Evaluating the Carrot Rewards App, a Population-Level Incentive-Based Intervention Promoting Step Counts Across Two Canadian Provinces: Quasi-Experimental Study

Effectiveness of holistic mobile health interventions on diet, and physical, and mental health outcomes: a systematic review and meta-analysis

Background

Good physical and mental health are essential for healthy ageing. Holistic mobile health (mHealth) interventions-including at least three components: physical activity, diet, and mental health-could support both physical and mental health and be scaled to the population level. This review aims to describe the characteristics of holistic mHealth interventions and their effects on related behavioural and health outcomes among adults from the general population.

Methods

In this systematic review and meta-analysis, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, Scopus, China National Knowledge Infrastructure, and Google Scholar (first 200 records). The initial search covered January 1, 2011, to April 13, 2022, and an updated search extended from April 13, 2022 to August 30, 2023. Randomised controlled trials (RCTs) and non-randomised studies of interventions (NRSIs) were included if they (i) were delivered via mHealth technologies, (ii) included content on physical activity, diet, and mental health, and (iii) targeted adults (≥18 years old) from the general population or those at risk of non-communicable diseases (NCDs) or mental disorders. Studies were excluded if they targeted pregnant women (due to distinct physiological responses), individuals with pre-existing NCDs or mental disorders (to emphasise prevention), or primarily utilised web, email, or structured phone support (to focus on mobile technologies without exclusive human support). Data (summary data from published reports) extraction and risk-of-bias assessment were completed by two reviewers using a standard template and Cochrane risk-of-bias tools, respectively. Narrative syntheses were conducted for all studies, and random-effects models were used in the meta-analyses to estimate the pooled effect of interventions for outcomes with comparable data in the RCTs. The study was registered in PROSPERO, CRD42022315166.

Findings

After screening 5488 identified records, 34 studies (25 RCTs and 9 pre-post NRSIs) reported in 43 articles with 5691 participants (mean age 39 years, SD 12.5) were included. Most (91.2%, n = 31/34) were conducted in high-income countries. The median intervention duration was 3 months, and only 23.5% (n = 8/34) of studies reported follow-up data. Mobile applications, short-message services, and mobile device-compatible websites were the most common mHealth delivery modes; 47.1% (n = 16/34) studies used multiple mHealth delivery modes. Of 15 studies reporting on weight change, 9 showed significant reductions (6 targeted on individuals with overweight or obesity), and in 10 studies reporting perceived stress levels, 4 found significant reductions (all targeted on general adults). In the meta-analysis, holistic mHealth interventions were associated with significant weight loss (9 RCTs; mean difference -1.70 kg, 95% CI -2.45 to -0.95; I2 = 89.00%) and a significant reduction in perceived stress levels (6 RCTs; standardised mean difference [SMD] -0.32; 95% CI -0.52 to -0.12; I2 = 14.52%). There were no significant intervention effects on self-reported moderate-to-vigorous physical activity (5 RCTs; SMD 0.21; 95%CI -0.25 to 0.67; I2 = 74.28%) or diet quality scores (5 RCTs; SMD 0.21; 95%CI -0.47 to 0.65; I2 = 62.27%). All NRSIs were labelled as having a serious risk of bias overall; 56% (n = 14/25) of RCTs were classified as having some concerns, and the others as having a high risk of bias.

Interpretation

Findings from identified studies suggest that holistic mHealth interventions may aid reductions in weight and in perceived stress levels, with small to medium effect sizes. The observed effects on diet quality scores and self-reported moderate-to-vigorous physical activity were less clear and require more research. High-quality RCTs with longer follow-up durations are needed to provide more robust evidence. To promote population health, future research should focus on vulnerable populations and those in middle- and low-income countries. Optimal combinations of delivery modes and components to improve efficacy and sustain long-term effects should also be explored.

Funding

National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme and Physical Activity and Nutrition Determinants in Asia (PANDA) Research Programme.

Does participation in municipality-initiated incentivized health promotion programs promote physical activity among the physically inactive? Verification in six Japanese municipalities

Background

Physical activity can prevent lifestyle-related diseases, such as hypertension and diabetes. However, many people in society are physically inactive, especially middle-aged and older adults over 40. Therefore, this study examined the effects of a municipality-led incentivized health promotion program to clarify: (1) whether the average number of steps increased from the preparticipation period to the 18th month following the program; and (2) whether such increases were influenced by financial incentives.

Design and methods

This study analyzed gender, age, step, and incentive type data from 5688 middle-aged and elderly participants in an incentivized health promotion program. The incentives were regional gift certificates that could only be used in local areas, national gift certificates, common points that could be used in department and convenience stores nationwide, and donations. The incentives were worth a maximum of 24,000 yen per year.

Results

Both the physically active group and the physically inactive group had increased the step count markedly 18 months post-participation; however those participants chosing financial incentives showed significantly higher steps after 18 months than those who opted for non-financial incentives (time: p < 0.05, time × group: p < 0.05).

Conclusions

Municipality-led financially incentivized health promotion programs could motivate increases in the average number of steps taken by those physically inactive residents.

Effects of Mobile-Based Financial Incentive Interventions for Adults at Risk of Developing Hypertension: Feasibility Randomized Controlled Trial

BACKGROUND

Hypertension is the leading modifiable risk factor for cardiovascular disease and mortality. Adopting lifestyle modifications, like increasing physical activity (PA), can be an effective strategy in blood pressure (BP) control, but many adults do not meet the PA guidelines. Financial incentive interventions have the power to increase PA levels but are often limited due to cost. Further, mobile health technologies can make these programs more scalable. There is a gap in the literature about the most feasible and effective financial incentive PA framework; thus, pay-per-minute (PPM) and self-funded investment incentive (SFII) frameworks were explored.

OBJECTIVE

The aims were to (1) determine the feasibility (recruitment, engagement, and acceptability) of an 8-week mobile-based PPM and SFII hypertension prevention PA program and (2) explore the effects of PPM and SFII interventions relative to a control on the PA levels, BP, and PA motivation.

METHODS

In total, 55 adults aged 40-65 years not meeting the Canadian PA guidelines were recruited from Facebook and randomized into the following groups: financial incentive groups, PPM or SFII, receiving up to CAD $20 each (at the time of writing: CAD $1=US $0.74), or a control group without financial incentive. PPM participants received CAD $0.02 for each minute of moderate-to-vigorous PA (MVPA) per week up to the PA guidelines and the SFII received CAD $2.50 for each week they met the PA guidelines. Feasibility outcome measures (recruitment, engagement, and acceptability) were assessed. Secondary outcomes included changes in PA outcomes (MVPA and daily steps) relative to baseline were compared among PPM, SFII, and control groups at 4 and 8 weeks using linear regressions. Changes in BP and relative autonomy index relative to baseline were compared among the groups at follow-up.

RESULTS

Participants were randomized to the PPM (n=19), SFII (n=18), or control (n=18) groups. The recruitment, retention rate, and engagement were 77%, 75%, and 65%, respectively. The intervention received overall positive feedback, with 90% of comments praising the intervention structure, financial incentive, and educational materials. Relative to the control at 4 weeks, the PPM and SFII arms increased their MVPA with medium effect (PPM vs control: η²_p=0.06, mean 117.8, SD 514 minutes; SFII vs control: η²_p=0.08, mean 145.3, SD 616 minutes). At 8 weeks, PPM maintained a small effect in MVPA relative to the control (η²_p=0.01, mean 22.8, SD 249 minutes) and SFII displayed a medium effect size (η²_p=0.07, mean 113.8, SD 256 minutes). Small effects were observed for PPM and SFII relative to the control for systolic blood pressure (SBP) and diastolic blood pressure (DBP) (PPM: η²_p=0.12, Δmean SBP 7.1, SD 23.61 mm Hg; η²_p=0.04, Δmean DBP 3.5, SD 6.2 mm Hg; SFII: η²_p=0.01, Δmean SBP -0.4, SD 1.4 mm Hg; η²_p=0.02, Δmean DBP -2.3, SD 7.7 mm Hg) and relative autonomy index (PPM: η²_p=0.01; SFII: η²_p=0.03).

CONCLUSIONS

The feasibility metrics and preliminary findings suggest that a future full-scale randomized controlled trial examining the efficacy of PPM and SFII relative to a control is feasible, and studies with longer duration are warranted.

Physical Activity Trends Among Adults in a National Mobile Health Program: A Population-Based Cohort Study of 411,528 Adults

Physical inactivity is a global public health challenge, and effective, large-scale interventions are needed. We examined the effectiveness of a population-wide mobile health (mHealth) intervention in Singapore, National Steps Challenge Season 3 (NSC3) and 2 booster challenges (Personal Pledge and Corporate Challenge). The study includes 411,528 participants. We used regression discontinuity design and difference-in-difference with fixed-effects regression to examine the association of NSC3 and the additional booster challenges on daily step counts. Participants tended to be female (58.5%), with an average age of 41.5 years (standard deviation, 13.9) and body mass index (weight (kg)/height (m)2) of 23.8 (standard deviation, 4.5). We observed that NSC3 was associated with a mean increase of 1,437 steps (95% confidence interval (CI): 1,408, 1,467) per day. Enrollments in Personal Pledge and Corporate Challenge were associated with additional mean increases of 1,172 (95% CI: 1,123, 1,222) and 896 (95% CI: 862, 930) steps per day, respectively. For NSC3, the associated mean increase in the step counts across different sex and age groups varied, with greater increases for female participants and those in the oldest age group. We provide real-world evidence suggesting that NSC3 was associated with improvements in participants' step counts. Results suggest NSC3 is an effective and appealing population-wide mHealth physical activity intervention.

Promoting participation in remote digital health studies: An expert interview study

Background

Remote digital health studies are on the rise and promise to reduce the operational inefficiencies of in-person research. However, they encounter specific challenges in maintaining participation (enrollment and retention) due to their exclusive reliance on technology across all study phases.

Objective

The goal of this study was to collect experts' opinions on how to facilitate participation in remote digital health studies.

Method

We conducted 13 semi-structured interviews with principal investigators, researchers, and software developers who had recent experiences with remote digital health studies. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, we performed a thematic analysis and mapped various approaches to successful study participation.

Results

Our analyses revealed four themes: (1) study planning to increase participation, where experts suggest that remote digital health studies should be planned based on adequate knowledge of what motivates, engages, and disengages a target population; (2) participant enrollment, highlighting that enrollment strategies should be selected carefully, attached to adequate support, and focused on inclusivity; (3) participant retention, with strategies that minimize the effort and complexity of study tasks and ensure that technology is adapted and responsive to participant needs, and (4) requirements for study planning focused on the development of relevant guidelines to foster participation in future studies.

Conclusions

Our findings highlight the significant requirements for seamless technology and researcher involvement in enabling high remote digital health study participation. Future studies can benefit from collected experiences and the development of guidelines to inform planning that balances participant and scientific requirements.

Long-term usage of a commercial mHealth app: A "multiple-lives" perspective

Background

Emerging evidence suggests that individuals use mHealth apps in multiple disjointed ways in the real-world-individuals, for example, may engage, take breaks, and re-engage with these apps. To our knowledge, very few studies have adopted this 'multiple-live' perspective to analyze long-term usage of a physical activity (PA) app. This study aimed to examine the duration of use, as well as the frequency, length, and timing of streaks (uninterrupted periods of use) and breaks (uninterrupted periods of non-use) within a popular commercial PA app called Carrot Rewards over 12 months. We also examined sociodemographic correlates of usage.

Method

This retrospective observational study analyzed data from 41,207 Carrot Rewards users participating in the "Steps" walking program from June/July 2016 to June/July 2017. We measured four usage indicators: duration of use, frequency and length of streaks and breaks, time to first break, and time to resume second streak. We also extracted information regarding participants' age, gender, province, and proxy indicators of socioeconomic status derived from census data. We used descriptive statistics to summarize usage patterns, Kaplan-Meier curves to illustrate the time to first break and time to resume second streak. We used linear regressions and Cox Proportional Hazard regression models to examine sociodemographic correlates of usage.

Results

Over 60% of the participants used Carrot Rewards for ≥6 months and 29% used it for 12 months (mean = 32.59 ± 18.435 weeks). The frequency of streaks and breaks ranged from 1 to 9 (mean = 1.61 ± 1.04 times). The mean streak and break length were 20.22 ± 18.26 and 16.14 ± 15.74 weeks, respectively. The median time to first break was 18 weeks across gender groups and provinces; the median time for participants to resume the second streak was between 12 and 32 weeks. Being female, older, and living in a community with greater post-secondary education levels were associated with increased usage.

Conclusion

This study provides empirical evidence that long-term mHealth app usage is possible. In this context, it was common for users to take breaks and re-engage with Carrot Rewards. When designing and evaluating PA apps, therefore, interventionists should consider the 'multiple-lives' perspective described here, as well as the impact of gender and age.

Physical Activity Among Older Women Living in Rural Areas in Canada: A Scoping Review

Although there is strong evidence linking physical activity in older age with wellbeing and health benefits, these relationships tend to be under-researched in a rural context. A scoping review was conducted to identify what is known about physical activity among older women living in rural Canada. The search strategy was intentionally broad, with eight databases, academic journals, and websites scanned for research and grey literature related to Canada, in English, from 2000 to 2022. 33 studies, reviews, and reports were included in the final selection. These articles indicate that physical activity among older women living in rural Canada is influenced by a multitude of layers, contexts, conditions, and environments, with outcomes dependent on a mixture of personal, relational, community, societal, and governmental factors. In general, the women are committed to creating and maintaining an active lifestyle, and supporting their local rural community to enable these activities. While the social environment can proscribe physical activity through ageist attitudes and restrictive socio-cultural norms, social support from family and community members mitigates against these constraints. Rural infrastructure, geography, climate (seasons and weather), as well as transportation and policy issues may also impede the physical activity opportunities of the women. Some of the problems related to sustaining and resourcing recreational activity in rural communities are partly addressed through the implications presented in the articles. Among others, these suggestions relate to leadership and learning opportunities, public engagement, and the partnership working involved in supporting physical activity in rural areas. However, further research is long overdue.

Evaluation of a Population-Wide Mobile Health Physical Activity Program in 696 907 Adults in Singapore

Background

Evidence of scaled‐up physical activity interventions is scarce. This study evaluates the uptake, engagement, and effectiveness of one such intervention program.

Methods and Results

The program was open to individuals aged ≥17 years in Singapore. The main intervention components comprised device‐based daily physical activity recording paired with step count goals and financial rewards. According to the different reward opportunities, we divided the evaluation period (August 2017 to June 2018) into the baseline monitoring phase, the main challenge phase, and the maintenance phase. Uptake was assessed by the number of individuals registered, and engagement by the step recording duration after registration. The effectiveness was defined as changes in mean daily step count from baseline to the main challenge phase and the maintenance phase. A total of 696 907 participants registered, including more Singapore citizens (versus noncitizens), women, and younger (aged 17–39 years) individuals. The evaluation of engagement and effectiveness included 421 388 (60.5%) participants who provided plausible characteristic information and step count data. The median duration of engagement was 74 (IQR, 14–149) days. Compared with the baseline of 7509 (SD, 3467) steps, mean daily step count increased by 1579 (95% CI, 1564–1594) steps during the main challenge phase and 934 (95% CI, 916–952) steps during the maintenance phase. Greater engagement and activity increase were found in participants who are citizens, women, aged ≥40 years, non‐obese, and using separate wearables (versus smartphones).

Conclusions

Mobile health physical activity interventions can successfully reach a large population and be effective in increasing physical activity, despite declining program engagement over time.

Enrollment and retention of participants in remote digital health studies: a scoping review and framework proposal (Preprint)

Background

Objective

Methods

Results

Conclusions

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.

User-centered design features for digital health applications to support physical activity behaviors in solid organ transplant recipients: A qualitative study

INTRODUCTION

Digital health tools may be effective in engaging solid organ transplant (SOT) recipients in physical activity (PA). This study examined the perspectives of SOT recipients regarding PA, and desired features for digital health tools.

METHODS

Semi-structured interviews were used to explore perspectives of SOT recipients about barriers and motivators to physical activity, and core features of a digital health tool to support PA. Interviews were analyzed via thematic analysis.

RESULTS

Participants included 21 SOT recipients (11 men, 10 women, 21-78 years, 1.5-16 years post-transplant) from various organ groups (four heart, five kidney, five liver, three lung, and four multi-organ). Barriers to PA included risk aversion, managing non-linear health trajectories, physical limitations and lack of access to appropriate fitness training. Facilitators of PA included desire to live long and healthy lives, renewed physical capabilities, access to appropriate fitness guidelines and facilities. Desired features of a digital health tool included a reward system, affordability, integration of multiple functions, and the ability to selectively share information with healthcare professionals and peers.

CONCLUSIONS

SOT recipients identified the desired features of a digital health tool, which may be incorporated into future designs of digital and mobile health applications to support PA in SOT recipients.

Socio-demographic determinants of physical activity and app usage from smartphone data

The increasing ubiquity of smartphones provides a potential new data source to capture physical activity behaviours. Though not designed as a research tool, these secondary data have the potential to capture a large population over a more extensive spatial area and with longer temporality than current methods afford. This paper uses one such secondary data source from a commercial app designed to incentivise activity. We explore the new insights these data provide, alongside the sociodemographic profile of those using physical activity apps, to gain insight into both physical activity behaviour and determinants of app usage in order to evaluate the suitability of the app in providing insights into the physical activity of the population. We find app usage to be higher in females, those aged 25-50, and users more likely to live in areas where a higher proportion of the population are of a lower socioeconomic status. We ascertain longer-term patterns of app usage with increasing age and more male users reaching physical activity guideline recommendations despite longer daily activity duration recorded by female users. Additionally, we identify key weekly and seasonal trends in physical activity. This is one of the first studies to utilise a large volume of secondary physical activity app data to co-investigate usage alongside activity behaviour captured.

Designing the Optimal Digital Health Intervention for Patients' Use Before and After Elective Orthopedic Surgery: Qualitative Study

Background

Health behavior changes made by patients during the perioperative period can impact the outcomes and success of elective surgeries. However, there remains a limited understanding of how best to support patients during this time, particularly through the use of digital health interventions. Recognizing and understanding the potential unmet needs of elective orthopedic surgery patients is central to motivating healthier behavior change, improving recovery, and optimizing overall surgical success in the short and long term.

Objective

The aim of this study is to explore patient perspectives on technology features that would help support them to change their lifestyle behaviors during the pre- and postoperative periods, and that could potentially maintain long-term healthy lifestyles following recovery.

Methods

Semistructured interviews with pre- and postoperative elective orthopedic patients were conducted between May and June 2020 using telephone and video call–based software. Patient perspectives on the use of digital technologies to complement current surgical care and support with lifestyle behavior changes were discussed. Interviews were audio recorded and transcribed verbatim. Reflexive thematic analysis enabled the development of themes from the data, with QSR NVivo software (version 12) facilitating data management. Ethical approval was obtained from the National Health Service Health Research Authority.

Results

A total of 18 participants were interviewed. Four themes were developed from the data regarding the design and functionality of digital technologies to best support the perioperative journey. These center around an intervention’s ability to incorporate interactive, user-centered features; direct a descriptive and structured recovery; enable customizable, patient-controlled settings; and deliver both general and specific surgical advice in a timely manner. Interventions that are initiated preoperatively and continued postoperatively were perceived as beneficial. Interventions designed with personalized milestones were found to better guide patients through a structured recovery. Individualized tailoring of preparatory and recovery information was desired by patients with previously high levels of physical activity before surgery. The use of personalized progression-based exercises further encouraged physical recovery; game-like rewards and incentives were regarded as motivational for making and sustaining health behavior change. In-built video calling and messaging features offered connectivity with peers and clinicians for supported care delivery.

Conclusions

Specific intervention design and functionality features can provide better, structured support for elective orthopedic patients across the entire surgical journey and beyond. This study provides much-needed evidence relating to the optimal design and timing of digital interventions for elective orthopedic surgical patients. Findings from this study suggest a desire for personalized perioperative care, in turn, supporting patients to make health behavior changes to optimize surgical success. These findings should be used to influence future co-design projects to enable the design and implementation of patient-focused, tailored, and targeted digital health technologies within modern health care settings.

User Engagement With Smartphone Apps and Cardiovascular Disease Risk Factor Outcomes: Systematic Review

Background

The use of mobile health (mHealth) interventions, including smartphone apps, for the prevention of cardiovascular disease (CVD) has demonstrated mixed results for obesity, hypercholesterolemia, diabetes, and hypertension management. A major factor attributing to the variation in mHealth study results may be mHealth user engagement.

Objective

This systematic review aims to determine if user engagement with smartphone apps for the prevention and management of CVD is associated with improved CVD health behavior change and risk factor outcomes.

Methods

We conducted a comprehensive search of PubMed, CINAHL, and Embase databases from 2007 to 2020. Studies were eligible if they assessed whether user engagement with a smartphone app used by an individual to manage his or her CVD risk factors was associated with the CVD health behavior change or risk factor outcomes. For eligible studies, data were extracted on study and sample characteristics, intervention description, app user engagement measures, and the relationship between app user engagement and the CVD risk factor outcomes. App user engagement was operationalized as general usage (eg, number of log-ins or usage days per week) or self-monitoring within the app (eg, total number of entries made in the app). The quality of the studies was assessed.

Results

Of the 24 included studies, 17 used a randomized controlled trial design, 4 used a retrospective analysis, and 3 used a single-arm pre- and posttest design. Sample sizes ranged from 55 to 324,649 adults, with 19 studies recruiting participants from a community setting. Most of the studies assessed weight loss interventions, with 6 addressing additional CVD risk factors, including diabetes, sleep, stress, and alcohol consumption. Most of the studies that assessed the relationship between user engagement and reduction in weight (9/13, 69%), BMI (3/4, 75%), body fat percentage (1/2, 50%), waist circumference (2/3, 67%), and hemoglobin A_1c (3/5, 60%) found statistically significant results, indicating that greater app user engagement was associated with better outcomes. Of 5 studies, 3 (60%) found a statistically significant relationship between higher user engagement and an increase in objectively measured physical activity. The studies assessing the relationship between user engagement and dietary and diabetes self-care behaviors, blood pressure, and lipid panel components did not find statistically significant results.

Conclusions

Increased app user engagement for prevention and management of CVD may be associated with improved weight and BMI; however, only a few studies assessed other outcomes, limiting the evidence beyond this. Additional studies are needed to assess user engagement with smartphone apps targeting other important CVD risk factors, including dietary behaviors, hypercholesterolemia, diabetes, and hypertension. Further research is needed to assess mHealth user engagement in both inpatient and outpatient settings to determine the effect of integrating mHealth interventions into the existing clinical workflow and on CVD outcomes.

Impact of COVID-19 on Physical Activity Among 10,000 Steps Members and Engagement With the Program in Australia: Prospective Study

BACKGROUND

Physical activity is an important health behavior, due to its association with many physical and mental health conditions. During distressing events, such as the COVID-19 pandemic, there is a concern that physical activity levels may be negatively impacted. However, recent studies have shown inconsistent results. Additionally, there is a lack of studies in Australia on this topic.

OBJECTIVE

The aim of this study is to investigate changes in physical activity reported through the 10,000 Steps program and changes in engagement with the program during the COVID-19 pandemic.

METHODS

Data between January 1, 2018, and June 30, 2020, from registered members of the 10,000 Steps program, which included 3,548,825 days with step data, were used. The number of daily steps were logged manually by the members or synced automatically from their activity trackers connected to the program. Measures on program usage were the number of new registered members per day, the number of newly registered organizations per day, the number of steps logged per day, and the number of step entries per day. Key dates used for comparison were as follows: the first case with symptoms in Wuhan, China; the first case reported in Australia; the implementation of a 14-day ban for noncitizens arriving in Australia from China; the start of the lockdown in Australia; and the relaxing of restrictions by the Australian Government. Wilcoxon signed-rank tests were used to test for significant differences in number of steps between subgroups, between engagement measures in 2019 versus 2020, and before and after an event.

RESULTS

A decrease in steps was observed after the first case in Australia was reported (1.5%; P=.02) and after the start of the lockdown (3.4%; P<.001). At the time that the relaxing of restrictions started, the steps had already recovered from the lockdown. Additionally, the trends were consistent across genders and age groups. New South Wales, Australian Capital Territory, and Victoria had the greatest step reductions, with decreases of 7.0% (P<.001), 6.2% (P=.02), and 4.7% (P<.001), respectively. During the lockdown, the use of the program increased steeply. On the peak day, there were more than 9000 step entries per day, with nearly 100 million steps logged per day; in addition, more than 450 new users and more than 15 new organizations registered per day, although the numbers decreased quickly when restrictions were relaxed. On average per day, there were about 55 new registered users (P<.001), 2 new organizations (P<.001), 25.6 million steps (P<.001), and 2672 log entries (P<.001) more in 2020 compared to the same period in 2019.

CONCLUSIONS

The pandemic has had negative effects on steps among Australians across age groups and genders. However, the effect was relatively small, with steps recovering quickly after the lockdown. There was a large increase in program usage during the pandemic, which might help minimize the health impact of the lockdown and confirms the important role of physical activity programs during times of distress and lockdowns.

Adding team-based financial incentives to the Carrot Rewards physical activity app increases daily step count on a population scale: a 24-week matched case control study

Background

Mobile health applications (mHealth apps) targeting physical inactivity have increased in popularity yet are usually limited by low engagement. This study examined the impact of adding team-based incentives (Step Together Challenges, STCs) to an existing mHealth app (Carrot Rewards) that rewarded individual physical activity achievements.

Methods

A 24-week quasi-experimental study (retrospective matched pairs design) was conducted in three Canadian provinces (pre-intervention: weeks 1–12; intervention: weeks 13–24). Participants who used Carrot Rewards and STCs (experimental group) were matched with those who used Carrot Rewards only (controls) on age, gender, province and baseline mean daily step count (±500 steps/d). Carrot Rewards users earned individual-level incentives (worth $0.04 CAD) each day they reached a personalized daily step goal. With a single partner, STC users could earn team incentives ($0.40 CAD) for collaboratively reaching individual daily step goals 10 times in seven days (e.g., Partner A completes four goals and Partner B completes six goals in a week).

Results

The main analysis included 61,170 users (mean age = 32 yrs.; % female = 64). Controlling for pre-intervention mean daily step count, a significant difference in intervention mean daily step count favoured the experimental group (p < 0.0001; η_p² = 0.024). The estimated marginal mean group difference was 537 steps per day, or 3759 steps per week (about 40 walking min/wk). Linear regression suggested a dose-response relationship between the number of STCs completed (app engagement) and intervention mean daily step count (adjusted R² = 0.699) with each new STC corresponding to approximately 200 more steps per day.

Conclusion

Despite an explosion of physical activity app interest, low engagement leading to small or no effects remains an industry hallmark. In this paper, we found that adding modest team-based incentives to the Carrot Rewards app increased mean daily step count, and importantly, app engagement moderated this effect. Others should consider novel small-teams based approaches to boost engagement and effects.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-020-01043-1.

Daily Step Counts from the First Thailand National Steps Challenge in 2020: A Cross-Sectional Study

Thailand’s first national steps challenge has been implemented in 2020 with the goal to raise the level of physical activity nationwide by monitoring achievements through a smartphone application. This study examined the daily step counts of participants in the first national steps challenge. Six data points from 186,653 valid participants were retrieved and analyzed in five periods using Poisson regression. The mean daily steps peaked at 3196 in Period 1, and steadily dropped to 1205 in Period 5. The daily steps per period were analyzed using the participants’ characteristics, such as the type of participant, sex, age, body mass index, and area of residence. The overall mean daily steps of the participants meant physical activity was far below the recommended level and tended to drop in later periods. The general population achieved significantly higher mean daily steps than public health officers or village health volunteers (24.0% by multivariate analysis). Participants who were female, younger (<45 years), obese (body mass index > 30), and living in rural areas had fewer mean daily steps (13.8%, 44.3%, 12.7%, and 14.7% by multivariate analysis, respectively), with statistical significance. In the future, the national steps challenge should be continuously implemented by counting all steps throughout a day, using more strategies to draw attention and raise motivation, advocating for more participants, as well as reporting the whole day step counts instead of distance.

Examining Responsiveness to an Incentive-Based Mobile Health App: Longitudinal Observational Study

Background

The Carrot Rewards app was developed as part of a public-private partnership to reward Canadians with loyalty points for downloading the app, referring friends, completing educational health quizzes, and health-related behaviors with long-term objectives of increasing health knowledge and encouraging healthy behaviors. During the first 3 months after program rollout in British Columbia, a number of program design elements were adjusted, creating observed differences between groups of users with respect to the potential impact of program features on user engagement levels.

Objective

This study examines the impact of reducing reward size over time and explored the influence of other program features such as quiz timing, health intervention content, and type of reward program on user engagement with a mobile health (mHealth) app.

Methods

Participants in this longitudinal, nonexperimental observational study included British Columbia citizens who downloaded the app between March and July 2016. A regression methodology was used to examine the impact of changes to several program design features on quiz offer acceptance and engagement with this mHealth app.

Results

Our results, based on the longitudinal app use of 54,917 users (mean age 35, SD 13.2 years; 65.03% [35,647/54,917] female), indicated that the key drivers of the likelihood of continued user engagement, in order of greatest to least impact, were (1) type of rewards earned by users (eg, movies [+355%; P<.001], air travel [+210%; P<.001], and grocery [+140%; P<.001] relative to gas), (2) time delay between early offers (−64%; P<.001), (3) the content of the health intervention (eg, healthy eating [−10%; P<.001] vs exercise [+20%, P<.001] relative to health risk assessments), and (4) changes in the number of points offered. Our results demonstrate that reducing the number of points associated with a particular quiz by 10% only led to a 1% decrease in the likelihood of offer response (P<.001) and that each of the other design features had larger impacts on participant retention than did changes in the number of points.

Conclusions

The results of this study demonstrate that this program, built around the principles of behavioral economics in the form of the ongoing awarding of a small number of reward points instantly following the completion of health interventions, was able to drive significantly higher engagement levels than those demonstrated in previous literature exploring the intersection of mHealth apps and financial incentives. Previous studies have demonstrated the presence of incentive matters to user engagement; however, our results indicate that the number of points offered for these reward point–based health interventions is less important than other program design features such as the type of reward points being offered, the timing of intervention and reward offers, and the content of the health interventions in driving continued engagement by users.

Effects of Incentives on Adherence to a Web-Based Intervention Promoting Physical Activity: Naturalistic Study

Background

Despite many advantages of web-based health behavior interventions such as wide accessibility or low costs, these interventions are often accompanied by high attrition rates, particularly in usage under real-life conditions. It would therefore be helpful to implement strategies such as the use of financial incentives to motivate program participation and increase adherence.

Objective

This naturalistic study examined real-life usage data of a 12-week web-based physical activity (PA) intervention (Fitness Coach) among insurants who participated in an additional incentive program (incentive group) and those who did not (nonincentive group). Users in the incentive group had the perspective of receiving €30 (about US $33) cash back at the end of the intervention.

Methods

Registration and real-life usage data as part of routine data management and evaluation of the Fitness Coach were analyzed between September 2016 and June 2018. Depending on the duration of use and the weekly recording of tasks, 4 adherence groups (low, occasional, strong, and complete adherence) were defined. Demographic characteristics were collected by a self-reported questionnaire at registration. We analyzed baseline predictors and moderators of complete adherence such as participation in the program, age, gender, and BMI using binary logistic regressions.

Results

A total of 18,613 eligible persons registered for the intervention. Of these, 15,482 users chose to participate in the incentive program (incentive group): mean age 42.4 (SD 14.4) years, mean BMI 24.5 (SD 4.0) kg/m², median (IQR) BMI 23.8 (21.7-26.4) kg/m²; 65.12% (10,082/15,482) female; and 3131 users decided not to use the incentive program (nonincentive group): mean age 40.7 (SD 13.4) years, mean BMI 26.2 (SD 5.0) kg/m², median BMI 25.3 (IQR 22.6-28.7) kg/m²; 72.18% (2260/3131) female. At the end of the intervention, participants in the incentive program group showed 4.8 times higher complete adherence rates than those in the nonincentive program group (39.2% vs 8.1%), also yielding significantly higher odds to complete the intervention (odds ratio [OR] 12.638) for the incentive program group. Gender significantly moderated the effect with men in the incentive group showing higher odds to be completely adherent than women overall and men in the nonincentive group (OR 1.761). Furthermore, older age and male gender were significant predictors of complete adherence for all participants, whereas BMI did not predict intervention completion.

Conclusions

This is the first naturalistic study in the field of web-based PA interventions that shows the potential of even small financial incentives to increase program adherence. Male users, in particular, seem to be strongly motivated by incentives to complete the intervention. Based on these findings, health care providers can use differentiated incentive systems to increase regular participation in web-based PA interventions.

Commercial app use linked with sustained physical activity in two Canadian provinces: a 12-month quasi-experimental study

Background

Top tier commercial physical activity apps rarely undergo peer-reviewed evaluation. Even fewer are assessed beyond six months, the theoretical threshold for behaviour maintenance. The purpose of this study was to examine whether a multi-component commercial app rewarding users with digital incentives for walking was associated with an increase in physical activity over one year.

Methods

This 12-month quasi-experimental study was conducted in two Canadian provinces (n = 39,113 participants). Following a two-week baseline period, participants earned digital incentives ($0.04 CAD/day) every day they reached a personalized daily step goal. Mixed-effects models estimated changes in weekly mean daily step count between the baseline period and the last two recorded weeks. Models were fit for several engagement groups and separately by baseline physical activity status within engagement groups.

Results

Nearly half of participants (43%) were categorized as physically inactive at baseline (fewer than 5000 daily steps), and 60% engaged with the app for at least six months [‘Regular’ (24–51 weeks of step data) or ‘Committed’ sub-groups (52 weeks)]. Weekly mean daily step count increased for physically inactive users regardless of engagement status (P < .0001). The increase was largest for ‘Regular’ and ‘Committed’ participants—1215 and 1821 steps/day, respectively. For physically active participants, step count increases were only observed in the ‘Committed’ sub-group (P < .0001). Effect sizes were modest-to-medium depending on the sub-group analyzed.

Conclusions

A commercial app providing small but immediate digital incentives for individualized goals was associated with an increased weekly mean daily step count on a population-scale over one year. This effect was more evident for physically inactive and more engaged participants.

Nudging to move: a scoping review of the use of choice architecture interventions to promote physical activity in the general population

Background

Nudges are used to alter lifestyles and thus curb the rise of non-communicable diseases. Physical activity is a core prevention strategy to reduce the burden of non-communicable diseases. This paper aims to (1) give an overview of the scope of interventions using choice architecture techniques to promote physical activity at the population levels and (2) identify research gaps by analysing the different approaches in terms of class and type of intervention used.

Methods

A systematic electronic database search was combined with snowball citation sampling of a starter set of publications to search for studies published through October 2018 reporting interventions to promote physical activity at the population level using choice architecture techniques. The methodology of the Joanna Briggs Institute for Scoping Reviews was applied.

Results

In all, 35 publications were included. Most of the interventions used point-of-choice prompts tested at railway stations, shopping malls and airports (N = 27). Eight studies were online studies. While all studies were aimed at the general population, details, if reported at all, were vague and basic. All studies focused on individual-level lifestyle behaviour. None of the studies attempted to alter population-based lifestyle behaviour. Online and “real-world” approaches were rarely combined. Neither, interventions targeting meso- and macro-level structures nor combinations of individual-level and specific meso- or macro-level interventions were found.

Conclusion

Nudging is in principle an effective approach to promote physical activity within the general population. However, there are large gaps in research. Available opportunities have not yet been exhausted. Further research is needed that is explicitly based on behavioural insights and covering the full range of nudging approaches, particularly focussing on theoretical developments, practical feasibility tests and scale-up activities.

Electronic supplementary material

The online version of this article (10.1186/s12966-019-0844-z) contains supplementary material, which is available to authorized users.

Financial incentives for physical activity in adults: systematic review and meta-analysis

Objective

The use of financial incentives to promote physical activity (PA) has grown in popularity due in part to technological advances that make it easier to track and reward PA. The purpose of this study was to update the evidence on the effects of incentives on PA in adults.

Data sources

Medline, PubMed, Embase, PsychINFO, CCTR, CINAHL and COCH.

Eligibility criteria

Randomised controlled trials (RCT) published between 2012 and May 2018 examining the impact of incentives on PA.

Design

A simple count of studies with positive and null effects (‘vote counting’) was conducted. Random-effects meta-analyses were also undertaken for studies reporting steps per day for intervention and post-intervention periods.

Results

23 studies involving 6074 participants were included (64.42% female, mean age = 41.20 years). 20 out of 22 studies reported positive intervention effects and four out of 18 reported post-intervention (after incentives withdrawn) benefits. Among the 12 of 23 studies included in the meta-analysis, incentives were associated with increased mean daily step counts during the intervention period (pooled mean difference (MD), 607.1; 95% CI: 422.1 to 792.1). Among the nine of 12 studies with post-intervention daily step count data incentives were associated with increased mean daily step counts (pooled MD, 513.8; 95% CI:312.7 to 714.9).

Conclusion

Demonstrating rising interest in financial incentives, 23 RCTs were identified. Modest incentives ($1.40 US/day) increased PA for interventions of short and long durations and after incentives were removed, though post-intervention ‘vote counting’ and pooled results did not align. Nonetheless, and contrary to what has been previously reported, these findings suggest a short-term incentive ‘dose’ may promote sustained PA.

What is the role of smartphones on physical activity promotion? A systematic review and meta-analysis

OBJECTIVES

To identify and evaluate the effect of interventions that used cell phones as a means to promote physical activity (PA).

METHODS

The databases searched were MedLine/PubMed, Scopus, SPORTDiscus, PsycINFO, Science Direct, Lilacs, and SciELO. After removing duplicates, applying exclusion criteria, and checking the reference lists, 45 studies were reviewed. The Downs and Black (D&B) scale measured methodological quality, and a random effect model was used to compute the meta-analysis of PA by the reported unit (minutes per day or steps per day), delivery agent (application (APP), SMS, or other), and PA measurement (questionnaire, accelerometer, pedometer).

RESULTS

Mobile phone-based PA interventions were efficient in increasing both minutes [10.49; CI (3.37-17.60); p = 0.004] and steps per day [735.17; CI (227.72-1242.61); p = 0.005] in adults when compared to baseline. Furthermore, APP-based interventions were able to increase the number of steps (p = 0.04) and minutes per day of PA (p = 0.04) in adults. Also, 85% of included manuscripts were classified as moderate- to high-quality articles.

CONCLUSIONS

Mobile phone-based PA interventions, inclusive those delivery by APP, were effective to increase minutes and steps per day in adults.