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.

Are parent-reported sleep logs essential? A comparison of three approaches to guide open source accelerometry-based nocturnal sleep processing in children.

We examined the comparability of children's nocturnal sleep estimates using accelerometry data, processed with and without a sleep log. In a secondary analysis, we evaluated factors associated with disagreement between processing approaches. Children (n = 722, age 5-12 years) wore a wrist-based accelerometer for 14 days during Autumn 2020, Spring 2021, and/or Summer 2021. Outcomes included sleep period, duration, wake after sleep onset (WASO), and timing (onset, midpoint, waketime). Parents completed surveys including children's nightly bed/wake time. Data were processed with parent-reported bed/wake time (sleep log), the Heuristic algorithm looking at Distribution of Change in Z-Angle (HDCZA) algorithm (no log), and an 8 p.m.-8 a.m. window (generic log) using the R-package 'GGIR' (version 2.6-4). Mean/absolute bias and limits of agreement were calculated and visualised with Bland-Altman plots. Associations between child, home, and survey characteristics and disagreement were examined with tobit regression. Just over half of nights demonstrated no difference in sleep period between sleep log and no log approaches. Among all nights, the sleep log approach produced longer sleep periods (9.3 min; absolute mean bias [AMB] = 28.0 min), shorter duration (1.4 min; AMB = 14.0 min), greater WASO (11.0 min; AMB = 15.4 min), and earlier onset (13.4 min; AMB = 17.4 min), midpoint (8.8 min; AMB = 15.3 min), and waketime (3.9 min; AMB = 14.8 min) than no log. Factors associated with discrepancies included smartphone ownership, bedroom screens, nontraditional parent work schedule, and completion on weekend/summer nights (range = 0.4-10.2 min). The generic log resulted in greater AMB among sleep outcomes. Small mean differences were observed between nights with and without a sleep log. Discrepancies existed on weekends, in summer, and for children with smartphones and screens in the bedroom.

Accelerometer-Assessed Physical Activity in People with Type 2 Diabetes: Accounting for Sleep when Determining Associations with Markers of Health.

High physical activity levels during wake are beneficial for health, while high movement levels during sleep are detrimental to health. Our aim was to compare the associations of accelerometer-assessed physical activity and sleep disruption with adiposity and fitness using standardized and individualized wake and sleep windows. People (N = 609) with type 2 diabetes wore an accelerometer for up to 8 days. Waist circumference, body fat percentage, Short Physical Performance Battery (SPPB) test score, sit-to-stands, and resting heart rate were assessed. Physical activity was assessed via the average acceleration and intensity distribution (intensity gradient) over standardized (most active 16 continuous hours (M16h)) and individualized wake windows. Sleep disruption was assessed via the average acceleration over standardized (least active 8 continuous hours (L8h)) and individualized sleep windows. Average acceleration and intensity distribution during the wake window were beneficially associated with adiposity and fitness, while average acceleration during the sleep window was detrimentally associated with adiposity and fitness. Point estimates for the associations were slightly stronger for the standardized than for individualized wake/sleep windows. In conclusion, standardized wake and sleep windows may have stronger associations with health due to capturing variations in sleep durations across individuals, while individualized windows represent a purer measure of wake/sleep behaviors.

Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0-5 years old).

BACKGROUND: Accurate accelerometer-based methods are required for assessment of 24-h physical behavior in young children. We aimed to summarize evidence on measurement properties of accelerometer-based methods for assessing 24-h physical behavior in young children. METHODS: We searched PubMed (MEDLINE) up to June 2021 for studies evaluating reliability or validity of accelerometer-based methods for assessing physical activity (PA), sedentary behavior (SB), or sleep in 0-5-year-olds. Studies using a subjective comparison measure or an accelerometer-based device that did not directly output time series data were excluded. We developed a Checklist for Assessing the Methodological Quality of studies using Accelerometer-based Methods (CAMQAM) inspired by COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN). RESULTS: Sixty-two studies were included, examining conventional cut-point-based methods or multi-parameter methods. For infants (0-12 months), several multi-parameter methods proved valid for classifying SB and PA. From three months of age, methods were valid for identifying sleep. In toddlers (1-3 years), cut-points appeared valid for distinguishing SB and light PA (LPA) from moderate-to-vigorous PA (MVPA). One multi-parameter method distinguished toddler specific SB. For sleep, no studies were found in toddlers. In preschoolers (3-5 years), valid hip and wrist cut-points for assessing SB, LPA, MVPA, and wrist cut-points for sleep were identified. Several multi-parameter methods proved valid for identifying SB, LPA, and MVPA, and sleep. Despite promising results of multi-parameter methods, few models were open-source. While most studies used a single device or axis to measure physical behavior, more promising results were found when combining data derived from different sensor placements or multiple axes. CONCLUSIONS: Up to age three, valid cut-points to assess 24-h physical behavior were lacking, while multi-parameter methods proved valid for distinguishing some waking behaviors. For preschoolers, valid cut-points and algorithms were identified for all physical behaviors. Overall, we recommend more high-quality studies evaluating 24-h accelerometer data from multiple sensor placements and axes for physical behavior assessment. Standardized protocols focusing on including well-defined physical behaviors in different settings representative for children's developmental stage are required. Using our CAMQAM checklist may further improve methodological study quality. PROSPERO REGISTRATION NUMBER: CRD42020184751.

Association of physical activity, sedentary behaviour, and daylight exposure with sleep in an ageing population: findings from the Whitehall accelerometer sub-study.

BACKGROUND: Ageing is accompanied by changes in sleep, while poor sleep is suggested as a risk factor for several health outcomes. Non-pharmacological approaches have been proposed to improve sleep in elderly; their impact remains to be investigated. The aim of this study was to examine the independent day-to-day associations of physical behaviours and daylight exposure with sleep characteristics among older adults. METHODS: Data were drawn from 3942 participants (age range: 60-83 years; 27% women) from the Whitehall II accelerometer sub-study. Day-to-day associations of objectively-assessed daytime physical behaviours (sedentary behaviour, light-intensity physical activity (LIPA), moderate-to-vigorous physical activity (MVPA), mean acceleration, physical activity chronotype) and daylight exposure (proportion of waking window with light exposure > 1000 lx and light chronotype) with sleep characteristics were examined using mixed models. RESULTS: A 10%-increase in proportion of the waking period spent sedentary was associated with 5.12-minute (4.31, 5.92) later sleep onset and 1.76-minute shorter sleep duration (95%confidence interval: 0.86, 2.66). Similar increases in LIPA and MVPA were associated with 6.69 (5.67, 7.71) and 4.15 (2.49, 5.81) earlier sleep onset respectively and around 2-minute longer sleep duration (2.02 (0.87, 3.17) and 2.23 (0.36, 4.11), respectively), although the association was attenuated for MVPA after adjustment for daylight exposure (1.11 (- 0.84, 3.06)). A 3-hour later physical activity chronotype was associated with a 4.79-minute later sleep onset (4.15, 5.43) and 2.73-minute shorter sleep duration (1.99, 3.47). A 10%-increase in proportion of waking period exposed to light> 1000 lx was associated with 1.36-minute longer sleep (0.69, 2.03), independently from mean acceleration. Associations found for sleep duration were also evident for duration of the sleep windows with slightly larger effect size (for example, 3.60 (2.37, 4.82) minutes for 10%-increase in LIPA), resulting in associations with sleep efficiency in the opposite direction (for example, - 0.29% (- 0.42, - 0.16) for 10%-increase in LIPA). Overall, associations were stronger for women than for men. CONCLUSIONS: In this study, higher levels of physical activity and daylight exposure were associated with slightly longer sleep in older adults. Given the small effect sizes of the associations, increased physical activity and daylight exposure might not be enough to improve sleep.

Advanced analytical methods to assess physical activity behavior using accelerometer time series: A scoping review.

Physical activity (PA) is a complex human behavior, which implies that multiple dimensions need to be taken into account in order to reveal a complete picture of the PA behavior profile of an individual. This scoping review aimed to map advanced analytical methods and their summary variables, hereinafter referred to as wearable-specific indicators of PA behavior (WIPAB), used to assess PA behavior. The strengths and limitations of those indicators as well as potential associations with certain health-related factors were also investigated. Three databases (MEDLINE, Embase, and Web of Science) were screened for articles published in English between January 2010 and April 2020. Articles, which assessed the PA behavior, gathered objective measures of PA using tri-axial accelerometers, and investigated WIPAB, were selected. All studies reporting WIPAB in the context of PA monitoring were synthesized and presented in four summary tables: study characteristics, details of the WIPAB, strengths, and limitations, and measures of association between those indicators and health-related factors. In total, 7247 records were identified, of which 24 articles were included after assessing titles, abstracts, and full texts. Thirteen WIPAB were identified, which can be classified into three different categories specifically focusing on (1) the activity intensity distribution, (2) activity accumulation, and (3) the temporal correlation and regularity of the acceleration signal. Only five of the thirteen WIPAB identified in this review have been used in the literature so far to investigate the relationship between PA behavior and health, while they may provide useful additional information to the conventional PA variables.

GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies.

The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers' decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.

Association of daily composition of physical activity and sedentary behaviour with incidence of cardiovascular disease in older adults.

BACKGROUND: Moderate-to-vigorous physical activity (MVPA) is proposed as key for cardiovascular diseases (CVD) prevention. At older ages, the role of sedentary behaviour (SB) and light intensity physical activity (LIPA) remains unclear. Evidence so far is based on studies examining movement behaviours as independent entities ignoring their co-dependency. This study examines the association between daily composition of objectively-assessed movement behaviours (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 during waking period were extracted over 7 days. Compositional Cox regression was used to estimate the hazard ratio (HR) for incident CVD for daily compositions of movement behaviours characterized by 10 (20 or 30) minutes greater duration in one movement behaviour accompanied by decrease in another behaviour, while keeping the third behaviour constant, compared to reference composition. Analyses were adjusted for sociodemographic, lifestyle, cardiometabolic risk factors and multimorbidity index. RESULTS: Of the 3319 participants, 299 had an incident CVD over a mean (SD) follow-up of 6.2 (1.3) years. Compared to daily movement behaviour composition with MVPA at recommended 21 min per day (150 min/week), composition with additional 10 min of MVPA and 10 min less SB was 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: Among older adults, an increase in MVPA duration at the expense of time in either SB or LIPA was found associated with lower incidence of CVD. This study lends support to public health guidelines encouraging increase in MVPA or at least maintain MVPA at current duration.

Joint association between accelerometry-measured daily combination of time spent in physical activity, sedentary behaviour and sleep and all-cause mortality: a pooled analysis of six prospective cohorts using compositional analysis.

OBJECTIVE: To examine the joint associations of daily time spent in different intensities of physical activity, sedentary behaviour and sleep with all-cause mortality. METHODS: Federated pooled analysis of six prospective cohorts with device-measured time spent in different intensities of physical activity, sedentary behaviour and sleep following a standardised compositional Cox regression analysis. PARTICIPANTS: 130 239 people from general population samples of adults (average age 54 years) from the UK, USA and Sweden. MAIN OUTCOME: All-cause mortality (follow-up 4.3-14.5 years). RESULTS: Studies using wrist and hip accelerometer provided statistically different results (I2=92.2%, Q-test p<0.001). There was no association between duration of sleep and all-cause mortality, HR=0.96 (95% CI 0.67 to 1.12). The proportion of time spent in moderate to vigorous physical activity was significantly associated with lower risk of all-cause mortality (HR=0.63 (95% CI 0.55 to 0.71) wrist; HR=0.93 (95% CI 0.87 to 0.98) hip). A significant association for the ratio of time spent in light physical activity and sedentary time was only found in hip accelerometer-based studies (HR=0.5, 95% CI 0.42 to 0.62). In studies based on hip accelerometer, the association between moderate to vigorous physical activity and mortality was modified by the balance of time spent in light physical activity and sedentary time. CONCLUSION: This federated analysis shows a joint dose-response association between the daily balance of time spent in physical activity of different intensities and sedentary behaviour with all-cause mortality, while sleep duration does not appear to be significant. The strongest association is with time spent in moderate to vigorous physical activity, but it is modified by the balance of time spent in light physical activity relative to sedentary behaviour.

Objectively-measured physical activity in children is influenced by social indicators rather than biological lifecourse factors: Evidence from a Brazilian cohort.

The aim of this study was to examine the longitudinal influences of early life social and biological indicators on objectively measured physical activity. All newborns in 2004 in the city of Pelotas, Southern Brazil were enrolled in a birth cohort study. At the age of 6years, a follow-up visit included objective assessment of overall physical activity (summarized in milli-g, 1mg=0.001g) by tri-axial wrist worn accelerometry. The associations between early life exposures, such as type of delivery, parity, birth weight, preterm delivery, maternal physical activity, socioeconomic position, and overall physical activity were examined. Valid accelerometry data were obtained from 2604 children (78.2% of the eligible individuals). Girls were less active than boys (ß=-8.65mg; 95% CI -10.0; -7.30). Higher socioeconomic position was related to lower activity levels (ß=-9.69mg. 95% CI -12.45; -6.93) and a similar association was found with maternal schooling. No associations were found with birthweight, type of delivery or preterm delivery. This study provides evidence for the role of some social factors in explaining children's physical activity behaviors, and minimizes the influence of some early life biological factors at determining physical activity levels.

Evaluation of raw acceleration sedentary thresholds in children and adults.

The aim was to develop sedentary (sitting/lying) thresholds from hip and wrist worn raw tri-axial acceleration data from the ActiGraph and GENEActiv, and to examine the agreement between free-living time spent below these thresholds with sedentary time estimated by the activPAL. Sixty children and adults wore an ActiGraph and GENEActiv on the hip and wrist while performing six structured activities, before wearing the monitors, in addition to an activPAL, for 24 h. Receiver operating characteristic (ROC) curves were used to determine sedentary thresholds based on activities in the laboratory. Agreement between developed sedentary thresholds during free-living and activPAL were assessed by Bland-Altman plots and by calculating sensitivity and specificity. Using laboratory data and ROC-curves showed similar classification accuracy for wrist and hip thresholds (Area under the curve = 0.84-0.92). Greatest sensitivity (97-98%) and specificity (74-78%) were observed for the wrist thresholds, with no large differences between brands. During free-living, Bland-Altman plots showed large mean individual biases and 95% limits of agreement compared with activPAL, with smallest difference for the ActiGraph wrist threshold in children (+30 min, P = 0.3). Sensitivity and specificity for the developed thresholds during free-living were low for both age groups and for wrist (Sensitivity, 68-88%, Specificity, 46-59%) and hip placements (Sensitivity, 89-97%, Specificity, 26-34%). Laboratory derived sedentary thresholds generally overestimate free-living sedentary time compared with activPAL. Wrist thresholds appear to perform better than hip thresholds for estimating free-living sedentary time in children and adults relative to activPAL, however, specificity for all the developed thresholds are low.

The effect of percutaneous coronary intervention on habitual physical activity in older patients.

BACKGROUND: Given the ongoing burden of cardiovascular disease and an ageing population, physical activity in patients with coronary artery disease needs to be emphasized. This study assessed whether sedentary behaviour and physical activity levels differed among older patients (≥75 years) following percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) consisting of ST-segment elevation myocardial infarction (STEMI) and non STEMI (NSTEMI) versus an elective admission control group of stable angina patients. METHODS: Sedentary behaviour and physical activity were assessed over a 7-day period using wrist-worn triaxial accelerometers (GENEActiv, Activinsights Ltd, UK) in 58 patients following PCI for, STEMI (n = 20) NSTEMI (n = 18) and stable angina (n = 20) upon discharge from a tertiary centre. Mean ± Standard deviation age was 79 ± 4 years (31% female). RESULTS: STEMI and NSTEMI patients spent more time in the low acceleration category (0-40 mg) reflecting sedentary time versus stable angina patients (1298 ± 59 and 1305 ± 66 vs. 1240 ± 92 min/day, p < 0.05). STEMI and NSTEMI patients spent less time in the 40-80 mg acceleration category reflecting low physical activity versus stable angina patients (95 ± 35 and 94 ± 41 vs. 132 ± 50 min/day, p < 0.05). Stable angina patients spent more time in the higher acceleration categories (80-120 and 120-160 mg) and moderate-to-vigorous physical activity (defined as 1 and 5 min/day bouts) versus NSTEMI patients (p < 0.05). For acceleration categories ≥160 mg, no differences were observed. CONCLUSIONS: Patients presenting with ACS and undergoing PCI spent more time in sedentary behaviour compared with stable angina patients.

Association between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors.

The correlation between objective and self-reported measures of physical activity varies between studies. We examined this association and whether it differed by demographic factors or socioeconomic status (SES). Data were from 3,975 Whitehall II (United Kingdom, 2012-2013) participants aged 60-83 years, who completed a physical activity questionnaire and wore an accelerometer on their wrist for 9 days. There was a moderate correlation between questionnaire- and accelerometer-assessed physical activity (Spearman's r = 0.33, 95% confidence interval: 0.30, 0.36). The correlations were higher in high-SES groups than in low-SES groups (P 's = 0.02), as defined by education (r = 0.38 vs. r = 0.30) or occupational position (r = 0.37 vs. r = 0.29), but did not differ by age, sex, or marital status. Of the self-reported physical activity, 68.3% came from mild activities, 25% from moderate activities, and only 6.7% from vigorous activities, but their correlations with accelerometer-assessed total physical activity were comparable (range of r 's, 0.21-0.25). Self-reported physical activity from more energetic activities was more strongly associated with accelerometer data (for sports, r = 0.22; for gardening, r = 0.16; for housework, r = 0.09). High-SES persons reported more energetic activities, producing stronger accelerometer associations in these groups. Future studies should identify the aspects of physical activity that are most critical for health; this involves better understanding of the instruments being used.

Children treated for severe acute malnutrition experience a rapid increase in physical activity a few days after admission.

OBJECTIVE: To assess physical activity at admission and during recovery from severe acute malnutrition. STUDY DESIGN: Ethiopian children who were admitted with severe acute malnutrition received a clinical examination each week to monitor their recovery during rehabilitation. Using accelerometry (24 h/d for 5 consecutive days) at admission and again after 10 days of rehabilitation, we assessed the level and changes of physical activity. RESULTS: Among 13 children included, the mean (SD) age was 31.1 months (15.5). At baseline, the day-night activity difference was relatively small, whereas the level of activity had substantially increased at follow-up. The diurnal mean acceleration level was significantly greater at follow-up for wrist (1158.8 vs 541.4 counts per minute, P = .003) but not hip movements (204.1 vs 141.5, P = .261). During daytime (6 a.m. to 10 p.m.), hip activity increased by 38% from baseline to follow-up (e(B) 1.38, 95% CI 1.17-1.62), and wrist activity more than doubled (e(B) 2.50, 95% CI 2.17-2.87). CONCLUSION: The level of physical activity among children with severe acute malnutrition is very low but increases rapidly during recovery. Accelerometry may be a useful approach in the recovery phase as an indicator of early improvement.

Association between profiles of accelerometer-measured daily movement behaviour and mortality risk: a prospective cohort study of British older adults.

Objectives: We identified profiles of wake-time movement behaviours (sedentary behaviours, light intensity physical activity and moderate-to-vigorous physical activity) based on accelerometer-derived features among older adults and then examined their association with all-cause mortality. Methods: Data were drawn from a prospective cohort of 3991 Whitehall II accelerometer substudy participants aged 60-83 years in 2012-2013. Daily movement behaviour profiles were identified using k-means cluster analysis based on 13 accelerometer-assessed features characterising total duration, frequency, bout duration, timing and activity intensity distribution of movement behaviour. Cox regression models were used to assess the association between derived profiles and mortality risk. Results: Over a mean follow-up of 8.1 (SD 1.3) years, a total of 410 deaths were recorded. Five distinct profiles were identified and labelled as 'active' (healthiest), 'active sitters', 'light movers', 'prolonged sitters', and 'most sedentary' (most deleterious). In model adjusted for sociodemographic, lifestyle, and health-related factors, compared with the 'active' profile, 'active sitters' (HR 1.57, 95% CI 1.01 to 2.44), 'light movers' (HR 1.75, 95% CI 1.17 to 2.63), 'prolonged sitters' (HR 1.67, 95% CI 1.11 to 2.51), 'most sedentary' (HR 3.25, 95% CI 2.10 to 5.02) profiles were all associated with a higher risk of mortality. Conclusion: Given the threefold higher mortality risk among those with a 'most sedentary' profile, public health interventions may target this group wherein any improvement in physical activity and sedentary behaviour might be beneficial.

Reliable measures of rest-activity rhythm fragmentation: how many days are needed?

BACKGROUND: A more fragmented, less stable rest-activity rhythm (RAR) is emerging as a risk factor for health. Accelerometer devices are increasingly used to measure RAR fragmentation using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probabilities (TP), self-similarity parameter (α), and activity balance index (ABI). These metrics were proposed in the context of long period of wear but, in real life, non-wear might introduce measurement bias. This study aims to determine the minimum number of valid days to obtain reliable fragmentation metrics. METHODS: Wrist-worn accelerometer data were drawn from the Whitehall accelerometer sub-study (age: 60 to 83 years) to simulate different non-wear patterns. Pseudo-simulated data with different numbers of valid days (one to seven), defined as < 1/3 of non-wear during both day and night periods, and with omission or imputation of non-wear periods were compared against complete data using intraclass correlation coefficient (ICC) and mean absolute percent error (MAPE). RESULTS: Five days with valid data (97.8% of participants) and omission of non-wear periods allowed an ICC ≥ 0.75 and MAPE ≤ 15%, acceptable cut points for reliability, for IS and ABI; this number was lower for TPs (two-three days), α and IV (four days). Overall, imputation of data did not provide better estimates. Findings were consistent across age and sex groups. CONCLUSIONS: The number of days of wrist accelerometer data with at least 2/3 of wear time for both day and night periods varies from two (TPs) to five (IS, ABI) days for reliable RAR measures among older adults.

Large-scale assessment of physical activity in a population using high-resolution hip-worn accelerometry: the German National Cohort (NAKO).

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

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.

Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study.

Background: Identification of new physical activity (PA) and sedentary behaviour (SB) features relevant for health at older age is important to diversify PA targets in guidelines, as older adults rarely adhere to current recommendations focusing on total duration. We aimed to identify accelerometer-derived dimensions of movement behaviours that predict mortality risk in older populations. Methods: We used data on 21 accelerometer-derived features of daily movement behaviours in 3991 participants of the UK-based Whitehall II accelerometer sub-study (25.8% women, 60-83 years, follow-up: 2012-2013 to 2021, mean = 8.3 years). A machine-learning procedure was used to identify core PA and SB features predicting mortality risk and derive a composite score. We estimated the added predictive value of the score compared to traditional sociodemographic, behavioural, and health-related risk factors. External validation in the Switzerland-based CoLaus study (N = 1329, 56.7% women, 60-86 years, follow-up: 2014-2017 to 2021, mean = 3.8 years) was conducted. Findings: In total, 11 features related to overall activity level, intensity distribution, bouts duration, frequency, and total duration of PA and SB, were identified as predictors of mortality in older adults and included in a composite score. Both in the derivation and validation cohorts, the score was associated with mortality (hazard ratio = 1.10 (95% confidence interval = 1.05-1.15) and 1.18 (1.10-1.26), respectively) and improved the predictive value of a model including traditional risk factors (increase in C-index = 0.007 (0.002-0.014) and 0.029 (0.002-0.055), respectively). Interpretation: The identified accelerometer-derived PA and SB features, beyond the currently recommended total duration, might be useful for screening of older adults at higher mortality risk and for diversifying PA and SB targets in older populations whose adherence to current guidelines is low. Funding: National Institute on Aging; UK Medical Research Council; British Heart Foundation; Wellcome Trust; French National Research Agency; GlaxoSmithKline; Lausanne Faculty of Biology and Medicine; Swiss National Science Foundation.

Objectively Measured Total Sedentary Time and Pattern of Sedentary Accumulation in Older Adults: Associations With Incident Cardiovascular Disease and All-Cause Mortality.

BACKGROUND: We examined associations of total duration and pattern of accumulation of objectively measured sedentary behavior (SB) with incident cardiovascular disease (CVD) and all-cause mortality among older adults. METHODS: Total sedentary time and 8 sedentary accumulation pattern metrics were extracted from accelerometer data of 3 991 Whitehall II study participants aged 60-83 years in 2012-2013. Incident CVD and all-cause mortality were ascertained up to March 2019. RESULTS: Two hundred and ninety-nine CVD cases and 260 deaths were recorded over a mean (standard deviation [SD]) follow-up of 6.2 (1.3) and 6.4 (0.8) years, respectively. Adjusting for sociodemographic and behavioral factors, 1-SD (100.2 minutes) increase in total sedentary time was associated with 20% higher CVD risk (hazard ratio [95% confidence interval]: 1.20 [1.05-1.37]). More fragmented SB was associated with reduced CVD risk (eg, 0.86 [0.76-0.97] for 1-SD [6.2] increase in breaks per sedentary hour). Associations were not evident once health-related factors and moderate-to-vigorous physical activity (MVPA) were considered. For all-cause mortality, associations with more fragmented SB (eg, 0.73 [0.59-0.91] for breaks per sedentary hour) were found only among the youngest older group (<74 years; p for interaction with age < .01) independently from all covariates. CONCLUSIONS: In this study, no associations of total sedentary time and sedentary accumulation patterns with incident CVD and all-cause mortality were found in the total sample once MVPA was considered. Our findings of reduced mortality risk with less total and more fragmented SB independent from MVPA among individuals <74 years need to be replicated to support the recent recommendations to reduce and fragment SB.