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.

Daily composition of movement behaviors with cardiovascular disease incidence in elderly

Background

Moderate-to-vigorous physical activity (MVPA) is proposed as key for cardiovascular diseases (CVD) prevention. At older ages, the role of sedentary behavior (SB) and light intensity physical activity (LIPA) remains unclear. Evidence so far is based on studies examining movement behaviors as independent entities ignoring their co-dependency. This study aims to examine the association between daily composition of objectively-assessed movement behaviors (MVPA, LIPA, SB) and incident CVD in older adults.

Methods

Whitehall II accelerometer sub-study participants free of CVD at baseline (N = 3319, 26.7% women, mean age=68.9 years in 2012-2013) wore a wrist-accelerometer from which times in SB, LIPA, and MVPA were extracted. Compositional Cox regression was used to estimate the hazard ratio (HR) for incident CVD for daily compositions of movement behaviors characterized by 10 (20 or 30) minutes greater duration in one movement behavior accompanied by decrease in another behavior, while keeping the third behavior constant, compared to reference composition.

Results

Of the 3319 participants, 299 had an incident CVD over a mean (SD) follow-up of 6.2 (1.3) years. Compared to individuals with daily movement behavior composition composed with MVPA at recommended 21 minutes per day (150 minutes/week), composition with additional 10 minutes of MVPA and 10 minutes less SB were associated with smaller risk reduction -8% (HR, 0.92; 95% CI, 0.87-0.99) - than the 14% increase in risk associated with a composition of similarly reduced time in MVPA and more time in SB (HR, 1.14; 95% CI, 1.02-1.27). For a given MVPA duration, the CVD risk did not differ as a function of LIPA and SB durations.

Conclusions

An increase in MVPA duration at the expense of time in either SB or LIPA was associated with lower risk of incident CVD. This study lends support to public health guidelines encouraging increase in MVPA or at least maintain MVPA at their current duration.

Key messages

Older adults should be encouraged to increase their moderate-to-vigorous physical activity or at least maintain at their current levels to lower risk of incident cardiovascular disease. Highly sedentary older adults should increase their moderate-to-vigorous physical activity by decreasing sedentary time rather than light-intensity activity to prevent cardiovascular disease.

Estimating sleep parameters using an accelerometer without sleep diary

Wrist worn raw-data accelerometers are used increasingly in large-scale population research. We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPT-window) was compared against sleep diary in 3752 participants (range = 60-82 years) and polysomnography in sleep clinic patients (N = 28) and in healthy good sleepers (N = 22). The SPT-window derived from the algorithm was 10.9 and 2.9 minutes longer compared with sleep diary in men and women, respectively. Mean C-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic-based and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.