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Record W4412521451 · doi:10.1186/s12891-025-08918-z

Co-development of an evidence-informed, theoretically driven exercise programme for people with chronic non-specific neck pain (the EPIC-Neck programme - “Exercise Prescription Improved through Co-design”)

2025· article· en· W4412521451 on OpenAlexaff
Jonathan Price, Alison Rushton, Natalie Ives, Kate Jolly, Colin Greaves

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsWestern University
FundersNational Institute for Health Research Applied Research Collaboration WestDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsMedicinePhysical therapyIntervention (counseling)Biopsychosocial modelNeck painPhysical medicine and rehabilitationSports medicineExercise prescriptionMedical prescriptionIntervention mappingAlternative medicineProtocol (science)NursingPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Guidelines recommend neck exercise as a key intervention for chronic non-specific neck pain, yet current exercise programmes show modest effects and poor patient engagement. This study aimed to co-develop a neck exercise programme that maximizes effectiveness and engagement. METHODS: Intervention Mapping steps 1-4 were employed with input from a diverse patient group (n = 17). In Step 1, outcomes/changes that the intervention aims to improve were synthesized from literature and patient workshops. To maximise engagement, Step 2 identified target behaviours (performance objectives), and their determinants from clinical guidelines, literature, and patient workshops. In Step 3, change techniques for each determinant were selected using the Theory and Techniques Tool and patient workshops. Techniques were organized into a logic model and framed within a "best fit" existing behaviour change theory to guide clinical practice. To maximise effectiveness, Step 2 identified exercise objectives from systematic reviews and expert consensus, describing the mechanisms through which exercise affects outcomes. Step 3 identified the most effective exercises and tailoring strategies to optimise exercise objectives. Resources to support delivery in clinical practice were co-developed with patients and physiotherapists in Step 4. RESULTS: The EPIC-Neck intervention aims to improve outcomes including pain, disability, function, sleep, mental well-being and relationship impact, based on individual patient needs. A biopsychosocial exercise prescription framework informs exercise tailoring to optimize neuromuscular function, pain self-efficacy, night pain, cognitive control, social support; and reduce catastrophic thinking/fear avoidance, depending on a patients desired outcome. Patients need to achieve four performance objectives to manage neck pain effectively with exercise: (1) performing specific neck exercises, (2) independently adapting and progressing their neck exercises, (3) using specific neck exercises during flare-ups, and (4) initiating general exercise. To maximise engagement, a facilitation guide was developed based on the Process Model of Lifestyle Behaviour Change. The guide addresses 35 determinants using 24 change techniques, including goal setting, motivation enhancement, social support, action planning, self-monitoring, problem-solving support, shared decision-making, and patient-centred communication. CONCLUSION: This study co-developed an evidence-informed, theoretically driven exercise programme designed to enhance both effectiveness and patient engagement. Future work will assess its feasibility and acceptability to patients and physiotherapists, and in the long-term establish its clinical and cost-effectiveness.

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)0.042
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.008
Research integrity0.0010.002
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 head0.019
GPT teacher head0.305
Teacher spread0.287 · 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 designQualitative
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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