Temporal physical activity patterns are associated with obesity in U.S. adults

Twenty-four-hour physical activity patterns associated with depressive symptoms: a cross-sectional study using big data-machine learning approach

BACKGROUND

Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of physical activity and depressive symptoms is poorly understood. In this exploratory study, we hypothesize that a particular temporal pattern of daily physical activity could be associated with depressive symptoms and might be a better marker than the total amount of physical activity.

METHODS

To address the hypothesis, we investigated the association between depressive symptoms and daily dominant activity behaviors based on 24-h temporal patterns of physical activity. We conducted a cross-sectional study on NHANES 2011-2012 data collected from the noninstitutionalized civilian resident population of the United States. The number of participants that had the whole set of physical activity data collected by the accelerometer is 6613. Among 6613 participants, 4242 participants had complete demography and Patient Health Questionnaire-9 (PHQ-9) questionnaire, a tool to quantify depressive symptoms. The association between activity-count behaviors and depressive symptoms was analyzed using multivariable logistic regression to adjust for confounding factors in sequential models.

RESULTS

We identified four physical activity-count behaviors based on five physical activity-counting patterns classified by unsupervised machine learning. Regarding PHQ-9 scores, we found that evening dominant behavior was positively associated with depressive symptoms compared to morning dominant behavior as the control group.

CONCLUSIONS

Our results might contribute to monitoring and identifying individuals with latent depressive symptoms, emphasizing the importance of nuanced activity patterns and their probability of assessing depressive symptoms effectively.

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health

BACKGROUND

Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders.

METHODS

Our method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20-65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003-2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data.

RESULTS

Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as "Early-morning movers" and the profile characterized as "Highest activity" both had lower levels of insulin (P < 0.01 for both), triglycerides (P < 0.05 and P < 0.01, respectively), HOMA-IR (P < 0.01 for both), and plasma glucose (P < 0.05 and P < 0.1, respectively) compared to the "Lowest activity" profile. No significant differences were observed for the "Least sedentary movers" profile compared to the "Lowest activity" profile.

CONCLUSIONS

Deep learning of movement behavior profiles revealed that, in addition to duration and patterns of movement behaviors, the timing of physical activity may also be crucial for gaining additional health benefits.

Temporal Dietary Pattern Cluster Membership Varies on Weekdays and Weekends but Both Link to Health

BACKGROUND

Energy and dietary quality are known to differ between weekdays and weekends. Data-driven approaches that incorporate time, amount, and duration of dietary intake have previously been used to partition participants' daily weekday dietary intake time series into clusters representing weekday temporal dietary patterns (TDPs) linked to health indicators in United States adults. Yet, neither the relationship of weekend day TDPs to health indicators nor how the TDP membership may change from weekday to weekend is known.

OBJECTIVES

This study was conducted to determine the association between TDPs on weekdays and weekend days and health indicators [diet quality, waist circumference (WC), body mass index (BMI), and obesity] and their overlap among participants.

METHODS

A weekday and weekend day 24-hour dietary recall of 9494 nonpregnant United States adults aged 20-65 years from the cross-sectional National Health and Nutrition Examination Survey 2007-2018 was used to determine the timing and amount of energy intake. Modified dynamic time warping and kernel k-means algorithm clustered participants into 4 TDPs on weekdays and weekend days. Multivariate regression models determined the associations between TDPs and health indicators, controlling for potential confounders and adjusting for the survey design and multiple comparisons. The percentages of overlap in cluster membership between TDPs on weekdays and weekend days were also determined.

RESULTS

United States adults with a TDP of evenly spaced, energy-balanced eating occasions, representing the TDP of more than one-third of all adults on weekdays and weekends, had significantly higher diet quality, lower BMI, WC, and odds of obesity when compared to those with other TDPs. Membership of most United States adults to TDPs varied from weekdays to weekends.

CONCLUSIONS

Both weekday and weekend TDPs were significantly associated with health indicators. TDP membership of most United States adults was not consistent on weekdays and weekends.

Temporal Patterns of Diet and Physical Activity and of Diet Alone Have More Numerous Relationships With Health and Disease Status Indicators Compared to Temporal Patterns of Physical Activity Alone

BACKGROUND

Daily temporal patterns of energy intake (temporal dietary patterns [TDPs]) and physical activity (temporal physical activity patterns [TPAPs]) have been independently and jointly (temporal dietary and physical activity patterns [TDPAPs]) associated with health and disease status indicators.

OBJECTIVE

The aim of this study was to compare the number and strength of association between clusters of daily TDPs, TPAPs, and TDPAPs and multiple health and disease status indicators.

DESIGN

This cross-sectional study used 1 reliable weekday dietary recall and 1 random weekday of accelerometer data to partition to create clusters of participants representing the 3 temporal patterns. Four clusters were created via kernel-k means clustering algorithm of the same constrained dynamic time warping distance computed over the time series for each temporal pattern.

PARTICIPANTS/SETTING

From the National Health and Nutrition Examination Survey (2003-2006), 1,836 US adults aged 20 through 65 years who were not pregnant and had valid diet, physical activity, sociodemographic, anthropometric, questionnaire, and health and disease status indicator data were included.

MAIN OUTCOME MEASURES

Health status indicators used as outcome measures were body mass index, waist circumference, fasting plasma glucose, hemoglobin A1c, triglycerides, high-density lipoprotein cholesterol, total cholesterol, and systolic and diastolic blood pressure; disease status indicators included obesity, type 2 diabetes mellitus, and metabolic syndrome.

STATISTICAL ANALYSES PERFORMED

Multivariate regression models determined associations between the clusters representing each pattern and health and disease status indicators, controlling for confounders and adjusting for multiple comparisons. The number of significant differences among clusters and adjusted R2 and Akaike information criterion compared the strength of associations between clusters of patterns and continuous and categorical health and disease status indicators.

RESULTS

TDPAPs showed 21 significant associations with health and disease status indicators, including body mass index, waist circumference, obesity, and type 2 diabetes; TDPs showed 19 significant associations; and TPAPs showed 8 significant associations.

CONCLUSIONS

TDPAPs and TDPs had stronger and more numerous associations with health and disease status indicators compared with TPAPs. Patterns representing the integration of daily dietary habits hold promise for early detection of obesity.

Higher walkability associated with increased physical activity and reduced obesity among United States adults

OBJECTIVE

This study examined associations among perceived neighborhood walkability, physical activity (PA), and obesity among United States adults.

METHODS

Data from the 2020 National Health Interview Survey were analyzed. Walkability was assessed using a summative scale and was categorized as low, medium, or high. PA was categorized as insufficient (0-149 min/wk) or sufficient (150+ min/wk). Multivariable regressions estimated an association between obesity and BMI and PA/walkability. Mediation analysis was used to partition contribution of PA as a mediator. Effect modification by race and ethnicity in the association between walkability and BMI was explored.

RESULTS

The sample included N = 31,568 adults. Compared with those in low-walkability neighborhoods, participants in high-walkability neighborhoods had increased odds of sufficient PA (odds ratio [OR] = 1.48; 95% CI: 1.30-1.69) and decreased obesity odds (OR = 0.76; 95% CI: 0.66-0.87). PA partially mediated the association between walkability and BMI (23.4%; 95% CI: 14.6%-62.7%). The association between walkability and BMI was modified by race and ethnicity (F[5,567]  = 2.75; p = 0.018). Among White, Black, Hispanic, and Asian adults, BMI decreased with increasing walkability; among American Indian/Alaska Native and multiracial/other adults, BMI increased with increasing walkability.

CONCLUSIONS

The findings highlight the importance of investing in the built environment to improve perceptions of walkability and promote PA and healthy weight, as well as developing interventions to target racial and ethnic disparities in these outcomes.

Joint temporal dietary and physical activity patterns: associations with health status indicators and chronic diseases

BACKGROUND

Diet and physical activity (PA) are independent risk factors for obesity and chronic diseases including type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS). The temporal sequence of these exposures may be used to create patterns with relations to health status indicators.

OBJECTIVES

The objectives were to create clusters of joint temporal dietary and PA patterns (JTDPAPs) and to determine their association with health status indicators including BMI, waist circumference (WC), fasting plasma glucose, glycated hemoglobin, triglycerides, HDL cholesterol, total cholesterol, blood pressure, and disease status including obesity, T2DM, and MetS in US adults.

METHODS

A 24-h dietary recall and random day of accelerometer data of 1836 participants from the cross-sectional NHANES 2003-2006 data were used to create JTDPAP clusters by constrained dynamic time warping, coupled with a kernel k-means clustering algorithm. Multivariate regression models determined associations between the 4 JTDPAP clusters and health and disease status indicators, controlling for potential confounders and adjusting for multiple comparisons.

RESULTS

A JTDPAP cluster with proportionally equivalent energy consumed at 2 main eating occasions reaching ≤1600 and ≤2200 kcal from 11:00 to 13:00 and from 17:00 to 20:00, respectively, and the highest PA counts among 4 clusters from 08:00 to 20:00, was associated with significantly lower BMI (P < 0.0001), WC (P = 0.0001), total cholesterol (P = 0.02), and odds of obesity (OR: 0.2; 95% CI: 0.1, 0.5) than a JTDPAP cluster with proportionally equivalent energy consumed reaching ≤1600 and ≤1800 kcal from 11:00 to 14:00 and from 17:00 to 21:00, respectively, and high PA counts from 09:00 to 12:00.

CONCLUSIONS

The joint temporally patterned sequence of diet and PA can be used to cluster individuals with meaningful associations to BMI, WC, total cholesterol, and obesity. Temporal patterns hold promise for future development of lifestyle patterns that integrate additional temporal and contextual activities.

Expanding the Capabilities of Nutrition Research and Health Promotion Through Mobile-Based Applications

Mobile-based applications are popular and prevalently used in the US population. Applications focusing on nutrition offer platforms for quantifying and changing behaviors to improve dietary intake. Such behavior changes can intervene in the relation of diet to promote health and prevent disease. Mobile applications offer a safe and convenient way to collect user data and share it back to users, researchers, and to health care providers. Other lifestyle factors like activity, sleep, and sedentary behavior, can also be quantified and included in investigations of how lifestyle is related to health. Yet, challenges in the assessment offered through mobile applications and effectiveness to change behavior still remain, including rigorous evaluation, demonstration of successful health improvement, and participant engagement. The data mobile applications generate, however, expands opportunities for discovery of the integrated and time-based nature of various daily activities in relation to health. This article is a summary of a symposium at Nutrition 2020 Live Online on the role of mobile applications as a tool for nutrition research and health promotion. The types and capabilities of mobile applications, challenges in their evaluation and use in research, and opportunities for the data they generate along with a specific example, are reviewed.