Improving Health for Older Adults With Pain Through Engagement: Protocol for Tailoring and Open Pilot Testing of a Mind-Body Activity Program Delivered Within Shared Medical Visits in an Underserved Community Clinic
Bibliographic record
Abstract
BACKGROUND: Chronic musculoskeletal pain is prevalent and disabling among older adults in underserved communities. Psychosocial pain management is more effective than pharmacological treatment in older adults. However, underserved community clinics often lack psychosocial treatments, in part because of a lack of trained providers. Shared medical appointments, in which patients undergo brief medical evaluation, monitoring, counseling, and group support, are an efficacious and cost-effective method for chronic disease management in underserved clinics, reducing the need for specialized providers. However, shared medical visits are often ineffective for chronic pain, possibly owing to lack of inclusion of skills most relevant for older adults (eg, pacing to increase engagement in daily activities). OBJECTIVE: We have described the protocol for the development and initial pilot effectiveness testing of the GetActive+ mind-body activity intervention for older adults with chronic pain. GetActive+ was adapted from GetActive, an evidence-based intervention that improved pain outcomes among mostly affluent White adults. We aim to establish the initial feasibility, acceptability, fidelity, and effectiveness of GetActive+ when delivered as part of shared medical appointments in a community clinic. METHODS: We conducted qualitative focus groups and individual interviews with providers (n=25) and English-speaking older adults (aged ≥55 y; n=18) with chronic pain to understand the pain experience in this population, perceptions about intervention content, and barriers to and facilitators of intervention participation and implementation in this setting. Qualitative interviews with Spanish-speaking older adults are in progress and will inform a future open pilot of the intervention in Spanish. We are currently conducting an open pilot study with exit interviews in English (n=30 individuals in total). Primary outcomes are feasibility (≥75% of patients who are approached agree to participate), acceptability (≥75% of patients who enrolled complete 8 out of 10 sessions; qualitative), and fidelity (≥75% of session components are delivered as intended). Secondary outcomes include physical function-self-reported, performance based (6-minute walk test), and objective (step count)-and emotional function (depression and anxiety). Other assessments include putative mechanisms (eg, mindfulness and pain catastrophizing). RESULTS: We began enrolling participants for the qualitative phase in November 2022 and the open pilot phase in May 2023. We completed the qualitative phase with providers and English-speaking patients, and the results are being analyzed using a hybrid, inductive-deductive approach. We conducted rapid analysis of these data to develop GetActive+ before the open pilot in English, including increasing readability and clarity of language, reducing the number of skills taught to increase time for individual check-ins and group participation, and increasing experiential exercises for skill uptake. CONCLUSIONS: We provide a blueprint for the refinement of a mind-body activity intervention for older adults with chronic pain in underserved community clinics and for incorporation within shared medical visits. It will inform a future, fully powered, effectiveness-implementation trial of GetActive+ to help address the chronic pain epidemic among older adults. TRIAL REGISTRATION: ClinicalTrials.gov NCT05782231; https://clinicaltrials.gov/study/NCT05782231. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52117.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".