ArticleScientific reports2025
Predicting and understanding non-adherence in chronic disease: cross-cohort validation and structural equation modeling of the SPUR 6/24 tool.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Shared decision-making and medication adherence among community adults with chronic diseases: a cross-sectional study in Hubei Province, China.BMC medical informatics and decision making · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The SPUR tool measures the risk of non-adherence for patients with chronic disease, as well as measuring the relative importance of thirteen behavioral drivers contributing to that risk. Over a period of four years, five different cohorts of patients in three countries and three different pathologies were studied to contribute to the elaboration and refinement of two patient-reported adherence measures: SPUR 6 and SPUR 24. This article examines the results of retrofitting of both of these tools to earlier patient cohorts as well as analyzing the pooled dataset via the use of both tools in order to further study the predictive potential of both. A further analysis was carried out using structural equation modeling both to test the structural validity of the SPUR tools and to examine both indirect and direct influence of the thirteen drivers on patient behavior.Direct comparisons of the SPUR tools to other patient-reported adherence measures across datasets and across the pooled dataset was carried out by analysis of Spearman's ranked correlation coefficients. The structural equation modeling was carried out using path analysis based on the decision-making schema hypothesized in the foundational SPUR article.The retrofitted analysis and the pooled data analysis both support the use of SPUR 6 and SPUR 24 to assess the risk of non-adherence of patients with chronic disease with respect to other widely used patient reported adherence measures. The structural equation modeling reinforced the hypothesis that the social and psychological drivers of SPUR have a significant indirect impact on non-adherence risk via the rational and usage drivers as well as their direct impact on non-adherence risk.SPUR 6 and SPUR 24 have demonstrated predictive value in assessing the risk of patient non-adherence as compared to their predecessors as well as to other widely-used patient adherence measures, across countries and pathologies. The social and psychological drivers of SPUR seem to drive behavior largely through their influence on rational and usage factors, indicating a cognitive rationalization process . These insights have direct implications for communication strategy towards patients in efforts to enhance medication adherence.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.