Abstract MP79: Physical Activity Patterns by Data-driven Model-based Clustering Improve Association with Cardiovascular Risk Factors in a Multiethnic Cohort: The Northern Manhattan Study

Background: Physical activity is a complex modifiable risk factor (RF) for cardiovascular disease (CVD). Current methods to measure physical activity are limited by the use of summary scores such as total metabolic equivalents score (METS). Hypothesis: Physical activity patterns derived by a data-driven clustering method are associated with CVD RFs independently of METS. Methods: The Northern Manhattan Study is a prospective cohort of older, urban-dwelling, multiethnic, stroke-free individuals. Questionnaires were used to capture multi-dimensions of leisure-time physical activity, which was summarized with METS (total activity minutes х intensity in MET). Participants were grouped into previously defined METS categories (less than 1, greater than 14, and 1-14), and also into clusters by multivariate finite mixture modeling based on activity frequency, duration, energy expenditure, and number of activity types. Bayesian information criterion was used to decide number of clusters. Associations between model-based clusters and 4 RFs (diabetes, hypertension, obesity, high waist circumference) were assessed in the entire cohort and in each METS category; associations between METS and RFs were assessed in each cluster. Chi-squared test was used. Results: Physical activity data were available in 3293, with mean age 69 years, 63% women, and 52% Hispanic. Six clusters were identified and labeled I-VI (Table 1). Model-based clusters were associated with all four RFs (all p≤0.01), with clusters V and VI having lower RFs prevalence than the others: the association with obesity prevailed among those with 1≤METS≤14 (p<0.01); and with hypertension among those with METS>14 (p=0.03). METS categories were associated with all four RFs in the entire cohort (all p≤0.04); METS and RFs became no longer significantly associated within clusters. Conclusions: A data-driven clustering method for depicting physical activity data is a principled, generalizable approach to form subgroups associated with CVD RFs independently of METS.

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Smartphone-recorded physical activity for estimating cardiorespiratory fitness

Scientific Reports ◽

10.1038/s41598-021-94164-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Micah T. Eades ◽

Athanasios Tsanas ◽

Stephen P. Juraschek ◽

Daniel B. Kramer ◽

Ernest Gervino ◽

...

Keyword(s):

Physical Activity ◽

Cardiorespiratory Fitness ◽

High Performance ◽

Stress Test ◽

Absolute Error ◽

Activity Data ◽

Physical Activity Data ◽

Cardiac Stress ◽

Metabolic Equivalents ◽

Multivariable Regression

AbstractWhile cardiorespiratory fitness is strongly associated with mortality and diverse outcomes, routine measurement is limited. We used smartphone-derived physical activity data to estimate fitness among 50 older adults. We recruited iPhone owners undergoing cardiac stress testing and collected recent iPhone physical activity data. Cardiorespiratory fitness was measured as peak metabolic equivalents of task (METs) achieved on cardiac stress test. We then estimated peak METs using multivariable regression models incorporating iPhone physical activity data, and validated with bootstrapping. Individual smartphone variables most significantly correlated with peak METs (p-values both < 0.001) included daily peak gait speed averaged over the preceding 30 days (r = 0.63) and root mean square of the successive differences of daily distance averaged over 365 days (r = 0.57). The best-performing multivariable regression model included the latter variable, as well as age and body mass index. This model explained 68% of variability in observed METs (95% CI 46%, 81%), and estimated peak METs with a bootstrapped mean absolute error of 1.28 METs (95% CI 0.98, 1.60). Our model using smartphone physical activity estimated cardiorespiratory fitness with high performance. Our results suggest larger, independent samples might yield estimates accurate and precise for risk stratification and disease prognostication.

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Physical Activity And Nurses’ Attribute Variables To Body Mass Index And Uric Acid

Jurnal Ilmiah Ilmu Keperawatan Indonesia ◽

10.33221/jiiki.v11i01.950 ◽

2021 ◽

Vol 11 (01) ◽

Author(s):

Ernawati Siagian ◽

Putri Agape Ramschie

Keyword(s):

Physical Activity ◽

Uric Acid ◽

Significant Relationship ◽

Health Workers ◽

Activity Patterns ◽

Sampling Technique ◽

P Value ◽

Moderate Physical Activity ◽

Activity Data ◽

Physical Activity Patterns

Introduction: Obesity among health workers hinder the effectiveness of health promotion, harm their health, and reduce productivity in the workplace. Objective: The purpose of this study is to describe the attribute variables, daily activity patterns, BMI and uric acid levels in nurses. Methods: The method in this study is a descriptive correlational using purposive sampling technique, amounting 50 respondents. International Physical Activity Questionnaire (IPAQ) was used for the physical activity data, BMI and checking uric acid levels. Results: The results showed Obese I BMI (58%), Obese II BMI (42%), Prediabetes fasting blood sugar levels (28%), Diabetes (4%). Moderate physical activity patterns is 80% METs- mins/week. Analysis showed that there is a significant relationship between age and physical activity patterns (METs- mins/week) with p value of 0,02<0,05. There is a significant relationship between marital status and physical activity patterns with p value of 0,005. There is also a significant relationship between chronic disease and uric acid levels with p value of 0,020<0,05. There is no significant relationship between BMI and uric acid levels with physical activity patterns (sig>0,05). Conclusion: Obesity among professional nurses has negative implications for the capacity, success, follow-up and safety of health services and the health of these nurses. Nurses need to achieve and maintain a healthy body weight.

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Visualizing Physical Activity Patterns among Community-Dwelling Older Adults: A Pilot Study

Sports ◽

10.3390/sports6040135 ◽

2018 ◽

Vol 6 (4) ◽

pp. 135 ◽

Cited By ~ 1

Author(s):

Monika Haga ◽

Katerina Vrotsou ◽

Ebba Bredland

Keyword(s):

Physical Activity ◽

Older Adults ◽

Visual Analysis ◽

Daily Life ◽

Activity Patterns ◽

Well Being ◽

Community Dwelling ◽

Intensity Level ◽

Physical Activity Patterns ◽

Metabolic Equivalents

Regular physical activity relates to physical and mental functioning in older people, and promoting physical activity has the potential to substantially reduce functional decline and improve well-being. Despite this, investigations of the physical activity quotient through participation in functional activities in everyday life have traditionally gained limited focus among older populations compared to leisure-time physical activity and exercise. Considering the accumulated evidence of the health benefits of low-intensity physical activity, exploring and measuring such activities in this population is highly relevant. The aim of this study was to visualize and describe older people’s physical activity patterns in daily life using a time-geographic approach in combination with the estimation of metabolic equivalents (METS). To exemplify the new method, a sample of nine retired men (65–82 years old, mean age 76.4 ± 5.8) with no homecare services from the municipality was recruited. In order to enable a visual analysis of the physical activity patterns in daily life, we developed the VISUAL-PA software, which is a visual analysis tool that includes METS to account for intensity and enables the analysis of distinct types and domains of physical activity. The VISUAL-PA software creates graphic outputs of physical activity patterns that enable the identification, visualization, and analysis of distinct types and intensities of physical activity in addition to sedentary behavior. The use of VISUAL-PA can contribute to a broader understanding of the complexity in physical activity patterns among older adults in terms of dimensions such as activity patterns and habits, domains, and intensity level. To strengthen the public health strategies that promote health and an active lifestyle, additional knowledge about physical activity patterns is necessary. Moreover, the visualization of physical activity can enable reflections on and awareness of activity habits and preferences, and thus facilitate behavior changes in older individuals.

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Faculty Opinions recommendation of Large-scale physical activity data reveal worldwide activity inequality.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727795643.793534116 ◽

2017 ◽

Author(s):

Mark A Febbraio

Keyword(s):

Physical Activity ◽

Large Scale ◽

Activity Data ◽

Physical Activity Data

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Physical activity patterns in chronic hemodialysis patients: Comparison of dialysis and nondialysis days

Journal of Renal Nutrition ◽

10.1053/j.jrn.2004.08.002 ◽

2005 ◽

Vol 15 (2) ◽

pp. 217-224 ◽

Cited By ~ 32

Author(s):

Karen M. Majchrzak ◽

Lara B. Pupim ◽

Kong Chen ◽

Cathi J. Martin ◽

Sheila Gaffney ◽

...

Keyword(s):

Physical Activity ◽

Activity Patterns ◽

Hemodialysis Patients ◽

Chronic Hemodialysis ◽

Physical Activity Patterns

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Physical activity patterns among walkers and compliance with public health recommendations

Medicine & Science in Sports & Exercise ◽

10.1097/00005768-200208000-00005 ◽

2002 ◽

Vol 34 (8) ◽

pp. 1255-1261 ◽

Cited By ~ 82

Author(s):

ANN P. RAFFERTY ◽

MATHEW J. REEVES ◽

HARRY B. MCGEE ◽

JAMES M. PIVARNIK

Keyword(s):

Public Health ◽

Physical Activity ◽

Activity Patterns ◽

Physical Activity Patterns

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Physical Activity Patterns After Retirement: The REGARDS Study

Innovation in Aging ◽

10.1093/geroni/igaa057.1704 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 528-529

Author(s):

Eric Shiroma ◽

J David Rhodes ◽

Aleena Bennet ◽

Monika M Safford ◽

Leslie MacDonald ◽

...

Keyword(s):

Physical Activity ◽

Light Intensity ◽

Racial Differences ◽

Activity Patterns ◽

Accelerometer Data ◽

Tv Viewing ◽

Major Life Events ◽

Major Life ◽

Physical Activity Patterns ◽

Regards Study

Abstract Major life events, such as retirement, may lead to dramatic shifts in physical activity (PA) patterns. However, there are limited empirical data quantifying the magnitude of these changes. Our aims were to objectively measure PA before and after retirement and to describe changes in participation in various types of PA. Participants were employed black and white men and women enrolled in REGARDS (REasons for Geographic and Racial Differences in Stroke), a national prospective cohort study (n=581, mean age 64 years, 25% black, 51% women). Participants met inclusion criteria if they retired between their first and second accelerometer wearing (2009-2013 and 2017-2018, respectively) and had valid accelerometer data (>4 days with >10 hours/day pre- and post-retirement). Accelerometer-based PA was categorized into average minutes per day spent in sedentary, light-intensity, and moderate-to-vigorous PA. Participants reported changes (less, same, more) in 12 types of PA. After retirement, participants decreased both sedentary time (by 36.3 minutes/day) and moderate-to-vigorous PA (by 5.6 minutes/day). Conversely, there was an increase in light-intensity PA (+18.1 minutes/day) after retirement. Participants reported changes in their participation level in various PA activities. For example, 41% reported an increased amount of TV viewing, 42% reported less walking, and 31% reported increased participation in volunteer activities. Findings indicate that retirement coincides with a change in the time spent in each intensity category and the time spent across a range of activity types. Further research is warranted to examine how these changes in physical activity patterns influence post-retirement health status.

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A roadmap: Social distancing physical activity data collection to protect those with intellectual disabilities

Journal of Intellectual Disabilities ◽

10.1177/17446295211009655 ◽

2021 ◽

pp. 174462952110096

Author(s):

Whitley J Stone ◽

Kayla M Baker

Keyword(s):

Physical Activity ◽

Data Collection ◽

Intellectual Disabilities ◽

Research Methods ◽

Virus Transmission ◽

Future Research ◽

Activity Data ◽

Physical Activity Data ◽

Social Distancing ◽

Novel Coronavirus

The novel coronavirus may impact exercise habits of those with intellectual disabilities. Due to the mandated discontinuation of face-to-face research, investigators must adapt projects to protect all involved while collecting objective physical activity metrics. This brief report outlines a modification process of research methods to adhere to social distancing mandates present during COVID-19. Actions taken included electronic consent and assent forms, an electronic survey, and mailing an accelerometer with included instructions. The amended research methods were implemented without risk for virus transmission or undue burden on the research team, participant, or caregiver. Recruitment was likely impacted by the coronavirus-mediated quarantine, plausibly resulting in bias. Objective physical activity data collection can be sufficiently modified to protect those with intellectual disabilities and investigators. Future research designs may require greater participant incentives and the creation of in-home participation.

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