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      Stress Management Apps With Regard to Emotion-Focused Coping and Behavior Change Techniques: A Content Analysis

      , Dipl -Psych, Dr Phil , 1 , , BSc, MSc 1 , , Dipl -Inform, Dr -Ing 1

      (Reviewer), (Reviewer)

      JMIR mHealth and uHealth

      JMIR Publications

      mHealth, mobile health, relaxation

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          Abstract

          Background

          Chronic stress has been shown to be associated with disease. This link is not only direct but also indirect through harmful health behavior such as smoking or changing eating habits. The recent mHealth trend offers a new and promising approach to support the adoption and maintenance of appropriate stress management techniques. However, only few studies have dealt with the inclusion of evidence-based content within stress management apps for mobile phones.

          Objective

          The aim of this study was to evaluate stress management apps on the basis of a new taxonomy of effective emotion-focused stress management techniques and an established taxonomy of behavior change techniques.

          Methods

          Two trained and independent raters evaluated 62 free apps found in Google Play with regard to 26 behavior change and 15 emotion-focused stress management techniques in October 2015.

          Results

          The apps included an average of 4.3 behavior change techniques (SD 4.2) and 2.8 emotion-focused stress management techniques (SD 2.6). The behavior change technique score and stress management technique score were highly correlated (r=.82, P=.01).

          Conclusions

          The broad variation of different stress management strategies found in this sample of apps goes in line with those found in conventional stress management interventions and self-help literature. Moreover, this study provided a first step toward more detailed and standardized taxonomies, which can be used to investigate evidence-based content in stress management interventions and enable greater comparability between different intervention types.

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          Most cited references 50

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          Psychosocial work environment and mental health--a meta-analytic review.

          To clarify the associations between psychosocial work stressors and mental ill health, a meta-analysis of psychosocial work stressors and common mental disorders was undertaken using longitudinal studies identified through a systematic literature review. The review used a standardized search strategy and strict inclusion and quality criteria in seven databases in 1994-2005. Papers were identified from 24,939 citations covering social determinants of health, 50 relevant papers were identified, 38 fulfilled inclusion criteria, and 11 were suitable for a meta-analysis. The Comprehensive Meta-analysis Programme was used for decision authority, decision latitude, psychological demands, and work social support, components of the job-strain and iso-strain models, and the combination of effort and reward that makes up the effort-reward imbalance model and job insecurity. Cochran's Q statistic assessed the heterogeneity of the results, and the I2 statistic determined any inconsistency between studies. Job strain, low decision latitude, low social support, high psychological demands, effort-reward imbalance, and high job insecurity predicted common mental disorders despite the heterogeneity for psychological demands and social support among men. The strongest effects were found for job strain and effort-reward imbalance. This meta-analysis provides robust consistent evidence that (combinations of) high demands and low decision latitude and (combinations of) high efforts and low rewards are prospective risk factors for common mental disorders and suggests that the psychosocial work environment is important for mental health. The associations are not merely explained by response bias. The impact of work stressors on common mental disorders differs for women and men.
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            Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis

            Background Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another. Objective The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors. Methods Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature. Results All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory. Conclusions Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings.
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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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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                February 2017
                23 February 2017
                : 5
                : 2
                Affiliations
                1Junior research group wearHEALTH Department of Computer Science University of Kaiserslautern KaiserslauternGermany
                Author notes
                Corresponding Author: Corinna Anna Christmann christmann@ 123456cs.uni-kl.de
                Article
                v5i2e22
                10.2196/mhealth.6471
                5344985
                28232299
                ©Corinna Anna Christmann, Alexandra Hoffmann, Gabriele Bleser. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 23.02.2017.

                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 JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

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                Original Paper
                Original Paper

                relaxation, mhealth, mobile health

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