Using Smartphones and Health Apps to Change and Manage Health Behaviors: A Population-Based Survey

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Abstract

Background

Chronic conditions are an increasing challenge for individuals and the health care system. Smartphones and health apps are potentially promising tools to change health-related behaviors and manage chronic conditions.

Objective

The aim of this study was to explore (1) the extent of smartphone and health app use, (2) sociodemographic, medical, and behavioral correlates of smartphone and health app use, and (3) associations of the use of apps and app characteristics with actual health behaviors.

Methods

A population-based survey (N=4144) among Germans, aged 35 years and older, was conducted. Sociodemographics, presence of chronic conditions, health behaviors, quality of life, and health literacy, as well as the use of the Internet, smartphone, and health apps were assessed by questionnaire at home visit. Binary logistic regression models were applied.

Results

It was found that 61.25% (2538/4144) of participants used a smartphone. Compared with nonusers, smartphone users were younger, did more research on the Internet, were more likely to work full-time and more likely to have a university degree, engaged more in physical activity, and less in low fat diet, and had a higher health-related quality of life and health literacy. Among smartphone users, 20.53% (521/2538) used health apps. App users were younger, less likely to be native German speakers, did more research on the Internet, were more likely to report chronic conditions, engaged more in physical activity, and low fat diet, and were more health literate compared with nonusers who had a smartphone. Health apps focused on smoking cessation (232/521, 44.5%), healthy diet (201/521, 38.6%), and weight loss (121/521, 23.2%). The most common app characteristics were planning (264/521, 50.7%), reminding (188/521, 36.1%), prompting motivation (179/521 34.4%), and the provision of information (175/521, 33.6%). Significant associations were found between planning and the health behavior physical activity, between feedback or monitoring and physical activity, and between feedback or monitoring and adherence to doctor’s advice.

Conclusions

Although there were many smartphone and health app users, a substantial proportion of the population was not engaged. Findings suggest age-related, socioeconomic-related, literacy-related, and health-related disparities in the use of mobile technologies. Health app use may reflect a user’s motivation to change or maintain health behaviors. App developers and researchers should take account of the needs of older people, people with low health literacy, and chronic conditions.

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Most cited references 19

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Increasing Physical Activity With Mobile Devices: A Meta-Analysis

Jason Fanning, Sean P Mullen, Edward McAuley (2012)

Background Regular physical activity has established physical and mental health benefits; however, merely one quarter of the U.S. adult population meets national physical activity recommendations. In an effort to engage individuals who do not meet these guidelines, researchers have utilized popular emerging technologies, including mobile devices (ie, personal digital assistants [PDAs], mobile phones). This study is the first to synthesize current research focused on the use of mobile devices for increasing physical activity. Objective To conduct a meta-analysis of research utilizing mobile devices to influence physical activity behavior. The aims of this review were to: (1) examine the efficacy of mobile devices in the physical activity setting, (2) explore and discuss implementation of device features across studies, and (3) make recommendations for future intervention development. Methods We searched electronic databases (PubMed, PsychINFO, SCOPUS) and identified publications through reference lists and requests to experts in the field of mobile health. Studies were included that provided original data and aimed to influence physical activity through dissemination or collection of intervention materials with a mobile device. Data were extracted to calculate effect sizes for individual studies, as were study descriptives. A random effects meta-analysis was conducted using the Comprehensive Meta-Analysis software suite. Study quality was assessed using the quality of execution portion of the Guide to Community Preventative Services data extraction form. Results Four studies were of “good” quality and seven of “fair” quality. In total, 1351 individuals participated in 11 unique studies from which 18 effects were extracted and synthesized, yielding an overall weight mean effect size of g = 0.54 (95% CI = 0.17 to 0.91, P = .01). Conclusions Research utilizing mobile devices is gaining in popularity, and this study suggests that this platform is an effective means for influencing physical activity behavior. Our focus must be on the best possible use of these tools to measure and understand behavior. Therefore, theoretically grounded behavior change interventions that recognize and act on the potential of smartphone technology could provide investigators with an effective tool for increasing physical activity.

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mHealth 2.0: Experiences, Possibilities, and Perspectives

Stefan Becker, Talya Miron-Shatz, Nikolaus Schumacher … (2014)

With more than 1 billion users having access to mobile broadband Internet and a rapidly growing mobile app market, all stakeholders involved have high hopes that this technology may improve health care. Expectations range from overcoming structural barriers to access in low-income countries to more effective, interactive treatment of chronic conditions. Before medical health practice supported by mobile devices ("mHealth") can scale up, a number of challenges need to be adequately addressed. From a psychological perspective, high attrition rates, digital divide of society, and intellectual capabilities of the users are key issues when implementing such technologies. Furthermore, apps addressing behavior change often lack a comprehensive concept, which is essential for an ongoing impact. From a clinical point of view, there is insufficient evidence to allow scaling up of mHealth interventions. In addition, new concepts are required to assess the efficacy and efficiency of interventions. Regarding technology interoperability, open standards and low-energy wireless protocols appear to be vital for successful implementation. There is an ongoing discussion in how far health care-related apps require a conformity assessment and how to best communicate quality standards to consumers. "Apps Peer-Review" and standard reporting via an "App synopsis" appear to be promising approaches to increase transparency for end users. With respect to development, more emphasis must be placed on context analysis to identify what generic functions of mobile information technology best meet the needs of stakeholders involved. Hence, interdisciplinary alliances and collaborative strategies are vital to achieve sustainable growth for "mHealth 2.0," the next generation mobile technology to support patient care.

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Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence.

Benjamin Harkin, Thomas Webb, Betty P I Chang … (2016)

Control theory and other frameworks for understanding self-regulation suggest that monitoring goal progress is a crucial process that intervenes between setting and attaining a goal, and helps to ensure that goals are translated into action. However, the impact of progress monitoring interventions on rates of behavioral performance and goal attainment has yet to be quantified. A systematic literature search identified 138 studies (N = 19,951) that randomly allocated participants to an intervention designed to promote monitoring of goal progress versus a control condition. All studies reported the effects of the treatment on (a) the frequency of progress monitoring and (b) subsequent goal attainment. A random effects model revealed that, on average, interventions were successful at increasing the frequency of monitoring goal progress (d+ = 1.98, 95% CI [1.71, 2.24]) and promoted goal attainment (d+ = 0.40, 95% CI [0.32, 0.48]). Furthermore, changes in the frequency of progress monitoring mediated the effect of the interventions on goal attainment. Moderation tests revealed that progress monitoring had larger effects on goal attainment when the outcomes were reported or made public, and when the information was physically recorded. Taken together, the findings suggest that monitoring goal progress is an effective self-regulation strategy, and that interventions that increase the frequency of progress monitoring are likely to promote behavior change.

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Author and article information

Contributors

Clemens Ernsting:

Institute of Medical SociologyCharité - Universitätsmedizin BerlinChariteplatz 1Berlin,Germany49 3045052921549 30450529972clemens.ernsting@charite.de

ORCID: http://orcid.org/0000-0003-1821-0340

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J. Med. Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: April 2017

Publication date (Electronic): 05 April 2017

Volume: 19

Issue: 4

Electronic Location Identifier: e101

Affiliations

[1] ¹Institute of Medical Sociology Charité - Universitätsmedizin Berlin BerlinGermany

[2] ²Faculty of Natural Sciences University of Stirling StirlingUnited Kingdom

[3] ³Pfizer Deutschland GmbH BerlinGermany

Author notes

Corresponding Author: Clemens Ernsting clemens.ernsting@ 123456charite.de

Author information

Clemens Ernsting http://orcid.org/0000-0003-1821-0340

Stephan U Dombrowski http://orcid.org/0000-0001-9832-2777

Monika Oedekoven http://orcid.org/0000-0002-0207-4068

Julie L O´Sullivan http://orcid.org/0000-0002-8991-9966

Melanie Kanzler http://orcid.org/0000-0002-9854-1184

Adelheid Kuhlmey http://orcid.org/0000-0003-4088-4675

Paul Gellert http://orcid.org/0000-0001-7492-7210

Article

Publisher ID: v19i4e101

DOI: 10.2196/jmir.6838

PMC ID: 5399221

PubMed ID: 28381394

SO-VID: c42066dc-86a0-4a61-8ab9-367e364917aa

Copyright © ©Clemens Ernsting, Stephan U Dombrowski, Monika Oedekoven, Julie L O´Sullivan, Melanie Kanzler, Adelheid Kuhlmey, Paul Gellert. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.04.2017.

License:

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.

History

Date received : 31 October 2016

Date revision requested : 1 December 2016

Date revision received : 27 December 2016

Date accepted : 26 February 2017

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