Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults' physical activity

Hourly step recommendations to achieve daily goals for working and older adults

BACKGROUND

The widespread use of physical activity trackers enables the collection of high-resolution health data, such as hourly step counts, to evaluate health promotion programmes. We aim to investigate how participants meet their daily step goals.

METHODS

We used 24-h steps data from the National Steps ChallengeTM Season 3, wherein participants were rewarded with vouchers when achieving specified goals of 5000, 7500, and 10,000 steps per day. We extracted data from 3075 participants' including a total of 52,346 participant-days. We modelled the hourly step counts using a two-part model, in which the distribution for step counts was allowed to depend on the sum of step counts up to the previous hour and participant demographics.

RESULTS

Participants have a mean age of 44.2 years (standard deviation = 13.9), and 40.4% are males. We show that on weekdays, the hourly mean step counts among participants aged 60 and above are higher than participants aged 30 to 59 from the start of the day till 6 p.m. We also find that participants who accumulate at least 7000 steps by 7 p.m. are associated with higher success of achieving 10,000 steps.

CONCLUSIONS

We provide recommendations on the hourly targets to achieve daily goals, based on different participants' characteristics. Future studies could experimentally test if prompts and nudges at the recommended times of day could promote reaching step goals.

The Impact of Monetary Incentives on Delay Discounting Within a Year-Long Physical Activity Intervention

BACKGROUND

Delay discounting is the depreciation in a reward's perceived value as a function of the time until receipt. Monetary incentive programs that provide rewards contingent on meeting daily physical activity (PA) goals may change participants' delay discounting preferences.

PURPOSE

Determine if monetary incentives provided in close temporal proximity to meeting PA goals changed delay discounting, and if such changes mediated intervention effects.

METHODS

Inactive adults (n = 512) wore accelerometers during a 12-month intervention where they received proximal monetary incentives for meeting daily moderate-to-vigorous PA (MVPA) goals or delayed incentives for study participation. Delay discount rate and average MVPA were assessed at baseline, end of intervention, and a 24-month follow-up. Using structural equation modeling, we tested effects of proximal versus delayed rewards on delay discounting and whether any changes mediated intervention effects on MVPA. PA self-efficacy was also evaluated as a potential mediator, and both self-efficacy and delay discounting were assessed as potential moderators of intervention effects.

RESULTS

Proximal rewards significantly increased participants' delay discounting (β = 0.238, confidence interval [CI]: -0.078, 0.380), indicating greater sensitivity to reinforcement timing. This change did not mediate incentive-associated increases in MVPA at the end of the 12-month intervention (β = -0.016, CI: -0.053, 0.019) or at a 24-month follow-up (β = -0.020, CI: -0.059, 0.018). Moderation effects were not found.

CONCLUSIONS

Incentive-induced increases in delay discounting did not deleteriously impact MVPA. This finding may help assuage concerns about using monetary incentives for PA promotion, but further research regarding the consequences of changes in delay discounting is warranted.

Automated, tailored adaptive mobile messaging to reduce alcohol consumption in help-seeking adults: A randomized controlled trial

AIMS

To test differential outcomes between three 6-month text-messaging interventions to reduce at-risk drinking in help-seeking adults.

DESIGN

A three-arm single-blind randomized controlled trial with 1-, 3-, 6- and 12-month follow-ups.

SETTING

United States. A fully remote trial without human contact, with participants recruited primarily via social media outlets.

PARTICIPANTS

Seven hundred and twenty-three adults (mean = 39.9 years, standard deviation = 10.0; 62.5% female) seeking to reduce their drinking were allocated to 6 months of baseline 'tailored statically' messaging (TS; n = 240), 'tailored adaptive' messaging (TA; n = 239) or 'drink tracking' messaging (DT; n = 244).

INTERVENTIONS

TS consisted of daily text messages to reduce harmful drinking that were tailored to demographics and alcohol use. TA consisted of daily, tailored text messages that were also adapted based on goal achievement and proactive prompts. DT consisted of a weekly assessment for self-reported drinking over the past 7 days.

MEASUREMENTS

The primary outcome measure was weekly sum of standard drinks (SSD) at 6-month follow-up. Secondary outcome measures included drinks per drinking day (DDD), number of drinking days (NDD) per week and heavy drinking days (HDD) at 1-, 3-, 6- and 12-month follow-ups.

FINDINGS

At 6 months, compared with DT, TA resulted in significant SSD reductions of 16.2 (from 28.7 to 12.5) drinks [adjusted risk ratio (aRR) = 0.80, 95% confidence interval (CI) = 0.71, 0.91] using intent-to-treat analysis. TA also resulted in significant improvements in DDD (aRR = 0.84; 95% CI = 0.77-0.92) and drinking days per week (b = -0.39; 95% CI = -0.67, -0.10), but not HDD compared with DT at 6 months. TA was not significantly different from TS at any time-point, except DDD at 6 months. All groups made improvements in SSD at 12-month follow-up compared with baseline with an average reduction of 12.9 drinks per week across groups.

CONCLUSIONS

Automated tailored mobile messaging interventions are scalable solutions that can reduce weekly alcohol consumption in remote help-seeking drinkers over time.

Precision Nutrition Unveiled: Gene-Nutrient Interactions, Microbiota Dynamics, and Lifestyle Factors in Obesity Management

BACKGROUND

Obesity is a complex metabolic disorder that is associated with several diseases. Recently, precision nutrition (PN) has emerged as a tailored approach to provide individualised dietary recommendations.

AIM

This review discusses the major intrinsic and extrinsic components considered when applying PN during the management of obesity and common associated chronic conditions.

RESULTS

The review identified three main PN components: gene-nutrient interactions, intestinal microbiota, and lifestyle factors. Genetic makeup significantly contributes to inter-individual variations in dietary behaviours, with advanced genome sequencing and population genetics aiding in detecting gene variants associated with obesity. Additionally, PN-based host-microbiota evaluation emerges as an advanced therapeutic tool, impacting disease control and prevention. The gut microbiome's composition regulates diverse responses to nutritional recommendations. Several studies highlight PN's effectiveness in improving diet quality and enhancing adherence to physical activity among obese patients. PN is a key strategy for addressing obesity-related risk factors, encompassing dietary patterns, body weight, fat, blood lipids, glucose levels, and insulin resistance.

CONCLUSION

PN stands out as a feasible tool for effectively managing obesity, considering its ability to integrate genetic and lifestyle factors. The application of PN-based approaches not only improves current obesity conditions but also holds promise for preventing obesity and its associated complications in the long term.

Precision Nutrition to Improve Risk Factors of Obesity and Type 2 Diabetes

PURPOSE OF REVIEW

Existing dietary and lifestyle interventions and recommendations, to improve the risk factors of obesity and type 2 diabetes with the target to mitigate this double global epidemic, have produced inconsistent results due to interpersonal variabilities in response to these conventional approaches, and inaccuracies in dietary assessment methods. Precision nutrition, an emerging strategy, tailors an individual's key characteristics such as diet, phenotype, genotype, metabolic biomarkers, and gut microbiome for personalized dietary recommendations to optimize dietary response and health. Precision nutrition is suggested to be an alternative and potentially more effective strategy to improve dietary intake and prevention of obesity and chronic diseases. The purpose of this narrative review is to synthesize the current research and examine the state of the science regarding the effect of precision nutrition in improving the risk factors of obesity and type 2 diabetes.

RECENT FINDINGS

The results of the research review indicate to a large extent significant evidence supporting the effectiveness of precision nutrition in improving the risk factors of obesity and type 2 diabetes. Deeper insights and further rigorous research into the diet-phenotype-genotype and interactions of other components of precision nutrition may enable this innovative approach to be adapted in health care and public health to the special needs of individuals. Precision nutrition provides the strategy to make individualized dietary recommendations by integrating genetic, phenotypic, nutritional, lifestyle, medical, social, and other pertinent characteristics about individuals, as a means to address the challenges of generalized dietary recommendations. The evidence presented in this review shows that precision nutrition markedly improves risk factors of obesity and type 2 diabetes, particularly behavior change.

The moderating impact of neighborhood walkability on mHealth interventions to increase moderate to vigorous physical activity for insufficiently active adults in a randomized trial

BACKGROUND

Ecological models suggest that interventions targeting specific behaviors are most effective when supported by the environment. This study prospectively examined the interactions between neighborhood walkability and an mHealth intervention in a large-scale, adequately powered trial to increase moderate-to-vigorous physical activity (MVPA).

METHODS

Healthy, insufficiently active adults (N = 512) were recruited purposefully from census block groups ranked on walkability (high/low) and socioeconomic status (SES, high/low). Participants were block-randomized in groups of four to WalkIT Arizona, a 12-month, 2 × 2 factorial trial evaluating adaptive versus static goal setting and immediate versus delayed financial reinforcement delivered via text messages. Participants wore ActiGraph GT9X accelerometers daily for one year. After recruitment, a walkability index was calculated uniquely for every participant using a 500-m street network buffer. Generalized linear mixed-effects hurdle models tested for interactions between walkability, intervention components, and phase (baseline vs. intervention) on: (1) likelihood of any (versus no) MVPA and (2) daily MVPA minutes, after adjusting for accelerometer wear time, neighborhood SES, and calendar month. Neighborhood walkability was probed at 5th, 25th, 50th, 75th, and 95th percentiles to explore the full range of effects.

RESULTS

Adaptive goal setting was more effective in increasing the likelihood of any MVPA and daily MVPA minutes, especially in lower walkable neighborhoods, while the magnitude of intervention effect declined as walkability increased. Immediate reinforcement showed a greater increase in any and daily MVPA compared to delayed reinforcement, especially relatively greater in higher walkable neighborhoods.

CONCLUSIONS

Results partially supported the synergy hypotheses between neighborhood walkability and PA interventions and suggest the potential of tailoring interventions to individuals' neighborhood characteristics.

TRIAL REGISTRATION

Preregistered at clinicaltrials.gov (NCT02717663).

A Systematic Review of eHealth Interventions to Promote Physical Activity in Adults with Obesity or Overweight

Use of information and communication technology to improve health, known as eHealth, is an emerging concept in healthcare that may present opportunities to promote physical activity in adults with obesity. The purpose of this research was to systematically review eHealth intervention studies to promote physical activity in adults with obesity. Five electronic databases were used. Two authors screened articles, assessed risk of bias, and extracted data independently. A qualitative data synthesis for summarizing the findings was performed using harvest plots. In the search, 2276 articles were identified, and 18 studies met all inclusion criteria. Study quality ranged from poor to good. The included studies varied in intervention technology (e.g., web-based), physical activity assessment (e.g., device-based), and control group (e.g., wait-list). Behavioral change techniques used in the included studies were consistent with some techniques (e.g., self-monitoring) known as effective in face-to-face interventions, but more efficiently employed in eHealth using information and communication technology. Overall, this systematic review showed that a web-based or physical activity monitor-based eHealth intervention had the potential to effectively promote physical activity in adults with obesity. Some recommendations for future eHealth interventions to promote physical activity in adults with obesity were provided (e.g., use of theory, accelerometers).

Efficacy of an mHealth Behavior Change Intervention for Promoting Physical Activity in the Workplace: Randomized Controlled Trial

BACKGROUND

Insufficient physical activity (PA) is a well-established risk factor for several noncommunicable diseases such as cardiovascular diseases, cancer, diabetes, depression, and dementia. The World Health Organization (WHO) advises that individuals engage in 150 minutes of moderate PA per week or 75 minutes of intense PA per week. According to the WHO's latest report, 23% of adults fail to meet the minimum recommended level of PA. The percentage was even higher in a recent global study that showed 27% of adults were insufficiently active and reported a 5% increase in the prevalence trend of insufficient PA between 2001 and 2016. The study also showed the rate of insufficient PA among countries varied significantly. For instance, it was estimated that 40% were insufficiently active in the United States, and the percentage was even higher in Saudi Arabia (more than 50%). Governments are actively developing policies and methods to successfully establish a PA-inducing environment that encourages a healthy lifestyle in order to address the global steady decline in PA.

OBJECTIVE

The purpose of this study was to determine the effectiveness of mobile health (mHealth) interventions, particularly SMS text messaging interventions, to improve PA and decrease BMI in healthy adults in the workplace.

METHODS

In this parallel, 2-arm randomized controlled trial, healthy adults (N=327) were randomized to receive an mHealth intervention (tailored text messages combined with self-monitoring (intervention; n=166) or no intervention (control; n=161). Adults who were fully employed in an academic institution and had limited PA during working hours were recruited for the study. Outcomes, such as PA and BMI, were assessed at baseline and 3 months later.

RESULTS

Results showed significant improvement in PA levels (weekly step counts) in the intervention group (β=1097, 95% CI 922-1272, P<.001). There was also a significant reduction in BMI (β=0.60, 95% CI 0.50-0.69, P<.001).

CONCLUSIONS

Combining tailored text messages and self-monitoring interventions to improve PA and lower BMI was significantly effective and has the potential to leverage current methods to improve wellness among the public.

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.

Effect of a WhatsApp walking trial on daily steps among female Saudi Arabian university students

Objective

Physical inactivity in Saudi women is high, even among young women (60% of university students are physically inactive). We aimed to investigate the effect of a physical activity intervention on daily walking among female students at a Saudi university.

Methods

Two hundred and seven female students (mean age: 22.6 ± 1.3; body mass index: 24.6 ± 5.9) participated in a parallel-group randomized trial. The intervention group wore pedometers and received health-promotion messages for 12 weeks through WhatsApp®; the control group received a similar number of messages unrelated to health. Average daily steps and self-reported activity were assessed at baseline and after 3 months. Analysis followed an intention-to-treat approach. Group differences in average daily steps were assessed using a two (group) by 2 (time) ANOVA. F-tests for main effects and the interaction were evaluated; P < 0.05 was considered significant.

Results

There was a significant group-by-time interaction, that is, the intervention group had a significantly higher change in daily steps compared to the control group (+576 vs. -525; F = 4.33, P = 0.04). Self-reported daily activity was not significantly different by group.

Conclusions

The intervention was effective at improving the number of daily steps in young women. Future studies could test this intervention among other student groups.

Setting goal difficulty in monetary incentives to physicians: evidence from an online health knowledge-sharing platform

Purpose

Prior studies on the impact of incentives on physicians’ online participation mainly focused on different incentives while ignoring the difficulty of setting monetary incentives efficiently. Based on goal-setting theory, the current research examines the relationship between incentives with goals of varying difficulty and professional health knowledge sharing (PHKS) in online health knowledge-sharing platforms (OHKSPs).

Design/methodology/approach

Four field experiments with different monetary incentives were conducted by one of China’s largest OHKSPs, with whom the researchers cooperated in data collection. Monthly panel data on 10,584 physicians were collected from September 2018 to December 2019. There were 9,376 physicians in the treatment group and 1,208 in the control group. The authors used a difference-in-difference (DID) model to explore the research question based on the same control group and the Chow test with seemingly unrelated estimation (sureg) to compare regression coefficients between four groups. Several robustness checks were performed to validate the main results, including a relative time model, multiple falsification tests and a DID estimation using the propensity score matching method.

Findings

The results show that the monetary incentive significantly positively affected the volume of physicians’ PHKS directly with negative spillover to the duration of physicians’ PHKS. Moreover, the positive effect of incentives with higher difficulty on the volume of physicians’ PHKS was significantly smaller than that of incentives with low difficulty. Finally, professional title had a positive moderating effect on the volume of goal difficulty setting and did not significantly moderate the effect on the duration of physicians’ PHKS.

Research limitations/implications

Some limitations of this study are: firstly, because the field experiments were enterprise benefit oriented, the treatment and control groups were not balanced. Secondly, the experiments for different incentive measures were relatively similar, making it challenging to validate a causal effect. Finally, more consideration should be given to the strategy for setting hierarchical incentives in future research.

Originality/value

The research indicates that monetary incentives have a bilateral effect on PHKS, i.e. a positive direct effect on the volume of physicians’ contributions and a negative spillover effect on the duration of physicians’ PHKS. The professional titles of physicians also moderate such bilateral switches of PHKS. Furthermore, when a physician’s energy is limited, the goal difficulty setting of the incentive mechanism tends to be low. The more difficult the incentives are, the more inefficient the effects on physicians’ PHKS will be.

Behavior change techniques in digital physical activity interventions for breast cancer survivors: a systematic review

Given the broad benefits of physical activity (PA) but low PA levels among breast cancer survivors (i.e., women who have received a breast cancer diagnosis), innovative and evidence-based techniques are needed to motivate and support exercise. This study systematically reviews the use of behavior change techniques (BCTs) in digital PA interventions for breast cancer survivors. Studies were retrieved from five electronic databases and were included if they (i) sampled exclusively female breast cancer survivors aged >18 years, (ii) involved a digital intervention with the primary purpose of increasing PA, (iii) included a BCT component, (iv) used a randomized or quasi-randomized design, and (v) were published from January 2000 to May 2022. Two coders independently extracted data. Twenty primary studies met the inclusion criteria and were included in this review. All interventions used at least one BCT (mean 4 ± 1, range 2-13); self-monitoring (85%) and goal setting (79%) were the most common BCTs. Twelve of 20 (60%) studies reported improvements in PA behavior in the intervention vs. control group, and self-monitoring and goal setting were the most commonly used BCTs in these studies. Of the 93 total BCTs, 66 were not used in any interventions in the review, including critical constructs for PA behavior change (e.g., biofeedback). BCTs, important facilitators of PA behavior change, are being underutilized in digital PA interventions for breast cancer survivors. Future research should incorporate more diverse BCTs to explore if they can add to the effectiveness of digital interventions for this population.

A randomized trial to promote physical activity in adult pre-hypertensive and hypertensive patients

The purpose of the pilot study was to determine the effectiveness of wearable activity trackers alone or in combination with behaviour change strategies for promoting physical activity (PA) among individuals with pre-hypertension or hypertension. A sample of 44 adults (68% female and mean age 55) were randomized to receive either a Fitbit Charge HR 3 alone (FB) or the Fitbit in combination with behaviour change strategies (i.e., goal setting, behaviour goal review, adaptive feedback) delivered by a trained health coach (FB+) for 12 weeks. Moderate and vigorous PA (MVPA), steps, and sedentary time (ST) were assessed using ActiGraph wGT3X-BT. The FB+ group significantly increased PA [+1854 (2518) steps/day, p < .01] and MVPA [+26 (34) mins/day, p < .05], and decreased their ST [-63 (73) mins/day, p < .01]. The FB group significantly increased  MVPA [+11 (16) mins/day, p < .05], and decreased their ST [-87 (117) mins/day, p < .01]. Participants in FB+ had a significantly greater increase in MVPA/day compared to FB only with a between-group effect size of 0.6 (p < .05). Using Fitbit for self-monitoring is effective in increasing PA and reducing ST among pre-hypertensive and hypertensive participants. Additional behaviour change support amplified the intervention effectiveness for promoting MVPA.

Evaluation Methods Applied to Digital Health Interventions: What Is Being Used beyond Randomised Controlled Trials?-A Scoping Review

Despite the potential of digital health interventions (DHIs), evaluations of their effectiveness face challenges. DHIs are complex interventions and currently established evaluation methods, e.g., the randomised controlled trial (RCT), are limited in their application. This study aimed at identifying alternatives to RCTs as potentially more appropriate evaluation approaches. A scoping review was conducted to provide an overview of existing evaluation methods of DHIs beyond the RCT. Cochrane Central Register of Controlled Trials, MEDLINE, Web of Science, and EMBASE were screened in May 2021 to identify relevant publications, while using defined inclusion and exclusion criteria. Eight studies were extracted for a synthesis comprising four alternative evaluation designs. Factorial designs were mostly used to evaluate DHIs followed by stepped-wedge designs, sequential multiple assignment randomised trials (SMARTs), and micro randomised trials (MRTs). Some of these methods allow for the adaptation of interventions (e.g., SMART or MRT) and the evaluation of specific components of interventions (e.g., factorial designs). Thus, they are appropriate for addressing some specific needs in the evaluation of DHIs. However, it remains unsolved how to establish these alternative evaluation designs in research practice and how to deal with the limitations of the designs.

Training Computers to See the Built Environment Related to Physical Activity: Detection of Microscale Walkability Features Using Computer Vision

The study purpose was to train and validate a deep learning approach to detect microscale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep learning models for eight microscale features guided by the Microscale Audit of Pedestrian Streetscapes Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train−correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N = 512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between microscale features and GIS-measured and participant-reported neighborhood macroscale walkability. Classifier precision, recall, and overall accuracy were all over >84%. Total microscale was associated with overall macroscale walkability (r = 0.30, p < 0.001). Positive associations were found between model-detected and self-reported sidewalks (r = 0.41, p < 0.001) and sidewalk buffers (r = 0.26, p < 0.001). The computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

A Digital Health Program Targeting Physical Activity Among Adolescents With Overweight or Obesity: Open Trial

BACKGROUND

Prior studies suggest that mobile health physical activity programs that provide only weekly or daily text-based health coaching evidence limit the efficacy in improving physical activity in adolescents with overweight or obesity. It is possible that incentives, combined with health coaching and daily feedback on goal success, may increase program efficacy; however, such programs have not yet been tested with adolescents with overweight and obesity.

OBJECTIVE

This study aims to examine the feasibility and acceptability of a 12-week, incentive-based, mobile health physical activity program with text-based health coaching, goal setting, and self-monitoring for adolescents with overweight or obesity. Program adherence and changes in tracked physical activity (ie, steps and active minutes while wearing a Fitbit [Google LLC]), body mass, and body fat are assessed.

METHODS

A total of 28 adolescents aged 13 to 18 years with a BMI ≥90th percentile participated in the program. Of the 28 participants, 2 (7%) were lost to follow-up; thus, data from 26 (93%) participants were used in analyses.

RESULTS

Participant-reported acceptability was high, with all mean ratings of text-based coaching, Fitbit use, and the overall program being >5 on a 7-point scale. In addition, 85% (23/26) of participants reported that they would like to continue to wear the Fitbit. Program adherence was also high, as participants wore the Fitbit on 91.1% (SD 12.6%) of days on average and met their weekly goals for an average of 7 (SD 3.5) of 11 possible weeks. There were no demographic (ie, sex, age, and baseline body mass) differences in the percentage of days participants wore their Fitbit. Across the 12-week study, there were significant improvements in tracked daily active minutes (P=.006) and steps (P<.001) and significant pre- to posttest improvements in body fat percentage (P=.04).

CONCLUSIONS

The pilot program improved adolescent physical activity and physical health. A larger factorial design trial with adaptive daily goals may clarify the role of each program component in driving physical activity.

Adaptive Goals and Reinforcement Timing to Increase Physical Activity in Adults: A Factorial Randomized Trial

INTRODUCTION

Potent lifestyle interventions to increase moderate-to-vigorous physical activity are urgently needed for population-level chronic disease prevention. This trial tested the independent and joint effects of a mobile health system automating adaptive goal setting and immediate financial reinforcement for increasing daily walking among insufficiently active adults.

STUDY DESIGN

Participants were randomized into a 2 (adaptive versus static goal setting) X 2 (immediate versus delayed financial incentive timing) condition factorial trial to increase walking.

SETTINGS/PARTICIPANTS

Participants (N=512 adults) were recruited between 2016 and 2018 and were 64.5% female, aged 18-60 years, 18.8% Hispanic, 6.1% African American, and 83% White.

INTERVENTION

Principles of reinforcement and behavioral economics directed intervention design.

MAIN OUTCOME MEASURES

Participants wore accelerometers daily (133,876 day-level observations) that remotely measured moderate-to-vigorous physical activity bout minutes of ≥3 minutes/day for 1 year. Primary outcomes were between-condition differences in (1) engaging ≥1 bout of moderate-to-vigorous physical activity on each day and (2) on days with ≥1 bout, daily total moderate-to-vigorous physical activity minutes.

RESULTS

Mixed-effects hurdle models tested treatment group X phase (time) interactions using an intent-to-treat approach in 2021. Engaging in any ambulatory moderate-to-vigorous physical activity was greater for Adaptive than for Static Goal groups (OR=2.34, 95% CI=2.10, 2.60 vs OR=1.66, 95% CI=1.50, 1.84; p<0.001) and for Immediate than for Static Reinforcement groups (OR=2.16 95% CI=1.94, 2.40 vs OR=1.77, 95% CI=1.59, 1.97; p<0.01). The Immediate Reinforcement group increased by 16.54 moderate-to-vigorous physical activity minutes/day, whereas the Delayed Reinforcement group increased by 9.91 minutes/day (p<0.001). The combined Adaptive Goals + Immediate Reinforcement group increased by 16.52 moderate-to-vigorous physical activity minutes/day, significantly more than that of either Delayed Reinforcement group.

CONCLUSIONS

This study offers automated and scalable-behavior change strategies for increasing walking among adults most at-risk for chronic diseases attributed to sedentary lifestyles.

TRIAL REGISTRATION

This study is registered at www.clinicaltrials.gov (ClinicalTrials.gov Identifier: NCT02717663).

Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review

Background: Sedentary behavior (SB) negatively impact health and is highly prevalent in the population. Digital behavior change interventions (DBCIs) have been developed to modify behaviors such as SB by technologies. However, it is unknown which behavior change techniques (BCTs) are most frequently employed in SB as well as the effect associated with DBCIs in this field. The aim of this systematic review was: (a) to evaluate the BCT most frequently employed in digital health including all technologies available and interventions aimed at increasing physical activity (PA), reducing sedentary time, and improving adherence to exercise in the clinical population, and (b) to review the effect associated with DBCIs in this field. Methods: The database used was Medline, as well as Scopus, Scielo, and Google Scholar. For the search strategy, we considered versions of behavior/behavioral, mHealth/eHealth/telemedicine/serious game/gamification. The terms related to PA and SB were included, the criteria for inclusion were randomized clinical trials (RCTs), adults, intervention based on digital media, and outcome variable lifestyle modification; a last 5 years filter was included. Michie's Taxonomy was used to identify BCTs. The study was registered under the number PROSPERO CRD42019138681. Results: Eighteen RCTs were included in the present systematic review, 5 of them healthy adults, and 13 of them with some illness. Studies included 2298 sedentary individuals who were followed up for 5 weeks-3 years. The most used BCTs were goal setting, problem solving, review outcomes/goals, feedback on behavior and outcomes of behavior, self-monitoring of behavior, social support, information about health consequences, and behavior practice/rehearsal. The effect associated with DBCIs showed improvements, among several related to PA and physiologic self-reported and anthropometric outcomes. Conclusion: The BCTs most used in digital health to change outcomes related to SB were goals and planning, feedback and monitoring, social support, natural consequences, repetition, and substitution. Besides these findings, DBCIs are influenced by several factors like the type of intervention, patients' preferences and values, or the number of BCTs employed. More research is needed to determine with precision which DBCIs or BCTs are the most effective to reduce SB in the clinical population.

The Role of Behavioral Economics in Improving Cardiovascular Health Behaviors and Outcomes

PURPOSE OF REVIEW

Behavioral economics represents a promising set of principles to inform the design of health-promoting interventions. Techniques from the field have the potential to increase quality of cardiovascular care given suboptimal rates of guideline-directed care delivery and patient adherence to optimal health behaviors across the spectrum of cardiovascular care delivery.

RECENT FINDINGS

Cardiovascular health-promoting interventions have demonstrated success in using a wide array of principles from behavioral economics, including loss framing, social norms, and gamification. Such approaches are becoming increasingly sophisticated and focused on clinical cardiovascular outcomes in addition to health behaviors as a primary endpoint. Many approaches can be used to improve patient decisions remotely, which is particularly useful given the shift to virtual care in the context of the COVID-19 pandemic. Numerous applications for behavioral economics exist in the cardiovascular care delivery space, though more work is needed before we will have a full understanding of ways to best leverage such applications in each clinical context.

Burstiness and Stochasticity in the Malleability of Physical Activity

This study examined whether patterns of self-organization in physical activity (PA) predicted long-term success in a yearlong PA intervention. Increased moderate to vigorous PA (MVPA) was targeted in insufficiently active adults (N = 512) via goal setting and financial reinforcement. The degree to which inverse power law distributions, which are reflective of self-organization, summarized (a) daily MVPA and (b) time elapsed between meeting daily goals (goal attainment interresponse times) was calculated. Goal attainment interresponse times were also used to calculate burstiness, the degree to which meeting daily goals clustered in time. Inverse power laws accurately summarized interresponse times, but not daily MVPA. For participants with higher levels of MVPA early in the study, burstiness in reaching goals was associated with long-term resistance to intervention, while stochasticity in meeting goals predicted receptiveness to intervention. These results suggest that burstiness may measure self-organizing resistance to change, while PA stochasticity could be a precondition for behavioral malleability.

A quality-improvement optimization pilot of BariFit, a mobile health intervention to promote physical activity after bariatric surgery

Bariatric surgery is the most effective treatment for severe obesity (body mass index >40), helping individuals lose, on average, 25%-29% of their body weight over the first year. However, many patients begin to plateau and regain weight within 12-24 months, and 20% of patients begin to regain weight within 6 months postsurgery. As physical activity (PA) is an important predictor of weight loss and maintenance postsurgery, there is a need for scalable, effective lifestyle interventions to help bariatric patients increase PA in order to maximize their weight loss and maintenance. To assess feasibility of using mobile health (mHealth) tools to support PA postsurgery, we conducted a quality-improvement optimization pilot of BariFit, an mHealth intervention that combines commercial devices and custom text messages. Fifty-one bariatric patients enrolled in a 16-week optimization pilot of BariFit. To assess feasibility, pre-post changes in PA were assessed using activPAL. In addition, the pilot randomized, using a 2 × 2 factorial design, two adaptive approaches to daily step goals (variable and 60th percentile goals) and provision of rest days (yes/no), and microrandomized provision of SMS-delivered activity suggestions five times a day for each participant. Adherence to using study equipment was over 95% at 16 weeks. Participants increased PA by 1,866 steps from baseline to end-of-study (p < .007). Participants who received variable step goals averaged 1,141 more steps per day (p = .096) than those who received 60th percentile goals. Activity suggestions had no effect. mHealth interventions are feasible for supporting PA postbariatric surgery.

Systematic review of context-aware digital behavior change interventions to improve health

Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013-2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users' health behaviors.

Smart wearable devices in cardiovascular care: where we are and how to move forward

Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage health. In the era of remote, decentralized and increasingly personalized patient care, catalysed by the COVID-19 pandemic, the cardiovascular community must familiarize itself with the wearable technologies on the market and their wide range of clinical applications. In this Review, we highlight the basic engineering principles of common wearable sensors and where they can be error-prone. We also examine the role of these devices in the remote screening and diagnosis of common cardiovascular diseases, such as arrhythmias, and in the management of patients with established cardiovascular conditions, for example, heart failure. To date, challenges such as device accuracy, clinical validity, a lack of standardized regulatory policies and concerns for patient privacy are still hindering the widespread adoption of smart wearable technologies in clinical practice. We present several recommendations to navigate these challenges and propose a simple and practical 'ABCD' guide for clinicians, personalized to their specific practice needs, to accelerate the integration of these devices into the clinical workflow for optimal patient care.

Use of Fitbit Devices in Physical Activity Intervention Studies Across the Life Course: Narrative Review

BACKGROUND

Commercial off-the-shelf activity trackers (eg, Fitbit) allow users to self-monitor their daily physical activity (PA), including the number of steps, type of PA, amount of sleep, and other features. Fitbits have been used as both measurement and intervention tools. However, it is not clear how they are being incorporated into PA intervention studies, and their use in specific age groups across the life course is not well understood.

OBJECTIVE

This narrative review aims to characterize how PA intervention studies across the life course use Fitbit devices by synthesizing and summarizing information on device selection, intended use (intervention vs measurement tool), participant wear instructions, rates of adherence to device wear, strategies used to boost adherence, and the complementary use of other PA measures. This review provides intervention scientists with a synthesis of information that may inform future trials involving Fitbit devices.

METHODS

We conducted a search of the Fitabase Fitbit Research Library, a database of studies published between 2012 and 2018. Of the 682 studies available on the Fitabase research library, 60 interventions met the eligibility criteria and were included in this review. A supplemental search in PubMed resulted in the inclusion of 15 additional articles published between 2019 and 2020. A total of 75 articles were reviewed, which represented interventions conducted in childhood; adolescence; and early, middle, and older adulthood.

RESULTS

There was considerable heterogeneity in the use of Fitbit within and between developmental stages. Interventions for adults typically required longer wear periods, whereas studies on children and adolescents tended to have more limited device wear periods. Most studies used developmentally appropriate behavior change techniques and device wear instructions. Regardless of the developmental stage and intended Fitbit use (ie, measurement vs intervention tool), the most common strategies used to enhance wear time included sending participants reminders through texts or emails and asking participants to log their steps or synchronize their Fitbit data daily. The rates of adherence to the wear time criteria were reported using varying metrics. Most studies supplemented the use of Fitbit with additional objective or self-reported measures for PA.

CONCLUSIONS

Overall, the heterogeneity in Fitbit use across PA intervention studies reflects its relative novelty in the field of research. As the use of monitoring devices continues to expand in PA research, the lack of uniformity in study protocols and metrics of reported measures represents a major issue for comparability purposes. There is a need for increased transparency in the prospective registration of PA intervention studies. Researchers need to provide a clear rationale for the use of several PA measures and specify the source of their main PA outcome and how additional measures will be used in the context of Fitbit-based interventions.

One Size Does Not Fit All: Financial Incentives Needed to Change Physical Exercise Levels for Different Groups

This study estimated the distribution of willingness to accept (WTA) for a physical activity behavior change intervention entailing the completion of 10,000 steps/day to shed light on which levels of incentives trigger a change in behavior for different proportions of the population and for more at-risk subgroups. An online contingent valuation (CV) survey was administered to 1,130 respondents in the Basque Autonomous Community, Spain. The survey queried respondents about their physical activity levels and intention to engage in physical activity before presenting the WTA questions. Nonparametric WTA values were estimated for the whole sample and for subsamples of active, inactive, and overweight and obese people. One-quarter of respondents would engage with the hypothetical program even without payment, but if a monetary incentive was offered them, they would take it. The median WTA for committing to complete 10,000 steps/day is €0.23 for the full sample, €0.21 for active, €0.25 for inactive, and €0.23 for overweight and obese people. The WTA at 75th percentile is €4 for the full sample, €1.70 for active, €10.80 for inactive, and €5 for overweight and obese respondents. WTA is positively affected by a person's lack of disposable time to increase their physical activity and, for inactive people, by their poor intention to become physically active.

Project Step: A Randomized Controlled Trial Investigating the Effects of Frequent Feedback and Contingent Incentives on Physical Activity

BACKGROUND

Financial incentives and feedback on behavior offer promise for promoting physical activity. However, evidence for the effect of each of these techniques is inadequate. The present study evaluated the effects of daily versus weekly feedback and incentives contingent on reaching a daily walking goal versus noncontingent incentives in a 2 × 2 trial.

METHODS

Participants (N = 57) had a body mass index >25 kg/m2 and were insufficiently active. Participants received a daily walking goal that adapted weekly.

RESULTS

Participants receiving daily feedback increased daily steps (P = .03) more than those receiving weekly feedback. Participants receiving contingent incentives did not significantly increase steps (P = .12) more than those receiving noncontingent incentives. A trend-level effect (P = .09) suggested that there may be an interaction such that the combination of daily feedback and contingent incentives is most effective.

CONCLUSIONS

Results indicate that feedback is an important component of remotely delivered PA interventions and that evaluating each component of low-intensity interventions may help to improve efficacy. Moreover, results indicate that possible synergistic effects of feedback and rewards should be investigated further to help optimize interventions.

The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining

Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application. Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running. Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies "monitoring of behavior," "feedback," "goal setting," "reminders," "rewards," and "providing instruction." An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team. Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed.

Promoting physical activity in young adult cancer survivors using mHealth and adaptive tailored feedback strategies: Design of the Improving Physical Activity after Cancer Treatment (IMPACT) randomized controlled trial

INTRODUCTION

Despite the health benefits of physical activity for cancer survivors, nearly 60% of young adult cancer survivors (YACS) are physically inactive. Few physical activity interventions have been designed specifically for YACS.

PURPOSE

To describe the rationale and design of the IMPACT (IMproving Physical Activity after Cancer Treatment) trial, which tests the efficacy of a theory-based, mobile physical activity intervention for YACS.

METHODS

A total of 280 physically inactive YACS (diagnosed at ages 18-39) will be randomized to a self-help control or intervention condition. All participants will receive an activity tracker and companion mobile app, cellular-enabled scale, individual videochat session, and access to a Facebook group. Intervention participants will also receive a 6-month mobile intervention based on social cognitive theory, which targets improvements in behavioral capability, self-regulation, self-efficacy, and social support, and incorporates self-regulation strategies and behavior change techniques. The program includes: behavioral lessons; adaptive goal-setting in response to individuals' changing activity patterns; tailored feedback based on objective data and self-report measures; tailored text messages; and Facebook prompts encouraging peer support. Assessments occur at baseline, 3, 6, and 12 months. The primary outcome is total physical activity min/week at 6 months (assessed via accelerometry); secondary outcomes include total physical activity at 12 months, sedentary behavior, weight, and psychosocial measures.

CONCLUSIONS

IMPACT uniquely focuses on physical activity in YACS using an automated tailored mHealth program. Study findings could result in a high-reach, physical activity intervention for YACS that has potential to be adopted on a larger scale and reduce cancer-related morbidity. ClinicalTrials.gov Identifier: NCT03569605.

Unraveling Mobile Health Exercise Interventions for Adults: Scoping Review on the Implementations and Designs of Persuasive Strategies

Background

It is unclear why some physical activity (PA) mobile health (mHealth) interventions successfully promote PA whereas others do not. One possible explanation is the variety in PA mHealth interventions—not only do interventions differ in the selection of persuasive strategies but also the design and implementation of persuasive strategies can vary. However, limited studies have examined the different designs and technical implementations of strategies or explored if they indeed influenced the effectiveness of the intervention.

Objective

This scoping review sets out to explore the different technical implementations and design characteristics of common and likely most effective persuasive strategies, namely, goal setting, monitoring, reminders, rewards, sharing, and social comparison. Furthermore, this review aims to explore whether previous mHealth studies examined the influence of the different design characteristics and technical operationalizations of common persuasive strategies on the effectiveness of the intervention to persuade the user to engage in PA.

Methods

An unsystematic snowball and gray literature search was performed to identify the literature that evaluated the persuasive strategies in experimental trials (eg, randomized controlled trial, pre-post test). Studies were included if they targeted adults, if they were (partly) delivered by a mobile system, if they reported PA outcomes, if they used an experimental trial, and when they specifically compared the effect of different designs or implementations of persuasive strategies. The study methods, implementations, and designs of persuasive strategies, and the study results were systematically extracted from the literature by the reviewers.

Results

A total of 29 experimental trials were identified. We found a heterogeneity in how the strategies are being implemented and designed. Moreover, the findings indicated that the implementation and design of the strategy has an influence on the effectiveness of the PA intervention. For instance, the effectiveness of rewarding was shown to vary between types of rewards; rewarding goal achievement seems to be more effective than rewarding each step taken. Furthermore, studies comparing different ways of goal setting suggested that assigning a goal to users might appear to be more effective than letting the user set their own goal, similar to using adaptively tailored goals as opposed to static generic goals. This study further demonstrates that only a few studies have examined the influence of different technical implementations on PA behavior.

Conclusions

The different implementations and designs of persuasive strategies in mHealth interventions should be critically considered when developing such interventions and before drawing conclusions on the effectiveness of the strategy as a whole. Future efforts are needed to examine which implementations and designs are most effective to improve the translation of theory-based persuasive strategies into practical delivery forms.

Wearable Activity Tracking Device Use in an Adolescent Weight Management Clinic: A Randomized Controlled Pilot Trial

BACKGROUND

The use of physical activity tracker devices has increased within the general population. However, there is limited medical literature studying the efficacy of such devices in adolescents with obesity. In this study, we explored the feasibility of using wearable activity tracking devices as an adjunct intervention on adolescents with obesity.

METHODS

Randomized controlled pilot trial evaluated the feasibility (attrition ≤50%) of an activity tracking intervention (ATI) and its effects on weight loss in adolescents with obesity enrolled in an adolescent weight management clinic (AWMC). Outcomes included feasibility (attrition rate) and absolute change in BMI. Differences between groups at 6, 12, and 18 weeks were examined.

RESULTS

Forty-eight participants were enrolled in the study. Eighteen subjects were randomly assigned to the ATI group and 30 to control. The average age was 14.5 years. Overall, the majority of participants were Hispanic (56%). Sexes were equally distributed. The average baseline BMI was 37.5 kg/m². At the study conclusion, the overall attrition rate was 52.1%, 44.4% in the ATI group versus 56.6% in the control group, with a differential attrition of 12.2%. The ATI and control groups each showed an absolute decrease in BMI of -0.25 and -2.77, respectively, with no significant differences between the groups.

CONCLUSION

The attrition rate in our study was >50%. Participation in the AWMC by the ATI and control groups resulted in maintenance of BMI and body weight for the study duration. However, the use of an activity tracking device was not associated with greater weight loss. This trial is registered with NCT03004378.

Effects of Goal Type and Reinforcement Type on Self-Reported Domain-Specific Walking Among Inactive Adults: 2×2 Factorial Randomized Controlled Trial

Background

WalkIT Arizona was a 2×2 factorial trial examining the effects of goal type (adaptive versus static) and reinforcement type (immediate versus delayed) to increase moderate to vigorous physical activity (MVPA) among insufficiently active adults. The 12-month intervention combined mobile health (mHealth) technology with behavioral strategies to test scalable population-health approaches to increasing MVPA. Self-reported physical activity provided domain-specific information to help contextualize the intervention effects.

Objective

The aim of this study was to report on the secondary outcomes of self-reported walking for transportation and leisure over the course of the 12-month WalkIT intervention.

Methods

A total of 512 participants aged 19 to 60 years (n=330 [64.5%] women; n=425 [83%] Caucasian/white, n=96 [18.8%] Hispanic/Latinx) were randomized into interventions based on type of goals and reinforcements. The International Physical Activity Questionnaire-long form assessed walking for transportation and leisure at baseline, and at 6 months and 12 months of the intervention. Negative binomial hurdle models were used to examine the effects of goal and reinforcement type on (1) odds of reporting any (versus no) walking/week and (2) total reported minutes of walking/week, adjusted for neighborhood walkability and socioeconomic status. Separate analyses were conducted for transportation and leisure walking, using complete cases and multiple imputation.

Results

All intervention groups reported increased walking at 12 months relative to baseline. Effects of the intervention differed by domain: a significant three-way goal by reinforcement by time interaction was observed for total minutes of leisure walking/week, whereas time was the only significant factor that contributed to transportation walking. A sensitivity analysis indicated minimal differences between complete case analysis and multiple imputation.

Conclusions

This study is the first to report differential effects of adaptive versus static goals for self-reported walking by domain. Results support the premise that individual-level PA interventions are domain- and context-specific and may be helpful in guiding further intervention refinement.

Trial Registration

Preregistered at clinicaltrials.gov: (NCT02717663) https://clinicaltrials.gov/ct2/show/NCT02717663

International Registered Report Identifier (IRRID)

RR2-10.1016/j.cct.2019.05.001

Contingency management for individuals with chronic health conditions: A systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Contingency management (CM) interventions involve providing reinforcement for engaging in a desired behavior, and have been shown to increase treatment adherence and promote abstinence in the treatment of substance use disorders. This review was conducted to systematically review the literature on the effectiveness of CM when applied to a range of medical conditions outside of substance use disorders.

METHOD

The authors identified a total of 24 randomized studies examining the effects of CM on four outcomes: weight change, physical activity, medication/device adherence, and viral load that were included in the qualitative summary, and 20 studies included in the meta-analysis.

RESULTS

CM was associated with positive outcomes for physical activity and medication/device adherence compared to control conditions. Findings with weight loss and viral load were more mixed, and evidence for publication bias was found for both outcomes. The effects of CM tend to dissipate when the contingency is removed. Heterogeneity was observed across most outcomes.

LIMITATIONS AND CONCLUSIONS

This review provides preliminary support for the use of CM in increasing physical activity and improving adherence to medication for chronic health conditions while reinforcement is ongoing. Future studies should examine potential moderators and identify strategies to maintain these changes over time.

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.

Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial

BACKGROUND

The Assistant to Lift your Level of activitY (Ally) app is a smartphone application that combines financial incentives with chatbot-guided interventions to encourage users to reach personalized daily step goals.

PURPOSE

To evaluate the effects of incentives, weekly planning, and daily self-monitoring prompts that were used as intervention components as part of the Ally app.

METHODS

We conducted an 8 week optimization trial with n = 274 insurees of a health insurance company in Switzerland. At baseline, participants were randomized to different incentive conditions (cash incentives vs. charity incentives vs. no incentives). Over the course of the study, participants were randomized weekly to different planning conditions (action planning vs. coping planning vs. no planning) and daily to receiving or not receiving a self-monitoring prompt. Primary outcome was the achievement of personalized daily step goals.

RESULTS

Study participants were more active and healthier than the general Swiss population. Daily cash incentives increased step-goal achievement by 8.1%, 95% confidence interval (CI): [2.1, 14.1] and, only in the no-incentive control group, action planning increased step-goal achievement by 5.8%, 95% CI: [1.2, 10.4]. Charity incentives, self-monitoring prompts, and coping planning did not affect physical activity. Engagement with planning interventions and self-monitoring prompts was low and 30% of participants stopped using the app over the course of the study.

CONCLUSIONS

Daily cash incentives increased physical activity in the short term. Planning interventions and self-monitoring prompts require revision before they can be included in future versions of the app. Selection effects and engagement can be important challenges for physical-activity apps.

CLINICAL TRIAL INFORMATION

This study was registered on ClinicalTrials.gov, NCT03384550.

The Exploration of Occupational Therapy Interventions to Address Sedentary Behavior and Pain Among Older Adults

This study aimed to determine the feasibility of an occupational therapy intervention to address sedentary behavior and pain among older adults residing in assisted living facilities. A single group pretest and posttest design was implemented using the Sedentary Behavior Questionnaire, actigraphy technology, and daily activity logs to measure sedentary behavior. The Short-Form McGill Pain Questionnaire was used to assess pain. Occupational therapy intervention focused on individualized goals related to participation in meaningful physical activities, which were established in collaboration with the participant and were tracked for 3 weeks. A total of 12 participants were recruited for this study. The mean number of steps and daily calories burned over the course of a week increased by 3,058.3 steps (30.82%) and 57.64 calories (19.96%), respectively. Individualized goal setting may have a positive impact on the reduction of pain and increasing activity levels among older adults residing in assisted living facilities.

Delay Discount Rate Moderates a Physical Activity Intervention Testing Immediate Rewards

Financial incentives can increase physical activity (PA), but differences in the immediacy of reward delivery and individual differences in delay discount rates (i.e., higher discount values associated with less tolerance for delayed rewards) may explain differential responding. The current study tested whether delay discount rate moderated the relative effectiveness of immediate financial rewards on increasing daily PA. Inactive, overweight adults (ages 18-60, N = 96) were randomized to receive either smaller, immediate goal-contingent rewards or larger, delayed rewards for participation. Delay discount rates were derived for those who completed the Monetary Choice Questionnaire (N = 85). Linear mixed models tested interactions between discount rate and intervention arm on changes in mean daily Fitbit-measured steps from baseline to intervention phases, and rates of change during the intervention phase. Across all groups, participants increased by 2258 steps/day on average from baseline to intervention and declined by 9 steps/day across the 4-month intervention phase. The mean increase in daily steps was greater for immediate reward-arm participants across all discount rates. Descriptive exploration of reward effects by delay discount rate suggested that the magnitude of reward effects decreased at higher discount rates. During the 4-month intervention phase, rates of decline in daily steps were similar in both reward arms, but declines became more pronounced at higher discount rates. Overall, intervention efficacy decreased with less tolerance for delays. The importance of financial reward immediacy for increasing PA appears to increase with greater delay discount rates.

Variable Magnitude and Frequency Financial Reinforcement is Effective at Increasing Adults' Free-Living Physical Activity

Financial rewards can increase health behaviors, but little research has quantified the effects of different reinforcement schedules on this process. This analysis compares the average moderate-to-vigorous physical activity (MVPA) associated with six distinct positive reinforcement schedules implemented within a physical activity promotion clinical trial. In this trial, participants (N = 512) wore an accelerometer for 1 year and were prescribed one of two types of MVPA goals: a static 30-min goal or an adaptive goal based on the MVPA produced over the previous 9 days. As participants met goals, they transitioned through a sequence of reinforcement stages, beginning with a continuous-fixed magnitude (CRF-FM), then CRF-variable magnitude (CRF-VM), followed by a series of variable ratio-VM (VR-VM) schedules. The average accumulation of MVPA bouts over the last 24 days of each stage was compared to each other. Average MVPA during stage transitions was also examined. The results indicated that immediate reinforcement resulted in more MVPA relative to a comparison group and that the relative effectiveness of adaptive versus static goals was dependent on the magnitude of daily MVPA goals. Schedule effects were qualitatively different for individuals who frequently met their daily goals (Large Intervention Effect subgroup) versus those who did not (Small Intervention Effect subgroup). For the Large Intervention Effect group, the CRF-VM schedule produced the most MVPA, in particular within the adaptive goal condition, with increases observed immediately upon encountering this schedule. In contrast, the CRF-FM schedule produced small amounts of MVPA. This pattern was reversed for the Small Intervention Effect subgroup, where the most MVPA was associated with the CRF-FM stage. Future interventions should focus on discriminating small versus large intervention effects as quickly as possible so that the optimal reinforcement schedule can be used.

A Mobile Health Team Challenge to Promote Stepping and Stair Climbing Activities: Exploratory Feasibility Study

Background

Mobile health (mHealth) approaches are growing in popularity as a means of addressing low levels of physical activity (PA).

Objective

This study aimed to determine the validity of wearables in measuring step count and floor count per day and assess the feasibility and effects of a 6-week team challenge intervention delivered through smartphone apps.

Methods

Staff and students from a public university were recruited between 2015 and 2016. In phase 1, everyone wore a Fitbit tracker (Charge or Charge HR) and an ActiGraph for 7 days to compare daily step count estimated by the two devices under free-living conditions. They were also asked to climb 4 bouts of floors in an indoor stairwell to measure floor count which was compared against direct observation. In phase 2, participants were allocated to either a control or intervention group and received a Fitbit tracker synced to the Fitbit app. Furthermore, the intervention group participants were randomized to 4 teams and competed in 6 weekly (Monday to Friday) real-time challenges. A valid day was defined as having 1500 steps or more per day. The outcomes were as follows: (1) adherence to wearing the Fitbit (ie, number of days in which all participants in each group were classified as valid users aggregated across the entire study period), (2) mean proportion of valid participants over the study period, and (3) the effects of the intervention on step count and floor count determined using multiple linear regression models and generalized estimating equations (GEEs) for longitudinal data analysis.

Results

In phase 1, 32 of 40 eligible participants provided valid step count data, whereas all 40 participants provided valid floor count data. The Fitbit trackers demonstrated high correlations (step count: Spearman ρ=0.89; P<.001; floor count: Spearman ρ=0.98; P<.001). The trackers overestimated step count (median absolute error: 17%) but accurately estimated floor count. In phase 2, 20 participants each were allocated to an intervention or control group. Overall, 24 participants provided complete covariates and valid PA data for analyses. Multiple linear regressions revealed that the average daily steps was 15.9% higher for the intervention group (95% CI −8.9 to 47.6; P=.21) during the final two intervention weeks; the average daily floors climbed was 39.4% higher (95% CI 2.4 to 89.7; P=.04). GEE results indicated no significant interaction effects between groups and the intervention week for weekly step count, whereas a significant effect (P<.001) was observed for weekly floor count.

Conclusions

The consumer wearables used in this study provided acceptable validity in estimating stepping and stair climbing activities, and the mHealth-based team challenge interventions were feasible. Compared with the control group, the participants in the intervention group climbed more stairs, so this can be introduced as an additional PA promotion target in the context of mHealth strategies. Methodologically rigorous studies are warranted to further strengthen this study’s findings.

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.

Effects of Bluetooth-Enabled Desk Ellipticals on Office Work Performance: Rationale, Design, and Protocol for a Randomized Trial With Overweight and Obese Adults

Background

Workplaces that provide opportunities for physical activity without requiring extra time for activity could help counteract the obesity epidemic. Desk ellipticals can contribute to activity-supportive workplace environments; however, the feasibility of engaging employees in pedaling ellipticals during simultaneous office work has not been well evaluated.

Objective

We aim to present the rationale and methods from an ongoing randomized trial with overweight and obese employees that will evaluate (1) the effects of pedaling a compact desk elliptical on work performance and (2) the influence of different incentive types and schedules on desk pedaling quantity.

Methods

Overweight and obese medical center employees are being recruited in dyads for a 2 (gift card type: healthier food vs Amazon) by 3 (gift card schedule: immediate incentive contingent on individual pedaling quantity; immediate incentive partially contingent on dyads’ joint pedaling quantity; and delayed noncontingent pedaling incentive) cluster randomized within-subjects factorial trial. All participants receive a Bluetooth-enabled desk elliptical for 4 weeks and access to a mobile app that provides real-time pedaling feedback. The primary aims are to assess (1) change in employee work performance from pre- to postelliptical installation via employee and supervisor ratings and (2) effects of gift card type and schedule on quantity of objectively measured desk pedaling completed.

Results

Data collection is ongoing. We expect to complete main outcome analyses in 2020.

Conclusions

This trial represents one of the earliest attempts to assess the effects of desk pedaling and pedaling-incentive types in real-world offices. It could help bridge the research-to-practice gap by providing evidence on whether desk pedaling can be sustained without compromising work performance.

International Registered Report Identifier (IRRID)

DERR1-10.2196/16275

Text Message Interventions for Physical Activity: A Systematic Review and Meta-Analysis

CONTEXT

Despite clear health benefits, many individuals fail to achieve the recommended levels of physical activity. Text message interventions to promote physical activity hold promise owing to the ubiquity of cell phones and the low expense of text message delivery.

EVIDENCE ACQUISITION

A systematic review and meta-analysis were performed to examine the impact of text message interventions on physical activity. Searches of PubMed, PsycINFO, Scopus, Cochrane, and ClinicalTrials.gov databases from inception to December 2017 were performed to identify studies investigating one-way text message interventionss to promote physical activity. A subset of RCTs, including an objective (accelerometer-based) physical activity outcome, were included in random-effects meta-analyses in 2018.

EVIDENCE SYNTHESIS

The systematic search revealed 944 articles. Of these, 59 were included in the systematic review (12 1-arm trials and 47 controlled trials; n=8,742; mean age, 42.2 years; 56.2% female). In meta-analyses of 13 studies (n=1,346), text message interventionss led to significantly greater objectively measured postintervention steps/day (Cohen's d=0.38, 95% CI=0.19, 0.58, n=10 studies). Analysis of postintervention moderate-to-vigorous physical activity found a similar but not statistically significant effect (Cohen's d=0.31, 95% CI= -0.01, 0.63, n=5 studies). Interventions with more components, tailored content, and interventions in medical populations led to nonsignificantly larger effect sizes compared with text message interventions without these features.

CONCLUSIONS

Text message interventions lead to higher objectively measured postintervention physical activity compared with control groups. More extensive, well-controlled studies are needed to examine this relationship further and identify characteristics of effective text message interventions.

Evaluating the Feasibility, Acceptability, and Effects of Deposit Contracts With and Without Daily Feedback to Promote Physical Activity

Background: Despite interest in financial incentive programs, evidence regarding the feasibility, acceptability, and effectiveness of deposit contracts (ie, use of participants’ own money as a financial reward) for increasing physical activity (PA) is limited. Furthermore, evidence regarding the use of feedback within incentive programs is limited. Purpose: To evaluate: (1) the feasibility and acceptability of deposit contracts for increasing objectively measured PA and (2) the effects of deposit contracts with or without ongoing feedback on PA. Methods: Participants (n = 24) were exposed to 3 conditions (1) self-monitoring, (2) incentive, and (3) incentive with feedback in an ABACABAC design, with the order of incentive conditions counterbalanced across participants. Results: Effect sizes suggest that individuals had a modest increase in PA during the incentive conditions compared with self-monitoring. Presentation order moderated results, such that individuals exposed to incentives with feedback first performed more poorly across both incentive conditions. In addition, individuals often cited the deposit contract as a reason for not enrolling, and those who did participate reported inadequate acceptability of the incentives and feedback. Conclusions: Results suggest that while deposit contracts may engender modest increases in PA, this type of incentive may not be feasible or acceptable for promoting PA.

Comparison of On-Site Versus Remote Mobile Device Support in the Framingham Heart Study Using the Health eHeart Study for Digital Follow-up: Randomized Pilot Study Set Within an Observational Study Design

Background

New electronic cohort (e-Cohort) study designs provide resource-effective methods for collecting participant data. It is unclear if implementing an e-Cohort study without direct, in-person participant contact can achieve successful participation rates.

Objective

The objective of this study was to compare 2 distinct enrollment methods for setting up mobile health (mHealth) devices and to assess the ongoing adherence to device use in an e-Cohort pilot study.

Methods

We coenrolled participants from the Framingham Heart Study (FHS) into the FHS–Health eHeart (HeH) pilot study, a digital cohort with infrastructure for collecting mHealth data. FHS participants who had an email address and smartphone were randomized to our FHS-HeH pilot study into 1 of 2 study arms: remote versus on-site support. We oversampled older adults (age ≥65 years), with a target of enrolling 20% of our sample as older adults. In the remote arm, participants received an email containing a link to enrollment website and, upon enrollment, were sent 4 smartphone-connectable sensor devices. Participants in the on-site arm were invited to visit an in-person FHS facility and were provided in-person support for enrollment and connecting the devices. Device data were tracked for at least 5 months.

Results

Compared with the individuals who declined, individuals who consented to our pilot study (on-site, n=101; remote, n=93) were more likely to be women, highly educated, and younger. In the on-site arm, the connection and initial use of devices was ≥20% higher than the remote arm (mean percent difference was 25% [95% CI 17-35] for activity monitor, 22% [95% CI 12-32] for blood pressure cuff, 20% [95% CI 10-30] for scale, and 43% [95% CI 30-55] for electrocardiogram), with device connection rates in the on-site arm of 99%, 95%, 95%, and 84%. Once connected, continued device use over the 5-month study period was similar between the study arms.

Conclusions

Our pilot study demonstrated that the deployment of mobile devices among middle-aged and older adults in the context of an on-site clinic visit was associated with higher initial rates of device use as compared with offering only remote support. Once connected, the device use was similar in both groups.

Applying machine learning to predict future adherence to physical activity programs

BACKGROUND

Identifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.

METHODS

We use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.

RESULTS

we had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.

CONCLUSIONS

DiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01280812 Registered on January 21, 2011.

Rationale, design, and baseline characteristics of WalkIT Arizona: A factorial randomized trial testing adaptive goals and financial reinforcement to increase walking across higher and lower walkable neighborhoods

Little change over the decades has been seen in adults meeting moderate-to-vigorous physical activity (MVPA) guidelines. Numerous individual-level interventions to increase MVPA have been designed, mostly static interventions without consideration for neighborhood context. Recent technologies make adaptive interventions for MVPA feasible. Unlike static interventions, adaptive intervention components (e.g., goal setting) adjust frequently to an individual's performance. Such technologies also allow for more precise delivery of "smaller, sooner incentives" that may result in greater MVPA than "larger, later incentives". Combined, these factors could enhance MVPA adoption. Additionally, a central tenet of ecological models is that MVPA is sensitive to neighborhood environment design; lower-walkable neighborhoods constrain MVPA adoption and maintenance, limiting the effects of individual-level interventions. Higher-walkable neighborhoods are hypothesized to enhance MVPA interventions. Few prospective studies have addressed this premise. This report describes the rationale, design, intervention components, and baseline sample of a study testing individual-level adaptive goal-setting and incentive interventions for MVPA adoption and maintenance over 2 years among adults from neighborhoods known to vary in neighborhood walkability. We scaled these evidenced-based interventions and tested them against static-goal-setting and delayed-incentive comparisons in a 2 × 2 factorial randomized trial to increase MVPA among 512 healthy insufficiently-active adults. Participants (64.3% female, M age = 45.5 ± 9.1 years, M BMI = 33.9 ± 7.3 kg/m2, 18.8% Hispanic, 84.0% White) were recruited from May 2016 to May 2018 from block groups ranked on GIS-measured neighborhood walkability and socioeconomic status (SES) and classified into four neighborhood types: "high walkable/high SES," "high walkable/low SES," "low walkable/high SES," and "low walkable/low SES." Results from this ongoing study will provide evidence for some of the central research questions of ecological models.

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.

Behavioral trials in the Arab Gulf States: A scoping review

The leading chronic conditions in Arab Gulf States are modifiable by lifestyle change. Available evidence suggests a paucity of experimental studies on these conditions. We aimed to review the published randomized controlled trials on behavioral modification in the Arab Gulf States. Three databases (PubMed, Embase, and Cochrane) were searched for related keywords, and the records were screened for eligible studies; data were abstracted on trial characteristics (e.g. publication year, study population, primary outcome, intervention, control, follow-up, and outcome results), and a quality assessment of the trials was made. A total of 16 trials were eligible; 50% did not provide sample size calculation, and 31% did not designate a primary outcome. A majority of the trials did not explain randomization or allocation concealment (50%), did not blind outcome assessors (69%) or adopt an intention-to-treat analysis (56%); and 82% of trials found a significant intervention effect. More behavioral trials should be conducted overall and specifically for conditions for which there are no trials (e.g. respiratory tract infection and road injury).

Randomised controlled trial of real-time feedback and brief coaching to reduce indoor smoking

BACKGROUND

Previous secondhand smoke (SHS) reduction interventions have provided only delayed feedback on reported smoking behaviour, such as coaching, or presenting results from child cotinine assays or air particle counters.

DESIGN

This SHS reduction trial assigned families at random to brief coaching and continuous real-time feedback (intervention) or measurement-only (control) groups.

PARTICIPANTS

We enrolled 298 families with a resident tobacco smoker and a child under age 14.

INTERVENTION

We installed air particle monitors in all homes. For the intervention homes, immediate light and sound feedback was contingent on elevated indoor particle levels, and up to four coaching sessions used prompts and praise contingent on smoking outdoors. Mean intervention duration was 64 days.

MEASURES

The primary outcome was 'particle events' (PEs) which were patterns of air particle concentrations indicative of the occurrence of particle-generating behaviours such as smoking cigarettes or burning candles. Other measures included indoor air nicotine concentrations and participant reports of particle-generating behaviour.

RESULTS

PEs were significantly correlated with air nicotine levels (r=0.60) and reported indoor cigarette smoking (r=0.51). Interrupted time-series analyses showed an immediate intervention effect, with reduced PEs the day following intervention initiation. The trajectory of daily PEs over the intervention period declined significantly faster in intervention homes than in control homes. Pretest to post-test, air nicotine levels, cigarette smoking and e-cigarette use decreased more in intervention homes than in control homes.

CONCLUSIONS

Results suggest that real-time particle feedback and coaching contingencies reduced PEs generated by cigarette smoking and other sources.

TRIAL REGISTRATION NUMBER

NCT01634334; Post-results.

Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial

Background

Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring.

Objective

The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data.

Methods

In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up.

Results

Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants.

Conclusions

This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost.

Trial Registration

ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d)

International Registered Report Identifier (IRRID)

DERR1-10.2196/11540