Integrating Sleep, Physical Activity, and Diet Quality to Estimate All-Cause Mortality Risk: A Combined Compositional Clustering and Survival Analysis of the National Health and Nutrition Examination Survey 2005-2006 Cycle

Patterns of dietary quality, physical activity, and sleep duration among cancer survivors and caregivers

Fruit and vegetable intake (FVI), moderate-to-vigorous physical activity (MVPA), and sleep duration are each independently associated with cancer-related and general health outcomes among cancer survivors. Past research suggests that health behaviors cluster among cancer survivors, with caregivers demonstrating similar patterns. This analysis examined co-occurrence of FVI, MVPA, and sleep duration among cancer survivors and informal cancer caregivers and identified sociodemographic and clinical correlates of health behavior engagement. Using data from the Health Information National Trends Survey (HINTS), an exploratory latent profile analysis (LPA) was conducted among those self-reporting a history of cancer or identifying as a cancer caregiver. The LPA model was fit with daily self-reported FVI (cups/d), MPVA (minutes/d) and sleep duration (hours/d). Multinomial logistic regression models were used to predict profile membership based on sociodemographic and clinical characteristics. Four health behavior profiles were identified (Least Engaged-No MVPA, Least Engaged-Low MVPA, Moderately Engaged, and Highly Engaged). The largest profile membership was Least Engaged-No MVPA, capturing 37% of the sample. Profiles were most distinguished by MVPA, with the lowest variance in sleep duration. Participants reporting higher FVI also often reported greater MVPA and longer sleep duration. Profile membership was significantly associated with age, relationship status, education, income, rurality, alcohol use, self-efficacy, psychological distress, BMI, and cancer type. This study identified four health behaviors patterns and sociodemographic correlates that distinguished those patterns among cancer survivors and caregivers drawn from a nationally representative sample. Results may help identify for whom health behavior interventions could be of greatest benefit.

Clusters of 24-hour movement behavior and diet and their relationship with health indicators among youth: a systematic review

Movement-related behaviors (physical activity [PA], sedentary behavior [SB], and sleep) and diet interact with each other and play important roles in health indicators in youth. This systematic review aimed to investigate how PA, SB, sleep, and diet cluster in youth by biological sex; and to examine which cluster are associated with health indicators. This study was registered in PROSPERO (number: CRD42018094826). Five electronic databases were assessed. Eligibility criteria allowed studies that included youth (aged 19 years and younger), and only the four behaviors {PA, SB, sleep, and diet (ultra-processed foods [UPF]; fruits and vegetables [FV])} analyzed by applying data-based cluster procedures. From 12,719 articles screened; 23 were included. Of these, four investigated children, and ten identified clusters by biological sex. Sixty-six mixed cluster were identified including, 34 in mixed-sex samples, 10 in boys and 11 in girls. The most frequent clusters in mixed-sex samples were "High SB UPF Low Sleep", "Low PA High SB Satisfactory Sleep", and "High PA". The main difference in profiles according to sex was that girls' clusters were characterized by high sleep duration, whereas boys' clusters by high PA. There were a few associations found between cluster types and health indicators, highlighting that youth assigned to cluster types with low PA exhibited higher adiposity. In conclusion, the youth presented a range of clusters of behaviors, typically exhibiting at least one unhealthy behavior. Similar patterns were observed in both sexes with the biggest difference in time of sleep for girls and PA for boys. These findings underscore the importance of intervention strategies targeting multiple behaviors simultaneously to enhance health risk profiles and indicators in children and adolescents.

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.

"We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults

Physical activity (PA) is known to be a risk factor for obesity and chronic diseases such as diabetes and metabolic syndrome. Few attempts have been made to pattern the time of physical activity while incorporating intensity and duration in order to determine the relationship of this multi-faceted behavior with health. In this paper, we explore a distance-based approach for clustering daily physical activity time series to estimate temporal physical activity patterns among U.S. adults (ages 20-65) from the National Health and Nutrition Examination Survey 2003-2006 (NHANES). A number of distance measures and distance-based clustering methods were investigated and compared using various metrics. These metrics include the Silhouette and the Dunn Index (internal criteria), and the associations of the clusters with health status indicators (external criteria). Our experiments indicate that using a distance-based cluster analysis approach to estimate temporal physical activity patterns through the day, has the potential to describe the complexity of behavior rather than characterizing physical activity patterns solely by sums or labels of maximum activity levels.

Heat and air quality related cause-based elderly mortalities and emergency visits

The combined effects of heat events and poor air quality conditions can severely affect population health. A novel correlational method was developed to assess the impact of the short-term variations of environmental variables (air pollutants and ambient conditions) on community health responses (mortalities and emergency department visits). A multi-dimensional clustering approach was proposed by combining hierarchical and k-means clustering to promote flexibility and robustness to improve the correlation procedure. The study focused on the health records of the elderly population and people diagnosed with cardiorespiratory causes. The study investigated multiple health records on different levels of investigation: total, elderly, cause-based, and elderly cause-based records. The developed method was validated by investigating the short-term impact of ambient air temperature, relative humidity, ground-level ozone, and fine particulate matter on the health records during hot and warm seasons in the municipalities of Mississauga and Brampton, Peel Region, Ontario, Canada for 15 years. The analysis confirmed the association between moderate levels of environmental variables and increased short-term daily total deaths and emergency department visits, while the elderly sector showed higher vulnerability to environmental changes. Furthermore, the association with extreme heat conditions and poor air quality levels was affirmed with cause-based mortalities and emergency visits; the correlation was strongest with elderly cause-based health records. Findings confirm that cardiorespiratory patients, especially elderly people, were at the greatest risk of poor environmental conditions.

The composition of data economy: a bibliometric approach and TCCM framework of conceptual, intellectual and social structure

Purpose

The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature.

Design/methodology/approach

The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus.

Findings

This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future.

Research limitations/implications

Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field.

Practical implications

The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.

Originality/value

This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.

Replacing Sedentary Behavior With Physical Activity of Different Intensities: Implications for Physical Function, Muscle Function, and Disability in Octogenarians Living in Long-Term Care Facilities

BACKGROUND

We investigated the associations of replacing sedentary behavior (SB) with physical activity of different intensities on the physical function of octogenarians living in long-term care facilities.

METHODS

This pooled study recruited 427 older adults aged 80 years and older (69.1% female; body mass index: 27.53). For 345 participants who provided valid data, we assessed device-measured time spent in SB, light-intensity physical activity (LIPA), and moderate to vigorous physical activity (MVPA). We assessed lower limb physical function, strength, mobility, and disability. We used compositional data analysis to investigate the associations of replacing SB with physical activity on the outcomes.

RESULTS

Reallocation of SB to LIPA and MVPA was associated with a higher number of 30-second Chair Stand cycles (LIPA: +0.21, MVPA: +1.81; P < .001), greater peak force (LIPA: +11.96 N, MVPA: +27.68 N; P < .001), peak power (LIPA: +35.82 W, MVPA: +92.73 W; P < .001), peak velocity (LIPA: +0.03 m/s, MVPA: +0.12 m/s; P < .001), higher levels of grip strength (LIPA: +0.68 kg, MVPA: +2.49 kg; P < .001), and less time in the Time Up and Go (LIPA: -7.63 s, MVPA: -12.43 s; P < .001).

CONCLUSIONS

Replacing SB with LIPA or MVPA is associated with physical function and disability of older adults living in long-term care facilities.

Co-Occurrence and Clustering of Sedentary Behaviors, Diet, Sugar-Sweetened Beverages, and Alcohol Intake among Adolescents and Adults: The Latin American Nutrition and Health Study (ELANS)

Poor diet, sedentary behaviors, sugar-sweetened beverages (SSB) and alcohol intake seem to co-exist in complex ways that are not well understood. The aim of this study was to provide an understanding of the extent to which unhealthy behaviors cluster in eight Latin America countries. A secondary aim was to identify socio-demographic characteristics associated with these behaviors by country. Data from adolescents and adults from the “Latin American Health and Nutrition Study” was used and the prevalence of screen-time, occupational and transportation–sedentary time, socializing with friends, poor diet, SSB and alcohol intake, alone and in combination, were identified. The eight Latin America (LA) countries added to analyses were: Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, and Venezuela. Logistic regression was used to estimate associations between ≥2 behaviors clustering, socio-demographics and weight status. Among 9218 individuals, the most prevalent behaviors were transportation and occupation–sedentary time, SSB and alcohol intake. Younger, female, married/living with a partner, low and middle-income and obese individuals had higher chances for these clustering behaviors. These results provide a multi-country level of understanding of the extent to which behaviors co-occur in the LA population.

Accumulation patterns of sedentary time and breaks and their association with cardiometabolic health markers in adults

Breaking up sedentary time with physical activity (PA) could modify the detrimental cardiometabolic health effects of sedentary time. Our aim was to identify profiles according to distinct accumulation patterns of sedentary time and breaks in adults, and to investigate how these profiles are associated with cardiometabolic outcomes. Participants (n = 4439) of the Northern Finland Birth Cohort 1966 at age 46 years wore a hip-worn accelerometer for 7 consecutive days during waking hours. Uninterrupted ≥1-min sedentary bouts were identified, and non-sedentary bouts in between two consecutive sedentary bouts were considered as sedentary breaks. K-means clustering was performed with 65 variables characterizing how sedentary time was accumulated and interrupted. Linear regression was used to determine the association of accumulation patterns with cardiometabolic health markers. Four distinct groups were formed as follows: "Couch potatoes" (n = 1222), "Prolonged sitters" (n = 1179), "Shortened sitters" (n = 1529), and "Breakers" (n = 509). Couch potatoes had the highest level of sedentariness and the shortest sedentary breaks. Prolonged sitters, accumulating sedentary time in bouts of ≥15-30 min, had no differences in cardiometabolic outcomes compared with Couch potatoes. Shortened sitters accumulated sedentary time in bouts lasting <15 min and performed more light-intensity PA in their sedentary breaks, and Breakers performed more light-intensity and moderate-to-vigorous PA. These latter two profiles had lower levels of adiposity, blood lipids, and insulin sensitivity, compared with Couch potatoes (1.1-25.0% lower values depending on the cardiometabolic health outcome, group, and adjustments for potential confounders). Avoiding uninterrupted sedentary time with any active behavior from light-intensity upwards could be beneficial for cardiometabolic health in adults.

Association of Physical Activity Intensity With Mortality: A National Cohort Study of 403 681 US Adults

IMPORTANCE

It is unclear whether, for the same amount of total physical activity, a higher proportion of vigorous physical activity (VPA) to total physical activity is associated with a greater reduction in mortality.

OBJECTIVE

To examine the association of the proportion of VPA to total physical activity (defined as moderate to vigorous physical activity [MVPA]) with all-cause mortality, cardiovascular disease mortality, and cancer mortality.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 403 681 adults from the National Health Interview Survey 1997-2013 who provided data on self-reported physical activity and were linked to the National Death Index records through December 31, 2015. Statistical analysis was performed from May 15, 2018, to August 15, 2020.

EXPOSURES

Proportion of VPA to total physical activity among participants performing any MVPA.

MAIN OUTCOMES AND MEASURES

All-cause mortality, cardiovascular disease mortality, and cancer mortality. Cox proportional hazards regression models were performed to estimate hazard ratios (HRs) and 95% CIs, adjusted for sociodemographic characteristics, lifestyle risk factors, and total physical activity.

RESULT

Among the 403 681 individuals (225 569 women [51.7%]; mean [SD] age, 42.8 [16.3] years) in the study, during a median 10.1 years (interquartile range, 5.4-14.6 years) of follow-up (407.3 million person-years), 36 861 deaths occurred. Mutually adjusted models considering the recommendations of moderate physical activity (MPA; 150-299 vs 0 minutes per week) and VPA (≥75-149 vs 0 minutes per week) showed similar associations for all-cause mortality (MPA: HR, 0.83; 95% CI, 0.80-0.87; and VPA: HR, 0.80; 95% CI, 0.76-0.84) and cardiovascular disease mortality (MPA: HR, 0.75; 95% CI, 0.68-0.83; and VPA: HR, 0.79; 95% CI, 0.70-0.91). For the same contrasts, VPA (HR, 0.89; 95% CI, 0.80-0.99) showed a stronger inverse association with cancer mortality compared with MPA (HR, 0.94; 95% CI, 0.86-1.02). Among participants performing any MVPA, a higher proportion of VPA to total physical activity was associated with lower all-cause mortality but not with cardiovascular disease and cancer mortality. For instance, compared with participants with 0% of VPA (no vigorous activity), participants performing greater than 50% to 75% of VPA to total physical activity had a 17% lower all-cause mortality (hazard ratio, 0.83; 95% CI, 0.78-0.88), independent of total MVPA. The inverse association between proportion of VPA to total physical activity and all-cause mortality was consistent across sociodemographic characteristics, lifestyle risk factors, and chronic conditions at baseline.

CONCLUSIONS AND RELEVANCE

This study suggests that, for the same volume of MVPA, a higher proportion of VPA to total physical activity was associated with lower all-cause mortality. Clinicians and public health interventions should recommend 150 minutes or more per week of MVPA but also advise on the potential benefits associated with VPA to maximize population health.

Sedentary Time and Metabolic Risk in Extremely Active Older Adults

OBJECTIVE

Increasing evidence suggests that time spent sedentary predicts increasing cardiometabolic risk independent of other physical activity. We objectively measured activity levels in active older adults and examined the association between sedentary behavior and the continuous metabolic syndrome risk score (cMSy).

RESEARCH DESIGN AND METHODS

Older adults (age ≥65 years) were recruited from the Whistler Masters ski team, a group of active older adults who undergo organized group training. Daily activity levels were recorded with accelerometers (SenseWear) worn for 7 days. A compositional approach was used to determine proportion of the time spent sedentary as compared with all other nonsedentary behaviors (isometric log-ratio transformation for time spent sedentary [ILR1]). Waist circumference, triglycerides, HDL, systolic blood pressure, and fasting glucose were measured, and cMSy was calculated using principal component analysis (sum of eigenvalues ≥1.0).

RESULTS

Fifty-four subjects (30 women and 24 men, mean ± SE age 71.4 ± 0.6 years) were recruited. Subjects demonstrated high levels of physical activity (2.6 ± 0.2 h light activity and 3.9 ± 0.2 h moderate/vigorous activity). In our final parsimonious model, ILR1 showed a significant positive association with increasing cMSy (standardized β = 0.368 ± 0.110, R ² = 0.40, P = 0.002), independent of age and biological sex.

CONCLUSIONS

Despite high levels of activity, ILR1 demonstrated a strong association with cMSy. This suggests that even in active older adults, sedentary behavior is associated with increasing cardiometabolic risk.

Recognition of 16-18-Year-Old Adolescents for Guiding Physical Activity Interventions: A Cross-Sectional Study

Adolescence is a rapid life stage requiring special attention wherein personal autonomy is developed to govern independent lifestyles. Unhealthy lifestyles are integral to prevailing adolescent physical inactivity patterns. Understudied 16-18-year-olds were investigated to establish physical activity prevalences and influencing health-related lifestyle factors. Adolescents were recruited randomly across 2017-2019 from Farnborough College of Technology and North Kent College, UK. Demographic and health-related lifestyle information were gathered anonymously and analysed using SAS® 9.4 software. Among the 414 adolescents included (48.3% male and 51.7% female), the mean (standard deviation (SD)) age was 16.9 (0.77). Approximately 15.2% smoked and 20.8% were overweight/obese. There were 54.8% perceiving themselves unfit and 33.3% spent >4 h/day on leisure-time screen-based activity. Around 80.4% failed to meet the recommended fruit/vegetable daily intake and 90.1% failed to satisfy UK National Physical Activity Guidelines, particularly females (p = 0.0202). Physical activity levels were significantly associated with gender, body mass index, smoking status, leisure sedentary screen-time, fruit/vegetable consumption and fitness perceptions. Those who were female, overweight/obese, non-smoking, having poor fitness perceptions, consuming low fruit/vegetables and engaging in excess screen-based sedentariness were the groups with lowest physical activity levels. Steering physical activity-oriented health interventions toward these at-risk groups in colleges may reduce the UK's burden of adolescent obesity.