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      Short- and Long-term Effects of a Mobile Phone App in Conjunction With Brief In-Person Counseling on Physical Activity Among Physically Inactive Women : The mPED Randomized Clinical Trial

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      , PhD, RN 1 , , , PhD 2 , , MS 3 , , PhD 3
      JAMA Network Open
      American Medical Association

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          Key Points

          Question

          Does use of a mobile phone–based physical activity education application (app) in conjunction with brief in-person counseling result in an increase of accelerometer-measured physical activity for 3 months and maintaining activity for an additional 6 months?

          Findings

          In this randomized clinical trial of 210 community-dwelling physically inactive women, the intervention achieved a statistically and clinically significant increase in total steps and time spent performing moderate to vigorous physical activity compared with the control group in the first 3 months. However, the group who continued use of the app, as compared with the group who discontinued app use, experienced no statistically significant effect on maintaining the increased activity in the following 6 months.

          Meaning

          The combination of a mobile phone app and brief in-person counseling increased objectively measured physical activity over 3 months, but use of the app for an additional 6 months did not help to maintain increased activity.

          Abstract

          This randomized clinical trial evaluates the effectiveness of a mobile phone app combined with brief in-person counseling for increasing physical activity levels among inactive women.

          Abstract

          Importance

          Mobile phone applications (apps) and activity trackers allow researchers to remotely deliver an intervention and monitor physical activity but have not been rigorously evaluated for longer periods.

          Objective

          To determine whether a mobile phone–based physical activity education app, in conjunction with brief in-person counseling, increases and then maintains levels of physical activity.

          Design, Setting, and Participants

          In this parallel randomized clinical trial, community-dwelling physically inactive women recruited between May 2011 and April 2014 were randomized in equal proportions into the control (n = 69), regular (n = 71), and plus (n = 70) groups. Data were analyzed using intention to treat from September 16, 2016, through June 30, 2018.

          Interventions

          The regular and plus groups were instructed to use the app on their mobile phone and an accelerometer every day for 3 months and attend brief in-person counseling. During the 6-month maintenance period, the plus group continued to use the app and accelerometer, while the regular group stopped using the app but continued using the accelerometer. The control group used the accelerometer throughout.

          Main Outcomes and Measures

          The primary and secondary outcomes were daily accelerometer-measured total steps and time spent in moderate to vigorous physical activity (MVPA).

          Results

          The 210 participants had a mean (SD) age of 52.4 (11.0) years. At baseline, the mean (SD) daily total steps by accelerometer in the control, regular, and plus groups were 5384 (2920), 5063 (2526), and 5837 (3235), respectively. During the 3-month intervention period, daily steps and MVPA increased in the combined regular and plus groups compared with the control group (between-group differences, 2060 steps per day; 95% CI, 1296-2825 steps per day; P < .001 and 18.2 min/d MVPA; 95% CI, 10.9-25.4 min/d MVPA; P < .001). During the subsequent 6-month maintenance period, mean activity level remained higher in the combined plus and regular groups than among controls (between-group difference, 1360 steps per day; 95% CI, 694-2026 steps per day; P <. 001), but trends in total daily steps and MVPA were similar in the plus and regular groups.

          Conclusions and Relevance

          In this trial, the intervention groups substantially increased their physical activity. However, use of both the app and accelerometer for an additional 6 months after the initial 3-month intervention did not help to maintain increases in physical activity compared with continued use of the accelerometer alone.

          Trial Registration

          ClinicalTrials.gov identifier: NCT01280812

          Related collections

          Most cited references20

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          Apps to promote physical activity among adults: a review and content analysis

          Background In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear. Methods The study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play. Results On average, the reviewed apps included 5 behavior change techniques (range 2–8). Techniques such as self-monitoring, providing feedback on performance, and goal-setting were used most frequently, whereas some techniques such as motivational interviewing, stress management, relapse prevention, self-talk, role models, and prompted barrier identification were not. No differences in the number of behavior change techniques between free and paid apps, or between the app stores were found. Conclusions The present study demonstrated that apps promoting physical activity applied an average of 5 out of 23 possible behavior change techniques. This number was not different for paid and free apps or between app stores. The most frequently used behavior change techniques in apps were similar to those most frequently used in other types of physical activity promotion interventions.
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            Physical activity assessment methodology in the Five-City Project.

            Previous measures of physical activity for epidemiologic studies were considered inadequate to meet the needs of a community-based health education trial. Therefore, new methods of quantifying the physical activity habits of communities were developed which are practical for large health surveys, provide information on the distribution of activity habits in the population, can detect changes in activity over time, and can be compared with other epidemiologic studies of physical activity. Independent self-reports of vigorous activity (at least 6 metabolic equivalents (METs) ), moderate activity (3-5 METs), and total energy expenditure (kilocalories per day) are described, and the physical activity practices of samples of California cities are presented. Relationships between physical activity measures and age, education, occupation, ethnicity, marital status, and body mass index are analyzed, and the reliabilities of the three activity indices are reported. The new assessment procedure is contrasted with nine other measures of physical activity used in community surveys.
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              Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm.

              We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P < 0·001) and locomotive (r 0·961, P < 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cut-off classification models. This method may be useful for field investigations as well as for self-monitoring by general users.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                24 May 2019
                May 2019
                24 May 2019
                : 2
                : 5
                : e194281
                Affiliations
                [1 ]Department of Physiological Nursing, Institute for Health & Aging, School of Nursing, University of California, San Francisco
                [2 ]Stanford Prevention Research Center, Stanford University, Palo Alto, California
                [3 ]Department of Epidemiology & Biostatistics, University of California, San Francisco
                Author notes
                Article Information
                Accepted for Publication: April 3, 2019.
                Published: May 24, 2019. doi:10.1001/jamanetworkopen.2019.4281
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Fukuoka Y et al. JAMA Network Open.
                Corresponding Author: Yoshimi Fukuoka, PhD, RN, Department of Physiological Nursing, Institute for Health & Aging, University of California, San Francisco, 2 Koret Way, N631, San Francisco, CA 94143 ( yoshimi.fukuoka@ 123456ucsf.edu ).
                Author Contributions: Dr Fukuoka had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Fukuoka, Haskell, Vittinghoff.
                Acquisition, analysis, or interpretation of data: Fukuoka, Lin, Vittinghoff.
                Drafting of the manuscript: Fukuoka.
                Critical revision of the manuscript for important intellectual content: All authors.
                Statistical analysis: Fukuoka, Lin, Vittinghoff.
                Obtained funding: Fukuoka.
                Administrative, technical, or material support: Fukuoka.
                Supervision: Fukuoka.
                Conflict of Interest Disclosures: Dr Fukuoka reported grants from the National Institutes of Health and the American Heart Association during the conduct of the study. No other disclosures were reported.
                Funding/Support: This project was supported by grant R01HL104147 from the National Heart, Lung, and Blood Institute and by the American Heart Association.
                Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
                Additional Contributions: We thank the following Data Safety and Monitoring Board members for the mPED study: Charles McCulloch, PhD, Department of Epidemiology and Biostatistics, University of California, San Francisco; Kathryn Lee, RN, PhD, Department of Family Health Care Nursing, University of California, San Francisco, and Kristine A. Madsen, MD, MPH, School of Public Health, University of California, Berkeley. We thank all the study participants, research staff, and administrators who made this study possible. None of these individuals received compensation for their contributions.
                Data Sharing Statement: See Supplement 3.
                Article
                zoi190187
                10.1001/jamanetworkopen.2019.4281
                6632135
                31125101
                8fc1d9f6-c50a-4df7-96d3-105e6b8946cc
                Copyright 2019 Fukuoka Y et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 7 February 2019
                : 2 April 2019
                : 3 April 2019
                Categories
                Research
                Original Investigation
                Online Only
                Nutrition, Obesity, and Exercise

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