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Evaluating Patient-Centered Mobile Health Technologies: Definitions, Methodologies, and Outcomes

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Date 2020 Nov 11
PMID 33174846
Citations 16
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Abstract

Several recently published studies and consensus statements have demonstrated that there is only modest (and in many cases, low-quality) evidence that mobile health (mHealth) can improve patient clinical outcomes such as the length of stay or reduction of readmissions. There is also uncertainty as to whether mHealth can improve patient-centered outcomes such as patient engagement or patient satisfaction. One principal challenge behind the "effectiveness" research in this field is a lack of common understanding about what it means to be effective in the digital space (ie, what should constitute a relevant outcome and how best to measure it). In this viewpoint, we call for interdisciplinary, conceptual clarity on the definitions, methodologies, and patient-centered outcomes frequently used in mHealth research. To formulate our recommendations, we used a snowballing approach to identify relevant definitions, outcomes, and methodologies related to mHealth. To begin, we drew heavily upon previously published detailed frameworks that enumerate definitions and measurements of engagement. We built upon these frameworks by extracting other relevant measures of patient-centered care, such as patient satisfaction, patient experience, and patient activation. We describe several definitional inconsistencies for key constructs in the mHealth literature. In an effort to achieve clarity, we tease apart several patient-centered care outcomes, and outline methodologies appropriate to measure each of these patient-care outcomes. By creating a common pathway linking definitions with outcomes and methodologies, we provide a possible interdisciplinary approach to evaluating mHealth technologies. With the broader goal of creating an interdisciplinary approach, we also provide several recommendations that we believe can advance mHealth research and implementation.

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References
1.
Nurmi J, Knittle K, Ginchev T, Khattak F, Helf C, Zwickl P . Engaging Users in the Behavior Change Process With Digitalized Motivational Interviewing and Gamification: Development and Feasibility Testing of the Precious App. JMIR Mhealth Uhealth. 2020; 8(1):e12884. PMC: 7055776. DOI: 10.2196/12884. View

2.
Maindal H, Sokolowski I, Vedsted P . Translation, adaptation and validation of the American short form Patient Activation Measure (PAM13) in a Danish version. BMC Public Health. 2009; 9:209. PMC: 2712471. DOI: 10.1186/1471-2458-9-209. View

3.
Xiong S, Berkhouse H, Schooler M, Pu W, Sun A, Gong E . Effectiveness of mHealth Interventions in Improving Medication Adherence Among People with Hypertension: a Systematic Review. Curr Hypertens Rep. 2018; 20(10):86. DOI: 10.1007/s11906-018-0886-7. View

4.
Evans D, Hopewell-Kelly N, Kok M, White J . Synthesising conceptual frameworks for patient and public involvement in research - a critical appraisal of a meta-narrative review. BMC Med Res Methodol. 2018; 18(1):116. PMC: 6202824. DOI: 10.1186/s12874-018-0572-0. View

5.
Radl-Karimi C, Nicolaisen A, Sodemann M, Batalden P, von Plessen C . Coproduction of healthcare service with immigrant patients: protocol of a scoping review. BMJ Open. 2018; 8(2):e019519. PMC: 5829924. DOI: 10.1136/bmjopen-2017-019519. View