The Effectiveness of a Web-Based Computer-Tailored Physical Activity Intervention Using Fitbit Activity Trackers: Randomized Trial

[eHealth applications for promotion of physical activity after visceral surgery : A systematic review]

BACKGROUND

eHealth applications can support early mobilization and physical activity (PA) after surgery. This systematic review provides an overview of eHealth services to enhance or record PA after visceral surgery interventions.

METHODS

Two electronic databases (MEDLINE PubMed and Web of Science) were systematically searched (November 2023). Articles were considered eligible if they were controlled trials and described digital devices used to promote PA after visceral surgery. The Cochrane risk of bias (RoB-2) tool was used to determine the methodological quality of studies.

RESULTS

A total of nine randomized controlled studies (RCT) were included in this systematic review. The studies differed with respect to the interventions, surgical indications and evaluation variables. The risk of bias of the individual studies was moderate. The six studies using activity trackers (AT) predominantly showed insignificant improvements in the postoperative step count. The more complex fitness applications could partially reveal significant advantages compared to the control groups and the home-based online training also showed a significant increase in functional capacity.

CONCLUSION

Activity tracking alone has so far failed to show clinically relevant effects. In contrast, the more complex eHealth applications revealed advantages compared to usual postoperative care. More high-quality studies are needed for evidence-based recommendations for eHealth services in conjunction with visceral surgery.

Does intervention engagement mediate physical activity change in a web-based computer-tailored physical activity intervention?-Secondary outcomes from a randomised controlled trial

Introduction

The relationship between intervention engagement and behaviour change may vary depending on the specific engagement metric being examined. To counter this composite engagement measures may provide a deeper understanding of the relationship between engagement and behaviour change, though few studies have applied such multidimensional engagement metrics. The aim of this secondary analysis of RCT data was to examine how a composite engagement score mediates the effect of a web-based computer-tailored physical activity intervention.

Methods

501 inactive Australian adults were randomised to a no-treatment control or intervention group. Intervention participants received 8 sessions of web-based personalised physical activity advice over a 12-week intervention period and the ability to complete action plans. Change in physical activity was assessed using Actigraph accelerometers at baseline, 3-months and 9-months. Engagement with the intervention (i.e., a composite score including frequency, intensity, duration and type) was continuously assessed during the intervention period using website tracking software and database metrics. Generalised structural equation models were used to examine how a composite engagement score mediated intervention effects at 3 months and 9 months.

Results

At 3 months, mediation analysis revealed that the intervention group had significantly higher engagement scores than the control group [a-path exp(b) = 6.462, 95% CI = 5.121-7.804, p < 0.001]. Further, increased engagement with the intervention platform was associated with an increased time spent in moderate-to-vigorous physical activity [ab-coefficient exp(b) = 1.008, 95% CI = 1.004-1.014, P < 0.001]; however, the magnitude of this effect was small. There were no significant mediation effects at the 9-month time point.

Discussion

The findings suggest that a composite intervention engagement score has a small positive influence on physical activity changes and that other factors (e.g., behaviour change techniques) are likely to be more important drivers of behaviour change.

The moderating effect of social support on the effectiveness of a web-based, computer-tailored physical activity intervention for older adults

This study aimed to assess the moderating effect of social support on the effectiveness of a web-based, computer-tailored physical activity intervention for older adults. In the Active for Life trial, 243 inactive adults aged 65+ years were randomised into: (1) tailoring + Fitbit (n = 78), (2) tailoring-only (n = 96) or (3) control (n = 69). For the current study, participants were categorised as having higher (n = 146) or lower (n = 97) social support based on the Duke Social Support Index (DSSI_10). Moderate-to-vigorous physical activity (MVPA) was measured through accelerometers at baseline and post-intervention. A linear mixed model analysis demonstrated that among participants with lower social support, the tailoring + Fitbit participants, but not the tailoring only participants increased their MVPA more than the control. Among participants with higher social support, no differences in MVPA changes were observed between groups. Web-based computer-tailored interventions with Fitbit integration may be more effective in older adults with lower levels of social support.

The Effectiveness of Strategies to Improve User Engagement With Digital Health Interventions Targeting Nutrition, Physical Activity, and Overweight and Obesity: Systematic Review and Meta-Analysis

BACKGROUND

Digital health interventions (DHIs) are effective in improving poor nutrition, physical inactivity, overweight and obesity. There is evidence suggesting that the impact of DHIs may be enhanced by improving user engagement. However, little is known about the overall effectiveness of strategies on engagement with DHIs.

OBJECTIVE

This study aims to assess the overall effectiveness of strategies to improve engagement with DHIs targeting nutrition, physical activity, and overweight or obesity and explore associations between strategies and engagement outcomes. The secondary aim was to explore the impact of these strategies on health risk outcomes.

METHODS

The MEDLINE, Embase, PsycINFO, CINAHL, CENTRAL, Scopus, and Academic Source Complete databases were searched up to July 24, 2023. Eligible studies were randomized controlled trials that evaluated strategies to improve engagement with DHIs and reported on outcomes related to DHI engagement (use or user experience). Strategies were classified according to behavior change techniques (BCTs) and design features (eg, supplementary emails). Multiple-variable meta-analyses of the primary outcomes (usage and user experience) were undertaken to assess the overall effectiveness of strategies. Meta-regressions were conducted to assess associations between strategies and use and user experience outcomes. Synthesis of secondary outcomes followed the "Synthesis Without Meta-Analysis" guidelines. The methodological quality and evidence was assessed using the Cochrane risk-of-bias tool, and the Grading of Recommendations Assessment, Development, and Evaluation tool respectively.

RESULTS

Overall, 54 studies (across 62 publications) were included. Pooled analysis found very low-certainty evidence of a small-to-moderate positive effect of the use of strategies to improve DHI use (standardized mean difference=0.33, 95% CI 0.20-0.46; P<.001) and very low-certainty evidence of a small-to-moderate positive effect on user experience (standardized mean difference=0.29, 95% CI 0.07-0.52; P=.01). A significant positive association was found between the BCTs social support (effect size [ES]=0.40, 95% CI 0.14-0.66; P<.001) and shaping knowledge (ES=0.39, 95% CI 0.03-0.74; P=.03) and DHI use. A significant positive association was found among the BCTs social support (ES=0.70, 95% CI 0.18-1.22; P=.01), repetition and substitution (ES=0.29, 95% CI 0.05-0.53; P=.03), and natural consequences (ES=0.29, 95% CI 0.05-0.53; P=.02); the design features email (ES=0.29, 95% CI 0.05-0.53; P=.02) and SMS text messages (ES=0.34, 95% CI 0.11-0.57; P=.01); and DHI user experience. For secondary outcomes, 47% (7/15) of nutrition-related, 73% (24/33) of physical activity-related, and 41% (14/34) of overweight- and obesity-related outcomes reported an improvement in health outcomes.

CONCLUSIONS

Although findings suggest that the use of strategies may improve engagement with DHIs targeting such health outcomes, the true effect is unknown because of the low quality of evidence. Future research exploring whether specific forms of social support, repetition and substitution, natural consequences, emails, and SMS text messages have a greater impact on DHI engagement is warranted.

TRIAL REGISTRATION

PROSPERO CRD42018077333; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=77333.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data

BACKGROUND

Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices.

OBJECTIVE

To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data.

METHODS

Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared.

RESULTS

In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively.

CONCLUSIONS

The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices.

Influencing health behaviour using smart building interventions for people with dementia and mild cognitive impairment: expert interviews and a systematic literature review

PURPOSE

Behaviour can have an influence on (coping with) chronic conditions such as dementia. Assistive technology can stimulate the daily behaviour of people with dementia, but the mechanisms through which this happens are unclear. Therefore, this paper focuses on potential behaviour change mechanisms, that can be employed in smart building interventions for people with dementia or MCI.

METHODS

This research uses expert interviews with medical experts (n = 9) and a systematic literature review of smart building interventions stimulating health behaviour (n = 12).

RESULTS

Results show how facilitation, incentive motivation (i.e., feedback), observational learning and self-efficacy are most promising according to medical experts; if they are appropriately personalised towards needs, preferences as well as abilities. The literature review shows how most of the examined research uses facilitation and incentive motivation to stimulate behaviour. Although positive results are reported in all studies, methodological quality could be improved.

CONCLUSION

For the design of smart building interventions for people with MCI or dementia, facilitation and incentive motivation seem to be promising behaviour change mechanisms. Outcome expectation, observational learning and self-efficacy could reinforcing the aforementioned mechanisms. Future research should focus on how different (environmental, digital) cues can be personalized and can adapt over time, as dementia progresses.IMPLICATIONS FOR REHABILITATIONAssistive technology for people with dementia can have an effect on (health) behaviour, which may in turn influence coping strategies or quality of life.Behaviour change mechanisms can inform the design of assistive technology such as smart building interventions.Facilitation, Incentive Motivation, Observational Learning and Self-efficacy seem promising behaviour change mechanisms for people with dementia or MCI.In any intervention for people with dementia, personalized and adaptable cues are of vital importance.

Effectiveness of social media with or without wearable devices to improve physical activity and reduce sedentary behavior: A randomized controlled trial of Chinese postgraduates

The present study was aimed to verify whether an integrating of wearable activity tracker device and a social media intervention strategy would be better than a standalone social media intervention for improving physical activity (PA) and reducing sedentary time for Chinese postgraduate population. A total of 42 full-time postgraduate students participated in this study, which were randomized to receive a 4-week social media intervention through WeChat either with (Wearable Device group) or without (control group) a wearable activity tracker device. Energy expenditure, step counts, moderate to vigorous physical activity time (MVPA) and sedentary time were assessed before and after the intervention. Besides, anthropometric parameters of body weight, body mass index, body fat rate, waist-to-hip ratio, as well as self-reported quality of life were also evaluated. It was found that both energy expenditure and step counts were significantly increased, while sedentary time was significantly reduced during the post-intervention test compared to the baseline test for Wearable Device group. No significant difference of PA was found for the control group. The results demonstrated that the integrating of wearable activity tracker device and a social media intervention was effective in promoting PA, while a standalone social media intervention may have no effect on the influence of PA for Chinese postgraduates.

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.

Engagement, acceptability, usability and satisfaction with Active for Life, a computer-tailored web-based physical activity intervention using Fitbits in older adults

BACKGROUND

Preliminary evidence suggests that web-based physical activity interventions with tailored advice and Fitbit integration are effective and may be well suited to older adults. Therefore, this study aimed to examine the engagement, acceptability, usability, and satisfaction with 'Active for Life,' a web-based physical activity intervention providing computer-tailored physical activity advice to older adults.

METHODS

Inactive older adults (n = 243) were randomly assigned into 3 groups: 1) tailoring + Fitbit, 2) tailoring only, or 3) a wait-list control. The tailoring + Fitbit group and the tailoring-only group received 6 modules of computer-tailored physical activity advice over 12 weeks. The advice was informed by objective Fitbit data in the tailoring + Fitbit group and self-reported physical activity in the tailoring-only group. This study examined the engagement, acceptability, usability, and satisfaction of Active for Life in intervention participants (tailoring + Fitbit n = 78, tailoring only n = 96). Wait-list participants were not included. Engagement (Module completion, time on site) were objectively recorded through the intervention website. Acceptability (7-point Likert scale), usability (System Usability Scale), and satisfaction (open-ended questions) were assessed using an online survey at post intervention. ANOVA and Chi square analyses were conducted to compare outcomes between intervention groups and content analysis was used to analyse program satisfaction.

RESULTS

At post-intervention (week 12), study attrition was 28% (22/78) in the Fitbit + tailoring group and 39% (37/96) in the tailoring-only group. Engagement and acceptability were good in both groups, however there were no group differences (module completions: tailoring + Fitbit: 4.72 ± 2.04, Tailoring-only: 4.23 ± 2.25 out of 6 modules, p = .14, time on site: tailoring + Fitbit: 103.46 ± 70.63, Tailoring-only: 96.90 ± 76.37 min in total, p = .56, and acceptability of the advice: tailoring + Fitbit: 5.62 ± 0.89, Tailoring-only: 5.75 ± 0.75 out of 7, p = .41). Intervention usability was modest but significantly higher in the tailoring + Fitbit group (tailoring + Fitbit: 64.55 ± 13.59, Tailoring-only: 57.04 ± 2.58 out of 100, p = .003). Participants reported that Active for Life helped motivate them, held them accountable, improved their awareness of how active they were and helped them to become more active. Conversely, many participants felt as though they would prefer personal contact, more detailed tailoring and more survey response options.

CONCLUSIONS

This study supports web-based physical activity interventions with computer-tailored advice and Fitbit integration as engaging and acceptable in older adults.

TRIAL REGISTRATION

Australian and New Zealand Clinical Trials Registry: ACTRN12618000646246. Registered April 23 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374901.

Comparison of Oura Smart Ring Against ActiGraph Accelerometer for Measurement of Physical Activity and Sedentary Time in a Free-Living Context

Smart rings, such as the Oura ring, might have potential in health monitoring. To be able to identify optimal devices for healthcare settings, validity studies are needed. The aim of this study was to compare the Oura smart ring estimates of steps and sedentary time with data from the ActiGraph accelerometer in a free-living context. A cross-sectional observational study design was used. A convenience sample of healthy adults (n = 42) participated in the study and wore an Oura smart ring and an ActiGraph accelerometer on the non-dominant hand continuously for 1 week. The participants completed a background questionnaire and filled out a daily log about their sleeping times and times when they did not wear the devices. The median age of the participants (n = 42) was 32 years (range, 18-46 years). In total, 191 (61% of the potential) days were compared. The Oura ring overestimated the step counts compared with the ActiGraph. The mean difference was 1416 steps (95% confidence interval, 739-2093 steps). Daily sedentary time was also overestimated by the ring; the mean difference was 17 minutes (95% confidence interval, -2 to 37 minutes). The use of the ring in nursing interventions needs to be considered.

The Effectiveness of Digital Insomnia Treatment with Adjunctive Wearable Technology: A Pilot Randomized Controlled Trial

OBJECTIVE

This pilot trial aimed to provide evidence for whether the integration of a wearable device with digital behavioral therapy for insomnia (dBTi) improves treatment outcomes and engagement.

PARTICIPANTS AND METHODS

One hundred and twenty-eight participants with insomnia symptoms were randomized to a 3-week dBTi program (SleepFix®) with a wearable device enabling sleep data synchronization (dBTi+wearable group; n = 62) or dBTi alone (n = 66). Participants completed the Insomnia Severity Index (ISI) and modified Pittsburgh Sleep Quality Index (PSQI) parameters: wake-after-sleep-onset (WASO), sleep-onset-latency (SOL), and total sleep time (TST) at baseline and weeks 1, 2, 3, and primary endpoint of week 6 and follow-up at 12 weeks. Engagement was measured by the number of daily sleep diaries logged in the app.

RESULTS

There was no difference in ISI change scores between the groups from pre- to post-treatment (Cohen's d= 0.7, p= .061). The dBTi+wearable group showed greater improvements in WASO (d= 0.8, p = .005) and TST (d= 0.3, p= .049) compared to the dBTi group. Significantly greater engagement (sleep diary entries) was observed in the dBTi+wearable group (mean = 22.4, SD = 10.0) compared to the dBTi group (mean = 14.1, SD = 14.2) (p = .010).

CONCLUSIONS

This pilot trial found that integration of wearable device with a digital insomnia therapy enhanced user engagement and led to improvements in sleep parameters compared to dBTi alone. These findings suggest that adjunctive wearable technologies may improve digital insomnia therapy effectiveness.

Machine Learning in Health Promotion and Behavioral Change: Scoping Review

BACKGROUND

Despite health behavioral change interventions targeting modifiable lifestyle factors underlying chronic diseases, dropouts and nonadherence of individuals have remained high. The rapid development of machine learning (ML) in recent years, alongside its ability to provide readily available personalized experience for users, holds much potential for success in health promotion and behavioral change interventions.

OBJECTIVE

The aim of this paper is to provide an overview of the existing research on ML applications and harness their potential in health promotion and behavioral change interventions.

METHODS

A scoping review was conducted based on the 5-stage framework by Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews) guidelines. A total of 9 databases (the Cochrane Library, CINAHL, Embase, Ovid, ProQuest, PsycInfo, PubMed, Scopus, and Web of Science) were searched from inception to February 2021, without limits on the dates and types of publications. Studies were included in the review if they had incorporated ML in any health promotion or behavioral change interventions, had studied at least one group of participants, and had been published in English. Publication-related information (author, year, aim, and findings), area of health promotion, user data analyzed, type of ML used, challenges encountered, and future research were extracted from each study.

RESULTS

A total of 29 articles were included in this review. Three themes were generated, which are as follows: (1) enablers, which is the adoption of information technology for optimizing systemic operation; (2) challenges, which comprises the various hurdles and limitations presented in the articles; and (3) future directions, which explores prospective strategies in health promotion through ML.

CONCLUSIONS

The challenges pertained to not only the time- and resource-consuming nature of ML-based applications, but also the burden on users for data input and the degree of personalization. Future works may consider designs that correspondingly mitigate these challenges in areas that receive limited attention, such as smoking and mental health.

The Effectiveness of a Computer-Tailored Web-Based Physical Activity Intervention Using Fitbit Activity Trackers in Older Adults (Active for Life): Randomized Controlled Trial

BACKGROUND

Physical activity is an integral part of healthy aging; yet, most adults aged ≥65 years are not sufficiently active. Preliminary evidence suggests that web-based interventions with computer-tailored advice and Fitbit activity trackers may be well suited for older adults.

OBJECTIVE

The aim of this study was to examine the effectiveness of Active for Life, a 12-week web-based physical activity intervention with 6 web-based modules of computer-tailored advice to increase physical activity in older Australians.

METHODS

Participants were recruited both through the web and offline and were randomly assigned to 1 of 3 trial arms: tailoring+Fitbit, tailoring only, or a wait-list control. The computer-tailored advice was based on either participants' Fitbit data (tailoring+Fitbit participants) or self-reported physical activity (tailoring-only participants). The main outcome was change in wrist-worn accelerometer (ActiGraph GT9X)-measured moderate to vigorous physical activity (MVPA) from baseline to after the intervention (week 12). The secondary outcomes were change in self-reported physical activity measured by means of the Active Australia Survey at the midintervention point (6 weeks), after the intervention (week 12), and at follow-up (week 24). Participants had a face-to-face meeting at baseline for a demonstration of the intervention and at baseline and week 12 to return the accelerometers. Generalized linear mixed model analyses were conducted with a γ distribution and log link to compare MVPA and self-reported physical activity changes over time within each trial arm and between each of the trial arms.

RESULTS

A total of 243 participants were randomly assigned to tailoring+Fitbit (n=78, 32.1%), tailoring only (n=96, 39.5%), and wait-list control (n=69, 28.4%). Attrition was 28.8% (70/243) at 6 weeks, 31.7% (77/243) at 12 weeks, and 35.4% (86/243) at 24 weeks. No significant overall time by group interaction was observed for MVPA (P=.05). There were no significant within-group changes for MVPA over time in the tailoring+Fitbit group (+3%, 95% CI -24% to 40%) or the tailoring-only group (-4%, 95% CI -24% to 30%); however, a significant decline was seen in the control group (-35%, 95% CI -52% to -11%). The tailoring+Fitbit group participants increased their MVPA 59% (95% CI 6%-138%) more than those in the control group. A significant time by group interaction was observed for self-reported physical activity (P=.02). All groups increased their self-reported physical activity from baseline to week 6, week 12, and week 24, and this increase was greater in the tailoring+Fitbit group than in the control group at 6 weeks (+61%, 95% CI 11%-133%).

CONCLUSIONS

A computer-tailored physical activity intervention with Fitbit integration resulted in improved MVPA outcomes in comparison with a control group in older adults.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12618000646246; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12618000646246.

A distance-based, randomized controlled trial for reducing sedentary behavior among prostate cancer survivors: a study protocol

BACKGROUND

Prostate cancer survivors (PCS) experience long-term side effects beyond treatment such as fatigue, depression and anxiety. Quality and engaging supportive care programs are needed to reduce these chronic and debilitating effects. Independent of physical activity (PA), high volumes of sedentary behavior (SB) are associated with chronic disease-related risk factors and poorer cancer-specific quality of life (QoL). Simultaneously increasing PA and decreasing SB may be an effective health promotion strategy. Given that PCS may face several barriers to engaging in supervised programs, there is a need to develop and assess the efficacy of interventions that employ distance-based approaches for behavior change. The primary aim of this study is to determine the effects of a 12-week intervention (Fitbit + behavioral counselling) vs. Fitbit-only control group in reducing SB among PCS. Secondary outcomes include light-intensity PA, QoL, motivational outcomes, and patient satisfaction.

METHODS

This two-armed, randomized controlled trial will recruit inactive PCS (stage I-IV) across Canada who self-report engaging in >8 hours/day of SB. Participants will be randomized to the intervention (n=60; Fitbit and behavioral support) or active control group (n=60; Fitbit-only). The intervention consists of the use of a Fitbit and a series of six behavioral support sessions (two group, four individual) to aid PCS in gradually replacing SB with light-intensity PA by increasing their daily step counts to 3,000 steps above their baseline values. The Fitbit-only control condition will receive a Fitbit and public health PA resources. The primary outcome is change in SB measured objectively using activPAL inclinometers. All secondary outcomes will be measured via self-report, except for PA which will be measuring using Fitbits. Data will be collected at baseline, post-intervention, and at 6-month post-intervention.

DISCUSSION

Reducing SB and increasing light-intensity PA plays an important, yet often undervalued role in the health and well-being of PCS. This study will create a unique distance-based platform that can be used by clinical and community-based organizations as a low-cost, supportive care tool to improve health outcomes for PCS.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT05214937 . Registered January 28, 2022 Protocol version: v.1.

Supporting Behavior Change in Sedentary Adults via Real-time Multidimensional Physical Activity Feedback: Mixed Methods Randomized Controlled Trial

BACKGROUND

Increasing physical activity (PA) behavior remains a public health priority, and wearable technology is increasingly being used to support behavior change efforts. Using wearables to capture and provide comprehensive, visually persuasive, multidimensional feedback with real-time support may be a promising way of increasing PA in inactive individuals.

OBJECTIVE

This study aims to explore whether a 6-week self-monitoring intervention using composite web-based multidimensional PA feedback with real-time daily feedback supports increased PA in adults.

METHODS

A 6-week, mixed methods, 2-armed exploratory randomized controlled trial with 6-week follow-up was used, whereby low to moderately active (PA level [PAL] <2.0) adults (mean age 51.3 years, SD 8.4 years; women 28/51, 55%) were randomly assigned to receive the self-monitoring intervention (36/51, 71%) or waiting list control (15/51, 29%). Assessment of PA across multiple health-harnessing PA dimensions (eg, PAL, weekly moderate to vigorous intensity PA, sedentary time, and steps), psychosocial cognitions (eg, behavioral regulation, barrier self-efficacy, and habit strength), and health were made at the prerandomization baseline at 6 and 12 weeks. An exploratory analysis of the mean difference and CIs was conducted using the analysis of covariance model. After the 12-week assessment, intervention participants were interviewed to explore their views on the program.

RESULTS

There were no notable differences in any PA outcome immediately after the intervention; however, at 12 weeks, moderate-to-large effects were observed with a mean difference in PAL of 0.09 (95% CI 0.02-0.15; effect size [Hedges g] 0.8), daily moderate-intensity PA of 24 (95% CI 0-45; Hedges g=0.6) minutes, weekly moderate-to-vigorous intensity PA of 195 (95% CI 58-331; Hedges g=0.8) minutes, and steps of 1545 (95% CI 581-2553; Hedges g=0.7). Descriptive analyses suggested that the differences in PA at 12 weeks were more pronounced in women and participants with lower baseline PA levels. Immediately after the intervention, there were favorable differences in autonomous motivation, controlled motivation, perceived competence for PA, and barrier self-efficacy, with the latter sustained at follow-up. Qualitative data implied that the intervention was highly informative for participants and that the real-time feedback element was particularly useful in providing tangible, day-to-day behavioral support.

CONCLUSIONS

Using wearable trackers to capture and present sophisticated multidimensional PA feedback combined with discrete real-time support may be a useful way of facilitating changes in behavior. Further investigation into the ways of optimizing the use of wearables in inactive participants and testing the efficacy of this approach via a robust study design is warranted.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02432924; https://clinicaltrials.gov/ct2/show/NCT02432924.

Acceptability, usefulness, and satisfaction with a web-based video-tailored physical activity intervention: The TaylorActive randomized controlled trial

PURPOSE

This study aimed to examine the usage, acceptability, usability, perceived usefulness, and satisfaction of a web-based video-tailored physical activity (PA) intervention (TaylorActive) in adults.

METHODS

In 2013-2014, 501 Australian adults aged 18+ years were randomized into a video-tailored intervention, text-tailored intervention, or control group. Over 3 months, the intervention groups received access to 8 sessions of personally tailored PA advice delivered via the TaylorActive website. Only the delivery method differed between the intervention groups: video-tailored vs. text-tailored. Google Analytics and telephone surveys conducted at post intervention (3 months) were used to assess intervention usage, acceptability, usability, perceived usefulness, and satisfaction. Quantitative and qualitative process data were analyzed using descriptive statistics and thematic content analysis.

RESULTS

Of 501 recruited adults, 259 completed the 3-month post-intervention survey (52% retention). Overall, usage of the TaylorActive website with respect to number of website visits, intervention sessions, and action plans completed was modest in both the video-tailored (7.6 ± 7.2 visits, mean ± SD) and text-tailored (7.3 ± 5.4 visits) groups with no significant between-group differences. The majority of participants in all groups used the TaylorActive website less than once in 2 weeks (66.7% video-tailored, 62.7% text-tailored, 87.5% control; p < 0.001). Acceptability was rated mostly high in all groups and, in some instances, significantly higher in the intervention groups compared to the control group (p < 0.010). Usability was also rated high; mean Systems Usability Scores were 77.3 (video-tailored), 75.7 (text-tailored), and 74.1 (control) with no significant between-group differences. Perceived usefulness of the TaylorActive intervention was low, though mostly rated higher in the intervention groups compared to the control group (p < 0.010). Satisfaction with the TaylorActive website was mixed. Participants in both intervention groups liked its ease of use, personalized feedback, and tracking of progress, but also found completing action plans and survey questions for each session repetitive and tedious.

CONCLUSION

Providing personally tailored PA advice on its own (through either video or text) is likely insufficient to ensure good retention, usage, perceived usefulness, and satisfaction with a web-based PA intervention. Strategies to address this may include the incorporation of additional intervention components such as activity trackers, social interactions, gamification, as well as the use of advanced artificial intelligence and machine learning technologies to allow more personalized dialogue with participants.

Effectiveness of physical activity monitors in adults: systematic review and meta-analysis

OBJECTIVE

To estimate the effectiveness of physical activity monitor (PAM) based interventions among adults and explore reasons for the heterogeneity.

DESIGN

Systematic review and meta-analysis.

STUDY SELECTION

The electronic databases MEDLINE, Embase, SPORTDiscus, CINAHL, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched on 4 June 2021. Eligible randomised controlled trials compared interventions in which adults received feedback from PAMs with control interventions in which no feedback was provided. No restrictions on type of outcome measurement, publication date, or language were applied.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently extracted data and assessed risk of bias. Random effects meta-analyses were used to synthesise the results. The certainty of evidence was rated by the Grading of Recommendations Assessment and Evaluation (GRADE) approach.

MAIN OUTCOME MEASURES

The three primary outcomes of interest were physical activity, moderate to vigorous physical activity, and sedentary time.

RESULTS

121 randomised controlled trials with 141 study comparisons, including 16 743 participants, were included. The PAM based interventions showed a moderate effect (standardised mean difference 0.42, 95% confidence interval 0.28 to 0.55) on physical activity, equivalent to 1235 daily steps; a small effect (0.23, 0.16 to 0.30) on moderate to vigorous physical activity, equivalent to 48.5 weekly minutes; and a small insignificant effect (-0.12, -0.25 to 0.01) on sedentary time, equal to 9.9 daily minutes. All outcomes favoured the PAM interventions.

CONCLUSIONS

The certainty of evidence was low for the effect of PAM based interventions on physical activity and moderate for moderate to vigorous physical activity and sedentary time. PAM based interventions are safe and effectively increase physical activity and moderate to vigorous physical activity. The effect on physical activity and moderate to vigorous physical activity is well established but might be overestimated owing to publication bias.

STUDY REGISTRATION

PROSPERO CRD42018102719.

Feasibility, Usability, and Effectiveness of a Machine Learning-Based Physical Activity Chatbot: Quasi-Experimental Study

BACKGROUND

Behavioral eHealth and mobile health interventions have been moderately successful in increasing physical activity, although opportunities for further improvement remain to be discussed. Chatbots equipped with natural language processing can interact and engage with users and help continuously monitor physical activity by using data from wearable sensors and smartphones. However, a limited number of studies have evaluated the effectiveness of chatbot interventions on physical activity.

OBJECTIVE

This study aims to investigate the feasibility, usability, and effectiveness of a machine learning-based physical activity chatbot.

METHODS

A quasi-experimental design without a control group was conducted with outcomes evaluated at baseline and 6 weeks. Participants wore a Fitbit Flex 1 (Fitbit LLC) and connected to the chatbot via the Messenger app. The chatbot provided daily updates on the physical activity level for self-monitoring, sent out daily motivational messages in relation to goal achievement, and automatically adjusted the daily goals based on physical activity levels in the last 7 days. When requested by the participants, the chatbot also provided sources of information on the benefits of physical activity, sent general motivational messages, and checked participants' activity history (ie, the step counts/min that were achieved on any day). Information about usability and acceptability was self-reported. The main outcomes were daily step counts recorded by the Fitbit and self-reported physical activity.

RESULTS

Among 116 participants, 95 (81.9%) were female, 85 (73.3%) were in a relationship, 101 (87.1%) were White, and 82 (70.7%) were full-time workers. Their average age was 49.1 (SD 9.3) years with an average BMI of 32.5 (SD 8.0) kg/m2. Most experienced technical issues were due to an unexpected change in Facebook policy (93/113, 82.3%). Most of the participants scored the usability of the chatbot (101/113, 89.4%) and the Fitbit (99/113, 87.6%) as at least "OK." About one-third (40/113, 35.4%) would continue to use the chatbot in the future, and 53.1% (60/113) agreed that the chatbot helped them become more active. On average, 6.7 (SD 7.0) messages/week were sent to the chatbot and 5.1 (SD 7.4) min/day were spent using the chatbot. At follow-up, participants recorded more steps (increase of 627, 95% CI 219-1035 steps/day) and total physical activity (increase of 154.2 min/week; 3.58 times higher at follow-up; 95% CI 2.28-5.63). Participants were also more likely to meet the physical activity guidelines (odds ratio 6.37, 95% CI 3.31-12.27) at follow-up.

CONCLUSIONS

The machine learning-based physical activity chatbot was able to significantly increase participants' physical activity and was moderately accepted by the participants. However, the Facebook policy change undermined the chatbot functionality and indicated the need to use independent platforms for chatbot deployment to ensure successful delivery of this type of intervention.

The Effect of Commercial Activity Tracker Based Physical Activity Intervention on Body Composition and Cardiometabolic Health Among Recent Retirees

The REACT is a commercial activity tracker based intervention, which primarily aimed to increase physical activity. This study examines the secondary outcomes of the physical activity intervention on body composition and cardiometabolic health indicators. Overall 231 recently retired Finnish men and women [65.2 (SD 1.1) years, 83% women] took part to the study. The participants were randomized into intervention (n = 117) and control (n = 114) groups. The intervention group members used a commercial activity tracker (Polar Loop 2, Polar, Kempele, Finland) with a daily activity goal and inactivity alerts every day for 12 months. Controls received no intervention. Secondary health outcomes included body weight, fat mass, fat free mass, waist circumference, blood pressure, indicators of glucose and lipid metabolisms, and high-sensitivity C-reactive protein, and they were measured at baseline and at 12-months end point. Hierarchical linear mixed models were used to examine the differences between the groups over time, and no differences in the mean changes of the body composition and cardiometabolic health indicators between the groups were found (group*time interaction &gt;0.20 for all measures). Fat free mass, waist circumference, blood pressure, and low density lipoprotein levels decreased in both groups over the 12 months. These findings state that 1-year daily use of commercial activity tracker does not induce different cardiometabolic health effects when compared to the non-user controls among general population of recent retirees.

The Effect of Consumer-based Activity Tracker Intervention on Physical Activity among Recent Retirees-An RCT Study

PURPOSE

The randomized controlled trial REACT (NCT03320746) examined the effect of a 12-month consumer-based activity tracker intervention on accelerometer-measured physical activity among recent retirees.

METHODS

Altogether 231 recently retired Finnish adults (age, 65.2 ± 1.1 yr, mean ± SD; 83% women) were randomized to intervention and control groups. Intervention participants were requested to wear a commercial wrist-worn activity tracker (Polar Loop 2; Polar, Kempele, Finland) for 12 months, to try to reach the daily activity goals shown on the tracker display, and to upload their activity data to a Web-based program every week. The control group received no intervention. Accelerometer-based outcome measurements of daily total, light physical activity (LPA), and moderate to vigorous (MVPA) physical activity were conducted at baseline and at 3-, 6-, and 12-month time points. Hierarchical linear mixed models were used to examine the differences between the groups over time. All analyses were performed by intention-to-treat principle and adjusted for wake wear time.

RESULTS

The use of a commercial activity tracker did not increase daily total activity, LPA, or MVPA over the 12-months period when compared with nonuser controls (group-time interaction, P = 0.39, 0.23, and 0.77, respectively). There was an increase in LPA over the first 6 months in both the intervention (26 min·d-1, 95% confidence interval [CI] = 13 to 39) and the control (14 min·d-1, 95% CI = 1 to 27) groups, but the difference between the groups was not significant (12 min·d-1, 95% CI = -6 to 30). In both groups, LPA decreased from 6 to 12 months.

CONCLUSION

The 12-month use of a commercial activity tracker does not appear to elicit significant changes in the daily total activity among a general population sample of recent retirees, thus highlighting the need to explore other alternatives to increase physical activity in this target group.

The Association Between Logging Steps Using a Website, App, or Fitbit and Engaging With the 10,000 Steps Physical Activity Program: Observational Study

Background

Engagement is positively associated with the effectiveness of digital health interventions. It is unclear whether tracking devices that automatically synchronize data (eg, Fitbit) produce different engagement levels compared with manually entering data.

Objective

This study examines how different step logging methods in the freely available 10,000 Steps physical activity program differ according to age and gender and are associated with program engagement.

Methods

A subsample of users (n=22,142) of the free 10,000 Steps physical activity program were classified into one of the following user groups based on the step-logging method: Website Only (14,617/22,142, 66.01%), App Only (2100/22,142, 9.48%), Fitbit Only (1705/22,142, 7.7%), Web and App (2057/22,142, 9.29%), and Fitbit Combination (combination of web, app, and Fitbit; 1663/22,142, 7.51%). Generalized linear regression and binary logistic regression were used to examine differences between user groups’ engagement and participation parameters. The time to nonusage attrition was assessed using Cox proportional hazards regression.

Results

App Only users were significantly younger and Fitbit user groups had higher proportions of women compared with other groups. The following outcomes were significant and relative to the Website Only group. The App Only group had fewer website sessions (odds ratio [OR] −6.9, 95% CI −7.6 to −6.2), whereas the Fitbit Only (OR 10.6, 95% CI 8.8-12.3), Web and App (OR 1.5, 95% CI 0.4-2.6), and Fitbit Combination (OR 8.0; 95% CI 6.2-9.7) groups had more sessions. The App Only (OR −0.7, 95% CI −0.9 to −0.4) and Fitbit Only (OR −0.5, 95% CI −0.7 to −0.2) groups spent fewer minutes on the website per session, whereas the Fitbit Combination group (OR 0.2, 95% CI 0.0-0.5) spent more minutes. All groups, except the Fitbit Combination group, viewed fewer website pages per session. The mean daily step count was lower for the App Only (OR −201.9, 95% CI −387.7 to −116.0) and Fitbit Only (OR −492.9, 95% CI −679.9 to −305.8) groups but higher for the Web and App group (OR 258.0, 95% CI 76.9-439.2). The Fitbit Only (OR 5.0, 95% CI 3.4-6.6), Web and App (OR 7.2, 95% CI 5.9-8.6), and Fitbit Combination (OR 15.6, 95% CI 13.7-17.5) groups logged a greater number of step entries. The App Only group was less likely (OR 0.65, 95% CI 0.46-0.94) and other groups were more likely to participate in Challenges. The mean time to nonusage attrition was 35 (SD 26) days and was lower than average in the Website Only and App Only groups and higher than average in the Web and App and Fitbit Combination groups.

Conclusions

Using a Fitbit in combination with the 10,000 Steps app or website enhanced engagement with a real-world physical activity program. Integrating tracking devices that synchronize data automatically into real-world physical activity interventions is one strategy for improving engagement.

Long-term Effectiveness of mHealth Physical Activity Interventions: Systematic Review and Meta-analysis of Randomized Controlled Trials

BACKGROUND

Mobile health (mHealth) interventions can increase physical activity (PA); however, their long-term impact is not well understood.

OBJECTIVE

The primary aim of this study is to understand the immediate and long-term effects of mHealth interventions on PA. The secondary aim is to explore potential effect moderators.

METHODS

We performed this study according to the Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed, the Cochrane Library, SCOPUS, and PsycINFO in July 2020. Eligible studies included randomized controlled trials of mHealth interventions targeting PA as a primary outcome in adults. Eligible outcome measures were walking, moderate-to-vigorous physical activity (MVPA), total physical activity (TPA), and energy expenditure. Where reported, we extracted data for 3 time points (ie, end of intervention, follow-up ≤6 months, and follow-up >6 months). To explore effect moderators, we performed subgroup analyses by population, intervention design, and control group type. Results were summarized using random effects meta-analysis. Risk of bias was assessed using the Cochrane Collaboration tool.

RESULTS

Of the 2828 identified studies, 117 were included. These studies reported on 21,118 participants with a mean age of 52.03 (SD 14.14) years, of whom 58.99% (n=12,459) were female. mHealth interventions significantly increased PA across all the 4 outcome measures at the end of intervention (walking standardized mean difference [SMD] 0.46, 95% CI 0.36-0.55; P<.001; MVPA SMD 0.28, 95% CI 0.21-0.35; P<.001; TPA SMD 0.34, 95% CI 0.20-0.47; P<.001; energy expenditure SMD 0.44, 95% CI 0.13-0.75; P=.01). Only 33 studies reported short-term follow-up measurements, and 8 studies reported long-term follow-up measurements in addition to end-of-intervention results. In the short term, effects were sustained for walking (SMD 0.26, 95% CI 0.09-0.42; P=.002), MVPA (SMD 0.20, 95% CI 0.05-0.35; P=.008), and TPA (SMD 0.53, 95% CI 0.13-0.93; P=.009). In the long term, effects were also sustained for walking (SMD 0.25, 95% CI 0.10-0.39; P=.001) and MVPA (SMD 0.19, 95% CI 0.11-0.27; P<.001). We found the study population to be an effect moderator, with higher effect scores in sick and at-risk populations. PA was increased both in scalable and nonscalable mHealth intervention designs and regardless of the control group type. The risk of bias was rated high in 80.3% (94/117) of the studies. Heterogeneity was significant, resulting in low to very low quality of evidence.

CONCLUSIONS

mHealth interventions can foster small to moderate increases in PA. The effects are maintained long term; however, the effect size decreases over time. The results encourage using mHealth interventions in at-risk and sick populations and support the use of scalable mHealth intervention designs to affordably reach large populations. However, given the low evidence quality, further methodologically rigorous studies are warranted to evaluate the long-term effects.

Examining moderators of the effectiveness of a web- and video-based computer-tailored physical activity intervention

Understanding for whom behaviour change interventions work is important, however there is a lack of studies examining potential moderators in such interventions. This study investigated potential moderators on the effectiveness of a computer-tailored intervention to increase physical activity among Australian adults. People who had <150 min of moderate-vigorous physical activity (MVPA) a week, able to speak and read English, aged ≥18 years, lived in Australia, and had internet access were eligible to participate. Participants recruited through social media, emails, and third-party databases, were randomly assigned to either the control (n = 167) or intervention groups (n = 334). Physical activity was measured objectively by ActiGraph GT3X and also by self-report at baseline and three months. Three-way interaction terms were tested to identify moderators (i.e., demographic characteristics, BMI, and perceived neighbourhood walkability). The results showed that the three-way interaction was marginally significant for sex on accelerometer measured MVPA/week (p = 0.061) and steps/day (p = 0.047). The intervention appeared to be more effective for women compared to men. No significant three-way interactions were found for the other potential moderators. Strategies to improve levels of personalisation may be needed so that physical activity interventions can be better tailored to different subgroups, especially sex, and therefore improve intervention effectiveness.

Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression

OBJECTIVE

To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

PubMed and seven additional databases, from 2007 to 2020.

STUDY SELECTION

Randomised controlled trials in adults (18-65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted.

DATA EXTRACTION AND SYNTHESIS

We conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses.

MAIN OUTCOME MEASURES

Physical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents.

RESULTS

Thirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I²=69%, T ²=0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metaregression.

CONCLUSION

Interventions using apps or trackers seem to be effective in promoting physical activity. Longer studies are needed to assess the impact of different intervention components on long-term engagement and effectiveness.

A Mobile Social Networking App for Weight Management and Physical Activity Promotion: Results From an Experimental Mixed Methods Study

BACKGROUND

Smartphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users.

OBJECTIVE

This 6-month pilot study on a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and to assess user perspectives on the intervention, particularly on social comparison and automated self-monitoring and feedback features.

METHODS

This was a mixed methods study involving a one-arm, pre-post quasi-experimental pilot with postintervention interviews and focus groups. Healthy young adults used a social networking mobile app intervention integrated with wireless tracking devices (a weight scale and a physical activity tracker) for 6 months. Quantitative results were analyzed separately for 2 groups-underweight-normal and overweight-obese BMI-using t tests and Wilcoxon sum rank, Wilcoxon signed rank, and chi-square tests. Weekly BMI change in participants was explored using linear mixed effects analysis. Interviews and focus groups were analyzed inductively using thematic analysis.

RESULTS

In total, 55 participants were recruited (mean age of 23.6, SD 4.6 years; 28 women) and 45 returned for the final session (n=45, 82% retention rate). There were no differences in BMI from baseline to postintervention (6 months) and between the 2 BMI groups. However, at 4 weeks, participants' BMI decreased by 0.34 kg/m² (P<.001), with a loss of 0.86 kg/m² in the overweight-obese group (P=.01). Participants in the overweight-obese group used the app significantly less compared with individuals in the underweight-normal BMI group, as they mentioned negative feelings and demotivation from social comparison, particularly from upward comparison with fitter people. Participants in the underweight-normal BMI group were avid users of the app's self-monitoring and feedback (P=.02) and social (P=.04) features compared with those in the overweight-obese group, and they significantly increased their daily step count over the 6-month study duration by an average of 2292 steps (95% CI 898-3370; P<.001). Most participants mentioned a desire for a more personalized intervention.

CONCLUSIONS

This study shows the effects of different interventions on participants from higher and lower BMI groups and different perspectives regarding the intervention, particularly with respect to its social features. Participants in the overweight-obese group did not sustain a short-term decrease in their BMI and mentioned negative emotions from app use, while participants in the underweight-normal BMI group used the app more frequently and significantly increased their daily step count. These differences highlight the importance of intervention personalization. Future research should explore the role of personalized features to help overcome personal barriers and better match individual preferences and needs.

Are web-based personally tailored physical activity videos more effective than personally tailored text-based interventions? Results from the three-arm randomised controlled TaylorActive trial

Objectives

Some online, personally tailored, text-based physical activity interventions have proven effective. However, people tend to ‘skim’ and ‘scan’ web-based text rather than thoroughly read their contents. In contrast, online videos are more engaging and popular. We examined whether web-based personally tailored physical activity videos were more effective in promoting physical activity than personally tailored text and generic information.

Methods

501 adults were randomised into a video-tailored intervention, text-tailored intervention or control. Over a 3-month period, intervention groups received access to eight sessions of web-based personally tailored physical activity advice. Only the delivery method differed between intervention groups: tailored video versus tailored text. The primary outcome was 7-day ActiGraph-GT3X+ measured moderate-to-vigorous physical activity (MVPA) assessed at 0, 3 and 9 months. Secondary outcomes included self-reported MVPA and website engagement. Differences were examined using generalised linear mixed models with intention-to-treat and multiple imputation.

Results

Accelerometer-assessed MVPA increased 23% in the control (1.23 (1.06, 1.43)), 12% in the text-tailored (1.12 (0.95, 1.32)) and 28% in the video-tailored (1.28 (1.06, 1.53)) groups at the 3-month follow-up only, though there were no significant between-group differences. Both text-tailored (1.77 (1.37, 2.28]) and video-tailored (1.37 (1.04, 1.79)) groups significantly increased self-reported MVPA more than the control group at 3 months only, but there were no differences between video-tailored and text-tailored groups. The video-tailored group spent significantly more time on the website compared with text-tailored participants (90 vs 77 min, p=0.02).

Conclusions

The personally tailored videos were not more effective than personally tailored text in increasing MVPA. The findings from this study conflict with pilot study outcomes and previous literature. Process evaluation and mediation analyses will provide further insights.

Trial registration number

ACTRN12615000057583

Optimising Web-Based Computer-Tailored Physical Activity Interventions for Prostate Cancer Survivors: A Randomised Controlled Trial Examining the Impact of Website Architecture on User Engagement

BACKGROUND

Web-based computer-tailored interventions can assist prostate cancer survivors to become more physically active by providing personally relevant behaviour change support. This study aimed to explore how changing the website architecture (free choice vs. tunnelled) impacted engagement within a physical activity computer-tailored intervention targeting prostate cancer survivors.

METHODS

On a 2:2:1 ratio, 71 Australian prostate cancer survivors with local or locally advanced disease (mean age: 66.6 years ± 9.66) were randomised into either a free-choice (N = 27), tunnelled (N = 27) or minimal intervention control arm (N =17). The primary outcome was differences in usage of the physical activity self-monitoring and feedback modules between the two intervention arms. Differences in usage of other website components between the two intervention groups were explored as secondary outcomes. Further, secondary outcomes involving comparisons between all study groups (including the control) included usability, personal relevance, and behaviour change.

RESULTS

The average number of physical activity self-monitoring and feedback modules accessed was higher in the tunnelled arm (M 2.6 SD 1.3) compared to the free-choice arm (M 1.5 SD 1.4), p = 0.01. However, free-choice participants were significantly more likely to have engaged with the social support (p = 0.008) and habit formation (p = 0.003) 'once-off' modules compared to the standard tunnelled arm. There were no other between-group differences found for any other study outcomes.

CONCLUSION

This study indicated that website architecture influences behavioural engagement. Further research is needed to examine the impact of differential usage on mechanisms of action and behaviour change.

Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis

BACKGROUND

Unhealthy behaviors, such as physical inactivity, sedentary lifestyle, and unhealthful eating, remain highly prevalent, posing formidable challenges in efforts to improve cardiovascular health. While traditional interventions to promote healthy lifestyles are both costly and effective, wearable trackers, especially Fitbit devices, can provide a low-cost alternative that may effectively help large numbers of individuals become more physically fit and thereby maintain a good health status.

OBJECTIVE

The objectives of this meta-analysis are (1) to assess the effectiveness of interventions that incorporate a Fitbit device for healthy lifestyle outcomes (eg, steps, moderate-to-vigorous physical activity, and weight) and (2) to identify which additional intervention components or study characteristics are the most effective at improving healthy lifestyle outcomes.

METHODS

A systematic review was conducted, searching the following databases from 2007 to 2019: MEDLINE, EMBASE, CINAHL, and CENTRAL (Cochrane). Studies were included if (1) they were randomized controlled trials, (2) the intervention involved the use of a Fitbit device, and (3) the reported outcomes were related to healthy lifestyles. The main outcome measures were related to physical activity, sedentary behavior, and weight. All the studies were assessed for risk of bias using Cochrane criteria. A random-effects meta-analysis was conducted to estimate the treatment effect of interventions that included a Fitbit device compared with a control group. We also conducted subgroup analysis and fuzzy-set qualitative comparative analysis (fsQCA) to further disentangle the effects of intervention components.

RESULTS

Our final sample comprised 41 articles reporting the results of 37 studies. For Fitbit-based interventions, we found a statistically significant increase in daily step count (mean difference [MD] 950.54, 95% CI 475.89-1425.18; P<.001) and moderate-to-vigorous physical activity (MD 6.16, 95% CI 2.80-9.51; P<.001), a significant decrease in weight (MD -1.48, 95% CI -2.81 to -0.14; P=.03), and a nonsignificant decrease in objectively assessed and self-reported sedentary behavior (MD -10.62, 95% CI -35.50 to 14.27; P=.40 and standardized MD -0.11, 95% CI -0.48 to 0.26; P=.56, respectively). In general, the included studies were at low risk for bias, except for performance bias. Subgroup analysis and fsQCA demonstrated that, in addition to the effects of the Fitbit devices, setting activity goals was the most important intervention component.

CONCLUSIONS

The use of Fitbit devices in interventions has the potential to promote healthy lifestyles in terms of physical activity and weight. Fitbit devices may be useful to health professionals for patient monitoring and support.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42019145450; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019145450.

CaRE @ Home: Pilot Study of an Online Multidimensional Cancer Rehabilitation and Exercise Program for Cancer Survivors

BACKGROUND

Although facility-based cancer rehabilitation and exercise programs exist, patients are often unable to attend due to distance, cost, and other competing obligations. There is a need for scalable remote interventions that can reach and serve a larger population.

METHODS

We conducted a mixed methods pilot study to assess the feasibility, acceptability and impact of CaRE@Home: an 8-week online multidimensional cancer rehabilitation and exercise program. Feasibility and acceptability data were captured by attendance and adherence metrics and through qualitative interviews. Preliminary estimates of the effects of CaRE@Home on patient-reported and physically measured outcomes were calculated.

RESULTS

A total of n = 35 participated in the study. Recruitment (64%), retention (83%), and adherence (80%) rates, along with qualitative findings, support the feasibility of the CaRE@Home intervention. Acceptability was also high, and participants provided useful feedback for program improvements. Disability (WHODAS 2.0) scores significantly decreased from baseline (T1) to immediately post-intervention (T2) and three months post-intervention (T3) (p = 0.03 and p = 0.008). Physical activity (GSLTPAQ) levels significantly increased for both Total LSI (p = 0.007 and p = 0.0002) and moderate to strenuous LSI (p = 0.003 and p = 0.002) from baseline to T2 and T3. Work productivity (iPCQ) increased from T1 to T3 (p = 0.026). There was a significant increase in six minute walk distance from baseline to T2 and T3 (p < 0.001 and p = 0.010) and in grip strength from baseline to T2 and T3 (p = 0.003 and p < 0.001).

CONCLUSIONS

Results indicate that the CaRE@Home program is a feasible and acceptable cancer rehabilitation program that may help cancer survivors regain functional ability and decrease disability. In order to confirm these findings, a controlled trial is required.

A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study

Background

Poor diet and physical inactivity are leading modifiable causes of death and disease. Advances in artificial intelligence technology present tantalizing opportunities for creating virtual health coaches capable of providing personalized support at scale.

Objective

This proof of concept study aimed to test the feasibility (recruitment and retention) and preliminary efficacy of physical activity and Mediterranean-style dietary intervention (MedLiPal) delivered via artificially intelligent virtual health coach.

Methods

This 12-week single-arm pre-post study took place in Adelaide, Australia, from March to August 2019. Participants were inactive community-dwelling adults aged 45 to 75 years, recruited through news stories, social media posts, and flyers. The program included access to an artificially intelligent chatbot, Paola, who guided participants through a computer-based individualized introductory session, weekly check-ins, and goal setting, and was available 24/7 to answer questions. Participants used a Garmin Vivofit4 tracker to monitor daily steps, a website with educational materials and recipes, and a printed diet and activity log sheet. Primary outcomes included feasibility (based on recruitment and retention) and preliminary efficacy for changing physical activity and diet. Secondary outcomes were body composition (based on height, weight, and waist circumference) and blood pressure.

Results

Over 4 weeks, 99 potential participants registered expressions of interest, with 81 of those screened meeting eligibility criteria. Participants completed a mean of 109.8 (95% CI 1.9-217.7) more minutes of physical activity at week 12 compared with baseline. Mediterranean diet scores increased from a mean of 3.8 out of 14 at baseline, to 9.6 at 12 weeks (mean improvement 5.7 points, 95% CI 4.2-7.3). After 12 weeks, participants lost an average 1.3 kg (95% CI –0.1 to –2.5 kg) and 2.1 cm from their waist circumference (95% CI –3.5 to –0.7 cm). There were no significant changes in blood pressure. Feasibility was excellent in terms of recruitment, retention (90% at 12 weeks), and safety (no adverse events).

Conclusions

An artificially intelligent virtual assistant-led lifestyle-modification intervention was feasible and achieved measurable improvements in physical activity, diet, and body composition at 12 weeks. Future research examining artificially intelligent interventions at scale, and for other health purposes, is warranted.

Low-Cost Consumer-Based Trackers to Measure Physical Activity and Sleep Duration Among Adults in Free-Living Conditions: Validation Study

Background

Wearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption.

Objective

This study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2.

Methods

A free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0.

Results

Large variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA.

Conclusions

This study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers.

Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health

Objectives

Recently, wearable device technology has gained more popularity in supporting a healthy lifestyle. Hence, researchers have begun to put significant efforts into studying the direct and indirect benefits of wearable devices for health and wellbeing. This paper summarizes recent studies on the use of consumer wearable devices to improve physical activity, mental health, and health consciousness.

Methods

A thorough literature search was performed from several reputable databases, such as PubMed, Scopus, ScienceDirect, arXiv, and bioRxiv mainly using “wearable device research” as a keyword, no earlier than 2018. As a result, 25 of the most recent and relevant papers included in this review cover several topics, such as previous literature reviews (9 papers), wearable device accuracy (3 papers), self-reported data collection tools (3 papers), and wearable device intervention (10 papers).

Results

All the chosen studies are discussed based on the wearable device used, complementary data, study design, and data processing method. All these previous studies indicate that wearable devices are used either to validate their benefits for general wellbeing or for more serious medical contexts, such as cardiovascular disorders and post-stroke treatment.

Conclusions

Despite their huge potential for adoption in clinical settings, wearable device accuracy and validity remain the key challenge to be met. Some lessons learned and future projections, such as combining traditional study design with statistical and machine learning methods, are highlighted in this paper to provide a useful overview for other researchers carrying out similar research.

Implementation and Effects of Information Technology-Based and Print-Based Interventions to Promote Physical Activity Among Community-Dwelling Older Adults: Protocol for a Randomized Crossover Trial

BACKGROUND

Despite the known health benefits of physical activity (PA), less than half and less than one-third of older adults in Germany reach the PA recommendations for endurance training and strength training, respectively, of the World Health Organization. The aim of this study is to investigate the implementation and effectiveness over the course of 9 months of two interventions (information technology [IT]-based vs print-based) for PA promotion among initially inactive older adults in a randomized, crossover trial. This study is part of a large research consortium (2015-2021) investigating different aspects of PA promotion. The IT-based intervention was previously developed and refined, while the print-based intervention was newly developed during this funding phase.

OBJECTIVE

We aim to compare the effectiveness and examine the preferences of study participants regarding both delivery modes.

METHODS

Our target sample size was 390 initially inactive community-dwelling older adults aged ≥60 years at baseline (3-month follow-up [T1]: expected n=300; 9-month follow-up [T2]: expected n=240) who were randomized to one of two interventions for self-monitoring PA: IT-based (50%) or print-based (50%) intervention. In addition, 30% of the IT-based intervention group received a PA tracker. At T1, participants in both groups could choose whether they prefered to keep their assigned intervention or cross over to the other group for the following 6 months (T2). Participants' intervention preferences at baseline were collected retrospectively to run a post hoc matched-mismatched analysis. During the initial 3-month intervention period, both intervention groups were offered weekly group sessions that were continued monthly between T1 and T2. A self-administered questionnaire and 3D accelerometers were employed to assess changes in PA between baseline, T1, and T2. Adherence to PA recommendations, attendance at group sessions, and acceptance of the interventions were also tracked.

RESULTS

The funding period started in February 2018 and ends in January 2021. We obtained institutional review board approval for the study from the Medical Association in Bremen on July 3, 2018. Data collection was completed on January 31, 2020, and data cleaning and analysis started in February 2020. We expect to publish the first results by the end of the funding period.

CONCLUSIONS

Strategies to promote active aging are of particular relevance in Germany, as 29% of the population is projected to be ≥65 years old by 2030. Regular PA is a key contributor to healthy aging. This study will provide insights into the acceptance and effectiveness of IT-based vs print-based interventions to promote PA in initially inactive individuals aged ≥60 years. Results obtained in this study will improve the existing evidence base on the effectiveness of community-based PA interventions in Germany and will inform efforts to anchor evidence-based PA interventions in community structures and organizations via an allocation of permanent health insurance funds.

TRIAL REGISTRATION

German Registry of Clinical Trials DRKS00016073; https://tinyurl.com/y983586m.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/15168.

A Social Networking and Gamified App to Increase Physical Activity: Cluster RCT

INTRODUCTION

Appealing approaches to increasing physical activity levels are needed. This study evaluated whether a social and gamified smartphone app (Active Team) could be one such approach.

STUDY DESIGN

A 3-group cluster RCT compared the efficacy of Active Team with a basic self-monitoring app and waitlist control group.

SETTING/PARTICIPANTS

Australian adults (N=444, mean age of 41 years, 74% female) were recruited in teams (n=121) and randomly assigned (1:1:1) to the Active Team (n=141, 39 teams), self-monitoring app (n=160, 42 teams), or waitlist group (n=143, 40 teams). Data were collected in 2016-2017, and analysis was conducted in 2018-2019.

INTERVENTION

Active Team is a 100-day app-based, gamified, online social networking physical activity intervention.

MAIN OUTCOME MEASURES

The primary outcome was change in objective physical activity from baseline to 3-month follow-up. Secondary outcomes included objective physical activity at 9 months and self-reported physical activity, quality of life, depression, anxiety and stress, well-being, and engagement.

RESULTS

Mixed models indicated no significant differences in objective physical activity between groups at 3 (F=0.17, p=0.84; Cohen's d=0.03, 95% CI= -0.21, 0.26) or 9 months (F=0.23, p=0.92; d=0.06, 95% CI= -0.17, 0.29) and no significant differences for secondary outcomes of quality of life, depression, anxiety and stress, or well-being. Self-reported moderate-to-vigorous physical activity was significantly higher in the Active Team group at the 9-month follow-up (F=3.05, p=0.02; d=0.50, 95% CI=0.26, 0.73). Engagement was high; the Active Team group logged steps on an average of 72 (SD=35) days and used the social and gamified features an average of 89 (SD=118) times.

CONCLUSIONS

A gamified, online social networking physical activity intervention did not change objective moderate-to-vigorous physical activity, though it did increase self-reported moderate-to-vigorous physical activity and achieve high levels of engagement. Future work is needed to understand if gamification, online social networks, and app-based approaches can be leveraged to achieve positive behavior change.

TRIAL REGISTRATION

This study is registered at Australian and New Zealand Clinical Trial Registry (protocol: ANZCTR12617000113358).

Feasible but Not Yet Efficacious: A Scoping Review of Wearable Activity Monitors in Interventions Targeting Physical Activity, Sedentary Behavior, and Sleep

Purpose of Review

The present review aimed to explore the range and characteristics of interventions that utilize WAM and descriptively summarize the efficacy of these interventions.

Recent Findings

A total of 65 articles (61 studies) were included in this review. Most of the WAM-based interventions (n=58) were designed to improve physical activity (PA). Interventions targeting sedentary behavior (SB) were much less common (n=12), and even less frequent were WAM-based sleep interventions (n=3). Most studies tested the feasibility of WAM-based interventions; hence, efficacy of these interventions in improving PA, SB, and/or sleep could not be conclusively determined. Nonetheless, WAM-based interventions showed considerable potential in increasing PA and decreasing SB.

Summary

WAM-based PA interventions exhibited preliminary efficacy in increasing PA. Although not as many interventions were focused on SB, current interventions also showed potential in decreasing sedentary time. Meanwhile, more evidence is needed to determine the utility of WAM in improving sleep. Major challenges with including WAM as part of interventions are reduced engagement in using the devices over time and the rapid changes in technology resulting in devices becoming obsolete soon after completion of an efficacy trial.

Acceptability of a computer-tailored and pedometer-based socio-cognitive intervention in a secondary coronary heart disease prevention program: A qualitative study

When developing an innovative intervention, its acceptability to patients, health care professionals and managers must be considered to ensure the implementation into practice. This study aims to identify factors influencing the acceptability of a computer-tailored and pedometer-based socio-cognitive intervention for patients with heart disease. Focus group interviews were conducted in two outlying regions of the province of Quebec (Canada). The Theory of Planned Behavior formed the theoretical basis of the interview guide. Two researchers performed verbatim analysis independently until consensus was achieved. The sample included 44 participants divided into six groups (patients n = 7 + 8, health care professionals n = 8 + 8, managers n = 6 + 7). Health care professionals and managers mentioned benefits concerning partners’ opportunity to improve assessment and monitoring. Patients believed the intervention could be useful to improve adherence to physical activity. Additional benefits indicated were self-monitoring behavior and improved health-related outcomes. However, patients expressed concern about the online security, fearing possible data breach. Some clinicians felt the pedometer may not be able to evaluate physical activities other than walking. With regard to behavioral control, a web application and pedometer must be easy to use and compatible with services already in place. Further barriers include level of literacy, cost and the various difficulties associated with wearing a pedometer. Findings suggest that, to improve the acceptability of a computer-tailored and pedometer-based socio-cognitive intervention, users must be assured of a secure website, validated, affordable and easy-to-use pedometers, and an intervention adapted to their level of literacy.

Efficacy of a computer-tailored web-based physical activity intervention using Fitbits for older adults: a randomised controlled trial protocol

INTRODUCTION

Physical activity is an integral part of healthy ageing, yet the majority of older adults 65+ years are not sufficiently active. Web-based physical activity interventions hold much promise to reach older adults. Preliminary evidence suggests that web-based interventions with tailored advice and Fitbits may be well suited for older adults.

METHODS AND ANALYSIS

This study aims to test the effectiveness of 'Active for Life', a 12-week computer-tailored web-based physical activity intervention using Fitbits for older adults. We will recruit 300 participants who will be randomly assigned to one of three trial arms: (1) web-based physical activity intervention with tailored advice only, (2) web-based physical activity intervention with tailored advice and Fitbit or (3) a wait-list control. The primary outcome, objective moderate to vigorous physical activity (MVPA) and secondary outcomes of objective sedentary behaviour, objective sleep, quality of life, social support, physical function and satisfaction with life will be assessed at baseline and week 12. The secondary outcomes of self-reported physical activity, sitting time and sleep will be assessed at baseline, week 6, 12 and 24. Website usability and participant satisfaction will be assessed at week 12 and website usage and intervention fidelity will be assessed from week 1 to 24. Intention-to-treat linear mixed model analyses will be used to test for group (tailoring only, tailoring +Fitbit, control) differences on changes in the main outcome, MVPA and secondary outcomes. Generalised linear models will be used to compare intervention groups (tailoring only, tailoring +Fitbit) on website usability, participant satisfaction, website usage and intervention fidelity.

ETHICS AND DISSEMINATION

The study has received ethics approval from the Central Queensland University Human Research Ethics Committee (H16/12-321). Study outcomes will be disseminated through peer-reviewed publications and academic conferences and used to inform improvements and dissemination of a tailored, web-based physical activity intervention for adults 65+ years.

TRIAL REGISTRATION NUMBER

Australian and New Zealand Clinical Trials Registry Number: ACTRN12618000646246.

User Engagement and Attrition in an App-Based Physical Activity Intervention: Secondary Analysis of a Randomized Controlled Trial

Background

The success of a mobile phone app in changing health behavior is thought to be contingent on engagement, commonly operationalized as frequency of use.

Objective

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.

Methods

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.

Results

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; F₂=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; F₁=6.385; P=.01) than younger users (mean 155.6, 95% CI 128.5-182.6). Super users were 4.6 years older (t₂₉₇=3.6; P<.001) and less likely to have a BMI in the obese range (χ²₂=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_1,272=4.76; P=.03).

Conclusions

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.

Trial Registration

Australian New Zealand Clinical Trials Registry ACTRN12617000113358; https://www.anzctr.org.au/ACTRN12617000113358.aspx

Effects of two web-based interventions promoting physical activity among older adults compared to a delayed intervention control group in Northwestern Germany: Results of the PROMOTE community-based intervention trial

Regular physical activity (PA) is of central importance for healthy ageing. However, in Germany, only 42% of older adults currently reach the PA recommendations of the World Health Organization. The aim of this study was to examine the effects of two web-based interventions on PA in adults aged 65-75 years living in Northwestern Germany compared to a delayed intervention control group (CG). 589 older adults were randomized to one of the three groups. Participants in intervention group 1 (IG1) received access to a web-based intervention for ten weeks assisting them in self-tracking PA behavior. Participants in IG2 received the intervention of IG1 and additionally an activity tracker to objectively track PA behavior. To analyze differences in objectively measured moderate-to-vigorous PA and sedentary time between baseline and follow-up (12 weeks after baseline), linear mixed models were used. The interaction effects revealed a decrease in minutes spent on moderate-to-vigorous PA in bouts of 10 min by 11 min per week in IG1 participants (β = -11.08, 95% CI: (-35.03; 12.87)). In comparison, IG2 participants were 7 min more physically active at follow-up (β = 7.48, 95% CI: (-17.64; 32.60)). Sedentary time in bouts of 30 min per week increased in IG1 participants (β = 106.77, 95% CI: (-47.69; 261.23)) and decreased in IG2 participants at follow-up (β = -16.45, 95% CI: (-178.83; 145.94)). Participation in the two web-based interventions did not lead to significant increases in moderate-to-vigorous PA or significant decreases in sedentary time compared to the CG. The study was registered at the German Clinical Trials Register (DRKS00010052, 07-11-2016).

Behavior Change Techniques Incorporated in Fitness Trackers: Content Analysis

BACKGROUND

The use of fitness trackers as tools of self-management to promote physical activity is increasing. However, the content of fitness trackers remains unexplored.

OBJECTIVE

The aim of this study was to use the Behavior Change Technique Taxonomy v1 (BCTTv1) to examine if swim-proof fitness trackers below Aus $150 (US$ 105) incorporate behavior change techniques (BCTs) that relate to self-management strategies to increase physical activity and reduce sedentary behavior and to determine if content of the fitness trackers correspond to physical activity guidelines.

METHODS

A total of two raters used the BCTTv1 to code 6 fitness trackers that met the inclusion criteria. The inclusion criteria were the ability to track activity, be swim proof, be compatible with Android and Apple operating systems, and cost below Aus $150.

RESULTS

All fitness trackers contained BCTs known to promote physical activity, with the most frequently used BCTs overlapping with self-management strategies, including goal setting, self-monitoring, and feedback on behavior. Fitbit Flex 2 (Fitbit Inc) contained the most BCTs at 20. Huawei Band 2 Pro (Huawei Technologies) and Misfit Shine 2 (Fossil Group) contained the least BCTs at 11.

CONCLUSIONS

Fitness trackers contain evidence-based BCTs that overlap with self-management strategies, which have been shown to increase physical activity and reduce sedentary behavior. Fitness trackers offer the prospect for physical activity interventions that are cost-effective and easily accessed by a wide population.