D Pinto1, U Bockenholt2, J Lee3, R W Chang4, L Sharma5, D J Finn6, A W Heinemann7, J L Holl8, P Hansen9. 1. Marquette University, Walter Schroeder Complex, Suite 346, P.O. Box 1881, Milwaukee, WI 53201-1881, USA. Electronic address: d.pinto@marquette.edu. 2. Northwestern University, Kellogg School of Management, Evanston, IL, USA. 3. Department of Preventive Medicine - Biostatistics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. 4. Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. 5. Department of Medicine (Rheumatology), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. 6. Center for Healthcare Studies, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. 7. Shirley Ryan AbilityLab, Chicago, IL, USA. 8. Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. 9. Department of Economics, University of Otago, Dunedin, New Zealand.
Abstract
OBJECTIVE: To investigate individual preferences for physical activity (PA) attributes in adults with chronic knee pain, to identify clusters of individuals with similar preferences, and to identify whether individuals in these clusters differ by their demographic and health characteristics. DESIGN: An adaptive conjoint analysis (ACA) was conducted using the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method to determine preference weights representing the relative importance of six PA attributes. Cluster analysis was performed to identify clusters of participants with similar weights. Chi-square and ANOVA were used to assess differences in individual characteristics by cluster. Multinomial logistic regression was used to assess associations between individual characteristics and cluster assignment. RESULTS: The study sample included 146 participants; mean age 65, 72% female, 47% white, non-Hispanic. The six attributes (mean weights in parentheses) are: health benefit (0.26), enjoyment (0.24), convenience (0.16), financial cost (0.13), effort (0.11) and time cost (0.10). Three clusters were identified: Cluster 1 (n = 33): for whom enjoyment (0.35) is twice as important as health benefit; Cluster 2 (n = 63): for whom health benefit (0.38) is most important; and Cluster 3 (n = 50): for whom cost (0.18), effort (0.18), health benefit (0.17) and enjoyment (0.18) are equally important. Cluster 1 was healthiest, Cluster 2 most self-efficacious, and Cluster 3 was in poorest health. CONCLUSIONS: Patients with chronic knee pain have preferences for PA that can be distinguished effectively using ACA methods. Adults with chronic knee pain, clustered by PA preferences, share distinguishing characteristics. Understanding preferences may help clinicians and researchers to better tailor PA interventions.
OBJECTIVE: To investigate individual preferences for physical activity (PA) attributes in adults with chronic knee pain, to identify clusters of individuals with similar preferences, and to identify whether individuals in these clusters differ by their demographic and health characteristics. DESIGN: An adaptive conjoint analysis (ACA) was conducted using the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method to determine preference weights representing the relative importance of six PA attributes. Cluster analysis was performed to identify clusters of participants with similar weights. Chi-square and ANOVA were used to assess differences in individual characteristics by cluster. Multinomial logistic regression was used to assess associations between individual characteristics and cluster assignment. RESULTS: The study sample included 146 participants; mean age 65, 72% female, 47% white, non-Hispanic. The six attributes (mean weights in parentheses) are: health benefit (0.26), enjoyment (0.24), convenience (0.16), financial cost (0.13), effort (0.11) and time cost (0.10). Three clusters were identified: Cluster 1 (n = 33): for whom enjoyment (0.35) is twice as important as health benefit; Cluster 2 (n = 63): for whom health benefit (0.38) is most important; and Cluster 3 (n = 50): for whom cost (0.18), effort (0.18), health benefit (0.17) and enjoyment (0.18) are equally important. Cluster 1 was healthiest, Cluster 2 most self-efficacious, and Cluster 3 was in poorest health. CONCLUSIONS:Patients with chronic knee pain have preferences for PA that can be distinguished effectively using ACA methods. Adults with chronic knee pain, clustered by PA preferences, share distinguishing characteristics. Understanding preferences may help clinicians and researchers to better tailor PA interventions.
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