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Record W4415299690 · doi:10.2196/preprints.86021

Digital Peer-Support for Family Caregivers of Individuals with Neuromuscular Disease: A Randomized Controlled Trial (Preprint)

2025· preprint· W4415299690 on OpenAlexaboutno aff
Samantha Mekhuri, Joseph Munn, Francine Buchanan, Nouma Hammash, Munazzah Ambreen, Sara Ahola Kohut, Louise Rose, Reshma Amin

Bibliographic record

Venuenot available
Typepreprint
Language
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsFamily caregiversRandomized controlled trialIntervention (counseling)Depression (economics)Caregiver burdenPsychological interventionAnalysis of covariancePrimary caregiver

Abstract

fetched live from OpenAlex

BACKGROUND Neuromuscular diseases (NMD) affect nerves and muscles resulting in weakness and often profound disability. Family caregivers of individuals with NMD experience significant burden and negative health effects. Peer support may help to ameliorate these negative impacts. OBJECTIVE To evaluate the effect of a digital peer-support intervention compared to usual care on caregiver mastery, competence, stress, burden, anxiety, and depression among family caregivers of individuals with NMD. METHODS We conducted a parallel group randomized controlled superiority trial. We recruited family caregivers of individuals (children and adults) with NMD. The 12-week intervention comprised access to a trained peer mentor via aTouchAway (Aetonix, Canada) and weekly digital discussion forums. Primary outcome was caregiver mastery (Pearlin Mastery Scale (PMS)) adjusted for baseline score. Secondary outcomes included caregiver stress, competence, burden, anxiety, and depression. We calculated adjusted (for baseline score) mean differences (aMDs) using analysis of covariance and generated multivariable linear regression models exploring associations with the intervention and caregiver age, years of caregiving, care recipient medical diagnosis, care recipient ventilation type, adjusting for baseline outcome scores. RESULTS We recruited 100 participants, mean (SD) age 46.8 (11.6) years, 70% mothers, with mean (SD) length of caregiving 11.8 (7.6) years. We found no difference in 12-week PMS scores (aMD 0.67; 95% CI -1.7 to 3.1). We also found no difference in any of our secondary outcomes. Mentors and participants sent a mean (SD) of 21.3 (33.3) and 17.7 (33.0) messages, respectively. Overall, 62% of participants and 92% of mentors engaged in at least one program element for ≥ 8 of the 12 weeks. CONCLUSIONS Our 12-week digital peer support program had no effect on caregiver mastery or other caregiving or psychological outcomes. This might be due to only moderate fidelity. Future research should focus on peer support interventions that are better tailored to improve intervention fidelity and ultimately caregiver well-being. CLINICALTRIAL ClinicalTrials.gov NCT05070624; https://clinicaltrials.gov/study/NCT05070624

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized triallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized triallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.028
GPT teacher head0.337
Teacher spread0.309 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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