Background: Determining patterns of physical activity throughout the day could assist in developing more personalized interventions or physical activity guidelines in general, and in particular for women, who are less likely to be physically active than men.
Objective: The aims of this report are to identify clusters of women based on accelerometer measured raw metabolic equivalent values (METs) and a normalized version of the METs ≥ 3 data and to compare sociodemographic and cardio metabolic risks among these identified clusters.
Methods: 215 women wearing an accelerometer for at least 8 hours per day for the last 7 days prior to the randomization visit were analyzed. A K-means clustering method, the Lloyd’s algorithm, was used. To choose the number of clusters, we used the elbow method, looking at the percentage of variance explained as a function of the number of clusters.
Results: The results of k means cluster analyses of raw METs revealed three different clusters (Figure 1) and the Low Active Group (n=102) had the highest depressive symptoms score compared to the Afternoon Active (n=65) and Morning Active (n=48) groups (overall P < .001). Based on a normalized version of the METs ≥ 3 data Figure 2), the moderate to vigorous physical activity (MVPA) Evening Peak group (n = 108) had higher BMI, and waist and hip circumference than the MVPA Noon Peak group (n=61) (overall P =.03, .02, and .03 respectively).
Conclusions: Categorizing physical inactive individuals into more specific activity patterns could aid in creating timing, frequency, duration, and intensity of physical activity interventions for women. Further research is needed to confirm these cluster groups using a large national dataset.