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      Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior Change: Qualitative Study

      , PhD , 1 , , PhD 1 , , BSC(Hons) 1 , , PhD 1

      (Reviewer), (Reviewer)

      Journal of Medical Internet Research

      JMIR Publications Inc.

      mobile phone, cellular phone, behavior, health, qualitative research, focus groups

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          Abstract

          Background

          There is increasing interest from academics and clinicians in harnessing smartphone applications (apps) as a means of delivering behavioral interventions for health. Despite the growing availability of a range of health-related apps on the market, academic research on the development and evaluation of such apps is in the relatively early stages. A few existing studies have explored the views of various populations on using mobile phones for health-related issues and some studies are beginning to report user feedback on specific apps. However, there remains little in depth research on users’ (and potential users’) experiences and views on a wide range of features and technologies that apps are, or will soon be, capable of. In particular, research on young adults is lacking, which is an unfortunate omission considering that this group comprises of a good number of mobile technology adoptors.

          Objective

          The current study sought to explore young adults’ perspectives on apps related to health behavior change. It sought their experiences and views of features that might support health behavior change and issues that contribute to interest in and willingness to use such apps.

          Methods

          Four focus groups were conducted with 19 students and staff at a University in the United Kingdom. Participants included 13 females and 6 males with a mean age of 23.79 (SD 7.89). The focus group discussions centred on participants’ experiences of using smartphone apps to support a healthy lifestyle, and their interest in and feelings about features and capabilities of such apps. The focus groups were recorded, transcribed, and analyzed using inductive thematic analysis.

          Results

          Study findings suggested that young, currently healthy adults have some interest in apps that attempt to support health-related behavior change. Accuracy and legitimacy, security, effort required, and immediate effects on mood emerged as important influences on app usage. The ability to record and track behavior and goals and the ability to acquire advice and information “on the go” were valued. Context-sensing capabilities and social media features tended to be considered unnecessary and off-putting.

          Conclusions

          This study provided insight into the opportunities and challenges involved in delivering health-related behavioral interventions through smartphone apps. The findings suggested a number of valued features and characteristics that app developers may wish to consider when creating health behavior apps. Findings also highlighted several major challenges that appeared to need further consideration and research to ensure the development of effective and well-accepted behavior change apps.

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          Most cited references 32

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          Harnessing Context Sensing to Develop a Mobile Intervention for Depression

          Background Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder. Objective The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing. Methods We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients’ mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients’ self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks. Results Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (betaweek = –.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (betaweek = –.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (bweek = –.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (betaweek = –.71, P < .001, per-protocol Cohen d = 2.58). Conclusions Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed. Trial Registration Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n)
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            Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current Applications Compared Against Evidence-Based Guidelines

            Background Interest in mobile health (mHealth) applications for self-management of diabetes is growing. In July 2009, we found 60 diabetes applications on iTunes for iPhone; by February 2011 the number had increased by more than 400% to 260. Other mobile platforms reflect a similar trend. Despite the growth, research on both the design and the use of diabetes mHealth applications is scarce. Furthermore, the potential influence of social media on diabetes mHealth applications is largely unexplored. Objective Our objective was to study the salient features of mobile applications for diabetes care, in contrast to clinical guideline recommendations for diabetes self-management. These clinical guidelines are published by health authorities or associations such as the National Institute for Health and Clinical Excellence in the United Kingdom and the American Diabetes Association. Methods We searched online vendor markets (online stores for Apple iPhone, Google Android, BlackBerry, and Nokia Symbian), journal databases, and gray literature related to diabetes mobile applications. We included applications that featured a component for self-monitoring of blood glucose and excluded applications without English-language user interfaces, as well as those intended exclusively for health care professionals. We surveyed the following features: (1) self-monitoring: (1.1) blood glucose, (1.2) weight, (1.3) physical activity, (1.4) diet, (1.5) insulin and medication, and (1.6) blood pressure, (2) education, (3) disease-related alerts and reminders, (4) integration of social media functions, (5) disease-related data export and communication, and (6) synchronization with personal health record (PHR) systems or patient portals. We then contrasted the prevalence of these features with guideline recommendations. Results The search resulted in 973 matches, of which 137 met the selection criteria. The four most prevalent features of the applications available on the online markets (n = 101) were (1) insulin and medication recording, 63 (62%), (2) data export and communication, 61 (60%), (3) diet recording, 47 (47%), and (4) weight management, 43 (43%). From the literature search (n = 26), the most prevalent features were (1) PHR or Web server synchronization, 18 (69%), (2) insulin and medication recording, 17 (65%), (3) diet recording, 17 (65%), and (4) data export and communication, 16 (62%). Interestingly, although clinical guidelines widely refer to the importance of education, this is missing from the top functionalities in both cases. Conclusions While a wide selection of mobile applications seems to be available for people with diabetes, this study shows there are obvious gaps between the evidence-based recommendations and the functionality used in study interventions or found in online markets. Current results confirm personalized education as an underrepresented feature in diabetes mobile applications. We found no studies evaluating social media concepts in diabetes self-management on mobile devices, and its potential remains largely unexplored.
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              The Smartphone Psychology Manifesto.

              By 2025, when most of today's psychology undergraduates will be in their mid-30s, more than 5 billion people on our planet will be using ultra-broadband, sensor-rich smartphones far beyond the abilities of today's iPhones, Androids, and Blackberries. Although smartphones were not designed for psychological research, they can collect vast amounts of ecologically valid data, easily and quickly, from large global samples. If participants download the right "psych apps," smartphones can record where they are, what they are doing, and what they can see and hear and can run interactive surveys, tests, and experiments through touch screens and wireless connections to nearby screens, headsets, biosensors, and other peripherals. This article reviews previous behavioral research using mobile electronic devices, outlines what smartphones can do now and will be able to do in the near future, explains how a smartphone study could work practically given current technology (e.g., in studying ovulatory cycle effects on women's sexuality), discusses some limitations and challenges of smartphone research, and compares smartphones to other research methods. Smartphone research will require new skills in app development and data analysis and will raise tough new ethical issues, but smartphones could transform psychology even more profoundly than PCs and brain imaging did.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications Inc. (Toronto, Canada )
                1439-4456
                1438-8871
                April 2013
                18 April 2013
                : 15
                : 4
                Affiliations
                1Academic Unit of Psychology University of Southampton SouthamptonUnited Kingdom
                Author notes
                Corresponding Author: Laura Dennison l.k.dennison@ 123456soton.ac.uk
                Article
                v15i4e86
                10.2196/jmir.2583
                3636318
                23598614
                ©Laura Dennison, Leanne Morrison, Gemma Conway, Lucy Yardley. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.04.2013.

                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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                Categories
                Original Paper

                Medicine

                focus groups, qualitative research, health, behavior, cellular phone, mobile phone

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