shows the characteristics of the intervention and control groups at baseline. The control group had significantly more married participants than the intervention group. Three behaviors are significantly different at baseline, regular exercise, managing stress, and attention to fat in the diet and were more likely to be performed by members of the control group. No other significant differences between the two groups were observed.
Baseline Characteristics by Intervention/Control Status
Does Activation Change?
The findings indicate that activation levels increased over time. Using a repeated measures general linear model, and assessing the total study population, time was a statistically significant predictor of activation levels (F = 45.1, p < 0.001). An assessment of the impact of the intervention on activation levels was also conducted. shows changes in activation levels for the intervention and control groups. No difference in activation were observed at baseline, however, the intervention group increased activation scores significantly above those in the control group by 6 weeks (F = 13.44, p < 0.001). By 6 months differences in activation between intervention and control group members had declined, largely because the control group also gained in activation over the study period. Because both groups gained in activation, differences were no longer statistically significant by 6 months (F = 2.344, p = 0.127). The impact that the intervention had on changes in activation and changes in behavior are examined in more depth in the multivariate portion of the analysis ().
Changes in Activation Scores by Intervention and Control Group, ***F = 13.442, p = 0.000
Repeated Measures General Linear Model, Self-Management Behavior Change over 6 Months (F-Values with Significance)
Are There Different Trajectories of Change in Activation?
Mplus growth mixture model analysis with continuous latent class indicators (linear PAM-13 scores) was conducted to determine if useful activation growth classes could be identified. Four different growth class models were evaluated (the model fit indices are available on request). While the difference in fit of the four models was not large, the two class model was selected for use in this analysis on the pragmatic criteria of having a sufficient number of cases in the activation growth classes to conduct an analysis of how activation growth class membership is related to behavior change over time. These two growth classes are labeled stable or no change in activation and increased activation. shows the mean activation score of these two growth classes at each wave.
Estimated Marginal Means of Activation by Wave by Activation Growth Class
In a repeated measures general linear model there was a significant wave by growth class interaction for activation (F = 47.71, p < 0.0001; ). Post hoc tests (95 percent CI) for the growth class effect indicated that in the increased growth class activation significantly increased at each wave while the stable growth class significantly increased from baseline to 6 weeks and significantly decreased from 6 weeks to 6 months. The “increased” class was significantly more activated at baseline than was the stable class by an observed difference of almost nine points. By 6 months, however, the difference was almost 26 points. It is informative that only about 10 percent of all respondents were in the “increased” activation growth class.
Does the Intervention Predict Activation Growth Class Membership?
shows the cross tabulation of growth classes and intervention and control groups. The chances of being in the “increased” growth class do not significantly differ between the intervention and control groups (χ2 = 0.11, p = 0.80).
Activation Growth Classes by Intervention/Control and Groups
The effect of the intervention on membership in growth class was also examined using a multivariate approach. A repeated measures general linear model with activation growth class and intervention/control group status as fixed factors was examined. There was no significant within subjects group (intervention/control) effect on activation over time (F = 1.98, p = 0.139), and no significant growth class and group (intervention/control) interaction (F < 1).
Do Changes in Activation Predict Changes in Behavior?
In this portion of the analysis we assess both the impact of the intervention and membership in a growth class on self-management behavior change over time. A repeated measures general linear model analysis was conducted for each of the 18 self-management behaviors. The same model was used for all analyses with group (intervention versus control) and activation growth class (stable versus increased) as fixed factors and age, baseline HRQoL, baseline depression, and a measure of social desirability as covariates (HRQoL is significantly higher at baseline for the “increased” class and depression is significantly lower for them. There are no differences at baseline in social desirability in the growth classes).
The interaction effects of group (intervention/control) × activation growth class × time were evaluated for each behavior. As these effects are all interaction terms the null hypothesis being tested is that there are no differences in behavior over time. A significant within subjects effect means that the mean behavior over time differs by the categories of the fixed factor variable(s). Apart from the usual difficulties of repeated measures post hoc tests with estimated marginal means, the small number of participants in the “increased” activation growth class creates cell sizes in examining the effects of activation growth class and group on behaviors that seriously limit the power of any post hoc test as well as the within subjects effects tests. Even if these statistical power issues were not present, we are far less interested in inference about the difference between individual behavior means at discrete time points than in determining if self-management behavior change over time has any consistent pattern by activation growth class and the intervention. The analysis, therefore, focuses on the within and between subjects effects of group and activation growth class.
The repeated measures general linear model analysis was applied to each of the 18 discrete self-management behaviors. The results of these tests are shown in . For six of the 18 behaviors there was a significant (p < 0.05) difference between the activation growth classes in the behavior pattern over time (growth class × time effect). These self-management behaviors included: engaging in regular exercise, managing stress, paying attention to amount of fat in diet, keeping a BP diary, keeping a glucose diary, and taking diabetes medications as recommended.
To examine overall differences on the 18 behaviors between the two growth classes, change scores were calculated for the 18 behaviors. Although both groups saw increases in positive behaviors, the “increasing” growth class saw a greater degree of increase in 14 of the 18 behaviors, compared with the stable growth class. Using the sign test (Siegel and Castellan 1988
) we tested whether the differences in improved behaviors are statistically significant. Assuming the null hypothesis, or no differences in increases, the chance that 14 out of the 18 behaviors would show greater improvements for the increasing growth class, as compared with the stable growth class, are 996 out of 1,000 or p
We also examined the initiation of behaviors after the baseline among both growth classes. Members of the “increased” growth class were more likely to initiate two behaviors: maintaining recommended weight and attention to fat in the diet, than those in the “stable” growth class during the study period. Among the “increased” growth class, who did not pay attention to fat in their diet at baseline, 85 percent initiated the behavior over the 6 months study period. Among members of “stable” growth class, 53 percent of those not paying attention to fat in their diet at baseline, began to attend to it over the study period (χ2 4.8, p < 0.03). A similar pattern was observed for maintaining recommended weight. Among the members of the “increasing” growth class who were not maintaining recommended weight at baseline, 30 percent had initiated this behavior over the 6 months study period. Among “stable” growth class members, only 14 percent initiated this behavior over the study period (χ2 5.5, p < 0.02).
There was a significant between subjects growth class effect for 11 of the 18 behaviors in that the mean behavior over the three waves differed by growth class. In all 11 behaviors the overall mean was higher (better self-management behavior) in the “increased” activation growth class. The significant between subjects effect for activation growth class occurred for six behaviors for which there was no significant within subjects growth class effect. There were high rates of in engaging in four of these behaviors, (ask about complications 78 percent; read about complications 90 percent; read food labels 90 percent; and know recommended weight 88 percent) at baseline among the “increased” growth class. Part of the reason for no significant within subjects change over time for the increased growth class in these four behaviors is that there was little improvement available (a ceiling effect) and with the small sample size a difference in behavior patterns between the “increased” and “stable” classes is not statistically detectable. For the two arthritis-specific behaviors the observed patterns of behavior over time were very different for the “increased” and “stable” activation growth classes, but the lack of statistical power results in failure to identify a significant within subjects effect by growth class.
Group (intervention/control) had a significant within subjects effect, that was not modified by an interaction with growth class, for two self-management behaviors (regular exercise and taking diabetes medication as recommended). The behavior change patterns over time are somewhat less clear than those for the growth classes. For regular exercise the intervention group increased over time while the control group did not (control: baseline behavior score = 2.9; 6 weeks = 2.8, and 6 months = 2.9; intervention: baseline score 2.8; 6 weeks = 3.2 and 6 months = 3.2). For taking diabetes medication as physician recommends the control group improved over time while the intervention group slightly increased at 6 weeks and then declined (control: baseline behavior score = 2.7; 6 weeks = 2.6, and 6 months = 3.0; intervention: baseline score 2.5; 6 weeks = 2.7 and 6 months = 2.6). There was also a significant between subjects group effect for two self-management behaviors. For ability to maintain recommended weight, the mean behavior over all three waves was significantly (F = 4.59, p = 0.033) better in the control group (M = 2.47) than in the intervention group (M = 2.14). For keeping a written diary of BP the overall mean behavior was also significantly better (F = 3.89, p = 0.05) in the control group (M = 2.47) than in the intervention group (M = 2.14).
For one behavior (check BP at least once a week), there was a significant within subjects group × class × time interaction (). Inspection of the mean behavior scores revealed that of the four group × class combinations only one had any sign of change in behavior over time; the increased activation growth class in the control group had a notable improvement in behavior from baseline to 6 months (baseline M = 2.69; 6 months M = 3.31).
What Factors Predict Activation Growth Class Membership?
With the identification of two activation growth classes that have clearly different activation trajectories over the three waves of the study, it is important to investigate the characteristics of these growth classes. It is reasonable to suggest that activation and subclinical depression are related as depressive symptoms entail a general deactivation. At each of the three waves greater depression is associated with lower activation (baseline r = −0.365, 6 weeks r = −0.444, 6 months r = −0.408 p < 0.0001 all waves). Better HRQoL is also associated with greater activation (baseline r = 0.301, 6 weeks r = 0.326, 6 months r = 0.345, p < 0.001 all waves). Further, depression and HRQoL are strongly negatively correlated (baseline r = −0.731, 6 weeks r = −0.711, 6 months r = −0.708, p < 0.001 all waves).
To examine the relationship between activation growth class and depression over the three waves a repeated measures general linear model analysis was conducted with activation growth classes and group (intervention/control) as fixed factors, depression as the repeated measure, and age, baseline HRQoL, and social desirability as covariates. This model evaluates group and activation growth class “effects” on depression over time while controlling for the effects of the covariates. The within subjects effects revealed that only activation growth class (F = 4.84, p = 0.009, Greenhouse–Geisser adjusted) and HRQoL (F = 4.13, p = 0.013) had a significant “effect” on depression over time. HRQoL had a large between subjects effect (F = 441.51, p < 0.0001, partial η2 = 0.53). The only other between subjects effect was for activation growth class (F = 25.94, p < 0.0001, partial η2 = 0.06). As shown in the profile plot () the “increased” activation growth class not only started with less depression than the stable class, but steadily declined in depression (6 months < baseline, 95 percent CI). The “stable” activation growth class remained relatively stable in depression (6 months and baseline not significantly different, 95 percent CI). We also examined the relationship between simple depression change score and behavior change score for each of the tested behaviors. For nine of the 18 behaviors there was a significant (p < 0.05) negative correlation (decreased depression baseline to 6 months related to increased behavior baseline to 6 months). The significant correlations were small to moderate (−0.139 to −0.338). For eight of the 18 behaviors there was a significant positive (better HRQoL and increased behavior) correlation between change in HRQoL and baseline to 6 months behavior change with the significant correlation ranging from 0.104 to 0.308.
Estimated Marginal Means of Depression by Wave by Activation Growth Class