Tracking Health Data Is Not Enough: A Qualitative Exploration of the Role of Healthcare Partnerships and mHealth Technology to Promote Physical Activity and to Sustain Behavior Change

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Abstract

Background

Despite the recent explosion of the mobile health (mHealth) industry and consumer acquisition of mHealth tools such as wearable sensors and applications (apps), limited information is known about how this technology can sustain health behavior change and be integrated into health care.

Objective

The objective of the study was to understand potential users’ views of mHealth technology, the role this technology may have in promoting individual activity goals aimed at improving health, and the value of integrating mHealth technology with traditional health care.

Methods

Four focus groups were conducted with adults interested in sharing their views on how mHealth technology could support wellness programs and improve health. Participants (n=30) were enrolled from an employee population at an academic health institution. Qualitative thematic analysis was used to code transcripts and identify overarching themes.

Results

Our findings suggest that tracking health data alone may result in heightened awareness of daily activity, yet may not be sufficient to sustain use of mHealth technology and apps, which often have low reuse rates. Participants suggested that context, meaning, and health care partnerships need to be incorporated to engage and retain users. In addition to these findings, drivers for mHealth technology previously identified in the literature, including integration and control of health data were confirmed in this study.

Conclusions

This study explores ways that mHealth technologies may be used to not only track data, but to encourage sustained engagement to achieve individual health goals. Implications of these findings include recommendations for mHealth technology design and health care partnership models to sustain motivation and engagement, allowing individuals to achieve meaningful behavior change.

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Most cited references 34

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Effectiveness of mobile phone messaging in prevention of type 2 diabetes by lifestyle modification in men in India: a prospective, parallel-group, randomised controlled trial.

Lisa G Johnston, Nish Chaturvedi, K George MM Alberti … (2013)

Type 2 diabetes can often be prevented by lifestyle modification; however, successful lifestyle intervention programmes are labour intensive. Mobile phone messaging is an inexpensive alternative way to deliver educational and motivational advice about lifestyle modification. We aimed to assess whether mobile phone messaging that encouraged lifestyle change could reduce incident type 2 diabetes in Indian Asian men with impaired glucose tolerance. We did a prospective, parallel-group, randomised controlled trial between Aug 10, 2009, and Nov 30, 2012, at ten sites in southeast India. Working Indian men (aged 35-55 years) with impaired glucose tolerance were randomly assigned (1:1) with a computer-generated randomisation sequence to a mobile phone messaging intervention or standard care (control group). Participants in the intervention group received frequent mobile phone messages compared with controls who received standard lifestyle modification advice at baseline only. Field staff and participants were, by necessity, not masked to study group assignment, but allocation was concealed from laboratory personnel as well as principal and co-investigators. The primary outcome was incidence of type 2 diabetes, analysed by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT00819455. We assessed 8741 participants for eligibility. 537 patients were randomly assigned to either the mobile phone messaging intervention (n=271) or standard care (n=266). The cumulative incidence of type 2 diabetes was lower in those who received mobile phone messages than in controls: 50 (18%) participants in the intervention group developed type 2 diabetes compared with 73 (27%) in the control group (hazard ratio 0·64, 95% CI 0·45-0·92; p=0·015). The number needed to treat to prevent one case of type 2 diabetes was 11 (95% CI 6-55). One patient in the control group died suddenly at the end of the first year. We recorded no other serious adverse events. Mobile phone messaging is an effective and acceptable method to deliver advice and support towards lifestyle modification to prevent type 2 diabetes in men at high risk. The UK India Education and Research Initiative, the World Diabetes Foundation. Copyright © 2013 Elsevier Ltd. All rights reserved.

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The eHealth Enhanced Chronic Care Model: A Theory Derivation Approach

Perry Gee, Deborah Greenwood, Debora Paterniti … (2015)

Background Chronic illnesses are significant to individuals and costly to society. When systematically implemented, the well-established and tested Chronic Care Model (CCM) is shown to improve health outcomes for people with chronic conditions. Since the development of the original CCM, tremendous information management, communication, and technology advancements have been established. An opportunity exists to improve the time-honored CCM with clinically efficacious eHealth tools. Objective The first goal of this paper was to review research on eHealth tools that support self-management of chronic disease using the CCM. The second goal was to present a revised model, the eHealth Enhanced Chronic Care Model (eCCM), to show how eHealth tools can be used to increase efficiency of how patients manage their own chronic illnesses. Methods Using Theory Derivation processes, we identified a “parent theory”, the Chronic Care Model, and conducted a thorough review of the literature using CINAHL, Medline, OVID, EMBASE PsychINFO, Science Direct, as well as government reports, industry reports, legislation using search terms “CCM or Chronic Care Model” AND “eHealth” or the specific identified components of eHealth. Additionally, “Chronic Illness Self-management support” AND “Technology” AND several identified eHealth tools were also used as search terms. We then used a review of the literature and specific components of the CCM to create the eCCM. Results We identified 260 papers at the intersection of technology, chronic disease self-management support, the CCM, and eHealth and organized a high-quality subset (n=95) using the components of CCM, self-management support, delivery system design, clinical decision support, and clinical information systems. In general, results showed that eHealth tools make important contributions to chronic care and the CCM but that the model requires modification in several key areas. Specifically, (1) eHealth education is critical for self-care, (2) eHealth support needs to be placed within the context of community and enhanced with the benefits of the eCommunity or virtual communities, and (3) a complete feedback loop is needed to assure productive technology-based interactions between the patient and provider. Conclusions The revised model, eCCM, offers insight into the role of eHealth tools in self-management support for people with chronic conditions. Additional research and testing of the eCCM are the logical next steps.

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Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults

Mashfiqui Rabbi, Angela Pfammatter, Mi Zhang … (2015)

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

Contributors

Sheridan W Miyamoto:

College of NursingThe Pennsylvania State University201 Nursing Sciences BuildingUniversity Park, CA, 16801United States1 814 863 41411 916 456 3257smiyamoto@psu.edu

ORCID: http://orcid.org/0000-0003-0494-1839

Journal

Journal ID (nlm-ta): JMIR Mhealth Uhealth

Journal ID (iso-abbrev): JMIR Mhealth Uhealth

Journal ID (publisher-id): JMU

Title: JMIR mHealth and uHealth

Publisher: JMIR Publications Inc. (Toronto, Canada )

ISSN (Electronic): 2291-5222

Publication date Collection: Jan-Mar 2016

Publication date (Electronic): 20 January 2016

Volume: 4

Issue: 1

Electronic Location Identifier: e5

Affiliations

[1] ¹College of Nursing The Pennsylvania State University University Park, CAUnited States

[2] ²Schools of Health and Clinical and Translational Science Center University of California Davis Sacramento, CAUnited States

[3] ³Betty Irene Moore School of Nursing University of California Davis Sacramento, CAUnited States

[4] ⁴Department of Computer Science University of California Davis Davis, CAUnited States

[5] ⁵Department of Physical Medicine and Rehabilitation University of California Davis Sacramento, CAUnited States

Author notes

Corresponding Author: Sheridan W Miyamoto smiyamoto@ 123456psu.edu

Author information

Sheridan W Miyamoto http://orcid.org/0000-0003-0494-1839

Stuart Henderson http://orcid.org/0000-0002-8634-7130

Heather M Young http://orcid.org/0000-0002-1691-3993

Amit Pande http://orcid.org/0000-0002-5898-3404

Article

Publisher ID: v4i1e5

DOI: 10.2196/mhealth.4814

PMC ID: 4758807

PubMed ID: 26792225

SO-VID: 99f051e8-d9c0-4766-8496-2aa1a801312e

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 JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 15 June 2015

Date revision requested : 7 July 2015

Date revision received : 31 August 2015

Date accepted : 18 October 2015

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