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Record W4413191381 · doi:10.2196/73554

Digitally Mediated Occupational Therapy to Increase Physical Activity in Urban and Rural Breast Cancer Survivors: Protocol for a Single-Arm Feasibility Trial

2025· article· en· W4413191381 on OpenAlexvenueno aff
Tara C. Klinedinst, Zachary Pope, Michael C Robertson, Audrey Wint, Christina Henson, Darla E. Kendzor

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintProtocol (science)Breast cancerMedicinePhysical therapyOccupational therapyCancerGerontologyAlternative medicineComputer scienceWorld Wide WebInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The 5-year survival rate for breast cancer (BC) has increased in recent years. However, functional limitations associated with BC treatment (eg, loss of strength, fatigue, and lymphedema) often have far-reaching effects on survivors' physical and mental health. Aerobic physical activity (PA) and muscle-strengthening exercise (MSE) can reduce functional limitations, and occupational therapy (OT) can support these health-promoting behaviors after treatment. Yet, barriers to access among BC survivors (eg, time burden and distance to the OT clinic) limit participation in OT programing. This is particularly true in Oklahoma, where 33% of residents live in rural counties. Digital technologies (eg, telehealth) can help urban and rural BC survivors circumvent these barriers. OBJECTIVE: We are investigating the feasibility of a novel OT program among urban and rural BC survivors that features (1) 8 once-weekly telehealth OT sessions targeting constructs grounded in Self-Determination Theory (SDT), and (2) self-regulatory strategies known to support aerobic PA and MSE in BC survivors including self-monitoring via a wearable PA tracker, goal setting, and the provision of timely feedback. METHODS: This is a single-arm feasibility trial. We are recruiting 38 BC survivors using community-based recruitment approaches and via referral from collaborating oncologists. Participants include individuals who have undergone primary treatment and/or breast-conserving surgery or mastectomy for BC in the last 24 months and who do not meet recommended PA levels at the time of enrollment. We will assess self-reported program acceptability and feasibility via recruitment rates, study retention, and protocol adherence. We will also evaluate program safety by tracking BC-related lymphedema events, musculoskeletal injuries, and other adverse events. Finally, we will assess changes in aerobic PA, MSE, and health-related quality of life during the program period using accelerometry and self-report measurement tools. RESULTS: We received funding in March 2024 and institutional review board approval in September 2024. We began recruiting in November 2024. We anticipate completing data collection in early 2026. We hypothesize that the SDT-grounded OT program will be acceptable, feasible, and safe. We also expect pre- to post-program improvements in (1) SDT-informed determinants of PA, (2) levels of aerobic PA and MSE engagement, and (3) health-related quality of life. CONCLUSIONS: The novel OT program under investigation is designed to decrease barriers to engaging in aerobic PA and MSE among people who have undergone various BC treatments. It is centered on facilitating a successful transition from active treatment to the posttreatment period and combines OT with the benefits of telehealth delivery and health behavior change theory. This program is amenable to wide-scale dissemination and, if shown to be acceptable and feasible, will represent a promising approach to supportive cancer care. TRIAL REGISTRATION: ClinicalTrials.gov NCT06671730; https://clinicaltrials.gov/study/NCT06671730. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/73554.

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)0.024
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation 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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.062
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.021
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0620.012

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 head0.251
GPT teacher head0.563
Teacher spread0.312 · 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 designNon-randomized trial
Domainnot available
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

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