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Background Wearable activity trackers offer considerable promise for helping users to adopt healthier lifestyles. This study aimed to explore users’ experience of activity trackers, including usage patterns, sharing of data to social media, perceived behaviour change (physical activity, diet and sleep), and technical issues/barriers to use. Methods A cross-sectional online survey was developed and administered to Australian adults who were current or former activity tracker users. Results were analysed descriptively, with differences between current and former users and wearable brands explored using independent samples t -tests, Mann-Whitney, and chi square tests. Results Participants included 200 current and 37 former activity tracker users (total N  = 237) with a mean age of 33.1 years (SD 12.4, range 18–74 years). Fitbit (67.5%) and Garmin devices (16.5%) were most commonly reported. Participants typically used their trackers for sustained periods (5–7 months) and most intended to continue usage. Participants reported they had improved their physical activity (51–81%) more commonly than they had their diet (14–40%) or sleep (11–24%), and slightly more participants reported to value the real time feedback (89%) compared to the long-term monitoring (78%). Most users (70%) reported they had experienced functionality issues with their devices, most commonly related to battery life and technical difficulties. Conclusions Results suggest users find activity trackers appealing and useful tools for increasing perceived physical activity levels over a sustained period.

Engagement in online programs is difficult to maintain. Gamification is the recent trend that offers to increase engagement through the inclusion of game-like features like points and badges, in non-game contexts. This review will answer the following question, 'Are gamification strategies effective in increasing engagement in online programs?' Eight databases (Web of Science, PsycINFO, Medline, INSPEC, ERIC, Cochrane Library, Business Source Complete and ACM Digital Library) were searched from 2010 to the 28th of October 2015 using a comprehensive search strategy. Eligibility criteria was based on the PICOS format, where \"population\" included adults, \"intervention\" involved an online program or smart phone application that included at least one gamification feature. \"Comparator\" was a control group, \"outcomes\" included engagement and \"downstream\" outcomes which occurred as a result of engagement; and \"study design\" included experimental studies from peer-reviewed sources. Effect sizes (Cohens d and 95% confidence intervals) were also calculated. 1017 studies were identified from database searches following the removal of duplicates, of which 15 met the inclusion criteria. The studies involved a total of 10,499 participants, and were commonly undertaken in tertiary education contexts. Engagement metrics included time spent (n = 5), volume of contributions (n = 11) and occasions visited to the software (n = 4); as well as downstream behaviours such as performance (n = 4) and healthy behaviours (n = 1). Effect sizes typically ranged from medium to large in direct engagement and downstream behaviours, with 12 out of 15 studies finding positive significant effects in favour of gamification. Gamification is effective in increasing engagement in online programs. Key recommendations for future research into gamification are provided. In particular, rigorous study designs are required to fully examine gamification's effects and determine how to best achieve sustained engagement.

Smartphone apps are a promising tool for delivering accessible and appealing physical activity interventions. Given the large growth of research in this field, there are now enough studies using the \"gold standard\" of experimental design-the randomized controlled trial design-and employing objective measurements of physical activity, to support a meta-analysis of these scientifically rigorous studies. This systematic review and meta-analysis aimed to determine the effectiveness of smartphone apps for increasing objectively measured physical activity in adults. A total of 7 electronic databases (EMBASE, EmCare, MEDLINE, Scopus, Sport Discus, The Cochrane Library, and Web of Science) were searched from 2007 to January 2018. Following the Population, Intervention, Comparator, Outcome and Study Design format, studies were eligible if they were randomized controlled trials involving adults, used a smartphone app as the primary or sole component of the physical activity intervention, used a no- or minimal-intervention control condition, and measured objective physical activity either in the form of moderate-to-vigorous physical activity minutes or steps. Study quality was assessed using a 25-item tool based on the Consolidated Standards of Reporting Trials checklist. A meta-analysis of study effects was conducted using a random effects model approach. Sensitivity analyses were conducted to examine whether intervention effectiveness differed on the basis of intervention length, target behavior (physical activity alone vs physical activity in combination with other health behaviors), or target population (general adult population vs specific health populations). Following removal of duplicates, a total of 6170 studies were identified from the original database searches. Of these, 9 studies, involving a total of 1740 participants, met eligibility criteria. Of these, 6 studies could be included in a meta-analysis of the effects of physical activity apps on steps per day. In comparison with the control conditions, smartphone apps produced a nonsignificant (P=.19) increase in participants' average steps per day, with a mean difference of 476.75 steps per day (95% CI -229.57 to 1183.07) between groups. Sensitivity analyses suggested that physical activity programs with a duration of less than 3 months were more effective than apps evaluated across more than 3 months (P=.01), and that physical activity apps that targeted physical activity in isolation were more effective than apps that targeted physical activity in combination with diet (P=.04). Physical activity app effectiveness did not appear to differ on the basis of target population. This meta-analysis provides modest evidence supporting the effectiveness of smartphone apps to increase physical activity. To date, apps have been most effective in the short term (eg, up to 3 months). Future research is needed to understand the time course of intervention effects and to investigate strategies to sustain intervention effects over time.

Physical inactivity and sedentary behavior are significant risk factors for various non-communicable diseases. Bangkok, Thailand's capital, is one of the fastest-growing metropolitans in Southeast Asia. Few studies have investigated the epidemiology of physical activity and sedentary behavior among Bangkok residents. This study aims to investigate the prevalence of combined physical activity and sedentary behavior patterns among Bangkok residents and examine relationships between participants' characteristics and the combined movement patterns. We analyzed data from the nationally representative 2021 Health Behavior Survey conducted by the Thailand National Statistical Office. The Global Physical Activity Questionnaire was used to assess physical activity and sedentary behavior. 'Sufficiently active' was defined as meeting the World Health Organization's guidelines for aerobic physical activity ([greater than or equal to]150 minutes of moderate-to-vigorous physical activity per week). 'Low sedentary time' was defined as sitting for [less than or equal to]7 hours per day. Participants were categorized into one of four movement patterns: highly active/low sedentary, highly active/highly sedentary, low active/low sedentary, and low active/highly sedentary. Multinomial logistic regression was used to identify the factors associated with each group of four movement patterns. Among the 3,137 individuals included in the study, the majority were categorized as highly active/highly sedentary (64.8%), followed by highly active/low sedentary (17.9%) and low active/highly sedentary (14.3%). Only a few (3.0%) of participants were categorized as being low active/low sedentary. Compared to males, female participants had a significantly higher likelihood of belonging to the highly active/low sedentary (AOR = 1.69, 95%CI: 1.25, 2.28) or highly active/highly sedentary (AOR = 1.51, 95%CI: 1.19, 1.93) group, rather than the low active/high sedentary group. Compared to unemployed/retired participants, those in labor-intensive occupations had a significantly higher likelihood of being in the highly active/low sedentary group (AOR = 1.89, 95%CI: 1.22, 2.94). Compared to participants with no chronic physical conditions, participants who reported multimorbidity had a significantly lower likelihood of being in the highly active/low sedentary group (AOR = 0.60, 95%CI: 0.37, 0.98). This study provides valuable insights into the patterns of physical activity and sedentary behavior among residents of Bangkok using up-to-date data. The majority belonged to the highly active/highly sedentary group, followed by the highly active/low sedentary group. Correlates such as sex, occupation, and chronic conditions were associated with these patterns. Targeted interventions in recreational activities, workplaces, and urban areas, including screen time control measures, movement breaks and improved built environments, are crucial in reducing sedentary behavior and promoting physical activity.

Background Immobility is major contributor to poor outcomes for older people during hospitalisation with an acute medical illness. Yet currently there is no specific mobility guidance for this population, to facilitate sustainable changes in practice. This study aimed to generate draft physical activity (PA) and sedentary behaviour (SB) recommendations for older adults’ during hospitalisation for an acute medical illness. Methods A 4-Round online Delphi consensus survey was conducted. International researchers, medical/nursing/physiotherapy clinicians, academics from national PA/SB guideline development teams, and patients were invited to participate. Round 1 sought responses to open-ended questions. In Rounds 2–3, participants rated the importance of items using a Likert scale (1–9); consensus was defined a priori as: ≥70% of respondents rating an item as “critical” (score ≥ 7) and ≤ 15% of respondents rating an item as “not important” (score ≤ 3). Round 4 invited participants to comment on draft statements derived from responses to Rounds 1–3; Round 4 responses subsequently informed final drafting of recommendations. Results Forty-nine people from nine countries were invited to each Round; response rates were 94, 90, 85 and 81% from Rounds 1–4 respectively. 43 concepts (items) from Rounds 2 and 3 were incorporated into 29 statements under themes of PA, SB, people and organisational factors in Round 4. Examples of the final draft recommendations (being the revised version of statements with highest participant endorsement under each theme) were: “ some PA is better than none”, “ older adults should aim to minimise long periods of uninterrupted SB during waking hours while hospitalised”, “when encouraging PA and minimising SB, people should be culturally responsive and mindful of older adults’ physical and mental capabilities” and “opportunities for PA and minimising SB should be incorporated into the daily care of older adults with a focus on function, independence and activities of daily living”. Conclusions These world-first consensus-based statements from expert and stakeholder consultation provide the starting point for recommendations to address PA and SB for older adults hospitalised with an acute medical illness. Further consultation and evidence review will enable validation of these draft recommendations with examples to improve their specificity and translation to clinical practice.

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. 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 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. 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.

Background Although gender is an important determinant of health behaviour with males less likely to perform health-protective behaviours, samples in health behaviour research are heavily biased towards females. This study investigated the use of online social network, Facebook, to reach and recruit inactive males to a team-based, social, and gamified physical activity randomised controlled trial. Methods Methodological techniques included a narrative literature review, survey of inactive males ( n  = 34) who rated advertisement images and text captions on scales of 1–10, and trial Facebook-delivered recruitment campaigns. Advertisement effectiveness was measured by cost-per-click to the study website, number of expressions of interest, and study enrolments from males. Results Survey results showed that vibrant images of men exercising accompanied by concise captions (< 35 words) were most effective. An advertising campaign incorporating these components achieved a cost-per-click of $0.60, with 80% of n  = 50 expressions of interest being from men, a marked improvement from baseline campaigns in which only 11% of expressions of interest were from men. Despite this, men who were recruited through the targeted campaign failed to enrol into the study, primarily due to reluctance to invite friends to join their team. An alternative strategy of encouraging females to invite men boosted male participation from 18% of the sample at baseline to 29% in the targeted recruitment phase. Conclusions Evidence-based approaches can improve Facebook recruitment outcomes, however, there are complex barriers hindering male recruitment to health behaviour studies that may necessitate multi-faceted strategies including involvement of family and friends.

The success of a mobile phone app in changing health behavior is thought to be contingent on engagement, commonly operationalized as frequency of use. This subgroup analysis of the 2 intervention arms from a 3-group randomized controlled trial aimed to examine user engagement with a 100-day physical activity intervention delivered via an app. Rates of engagement, associations between user characteristics and engagement, and whether engagement was related to intervention efficacy were examined. Engagement was captured in a real-time log of interactions by users randomized to either a gamified (n=141) or nongamified version of the same app (n=160). Physical activity was assessed via accelerometry and self-report at baseline and 3-month follow-up. Survival analysis was used to assess time to nonuse attrition. Mixed models examined associations between user characteristics and engagement (total app use). Characteristics of super users (top quartile of users) and regular users (lowest 3 quartiles) were compared using t tests and a chi-square analysis. Linear mixed models were used to assess whether being a super user was related to change in physical activity over time. Engagement was high. Attrition (30 days of nonuse) occurred in 32% and 39% of the gamified and basic groups, respectively, with no significant between-group differences in time to attrition (P=.17). Users with a body mass index (BMI) in the healthy range had higher total app use (mean 230.5, 95% CI 190.6-270.5; F =8.67; P<.001), compared with users whose BMI was overweight or obese (mean 170.6, 95% CI 139.5-201.6; mean 132.9, 95% CI 104.8-161.0). Older users had higher total app use (mean 200.4, 95% CI 171.9-228.9; F =6.385; P=.01) than younger users (mean 155.6, 95% CI 128.5-182.6). Super users were 4.6 years older (t =3.6; P<.001) and less likely to have a BMI in the obese range (χ =15.1; P<.001). At the 3-month follow-up, super users were completing 28.2 (95% CI 9.4-46.9) more minutes of objectively measured physical activity than regular users (F =4.76; P=.03). Total app use was high across the 100-day intervention period, and the inclusion of gamified features enhanced engagement. Participants who engaged the most saw significantly greater increases to their objectively measured physical activity over time, supporting the theory that intervention exposure is linked to efficacy. Further research is needed to determine whether these findings are replicated in other app-based interventions, including those experimentally evaluating engagement and those conducted in real-world settings. Australian New Zealand Clinical Trials Registry ACTRN12617000113358; https://www.anzctr.org.au/ACTRN12617000113358.aspx.

ObjectivesCurrent evidence is unclear due to methodological limitations. We bridge critical knowledge gaps by quantifying the longitudinal changes in movement behaviours and their correlates from early childhood through adolescence.DesignLongitudinal observational cohort study.SettingGeneral healthy child and adolescent sample in Singapore.ParticipantsGrowing Up in Singapore Towards healthy Outcomes study participants.Primary and secondary outcome measuresWe used wrist-worn accelerometry and proxy-reported data to examine movement behaviours (sleep, inactivity, light physical activity (PA; LPA) and moderate-to-vigorous PA (MVPA) and screen-viewing) at ages 5.5, 8, 10 and 12 years and the sociodemographic and maternal lifestyle-related correlates using linear regression models with generalised estimating equations.ResultsAmong 837 children, sleep, LPA and MVPA declined by 3% (from 9.1 to 8.8 hours/day), 24% (from 5.8 to 4.4 hours/day) and 44% (from 71.3 to 40.1 min/day), respectively, while inactivity and screen viewing increased by 26% (from 8.0 to 10.1 hours/day) and 155% (from 1.8 to 4.6 hours/day), respectively, from ages 5.5 to 12 years. The greatest annual increase in inactivity (0.6 hour/annum) and screen-viewing (0.8 hour/annum) and decrease in LPA (0.3 hour/annum) and MVPA (10.4 min/annum) occurred from ages 8 to 10 years. Girls of Malay ethnicity and lower socioeconomic status, and whose mothers had less favourable movement behaviours, had significantly less sleep, higher inactivity and screen-viewing and/or lower PA. Maternal PA levels and/or sitting time were associated with children’s sleep, inactivity and MVPA up to age 8 years, while maternal sitting and screen-viewing behaviours were associated with children’s screen-viewing at all ages.ConclusionsUsing contemporaneous datasets relevant to the present day, we confirmed that children become less physically active and have longer screen-viewing as they transition into adolescence and highlighted characteristics to be prioritised in future interventions.

Introduction University years represent a crucial period in a student’s life, with mounting pressures on mental health and the formation of lifestyle habits that may endure into adulthood. This study examined the associations between movement and dietary behaviours and mental health among university students. It further explored potential sex-specific differences in these associations. Methods This cross-sectional study used data (2020–2022) from the Health@NUS prospective cohort study. Six behaviours—moderate-to-vigorous physical activity (MVPA), sedentary behaviour, sleep duration, and intake of fruit, vegetables, and unhealthy food—were assessed and categorised as either healthy or unhealthy based on established guidelines. Mental health was measured using the 6-item Kessler Psychological Distress Scale and the 5-item World Health Organisation Well-Being Index. Multivariable linear regression was used to analyse the associations between the behaviours and mental health, and to evaluate effect modification by sex. Results Among 773 students (mean age 22.7 years, 56.8% female), 23.3% engaged in 4–6 healthy behaviours; 14.1% and 37.7% reported high distress and poor well-being, respectively. Compared to students engaging in 0–1 healthy behaviours, those engaging in 2 or more healthy behaviours reported less distress and greater well-being. Students who engaged in 4–6 behaviours reported the lowest distress (females: -1.51, 95% CI -2.75, -0.27; males: -1.72, 95% CI -3.06, -0.39) and the best well-being (females: 10.66, 95% CI 6.04, 15.23; males: 9.98, 95% CI 6.04, 15.23). For individual behaviours, adequate sleep and less intake of unhealthy foods were associated with both less distress and better mental well-being, whereas sufficient MVPA and less sedentary behaviour were associated with better well-being. Additionally, among female students, less sedentary behaviour and unhealthy food intake were associated with better well-being. Conclusions The more healthy movement and dietary behaviours that students engaged in, the better their reported mental health outcomes. The type of behaviour and sex also appear to play a role. These findings highlight the need for interventions addressing both lifestyle behaviours and mental health in university students, potentially using sex-tailored strategies. Clinical trial number NCT05154227 (registered on 16 September 2021; https://www.clinicaltrials.gov/)