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JMIR Public Health Surveill. 2018 Feb 1;4(1):e10. doi: 10.2196/publichealth.9138.

Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis.

Author information

1
Department of Physiological Nursing/Institute for Health & Aging, University of California, San Francisco, San Francisco, CA, United States.
2
Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, CA, United States.
3
Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, United States.
4
Stanford Prevention Research Center, Stanford University, Palo Alto, CA, United States.
5
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States.

Abstract

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 baseline raw metabolic equivalent of task (MET) values and a normalized version of the METs ≥3 data, and to compare sociodemographic and cardiometabolic risks among these identified clusters.

METHODS:

A total of 215 women who were enrolled in the Mobile Phone Based Physical Activity Education (mPED) trial and wore an accelerometer for at least 8 hours per day for the 7 days prior to the randomization visit were analyzed. The k-means clustering method and the Lloyd algorithm were used on the data. We used the elbow method to choose the number of clusters, looking at the percentage of variance explained as a function of the number of clusters.

RESULTS:

The results of the k-means cluster analyses of raw METs revealed three different clusters. The unengaged group (n=102) had the highest depressive symptoms score compared with the afternoon engaged (n=65) and morning engaged (n=48) groups (overall P<.001). Based on a normalized version of the METs ≥3 data, the moderate-to-vigorous physical activity (MVPA) evening peak group (n=108) had a higher body mass index (P=.03), waist circumference (P=.02), and hip circumference (P=.03) than the MVPA noon peak group (n=61).

CONCLUSIONS:

Categorizing physically 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.

TRIAL REGISTRATION:

ClinicalTrials.gov NCT01280812; https://clinicaltrials.gov/ct2/show/NCT01280812 (Archived by WebCite at http://www.webcitation.org/6vVyLzwft).

KEYWORDS:

accelerometer; body mass index; cluster analysis; mHealth; machine learning; metabolism; physical activity; primary prevention; randomized controlled trial; women

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