Psychometric evaluation of the Adelphi Adherence Questionnaire (ADAQ©) in adults with osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Medication non-adherence is a common issue in chronic illness. The World Health Organization has recognized a need for a valid and reliable method of measuring adherence to understand and mitigate non-adherence. This study aimed to psychometrically evaluate the English version of the Adelphi Adherence Questionnaire (ADAQ©), a questionnaire designed to assess patient-reported medication adherence across multiple therapy areas, in patients with Osteoarthritis (OA). METHODOLOGY: Data from the Adelphi OA Disease Specific Programme™, a survey of physicians and their consulting adult patients with OA conducted in the United States, November 2020 to March 2021, was used to assess the psychometric properties of the ADAQ. Patients completed the ADAQ, Adherence to Refills and Medication Scale (ARMS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), and EQ-5D-3L. The measurement model of the 13-item ADAQ was assessed and refined using latent variable modelling (Multiple Indicator Multiple Cause, confirmatory and exploratory factor analyses, item response theory, Mokken scaling, and bifactor analyses). Correlational analyses (Spearman's rank and polyserial as appropriate) with ARMS, WOMAC, and EQ-5D-3L scores assessed construct validity. Anchor- and distribution-based analyses were performed to estimate between-group clinically important differences (CID). RESULTS: Overall, 723 patients were included in this analysis (54.5% female, 69.0% aged ≥ 60). Latent variable modelling indicated a unidimensional reflective model was appropriate, with a bifactor model confirming an 11-item essentially unidimensional score. Items 12 and 13 were excluded from scoring as they measured a different concept. The ADAQ had high internal reliability with omega hierarchical and Cronbach's alpha coefficients of 0.89 and 0.97, respectively. Convergent validity was supported by moderate correlations with items of the ARMS, and physician-reported adherence and compliance. Mean differences in ADAQ score between high and low adherence groups yielded CID estimates between 0.49 and 1.05 points, with a correlation-weighted average of 0.81 points. CONCLUSION: This scoring model showed strong construct validity and internal consistency reliability when assessing medication adherence in OA. Future work should focus on confirming validity across a range of disease areas.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".