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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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          Abstract

          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.

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          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 Network Open
                JAMA Netw Open
                American Medical Association (AMA)
                2574-3805
                May 03 2019
                May 24 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
                Article
                10.1001/jamanetworkopen.2019.4281
                8fc1d9f6-c50a-4df7-96d3-105e6b8946cc
                © 2019
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