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      Mobile Health Apps to Facilitate Self-Care: A Qualitative Study of User Experiences

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      PLoS ONE
      Public Library of Science

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          Abstract

          Objective

          Consumers are living longer, creating more pressure on the health system and increasing their requirement for self-care of chronic conditions. Despite rapidly-increasing numbers of mobile health applications (‘apps’) for consumers’ self-care, there is a paucity of research into consumer engagement with electronic self-monitoring. This paper presents a qualitative exploration of how health consumers use apps for health monitoring, their perceived benefits from use of health apps, and suggestions for improvement of health apps.

          Materials and Methods

          ‘Health app’ was defined as any commercially-available health or fitness app with capacity for self-monitoring. English-speaking consumers aged 18 years and older using any health app for self-monitoring were recruited for interview from the metropolitan area of Perth, Australia. The semi-structured interview guide comprised questions based on the Technology Acceptance Model, Health Information Technology Acceptance Model, and the Mobile Application Rating Scale, and is the only study to do so. These models also facilitated deductive thematic analysis of interview transcripts. Implicit and explicit responses not aligned to these models were analyzed inductively.

          Results

          Twenty-two consumers (15 female, seven male) participated, 13 of whom were aged 26–35 years. Eighteen participants reported on apps used on iPhones. Apps were used to monitor diabetes, asthma, depression, celiac disease, blood pressure, chronic migraine, pain management, menstrual cycle irregularity, and fitness. Most were used approximately weekly for several minutes per session, and prior to meeting initial milestones, with significantly decreased usage thereafter. Deductive and inductive thematic analysis reduced the data to four dominant themes: engagement in use of the app; technical functionality of the app; ease of use and design features; and management of consumers’ data.

          Conclusions

          The semi-structured interviews provided insight into usage, benefits and challenges of health monitoring using apps. Understanding the range of consumer experiences and expectations can inform design of health apps to encourage persistence in self-monitoring.

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          Most cited references34

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          Meta-analysis: chronic disease self-management programs for older adults.

          Although enthusiasm is growing for self-management programs for chronic conditions, there are conflicting data regarding their effectiveness and no agreement on their essential components. To assess the effectiveness and essential components of self-management programs for hypertension, osteoarthritis, and diabetes mellitus. The authors searched multiple sources dated through September 2004, including the Cochrane Library, MEDLINE, PsycINFO, and Nursing and Allied Health databases, and bibliographies of 87 previous reviews. Randomized trials that compared outcomes of self-management interventions with a control or with usual care for diabetes mellitus, osteoarthritis, or hypertension; outcomes included hemoglobin A1c level, fasting blood glucose level, weight, blood pressure, pain, or function. Two reviewers independently identified trials and extracted data regarding whether the intervention used tailored adjustments to meet individual patient needs, a group setting, feedback, and psychological services, and whether the intervention was provided by the patient's usual physician. Of 780 studies screened, 53 studies contributed data to the random-effects meta-analysis (26 diabetes studies, 14 osteoarthritis studies, and 13 hypertension studies). Self-management interventions led to a statistically and clinically significant pooled effect size of -0.36 (95% CI, -0.52 to -0.21) for hemoglobin A1c, equivalent to a reduction in hemoglobin A1c level of about 0.81%. Self-management interventions decreased systolic blood pressure by 5 mm Hg (effect size, -0.39 [CI, -0.51 to -0.28]) and decreased diastolic blood pressure by 4.3 mm Hg (effect size, -0.51 [CI, -0.73 to -0.30]). Pooled effects of self-management interventions were statistically significant but clinically trivial for pain and function outcomes for osteoarthritis. No consistent results supported any of the 5 characteristics examined as essential for program success. Studies had variable quality, and possible publication bias was evident. Self-management programs for diabetes mellitus and hypertension probably produce clinically important benefits. The elements of the programs most responsible for benefits cannot be determined from existing data, and this inhibits specification of optimally effective or cost-effective programs. Osteoarthritis self-management programs do not appear to have clinically beneficial effects on pain or function.
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            Evidence Suggesting That a Chronic Disease Self-Management Program Can Improve Health Status While Reducing Hospitalization

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              Just a Fad? Gamification in Health and Fitness Apps

              Background Gamification has been a predominant focus of the health app industry in recent years. However, to our knowledge, there has yet to be a review of gamification elements in relation to health behavior constructs, or insight into the true proliferation of gamification in health apps. Objective The objective of this study was to identify the extent to which gamification is used in health apps, and analyze gamification of health and fitness apps as a potential component of influence on a consumer’s health behavior. Methods An analysis of health and fitness apps related to physical activity and diet was conducted among apps in the Apple App Store in the winter of 2014. This analysis reviewed a sample of 132 apps for the 10 effective game elements, the 6 core components of health gamification, and 13 core health behavior constructs. A regression analysis was conducted in order to measure the correlation between health behavior constructs, gamification components, and effective game elements. Results This review of the most popular apps showed widespread use of gamification principles, but low adherence to any professional guidelines or industry standard. Regression analysis showed that game elements were associated with gamification (P<.001). Behavioral theory was associated with gamification (P<.05), but not game elements, and upon further analysis gamification was only associated with composite motivational behavior scores (P<.001), and not capacity or opportunity/trigger. Conclusions This research, to our knowledge, represents the first comprehensive review of gamification use in health and fitness apps, and the potential to impact health behavior. The results show that use of gamification in health and fitness apps has become immensely popular, as evidenced by the number of apps found in the Apple App Store containing at least some components of gamification. This shows a lack of integrating important elements of behavioral theory from the app industry, which can potentially impact the efficacy of gamification apps to change behavior. Apps represent a very promising, burgeoning market and landscape in which to disseminate health behavior change interventions. Initial results show an abundant use of gamification in health and fitness apps, which necessitates the in-depth study and evaluation of the potential of gamification to change health behaviors.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                23 May 2016
                2016
                : 11
                : 5
                : e0156164
                Affiliations
                [001]School of Pharmacy, Curtin University, Perth, Western Australia, Australia
                University of Groningen, University Medical Center Groningen, NETHERLANDS
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KA LE OB. Performed the experiments: KA. Analyzed the data: KA LE OB. Wrote the paper: KA LE OB.

                Article
                PONE-D-16-02829
                10.1371/journal.pone.0156164
                4876999
                27214203
                831c5848-f0d3-4bdf-9f03-b0e5d7f229ee
                © 2016 Anderson et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 21 January 2016
                : 10 May 2016
                Page count
                Figures: 0, Tables: 3, Pages: 21
                Funding
                The authors have no support or funding to report.
                Categories
                Research Article
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Diagnostic Medicine
                Signs and Symptoms
                Headaches
                Migraine
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Signs and Symptoms
                Headaches
                Migraine
                Medicine and Health Sciences
                Pulmonology
                Asthma
                Medicine and Health Sciences
                Vascular Medicine
                Blood Pressure
                Computer and Information Sciences
                Data Management
                Engineering and Technology
                Electronics
                Consumer Electronics
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Sleep
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Sleep
                Medicine and Health Sciences
                Health Care
                Health Care Policy
                Health Systems Strengthening
                Custom metadata
                Limited data can be made available to researchers who meet the criteria for access to confidential data. Due to the qualitative nature of these data, the interview transcripts contain personal information that potentially identifies participants and would breach participant confidentiality if made publicly available. Data requests may be sent to the corresponding author.

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