Comparing three ways to increase physical activity in patients with depression and cardiovascular disease : the healthy hearts healthy minds study / Louisa G. Sylvia.

Format:

Book

Author/Creator:

Sylvia, Louisa G., author.

Language:

English

Subjects (All):

Heart--Diseases--Patients--Rehabilitation.

Heart.

Chronic diseases--Exercise therapy.

Chronic diseases.

Physical Description:

1 online resource (69 pages) : illustrations

Other Title:

Comparing three ways to increase physical activity in patients with depression and cardiovascular disease

Place of Publication:

Washington, DC : Patient-Centered Outcomes Research Institute, 2022.

Summary:

BACKGROUND: Individuals with depression are 4 times more likely to die from cardiovascular disease (CVD) than are those without depression, and the cumulative effects of this comorbidity can result in a loss of 8 to 25 years of life expectancy. Conversely, people with CVD have not only 3 times the rate of depression compared with the general population, but they die at higher rates if they have comorbid depression. Exercise improves both depression and risk factors for CVD, yet most Americans do not exercise regularly, especially those with depression or heart disease. Exercise reduces the risk of dying by CVD and, for some individuals, can have antidepressant effects similar to those of medications. OBJECTIVES: The purpose of this study was to compare empirically supported psychotherapy interventions to increase exercise in individuals with a history of depression who had or were at risk for CVD (defined as <150 minutes of physical activity [PA] per week). The primary aim of this 3-arm randomized trial was to compare internet-based, self-guided cognitive-behavioral therapy (CBT) plus an activity tracker (Fitbit), self-guided mindfulness-based cognitive therapy (MBCT) plus Fitbit, and Fitbit alone. The primary outcome was increased exercise measured by increased daily steps measured by the activity tracker. The secondary aim was to determine the heterogeneity of treatment effects (HTE; predictors and moderators of treatment response) to both interventions. METHODS: We recruited adult participants (N = 340; mean [SD] age, 43 [11] years) who had received a diagnosis of unipolar depression and had or were at risk for CVD. Individuals who were already exercising regularly, were using a Fitbit, or were unable to exercise were excluded from the study. Participants were recruited from 2 online patient-powered research networks, MoodNetwork (individuals with mood disorders) and Health eHeart (focused on cardiovascular health). Eligible participants were then randomized to 1 of 3 groups: CBT+Fitbit, MBCT+Fitbit, or Fitbit only through a central randomization program, and then started the intervention (CBT or MBCT). The primary outcome of exercise was defined as the change in the number of steps taken per day as measured by the Fitbit at posttreatment (8 weeks) and follow-up (16 weeks). For the primary aim, we hypothesized that CBT will be superior to MBCT in increasing daily steps over the course of 8 weeks of treatment and after 8 additional weeks of follow-up (16 weeks total). To assess this, we used data provided by the maker of Fitbit to determine the total number of steps. We conducted general linear mixed models that accounted for the covariance of observations within participants. We assessed potential treatment moderators (eg, age, depression, stress, self-efficacy, well-being, [hypo]mania, weight, cigarette smoking status) to explore whether certain characteristics could be used to match treatments to specific subpopulations. We did not have any a priori hypotheses for our exploratory second aim to examine moderators of the main outcome (ie, whether certain populations of individuals might do better with one treatment over the other to increase their daily number of steps); therefore, we conducted HTE analyses. For the HTE analyses, we added appropriate interaction terms to the linear mixed model and used a likelihood ratio test to assess whether the estimated treatment effect differed across levels of the moderator. A significant interaction model would indicate that the HTE variable moderated differences between groups in daily steps changed over time. RESULTS: The average number of daily steps changed by +2.8 steps per day (95% CI, −1.5 to +7.0) in the MBCT+Fitbit group and by +2.9 steps/day (95% CI, −1.3 to +7.0) in the CBT+Fitbit group (ie, both increased) but changed by −8.2 steps/day (95% CI, −14.5 to −1.9) in the Fitbit-only group (ie, decreased). The changes in average daily steps were not different between the MBCT+Fitbit and CBT+Fitbit groups (P = .97), but both were different from those of the Fitbit-only group across the initial 8-week period (P = .005 and .004, respectively). Group differences were not maintained across the entire 16-week follow-up period. Some moderators were identified in exploratory analyses, including comorbid anxiety disorders, PA (as assessed via the International Physical Activity Questionnaire), and employment status (see Table 4 and Table 5). During the first week of the study, participants took an average (SD) of 4778 (2421) (0%, 25%, 50%, 75%, 100% quantiles = 0, 3013, 4399, 6207, 13,372) steps per day. Over the 16-week follow-up period, participants provided an average (SD) of 81 (34) (0%, 25%, 50%, 75%, 100% quantiles = 1, 61, 95, 111, 112) days of Fitbit data, and the average within-participant SD in daily steps was 2505. CONCLUSIONS: We found that online behavioral interventions yielded statistically significant but not clinically meaningful changes in daily steps at week 8; moreover, this gain was not maintained at week 16. Our results suggest that an activity tracker with or without self-guided web-based psychotherapy interventions has limited effects on PA in participants with a history of depression who have or are at risk for CVD. LIMITATIONS: This study had the following limitations: (1) potential treatment moderators, including adverse events, were self-reported; (2) there were technical/syncing issues with the Fitbit; and (3) we had a largely White, female, and highly educated sample, which hindered the generalizability of the results to other populations.

Contents:

ABSTRACT 4

BACKGROUND 6

Aims and Hypotheses 7

Significance and Potential Impact 8

PATIENT AND OTHER STAKEHOLDER ENGAGEMENT 9

METHODS 11

Study Overview 11

Study Setting 12

Overview of Study Procedures 12

Table 1 Baseline Demographics and Clinical Characteristics by Randomized

Intervention Groupa 14

Interventions and Comparators or Controls 16

Design Consideration: Why Do a Comparative Effectiveness Trial of CBT vs MBCT? 19

Statistical Analysis for Primary Outcomes 20

Study Outcomes 20

Sample Size Calculations and Power 23

Table 2 Anticipated Final Racial or Ethnic and Gender Enrollment 24

Figures 1 and 2 25

Time Frame for the Study 25

Table 3 Study Timeline 26

Data Collection and Sources 26

Analytical and Statistical Approaches 27

Changes to the Original Study Protocol 28

RESULTS 30

Participants 30

Figure 3 CONSORT Chart 31

Aim 1: Primary Outcome-Daily Steps 32

Figure 4 Intervention Groups Over Time 34

Aim 2: HTE 35

Table 4 Second Aim: HTE Analysis at 8 Weeks 36

Table 5 Second Aim: HTE Analysis at 16 Weeks 38

Table 6 Analysis of Primary Study End Point (Daily Steps Measured via Fitbit) Using Linear Mixed-Effects Models: 8 Weeks and 16 Weeks 40

DISCUSSION 42

Summary of Results 42

Subgroup Analyses or HTE 42

Results in Context 43

Study Limitations 43

Future Research 44

CONCLUSIONS 45

REFERENCES 46

RELATED PUBLICATIONS 54

APPENDICES 55

Appendix A Schedule of Assessments 55

Appendix B Baseline Demographics Among 314 Participants 57

Appendix C Baseline Demographics Among 340 Participants 59

Appendix D Analysis of Primary Study End Points Adjusted for Number of Days Between Study Screening and Randomization, History of Smoking at Baseline, and Baseline Employment Status 62

Appendix E Analysis of Primary Study End Points Adjusted for Postrandomization

Intervention Sessions 64

Appendix F Select Pairwise Group Differences for Exploratory Moderator Analyses 66.

Notes:

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