SPUR was developed as a novel PROM to better understand adherence and its drivers, valid across all chronic conditions and cultures. The SPUR adherence model and tool continue to be tested and proven in additional pathologies, geographies, and languages. Indeed, with SPUR, instead of having one point of data on non-adherence, through one questionnaire, a researcher can paint a picture of a population with 18 points of data.
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Citation and link to original article
Purpose: People living with COPD who struggle to take their medicines often experience poorer health outcomes such as exacerbations of symptoms, more frequent and lengthy hospital admissions, and worsening mortality rates. This study aimed to evaluate the psychometric properties of the previously validated SPUR-27 model, a multi-factorial model of medication adherence.
Patients and Methods: This cross-sectional study was conducted with 100 adult patients living with COPD in a hospital setting in Southwest London. Medication adherence was assessed using a shortened SPUR model (SPUR-27) against the validated Inhaler Adherence Scale (IAS) as a comparator. In addition, objective medication adherence data, presented as the Medication Possession Ratio (MPR), were derived from patient medical and pharmacy records. The COPD Assessment Tool (CAT) score was used to
examine the relationship between medication adherence and COPD symptom severity. Reliability of SPUR-27 was assessed using internal consistency estimates. Exploratory factor analysis, partial confirmatory factor analysis, and maximum likelihood analysis were conducted in conjunction with construct, concurrent, and known-group validity testing to explore the psychometric properties of the SPUR model in this population.
Results: A 7-factor model for SPUR-27 was derived with adequate factor loadings. SPUR (α=0.893) observed strong internal consistency (>0.8). The model was significantly positively correlated with IAS score (p<0.001) as well as MPR (p<0.01). A significant (p<0.01) relationship between poor medication adherence and worsening symptom severity, as defined by the CAT score, was identified for SPUR (χ2 = 8.570) using Chi-Square analysis. Furthermore, SPUR-27 demonstrated early evidence of validity with
good incremental fit indices: NFI (0.96), TFI (0.97), and CFI (0.93) were all reported as >0.9 in addition to the RMSEA, which was <0.08 (0.059).
Conclusion: SPUR demonstrated strong psychometric properties in patients living with COPD. Further work should look to examine the test–retest reliability of the model and its application in broader sample populations.
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We are embarking on an exciting new period for SPUR, as we investigate its potential. There are two major avenues of research,
Citation and link to original article
Purpose: Poor medication adherence (MA) is linked to an increased likelihood of hospital admission. Early interventions to address MA may reduce this risk and associated health-care costs. This study aimed to evaluate a holistic Patient Reported Outcome Measure (PROM) of MA, known as SPUR, as a predictor of general admission and early readmission in patients living with Type 2 Diabetes.
Patients and Methods: An observational study design was used to assess data collected over a 12-month period including 6-month retrospective and 6-month prospective monitoring of the number of admissions and early readmissions (admissions occurring within 30 days of discharge) across the cohort. Patients (n = 200) were recruited from a large South London NHS Trust. Covariates of interest included: age, ethnicity, gender, level of education, income, the number of medicines and medical conditions, and a Covid-19 diagnosis. A Poisson or negative binomial model was employed for count outcomes, with the exponentiated coefficient indicating incident ratios (IR) [95% CI]. For binary outcomes (Coefficient, [95% CI]), a logistic regression model was developed.
Results: Higher SPUR scores (increased adherence) were significantly associated with a lower number of admissions (IR = 0.98,[0.96, 1.00]). The number of medical conditions (IR = 1.07, [1.01, 1.13]), age ≥80 years (IR = 5.18, [1.01, 26.55]), a positive Covid-19 diagnosis during follow-up (IR = 1.83, [1.11, 3.02]) and GCSE education (IR = 2.11, [1.15,3.87]) were factors associated with a greater risk of admission. When modelled as a binary variable, only the SPUR score (−0.051, [−0.094, −0.007]) was significantly predictive of an early readmission, with patients reporting higher SPUR scores being less likely to experience an early readmission.
Conclusion: Higher levels of MA, as determined by SPUR, were significantly associated with a lower risk of general admissions and
early readmissions among patients living with Type 2 Diabetes.
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Results: The first objective, determining the ability of SPUR to identify patients at risk of non-adherence in hypertension, was demonstrated via correlations with existing PROMs. The ability of SPUR to flag patients at risk of depression and anxiety represents an important extension of its value. In a digital adherence technology (DAT), adding just one item from the PHQ-8 or GAD-7 in a dynamic interface, such as SPUR, does not represent an important increase in testing burden for the patient and can greatly enhance both the feedback to the patient as well as providing an important indication to the DAT itself as well as to the patient.
We are processing this data per your request only. We are hosting the data internally. We will not share it. This information will be used to send you information from the author's team, and you will not subscribed to any newsletters other marketing materials. We store the data for maximum of 3 years on secure servers. You can find out how to send a request to access your data, request to delete your data, correct any inaccuracies or restrict our processing of your data. You have the right to lodge a complaint about the way we handle your data with French Data Protection authority CNIL or you can contact our DPO at
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