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Record W4403020484 · doi:10.2196/57635

Patient Engagement in a Mobile App–Based Rehabilitation Program for Total Hip or Knee Arthroplasty: Secondary Data Analysis of a Randomized Controlled Trial

2024· article· en· W4403020484 on OpenAlexvenueno aff
Qingling Wang, Regina Lee, Sharyn Hunter, Aiyong Zhu, Sally Wai‐Chi Chan

Bibliographic record

VenueJMIR mhealth and uhealth · 2024
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationPsychological interventionPhysical therapyRandomized controlled trialMedicineArthroplastyPatient educationHealth careNursingSurgery

Abstract

fetched live from OpenAlex

Background Health care professionals use mobile apps to support patients’ rehabilitation after total hip or knee arthroplasty. Understanding patient engagement in such mobile health interventions can help tailor these interventions to better support patients. Objective This study aimed to investigate patient engagement in a mobile app–based arthroplasty rehabilitation program and to investigate the association between patient engagement and their characteristics. Methods Data were extracted from a pool of 42 participants in the experimental arm of a randomized controlled trial that used a mobile app (WeChat [Tencent Holdings Limited])–based program to support patients’ rehabilitation after total hip or knee arthroplasty. The primary outcomes were the number of days the participants accessed the program and completed recommended rehabilitation tasks. Secondary outcomes included data on the participants’ posts on a discussion forum, messages sent by the participants, access to the program components, and reading and sharing the program content. Generalized linear models were used to analyze the association between patient engagement and personal characteristics. Results The participants reported in a rehabilitation diary accessing the program on a mean of 5.2 (SD 2) days per week and completing recommended rehabilitation tasks on a mean of 6.5 (SD 0.8) days per week. The majority (31/42, 74%) posted on the discussion forum, with a mean of 18.1 (SD 21.2) posts. Most participants (37/42, 88%) sent messages to health care professionals, with a mean of 14 (SD 15.9) messages. The program components were visited for a total of 525 times. The program content was read 898 times and shared 82 times in total. Generalized linear models showed that both primary outcomes, the number of days the participants accessed the program (B=6.46, 95% CI 1.98-15.35; χ21=11.1, P=.001) and the number of days they completed rehabilitation tasks (B=2.65, 95% CI 0.45-5.48; χ21=5.7, P=.02), were positively associated with having a high school education or above. In addition, the number of posts on the discussion forum was positively associated with living with family, having a high school education or above, undergoing total knee arthroplasty, having comorbidities, and the score of self-efficacy but was negatively associated with age. The number of messages sent by the participants was positively associated with having a high school education or above, having comorbidities, and the score of self-efficacy. Conclusions Patient engagement in mobile arthroplasty rehabilitation is associated with their education level, cohabitation status, age, type of surgery, presence of comorbidities, and sense of self-efficacy. Program developers can consider these characteristics and use strategies, such as family involvement, in the design of mobile arthroplasty rehabilitation programs to enhance patient engagement in such interventions. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12621000867897; https://tinyurl.com/mtdw25fp

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)0.028
metaresearch head score (Gemma)0.056
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.056
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0130.018
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0130.001

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 head0.035
GPT teacher head0.385
Teacher spread0.350 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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