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Record W2735322872 · doi:10.3138/ptc.2016-87

Patients' Perspectives on and Experiences of Home Exercise Programmes Delivered with a Mobile Application

2017· article· en· W2735322872 on OpenAlexaffvenue
Hillary Abramsky, Puneet Kaur, Mikale Robitaille, Leanna Taggio, Paul K. Kosemetzky, Hillary Foster, Barbara E. Gibson, Maggie Bergeron, Patrick Jachyra

Bibliographic record

VenuePhysiotherapy Canada · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalToronto Rehabilitation InstituteUniversity of Toronto
FundersChildren's Hospital Foundation
KeywordsGeneral partnershipMedicinePhysical therapyQualitative researchPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Purpose: We explored patients' perspectives on home exercise programmes (HEPs) and their experiences using a mobile application designed to facilitate home exercise. Method: Data were generated using qualitative, semi-structured, face-to-face interviews with 10 participants who were receiving outpatient physiotherapy. Results: Establishing a therapeutic partnership between physiotherapists and patients enabled therapists to customize the HEPs to the patients' lifestyles and preferences. Analysis suggests that using the mobile application improved participants' ability to integrate the HEP into their daily life and was overwhelmingly preferred to traditional paper handouts. Conclusions: The results suggest that efforts to engage patients in HEPs need to take their daily lives into account. To move in this direction, sample exercise prescription questions are offered. Mobile applications do not replace the clinical encounter, but they can be an effective tool and an extension of delivering personalized HEPs in an existing therapeutic partnership.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.009
GPT teacher head0.358
Teacher spread0.349 · 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 designOther design
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

Citations7
Published2017
Admission routes2
Has abstractyes

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