Why we need more than just randomized controlled trials to establish the effectiveness of online social networks for health behavior change

Despite their popularity and potential to promote health in large populations, the effectiveness of online social networks (e.g., Facebook) to improve health behaviors has been somewhat disappointing. Most of the research examining the effectiveness of such interventions has used randomized controlled trials (RCTs). It is asserted that the modest outcomes may be due to characteristics specific to both online social networks and RCTs. The highly controlled nature of RCTs stifles the dynamic nature of online social networks. Alternative and ecologically valid research designs that evaluate online social networks in real-life conditions are needed to advance the science in this area

Day type and the relationship between weight status and sleep duration in children and adolescents

Chronic reduced quality and duration of sleep have been associated with a range of physical and psychosocial disturbances in both adults and children, including: impaired attention, memory, creativity, learning and academic performance;1 motor skill deficits; 2 greater emotional lability, increased impulsivity, aggression and hyperactivity; 3 and increased potential for alcohol and drug abuse in adulthood.

Are health behavior change interventions that use online social networks effective? : A systematic review

Background: The dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change.Objective: The intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions.Methods: Eight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where “population” included child or adult populations, including healthyand disease populations; “intervention” involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; “comparator” was either a control group or within subject in the case of pre-post study designs; “outcomes” included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg,self-efficacy); and “study design” included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen’s d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intendedusage) with the social networking component of the interventions were scrutinized.Results: A total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online health social network websites (n=2), research health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine ofthe 10 included studies reported significant improvements in some aspect of health behavior change or outcomes related to behavior change. Effect sizes for behavior change ranged widely from −0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement andfidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity).Conclusions: To date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether behavior change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided

Do birds of a feather flock together within a team-based physical activity intervention? A social network analysis

Background: Homophily is the tendency to associate with friends similar to ourselves. This study explored the effects of homophily on team formation in a physical activity challenge in which “captains” signed up their Facebook friends to form teams. Methods: This study assessed whether participants (n = 430) were more similar to their teammates than to nonteammates with regard to age, sex, education level, body mass index, self-reported and objectively measured physical activity, and negative emotional states; and whether captains were more similar to their own teammates than to nonteammates. Variability indices were calculated for each team, and a hypothetical variability index, representing that which would result from randomly assembled teams, was also calculated. Results: Within-team variability was less than that for random teams for all outcomes except education level and depression, with differences (SDs) ranging from +0.15 (self-reported physical activity) to +0.47 (age) (P < .001 to P = .001). Captains were similar to their teammates except in regard to age, with captains being 2.6 years younger (P = .003). Conclusions: Results support hypotheses that self-selected teams are likely to contain individuals with similar characteristics, highlighting potential to leverage team-based health interventions to target specific populations by instructing individuals with risk characteristics to form teams to help change behavior. © 2019 Human Kinetics, Inc

Are health behavior change interventions that use online social networks effective? : A systematic review

Background: The dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change.Objective: The intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions.Methods: Eight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where “population” included child or adult populations, including healthyand disease populations; “intervention” involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; “comparator” was either a control group or within subject in the case of pre-post study designs; “outcomes” included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg,self-efficacy); and “study design” included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen’s d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intendedusage) with the social networking component of the interventions were scrutinized.Results: A total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online health social network websites (n=2), research health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine ofthe 10 included studies reported significant improvements in some aspect of health behavior change or outcomes related to behavior change. Effect sizes for behavior change ranged widely from −0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement andfidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity).Conclusions: To date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether behavior change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided

"Active Team" a social and gamified app-based physical activity intervention: Randomised controlled trial study protocol

© 2017 The Author(s). Background: Physical inactivity is a leading preventable cause of chronic disease and premature death globally, yet over half of the adult Australian population is inactive. To address this, web-based physical activity interventions, which have the potential to reach large numbers of users at low costs, have received considerable attention. To fully realise the potential of such interventions, there is a need to further increase their appeal to boost engagement and retention, and sustain intervention effects over longer periods of time. This randomised controlled trial aims to evaluate the efficacy of a gamified physical activity intervention that connects users to each other via Facebook and is delivered via a mobile app. Methods: The study is a three-group, cluster-RCT. Four hundred and forty (440) inactive Australian adults who use Facebook at least weekly will be recruited in clusters of three to eight existing Facebook friends. Participant clusters will be randomly allocated to one of three conditions: (1) waitlist control condition, (2) basic experimental condition (pedometer plus basic app with no social and gamification features), or (3) socially-enhanced experimental condition (pedometer plus app with social and gamification features). Participants will undertake assessments at baseline, three and nine months. The primary outcome is change in total daily minutes of moderate-to-vigorous physical activity at three months measured objectively using GENEActive accelerometers [Activeinsights Ltd., UK]. Secondary outcomes include self-reported physical activity, depression and anxiety, wellbeing, quality of life, social-cognitive theory constructs and app usage and engagement. Discussion: The current study will incorporate novel social and gamification elements in order to examine whether the inclusion of these components increases the efficacy of app-based physical activity interventions. The findings will be used to guide the development and increase the effectiveness of future health behaviour interventions. Trial registration: This trial was registered with the Australian and New Zealand Clinical Trial Registry (ACTRN12617000113358, date of registration 23 January, 2017)

Validity and bias on the online active Australia survey: Activity level and participant factors associated with self-report bias

BACKGROUND: This study examined the criterion validity of the online Active Australia Survey, using accelerometry as the criterion, and whether self-report bias was related to level of activity, age, sex, education, body mass index and health-related quality of life. METHODS: The online Active Australia Survey was validated against the GENEActiv accelerometer as a direct measure of activity. Participants (n = 344) wore an accelerometer for 7 days, completed the Active Australia Survey, and reported their health and demographic characteristics. A Spearman's rank coefficient examined the association between minutes of moderate-to-vigorous physical activity recorded on the Active Australia Survey and GENEActiv accelerometer. A Bland-Altman plot illustrated self-report bias (the difference between methods). Linear mixed effects modelling was used to examine whether participant factors predicted self-report bias. RESULTS: The association between moderate-to-vigorous physical activity reported on the online Active Australia Survey and accelerometer was significant (rs = .27, p < .001). Participants reported 4 fewer minutes per day on the Active Australia Survey than was recorded by accelerometry (95% limits of agreement -104 - 96 min) but the difference was not significant (t(343) = -1.40, p = .16). Self-report bias was negatively associated with minutes of accelerometer-recorded moderate-to-vigorous physical activity and positively associated with mental health-related quality of life. CONCLUSIONS: The online Active Australia Survey showed limited criterion validity against accelerometry. Self-report bias was related to activity level and mental health-related quality of life. Caution is recommended when interpreting studies using the online Active Australia Survey

Psychometric properties of the PERMA Profiler for measuring wellbeing in Australian adults

Introduction This study evaluated the psychometric properties of the PERMA Profiler, a 15-item self-report measurement tool designed to measure Seligman’s five pillars of wellbeing: Positive emotions, Relationships, Engagement, Meaning, and Accomplishment. Methods Australian adults (N = 439) completed the PERMA Profiler and measures of physical and mental health (SF-12), depression, anxiety, stress (DASS 21), subjective physical activity (Active Australia Survey), and objective activity and sleep (GENEActiv accelerometer). Internal consistency was examined using Cronbach’s alpha and associations between theoretically related constructs examined using Pearson’s correlation. Model fit in comparison with theorised models was examined via Confirmatory Factor Analysis. Results Results indicated acceptable internal consistency for overall PERMA Profiler scores and all subscales (α range = 0.80–0.93) except Engagement (α = 0.66). Moderate associations were found between PERMA Profiler wellbeing scores with subjective constructs (e.g. depression, anxiety, stress; r = -0.374 - -0.645, p = <0.001) but not objective physical activity or sleep. Data failed to meet model fit criteria for neither the theorised five-factor nor an alternative single-factor structure. Conclusions Findings were mixed, providing strong support for the scale’s internal consistency and moderate support for congervent and divergent validity, albeit not in comparison to objectively captured activity outcomes. We could not replicate the theorised data structure nor an alternative, single factor structure. Results indicate insufficient psychometric properties of the PERMA Profiler. © 2019 Ryan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Can a school-based sleep education programme improve sleep knowledge, hygiene and behaviours using a randomised controlled trial

Objectives: The present study investigated the effectiveness of a school-based sleep education programme in improving key sleep behaviours, sleep knowledge, and sleep hygiene. Design and methods: A cross-sectional cluster-randomised controlled trial with two groups (Intervention and Control) and three assessment time points [baseline, immediately post intervention (6 weekspost baseline) and follow-up (18 weeks post baseline)] was employed. A total of 296 students (mean age = 12.2 ± 0.6 years; 59% female) from 12 schools in Adelaide, South Australia, were recruited, with 149 participants in the Intervention group and 147 in the Control group. The intervention consisted of four classroom lessons delivered at weekly intervals, followed by a group project on sleep topics, which students presented at a parental information evening. Sleep patterns were assessed objectively (actigraphy,n = 175) and subjectively (time-use recall, n = 251) at three time points. Sleep knowledge and sleep hygiene (n = 296) were also measured. Results: Generalised estimating equations were used to compare changes in the Intervention and Control groups. The programme increased time in bed by 10 min (p = 0.03) for the Intervention group relative to the Control group, due to a 10-min delay in wake time (p = 0.00). These changes were not sustained at follow-up. There was no impact on sleep knowledge or sleep hygiene. Conclusion: Investment in the sleep health of youth through sleep education is important but changes to sleep patterns are difficult to achieve. More intensive programmes, programmes with a different focus or programmes targeting different age groups may be more effective

Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions

Objective: Physical inactivity is a leading modifiable cause of death and disease worldwide. Population-based interventions to increase physical activity are needed. Existing automated expert systems (e.g., computer-tailored interventions) have significant limitations that result in low long-term effectiveness. Therefore, innovative approaches are needed. This special communication aims to describe and discuss a novel mHealth intervention approach that proactively offers participants with hyper-personalised intervention content adjusted in real-time. Methods: Using machine learning approaches, we propose a novel physical activity intervention approach that can learn and adapt in real-time to achieve high levels of personalisation and user engagement, underpinned by a likeable digital assistant. It will consist of three major components: (1) conversations: to increase user's knowledge on a wide range of activity-related topics underpinned by Natural Language Processing; (2) nudge engine: to provide users with hyper-personalised cues to action underpinned by reinforcement learning (i.e., contextual bandit) and integrating real-time data from activity tracking, GPS, GIS, weather, and user provided data; (3) Q&A: to facilitate users asking any physical activity related questions underpinned by generative AI (e.g., ChatGPT, Bard) for content generation. Results: The detailed concept of the proposed physical activity intervention platform demonstrates the practical application of a just-in-time adaptive intervention applying various machine learning techniques to deliver a hyper-personalised physical activity intervention in an engaging way. Compared to traditional interventions, the novel platform is expected to show potential for increased user engagement and long-term effectiveness due to: (1) using new variables to personalise content (e.g., GPS, weather), (2) providing behavioural support at the right time in real-time, (3) implementing an engaging digital assistant and (4) improving the relevance of content through applying machine learning algorithms. Conclusion: The use of machine learning is on the rise in every aspect of today's society, however few attempts have been undertaken to harness its potential to achieve health behaviour change. By sharing our intervention concept, we contribute to the ongoing dialogue on creating effective methods for promoting health and well-being in the informatics research community. Future research should focus on refining these techniques and evaluating their effectiveness in controlled and real-world circumstances