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Record W4394951574 · doi:10.3233/jpd-230266

Adherence to Non-Pharmacological Interventions in Parkinson’s Disease: A Rapid Evidence Assessment of the Literature

2024· review· en· W4394951574 on OpenAlexafffund
John Li, Nimrit Aulakh, Ivan Culum, Angela Roberts

Bibliographic record

VenueJournal of Parkinson s Disease · 2024
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNational Institutes of HealthCanada Research Chairs
KeywordsParkinson's diseasePsychological interventionMedicineDiseaseIntensive care medicinePsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Background: Low adherence to non-pharmacological interventions can impact treatment effectiveness. Yet, there is limited information on adherence barriers and facilitators to non-pharmacological interventions in Parkinson's disease (PD). Objective: 1) To examine the quality of adherence reporting and 2) to identify key determinants of adherence to PD non-pharmacological interventions. Methods: A rapid evidence assessment was conducted, following PRISMA guidelines, that included controlled studies of exercise, physiotherapy, occupational therapy, speech-language therapy with explicit reporting of 'adherence' OR 'compliance', published in the last 15 years. Data extracted included: adherence rates, adherence outcomes, and factors associated with adherence. A collaborative thematic analysis was conducted to identify determinants of adherence. Results: The search yielded 2,445 articles of which 114 met criteria for full screening with 45 studies meeting all inclusion criteria. High quality adherence data that aligned with the intervention goals were reported by 22.22%(N = 10) of studies, with the majority reporting attendance/attrition rates only 51.11%(N = 23). Four major themes (34 subthemes) emerged: disease and health, personal, program design, and system and environmental. Conclusions: There has been limited progress in the quality of adherence reporting in PD non-pharmacological interventions over the last decade. Acknowledging this limitation, key determinants of adherence included: alignment with personal beliefs, attitudes, and expectations; the demands of the intervention and worsening disease symptoms and personal/time obligations; and accessibility and safety concerns. Program design elements found to facilitate adherence included: opportunities for social engagement and in-person offerings linked to higher levels of interventionist support, performative feedback, and social reinforcement.

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)0.113
metaresearch head score (Gemma)0.242
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.113
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1130.242
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0090.009
Bibliometrics0.0370.023
Science and technology studies0.0020.002
Scholarly communication0.0090.009
Open science0.0040.007
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.113
GPT teacher head0.449
Teacher spread0.336 · 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 designSystematic review
Domainnot available
GenreReview

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 routes2
Has abstractyes

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