was associated with a reduction of the incidence of chronic major diseases. The strongest evidence exists for colon cancer, breast cancer, and cardiovascular diseases. The maximal magnitudes of the risk reduction reported were 22% for colorectal cancer, 75% for breast cancer, and 49% for cardiovascular diseases.

Researchers in Taiwan performed a prospective cohort study on 416,175 individuals between 1996 and 2008 to evaluate the effect physical activity has on life expectancy. Participants were placed in 5 categories on the basis of the amount of weekly exercise indicated in a self-administered questionnaire. The 5 categories were inactivity, low, medium, high, and very high activity. Results showed that compared with individuals in the inactive group, those in the low activity group, who exercised for an average of 15 minutes per day, had a 14% reduced risk of all-cause mortality and had a 3-year longer life expectancy. Each additional 15 minutes of daily exercise beyond the minimum amount of 15 minutes a day further reduced all-cause mortality by 4% and all-cancer mortality by 1%. These benefits were applicable to all age groups and both sexes, and to those with cardiovascular disease risks.

Recent data show that areas with high obesity rates have low exercise rates and areas with low obesity rates have high exercise rates. Data from Healthy People 2020 demonstrate that we can subjectively measure physical activity as a vital sign, but we have not figured out how to substantially increase the percentage of the population who are physically active. Therefore just measuring exercise rates does not increase exercise rates. Social networking may be a new innovative way to increase exercise rates. During the past few years there has been a growing interest in understanding behavior change of an individual and of a population around the issue of physical activity. Changing behaviors is a key step to long-term health and disease prevention.

The 2008 Physical Activity Guidelines for Americans for children and adults recommends at least 60 minutes per day of physical activity for children and 30 minutes per day for adults. Despite evidence of the health benefits of physical activity as outlined above, most US adults and children are not meeting the recommendation in the 2008 Physical Activity Guidelines for Americans. Data from Healthy People 2020 show that the percentage of adults engaging in regular physical activity increased only from 43.5% in 2008 to 48.8% in 2011. Data from the Centers for Disease Control and Prevention show that only 1 in 3 adults who have seen a physician have been advised to be physically active.

Despite advice from physicians, most patients do not meet daily exercise goals. Research has shown there are many barriers to getting Americans to be more active. Although level of education and economic status seem to play a critical role in being physically active, there also appear to be other factors that prevent physical activity for all age groups. A central theme appears to be support from family and friends and the environment we live in. It is clear there is much work to do to help Americans be more active. This information suggests we need to use a broader systemic organized approach to understanding the correlates of physical activity. Just asking patients about physical activity is not enough to make them more physically active.

Physicians and community leaders need to better understand community influences on physical activity. Because current interventions have not significantly increased physical activity rates, community leaders including physicians must work together to understand how we can increase physical activity rates to prevent disease.

The purpose of this article is to outline 5 strategies to increase physical activity in Americans. To achieve total health in America, we must encourage Americans to be more physically active. The goal is to increase the number of people who are physically active at least 30 minutes per day. A 5-step strategy to help America move is outlined below.

STRATEGIES TO INCREASE PHYSICAL ACTIVITY

Measure Physical Activity as a Vital Sign

Healthy People 2020 has been measuring activity rates by survey since 2008. Since that time, exercise rates in America have increased by only a few percentage points. One intervention that may significantly increase exercise rates in America is to implement a national process in which during each visit with a physician, the patient is asked the number of minutes per week the patient participates in a physical activity. This question lets the patient know that exercise is an important part of health just like other vital signs of health (eg, blood pressure). If the patient is not meeting exercise goals, then the physician can start a conversation about the importance of physical activity in health. Ultimately, patients must be responsible for their health and their level of physical activity. Understanding what is expected of them is the first step. The second step is learning how to change behavior from inactivity to daily physical activity.

Encourage Patients to be Physically Active at Least 150 Minutes Per Week

Behavior change occurs when motivation aligns with ability and triggers. Good health should be the main motivator for an individual to be physically active. As stated above, physical activity has many health benefits. Some patients will need education on how to be physically active (ability) and triggers to remind them to be physically active.

Behavioral interventions have been shown to significantly increase physical activity that results in improved control of diabetes and body mass index. Behavior interventions can prompt patients to be more physically active. Brown et al reported that behavior interventions have been effective with diverse populations and in a variety of settings. For example, one study showed that a point-of-decision prompt to encourage the use of stairs (ie, a sign next to the elevator that encourages using the stairs to improve health) was effective in increasing the number of people who choose to use the stairs instead of taking an elevator.

Promoting healthy behaviors is a key part of helping patients to be more active. The US Department of Health and Human Services published Physical Activity and Health: A Report of the Surgeon General in 1996. This report supports the fact that activity is better than inactivity and activity has health care benefits. Patients can help themselves to be more active by building social networks. Social support interventions focus on changing physical activity behavior through building, strengthening, and maintaining social networks that provide supportive relationships for behavior change. These include setting up buddy systems with friends and families to hold each other accountable for meeting weekly physical activity goals.

Create Healthy Environments by Making It Easier to be Physically Active Where We Live, Learn, Work, Play, and Pray

Patients have complex lives and have many barriers to being physically active. The time has come to work together to create environments where it is easier to be physically active. This may include a family being physically active together on weekends, students participating in school-based physical education programs, employees participating in employer-based physical activity programs, and faith-based groups encouraging community members to be physically active at faith-based functions.

An identified barrier to physical activity is the lack of safe environments to walk, bike, and play. Creation of places for physical activity combined with education on where to find places to be physically active will certainly help adults and children to be more physically active. Physicians and patients should work together with community leaders to promote communitywide campaigns and support families who want to be physically active. These communitywide campaigns can form multidisciplinary teams that focus on using community resources to promote physical activity where we live, learn, work, play, and pray. These interventions need not be complicated and may be as simple as asking people to walk to school instead of driving. Other more difficult interventions may involve changing our infrastructure to promote health and wellness. These include healthy physical environments (eg, accessible stairwells for walking), healthy activity at work (eg, time and space for physical activities), and activating physicians and patients to work with employer groups to implement lifestyle management programs into the workplace environment.

Monitor Disease Incidence of Patients Who are Physically Active vs Those Who are not Physically Active

Healthy People 2020 has developed physical activity objectives and goals for the future. The objectives are aligned with the 2008 Physical Activity Guidelines for Americans and set objectives and goals for adults, children, schools, and physicians. Although the goals appear to be conservative, they do represent an initial attempt to get Americans moving more by the year 2020. Of note is the expectation for schools to increase the percentage of time students have for physical education and for physicians to counsel patients who will most benefit from physical activity (those with obesity and comorbidities).

Once we set goals it will be important for physicians and health plans to monitor disease incidence as it relates to physical activity to demonstrate the relationship between physical activity, wellness, and health care affordability. Physical activity will lower health care costs, prevent preventable disease, and save lives. Monitoring the relationship between health care costs and disease incidence for individuals who are physically active compared with individuals who are not physically active will help us understand how physical activity can be used as medicine to treat and prevent disease.

Spread Best Practices

To help Americans meet physical activity goals, physicians and patients will need to identify and spread best practices. To achieve total health in the future, we will need healthy people and healthy communities to promote and maintain health. Because patients spend very little time with their physicians, our communities must be healthy and self-learning. Part of self-learning is identifying something that works and sharing with other communities. Every day thousands of patients look for ways to be more active. Currently there is no organized systematic Web-based approach to locate these services that also allows for community organizations to see the services previously accessed by a patient. In addition, there is no objective way to know whether a service provided to a community improves health care outcomes. We must develop a Web-based physical activity resource to help match patients with a specific community resource that will help patients be more active where they live, learn, work, play, and pray. This program will help identify a physical activity best practice in one patient or community and share the resource or idea with another patient or community. Many communities have a plethora of opportunities; others do not. Sharing what works in one community with other communities will encourage America to be more active. One of the best ways to accomplish this goal is to publish data on disease incidence and physical activity on the Internet and to let communities around the country see which communities have lowered their disease incidence by becoming more active. Successful communities can share with other communities how they overcame barriers to success and became more physically active.

CONCLUSION

It is clear that the wide range of policies and programs described in this paper are needed to promote more physically active lifestyles for the patients we serve. Like tobacco use and obesity prevention, public health goals for physical activity are more likely to be achieved if policies and interventions are guided by approaches known to effectively meet the needs of all members of the communities we serve. To achieve success we should establish a program to evaluate and to learn from other programs so we can identify and spread best practices quickly and efficiently. The key to success will be overcoming barriers to helping patients become more physically active. Attention should be given to addressing both the challenges of individual behavior change and the challenges of overcoming environmental barriers that inhibit a populationwide effort to transition from an inactive to an active lifestyle. Resources for walking paths, bike paths, parks, and community outreach programs will encourage physical activity in daily living and should be linked to health care cost savings as an opportunity to prevent disease and make health care more affordable. Health care systems cannot do this alone and must partner with communities to achieve success.

To accomplish our goal of achieving high rates of physical activity for all Americans, we will need behavior medicine specialists, sociologists, physiologists, recreation specialists, physicians, architects, city planners, and engineers working together to engage schools, worksites, religious institutions, and communities to create opportunities to promote physical activity. To create lasting behavior change, patients must do patient work, physicians must do physician work, community leaders must do community leader work, and government leaders must do government leader work. There is no easy fix to the inactivity crisis we have in America. Measuring blood pressure as a vital sign without adjusting treatment may not improve blood pressure control rates. However, holding physicians and patients accountable for blood pressure rates has significantly increased the percentage of the population whose blood pressure is now under control with medication and/or lifestyle management.

To improve physical activity rates, we must hold patients and physicians accountable for physical activity as a vital sign. The first stepis to measure physical activity as a vital sign. The second step is to motivate patients who are inactive to be more physically active. The third step is to create environments and triggers that give people the ability to be more active. The fourth step is to show that patients who are physically active have lower disease incidence. The fifth and final step is to identify and to spread best practices. Although these strategies appear easy and straightforward, the implementation of these strategies across America will require hard work and widespread policy change. This should lead to longer-lasting changes where we live, learn, work, play, and pray that ultimately will prevent preventable disease, lower health care costs, and save lives.

Disclosure Statement
The author(s) have no conflicts of interest to disclose.
Acknowledgment
Mary Corrado, ELS, provided editorial assistance.
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41. Marcus BH, Emmons KM, Simkin-Silverman LR, et al. Evaluation of motivationally tailored vs standard self-help physical activity interventions at the workplace. Am J Health Promot 1998 Mar-Apr;12(4):246-53. DOI: https://doi.org/10.4278/0890-1171-12.4.246.
42. King AC, Carl F, Birkel L, Haskell WL. Increasing exercise among blue-collar employees: the tailoring of worksite programs to meet specific needs. Prev Med 1988 May;17(3):357-65. DOI: https://doi.org/10.1016/0091-7435(88)90010-2.
43. Active transportation: making the link from transportation to physical activity and obesity [Internet]. San Diego, CA: Active Living Research; 2009 [cited 2015 Apr 1]. Available from: http://activelivingresearch.org/sites/default/files/ALR_Brief_ActiveTransportation_0.pdf.
44. Lee V, Mikkelsen L, Srikantharajah J, Cohen L. Strategies for enhancing the built environment to support healthy eating and active living [Internet]. Oakland, CA: Healthy Eating Active Living Convergence Partnership; 2008 [cited 2015 Apr 1]. Available from: www.convergencepartnership.org/atf/cf/%7B245a9b44-6ded-4abd-a392-ae583809e350%7D/CP_BUILT%20ENVIRONMENT21-07.11.11.PDF.
45. Transportation Research Board special report 282. Does the built environment influence physical activity? Examining the evidence. Washington, DC: Committee on Physical Activity, Health, Transportation, and Land Use; Transportation Research Board, Institute of Medicine of the National Academies; 2005 Jan 11.
46. Glickman D, Parker L, Sim LJ, Cook HDV, Miller EA, editors. Accelerating progress in obesity prevention: solving the weight of the nation [Internet]. Washington, DC: Committee on Accelerating Progress in Obesity Prevention Food and Nutrition Board, Institute of Medicine of the National Academies; 2012 May 8 [cited 2013 Apr 19]. Available from: www.iom.edu/Reports/2012/Accelerating-Progress-in-Obesity-Prevention.aspx.

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strategies to increase physical activity

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Strategies to Increase Physical Activity

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Phillip Tuso, MD, FACP, FASN Perm J 2015 Fall; 19(4):84-88 [Full Citation] https://doi.org/10.7812/TPP/14-242 The benefits of physical activity are well known and well publicized. Healthy People 2020 has determined that physical activity is one of their key interventions to improve health in America.

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Selected research on screen time and children

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Selected research on screen time and children

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Activities for Kids | Kids' Activities - Change4Life

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Build some physical activity into your child's day, from 10 Minute Shake Up games to sports the whole family can enjoy.

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Screen time and young children: Promoting health and development in a digital world

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Screen time and young children: Promoting health and development in a digital world

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Abstract

The digital landscape is evolving more quickly than research on the effects of screen media on the development, learning and family life of young children. This statement examines the potential benefits and risks of screen media in children younger than 5 years, focusing on developmental, psychosocial and physical health. Evidence-based guidance to optimize and support children's early media experiences involves four principles: minimizing, mitigating, mindfully using and modelling healthy use of screens. Knowing how young children learn and develop informs best practice strategies for health care providers.

BACKGROUND AND METHODOLOGY

Exposure to digital media in Canadian family life is increasing, as are concerns about how screen time affects children and families. This statement examines potential benefits and risks of screen exposure and use on children younger than 5 years, and provides evidence-based guidance for health professionals to help families. 'Screen time' refers to time spent with any screen, including smart phones, tablets, television, video games, computers or wearable technology. 'Digital media' refers to content transmitted over the Internet or computer networks on all devices, unless particular ones are specified.

A 2016 survey of Canadian Paediatric Society (CPS) members found that parents sought advice about their children's screen time in four main areas: duration (how much is too much?), setting limits, the effects on health and well-being and optimal content. A literature search (1) into the effects of screen media on children younger than 5 years included systematic reviews, guidelines and policy statements on screen media exposure and use. Potential benefits and risks were categorized into developmental, psychosocial and physical domains. Recommendations are based on evidence and expert consensus. Screen time in older children and adolescents, children with neurodevelopmental disorders and environmental health concerns are beyond the scope of this statement.

WHY FOCUS ON THIS AGE GROUP?

Research recognizes important developmental differences between preschool and later childhood, and TV-based data have established that a child's earliest screen encounters can be formative (2-4).

They may be habit-forming, and early overexposure increases the likelihood of overuse in later life ( 4, 5).

Health routines, including family media use, are established more easily in early childhood than later on (4).

Screen use tends to increase over time to include more entertainment (versus exclusively educational viewing) (6-8).

Some important trends are reshaping early childhood:

Active Healthy Kids Canada reported in 2014 that children 3 to 5 years old spend an average of 2 hours per day in front of screens ( 9). Only 15% of preschoolers met current Canadian Sedentary Behaviour Guidelines for the Early Years, which recommend limiting screen time to less than 1 hour per day ( 10).

TV still dominates total screen time and appears to be increasing for this age group ( 11). Many preschoolers accumulate screen time at home and in child care ( 5, 12), from a variety of screens that are easily transportable.

In the USA, rates of mobile media use among 2- to 4-year-olds increased from 39% to 80% between 2011 and 2013 ( 12-15). A recent UK study found that about 51% of infants 6- to 11-months-old use a touch screen daily ( 16).

A 2012 US study found that the average child between 8 months and 8 years of age is exposed to nearly 4 hours of background TV on a typical day (17).

IMPACTS OF SCREEN MEDIA ON DEVELOPMENT

Whether early exposure to screen media changes the developing brain is not known, and published research on how (and how much) children younger than 5 years of age actually learn from screens remains limited ( 6, 18-21). Still, studies show that while babies do not absorb content from TV, it can catch and hold their attention. They can imitate specific actions they see on screen between 6 and 14 months, and remember brief sequences by 18 months ( 6).

Potential benefits for development

Beginning at about 2 years, quality TV-well-designed, age-appropriate programs with specific educational goals-can provide an additional route to early language and literacy for children ( 30). Quality programming also fosters aspects of cognitive development, including positive racial attitudes and imaginative play ( 31). Early evidence suggests that interactive media, specifically applications that involve contingent responses from an adult (i.e., timely reactions to what a child says or does), can help children retain taught information. This responsiveness, when coupled with age-appropriate content, timing and intensity of action, can teach new words to 24-month-olds ( 21, 22, 32). There is early evidence that interactive 'learn-to-read' apps and e-books can build early literacy by providing practice with letters, phonics and word recognition ( 21, 33). However, while screens may help with language learning when quality content is co-viewed and discussed with a parent or caregiver ( 34), preschoolers learn best (i.e., in expressive and vocabulary terms) from live, direct and dynamic interactions with caring adults ( 35).

Risks for development

Research examining TV exposure has demonstrated associations, although not direct causal relationships, between heavy early screen exposure (more than 2 hours/day by infants younger than 12 months in one study) and significant language delays ( 26, 36). Evidence of an association between screen time and attentional difficulties is mixed, with negative effects only clearly apparent when exposure is extremely high (i.e., more than 7 hours/day) ( 6, 37). High exposure to background TV has been found to negatively affect language use and acquisition, attention, cognitive development and executive function in children younger than 5 years. It also reduces the amount and quality of parent-child interaction and distracts from play ( 17, 22, 35, 38). While e-books have been shown to offer benefits with children's reading engagement, parents appear to use fewer reading strategies during these interactions. Further, e-book sound effects and animation can interfere with story comprehension and event sequencing in preschoolers, when compared with paper books ( 21, 39-42).

Some studies associate prolonged TV viewing with lower cognitive abilities, especially related to short-term memory, early reading and math skills and language development ( 12, 20, 43-45). Fast-paced or violent content can negatively impact executive function ( 5, 46), and these effects may be cumulative. The inability of young children (especially those younger than 2 years) to distinguish everyday reality from what happens on screen, along with their efforts to make sense of competing experiential realms, may interfere with and impede executive function ( 6, 47).

What makes the difference? Minimizing and mitigating screen time

Because there are no proven benefits of media exposure for infants and toddlers, and some known developmental risks ( 20, 22, 26, 48), counsel parents to minimize young children's screen time. Minimizing screen time leaves more time for face-to-face interactions, which is how young children learn best.

When children watch educational, age-appropriate content with an engaged adult, screen time can be a positive learning experience. When adults mitigate screen time, they:

Watch with children. Adults can connect what is being viewed with real life, and build language and cognitive skills, such as attention, memory and thinking ( 6, 38, 49). Shared screen time also avoids the disadvantages of solitary viewing.

Actively curate children's screen activities by prioritizing educational content or apps, avoiding mainstream or commercial programs and using a media classification rating (e.g., the Canadian Home Video Rating System) to guide viewing choices.

Combine touch screen use with creative or active play (50).

Although a 'digital divide' between households with and without Internet access persists in Canada, learning apps on mobile devices may help bridge the gap ( 12). In 2016, the Canadian Radio-television and Telecommunications Commission pledged to provide Internet access as a basic service for all Canadians ( 11). However, even as screen-based educational content becomes increasingly accessible to all families, a new gap may be opening. Children whose parents have the ability to mentor and curate screen encounters may reap benefits that are less accessible in families with fewer financial resources or parents who cannot be as involved. Health providers should be alert to this gap, which may be reflected in other parent-child interactions ( 51).

THE PSYCHOSOCIAL IMPACTS OF SCREEN MEDIA

Co-viewing quality screen content can affect more than children's learning. Studies have shown that parents can positively influence children's social adaptive skills, sleep patterns and behaviours by being involved with and setting limits on their screen time ( 22, 52). Television research shows that socioeconomic factors can shape the content and mediation of screen use. Further, TV viewing has been found to be negatively associated with school readiness skills, especially as family income decreases ( 53). However, the time spent viewing-across diverse middle-income households, for example-is stable ( 6, 7, 12). In fact, socioeconomic status appears to have little bearing on the degree to which families comply with current screen guidelines ( 54). Raising awareness around how children learn best and their need for screen time limits is important for all families, regardless of economic circumstances.

Potential psychosocial benefits

Quality content can enhance social and language skills for all children aged 2 years and older, particularly for children living in poverty or who are otherwise disadvantaged ( 26, 30). Educational TV reaches children in lower-income homes almost as much as higher-income homes, and among children whose families own a laptop or mobile device, barriers to accessing and using educational content have almost disappeared ( 12). Well-designed, age-appropriate educational programs and screen activities can be powerfully pro-social, helping children to learn antiviolence attitudes, empathy, tolerance and respect ( 31, 55). Appropriately used, screen time can calm a child who is overexcited or distressed (e.g., during a medical procedure) ( 15, 56). But screen learning can affect behaviour both positively and negatively, so ensuring quality content is crucial ( 57).

Psychosocial risks

Parents report that their own use of mobile technology demands more intense attention than other distractions, such as reading books or watching TV. Smartphones blur the line between work and home life, timing is unpredictable and responding often requires emotional investment. In a recent study, parents reported that shifting attention between screens and family life can be stressful, tiring and reduces their ability to interact 'in the moment' with children (62).

Recent evidence suggests an association between elevated levels of TV exposure at age 2 and self-reported victimization, social isolation, proactive aggression and anti-social behaviours in middle childhood ( 63, 64). Excessive TV viewing (more than 2 hours/day) has been clearly associated with early childhood self-regulation difficulties ( 26, 60). Such effects could be more pronounced in children with special behavioural needs, and self-perpetuating because parents are more likely to use screen media to pacify a child with challenging behaviours ( 22, 60). Research shows that excessive levels of screen media exposure are associated more strongly with low levels of stimulation in the home and low parental involvement than with socioeconomic position ( 6, 12, 65).

While quality educational programming is a leading mitigator of psychosocial risk on any screen, studies show that children's access to and choice of optimal content peaks early: at 2 to 4 years old ( 7, 8, 12). As children age, they are more likely to view entertainment programming.

What makes the difference? Mindful use of screen time

Children younger than 5 years learn best from live, immersive interactions with family members and caregivers. Given the choice, they will nearly always opt for talking, playing or being read to over screen time in any form ( 22). By using screen time mindfully, parents and caregivers:

Actively enhance-and limit-media encounters by choosing them together and purposefully ('Let's watch or play this content, at this time, for this reason ').

Limit screen use in public places and during family routines, such as at meals. Family times are prime opportunities for social learning.

Select content from quality, non-commercial sources, to minimize exposure to advertising.

Pay attention to messages about gender, body image, violence, diversity and social issues when choosing content (69).

THE IMPACTS OF SCREEN MEDIA ON PHYSICAL HEALTH

For preschool children, physical activity is associated with improved measures of health, while sedentary behaviour is associated with less favourable health outcomes (70).

Potential benefits for physical health

Children's screen time does not have to be passive; digital media use can encourage and complement physical activity ( 51). Especially after age 3, children respond to activity-based programming when it is fun, designed for them and encourages imitation or participation ( 39, 71). One study found that active video games could acutely increase light-to-moderate or moderate-to-vigorous physical activity (PA) in the short term ( 72). Families and child care programs may consider using fun, age-appropriate movement (e.g., yoga or dance) and fitness apps or console games to integrate more PA into daily routines ( 51, 73, 74). Mobile devices with apps for exploring the natural world can enhance outdoor PA ( 50). Quality content connects on- with off-screen experiences, fosters engagement with caregivers and peers and supports active, imaginative play ( 51, 75, 76). One 2010 study found that while time spent watching commercial TV was significantly correlated with BMI, time spent watching non-commercial educational TV was not ( 77).

Risks for physical health

Although evidence for an association between screen time and body mass index in very young children is inconclusive, several studies have suggested that risks for overweight established by early screen use can persist into later life ( 2-6, 12, 78, 79). TV viewing in the early years can become routine, placing children who are heavy viewers at higher risk for being sedentary or overweight ( 35). One 2012 Canadian study found that children who watched just 1 hour of TV/day were 50% more likely to be overweight than those who watched less ( 80). Another study from 2009 measuring PA and body fat in middle-class preschoolers linked TV viewing to higher body fat, and found this relationship did not change when children's variable PA rates were taken into account ( 81).

While reinforcing sedentary behaviours, commercial TV also exposes children to advertisements for unhealthy foods and encourages snacking, which increases overall food intake ( 82, 83). A 2012 systematic review of TV watching and adverse dietary effects in children 2 to 6 years old found most studies reported negative effects with as little as 1 hour/day of viewing ( 84).

The amount of time spent viewing screens before bedtime is associated with an increase in sleep problems for this age group ( 6, 85), and evidence suggests that volume of screen time-rather than content alone-is detrimental to sleep patterns ( 45, 53, 86, 87). The presence of any electronic device in a bedroom is associated with fewer minutes of sleep per night, due in part to melatonin suppression ( 26, 85).

What makes the difference? Modelling screen time

Children younger than 5 years require active play and quality family time to develop essential life skills, such as language, self-regulation and creative thinking. Regardless of age, children should not have to compete with screens for parental attention. When parents model healthy screen habits, they:

Minimize their own screen use when young children are present, especially for mealtimes, play and other prime opportunities for social learning.

Prioritize interactions with children through conversation, play and healthy, active routines.

Choose when to use media together, and turn off screens when they are not in use.

Help children to recognize and question advertising messages, stereotyping and other problematic content, and ensure that media used in the presence of children is free of such content.

RECOMMENDATIONS

To promote child health and development in a digital world, physicians and other health care providers should counsel parents and caregivers of young children on the appropriate use of screen time. Specific recommendations include the following:
Minimize screen time:

Screen time for children younger than 2 years is not recommended.

For children 2 to 5 years, limit routine or regular screen time to less than 1 hour per day.

Ensure that sedentary screen time is not a routine part of child care for children younger than 5 years.

Maintain daily 'screen-free' times, especially for family meals and book-sharing.

Avoid screens for at least 1 hour before bedtime, given the potential for melatonin-suppressing effects.

(reduce) the associated with screen time:

Be present and engaged when screens are used and, whenever possible, co-view with children.

Be aware of content and prioritize educational, age-appropriate and interactive programming.

Use parenting strategies that teach self-regulation, calming and limit-setting.

As a family, be mindful about the use of screen time:

Conduct a self-assessment of current screen habits and develop a family media plan for when, how and where screens may (and may not) be used.

Help children recognize and question advertising messages, stereotyping and other problematic content.

Remember: too much screen time means lost opportunities for teaching and learning.

Be reassured that there is no evidence to support introducing technology at an early age.

Adults should model healthy screen use:

Choose healthy alternatives, such as reading, outdoor play and creative, hands-on activities.

Turn off their devices at home during family time.

Turn off screens when not in use and avoid background TV.

Ten Questions to Consider Asking Families With Young Children

1. What kind of screens are in your home (e.g., TV, tablet, computer, smartphone)? Which does your child use?

2. Is watching TV or programs/movies on other devices a shared family activity and a common way to relax? How often is a screen on in the background although no one is really watching?

3. Does anyone in the family use screens during mealtimes?

4. What do you watch with your child? What does your child watch alone?

5. Do you encourage or discourage conversation with your child while you are using screens?

6. Do you ever watch adult/commercial programming with your child?

7. Does your child use screens while you do chores around the home? Often? Sometimes?

8. Are there any screen-based activities in your child's day care program? Do you know how much these are used?

9. Does your child use any kind of screen before bedtime? How long before bedtime? Is there a TV or computer in your child's bedroom? Does your child take mobile devices into the bedroom?

10. Does your family have rules or guidelines for screen use that everyone understands and shares?

CPS DIGITAL HEALTH TASK FORCE

Members: Stacey Bélanger MD (CPS Mental Health and Developmental Disabilities Committee); Ruth Grimes MD (CPS Board Representative); Janice Heard MD (CPS Public Education Advisory Committee); Matthew Johnson (Director of Education, MediaSmarts); Elizabeth Moreau (Director, CPS Communications and Knowledge Translation); Mark Norris MD (CPS Adolescent Health Committee); Alyson Shaw MD (Chair, CPS Literacy Promotion Advisory Group); Richard Stanwick MD (CPS Public Education Advisory Committee); Jackie Van Lankveld (Manager, Speech Services, Niagara Children's Centre); Robin Williams MD (Chair, CPS Early Years Task Force)

Acknowledgements

The development of this statement was made possible through an unrestricted grant from TELUS. This statement was reviewed by the Community Paediatrics Committee, the Early Years Task Force and the Mental Health and Developmental Disabilities Committee of the Canadian Paediatric Society. It was also reviewed by representatives of the College of Family Physicians of Canada. Special thanks are due to Professor Mary L. Courage, of Memorial University of Newfoundland and Labrador, to Dr Claire M. A. LeBlanc, with McGill University Health Centre, and to Dr Mark Tremblay, of the Healthy Active Living and Obesity Research Group (HALO), for their reviews and feedback. Thanks also to Jennie Strickland, for statement drafting, and to Jessie McGowan, for reviewing the literature.

References

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Screen time and young children: Promoting health and development in a digital world

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The digital landscape is evolving more quickly than research on the effects of screen media on the development, learning and family life of young children. This statement examines the potential benefits and risks of screen media in children younger than 5 years, focusing on developmental, psychosocial and physical health.