Russell E Glasgow1, Lisa A Strycker2, Diane K King3, Deborah J Toobert2. 1. Implementation Science, Division of Cancer Control and Population Science, National Cancer Institute, Rockville, Maryland (REG). 2. Oregon Research Institute, Eugene, Oregon(LAS, DJT) 3. University of Alaska, Anchorage, Alaska (DKK)
Abstract
BACKGROUND: Efforts to predict success in chronic disease management programs have been generally unsuccessful. OBJECTIVE: To identify patient subgroups associated with success at each of 6 steps in a diabetes self-management (DSM) program. DESIGN: Using data from a randomized trial, recursive partitioning with signal detection analysis was used to identify subgroups associated with 6 sequential steps of program success: agreement to participate, completion of baseline, initial website engagement, 4-month behavior change, later engagement, and longer-term maintenance. SETTING: The study was conducted in 5 primary care clinics within Kaiser Permanente Colorado. PATIENTS: Different numbers of patients participated in each step, including 2076, 544, 270, 219, 127, and 89. All measures available were used to address success at each step. Intervention. Participants were randomized to receive either enhanced usual care or 1 of 2 Internet-based DSM programs: 1) self-administered, computer-assisted self-management and 2) the self-administered program with the addition of enhanced social support. MEASUREMENTS: Two sets of potential predictor variables and 6 dichotomous outcomes were created. RESULTS: Signal detection analysis differentiated successful and unsuccessful subgroups at all but the final step. Different patient subgroups were associated with success at these different steps. Demographic factors (education, ethnicity, income) were associated with initial participation but not with later steps, and the converse was true of health behavior variables. LIMITATIONS: Analyses were limited to one setting, and the sample sizes for some of the steps were modest. CONCLUSIONS: Signal detection and recursive partitioning methods may be useful for identifying subgroups that are more or less successful at different steps of intervention and may aid in understanding variability in outcomes.
RCT Entities:
BACKGROUND: Efforts to predict success in chronic disease management programs have been generally unsuccessful. OBJECTIVE: To identify patient subgroups associated with success at each of 6 steps in a diabetes self-management (DSM) program. DESIGN: Using data from a randomized trial, recursive partitioning with signal detection analysis was used to identify subgroups associated with 6 sequential steps of program success: agreement to participate, completion of baseline, initial website engagement, 4-month behavior change, later engagement, and longer-term maintenance. SETTING: The study was conducted in 5 primary care clinics within Kaiser Permanente Colorado. PATIENTS: Different numbers of patients participated in each step, including 2076, 544, 270, 219, 127, and 89. All measures available were used to address success at each step. Intervention. Participants were randomized to receive either enhanced usual care or 1 of 2 Internet-based DSM programs: 1) self-administered, computer-assisted self-management and 2) the self-administered program with the addition of enhanced social support. MEASUREMENTS: Two sets of potential predictor variables and 6 dichotomous outcomes were created. RESULTS: Signal detection analysis differentiated successful and unsuccessful subgroups at all but the final step. Different patient subgroups were associated with success at these different steps. Demographic factors (education, ethnicity, income) were associated with initial participation but not with later steps, and the converse was true of health behavior variables. LIMITATIONS: Analyses were limited to one setting, and the sample sizes for some of the steps were modest. CONCLUSIONS: Signal detection and recursive partitioning methods may be useful for identifying subgroups that are more or less successful at different steps of intervention and may aid in understanding variability in outcomes.
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