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      The use of mobile devices for physical activity tracking in older adults’ everyday life

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          Abstract

          Objective

          The tracking of one’s own physical activity with mobile devices is a way of monitoring and motivating oneself to remain healthy. Older adults’ general use of mobile devices for physical activity tracking has not yet been examined systematically. The study aimed to describe the use of physical activity trackers, smartwatches and smartphones, or tablets for tracking physical activity and to examine the reasons for the use of these technologies.

          Methods

          Participants aged ≥50 years ( N = 1013) living in Switzerland were interviewed in a telephone survey. To address the research questions, we calculated descriptive frequency distributions, tested for differences between groups, and performed logistic regression analyses.

          Results

          Descriptive and multivariate analyses showed that (a) 20.5% of participants used mobile devices for physical activity tracking; (b) men, younger individuals, those with a strong interest in new technology, and those who frequently exercised had a higher likelihood of using mobile devices for physical activity tracking; and (c) participants more often agreed with reasons for use relating to tracking physical activity and motivating oneself to remain healthy than they did with reasons relating to social factors.

          Conclusions

          The study presented representative data about the actual use of mobile tracking technology in persons over 50 years of age. Today, mainly active and younger elderly (mostly men) with a high interest in technology are using tracking technologies. Results indicate a need for further studies on motivational and usability aspects regarding the use of mobile health tracking devices by older adults.

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          Most cited references43

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          Barriers and motivations to exercise in older adults.

          Although exercise is an established component in the management of many chronic diseases associated with aging, activity levels tend to progressively decline with increasing age. Given the growing proportion of older adults, these suboptimal levels of physical activity represent an increasing public health problem. The predicators of adherence elucidated in younger adults are unreliable in elderly populations. Age-specific barriers and motivators unique to this cohort are relevant and must be acknowledged. The identification of reliable predictors of exercise adherence will allow healthcare providers to effectively intervene and change patterns of physical activity in sedentary elderly. In particular, because older patients respect their physician's advice and have regular contact with their family doctor, physicians can play a key and pivotal role in the initiation and maintenance of exercise behavior among the older population.
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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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              The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.

              A key contemporary trend emerging in big data science is the quantified self (QS)-individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual's continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity-barriers to widespread adoption and a critique regarding scientific soundness-but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality.
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                Author and article information

                Journal
                Digit Health
                Digit Health
                DHJ
                spdhj
                Digital health
                SAGE Publications (Sage UK: London, England )
                2055-2076
                09 November 2017
                Jan-Dec 2017
                : 3
                : 2055207617740088
                Affiliations
                [1 ]University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Switzerland
                [2 ]Center of Competence for Gerontology, University of Zurich, Switzerland
                [3 ]Doctoral Program GROW “Gerontological Research on Well-Being”, University of Cologne, Germany
                [4 ]Working Area Research Methodology, University of Cologne, Germany
                Author notes
                [*]Alexander Seifert, University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Andreasstrasse 15, 8050 Zurich, Switzerland. Email: alexander.seifert@ 123456uzh.ch
                Author information
                http://orcid.org/0000-0003-3124-4588
                http://orcid.org/0000-0002-2067-7778
                Article
                10.1177_2055207617740088
                10.1177/2055207617740088
                6001246
                29942617
                89d61b28-5b66-4278-8562-a57f863169ef
                © The Author(s) 2017

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License ( http://www.creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 24 June 2017
                : 6 October 2017
                Categories
                Original Research
                Custom metadata
                January-December 2017

                activity tracker,smartwatch,smartphone,health monitoring,elderly

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