SPUR-27 - Psychometric Properties of a Patient-Reported Outcome Measure of Medication Adherence in Chronic Obstructive Pulmonary Disease.

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 (χ 2 = 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.

SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes.

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.

The SPUR adherence profiling tool: preliminary results of algorithm development.

OBJECTIVE: The SPUR (Social, Psychological, Usage, and Rational) Adherence Profiling Tool is a recently developed adaptive instrument for measuring key patient-level risk factors for adherence problems. This study describes the SPUR questionnaire's psychometric refinement and evaluation. METHODS: Data were collected through an online survey among individuals with type 2 diabetes in the United States. 501 participants completed multiple questionnaires, including SPUR and several validated adherence measures. A Partial Credit Model (PCM) analysis was performed to evaluate the structure of the SPUR tool and verify the assumption of a single underlying latent variable reflecting adherence. Partial least-squares discriminant analyses (PLS-DA) were conducted to identify which hierarchically-defined items within each dimension needed to be answered by a given patient. Lastly, correlations were calculated between the latent trait of SPUR adherence and other patient-reported adherence measures. RESULTS: Of the 45 candidate SPUR items, 39 proved to fit well to the PCM confirming that SPUR responses reflected one underlying latent trait hypothesized as non-adherence. Correlations between the latent trait of the SPUR tool and other adherence measures were positive, statistically significant, and ranged from 0.32 to 0.48 (p-values < .0001). The person-item map showed that the items reflected well the range of adherence behaviors from perfect adherence to high levels of non-adherence. The PLS-DA results confirmed the relevance of using four meta-items as filters to open or close subsequent items from their corresponding SPUR dimensions. CONCLUSIONS: The SPUR tool represents a promising new adaptive instrument for measuring adherence accurately and efficiently using the digital behavioral diagnostic tool.

Finalization and Validation of Questionnaire and Algorithm of SPUR, a New Adherence Profiling Tool.

Purpose: The SPUR (Social, Psychological, Usage and Rational) Adherence Profiling Tool is a recently developed adaptive instrument for assessing key patient-level drivers for non-adherence. This study describes the SPUR questionnaire's finalization and psychometric evaluation. Patients and Methods: Data were collected through an online survey among patients with type 2 diabetes included by general practitioners and diabetologists in France. The survey included four questionnaires, SPUR and three validated adherence measures: BMQ, MARS and ACCEPT. Item-level analysis and a partial credit model (PCM) were performed to refine the response option coding of SPUR items. The final item selection of SPUR was defined using a PCM and a principal component analysis (PCA). Construct validity, concurrent validity and known-groups validity were assessed on the final SPUR questionnaire. Results: A total of 245 patients (55% men, mean age of 63 years) completed the survey remotely and were included in this analysis. Refining response option coding allowed a better discrimination of patients on the latent trait. After item selection, a short, an intermediate, and a long form composed the final SPUR questionnaire. The short form will be used to screen patients for risk and then the other forms will allow the collection of further information to refine the risk assessment and decide the best levers for action. Results obtained were supportive of the construct validity of the forms. Their concurrent validity was demonstrated: moderate to high significant correlations were obtained with BMQ, MARS and ACCEPT scores. Their known-groups validity were shown with a logical pattern of higher scores obtained for patients considered non-adherent and significant differences between the scores obtained for patients considered adherent versus non-adherent. Conclusion: SPUR is a valid tool to evaluate the risk of non-adherence of patients, allowing effective intervention by providing insights into the respective individual reasons for lack of adherence.

SPUR: psychometric properties of a patient-reported outcome measure of medication adherence in type 2 diabetes.

INTRODUCTION: Poor medication adherence is associated with worsening patient health outcomes and increasing healthcare costs. A holistic tool to assess both medication adherence and drivers of adherence behaviour has yet to be developed. This study aimed to examine SPUR, a multifactorial patient-reported outcome measure of medication adherence in patients living with type 2 diabetes, with a view to develop a suitable model for psychometric analysis.Furthermore, the study aimed to explore the relationship between the SPUR model and socio-clinical factors of medication adherence. RESEARCH DESIGN AND METHODS: The study recruited 378 adult patients living with type 2 diabetes from a mix of community and secondary-care settings to participate in this non-interventional cross-sectional study. The original SPUR-45 tool was completed by participants with other patient-reported outcome measures for comparison, in addition to the collection of two objective adherence measures; HbA1c and the medication possession ratio (MPR). RESULTS: Factor and reliability analysis conducted on SPUR-45 produced a revised and more concise version (27-items) of the tool, SPUR-27, which was psychometrically assessed. SPUR-27 observed strong internal consistency with significant correlations to the other psychometric measures (Beliefs about Medication Questionnaire, Diabetes Treatment Satisfaction Questionnaire, Medicine Adherence Rating Scale) completed by participants. Higher SPUR-27 scores were associated with lower HbA1c values and a higher MPR, as well as other predicted socio-clinical factors such as higher income, increased age and lower body mass index. CONCLUSIONS: SPUR-27 demonstrated strong psychometric properties. Further work should look to examine the test-retest reliability of the model as well as examine transferability to other chronic conditions and broader population samples. Overall, the initial findings suggest that SPUR-27 is a reliable model for the multifactorial assessment of medication adherence among patients living with type 2 diabetes.

Development of the SPUR tool: a profiling instrument for patient treatment behavior.

BACKGROUND: Long-term treatment adherence is a worldwide concern, with nonadherence resulting from a complex interplay of behaviors and health beliefs. Determining an individual's risk of nonadherence and identifying the drivers of that risk are crucial for the development of successful interventions for improving adherence. Here, we describe the development of a new tool assessing a comprehensive set of characteristics predictive of patients' treatment adherence based on the Social, Psychological, Usage and Rational (SPUR) adherence framework. Concepts from existing self-reporting tools of adherence-related behaviors were identified following a targeted MEDLINE literature review and a subset of these concepts were then selected for inclusion in the new tool. SPUR tool items, simultaneously generated in US English and in French, were tested iteratively through two rounds of cognitive interviews with US and French patients taking long-term treatments for chronic diseases. The pilot SPUR tool, resulting from the qualitative analysis of patients' responses, was then adapted to other cultural settings (China and the UK) and subjected to further rounds of cognitive testing. RESULTS: The literature review identified 27 relevant instruments, from which 49 concepts were included in the SPUR tool (Social: 6, Psychological: 13, Usage: 11, Rational: 19). Feedback from US and French patients suffering from diabetes, multiple sclerosis, or breast cancer (n = 14 for the first round; n = 16 for the second round) indicated that the SPUR tool was well accepted and consistently understood. Minor modifications were implemented, resulting in the retention of 45 items (Social: 5, Psychological: 14, Usage: 10, Rational: 16). Results from the cognitive interviews conducted in China (15 patients per round suffering from diabetes, breast cancer or chronic obstructive pulmonary disease) and the UK (15 patients suffering from diabetes) confirmed the validity of the tool content, with no notable differences being identified across countries or chronic conditions. CONCLUSION: Our qualitative analyses indicated that the pilot SPUR tool is a promising model that may help clinicians and health systems to predict patient treatment behavior. Further steps using quantitative methods are needed to confirm its predictive validity and other psychometric properties.

The SPUR Model: A Framework for Considering Patient Behavior.

BACKGROUND: Medication nonadherence is a global problem that requires urgent attention. Roughly half of all drugs that are prescribed for chronic treatments are not taken by the patients in question. Initiatives designed to support patients and help them modify their behavior are enhanced by personalization, and a number of profiling tools exist to help customize such interventions. Most of these tools were originally designed as paper-based questionnaires, but the growth of digital adherence technologies (DATs) illuminate the need for the development of digital profiling systems that can interact with fully automated patient interfaces. OBJECTIVE: The objective of this study was to examine existing frameworks from medicine, psychology, sociology, consumer behavior, and economics to elaborate a comprehensive, quantitative profiling approach that can be used to drive the customization of patient support initiatives. RESULTS: Building primarily on Icek Ajzen's Theory of Planned Behavior (TPB), the Health Belief Model (HBM) was used to inform the beliefs about behavior posited in the TPB, while incorporating established factors regarding self-efficacy in the "control" elements of the TPB and selected social and psychological factors in the other constituents of the model. The resulting SPUR (Social, Psychological, Usage, Rational) framework represents a holistic, profiling tool with detailed, quantitative outputs that describe a patient's behavioral risks and the drivers of that risk. CONCLUSION: An interactive, digital questionnaire built around SPUR represents a potentially useful tool for those desirous of building interactive digital support programs for patients with chronic diseases.