Barriers to and Facilitators of Engagement With Remote Measurement Technology for Managing Health: Systematic Review and Content Analysis of Findings
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Abstract
BACKGROUND: Remote measurement technology refers to the use of mobile health technology to track and measure change in health status in real time as part of a person's everyday life. With accurate measurement, remote measurement technology offers the opportunity to augment health care by providing personalized, precise, and preemptive interventions that support insight into patterns of health-related behavior and self-management. However, for successful implementation, users need to be engaged in its use. OBJECTIVE: Our objective was to systematically review the literature to update and extend the understanding of the key barriers to and facilitators of engagement with and use of remote measurement technology, to guide the development of future remote measurement technology resources. METHODS: We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines involving original studies dating back to the last systematic review published in 2014. We included studies if they met the following entry criteria: population (people using remote measurement technology approaches to aid management of health), intervention (remote measurement technology system), comparison group (no comparison group specified), outcomes (qualitative or quantitative evaluation of the barriers to and facilitators of engagement with this system), and study design (randomized controlled trials, feasibility studies, and observational studies). We searched 5 databases (MEDLINE, IEEE Xplore, EMBASE, Web of Science, and the Cochrane Library) for articles published from January 2014 to May 2017. Articles were independently screened by 2 researchers. We extracted study characteristics and conducted a content analysis to define emerging themes to synthesize findings. Formal quality assessments were performed to address risk of bias. RESULTS: A total of 33 studies met inclusion criteria, employing quantitative, qualitative, or mixed-methods designs. Studies were conducted in 10 countries, included male and female participants, with ages ranging from 8 to 95 years, and included both active and passive remote monitoring systems for a diverse range of physical and mental health conditions. However, they were relatively short and had small sample sizes, and reporting of usage statistics was inconsistent. Acceptability of remote measurement technology according to the average percentage of time used (64%-86.5%) and dropout rates (0%-44%) was variable. The barriers and facilitators from the content analysis related to health status, perceived utility and value, motivation, convenience and accessibility, and usability. CONCLUSIONS: The results of this review highlight gaps in the design of studies trialing remote measurement technology, including the use of quantitative assessment of usage and acceptability. Several processes that could facilitate engagement with this technology have been identified and may drive the development of more person-focused remote measurement technology. However, these factors need further testing through carefully designed experimental studies. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42017060644; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=60644 (Archived by WebCite at http://www.webcitation.org/70K4mThTr).
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Many researchers have included analytic indicators as a study measure when evaluating apps, but they are not consistent or systematic in their selection.
“While many researchers have included analytic indicators as a study measure when evaluating apps, they are not consistent or systematic in their selection [26].”
From A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review (2019) · cites this paper as [26]
Simblett et al found that studies were reporting idiosyncratic measures of engagement and adherence that were not comparable across studies.
“In a recent review on the barriers and facilitators of engagement with remote measurement technology for managing health, Simblett et al found that studies were reporting idiosyncratic measures of engagement and adherence that were not comparable across studies [26].”
From A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review (2019) · cites this paper as [26]
Two recent systematic reviews identified the use of participatory research methods in eHealth intervention development as the 1 key factor in determining the acceptability and usability of most eHealth interventions.
“Overall, 2 recent systematic reviews on the topic have repeatedly identified the use of participatory research methods in eHealth intervention development as the 1 key factor in determining the acceptability and usability of most eHealth (especially electronic mental health [e-mental health]) interventions [44,50].”
From A multicomponent eHealth intervention for family Carers for people affected by psychosis: a coproduced design and build study (2019) · cites this paper as [50]
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Home-monitoring of vital capacity in people with a motor neuron disease
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- Authors
- Simblett, S., Greer, B., Matcham, F., Curtis, H., Polhemus, A., Ferrão, J., Gamble, P., & Wykes, T.
- Affiliations
- King's College London (GB)
- MSD (Czech Republic) (CZ)
- Corresponding author
- Simblett, Sara
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2018-07-12
- Volume
- 20
- Issue
- 7
- Pages
- e10480
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC6062692
- OpenAlex
- W2801980712
- Cited by
- 270
- References
- 39
- Citations by year
- 2026: 22 · 2025: 37 · 2024: 27 · 2023: 40 · 2022: 42 · 2021: 43 · 2020: 32 · 2019: 22 · 2018: 5
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Funders
- National Institute for Health and Care Research (NF-SI-0514-10028)
- European Commission (115902, HORIZON2020)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Demography › Technology Use by Older Adults
- Keywords
- remote monitoring, mobile health, user engagement, barriers to use, technology acceptability, technology usability, self-management, health behavior, dropout rates, usage statistics, personalized interventions, mental health, physical health, motivation
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Female, Humans, Male, Middle Aged, Patient Participation, Prospective Studies, Telemedicine, Young Adult, Remote Sensing Technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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