Advocating for Implementation of the Global Action Plan on Physical Activity: Challenges and Support Requirements.

BACKGROUND: There is limited understanding of the challenges experienced and supports required to aid effective advocacy of the Global Action Plan on Physical Activity (GAPPA). The purpose of this study was to assess the challenges experienced and supports needed to advocate for the GAPPA across countries of different income levels. METHODS: Stakeholders working in an area related to the promotion of physical activity were invited to complete an online survey. The survey assessed current awareness and engagement with the GAPPA, factors related to advocacy, and the perceived challenges and supports related to advocacy for implementation of the GAPPA. Closed questions were analyzed in SPSS, with a Pearson's chi-square test used to assess differences between country income level. Open questions were analyzed using inductive thematic analysis. RESULTS: Participants (n = 518) from 81 countries completed the survey. Significant differences were observed between country income level for awareness of the GAPPA and perceived country engagement with the GAPPA. Challenges related to advocacy included a lack of support and engagement, resources, priority, awareness, advocacy education and training, accessibility, and local application. Supports needed for future advocacy included guidance and support, cooperation and alliance, advocacy education and training, and advocacy resources. CONCLUSIONS: Although stakeholders from different country income levels experience similar advocacy challenges and required supports, how countries experience these can be distinct. This research has highlighted some specific ways in which those involved in the promotion of physical activity can be supported to scale up advocacy for the GAPPA. When implementing such supports, consideration of regional, geographic, and cultural barriers and opportunities is important to ensure they are effective and equitable.

Development of strategies to support home-based exercise adherence after stroke: a Delphi consensus.

OBJECTIVE: To develop a set of strategies to enhance adherence to home-based exercises after stroke, and an overarching framework to classify these strategies. METHOD: We conducted a four-round Delphi consensus (two online surveys, followed by a focus group then a consensus round). The Delphi panel consisted of 13 experts from physiotherapy, occupational therapy, clinical psychology, behaviour science and community medicine. The experts were from India, Australia and UK. RESULTS: In round 1, a 10-item survey using open-ended questions was emailed to panel members and 75 strategies were generated. Of these, 25 strategies were included in round 2 for further consideration. A total of 64 strategies were finally included in the subsequent rounds. In round 3, the strategies were categorised into nine domains-(1) patient education on stroke and recovery, (2) method of exercise prescription, (3) feedback and supervision, (4) cognitive remediation, (5) involvement of family members, (6) involvement of society, (7) promoting self-efficacy, (8) motivational strategies and (9) reminder strategies. The consensus from 12 experts (93%) led to the development of the framework in round 4. CONCLUSION: We developed a framework of comprehensive strategies to assist clinicians in supporting exercise adherence among stroke survivors. It provides practical methods that can be deployed in both research and clinical practices. Future studies should explore stakeholders' experiences and the cost-effectiveness of implementing these strategies.

An ontology-based modelling system (OBMS) for representing behaviour change theories applied to 76 theories.

Background: To efficiently search, compare, test and integrate behaviour change theories, they need to be specified in a way that is clear, consistent and computable. An ontology-based modelling system (OBMS) has previously been shown to be able to represent five commonly used theories in this way. We aimed to assess whether the OBMS could be applied more widely and to create a database of behaviour change theories, their constructs and propositions. Methods: We labelled the constructs within 71 theories and used the OBMS to represent the relationships between the constructs. Diagrams of each theory were sent to authors or experts for feedback and amendment. The 71 finalised diagrams plus the five previously generated diagrams were used to create a searchable database of 76 theories in the form of construct-relationship-construct triples. We conducted a set of illustrative analyses to characterise theories in the database. Results: All 71 theories could be satisfactorily represented using this system. In total, 35 (49%) were finalised with no or very minor amendment. The remaining 36 (51%) were finalised after changes to the constructs (seven theories), relationships between constructs (15 theories) or both (14 theories) following author/expert feedback. The mean number of constructs per theory was 20 (min. = 6, max. = 72), with the mean number of triples per theory 31 (min. = 7, max. = 89). Fourteen distinct relationship types were used, of which the most commonly used was 'influences', followed by 'part of'. Conclusions: The OBMS can represent a wide array of behavioural theories in a precise, computable format. This system should provide a basis for better integration and synthesis of theories than has hitherto been possible.

Health Gain, Cost Impacts, and Cost-Effectiveness of a Mass Media Campaign to Promote Smartphone Apps for Physical Activity: Modeling Study.

BACKGROUND: Physical activity smartphone apps are a promising strategy to increase population physical activity, but it is unclear whether government mass media campaigns to promote these apps would be a cost-effective use of public funds. OBJECTIVE: We aimed to estimate the health impacts, costs, and cost-effectiveness of a one-off national mass media campaign to promote the use of physical activity apps. METHODS: We used an established multistate life table model to estimate the lifetime health gains (in quality-adjusted life years [QALYs]) that would accrue if New Zealand adults were exposed to a one-off national mass media campaign to promote physical activity app use, with a 1-year impact on physical activity, compared to business-as-usual. A health-system perspective was used to assess cost-effectiveness. and a 3% discount rate was applied to future health gains and health system costs. RESULTS: The modeled intervention resulted in 28 QALYs (95% uncertainty interval [UI] 8-72) gained at a cost of NZ $81,000/QALY (2018 US $59,500; 95% UI 17,000-345,000), over the remaining life course of the 2011 New Zealand population. The intervention had a low probability (20%) of being cost-effective at a cost-effectiveness threshold of NZ $45,000 (US $32,900) per QALY. The health impact and cost-effectiveness of the intervention were highly sensitive to assumptions around the maintenance of physical activity behaviors beyond the duration of the intervention. CONCLUSIONS: A mass media campaign to promote smartphone apps for physical activity is unlikely to generate much health gain or be cost-effective at the population level. Other investments to promote physical activity, particularly those that result in sustained behavior change, are likely to have greater health impacts.

Effectiveness of a 3-Month Mobile Phone-Based Behavior Change Program on Active Transportation and Physical Activity in Adults: Randomized Controlled Trial.

BACKGROUND: Active transportation (AT; ie, walking and cycling as a mode for transportation) has been associated with decreased morbidity and mortality; however, low-cost and scalable intervention programs are lacking. OBJECTIVE: The goal of the research was to determine the effectiveness of a 3-month behavior change program delivered via a mobile phone app to promote AT (TravelVu Plus) on time spent in moderate-to-vigorous physical activity (MVPA). METHODS: For this 2-arm parallel randomized controlled trial, we recruited a population-based sample of 254 adults from Stockholm County who were aged 20 to 65 years and had access to a smartphone. On completion of 1-week baseline measures, the 254 participants were randomized to either the control or intervention group (1:1 ratio). Both groups had access to the standard TravelVu app (Trivector AB) for monitoring their AT for 6 months. The intervention group also received a 3-month behavior change program to promote AT (TravelVu Plus app). Assessors of outcomes were blinded to group allocation. Outcomes were objectively measured MVPA at 3 (primary) and 6 months. Secondary outcomes were AT, attitudes toward AT, and health-related quality of life at 3 and 6 months. RESULTS: No effect on MVPA was observed after 3 months (P=.29); however, at 6 months the intervention group had a greater improvement in MVPA than the controls (6.05 minutes per day [95% CI 0.36 to 11.74; P=.04]). A Bayesian analyses showed that there was a 98% probability that the intervention had any effect at 6 months, and a 63% probability that this effect was >5 minute MVPA per day. CONCLUSIONS: No effect on MVPA immediately after the intervention period (at 3 months) was observed; however, there was a delayed effect on MVPA (6 minutes per day) at 6 months, which corresponds to approximately 30% of the weekly MVPA recommendation. Our findings suggest that a behavior change program promoting AT delivered via an app may have a relevant effect on PA. TRIAL REGISTRATION: ClinicalTrials.gov NCT03086837; https://clinicaltrials.gov/ct2/show/NCT03086837. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12889-018-5658-4.

Correlates of sedentary behaviour in Asian adults: A systematic review.

The objective of this study is to systematically review the evidence on correlates of sedentary behaviour (SB) among Asian adults. We searched for studies that examined individual, environmental, and political/cultural correlates of total and domain-specific SB (transport, occupation, leisure, and screen time) in Asian adults published from 2000 onwards in nine scientific databases. Two reviewers independently screened identified references. Following quality assessment of included studies, we performed narrative synthesis that considered differences based on SB measurements, regions, and population characteristics (PROSPERO: CRD42018095268). We identified 13 249 papers of which we included 49, from four regions and 12 countries. Researchers conducted cross-sectional analyses and most relied on SB self-report for SB measurement. Of the 118 correlates studied, the following associations were consistent: higher age, living in an urban area (East Asia), and lower mental health with higher total SB; higher education with higher total and occupational SB; higher income with higher leisure-time SB; higher transit density with higher total SB in older East Asians; and being an unmarried women with higher SB in the Middle East. We encourage more research in non-high-income countries across regions, further exploration of important but neglected correlates using longitudinal designs and qualitative research, and the use of objective instruments to collect SB data.

Tailored Daily Activity: An Adaptive Physical Activity Smartphone Intervention.

Background:Nontailored and static goals may hinder behavior change. We investigated the feasibility and acceptability of an adaptive proof-of-concept smartphone-delivered intervention by using real-world movement data capture of physical activity (PA) and sedentary behavior (SB) to inform behavior change content delivery.Materials and Methods:A single-group 8-week study with pre- and post-intervention assessments was conducted in Auckland, New Zealand. Participants aged 17-69 years who owned an Android smartphone were recruited and used the application (app). Usage data, self-reported acceptability and PA and SB were assessed. Daily repeated measurement of PA and SB outcomes were analyzed through random-effects mixed models.Results:Participants (n = 69) were predominantly female (78%) with a mean age of 34.5 years (range 18-61). On average, participants opened the app on 11.4 days throughout the 8 weeks. Use decreased over time; 20% of participants opened the app every day. Feedback on behavior (73%), behavior substitution (71%), discrepancy between behavior and goal (58%) and goal setting (54%) were rated as the most useful behavior change techniques by participants. Time spent on light, moderate-to-vigorous intensity and total PA increased post-intervention, whereas time spent on SB decreased.Conclusions:The adaptive proof-of-concept app was considered acceptable, with preliminary support for its positive effects on PA and SB.

Physically active lessons in schools and their impact on physical activity, educational, health and cognition outcomes: a systematic review and meta-analysis.

OBJECTIVE: This review provides the first meta-analysis of the impact of physically active lessons on lesson-time and overall physical activity (PA), as well as health, cognition and educational outcomes. DESIGN: Systematic review and meta-analysis of controlled studies. Six meta-analyses pooled effects on lesson-time PA, overall PA, in-class educational and overall educational outcomes, cognition and health outcomes. Meta-analyses were conducted using the metafor package in R. Risk of bias was assessed using the Cochrane tool for risk of bias. DATA SOURCES: PubMed, Embase, PsycINFO, ERIC and Web of Science, grey literature and reference lists were searched in December 2017 and April 2019. STUDIES ELIGIBILITY CRITERIA: Physically active lessons compared with a control group in a randomised or non-randomised design, within single component interventions in general school populations. RESULTS: 42 studies (39 in preschool or elementary school settings, 27 randomised controlled trials) were eligible to be included in the systematic review and 37 of them were included across the six meta-analyses (n=12 663). Physically active lessons were found to produce large, significant increases in lesson-time PA (d=2.33; 95% CI 1.42 to 3.25: k=16) and small, increases on overall PA (d=0.32; 95% CI 0.18 to 0.46: k=8), large, improvement in lesson-time educational outcomes (d=0.81; 95% CI 0.47 to 1.14: k=7) and a small improvement in overall educational outcomes (d=0.36; 95% CI 0.09 to 0.63: k=25). No effects were seen on cognitive (k=3) or health outcomes (k=3). 25/42 studies had high risk of bias in at least two domains. CONCLUSION: In elementary and preschool settings, when physically active lessons were added into the curriculum they had positive impact on both physical activity and educational outcomes. These findings support policy initiatives encouraging the incorporation of physically active lessons into teaching in elementary and preschool setting. TRIAL REGISTRATION NUMBER: CRD42017076933.

Multifactorial e- and mHealth interventions for cardiovascular disease primary prevention: Protocol for a systematic review and meta-analysis of randomised controlled trials.

OBJECTIVE: Cardiovascular diseases (CVD) are a leading cause of mortality and disease burden. Preventative interventions to augment the population-level adoption of health lifestyle behaviours that reduce CVD risk are a priority. Face-to-face interventions afford individualisation and are effective for improving health-related behaviours and outcomes, but they are costly and resource intensive. Electronic and mobile health (e- and mHealth) approaches aimed at modifying lifestyle risk factors may be an effective and scalable approach to reach many individuals while preserving individualisation. This systematic review aims to (a) determine the effectiveness of multifactorial e- and mHealth interventions on CVD risk and on lifestyle-related cardiometabolic risk factors and self-management behaviours among adults without CVD; and (b) describe the evidence on adverse events and on the cost-effectiveness of these interventions. METHODS: Methods were detailed prior to the start of the review in order to improve conduct and prevent inconsistent decision making throughout the review. This protocol was prepared following the PRISMA-P 2015 statement. MEDLINE, CINAHL, Embase, PsycINFO, Web of Science, Cochrane Public Health Group Specialised Register and CENTRAL electronic databases will be searched between 1991 and September 2019. Eligibility criteria are: (a) population: community-dwelling adults; (b) intervention/comparison: randomised controlled trials comparing e- or mHealth CVD risk preventative interventions with usual care; and (c) outcomes: modifiable CVD risk factors. Selection of study reports will involve two authors independently screening titles and abstracts, followed by a full-text review of potentially eligible reports. Two authors will independently undertake data extraction and assess risk of bias. Where appropriate, meta-analysis of outcome data will be performed. DISCUSSION: This protocol describes the pre-specified methods for a systematic review that will provide quantitative and narrative syntheses of current multifactorial e- and mHealth CVD preventative interventions. A systematic review and meta-analysis will be conducted following the methods outlined in the Cochrane Handbook for Systematic Reviews of Interventions and reported according to PRISMA guidelines.

Early Career Professionals' (Researchers, Practitioners, and Policymakers) Role in Advocating, Disseminating, and Implementing the Global Action Plan on Physical Activity: ISPAH Early Career Network View.

Increasing population levels of physical activity (PA) can assist in achieving the United Nations sustainable development goals, benefiting multiple sectors and contributing to global prosperity. Practices and policies to increase PA levels exist at the subnational, national, and international levels. In 2018, the World Health Organization launched the first Global Action Plan on Physical Activity (GAPPA). The GAPPA provides guidance through a framework of effective and feasible policy actions for increasing PA, and requires engagement and advocacy from a wide spectrum of stakeholders for successful implementation of the proposed actions. Early career professionals, including researchers, practitioners, and policymakers, can play a major role with helping "all people being regularly active" by contributing to 4 overarching areas: (1) generation-of evidence, (2) dissemination-of key messages and evidence, (3) implementation-of the evidence-based actions proposed in the GAPPA, and (4) contributing to advocacy for robust national action plans on PA. The contribution of early career professionals can be achieved through 5 pathways: (1) research, (2) workplace/practice, (3) business, (4) policy, and (5) professional and public opinion. Recommendations of how early career professionals can contribute to the generation, dissemination, and implementation of the evidence and actions proposed by the GAPPA are provided.

Implementation of the goal-setting components in popular physical activity apps: Review and content analysis.

OBJECTIVE: Goal setting is an effective strategy to promote physical activity. Commercial apps that tackle physical activity often include goal setting; however, it is unknown whether the implementation of the goal-setting components is congruent with the theory. This study evaluated the quality of goal setting in popular free and paid physical activity apps by assessing the presence of effective goal-setting components. METHODS: A six-item scale was developed based on the goal-setting literature and used for coding each app for the presence/absence of goal-setting components (i.e. specificity, difficulty, action planning, timeframe, goal evaluation and goal re-evaluation). Cohen's Kappa was used to evaluate inter-rater reliability for each scale item. The number of goal-setting components included in the 40 apps was calculated and the difference between free and paid apps was assessed. RESULTS: All scale items achieved satisfactory inter-rater reliability except 'goal evaluation'. The most frequently included goal-setting components in popular physical activity apps were 'goal specificity' (95% of the apps) and 'goal timeframe' (67.5%). Conversely, only 47.5% and 25% of the apps implemented 'action planning' and 'goal difficulty', respectively, and none included 'goal re-evaluation'. No differences emerged between free and paid apps. CONCLUSIONS: The quality of the goal-setting strategy in popular physical activity apps could be improved by introducing components scarcely implemented to date. In particular, tailoring the goal difficulty to the users' ability level and re-evaluating the goals based on achievements should be implemented to increase the quality of goal setting.

The Smart City Active Mobile Phone Intervention (SCAMPI) study to promote physical activity through active transportation in healthy adults: a study protocol for a randomised controlled trial.

BACKGROUND: The global pandemic of physical inactivity represents a considerable public health challenge. Active transportation (i.e., walking or cycling for transport) can contribute to greater total physical activity levels. Mobile phone-based programs can promote behaviour change, but no study has evaluated whether such a program can promote active transportation in adults. This study protocol presents the design and methodology of The Smart City Active Mobile Phone Intervention (SCAMPI), a randomised controlled trial to promote active transportation via a smartphone application (app) with the aim to increase physical activity. METHODS/DESIGN: A two-arm parallel randomised controlled trial will be conducted in Stockholm County, Sweden. Two hundred fifty adults aged 20-65 years will be randomised to either monitoring of active transport via the TRavelVU app (control), or to a 3-month evidence-based behaviour change program to promote active transport and monitoring of active travel via the TRavelVU Plus app (intervention). The primary outcome is moderate-to-vigorous intensity physical activity (MVPA in minutes/day) (ActiGraph wGT3x-BT) measured post intervention. Secondary outcomes include: time spent in active transportation measured via the TRavelVU app, perceptions about active transportation (the Transport and Physical Activity Questionnaire (TPAQ)) and health related quality of life (RAND-36). Assessments are conducted at baseline, after the completed intervention (after 3 months) and 6 months post randomisation. DISCUSSION: SCAMPI will determine the effectiveness of a smartphone app to promote active transportation and physical activity in an adult population. If effective, the app has potential to be a low-cost intervention that can be delivered at scale. TRIAL REGISTRATION: ClinicalTrials.gov NCT03086837 ; 22 March, 2017.

Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change.

Few interventions to promote physical activity (PA) adapt dynamically to changes in individuals' behavior. Interventions targeting determinants of behavior are linked with increased effectiveness and should reflect changes in behavior over time. This article describes the application of two frameworks to assist the development of an adaptive evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary behaviors (SB). Intervention mapping was used to identify the determinants influencing uptake of PA and optimal behavior change techniques (BCTs). Behavioral intervention technology was used to translate and operationalize the BCTs and its modes of delivery. The intervention was based on the integrated behavior change model, focused on nine determinants, consisted of 33 BCTs, and included three main components: (1) automated capture of daily PA and SB via an existing smartphone application, (2) classification of the individual into an activity profile according to their PA and SB, and (3) behavior change content delivery in a dynamic fashion via a proof-of-concept application. This article illustrates how two complementary frameworks can be used to guide the development of a mobile health behavior change program. This approach can guide the development of future mHealth programs.

Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study.

BACKGROUND: The use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data-which includes billions of digital traces-offers scientists a new lens to examine PA in fine-grained detail and allows us to track people's geocoded movement patterns to determine their interaction with the environment. OBJECTIVE: The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data. METHODS: The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement. RESULTS: A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [-0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t6123=101.49, P<.001), and biases were larger during high-intensity activities (F3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps. CONCLUSIONS: The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics.

Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques.

BACKGROUND: The number of commercial apps to improve health behaviours in children is growing rapidly. While this provides opportunities for promoting health, the content and quality of apps targeting children and adolescents is largely unexplored. This review systematically evaluated the content and quality of apps to improve diet, physical activity and sedentary behaviour in children and adolescents, and examined relationships of app quality ratings with number of app features and behaviour change techniques (BCTs) used. METHODS: Systematic literature searches were conducted in iTunes and Google Play stores between May-November 2016. Apps were included if they targeted children or adolescents, focused on improving diet, physical activity and/or sedentary behaviour, had a user rating of at least 4+ based on at least 20 ratings, and were available in English. App inclusion, downloading and user-testing for quality assessment and content analysis were conducted independently by two reviewers. Spearman correlations were used to examine relationships between app quality, and number of technical app features and BCTs included. RESULTS: Twenty-five apps were included targeting diet (n = 12), physical activity (n = 18) and sedentary behaviour (n = 7). On a 5-point Mobile App Rating Scale (MARS), overall app quality was moderate (total MARS score: 3.6). Functionality was the highest scoring domain (mean: 4.1, SD: 0.6), followed by aesthetics (mean: 3.8, SD: 0.8), and lower scoring for engagement (mean: 3.6, SD: 0.7) and information quality (mean: 2.8, SD: 0.8). On average, 6 BCTs were identified per app (range: 1-14); the most frequently used BCTs were providing 'instructions' (n = 19), 'general encouragement' (n = 18), 'contingent rewards' (n = 17), and 'feedback on performance' (n = 13). App quality ratings correlated positively with numbers of technical app features (rho = 0.42, p < 0.05) and BCTs included (rho = 0.54, p < 0.01). CONCLUSIONS: Popular commercial apps to improve diet, physical activity and sedentary behaviour in children and adolescents had moderate quality overall, scored higher in terms of functionality. Most apps incorporated some BCTs and higher quality apps included more app features and BCTs. Future app development should identify factors that promote users' app engagement, be tailored to specific population groups, and be informed by health behaviour theories.

mHealth Technologies to Influence Physical Activity and Sedentary Behaviors: Behavior Change Techniques, Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: mHealth programs offer potential for practical and cost-effective delivery of interventions capable of reaching many individuals. PURPOSE: To (1) compare the effectiveness of mHealth interventions to promote physical activity (PA) and reduce sedentary behavior (SB) in free-living young people and adults with a comparator exposed to usual care/minimal intervention; (2) determine whether, and to what extent, such interventions affect PA and SB levels and (3) use the taxonomy of behavior change techniques (BCTs) to describe intervention characteristics. METHODS: A systematic review and meta-analysis following PRISMA guidelines was undertaken to identify randomized controlled trials (RCTs) comparing mHealth interventions with usual or minimal care among individuals free from conditions that could limit PA. Total PA, moderate-to-vigorous intensity physical activity (MVPA), walking and SB outcomes were extracted. Intervention content was independently coded following the 93-item taxonomy of BCTs. RESULTS: Twenty-one RCTs (1701 participants-700 with objectively measured PA) met eligibility criteria. SB decreased more following mHealth interventions than after usual care (standardised mean difference (SMD) -0.26, 95 % confidence interval (CI) -0.53 to -0.00). Summary effects across studies were small to moderate and non-significant for total PA (SMD 0.14, 95 % CI -0.12 to 0.41); MVPA (SMD 0.37, 95 % CI -0.03 to 0.77); and walking (SMD 0.14, 95 % CI -0.01 to 0.29). BCTs were employed more frequently in intervention (mean = 6.9, range 2 to 12) than in comparator conditions (mean = 3.1, range 0 to 10). Of all BCTs, only 31 were employed in intervention conditions. CONCLUSIONS: Current mHealth interventions have small effects on PA/SB. Technological advancements will enable more comprehensive, interactive and responsive intervention delivery. Future mHealth PA studies should ensure that all the active ingredients of the intervention are reported in sufficient detail.

Diabetic Mario: Designing and Evaluating Mobile Games for Diabetes Education.

Traditionally, diabetes education has relied on written materials, with limited resources available for children with diabetes. Mobile games can be effective and motivating tools for the promotion of children's health. In our earlier work, we proposed a novel approach for designing computer games aimed at educating children with diabetes. In this article, we apply our game design to a mobile Android game (Mario Brothers). We also introduce four heuristics that are specifically designed for evaluating the mobile game, by adapting traditional usability heuristics. Results of a pilot study (n = 12) to evaluate gameplay over 1-week showed that the children found the game engaging and improved their knowledge of healthy diet and lifestyle.

Telehealth exercise-based cardiac rehabilitation: a systematic review and meta-analysis.

OBJECTIVE: Despite proven effectiveness, participation in traditional supervised exercise-based cardiac rehabilitation (exCR) remains low. Telehealth interventions that use information and communication technologies to enable remote exCR programme delivery can overcome common access barriers while preserving clinical supervision and individualised exercise prescription. This meta-analysis aimed to determine the benefits of telehealth exCR on exercise capacity and other modifiable cardiovascular risk factors compared with traditional exCR and usual care, among patients with coronary heart disease (CHD). METHODS: CINAHL, The Cochrane Library, Embase, MEDLINE, PubMed and PsycINFO were searched from inception through 31 May 2015 for randomised controlled trials comparing telehealth exCR with centre-based exCR or usual care among patients with CHD. Outcomes included maximal aerobic exercise capacity, modifiable cardiovascular risk factors and exercise adherence. RESULTS: 11 trials (n=1189) met eligibility criteria and were included in the review. Physical activity level was higher following telehealth exCR than after usual care. Compared with centre-based exCR, telehealth exCR was more effective for enhancing physical activity level, exercise adherence, diastolic blood pressure and low-density lipoprotein cholesterol. Telehealth and centre-based exCR were comparably effective for improving maximal aerobic exercise capacity and other modifiable cardiovascular risk factors. CONCLUSIONS: Telehealth exCR appears to be at least as effective as centre-based exCR for improving modifiable cardiovascular risk factors and functional capacity, and could enhance exCR utilisation by providing additional options for patients who cannot attend centre-based exCR. Telehealth exCR must now capitalise on technological advances to provide more comprehensive, responsive and interactive interventions.

The association between the activity profile and cardiovascular risk.

OBJECTIVES: This study sought to better understand the interrelationships between physical activity and sedentary behaviour and the relationship to risk of cardiovascular disease (CVDR) in adults aged 30-75 years. DESIGN: Cross-sectional. METHODS: Data from two-year waves (2003-2004 and 2005-2006) of the National Health and Nutritional Examination survey were analysed in 2014. Accelerometer-derived time and proportion of time spent sedentary and on moderate-to-vigorous physical activity (MVPA) were calculated to generate four activity profiles based on cut-points to define low and high levels for the respective behaviours. Using health outcome data, CVDR was calculated for each person. Weighted multiple linear regression models were used to evaluate the predicted effects of sedentary and physical activity behaviours on the CVDR score, adjusting for participants' sex, age group, race, annual household income, and accelerometer wear time. RESULTS: The lowest CVDR was observed among Busy Exercisers (high MVPA and low sedentary; 8.5%), whereas Couch Potatoes (low MVPA and high sedentary) had the highest (18.6%). Compared with the reference group (Busy Exercisers), the activity profile associated with the highest CVDR was Couch Potatoes (adjusted mean difference 3.6, SE 0.38, p<0.0001). A smoothed three-dimensional response surface "risk landscape" was developed to better visualise the conjoint associations of MVPA and sedentary behaviour on CVDR for each activity profile. The association between MVPA was greater than that of sedentary behaviour; however, for people with low MVPA, shifts in sedentary behaviour may have the greatest impact on CVDR. CONCLUSIONS: Activity profiles that consider the interrelationships between physical activity and sedentary behaviour differ in terms of CVDR. Future interventions may need to be tailored to specific profiles and be dynamic enough to reflect change in the profile over time.

Apps for IMproving FITness and Increasing Physical Activity Among Young People: The AIMFIT Pragmatic Randomized Controlled Trial.

BACKGROUND: Given the global prevalence of insufficient physical activity (PA), effective interventions that attenuate age-related decline in PA levels are needed. Mobile phone interventions that positively affect health (mHealth) show promise; however, their impact on PA levels and fitness in young people is unclear and little is known about what makes a good mHealth app. OBJECTIVE: The aim was to determine the effects of two commercially available smartphone apps (Zombies, Run and Get Running) on cardiorespiratory fitness and PA levels in insufficiently active healthy young people. A second aim was to identify the features of the app design that may contribute to improved fitness and PA levels. METHODS: Apps for IMproving FITness (AIMFIT) was a 3-arm, parallel, randomized controlled trial conducted in Auckland, New Zealand. Participants were recruited through advertisements in electronic mailing lists, local newspapers, flyers posted in community locations, and presentations at schools. Eligible young people aged 14-17 years were allocated at random to 1 of 3 conditions: (1) use of an immersive app (Zombies, Run), (2) use of a nonimmersive app (Get Running), or (3) usual behavior (control). Both smartphone apps consisted of a fully automated 8-week training program designed to improve fitness and ability to run 5 km; however, the immersive app featured a game-themed design and narrative. Intention-to-treat analysis was performed using data collected face-to-face at baseline and 8 weeks, and all regression models were adjusted for baseline outcome value and gender. The primary outcome was cardiorespiratory fitness, objectively assessed as time to complete the 1-mile run/walk test at 8 weeks. Secondary outcomes were PA levels (accelerometry and self-reported), enjoyment, psychological need satisfaction, self-efficacy, and acceptability and usability of the apps. RESULTS: A total of 51 participants were randomized to the immersive app intervention (n=17), nonimmersive app intervention (n=16), or the control group (n=18). The mean age of participants was 15.7 (SD 1.2) years; participants were mostly NZ Europeans (61%, 31/51) and 57% (29/51) were female. Overall retention rate was 96% (49/51). There was no significant intervention effect on the primary outcome using either of the apps. Compared to the control, time to complete the fitness test was -28.4 seconds shorter (95% CI -66.5 to 9.82, P=.20) for the immersive app group and -24.7 seconds (95% CI -63.5 to 14.2, P=.32) for the nonimmersive app group. No significant intervention effects were found for secondary outcomes. CONCLUSIONS: Although apps have the ability to increase reach at a low cost, our pragmatic approach using readily available commercial apps as a stand-alone instrument did not have a significant effect on fitness. However, interest in future use of PA apps is promising and highlights a potentially important role of these tools in a multifaceted approach to increase fitness, promote PA, and consequently reduce the adverse health outcomes associated with insufficient activity. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12613001030763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12613001030763 (Archived by WebCite at http://www.webcitation.org/6aasfJVTJ).