Mobile Health Technology Evaluation
Open accessAbstract
Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.
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The evidence base for mHealth approaches improving health outcomes is lacking.
“Much recent mHealth literature has drawn attention to the fact that the evidence base for mHealth approaches improving health outcomes is lacking [40, 41].”
Design guidelines for mHealth applications are not readily available.
“While the design and the relevance of an mHealth app influence its uptake and ultimately its success, design guidelines are not readily available [4, 5].”
From A user-centred design framework for mHealth (2020) · cites this paper as [4]
The Introduction discusses limited evidence for mHealth efficacy, and “Using Open Infrastructure and Data Standards” proposes common research standards (page numbers unavailable). Neither passage addresses whether design guidelines for mHealth applications are readily available. Searched for app-design guidelines and guidelines. (LLM, conf=0.70)
Experimental — accuracy not yet measuredInterventions developed and tested may not have been reported as a scientific study; this is a well-known phenomenon in a field arguably driven by commercial developers as opposed to clinical-academic researchers.
“Also, interventions developed and tested may not have been reported as a scientific study; a well-known phenomenon in a field that is arguably driven by commercial developers as opposed to clinical-academic researchers (Kumar et al., 2013;Meurk, Leung, Hall, Head, & Whiteford, 2016;Musiat et al., 2014).”
From eHealth interventions for family carers of people with long term illness: A promising approach? (2018) · cites this paper as Kumar et al., 2013
Because technology advances much faster than research methods, digital mental health tools are often obsolete by the time they are validated.
“The rapid pace of technology development relative to the slow pace of research methods often results in mental health technologies that are outdated and obsolete by the time they are validated (6).”
From Three Problems With Current Digital Mental Health Research . . . and Three Things We Can Do About Them (2017) · cites this paper as [6]
The unrealized potential of mHealth apps is in part due to difficulties of generating a solid evidence base to guide clinical, policy, and regulatory decision making.
“This is, in part, due to the difficulties of generating a solid evidence base to guide clinical, policy, and regulatory decision making [90].”
From A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review (2019) · cites this paper as [90]
Improvement work may rely on the repeated adjustment and refinement of interventions, often in a series of experiential learning cycles, and deploy interventions that are intentionally adapted in light of emergent information and evaluation.
“Such work may instead rely on the repeated adjustment and refinement of interventions, often in a series of experiential learning cycles, and deploy interventions that are intentionally adapted in light of emergent information and evaluation.11–13”
From Demystifying theory and its use in improvement (2015) · cites this paper as [13]
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Smartphone-Based Psychotherapeutic Micro-Interventions to Improve Mood in a Real-World Setting
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Distinguishing features in the assessment of mHealth apps
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Mobile applications for the treatment of tobacco use and dependence
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Creating an mHealth App for Colorectal Cancer Screening: User-Centered Design Approach
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A Depression Prevention Intervention for Adolescents in the Emergency Department
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Technology to assess and support self-management in serious mental illness
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Toward Usable Evidence: Optimizing Knowledge Accumulation in HCI Research on Health Behavior Change
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Users’ Perspectives on mHealth Self-Management of Bipolar Disorder: Qualitative Focus Group Study
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“It also has the ability to reduce costs in health care and improve its accessibility [7].”
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Unmet Need: Improving mHealth Evaluation Rigor to Build the Evidence Base
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Variations in the Use of mHealth Tools: The VA Mobile Health Study
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Practical considerations in the design and development of smartphone apps for behavior change
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“This is problematic, as design likely plays a key role in uncovering what types of BITs are most efficacious, for which populations, and under which circumstances.(30) Therefore, a state-of-the-art review of the design of recently evaluated BITs for youth with internalizing disorders would enrich the field’s understan…”
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A Novel Compensatory Motion Detection Method Using Multiple Signals and Machine Learning
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Mobile Mental Health Virtual Communities: Challenges and Opportunities
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- Authors
- Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., Riley, W. T., Shar, A., Spring, B., Spruijt-Metz, D., Hedeker, D., Honavar, V., Kravitz, R., Craig Lefebvre, R., Mohr, D. C., Murphy, S. A., Quinn, C., Shusterman, V., & Swendeman, D.
- Affiliations
- University of Memphis (US)
- National Institutes of Health (US)
- Duke University (US)
- University of California San Diego (US)
- Robert Wood Johnson Foundation (US)
- Northwestern University (US)
- University of Illinois Chicago (US)
- U.S. National Science Foundation (US)
- University of California, Davis (US)
- University of Michigan (US)
- University of Maryland, Baltimore (US)
- University of Pittsburgh (US)
- University of California, Los Angeles (US)
- Corresponding author
- Nilsen, Wendy J.
- ORCID
- Venue
- American Journal of Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 45
- Issue
- 2
- Pages
- 228-236
- ISSN
- 0749-3797 (ISSN-L)
- PMC
- PMC3803146
- OpenAlex
- W2142505388
- Cited by
- 900
- References
- 41
- Citations by year
- 2026: 28 · 2025: 29 · 2024: 54 · 2023: 35 · 2022: 75 · 2021: 81 · 2020: 104 · 2019: 109 · 2018: 85 · 2017: 91 · 2016: 89 · 2015: 66 · 2014: 41 · 2013: 11
- FWCI
- 124.98
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (Z99 OD999999, UL1TR000150)
- National Institute of Mental Health (P30MH058107)
- National Institute on Drug Abuse (P50DA010075)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Keywords
- mobile health, wearable health technology, evidence generation, evaluation standards, intervention evaluation, outcome measurement, healthcare cost reduction, well-being
- MeSH terms
- Diffusion of Innovation, Forecasting, Humans, Quality of Health Care, Reproducibility of Results, Computer Security, Outcome Assessment, Health Care, Telemedicine, Biomedical Technology, Quality Improvement
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