Dataset Information

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

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

SUBMITTER: Fukuoka Y

PROVIDER: S-EPMC5814604 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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

Fukuoka Yoshimi Y Zhou Mo M Vittinghoff Eric E Haskell William W Goldberg Ken K Goldberg Ken K Aswani Anil A

JMIR public health and surveillance 20180201 1

<h4>Background</h4>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.<h4>Objective</h4>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 sociodemogr ...[more]

PMID: 29391341

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Project description:BackgroundThe aims of this report were 1) to describe the duration of moderate to vigorous physical activity (MVPA) and the proportion of participants meeting the recommended criterion of at least 150 min of MVPA per week as measured by the 7 Day Physical Activity Recall Questionnaire (7D-PAR) and accelerometer among women who were enrolled in the mPED trial; 2) to assess the level of agreement of the two measures using a Bland-Altman plot; and 3) to describe the positive and negative predictive values (PPV and NPV, respectively) of meeting the guidelines by calculating the percentage of women meeting the physical activity recommendation by the 7D-PAR who also met this recommendation based on data from the accelerometer.MethodsBaseline data on duration of MVPA from the mPED trial were analyzed for 215 women. Among the women who met the recommended criterion by the 7D-PAR (self-report), we calculated the proportion of individuals who also met it by the accelerometer (objective measure). A Bland Altman Plot was used to assess concordance between the two measures.ResultsThe mean age was 52.4 (±11.2) years; 54.4 % were white; and 48.8 % were single or divorced. While median MVPA was 160 min/week by the 7D-PAR, it was only 24 min/week in the accelerometer. A total of 117 women met the 150-min criterion by the 7D-PAR. Of these, only 18 also met the criterion by the objective measure (PPV 15.4 %, 95 % CI 9.4-23.2 %). Among the 98 women who did not meet the criterion by the 7D-PAR, none met it by the accelerometer (NPV 100 %). A Bland Altman plot showed the mean difference of +145 min between the two measures with a 95 % limit of agreement at + 471 to -181 min.ConclusionsThe large discrepancy between the self-reported and objective measures of MVPA meeting the 150-min criterion suggests that self-reported physical activity measures should be used with caution in intervention studies. While our data suggest that self-report could be used to identify a physically inactive sample, it would be likely to over-estimate the proportions of women who become active in one or both arms of trials of interventions promoting MVPA.Trial registrationClinicalTrials.gov NCT01280812.

Project description:Physical activity improves health and may delay the onset of several chronic diseases. For women in particular, the rate of these diseases accelerates at middle age; therefore it is important to identify the determinants of health-enhancing physical activity during midlife in this population. In this study, we focused on determinants that are unique to the female sex, such as childbearing and menopause. The main objective was to characterize the level of physical activity and differences between active and inactive middle-aged Finnish women. In addition, we examined the association of physical activity with female reproductive factors at midlife. The study population consisted of 647 women aged 48 to 55 years who participated in our Estrogenic Regulation of Muscle Apoptosis (ERMA) study during the period from 2015 to 2016. Physical activity was measured objectively using hip-worn accelerometers for seven consecutive days. The outcome measures included the amounts of light intensity physical activity and moderate to vigorous intensity physical activity accumulated in bouts of at least 10 minutes (MVPA10). MVPA10 was used to determine whether women were placed in the active (≥150 min/week) or inactive (<150 min/week) group. Multiple linear regression models were performed with physical activity measures as dependent variables and cumulative reproductive history index, menopausal symptoms, and pelvic floor dysfunction as independent variables. We found that a large portion (61%) of Finnish middle-aged women did not meet the physical activity recommendations of 150 minutes of MVPA10 per week. In the studied cohort, 78% of women experienced menopausal symptoms, and 54% exhibited pelvic floor dysfunction. Perceived menopausal symptoms were associated with greater light physical activity. Perceived pelvic floor dysfunction was associated with lower MVPA10. According to the fully adjusted multiple linear regression models, reproductive factors explained 6.0% of the variation of MVPA10 and 7.5% of the variation of light physical activity. The results increase our knowledge of the factors related to physical activity participation among middle-aged women and indicate that menopausal symptoms and pelvic floor dysfunction should be identified and considered when promoting physical activity for women during midlife. The results emphasize that awareness of female reproductive factors, especially menopausal symptoms and pelvic floor dysfunction, is important for physical activity counseling to effectively help women in performing and sustaining health-enhancing amounts of physical activity. Specifically, the condition of the pelvic floor should be taken into account when identifying the proper activity type and intensity level so that health benefits of physical activity can still be attained without worsening symptoms.

Project description:BackgroundWe investigated preoperative physical activity (PA) and sedentary behaviour (SB) in patients scheduled for elective cardiac procedures and compared them with population-based sample of Finnish adults.MethodsCardiac patients (n = 139) undergoing cardiac operations carried a triaxial accelerometer for seven days during the month before the procedure. Patients were categorised into four groups according to the procedure: percutaneous coronary intervention or coronary angiography (PCI-CA), coronary artery bypass grafting (CABG), aortic valve replacement (AVR) and mitral valve surgery (MVS). The raw accelerometer data was analyzed with dedicated algorithms to determine metabolic equivalents (METs, 3.5 mL/kg/min of oxygen consumption) of PA. The intensity of PA was divided into two categories: light (LPA, 1.5-2.9 METs) and moderate-to-vigorous (MVPA, ≥ 3.0 METs), while SB represented intensity < 1.5 MET without movements. SB and PA were described as daily means and accumulation from different bout lengths. Daily standing, steps and mean and peak MET-values were calculated. The results were compared between the patient groups and against the reference group from a population-based study FinFit2017.ResultsCardiac patients had fewer daily steps than the FinFit population (p = 0.01), and less SB accumulating from < 20 min bouts (p = 0.002) but more from 20 to 60 min bouts (p = 0.002). Particularly, CABG group had less daily MVPA (p = 0.002) and MVPA accumulating from > 10 min bouts (p < 0.001) than the FinFit population.ConclusionsWe found large differences in PA and SB between the patient groups and the FitFit population, CABG group having the worst activity profile. Also, the variation within the patient groups was wide, which should be considered to individualise the rehabilitation programs postoperatively. Trial registration clinicaltrials.gov (NCT03470246). Registered 19 March 2018, https://clinicaltrials.gov/ct2/show/NCT03470246.

Project description:BackgroundPhysical activity is associated with a variety of health benefits, but the biological mechanisms that explain these associations remain unclear. Metabolomics is a powerful tool to comprehensively evaluate global metabolic signature associated with physical activity and helps to pinpoint the pathways that mediate the health effects of physical activity. There has been limited research on metabolomics and habitual physical activity, and no metabolomics study has examined sedentary behaviour and physical activity of different intensities.MethodsIn a group of Chinese adults (N = 277), we used an untargeted approach to examine 328 plasma metabolites in relation to accelerometer-measured physical activity, including overall volume of physical activity (physical activity energy expenditure (PAEE) and duration of physically active time) and sedentary time, and measures related to different intensities of physical activity (moderate-to-vigorous activity (MVPA), light activity, average physical activity intensity).ResultsWe identified 11 metabolites that were associated with total activity, with a false discovery rate of 0.2 or lower. Notably, we observed generally lower levels of amino acids in the valine, leucine and isoleucine metabolism pathway and of carbohydrates in sugar metabolism among participants with higher activity levels. Moreover, we found that PAEE, time spent in light activity and duration of physically active time were associated with a similar metabolic pattern, whereas the metabolic signature associated with sedentary time mirrored this pattern. In contrast, average activity intensity and time spent in MVPA appeared to be associated with somewhat different metabolic patterns.ConclusionsOverall, the metabolomics patterns support a beneficial role of higher volume of physical activity in cardiometabolic health. Our findings identified candidate pathways and provide insight into the mechanisms underlying the health effects of physical activity.

Dataset Information

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

Publications

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

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