Group-Based Trajectory Analysis of Physical Activity Change in a US Weight Loss Intervention

Associations between daily step count trajectories and clinical outcomes among adults with comorbid obesity and depression

Purpose

To examine the relationship between features of daily measured step count trajectories and clinical outcomes among people with comorbid obesity and depression in the ENGAGE-2 Trial.

Methods

This post hoc analysis used data from the ENGAGE-2 trial where adults (n=106) with comorbid obesity (BMI ≥30.0 or 27.0 if Asian) and depressive symptoms (Patient Health Questionnaire-9 score ≥10) were randomized (2:1) to receive the experimental intervention or usual care. Daily step count trajectories over the first 60 days (Fitbit Alta HR) were characterized using functional principal component analyses. 7-day and 30-day trajectories were also explored. Functional principal component scores that described features of step count trajectories were entered into linear mixed models to predict weight (kg), depression (Symptom Checklist-20), and anxiety (Generalized Anxiety Disorder Questionnaire-7) at 2-months (2M) and 6-months (6M).

Results

Features of 60-day step count trajectories were interpreted as overall sustained high, continuous decline, and disrupted decline. Overall sustained high step count was associated with low anxiety (2M, β=-0.78, p<.05; 6M, β=-0.80, p<.05) and low depressive symptoms (6M, β=-0.15, p<.05). Continuous decline in step count was associated with high weight (2M, β=0.58, p<.05). Disrupted decline was not associated with clinical outcomes at 2M or 6M. Features of 30-day step count trajectories were also associated with weight (2M, 6M), depression (6M), and anxiety (2M, 6M); Features of 7-day step count trajectories were not associated with weight, depression, or anxiety at 2M or 6M.

Conclusions

Features of step count trajectories identified using functional principal component analysis were associated with depression, anxiety, and weight outcomes among adults with comorbid obesity and depression. Functional principal component analysis may be a useful analytic method that leverages daily measured physical activity levels to allow for precise tailoring of future behavioral interventions.

Changes in pedometer-measured physical activity are associated with weight loss and changes in body composition and fat distribution in response to reduced-energy diet interventions: The POUNDS Lost trial

AIMS

To examine whether changes in objectively measured physical activity (PA) are associated with weight loss and changes in body composition and fat distribution in response to weight-loss diet interventions.

METHODS

This study included 535 participants with overweight/ obesity, who were randomly assigned to four weight-loss diets varying in macronutrients. PA was measured objectively with pedometers, and body composition and fat distribution were measured using dual-energy X-ray absorptiometry and computed tomography scans at baseline, 6 months and 24 months.

RESULTS

From baseline to 6 months, when the maximum weight loss was achieved, each 1000-steps/d increment in PA was associated with a greater reduction in body weight (β[SE] = -0.48[0.11]) and waist circumference (β[SE] = -0.49[0.12]). Similar inverse associations were found in changes in body composition and fat distribution (P < 0.05 and false discovery rate qvalue < 0.1 for all). The trajectory of the above adiposity measures across the 24-month intervention period differed between the patterns of PA change. Participants with the largest increase in PA maintained their weight loss from 6 months to 24 months, while those with a smaller increase in PA regained their weight. In addition, dietary fat or protein intake significantly modified the associations between changes in PA and changes in body weight and waist circumference over 24 months (P_∆PA*diet  < 0.05).

CONCLUSIONS

Changes in objectively measured PA were inversely related to changes in body weight, body composition and fat distribution in response to weight-loss diets, and such associations were more evident in people on a high-fat or average-protein diet compared with a low-fat or high-protein diet.

Concurrent changes in physical activity and body mass index among 66 852 public sector employees over a 16-year follow-up: multitrajectory analysis of a cohort study in Finland

OBJECTIVES

To identify concurrent developmental trajectories of physical activity and body mass index (BMI) over time.

DESIGN

Prospective cohort study, repeated survey.

SETTING

Cohort study in Finland.

PARTICIPANTS

66 852 public sector employees, who have been followed up for 16 years.

OUTCOME MEASURES

Shapes of trajectories of changes in physical activity and BMI.

RESULTS

At baseline, mean age was 44.7 (SD 9.4) years, BMI 25.1 (SD 4.1) kg/m² and physical activity 27.7 (SD 24.8) MET hours/week. Four clusters of concurrent BMI and physical activity trajectories were identified: (1) normal weight (BMI <25 kg/m²) and high level of physical activity (30-35 MET hours/week), (2) overweight (BMI 25-30 kg/m²) and moderately high level of physical activity (25-30 MET hours/week), (3) obesity (BMI 30-35 kg/m²) and moderately low level of physical activity (20-25 MET hours/week) and (4) severe obesity (BMI >35 kg/m²) and low level of physical activity (<20 MET hours/week). In general, BMI increased and physical activity decreased during the follow-up. Decline in physical activity and increase in BMI were steeper among obese respondents with low level of physical activity.

CONCLUSIONS

Changes in BMI and physical activity might be interconnected. The results may be of interest for both clinicians and other stakeholders with respect to informing measures targeting increasing physical activity and controlling weight, especially among middle-aged people. Additionally, the information on the established trajectories may give individuals motivation to change their health behaviour.

Latent class growth modelling for the evaluation of intervention outcomes: example from a physical activity intervention

Intervention studies often assume that changes in an outcome are homogenous across the population, however this assumption might not always hold. This article describes how latent class growth modelling (LCGM) can be performed in intervention studies, using an empirical example, and discusses the challenges and potential implications of this method. The analysis included 110 young adults with mobility disability that had participated in a parallel randomized controlled trial and received either a mobile app program (n = 55) or a supervised health program (n = 55) for 12 weeks. The primary outcome was accelerometer measured moderate to vigorous physical activity (MVPA) levels in min/day assessed at baseline, 6 weeks, 12 weeks, and 1-year post intervention. The mean change of MVPA from baseline to 1-year was estimated using paired t-test. LCGM was performed to determine the trajectories of MVPA. Logistic regression models were used to identify potential predictors of trajectories. There was no significant difference between baseline and 1-year MVPA levels (4.8 min/day, 95% CI: -1.4, 10.9). Four MVPA trajectories, 'Normal/Decrease', 'Normal/Increase', 'Normal/Rapid increase', and 'High/Increase', were identified through LCGM. Individuals with younger age and higher baseline MVPA were more likely to have increasing trajectories of MVPA. LCGM uncovered hidden trajectories of physical activity that were not represented by the average pattern. This approach could provide significant insights when included in intervention studies. For higher accuracy it is recommended to include larger sample sizes.

A trajectory analysis of daily step counts during a physician-delivered intervention

OBJECTIVES

Higher steps are associated with lower mortality and cardiovascular event rates. We previously demonstrated that tailored physician-delivered step count prescriptions successfully increased steps/day in adults with type 2 diabetes mellitus (T2DM) and/or hypertension. In the present analysis, we examined patterns of step count change and the factors that influence different responses.

DESIGN

Longitudinal observational study METHODS: Active arm participants (n=118) recorded steps/day. They received a step count prescription from their physician every 3-4 months. We computed mean steps/day and changes from baseline for sequential 30-day periods. Group-based trajectory modeling was applied.

RESULTS

Four distinct trajectories of mean steps/day emerged, distinguishable by differences in baseline steps/day: sedentary (19%), low active (40%), somewhat active (30%) and active (11%). All four demonstrated similar upward slopes. Three patterns emerged for the change in steps from baseline: gradual decrease (30%), gradual increase with late decline (56%), and rapid increase with midpoint decline (14%); thus 70% had an increase from baseline. T2DM (odd ratios [OR]: 3.7, 95% CI 1.7, 7.7) and age (OR per 10-year increment: 2, 95% CI 1.3, 2.8) were both associated with starting at a lower baseline but participants from these groups were no less likely than others to increase steps/day.

CONCLUSIONS

T2DM and older age were associated with lower baseline values but were not indicators of likelihood of step count increases. A physician-delivered step count prescription and monitoring strategy has strong potential to be effective in increasing steps irrespective of baseline counts and other clinical and demographic characteristics.