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Record W3207657071 · doi:10.2196/30495

Patients’ Use of Mobile Health for Self-management of Knee Osteoarthritis: Results of a 6-Week Pilot Study

2021· article· en· W3207657071 on OpenAlexafffundvenue
Brittany Shewchuk, Lee A. Green, Tanya Barber, Jean Miller, Sylvia Teare, Denise Campbell‐Scherer, Kelly Mrklas, Linda Li, Nancy Marlett, Tracy Wasylak, Elena Lopatina, Deirdre McCaughey, Deborah A. Marshall

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of British ColumbiaAlberta Health ServicesAlberta HealthUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsUsabilitymHealthMedicineQuality of life (healthcare)Self-managementPhysical therapySystem usability scaleKnee painPatient satisfactionOsteoarthritisPsychological interventionNursingAlternative medicineWeb usabilityComputer science

Abstract

fetched live from OpenAlex

Background In a previous study, a prototype mobile health (mHealth) app was co-designed with patients, family physicians, and researchers to enhance self-management and optimize conservative management for patients with mild to moderate knee osteoarthritis (OA). Objective This study aims to evaluate the overall usability, quality, and effectiveness of the mHealth app prototype for aiding knee OA self-management from the perspectives of patients with OA and health care providers (HCPs). Methods Using methods triangulation of qualitative and quantitative data, we conducted a pilot evaluation of an mHealth app prototype that was codeveloped with patients and HCPs. We recruited adult patients aged ≥20 years with early knee OA (n=18) who experienced knee pain on most days of the month at any time in the past and HCPs (n=7) to participate. In the qualitative assessment, patient and HCP perspectives were elicited on the likeability and usefulness of app features and functionalities and the perceived impact of the app on patient-HCP communication. The quantitative assessment involved evaluating the app using usability, quality, and effectiveness metrics. Patient baseline assessments included a semistructured interview and survey to gather demographics and assess the quality of life (European Quality-of-Life 5-Dimension 5-Level Questionnaire [EQ-5D-5L]) and patient activation (patient activation measure [PAM]). Following the 6-week usability trial period, a follow-up survey assessed patients’ perceptions of app usability and quality and longitudinal changes in quality of life and patient activation. Semistructured interviews and surveys were also conducted with HCPs (n=7) at baseline to evaluate the usability and quality of the app prototype. Results Interviews with patients and HCPs revealed overall positive impressions of the app prototype features and functionalities related to likeability and usefulness. Between the baseline and follow-up patient assessments, the mean EQ-5D-5L scores improved from 0.77 to 0.67 (P=.04), and PAM scores increased from 80.4 to 87.9 (P=.01). Following the 6-week evaluation, patients reported a mean System Usability Scale (SUS) score of 57.8, indicating marginal acceptability according to SUS cutoffs. The mean number of goals set during the usability period was 2.47 (SD 3.08), and the mean number of activities completed for knee OA self-management during the study period was 22.2 (SD 17.8). Spearman rank correlation (rs) calculations revealed that the follow-up PAM scores were weakly correlated (rs=−0.32) with the number of goals achieved and the number (rs=0.19) of activities performed during the 6-week usability period. HCPs reported a mean SUS score of 39.1, indicating unacceptable usability. Conclusions This evidence-based and patient-centered app prototype represents a potential use of mHealth for improving outcomes and enhancing conservative care by promoting patient activation and patient-HCP communication regarding OA management. However, future iterations of the app prototype are required to address the limitations related to usability and quality.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.388
Teacher spread0.306 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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".

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Citations23
Published2021
Admission routes3
Has abstractyes

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