Dataset Information

An adaptive physical activity intervention for overweight adults: a randomized controlled trial.

ABSTRACT:

Background

Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible.

Objective

To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. The adaptive intervention was hypothesized to result in greater increases in steps per day than the static intervention.

Methods

Participants (N = 20) were randomized to one of two 6-month treatments: 1) static intervention (SI) or 2) adaptive intervention (AI). Inactive overweight adults (85% women, M = 36.9 ± 9.2 years, 35% non-white) in both groups received a pedometer, email and text message communication, brief health information, and biweekly motivational prompts. The AI group received daily step goals that adjusted up and down based on the percentile-rank algorithm and micro-incentives for goal attainment. This algorithm adjusted goals based on a moving window; an approach that responded to each individual's performance and ensured goals were always challenging but within participants' abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer's data.

Results

A random-effects repeated-measures model accounted for 180 repeated measures and autocorrelation. After adjusting for covariates, the treatment phase showed greater steps/day relative to the baseline phase (p<.001) and a group by study phase interaction was observed (p ?.017). The SI group increased by 1,598 steps/day on average between baseline and treatment while the AI group increased by 2,728 steps/day on average between baseline and treatment; a significant between-group difference of 1,130 steps/day (Cohen's d = .74).

Conclusions

The adaptive intervention outperformed the static intervention for increasing PA. The adaptive goal and feedback algorithm is a "behavior change technology" that could be incorporated into mHealth technologies and scaled to reach large populations.

Trial registration

ClinicalTrials.gov NCT01793064.

SUBMITTER: Adams MA

PROVIDER: S-EPMC3857300 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

An adaptive physical activity intervention for overweight adults: a randomized controlled trial.

Adams Marc A MA Sallis James F JF Norman Gregory J GJ Hovell Melbourne F MF Hekler Eric B EB Perata Elyse E

PloS one 20131209 12

<h4>Background</h4>Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible.<h4>Objective</h4>To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. T ...[more]

PMID: 24349392

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Project description:Addressing the obesity epidemic requires the development of effective, scalable interventions. Pedometers and Web-based programs are beneficial in increasing activity levels but might be enhanced by the addition of nonhuman coaching.We hypothesized that a virtual coach would increase activity levels, via step count, in overweight or obese individuals beyond the effect observed using a pedometer and website alone.We recruited 70 participants with a body mass index (BMI) between 25 and 35 kg/m(2) from the Boston metropolitan area. Participants were assigned to one of two study arms and asked to wear a pedometer and access a website to view step counts. Intervention participants also met with a virtual coach, an automated, animated computer agent that ran on their home computers, set goals, and provided personalized feedback. Data were collected and analyzed in 2008. The primary outcome measure was change in activity level (percentage change in step count) over the 12-week study, split into four 3-week time periods. Major secondary outcomes were change in BMI and participants' satisfaction.The mean age of participants was 42 years; the majority of participants were female (59/70, 84%), white (53/70, 76%), and college educated (68/70, 97%). Of the initial 70 participants, 62 completed the study. Step counts were maintained in intervention participants but declined in controls. The percentage change in step count between those in the intervention and control arms, from the start to the end, did not reach the threshold for significance (2.9% vs -12.8% respectively, P = .07). However, repeated measures analysis showed a significant difference when comparing percentage changes in step counts between control and intervention participants over all time points (analysis of variance, P = .02). There were no significant changes in secondary outcome measures.The virtual coach was beneficial in maintaining activity level. The long-term benefits and additional applications of this technology warrant further study.ClinicalTrials.gov NCT00792207; http://clinicaltrials.gov/ct2/show/NCT00792207 (Archived by WebCite at http://www.webcitation.org/63sm9mXUD).

Project description:BACKGROUND:Insufficient physical activity in the adult population is a global pandemic. Fun for Wellness (FFW) is a self-efficacy theory- and Web-based behavioral intervention developed to promote growth in well-being and physical activity by providing capability-enhancing opportunities to participants. OBJECTIVE:This study aimed to evaluate the effectiveness of FFW to increase physical activity in adults with obesity in the United States in a relatively uncontrolled setting. METHODS:This was a large-scale, prospective, double-blind, parallel-group randomized controlled trial. Participants were recruited through an online panel recruitment company. Adults with overweight were also eligible to participate, consistent with many physical activity-promoting interventions for adults with obesity. Also consistent with much of the relevant literature the intended population as simply adults with obesity. Eligible participants were randomly assigned to the intervention (ie, FFW) or the usual care (ie, UC) group via software code that was written to accomplish equal allocations to the FFW and UC groups. Data collection was Web based, fully automated, and occurred at three time points: baseline, 30 days after baseline (T2), and 60 days after baseline (T3). Participants (N=461) who were assigned to the FFW group (nFFW=219) were provided with 30 days of 24-hour access to the Web-based intervention. A path model was fit to the data consistent with the FFW conceptual model for the promotion of physical activity. RESULTS:There was evidence for a positive direct effect of FFW on transport-related physical activity self-efficacy (beta=.22, P=.02; d=0.23), domestic-related physical activity self-efficacy (beta=.22, P=.03; d=0.22), and self-efficacy to regulate physical activity (beta=.16, P=.01; d=0.25) at T2. Furthermore, there was evidence for a positive indirect effect of FFW on physical activity at T3 through self-efficacy to regulate physical activity at T2 (beta=.42, 95% CI 0.06 to 1.14). Finally, there was evidence for a null direct effect of FFW on physical activity (beta=1.04, P=.47; d=0.07) at T3. CONCLUSIONS:This study provides some initial evidence for both the effectiveness (eg, a positive indirect effect of FFW on physical activity through self-efficacy to regulate physical activity) and the ineffectiveness (eg, a null direct effect of FFW on physical activity) of the FFW Web-based behavioral intervention to increase physical activity in adults with obesity in the United States. More broadly, FFW is a scalable Web-based behavioral intervention that may effectively, although indirectly, promote physical activity in adults with obesity and therefore may be useful in responding to the global pandemic of insufficient physical activity in this at-risk population. Self-efficacy to regulate physical activity appears to be a mechanism by which FFW may indirectly promote physical activity in adults with obesity. TRIAL REGISTRATION:ClinicalTrials.gov, identifier: NCT03194854; https://clinicaltrials.gov/ct2/show/NCT03194854.

Project description:BackgroundCardiac rehabilitation (CR) is an exercise-based program prescribed after cardiac events associated with improved physical, mental, and social functioning; however, many patients return to a sedentary lifestyle leading to deteriorating functional capacity after discharge from CR. Physical activity (PA) is critical to avoid recurrence of cardiac events and mortality and maintain functional capacity. Leveraging mobile health (mHealth) strategies to increase adherence to PA is a promising approach. Based on the social cognitive theory, we sought to determine whether mHealth strategies (Movn mobile app for self-monitoring, supportive push-through messages, and wearable activity tracker) would improve PA and functional capacity over 2 months.ObjectiveThe objectives of this pilot randomized controlled trial were to examine preliminary effects of an mHealth intervention on group differences in PA and functional capacity and group differences in depression and self-efficacy to maintain exercise after CR.MethodsDuring the final week of outpatient CR, patients were randomized 1:1 to the intervention group or usual care. The intervention group downloaded the Movn mobile app, received supportive push-through messages on motivation and educational messages related to cardiovascular disease (CVD) management 3 times per week, and wore a Charge 2 (Fitbit Inc) activity tracker to track step counts. Participants in the usual care group wore a pedometer and recorded their daily steps in a diary. Data from the 6-minute walk test (6MWT) and self-reported questionnaires were collected at baseline and 2 months.ResultsWe recruited 60 patients from 2 CR sites at a community hospital in Northern California. The mean age was 68.0 (SD 9.3) years, and 23% (14/60) were female; retention rate was 85% (51/60). Our results from 51 patients who completed follow-up showed the intervention group had a statistically significant higher mean daily step count compared with the control (8860 vs 6633; P=.02). There was no difference between groups for the 6MWT, depression, or self-efficacy to maintain exercise.ConclusionsThis intervention addresses a major public health initiative to examine the potential for mobile health strategies to promote PA in patients with CVD. Our technology-based pilot mHealth intervention provides promising results on a pragmatic and contemporary approach to promote PA by increasing daily step counts after completing CR.Trial registrationClinicalTrials.gov NCT03446313; https://clinicaltrials.gov/ct2/show/NCT03446313.

Project description:BackgroundOnline social networks, such as Facebook™, have extensive reach, and they use technology that could enhance social support, an established determinant of physical activity. This combination of reach and functionality makes online social networks a promising intervention platform for increasing physical activity.PurposeTo test the efficacy of a physical activity intervention that combined education, physical activity monitoring, and online social networking to increase social support for physical activity compared to an education-only control.DesignRCT. Students (n=134) were randomized to two groups: education-only controls receiving access to a physical activity-focused website (n=67) and intervention participants receiving access to the same website with physical activity self-monitoring and enrollment in a Facebook group (n=67). Recruitment and data collection occurred in 2010 and 2011; data analyses were performed in 2011.Setting/participantsFemale undergraduate students at a large southeastern public university.InterventionIntervention participants were encouraged through e-mails, website instructions, and moderator communications to solicit and provide social support related to increasing physical activity through a physical activity-themed Facebook group. Participants received access to a dedicated website with educational materials and a physical activity self-monitoring tool.Main outcome measuresThe primary outcome was perceived social support for physical activity; secondary outcomes included self-reported physical activity.ResultsParticipants experienced increases in social support and physical activity over time but there were no differences in perceived social support or physical activity between groups over time. Facebook participants posted 259 times to the group. Two thirds (66%) of intervention participants completing a post-study survey indicated that they would recommend the program to friends.ConclusionsUse of an online social networking group plus self-monitoring did not produce greater perceptions of social support or physical activity as compared to education-only controls. Given their promising features and potential reach, efforts to further understand how online social networks can be used in health promotion should be pursued.Trial registrationThis study is registered at clinicaltrials.govNCT01421758.

Project description:BackgroundEmerging interventions that rely on and harness variability in behavior to adapt to individual performance over time may outperform interventions that prescribe static goals (e.g., 10,000 steps/day). The purpose of this factorial trial was to compare adaptive vs. static goal setting and immediate vs. delayed, non-contingent financial rewards for increasing free-living physical activity (PA).MethodsA 4-month 2 × 2 factorial randomized controlled trial tested main effects for goal setting (adaptive vs. static goals) and rewards (immediate vs. delayed) and interactions between factors to increase steps/day as measured by a Fitbit Zip. Moderate-to-vigorous PA (MVPA) minutes/day was examined as a secondary outcome.ResultsParticipants (N = 96) were mainly female (77%), aged 41 ± 9.5 years, and all were insufficiently active and overweight/obese (mean BMI = 34.1 ± 6.2). Participants across all groups increased by 2389 steps/day on average from baseline to intervention phase (p < .001). Participants receiving static goals showed a stronger increase in steps per day from baseline phase to intervention phase (2630 steps/day) than those receiving adaptive goals (2149 steps/day; difference = 482 steps/day, p = .095). Participants receiving immediate rewards showed stronger improvement (2762 step/day increase) from baseline to intervention phase than those receiving delayed rewards (2016 steps/day increase; difference = 746 steps/day, p = .009). However, the adaptive goals group showed a slower decrease in steps/day from the beginning of the intervention phase to the end of the intervention phase (i.e. less than half the rate) compared to the static goals group (-7.7 steps vs. -18.3 steps each day; difference = 10.7 steps/day, p < .001) resulting in better improvements for the adaptive goals group by study end. Rate of change over the intervention phase did not differ between reward groups. Significant goal phase x goal setting x reward interactions were observed.ConclusionsAdaptive goals outperformed static goals (i.e., 10,000 steps) over a 4-month period. Small immediate rewards outperformed larger, delayed rewards. Adaptive goals with either immediate or delayed rewards should be preferred for promoting PA.Trial registrationClinicalTrials.gov ID: NCT02053259 registered prospectively on January 31, 2014.

Project description:BACKGROUND:The high prevalence of physical inactivity worldwide calls for innovative and more effective ways to promote physical activity (PA). There are limited objective data on the effectiveness of Web-based personalized feedback on increasing PA in adults. OBJECTIVE:It is hypothesized that providing personalized advice based on PA measured objectively alongside diet, phenotype, or genotype information would lead to larger and more sustained changes in PA, compared with nonpersonalized advice. METHODS:A total of 1607 adults in seven European countries were randomized to either a control group (nonpersonalized advice, Level 0, L0) or to one of three personalized groups receiving personalized advice via the Internet based on current PA plus diet (Level 1, L1), PA plus diet and phenotype (Level 2, L2), or PA plus diet, phenotype, and genotype (Level 3, L3). PA was measured for 6 months using triaxial accelerometers, and self-reported using the Baecke questionnaire. Outcomes were objective and self-reported PA after 3 and 6 months. RESULTS:While 1270 participants (85.81% of 1480 actual starters) completed the 6-month trial, 1233 (83.31%) self-reported PA at both baseline and month 6, but only 730 (49.32%) had sufficient objective PA data at both time points. For the total cohort after 6 months, a greater improvement in self-reported total PA (P=.02) and PA during leisure (nonsport) (P=.03) was observed in personalized groups compared with the control group. For individuals advised to increase PA, we also observed greater improvements in those two self-reported indices (P=.006 and P=.008, respectively) with increased personalization of the advice (L2 and L3 vs L1). However, there were no significant differences in accelerometer results between personalized and control groups, and no significant effect of adding phenotypic or genotypic information to the tailored feedback at month 3 or 6. After 6 months, there were small but significant improvements in the objectively measured physical activity level (P<.05), moderate PA (P<.01), and sedentary time (P<.001) for individuals advised to increase PA, but these changes were similar across all groups. CONCLUSIONS:Different levels of personalization produced similar small changes in objective PA. We found no evidence that personalized advice is more effective than conventional "one size fits all" guidelines to promote changes in PA in our Web-based intervention when PA was measured objectively. Based on self-reports, PA increased to a greater extent with more personalized advice. Thus, it is crucial to measure PA objectively in any PA intervention study. TRIAL REGISTRATION:ClinicalTrials.gov NCT01530139; http://clinicaltrials.gov/show/NCT01530139 (Archived by WebCite at: http://www.webcitation.org/6XII1QwHz).

Dataset Information

An adaptive physical activity intervention for overweight adults: a randomized controlled trial.

Background

Objective

Methods

Results

Conclusions

Trial registration

Publications

An adaptive physical activity intervention for overweight adults: a randomized controlled trial.

Similar Datasets

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