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Record W3204762441 · doi:10.1177/20552076211045579

Exploring the relationship between the usability of a goal-oriented mobile health application and non-usage attrition in patients with multimorbidity: A blended data analysis approach

2021· article· en· W3204762441 on OpenAlexafffund
Farah Tahsin, Shawn Tracy, Edward Chau, Sarah Harvey, Mayura Loganathan, Brian McKinstry, Stewart W Mercer, Jason X Nie, Tim Ramsay, Kednapa Thavorn, Ted E. Palen, Jasvinei Sritharan, Carolyn Steele Gray

Bibliographic record

VenueDigital Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of OttawaOttawa HospitalBridgepoint Active HealthcareTrillium Health CentreUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsUsabilityAttritionComputer scienceData scienceWorld Wide WebPsychologyHuman–computer interactionMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Mobile health applications are increasingly used to support the delivery of health care services to a variety of patients. Based on data obtained from a pragmatic trial of the electronic Patient Reported Outcome (ePRO) app designed to support goal-oriented care primary care, this study aims to (1) examine how patient-reported usability changed over the one-year intervention period, and (2) explore participant attrition rate of the electronic Patient Reported Outcome app over one year study period. METHODS: We performed a secondary analysis of 44 older adults with complex chronic needs enrolled in the electronic Patient Reported Outcome-digital health intervention. App usage and attrition were measured using device-generated usage logs; usability was measured using the patient-reported post-study system usability questionnaire collected at 3, 6, 9, and 12 months. Research memos were used to interpret potential contextual contributing factors to patients' overall usage and usability score pattern. A data triangulation method of both quantitative and qualitative data was used to analyze and interpret study findings. RESULTS: While there was gradual attrition in the use of the ePRO app, patients' usability scores remained consistent throughout the study period. Qualitative memos suggested patients' encounters with technical difficulties and relationship dynamics with primary providers influenced patients' adherence to the ePRO app. CONCLUSION: This study highlights that the patient-provider relationship is a key determining factor that influences complex patients' continued engagement with a Mobile health app. The finding calls attention to the measurement of usability of a Mobile health app, its impact on attrition, and contributing factors that influence patients' attrition. Trial registration: Clinicaltrials.gov Identified NCT02917954.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.052
metaresearch head score (Gemma)0.125
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.125
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.179
GPT teacher head0.414
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2021
Admission routes2
Has abstractyes

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