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      Mobile App-Based Health Promotion Programs: A Systematic Review of the Literature

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          Abstract

          This study investigated the features and usefulness of mobile app-based health promotion programs for the general population. A comprehensive bibliographic search of studies on health promotion programs using mobile apps in peer-reviewed journals published in English up to November 2017 was performed using the PubMed, Embase, and CINAHL databases. The inclusion criteria were (1) randomized control trial designs; (2) assessed mobile app-based interventions to promote adult health conditions; 12 studies were ultimately included. The most common topics were diet and physical activity ( n = 8) and overall healthy lifestyle improvement ( n = 4). The purpose of the apps included providing feedback on one’s health status ( n = 9) and monitoring individual health status or behavior change ( n = 9). Across all studies, health outcomes were shown to be better for mobile app users compared to non-users. Mobile app-based health interventions may be an effective strategy for improving health promotion behaviors in the general population without diseases. This study suggests that mobile app use is becoming commonplace for a variety of health-promoting behaviors in addition to physical activity and weight control. Future research should address the feasibility and effectiveness of using mobile apps for health promotion in developing countries.

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          Mental Health Mobile Apps for Preadolescents and Adolescents: A Systematic Review

          Background There are an increasing number of mobile apps available for adolescents with mental health problems and an increasing interest in assimilating mobile health (mHealth) into mental health services. Despite the growing number of apps available, the evidence base for their efficacy is unclear. Objective This review aimed to systematically appraise the available research evidence on the efficacy and acceptability of mobile apps for mental health in children and adolescents younger than 18 years. Methods The following were systematically searched for relevant publications between January 2008 and July 2016: APA PsychNet, ACM Digital Library, Cochrane Library, Community Care Inform-Children, EMBASE, Google Scholar, PubMed, Scopus, Social Policy and Practice, Web of Science, Journal of Medical Internet Research, Cyberpsychology, Behavior and Social Networking, and OpenGrey. Abstracts were included if they described mental health apps (targeting depression, bipolar disorder, anxiety disorders, self-harm, suicide prevention, conduct disorder, eating disorders and body image issues, schizophrenia, psychosis, and insomnia) for mobile devices and for use by adolescents younger than 18 years. Results A total of 24 publications met the inclusion criteria. These described 15 apps, two of which were available to download. Two small randomized trials and one case study failed to demonstrate a significant effect of three apps on intended mental health outcomes. Articles that analyzed the content of six apps for children and adolescents that were available to download established that none had undergone any research evaluation. Feasibility outcomes suggest acceptability of apps was good and app usage was moderate. Conclusions Overall, there is currently insufficient research evidence to support the effectiveness of apps for children, preadolescents, and adolescents with mental health problems. Given the number and pace at which mHealth apps are being released on app stores, methodologically robust research studies evaluating their safety, efficacy, and effectiveness is promptly needed.
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            Texting and Mobile Phone App Interventions for Improving Adherence to Preventive Behavior in Adolescents: A Systematic Review

            Background Many preventable behaviors contribute to adolescent mortality and morbidity. Non-adherence to preventive measures represents a challenge and has been associated with worse health outcomes in this population. The widespread use of electronic communication technologies by adolescents, particularly the use of text messaging (short message service, SMS) and mobile phones, presents new opportunities to intervene on risk and preventive risk behavior, but little is known about their efficacy. Objective This study aimed to systematically evaluate evidence for the efficacy of text messaging and mobile phone app interventions to improve adherence to preventive behavior among adolescents and describe intervention approaches to inform intervention development. Methods This review covers literature published between 1995 and 2015. Searches included PubMed, Embase, CENTRAL, PsycINFO, CINAHL, INSPEC, Web of Science, Google Scholar, and additional databases. The search strategy sought articles on text messaging and mobile phone apps combined with adherence or compliance, and adolescents and youth. An additional hand search of related themes in the Journal of Medical Internet Research was also conducted. Two reviewers independently screened titles and abstracts, assessed full-text articles, and extracted data from articles that met inclusion criteria. Included studies reflect original research—experimental or preexperimental designs with text messaging or mobile phone app interventions—targeting adherence to preventive behavior among adolescents (12-24 years old). The preferred reporting items of systematic reviews and meta-analyses (PRISMA) guidelines were followed for reporting results, and findings were critically appraised against the Oxford Centre for Evidence-based Medicine criteria. Results Of 1454 records, 19 met inclusion criteria, including text messaging (n=15) and mobile phone apps (n=4). Studies targeted clinic attendance, contraceptive use, oral health, physical activity and weight management, sun protection, human papillomavirus (HPV) vaccination, smoking cessation, and sexual health. Most studies were performed in the United States (47%, 9/19), included younger adolescents (63%, 12/19), and had sample size <100 (63%, 12/19). Although most studies were randomized controlled trials (RCTs; 58%, 11/19), only 5 followed an intent-to-treat analysis. Only 6 of 19 studies (32%) incorporated a theoretical framework in their design. Most studies reported good feasibility with high acceptability and satisfaction. About half of the included studies (42%, 8/19) demonstrated significant improvement in preventive behavior with moderate standardized mean differences. As early efforts in this field to establish feasibility and initial efficacy, most studies were low to moderate in quality. Studies varied in sample size and methods of preventive behavior adherence or outcome assessment, which prohibited performing a meta-analysis. Conclusions Despite the promising feasibility and acceptability of text messaging and mobile phone apps in improving preventive behavior among adolescents, overall findings were modest in terms of efficacy. Further research evaluating the efficacy, effectiveness, and cost-effectiveness of these intervention approaches in promoting preventive behavior among adolescents is needed.
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              Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults

              Background A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users’ behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement. Objective MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user’s environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions. Methods MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior’s personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions. Results In qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior’s personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P<.001). Conclusions MyBehavior is a simple-to-use mobile phone app with preliminary evidence of efficacy. To the best of our knowledge, MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information (ie, manual food logging and automatic tracking of activity). Lessons learned about the difficulty of manual logging and usability concerns, as well as future directions, are discussed. Trial Registration ClinicalTrials.gov NCT02359981; https://clinicaltrials.gov/ct2/show/NCT02359981 (Archived by WebCite at http://www.webcitation.org/6YCeoN8nv).
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                13 December 2018
                December 2018
                : 15
                : 12
                : 2838
                Affiliations
                [1 ]College of Nursing, Yonsei University, 50-1, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; lmk425@ 123456yuhs.ac (M.L.); goshimak@ 123456naver.com (Y.K.); zzomi324@ 123456naver.com (M.C.); je8952@ 123456naver.com (J.J.); scarlet311@ 123456hanmail.net (H.J.)
                [2 ]Mo-im Kim Nursing Research Institute, College of Nursing, Yonsei University, 50-1, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; kjh129@ 123456outlook.kr
                Author notes
                [* ]Correspondence: hlee39@ 123456yuhs.ac ; Tel.: +82-2-2228-3373
                Author information
                https://orcid.org/0000-0002-1716-5843
                Article
                ijerph-15-02838
                10.3390/ijerph15122838
                6313530
                30551555
                582b69a9-ada5-45c5-9120-d55f2875629a
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 12 October 2018
                : 07 December 2018
                Categories
                Review

                Public health
                health promotion,mobile app,app-based intervention,smartphone
                Public health
                health promotion, mobile app, app-based intervention, smartphone

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