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      Maximizing Engagement in Mobile Health Studies : Lessons Learned and Future Directions

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          Abstract

          The widespread availability of smartphones, tablets, and smartwatches has led to exponential growth in the number of mobile health (mHealth) studies conducted. Although promising, the key challenge of all apps (both for research and nonresearch) is the high attrition rate of participants and users. Numerous factors have been identified as potentially influencing engagement, and it is important that researchers consider these and how best to overcome them within their studies. This article discusses lessons learned from attempting to maximize engagement in 2 successful UK mHealth studies—Cloudy with a Chance of Pain and Quality of Life, Sleep and Rheumatoid Arthritis.

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          Handling time varying confounding in observational research

          Goodarz Danaei, Mahyar Etminan, Mohammad Mansournia … (2017)
          Article 0 comments Cited 146 times – based on 0 reviews     Review now
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            The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors.

            Bruce Dobkin, Andrew Dorsch (2015)
            Mobile health tools that enable clinicians and researchers to monitor the type, quantity, and quality of everyday activities of patients and trial participants have long been needed to improve daily care, design more clinically meaningful randomized trials of interventions, and establish cost-effective, evidence-based practices. Inexpensive, unobtrusive wireless sensors, including accelerometers, gyroscopes, and pressure-sensitive textiles, combined with Internet-based communications and machine-learning algorithms trained to recognize upper- and lower-extremity movements, have begun to fulfill this need. Continuous data from ankle triaxial accelerometers, for example, can be transmitted from the home and community via WiFi or a smartphone to a remote data analysis server. Reports can include the walking speed and duration of every bout of ambulation, spatiotemporal symmetries between the legs, and the type, duration, and energy used during exercise. For daily care, this readily accessible flow of real-world information allows clinicians to monitor the amount and quality of exercise for risk factor management and compliance in the practice of skills. Feedback may motivate better self-management as well as serve home-based rehabilitation efforts. Monitoring patients with chronic diseases and after hospitalization or the start of new medications for a decline in daily activity may help detect medical complications before rehospitalization becomes necessary. For clinical trials, repeated laboratory-quality assessments of key activities in the community, rather than by clinic testing, self-report, and ordinal scales, may reduce the cost and burden of travel, improve recruitment and retention, and capture more reliable, valid, and responsive ratio-scaled outcome measures that are not mere surrogates for changes in daily impairment, disability, and functioning.
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              An Evaluation of Mobile Health Application Tools

              Preethi Sama, Zubin Eapen, Kevin Weinfurt … (2014)
              Background The rapid growth in the number of mobile health applications could have profound significance in the prevention of disease or in the treatment of patients with chronic disease such as diabetes. Objective The objective of this study was to describe the characteristics of the most common mobile health care applications available in the Apple iTunes marketplace. Methods We undertook a descriptive analysis of a sample of applications in the “health and wellness” category of the Apple iTunes Store. We characterized each application in terms of its health factor and primary method of user engagement. The main outcome measures of the analysis were price, health factors, and methods of user engagement. Results Among the 400 applications that met the inclusion criteria, the mean price of the most frequently downloaded paid applications was US $2.24 (SD $1.30), and the mean price of the most currently available paid applications was US $2.27 (SD $1.60). Fitness/training applications were the most popular (43.5%, 174/400). The next two most common categories were health resource (15.0%, 60/400) and diet/caloric intake (14.3%, 57/400). Applications in the health resource category constituted 5.5% (22/400) of the applications reviewed. Self-monitoring was the most common primary user engagement method (74.8%, 299/400). A total of 20.8% (83/400) of the applications used two or more user engagement approaches, with self-monitoring and progress tracking being the most frequent. Conclusions Most of the popular mobile health applications focus on fitness and self-monitoring. The approaches to user engagement utilized by these applications are limited and present an opportunity to improve the effectiveness of the technology.
              Article 0 comments Cited 61 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Katie L. Druce
                Journal
                Journal ID (nlm-ta): Rheum Dis Clin North Am
                Journal ID (iso-abbrev): Rheum. Dis. Clin. North Am
                Title: Rheumatic Diseases Clinics of North America
                Publisher: Elsevier
                ISSN (Print): 0889-857X
                ISSN (Electronic): 1558-3163
                Publication date PMC-release: 1 May 2019
                Publication date (Print): May 2019
                Volume: 45
                Issue: 2
                Pages: 159-172
                Affiliations
                [a ]Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, UK
                [b ]NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK
                Author notes
                [∗ ]Corresponding author. Katie.druce@ 123456manchester.ac.uk
                Article
                Publisher ID: S0889-857X(19)30004-3
                DOI: 10.1016/j.rdc.2019.01.004
                PMC ID: 6483978
                PubMed ID: 30952390
                SO-VID: 2ceda1d8-00e1-4895-908d-8b388611e9f5
                Copyright © © 2019 The Authors
                License:

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Categories
                Subject: Article

                Keywords: epidemiology,mhealth,methods,remote monitoring,rheumatic diseases,patient reported outcomes
                Data availability:
                Keywords: epidemiology, mhealth, methods, remote monitoring, rheumatic diseases, patient reported outcomes

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