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

Dietary acidity and bone mass in minority children and adolescents: a cross-sectional analysis from the MetA-Bone trial

Dietary acid load could be associated with bone mass, but there are limited and conflicting results. This secondary cross-sectional analysis evaluated these associations among 123 children/adolescents, mostly Hispanics. Dietary acid load seems to be associated with bone mass in boys, but these results should be confirmed through long-term studies.

BACKGROUND

Childhood/adolescence is key for developing strong bones. With the continuing rise in osteoporosis rates in Western societies, attention has turned to the possible impact of diets that create high levels of acid in the body. Some studies have shown an association between dietary acid load and bone mass, but there are limited studies in children/adolescents and none in Hispanics.

OBJECTIVE

This study aimed to evaluate the associations between dietary acid load (assessed as dietary potential renal acid load, PRAL, and protein to potassium ratio) and whole-body bone mineral content (BMC) and density (BMD) in a sample of 123 children and adolescents of predominantly Hispanic ethnicity.

DESIGN

This study is a secondary cross-sectional analysis.

RESULTS

Dietary PRAL was positively associated with BMC and BMD overall (p < 0.05) and with BMC in boys (p < 0.05) when adjusted for energy misreporting only. The vegetable protein to potassium ratio was inversely associated with BMC (p = 0.004) in boys only, after adjusting for important factors.

CONCLUSIONS

Dietary acid load seems to be associated with BMC in boys in this sample of primarily Hispanic children. Long-term studies are needed to confirm these results and to understand the importance of protein intake in relation to other key nutrients in bone mass acquisition among Hispanic boys.

Independent and Combined Associations of Physical Activity in Different Domains and Inflammatory Diet with Type 2 Diabetes: A Population-Based Cohort Study

OBJECTIVE

This study aims to explore the independent and combined associations of physical activity (PA) in different domains and inflammatory diet with type 2 diabetes mellitus (T2DM).

METHODS

Data from 8736 American adults from the NHANES 2007-2016 were used. PA in different domains was assessed using the self-reported Global Physical Activity Questionnaire, and dietary inflammatory index was estimated based on 24 h dietary recalls. T2DM diagnosis was determined by a combination of self-report and laboratory data. A multivariate modified Poisson regression model was used to explore the independent and combined associations of moderate-vigorous intensity physical activity (MVPA) and inflammatory diet with T2DM.

RESULTS

PA in the Work MVPA, Recreational MVPA and Total MVPA domains was independently associated with reduced risk of T2DM, and an inflammatory diet was independently associated with elevated risk of T2DM. In the combined analysis, the combination of active and anti-inflammatory within the Work MVPA, Recreational MVPA and Total MVPA fields was associated with the greatest reduced risk of T2DM, and always associated with decreased risk of T2DM in the active group.

CONCLUSIONS

Our study emphasizes that the combination of active PA and anti-inflammatory diet is closely associated with the reduced risk of T2DM, and suggests the combination of both for the prevention and treatment of T2DM.

Association Between Visceral Obesity Index and Diabetes: A Systematic Review and Meta-analysis

CONTENT

The correlation between visceral obesity index (VAI) and diabetes and accuracy of early prediction of diabetes are still controversial.

OBJECTIVE

This study aims to review the relationship between high level of VAI and diabetes and early predictive value of diabetes.

DATA SOURCES

The databases of PubMed, Cochrane, Embase, and Web of Science were searched until October 17, 2023.

STUDY SELECTION

After adjusting for confounding factors, the original study on the association between VAI and diabetes was analyzed.

DATA EXTRACTION

We extracted odds ratio (OR) between VAI and diabetes management after controlling for mixed factors, and the sensitivity, specificity, and diagnostic 4-grid table for early prediction of diabetes.

DATA SYNTHESIS

Fifty-three studies comprising 595 946 participants were included. The findings of the meta-analysis elucidated that in cohort studies, a high VAI significantly increased the risk of diabetes mellitus in males (OR = 2.83 [95% CI, 2.30-3.49]) and females (OR = 3.32 [95% CI, 2.48-4.45]). The receiver operating characteristic, sensitivity, and specificity of VAI for early prediction of diabetes in males were 0.64 (95% CI, .62-.66), 0.57 (95% CI, .53-.61), and 0.65 (95% CI, .61-.69), respectively, and 0.67 (95% CI, .65-.69), 0.66 (95% CI, .60-.71), and 0.61 (95% CI, .57-.66) in females, respectively.

CONCLUSION

VAI is an independent predictor of the risk of diabetes, yet its predictive accuracy remains limited. In future studies, determine whether VAI can be used in conjunction with other related indicators to early predict the risk of diabetes, to enhance the accuracy of prediction of the risk of diabetes.

Analytic Methods for Understanding the Temporal Patterning of Dietary and 24-H Movement Behaviors: A Scoping Review

Dietary and movement behaviors [physical activity (PA), sedentary behavior (SED), and sleep] occur throughout a 24-h day and involve multiple contexts. Understanding the temporal patterning of these 24-h behaviors and their contextual determinants is key to determining their combined effect on health. A scoping review was conducted to identify novel analytic methods for determining temporal behavior patterns and their contextual correlates. We searched Embase, ProQuest, and EBSCOhost databases in July 2022 to identify studies published between 1997 and 2022 on temporal patterns and their contextual correlates (e.g., locational, social, environmental, personal). We included 14 studies after title and abstract (n = 33,292) and full-text (n = 135) screening, of which 11 were published after 2018. Most studies (n = 4 in adults; n = 5 in children and adolescents), examined waking behavior patterns (i.e., both PA and SED) of which 3 also included sleep and 6 included contextual correlates. PA and diet were examined together in only 1 study of adults. Contextual correlates of dietary, PA, and sleep temporal behavior patterns were also examined. Machine learning with various clustering algorithms and model-based clustering techniques were most used to determine 24-h temporal behavior patterns. Although the included studies used a diverse range of methods, behavioral variables, and assessment periods, results showed that temporal patterns characterized by high SED and low PA were linked to poorer health outcomes, than those with low SED and high PA. This review identified temporal behavior patterns, and their contextual correlates, which were associated with adiposity and cardiometabolic disease risk, suggesting these methods hold promise for the discovery of holistic lifestyle exposures important to health. Standardized reporting of methods and patterns and multidisciplinary collaboration among nutrition, PA, and sleep researchers; statisticians; and computer scientists were identified as key pathways to advance future research on temporal behavior patterns in relation to health.

Association between Physical Activity and Phase Angle Obtained via Bioelectrical Impedance Analysis in South Korean Adults Stratified by Sex

This cross-sectional study aimed to examine the association of various aspects of physical activity, including intensity, duration, type, and purpose, with the phase angle (PhA), an objective indicator of health, in Korean adults after stratification by sex. Data from the 2022 Korean National Health and Nutrition Examination Survey, a nationwide, representative, population-based survey, were used. In total, 3996 participants were included in the study. Participants self-reported their weekly intensity, frequency, duration of engagement in physical activity. PhA was categorized into two groups on the basis of sex-specific averages. Multiple logistic regression analysis was used to investigate the relationship between physical activity and PhA, and proportional odds logistic regression analysis was performed to determine the association between physical activity and different subclasses of PhA. A positive association was found between sufficiently active aerobic physical activity and PhA compared with inactive physical activity (sufficiently active, male: odds ratio = 1.952, 95% confidence interval = 1.373-2.776; female: odds ratio = 1.333, 95% confidence interval = 1.019-1.745). This association was further strengthened when aerobic physical activity was accompanied by muscle-strengthening activity (sufficiently active with muscle-strengthening activity, male: aOR = 2.318, 95% CI = 1.512-3.554; female: aOR = 1.762, 95% CI = 1.215-2.556) and vigorous-intensity activities (sufficiently active with sufficient vigorous-intensity activity, male: aOR = 2.785, 95% CI = 1.647-4.709; female: aOR = 2.505, 95% CI = 1.441-4.356) and when there was more leisure-time physical activity than occupational physical activity (sufficiently active with more leisure-time physical activity, male: aOR = 2.158, 95% CI = 1.483-3.140; female: aOR = 1.457, 95% CI = 1.078-1.969). Furthermore, the inclusion of muscle-strengthening activity made a significant difference in the values of PhA for males with insufficiently active physical activity (aOR = 2.679, 95% CI = 1.560-4.602). For females with highly active physical activity (aOR = 1.521, 95% CI = 1.068-2.166), the inclusion of muscle-strengthening and vigorous-intensity activities were significantly associated with higher values for PhA. This study can be utilized to provide specific suggestions for better health programs and can change perception that only occupational physical activity is enough. This study also indicated that PhA can be used for personalized health assessments.

Conceptualization and Assessment of 24-H Timing of Eating and Energy Intake: A Methodological Systematic Review of the Chronic Disease Literature

Timing of eating (TOE) and energy intake (TOEI) has important implications for chronic disease risk beyond diet quality. The 2020 Dietary Guidelines Advisory Committee recommended developing consistent terminology to address the lack of TOE/TOEI standardization. The primary objective of this methodological systematic review was to characterize the conceptualization and assessment of TOE/TOEI within the chronic disease literature (International Prospective Register of Systematic Reviews registration number: CRD42021236621). Literature searches in Cumulative Index to Nursing and Allied Health Literature (CINAHL) Plus, Embase, PubMed, and Scopus were limited to English language publications from 2000 to August 2022. Eligible studies reported the association between TOE/TOEI and obesity, cardiovascular disease, type 2 diabetes mellitus, cancer, or a related clinical risk factor among adults (≥19 y) in observational and intervention studies. A qualitative synthesis described and compared TOE/TOEI conceptualization, definitions, and assessment methods across studies. Of the 7579 unique publications identified, 259 studies (observational [51.4 %], intervention [47.5 %], or both [1.2 %]) were eligible for inclusion. Key findings indicated that most studies (49.6 %) were conducted in the context of obesity and body weight. TOE/TOEI variables or assigned conditions conceptualized interrelated aspects of time and eating or energy intake in varying ways. Common TOE/TOEI conceptualizations included the following: 1) timepoint (specific time to represent when intake occurs, such as time of breakfast [74.8 %]); 2) duration (length of time or interval when intake does/does not occur, such as "eating window" [56.5 %]); 3) distribution (proportion of daily intake at a given time interval, such as "percentage of energy before noon" [29.8 %]); and 4) cluster (grouping individuals based on temporal ingestive characteristics [5.0 %]). Assessment, definition, and operationalization of 24-h TOE/TOEI variables varied widely across studies. Observational studies most often used surveys or questionnaires (28.9 %), whereas interventions used virtual or in-person meetings (23.8 %) to assess TOE/TOEI adherence. Overall, the diversity of terminology and methods solidifies the need for standardization to guide future research in chrononutrition and to facilitate inter-study comparisons.

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.

The Discovery of Data-Driven Temporal Dietary Patterns and a Validation of Their Description Using Energy and Time Cut-Offs

Data-driven temporal dietary patterning (TDP) methods were previously developed. The objectives were to create data-driven temporal dietary patterns and assess concurrent validity of energy and time cut-offs describing the data-driven TDPs by determining their relationships to BMI and waist circumference (WC). The first day 24-h dietary recall timing and amounts of energy for 17,915 U.S. adults of the National Health and Nutrition Examination Survey 2007−2016 were used to create clusters representing four TDPs using dynamic time warping and the kernel k-means clustering algorithm. Energy and time cut-offs were extracted from visualization of the data-derived TDPs and then applied to the data to find cut-off-derived TDPs. The strength of TDP relationships with BMI and WC were assessed using adjusted multivariate regression and compared. Both methods showed a cluster, representing a TDP with proportionally equivalent average energy consumed during three eating events/day, associated with significantly lower BMI and WC compared to the other three clusters that had one energy intake peak/day at 13:00, 18:00, and 19:00 (all p < 0.0001). Participant clusters of the methods were highly overlapped (>83%) and showed similar relationships with obesity. Data-driven TDP was validated using descriptive cut-offs and hold promise for obesity interventions and translation to dietary guidance.