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      Development of Smartphone Applications for Nutrition and Physical Activity Behavior Change

      research-article
      , BND (Hons) 1 , , , BAppSc/BSc (Hons) 1 , , PhD 2 , , PhD, MPhilPH 1
      (Reviewer), (Reviewer)
      JMIR Research Protocols
      JMIR Publications Inc.
      cellular phone, young adult, primary prevention, lifestyle, health behavior

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          Abstract

          Background

          Young adults (aged 18 to 35) are a population group at high risk for weight gain, yet we know little about how to intervene in this group. Easy access to treatment and support with self-monitoring of their behaviors may be important. Smartphones are gaining in popularity with this population group and software applications (“apps”) used on these mobile devices are a novel technology that can be used to deliver brief health behavior change interventions directly to individuals en masse, with potentially favorable cost-utility. However, existing apps for modifying nutrition or physical activity behaviors may not always reflect best practice guidelines for weight management.

          Objective

          This paper describes the process of developing four apps aimed at modifying key lifestyle behaviors associated with weight gain during young adulthood, including physical activity, and consumption of take-out foods (fast food), fruit and vegetables, and sugar-sweetened drinks.

          Methods

          The development process involved: (1) deciding on the behavior change strategies, relevant guidelines, graphic design, and potential data collection; (2) selecting the platform (Web-based versus native); (3) creating the design, which required decisions about the user interface, architecture of the relational database, and programming code; and (4) testing the prototype versions with the target audience (young adults aged 18 to 35).

          Results

          The four apps took 18 months to develop, involving the fields of marketing, nutrition and dietetics, physical activity, and information technology. Ten subjects provided qualitative feedback about using the apps. The slow running speed of the apps (due to a reliance on an active Internet connection) was the primary issue identified by this group, as well as the requirement to log in to the apps.

          Conclusions

          Smartphone apps may be an innovative medium for delivering individual health behavior change intervention en masse, but researchers must give consideration to the target population, available technologies, existing commercial apps, and the possibility that their use will be irregular and short-lived.

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          Most cited references40

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          In search of how people change. Applications to addictive behaviors.

          How people intentionally change addictive behaviors with and without treatment is not well understood by behavioral scientists. This article summarizes research on self-initiated and professionally facilitated change of addictive behaviors using the key trans-theoretical constructs of stages and processes of change. Modification of addictive behaviors involves progression through five stages--pre-contemplation, contemplation, preparation, action, and maintenance--and individuals typically recycle through these stages several times before termination of the addiction. Multiple studies provide strong support for these stages as well as for a finite and common set of change processes used to progress through the stages. Research to date supports a trans-theoretical model of change that systematically integrates the stages with processes of change from diverse theories of psychotherapy.
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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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              Global Recommendations on Physical Activity for Health

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                Author and article information

                Contributors
                Journal
                JMIR Res Protoc
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications Inc. (Toronto, Canada )
                1929-0748
                Jul-Dec 2012
                22 August 2012
                : 1
                : 2
                : e9
                Affiliations
                [1 ]School of Molecular Bioscience The University of Sydney SydneyAustralia
                [2 ]Sydney School of Public Health The University of Sydney SydneyAustralia
                Author notes
                Corresponding Author: Lana Hebden lana.hebden@ 123456sydney.edu.au
                Article
                v1i2e9
                10.2196/resprot.2205
                3626164
                23611892
                765c7db6-1fe8-4212-9a3d-9075239ce83d
                ©Lana Hebden, Amelia Cook, Hidde P. van der Ploeg, Margaret Allman-Farinelli. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 22.08.2012.

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

                History
                : 04 June 2012
                : 26 June 2012
                : 10 July 2012
                : 02 August 2012
                Categories
                Original Paper

                cellular phone,young adult,primary prevention,lifestyle,health behavior

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