Article Figures & Data
Figures
- Figure 1.
Path analysis depicting the associations between PDM, activation, medication adherence, and clinical outcome.
Hemoglobin A1c=glycated hemoglobin; LDL=low-density lipoprotein; PDM=participatory decision making.
Note: refer to Table 3⇑ for specific coefficients for each of the labeled paths: b1, b2, and b3.
Tables
Baseline Score Follow-up Score Variable Meaning of High Score n Mean (SD); Range n Mean (SD) P Value NA=not applicable. aDetermined by paired t test. Participatory decision making Greater participatory decision making 60 2.40 (1.14); 1–5 NA NA NA Patient activation Higher level of patient activation 162 12.48 (3.53); 3–18 143 13.31 (3.61) .014 Medication adherence More problems with medication adherence 156 1.23 (0.97); 0–4 145 1.10 (0.93) .017 - Table 2.
Parameters of the Latent Growth Curve Model for Hemoglobin A1c, Systolic Blood Pressure, and LDL Cholesterol
Model Parameters Measure Mean (SE) Sign Variance (SE) P Value Model Fit χ2 (df); CFI; RMSEA Hemoglobin A1c=glycated hemoglobin; CFI=Comparative Fit Index; df = degree of freedom; LDL = low-density lipoprotein; RMSEA = root mean square error of approximation. Hemoglobin A1c 26.9 (30); 0.99; 0.030 Baseline 7.05 (0.97) <.001 2.05 (.275) <.001 Rate of change −0.03 (0.015) <.10 0.10 (.017) <.001 Systolic blood pressure 57.0 (30); 0.97; 0.044 Baseline 130.41 (.98) <.001 177.38 (21.88) <.001 Rate of change −0.30 (0.15) <.05 2.52 (0.58) <.001 LDL cholesterol 60.6 (30); 0.93; 0.059 Baseline 102.89 (2.30) <.001 847.00 (116.20) <.001 Rate of change −2.89 (0.48) <.001 11.14 ( 5.80) <.01 - Table 3.
Regression Weights of Study Variables on Outcome Rates of Change From Structural Equation Models
Hemoglobin A1c Systolic Blood Pressure LDL Cholesterol Path Independent Variable Dependent Variable Regression Coefficient (SE) P Value Regression Coefficient (SE) P Value Regression Coefficient (SE) P Value A1 = glycated hemoglobin; LDL = low-density lipoprotein; PDM = participatory decision making. Note: See Figure 1 for path descriptions. aMeasured by Kaplan-Greenfield scale. bMeasured by Lorig communication scale. cStandardized regression coefficient. dMeasured by Morisky scale. b3 PDMa Patient activation follow-upb 0.44 (0.21); −0.14c .03 0.43 (0.21); 0.14c 0.04 0.42 (0.21); 0.14c .04 b2 Patient activation follow-up Medication adherence follow-upd −0.04 (0.02); −0.16c .02 −0.004 (0.02); −0.15c 0.02 −0.04 (0.02); −0.16c .02 b1 Medication adherence follow-up Rate of change in outcome 0.04 (0.02); 0.26c .05 0.04 (0.17); 0.05c 0.80 1.08 (0.53); 0.40c .04
The Article in Brief
Participatory Decision Making, Patient Activation, Medication Adherence, and Intermediate Clinical Outcomes in Type 2 Diabetes: A STARNet Study
Michael L. Parchman , and colleagues
Background Participatory decision making, in which patients actively participate in the medical visit, is associated with better outcomes in patients with chronic illnesses. This study of patients with type 2 diabetes aimed to shed light on the pathway between a clinician's participatory decision-making style, patient participation in the visit, and taking medication as prescribed.
What This Study Found Diabetes patients who reported a higher level of participatory decision making were more likely to actively participate in the medical visit. Active participation in the visit was associated with taking medications as prescribed, and taking medications as prescribed was associated with improved control of hemoglobin A1C levels and LDL cholesterol levels (but not blood pressure).
Implications
- Actively involving patients in decision making about their care requires adequate visit time and resources. This will require a fundamental restructuring and transformation of many primary care settings.
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