Rest-Activity Rhythm Is Associated With Obesity Phenotypes: A Cross-Sectional Analysis

Measures of fragmentation of rest activity patterns: mathematical properties and interpretability based on accelerometer real life data

Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index (ABI) metric, a transformation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical use in a large dataset.

Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study

BACKGROUND

This study aimed to investigate the relationships between adiposity and circadian rhythm and compare the measurement of circadian rhythm using both actigraphy and a smartphone app that tracks human-smartphone interactions.

OBJECTIVE

We hypothesized that the app-based measurement may provide more comprehensive information, including light-sensitive melatonin secretion and social rhythm, and have stronger correlations with adiposity indicators.

METHODS

We enrolled a total of 78 participants (mean age 41.5, SD 9.9 years; 46/78, 59% women) from both an obesity outpatient clinic and a workplace health promotion program. All participants (n=29 with obesity, n=16 overweight, and n=33 controls) were required to wear a wrist actigraphy device and install the Rhythm app for a minimum of 4 weeks, contributing to a total of 2182 person-days of data collection. The Rhythm app estimates sleep and circadian rhythm indicators by tracking human-smartphone interactions, which correspond to actigraphy. We examined the correlations between adiposity indices and sleep and circadian rhythm indicators, including sleep time, chronotype, and regularity of circadian rhythm, while controlling for physical activity level, age, and gender.

RESULTS

Sleep onset and wake time measurements did not differ significantly between the app and actigraphy; however, wake after sleep onset was longer (13.5, SD 19.5 minutes) with the app, resulting in a longer actigraphy-measured total sleep time (TST) of 20.2 (SD 66.7) minutes. The obesity group had a significantly longer TST with both methods. App-measured circadian rhythm indicators were significantly lower than their actigraphy-measured counterparts. The obesity group had significantly lower interdaily stability (IS) than the control group with both methods. The multivariable-adjusted model revealed a negative correlation between BMI and app-measured IS (P=.007). Body fat percentage (BF%) and visceral adipose tissue area (VAT) showed significant correlations with both app-measured IS and actigraphy-measured IS. The app-measured midpoint of sleep showed a positive correlation with both BF% and VAT. Actigraphy-measured TST exhibited a positive correlation with BMI, VAT, and BF%, while no significant correlation was found between app-measured TST and either BMI, VAT, or BF%.

CONCLUSIONS

Our findings suggest that IS is strongly correlated with various adiposity indicators. Further exploration of the role of circadian rhythm, particularly measured through human-smartphone interactions, in obesity prevention could be warranted.

Stability and Volatility of Human Rest-Activity Rhythms: Insights from Very Long Actograms (VLAs)

Importance

Wrist-worn activity monitors provide biomarkers of health by non-obtrusively measuring the timing and amount of rest and physical activity (rest-activity rhythms, RARs). The morphology and robustness of RARs vary by age, gender, and sociodemographic factors, and are perturbed in various chronic illnesses. However, these are cross-sectionally derived associations from recordings lasting 4-10 days, providing little insights into how RARs vary with time.

Objective

To describe how RAR parameters can vary or evolve with time (~months).

Design Setting and Participants

48 very long actograms ("VLAs", ≥90 days in duration) were identified from subjects enrolled in the STAGES (Stanford Technology, Analytics and Genomics in Sleep) study, a prospective cross-sectional, multi-site assessment of individuals > 13 years of age that required diagnostic polysomnography to address a sleep complaint. A single 3-year long VLA (author GD) is also described.

Exposures/Intervention

None planned.

Main Outcomes and Measures

For each VLA, we assessed the following parameters in 14-day windows: circadian/ultradian spectrum, pseudo-F statistic ("F"), cosinor amplitude, intradaily variability, interdaily stability, acrophase and estimates of "sleep" and non-wearing.

Results

Included STAGES subjects (n = 48, 30 female) had a median age of 51, BMI of 29.4kg/m2, Epworth Sleepiness Scale score (ESS) of 10/24 and a median recording duration of 120 days. We observed marked within-subject undulations in all six RAR parameters, with many subjects displaying ultradian rhythms of activity that waxed and waned in intensity. When appraised at the group level (nomothetic), averaged RAR parameters remained remarkably stable over a ~4 month recording period. Cohort-level deficits in average RAR robustness associated with unemployment or high BMI (>29.4) also remained stable over time.

Conclusions and Relevance

Through an exemplary set of months-long wrist actigraphy recordings, this study quantitatively depicts the longitudinal stability and dynamic range of human rest-activity rhythms. We propose that continuous and long-term actigraphy may have broad potential as a holistic, transdiagnostic and ecologically valid monitoring biomarker of changes in chronobiological health. Prospective recordings from willing subjects will be necessary to precisely define contexts of use.

Association between disruption of circadian activity rhythms and obesity

This cross-sectional study aimed to elucidate the association between disruption of circadian activity rhythms and obesity-related indices. This study included 69 older participants (male, n = 36; female, n = 33), and disruption of circadian activity rhythms in daily life was assessed using the cosinor method based on physical activity data. The daily physical activity data were fitted to a cosine curve to estimate the peak phase (acrophase), and the difference in the peak phase (time) for each day was determined. The standard deviation (SD) of the mean peak phase differences over 6 days was obtained. The higher the SD value, the more disturbed the circadian rhythm of activity. Based on the calculated values, the samples were divided into tertiles (small, medium, and large). A significant positive correlation was found between Log SD and body fat percentage on the disruption of circadian activity rhythms and obesity-related indices. In addition, positive associations were observed among body mass, body mass index, and Log SD. However, these associations were not observed in women. These results suggest that disruption of circadian activity rhythms is associated with obesity-related indices and may differ according to sex.

Rest-activity rhythms across the lifespan: cross-sectional findings from the US representative National Health and Nutrition Examination Survey

STUDY OBJECTIVES

Rest-activity rhythms (RAR) may mark development, aging, and physical and mental health. Understanding how they differ between people may inform intervention and health promotion efforts. However, RAR characteristics across the lifespan have not been well-studied. Therefore, we investigated the association between RAR measures with demographic and lifestyle factors in a US nationally representative study.

METHODS

RAR metrics of interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), and mean amplitude and timing of high (M10) and low (L5) activity were derived from 2011 to 2012 and 2013 to 2014 National Health and Nutrition Examination Survey (NHANES) actigraphy data. Population-weighted linear and logistic regression models were fit to examine the associations of age, gender, smoking, alcohol, season, body mass index (BMI), income-to-poverty ratio, and race/ethnicity with RAR. Significance was based on a false-discovery rate-corrected P-value of <0.05.

RESULTS

Among n = 12 526 NHANES participants (3-≥80 years), IS (higher = greater day-to-day regularity) and RA (higher = greater rhythm strength) generally decreased with age and were lower among males, whereas IV (higher = greater rhythm fragmentation) increased with age (p < 0.05). Dynamic changes in RAR trajectories were observed during childhood and adolescence. Income, BMI, smoking, and alcohol use were associated with RAR metrics, as well as season among children and teenagers (p < 0.05). RAR also differed by race/ethnicity (p < 0.05), with trajectories initially diverging in childhood and continuing into adulthood.

CONCLUSIONS

RAR differed by demographic and health-related factors, representing possible windows for public health intervention and sleep health promotion. RAR differences by race/ethnicity begin in childhood, are evident in early adolescence, and persist throughout adulthood.

Measures of fragmentation of rest activity patterns: mathematical properties and interpretability based on accelerometer real life data

Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index metric, an adaptation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical worth in a large dataset.

The rest-activity rhythm, genetic susceptibility and risk of type 2 diabetes: A prospective study in UK Biobank

AIMS

This study aims to examine the association between the rest-activity rhythm (RAR) and the incidence of type 2 diabetes (T2D).

MATERIALS AND METHODS

In total, 97 503 participants without diabetes in the UK Biobank cohort were recruited. Wearable accelerometry was used to monitor circadian behaviour. The parameters of RAR including inter-daily stability, intra-daily variability, relative amplitude (RA), most active continuous 10 h period (M10), and least active continuous 5 h period (L5) were calculated to evaluate the robustness and regularity of the RAR. The weighted polygenic risk score for T2D (T2D-PRS) was calculated. Cox proportion hazards models were used to evaluate the survival relationship and the joint and interaction effects of RAR parameters and T2D-PRS on the occurrence of T2D.

RESULTS

During 692 257 person-years follow-ups, a total of 2434 participants were documented. After adjustment for potential confounders, compared with participants in the highest quartile of RA and M10, the participants in the lowest quartile had a greater risk of T2D (HRRA  = 2.06, 95% CI: 1.76-2.41; HRM10  = 1.33, 95% CI: 1.19-1.49). Meanwhile, the highest quartile of L5 was related to a higher risk of T2D (HR = 1.78, 95% CI: 1.55-2.24). The joint analysis showed that the high T2D-PRS with the lowest quartile of RA and M10, or highest quartile of L5 jointly increased the risk of T2D (HRRA  = 4.46, 95% CI: 3.36-6.42; HRM10  = 3.15, 95% CI: 2.29-4.32; HRL5  = 3.09, 95% CI: 2.40-3.99). No modification effects of T2D-PRS on the association between the RAR parameters and risk of T2D were observed (p > .05).

CONCLUSION

The unbalanced RAR are associated with a greater risk of T2D, which are independent of known risk factors of T2D.

Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014

Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011-2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: "High activity", "Low activity", "Mild circadian rhythm (CR) disruption", "Severe CR disruption", and "Very low activity". Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05-0.07 log-unit, all p < 0.05) and accelerated biological aging (1.42 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09-0.12 log-unit, all p < 0.05), biological aging advance (1.28 years, p = 0.021), and all-cause mortality risk (HR = 1.58, p = 0.042). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that data from wearable accelerometers can help in identifying at-risk populations and personalize treatments for healthier aging.

Association of 24 h Behavior Rhythm with Non-Alcoholic Fatty Liver Disease among American Adults with Overweight/Obesity

Emerging evidence suggests that in addition to metabolic, genetic and environmental factors, circadian rhythm also plays a role in non-alcoholic fatty liver disease (NAFLD). This study aimed to explore the association of 24 h behavior rhythm (activity-rest and feeding-fasting rhythm) with NAFLD. A total of 4502 adult participants with overweight/obesity from the National Health and Nutrition Examination Survey (NHANES) 2011-2014 were included in the current study. The behavior rhythm indices were calculated and divided into quintiles for logistic regression models. Compared to those in the lowest quintile, participants in the highest quintile of relative amplitude (RA) had a lower risk of NAFLD (OR = 0.71, 95% CI, 0.55-0.91); participants in the highest quintile of the average activity of the least active continuous 5 h period (L5) were associated with a higher risk of NAFLD (OR = 1.35, 95% CI, 1.07-1.71). Additionally, participants in the highest quintile of fasting duration and feeding rhythm score were associated with a lower risk of NAFLD relative to those in the lowest quintile (OR = 0.76, 95% CI, 0.59-0.98 for fasting duration, OR = 0.74, 95% CI, 0.58-0.95 for feeding rhythm score). The associations were stronger among participants with obesity. No significant associations were found in the relationship of other behavior rhythm indices with NAFLD. This study indicated a significant association of 24 h behavior rhythm with NAFLD among American adults with overweight/obesity.

Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia

The association between physical activity (PA) and dyslipidemia is well known, but the relationship between a temporal pattern of PA and dyslipidemia remain unknown. Here, we aimed to identify the intensity and temporal patterns of PA clustered by the trajectory model and their relationship with dyslipidemia. The participants were 701 adults (305 males) aged 18−60 years undergoing continuous measurement of PA with Actigraph GT3X+ accelerometers for at least 3 days. A trajectory analysis was applied based on moderate-to-vigorous intensity physical activity (MVPA) accumulated values over every period per day. The association between PA and dyslipidemia was estimated using a logistic regression model. Four distinct PA trajectory groups in the population were identified (continued low, stable and moderate, late increasing, and early increasing). Specifically, the “moderate and stable group” was associated with a decreased rate of high TG (p < 0.05) and the “moderate and stable group” and “late increasing group” were associated with decreased rates of low HDL-C (p < 0.05). In conclusion, there were four activity trajectory groups in this population and the continued low PA trajectory was associated with a high prevalent rate of an abnormal lipid profile, and continued and moderate activity or late afternoon increasing activity might have lower HDL-C distribution.