Effectiveness of an Activity Tracker- and Internet-Based Adaptive Walking Program for Adults: A Randomized Controlled Trial

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Abstract

Background

The benefits of physical activity are well documented, but scalable programs to promote activity are needed. Interventions that assign tailored and dynamically adjusting goals could effect significant increases in physical activity but have not yet been implemented at scale.

Objective

Our aim was to examine the effectiveness of an open access, Internet-based walking program that assigns daily step goals tailored to each participant.

Methods

A two-arm, pragmatic randomized controlled trial compared the intervention to no treatment. Participants were recruited from a workplace setting and randomized to a no-treatment control (n=133) or to treatment (n=132). Treatment participants received a free wireless activity tracker and enrolled in the walking program, Walkadoo. Assessments were fully automated: activity tracker recorded primary outcomes (steps) without intervention by the participant or investigators. The two arms were compared on change in steps per day from baseline to follow-up (after 6 weeks of treatment) using a two-tailed independent samples t test.

Results

Participants (N=265) were 66.0% (175/265) female with an average age of 39.9 years. Over half of the participants (142/265, 53.6%) were sedentary (<5000 steps/day) and 44.9% (119/265) were low to somewhat active (5000-9999 steps/day). The intervention group significantly increased their steps by 970 steps/day over control ( P<.001), with treatment effects observed in sedentary ( P=.04) and low-to-somewhat active ( P=.004) participants alike.

Conclusions

The program is effective in increasing daily steps. Participants benefited from the program regardless of their initial activity level. A tailored, adaptive approach using wireless activity trackers is realistically implementable and scalable.

Trial Registration

Clinicaltrials.gov NCT02229409, https://clinicaltrials.gov/ct2/show/NCT02229409 (Archived by WebCite at http://www.webcitation.org/6eiWCvBYe)

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Most cited references 13

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Meta-analysis of workplace physical activity interventions.

Vicki S Conn, Adam Hafdahl, Pamela Cooper … (2009)

Most adults do not achieve adequate physical activity levels. Despite the potential benefits of worksite health promotion, no previous comprehensive meta-analysis has summarized health and physical activity behavior outcomes from such programs. This comprehensive meta-analysis integrated the extant wide range of worksite physical activity intervention research. Extensive searching located published and unpublished intervention studies reported from 1969 through 2007. Results were coded from primary studies. Random-effects meta-analytic procedures, including moderator analyses, were completed in 2008. Effects on most variables were substantially heterogeneous because diverse studies were included. Standardized mean difference (d) effect sizes were synthesized across approximately 38,231 subjects. Significantly positive effects were observed for physical activity behavior (0.21); fitness (0.57); lipids (0.13); anthropometric measures (0.08); work attendance (0.19); and job stress (0.33). The significant effect size for diabetes risk (0.98) is less robust given small sample sizes. The mean effect size for fitness corresponds to a difference between treatment minus control subjects' means on VO2max of 3.5 mL/kg/min; for lipids, -0.2 on the ratio of total cholesterol to high-density lipoprotein; and for diabetes risk, -12.6 mg/dL on fasting glucose. These findings document that some workplace physical activity interventions can improve both health and important worksite outcomes. Effects were variable for most outcomes, reflecting the diversity of primary studies. Future primary research should compare interventions to confirm causal relationships and further explore heterogeneity.

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An Adaptive Physical Activity Intervention for Overweight Adults: A Randomized Controlled Trial

Marc Adams, James Sallis, Gregory Norman … (2013)

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

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Workplace physical activity interventions: a systematic review.

Quyen G To, Ted T L Chen, Costan Magnussen … (2024)

To assess the effectiveness of workplace interventions in improving physical activity.

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Author and article information

Contributors

Nathan K Cobb:

Division of Pulmonary and Critical Care MedicineGeorgetown University Medical CenterMain Building, 4th Floor3800 Reservoir Road, NWWashington, DC, 20007United States1 202 444 88301 202 444 0032nkc4@georgetown.edu

ORCID: http://orcid.org/0000-0003-4210-226X

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J. Med. Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications Inc. (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: February 2016

Publication date (Electronic): 09 February 2016

Volume: 18

Issue: 2

Electronic Location Identifier: e34

Affiliations

[1] ¹MeYou Health LLC Boston, MAUnited States

[2] ²Division of General Internal Medicine The Johns Hopkins University School of Medicine Baltimore, MDUnited States

[3] ³Welch Center for Prevention, Epidemiology, and Clinical Research The Johns Hopkins University Baltimore, MDUnited States

[4] ⁴Department of Kinesiology Towson University Towson, MDUnited States

[5] ⁵Division of Pulmonary and Critical Care Medicine Georgetown University Medical Center Washington, DCUnited States

Author notes

Corresponding Author: Nathan K Cobb nkc4@ 123456georgetown.edu

Author information

Josée Poirier http://orcid.org/0000-0002-1302-869X

Wendy L Bennett http://orcid.org/0000-0001-9828-0706

Gerald J Jerome http://orcid.org/0000-0003-0612-2626

Nina G Shah http://orcid.org/0000-0003-4282-6647

Mariana Lazo http://orcid.org/0000-0002-3178-6734

Hsin-Chieh Yeh http://orcid.org/0000-0002-5738-0652

Jeanne M Clark http://orcid.org/0000-0003-1194-0092

Nathan K Cobb http://orcid.org/0000-0003-4210-226X

Article

Publisher ID: v18i2e34

DOI: 10.2196/jmir.5295

PMC ID: 4764789

PubMed ID: 26860434

SO-VID: 113180f6-2a49-4447-94aa-b25040621823

Copyright © ©Josée Poirier, Wendy L Bennett, Gerald J Jerome, Nina G Shah, Mariana Lazo, Hsin-Chieh Yeh, Jeanne M Clark, Nathan K Cobb. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 09.02.2016.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 28 October 2015

Date revision requested : 20 November 2015

Date revision received : 22 December 2015

Date accepted : 17 January 2016

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