Evidence map›Paper›PMID 39546781›Full record

ArticleJournal of medical Internet research2024

Human Factors, Human-Centered Design, and Usability of Sensor-Based Digital Health Technologies: Scoping Review.

Animesh Tandon, Bryan Cobb, Jacob Centra, Elena Izmailova, Nikolay V Manyakov, Samantha McClenahan, Smit Patel, Emre Sezgin, Srinivasan Vairavan, Bernard Vrijens and 2 more

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Animesh TandonDivision of Cardiology and Cardiovascular Medicine, Department of Heart, Vascular, and Thoracic, Children's Institute, Cleveland Clinic Children's, Cleveland, OH, United States.ORCID https://orcid.org/0000-0001-9769-8801
Bryan CobbHealthcare Innovations Delivery, Neurology, Medical Affairs, Genentech, San Francisco, CA, United States.ORCID https://orcid.org/0000-0001-7147-8720
Jacob CentraDigital Medicine Society, Boston, MA, United States.ORCID https://orcid.org/0009-0009-6691-7923
Elena IzmailovaKoneksa Health, New York, NY, United States.ORCID https://orcid.org/0000-0002-7150-1748
Nikolay V ManyakovData Science and Digital Health, Johnson & Johnson Innovative Medicine, Beerse, Belgium.ORCID https://orcid.org/0000-0001-9037-1400
Samantha McClenahanDigital Medicine Society, Boston, MA, United States.ORCID https://orcid.org/0000-0002-1792-9700
Smit PatelDigital Medicine Society, Boston, MA, United States.ORCID https://orcid.org/0000-0002-9186-0430
Emre SezginThe Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States.ORCID https://orcid.org/0000-0001-8798-9605
Srinivasan VairavanJohnson & Johnson Innovative Medicine, New Jersey, NJ, United States.ORCID https://orcid.org/0000-0001-7975-8436
Bernard VrijensAARDEX Group, Liège, Belgium.ORCID https://orcid.org/0000-0002-9090-1253
Jessie P BakkerDigital Medicine Society, Boston, MA, United States.ORCID https://orcid.org/0000-0002-2976-4747
Digital Health Measurement Collaborative Community (DATAcc) hosted by DiMeSee Acknowledgements, .

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreasing adoption of sensor-based digital health technologies (sDHTs) in recent years has cast light on the many challenges in implementing these tools into clinical trials and patient care at scale across diverse patient populations; however, the methodological approaches taken toward sDHT usability evaluation have varied markedly.

objectiveThis review aims to explore the current landscape of studies reporting data related to sDHT human factors, human-centered design, and usability, to inform our concurrent work on developing an evaluation framework for sDHT usability.

methodsWe conducted a scoping review of studies published between 2013 and 2023 and indexed in PubMed, in which data related to sDHT human factors, human-centered design, and usability were reported. Following a systematic screening process, we extracted the study design, participant sample, the sDHT or sDHTs used, the methods of data capture, and the types of usability-related data captured.

resultsOur literature search returned 442 papers, of which 85 papers were found to be eligible and 83 papers were available for data extraction and not under embargo. In total, 164 sDHTs were evaluated; 141 (86%) sDHTs were wearable tools while the remaining 23 (14%) sDHTs were ambient tools. The majority of studies (55/83, 66%) reported summative evaluations of final-design sDHTs. Almost all studies (82/83, 99%) captured data from targeted end users, but only 18 (22%) out of 83 studies captured data from additional users such as care partners or clinicians. User satisfaction and ease of use were evaluated for 83% (136/164) and 91% (150/164) of sDHTs, respectively; however, learnability, efficiency, and memorability were reported for only 11 (7%), 4 (2%), and 2 (1%) out of 164 sDHTs, respectively. A total of 14 (9%) out of 164 sDHTs were evaluated according to the extent to which users were able to understand the clinical data or other information presented to them (understandability) or the actions or tasks they should complete in response (actionability). Notable gaps in reporting included the absence of a sample size rationale (reported for 21/83, 25% of all studies and 17/55, 31% of summative studies) and incomplete sociodemographic descriptive data (complete age, sex/gender, and race/ethnicity reported for 14/83, 17% of studies).

conclusionsBased on our findings, we suggest four actionable recommendations for future studies that will help to advance the implementation of sDHTs: (1) consider an in-depth assessment of technology usability beyond user satisfaction and ease of use, (2) expand recruitment to include important user groups such as clinicians and care partners, (3) report the rationale for key study design considerations including the sample size, and (4) provide rich descriptive statistics regarding the study sample to allow a complete understanding of generalizability to other patient populations and contexts of use.

Indexed as

User-Centered DesignBiomedical TechnologyDigital HealthDigital TechnologyErgonomicsHumansWearable Electronic Devicescliniciansconnected caredecentralizeddigital healthergonomicshuman-centered designhuman factorsmobile phoneremotescreeningsensorssystematic scoping reviewusabilityuser experiencewearable

Identifiers

PMID39546781
PMCPMC11607562

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.