Identifying waking time in 24-h accelerometry data in adults using an automated algorithm

Identifying bedrest using 24-h waist or wrist accelerometry in adults

Objectives

To adapt and refine a previously-developed youth-specific algorithm to identify bedrest for use in adults. The algorithm is based on using an automated decision tree (DT) analysis of accelerometry data.

Design

Healthy adults (n = 141, 85 females, 19–69 years-old) wore accelerometers on the waist, with a subset also wearing accelerometers on the dominant wrist (n = 45). Participants spent ≈24-h in a whole-room indirect calorimeter equipped with a force-platform floor to detect movement.

Methods

Minute-by-minute data from recordings of waist-worn or wrist-worn accelerometers were used to identify bedrest and wake periods. Participants were randomly allocated to development (n = 69 and 23) and validation (n = 72 and 22) groups for waist-worn and wrist-worn accelerometers, respectively. The optimized DT algorithm parameters were block length, threshold, bedrest-start trigger, and bedrest-end trigger. Differences between DT classification and synchronized objective classification by the room calorimeter to bedrest or wake were assessed for sensitivity, specificity, and accuracy using a Receiver Operating Characteristic (ROC) procedure applied to 1-min epochs (n = 92,543 waist; n = 30,653 wrist).

Results

The optimal algorithm parameter values for block length were 60 and 45 min, thresholds 12.5 and 400 counts/min, bedrest-start trigger 120 and 400 counts/min, and bedrest-end trigger 1,200 and 1,500 counts/min, for the waist and wrist-worn accelerometers, respectively. Bedrest was identified correctly in the validation group with sensitivities of 0.819 and 0.912, specificities of 0.966 and 0.923, and accuracies of 0.755 and 0.859 by the waist and wrist-worn accelerometer, respectively. The DT algorithm identified bedrest/sleep with greater accuracy than a commonly used automated algorithm (Cole-Kripke) for wrist-worn accelerometers (p<0.001).

Conclusions

The adapted DT accurately identifies bedrest in data from accelerometers worn by adults on either the wrist or waist. The automated bedrest/sleep detection DT algorithm for both youth and adults is openly accessible as a package “PhysActBedRest” for the R-computer language.

Sedentary Behavior, Physical Activity, and Fitness-The Maastricht Study

PURPOSE

This cross-sectional study examined the mutual independent associations of sedentary behavior, lower intensity physical activity (LPA) and higher intensity physical activity (HPA) (an approximation of moderate to vigorous physical activity with cardiorespiratory fitness (CRF).

METHODS

Two thousand twenty-four participants were included from The Maastricht Study (mean ± SD age, 59.7 ± 8.1 yr; 49.6% men). With the activPAL3 activity monitor, we assessed sedentary time (ST), sedentary pattern variables (number of sedentary breaks, average sedentary bout duration, and number of prolonged sedentary bouts [≥30 min]), LPA, and HPA. CRF was calculated as maximum power output per kilogram body mass (Wmax·kg) estimated from a submaximal cycle ergometer test. Linear regression analyses and isotemporal substitution analyses were used to examine associations of ST, sedentary pattern variables, and HPA with CRF. Analyses were stratified by sex.

RESULTS

One hour of ST per day was associated with a lower Wmax·kg: Bmen = -0.03 (95% confidence interval [CI], -0.05 to -0.01) and Bwomen = -0.02 (95% CI, -0.04 to 0.00), independent of HPA. No statistically significant associations between sedentary patterns variables and CRF were observed. LPA was associated with a higher Wmax·kg: Bmen = 0.12 (95% CI, 0.07-0.17) and Bwomen = 0.12 (95% CI, 0.07-0.18). HPA was associated with a higher Wmax·kg: Bmen = 0.48 (95% CI, 0.38-0.58) and Bwomen = 0.27 (95% CI, 0.18-0.36). Replacing ST with LPA (Bmen, 0.08; 95% CI, 0.03-0.14; Bwomen, 0.10; 95% CI, 0.05-0.16) or with HPA (Bmen, 0.49; 95% CI, 0.39-0.59; Bwomen = 0.28; 95% CI, 0.19-0.36), but not with standing was associated with higher CRF.

CONCLUSIONS

Modest associations between sedentary behavior and CRF were observed. Replacing ST with LPA was associated with higher CRF, which could be of particular importance for individuals who cannot engage in HPA. Nonetheless, replacing ST with HPA was associated with greatest estimated change in CRF.

Objectively Measured Physical Activity and Sedentary Behavior in Successful Weight Loss Maintainers

OBJECTIVE

The objective of this study was to compare patterns of objectively measured moderate-to-vigorous physical activity (MVPA, ≥ 3.00 metabolic equivalents [METs]), light-intensity physical activity (LPA, 1.50-2.99 METs), and sedentary behavior (SB, < 1.50 METs) in successful weight loss maintainers (WLMs), normal weight controls (NC), and controls with overweight/obesity (OC).

METHODS

Participants (18-65 y) were recruited in three groups: WLM (maintaining ≥ 13.6-kg weight loss for ≥ 1 year, n = 30), NC (BMI matched to current BMI of WLM, n = 33), and OC (BMI matched to pre-weight loss BMI of WLM, n = 27). All participants wore the activPAL for 1 week.

RESULTS

Compared with OC and NC, WLM spent more awake time in total MVPA (WLM: 9.6 ± 3.9%, NC: 7.1 ± 2.1%, OC: 5.9 ± 2.0%; P < 0.01) and more time in sustained (≥ 10 min) bouts of MVPA (WLM: 39 ± 33, NC: 17 ± 14, OC: 9 ± 11 min/d; P < 0.01). Compared with OC, WLM and NC spent more awake time in LPA (WLM: 29.6 ± 7.9%, NC: 29.1 ± 8.3%, OC: 24.8 ± 6.7%; P = 0.04) and less awake time sedentary (WLM: 60.8 ± 9.3%, NC: 63.8 ± 9.5%, OC: 69.3 ± 7.5%; P < 0.01).

CONCLUSIONS

Results provide additional data supporting the important role of MVPA in weight loss maintenance and suggest notable differences in LPA and SB between normal weight individuals and those with overweight/obesity. Increasing LPA and/or decreasing SB may be additional potential targets for weight management interventions.

Replacement Effects of Sedentary Time on Metabolic Outcomes: The Maastricht Study

INTRODUCTION

Sedentary time has been associated with detrimental health effects, so in some countries, guidelines to reduce sedentary time have been developed. As reducing sedentary time inevitably results in more nonsedentary time, effects of this reduction may depend on the activity with which it is replaced.

PURPOSE

This study aimed to examine associations of theoretical reallocations of sedentary time to standing or stepping with cardiometabolic outcomes and type 2 diabetes.

METHODS

We included 2213 participants (51% men, mean ± SD age = 60.0 ± 8.1 yr) of the Maastricht Study who were asked to wear an accelerometer 24 h·d for a week. We calculated daily sedentary, standing, and stepping time. An isotemporal substitution modeling approach was applied to examine effects on waist circumference; body mass index; cholesterol, triacylglycerol, glucose, and insulin levels; metabolic syndrome; and type 2 diabetes.

RESULTS

Replacement of sedentary time (30 min·d) with stepping was associated with lower odds for metabolic syndrome (odds ratio [OR] = 0.72, 95% confidence interval [CI] = 0.66-0.78) and type 2 diabetes (OR = 0.79, 95% CI = 0.72-0.87), more favorable waist circumference (B = -1.42, 95% CI = -1.78 to -1.06), and body mass index (B = -0.48, 95% CI = -0.62 to -0.35) and improved cholesterol, triacylglycerol, glucose, and insulin levels. Replacing sedentary time with standing was associated with lower odds for metabolic syndrome and type 2 diabetes and favorable outcomes in waist circumference, cholesterol, triacylglycerol, and insulin levels.

CONCLUSION

Theoretical replacements of sedentary time with nonsedentary time (both standing and stepping) were associated with lower odds for metabolic syndrome, type 2 diabetes, and beneficial metabolic outcomes. These results could be important for the general population, including those who cannot meet physical activity guidelines. Consideration should be given to developing recommendations for daily reallocating sedentary time.

Benefits of Substituting Sitting with Standing and Walking in Free-Living Conditions for Cardiometabolic Risk Markers, Cognition and Mood in Overweight Adults

Background: We investigated whether substituting sitting with standing and self-perceived light walking in free-living conditions would improve cardiometabolic risk factors, mood, and cognition in overweight/obese adults.

Methods: In a randomized, cross-over study, 24 (m/f: 13/11) sedentary overweight/obese participants (64 ± 7 years, BMI 29 ± 2 kg/m²) followed two activity regimens of each 4 days in free-living conditions: “Sit”: sitting 13.5 h/day, standing 1.4 h/day, self-perceived light-intensity walking 0.7 h/day; for “SitLess” these activities lasted 7.6, 4.0, and 4.3 h/day, respectively. Meals were standardized and physical activity was assessed by accelerometry (activPAL). Insulin sensitivity (expressed as Matsuda-index based on an oral glucose tolerance test), circulating lipids, blood pressure, mood (pleasantness and arousal), and cognition were assessed on the morning after the activity regimens. Quality of life and sleep were assessed on the last day of the activity regimens.

Results: We observed that AUC (0–190 min) for insulin decreased by 20% after SitLess vs. Sit [10,125 (656) vs. 12,633 (818); p = 0.006]. Insulin sensitivity improved by 16% after SitLess vs. Sit [Matsuda-index, mean (SEM): 6.45 (0.25) vs. 5.58 (0.25) respectively; p = 0.007]. Fasting triglycerides, non-HDL-cholesterol, and apolipoprotein B decreased by 32, 7, and 4% respectively, whereas HDL-cholesterol increased by 7% after SitLess vs. Sit (all p < 0.01). Diastolic blood pressure was lower after SitLess vs. Sit (p < 0.05). Pleasantness (as one marker of mood status) after the oral glucose tolerance test was higher after SitLess vs. Sit (p < 0.05). There was no significant difference between regimens for cognition, quality of life and sleep.

Conclusions: Reducing sitting time in free-living conditions markedly improved insulin sensitivity, circulating lipids, and diastolic blood pressure. Substituting sitting with standing and self-perceived light walking is an effective strategy to improve cardiometabolic risk factors in overweight/obese subjects.

Sedentary Behavior Is Only Marginally Associated with Physical Function in Adults Aged 40-75 Years-the Maastricht Study

Background: In an aging population, regular physical activity (PA) and exercise have been recognized as important factors in maintaining physical function and thereby preventing loss of independence and disability. However, (older) adults spent the majority of their day sedentary and therefore insight into the consequences of sedentary behavior on physical function, independent of PA, is warranted. Objective: To examine the associations of objectively measured sedentary time (ST), patterns of sedentary behavior, overall PA, and higher intensity PA (HPA) with objective measures of physical function. Methods: This is a cross-sectional study in 1,932 men and women (aged 40-75 years) participating in The Maastricht Study. The activPAL3 was used to assess daily sedentary behavior: ST (h), sedentary breaks (n), prolonged (≥30 min) sedentary bouts (n), and to assess time spent in (H)PA (h). Measures of physical function included: covered distance during a 6 min walk test [6MWD (meters)], timed chair rise stand test performance [TCST_time (seconds)], grip strength (kg kg^-1), and elbow flexion and knee extension strength (Nm kg^-1). Linear regression analyses were used to examine associations between daily sedentary behavior and PA with physical function. Results: Every additional hour ST was associated with shorter 6MWD [B = -2.69 m (95% CI = -4.69; -0.69)] and lower relative elbow extension strength (B = -0.01 Nm kg^-1 (-0.02; 0.00). More sedentary breaks were associated with faster TCST_time: B = -0.55 s (-0.85; -0.26). Longer average sedentary bout duration was associated with slower TCST_time [B = 0.17 s (0.09; 0.25)] and lower knee extension strength [B = -0.01 Nm kg^-1 (-0.02; 0.00)]. Every hour of PA and HPA were associated with greater 6MWD [B_PA = 15.88 m (9.87; 21.89), B_HPA = 40.72 m (30.18; 51.25)], faster TCST_time [B_PA = -0.55 s (-1.03; -0.07), B_HPA = -2.25 s (-3.09; -1.41)], greater elbow flexion strength [B_PA = 0.03 Nm kg^-1 (0.01; 0.07)], [B_HPA = 0.05 Nm kg^-1 (0.01; 0.08)], and greater knee extension strength [B_PA = 0.04 Nm kg^-1 (0.01; 0.07)], [B_HPA = 0.13 Nm kg^-1 (0.06; 0.20)]. Conclusion: In adults aged 40-75 years, sedentary behavior appeared to be marginally associated with lower physical function, independent of HPA. This suggests that merely reducing sedentary behavior is insufficient to improve/maintain physical function. In contrast, engaging regularly in PA, in particular HPA, is important for physical function.

Identifying adults' valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocol

The activPAL monitor, often worn 24 h d^-1, provides accurate classification of sitting/reclining posture. Without validated automated methods, diaries-burdensome to participants and researchers-are commonly used to ensure measures of sedentary behaviour exclude sleep and monitor non-wear. We developed, for use with 24 h wear protocols in adults, an automated approach to classify activity bouts recorded in activPAL 'Events' files as 'sleep'/non-wear (or not) and on a valid day (or not). The approach excludes long periods without posture change/movement, adjacent low-active periods, and days with minimal movement and wear based on a simple algorithm. The algorithm was developed in one population (STAND study; overweight/obese adults 18-40 years) then evaluated in AusDiab 2011/12 participants (n  =  741, 44% men, aged  >35 years, mean  ±  SD 58.5  ±  10.4 years) who wore the activPAL3^™ (7 d, 24 h d^-1 protocol). Algorithm agreement with a monitor-corrected diary method (usual practice) was tested in terms of the classification of each second as waking wear (Kappa; κ) and the average daily waking wear time, on valid days. The algorithm showed 'almost perfect' agreement (κ  >  0.8) for 88% of participants, with a median kappa of 0.94. Agreement varied significantly (p  <  0.05, two-tailed) by age (worsens with age) but not by gender. On average, estimated wear time was approximately 0.5 h d^-1 higher than by the diary method, with 95% limits of agreement of approximately this amount  ±2 h d^-1. In free-living data from Australian adults, a simple algorithm developed in a different population showed 'almost perfect' agreement with the diary method for most individuals (88%). For several purposes (e.g. with wear standardisation), adopting a low burden, automated approach would be expected to have little impact on data quality. The accuracy for total waking wear time was less and algorithm thresholds may require adjustments for older populations.

Assessing Daily Physical Activity in Older Adults: Unraveling the Complexity of Monitors, Measures, and Methods

At the 67th Gerontological Society of America Annual Meeting, a preconference workshop was convened to discuss the challenges of accurately assessing physical activity in older populations. The advent of wearable technology (eg, accelerometers) to monitor physical activity has created unprecedented opportunities to observe, quantify, and define physical activity in the real-world setting. These devices enable researchers to better understand the associations of physical activity with aging, and subsequent health outcomes. However, a consensus on proper methodological use of these devices in older populations has not been established. To date, much of the validation research regarding device type, placement, and data interpretation has been performed in younger, healthier populations, and translation of these methods to older populations remains problematic. A better understanding of these devices, their measurement properties, and the data generated is imperative to furthering our understanding of daily physical activity, its effects on the aging process, and vice versa. The purpose of this article is to provide an overview of the highlights of the preconference workshop, including properties of the different types of accelerometers, the methodological challenges of employing accelerometers in older study populations, a brief summary of ongoing aging-related research projects that utilize different types of accelerometers, and recommendations for future research directions.

Physical Activity and Sedentary Behavior in Metabolically Healthy versus Unhealthy Obese and Non-Obese Individuals - The Maastricht Study

Background

Both obesity and the metabolic syndrome are associated with increased risk of cardiovascular diseases and type 2 diabetes. Although both frequently occur together in the same individual, obesity and the metabolic syndrome can also develop independently from each other. The (patho)physiology of “metabolically healthy obese” (i.e. obese without metabolic syndrome) and “metabolically unhealthy non-obese” phenotypes (i.e. non-obese with metabolic syndrome) is not fully understood, but physical activity and sedentary behavior may play a role.

Objective

To examine objectively measured physical activity and sedentary behavior across four groups: I) “metabolically healthy obese” (MHO); II) “metabolically unhealthy obese” (MUO); III)”metabolically healthy non-obese” (MHNO); and IV) “metabolically unhealthy non-obese” (MUNO).

Methods

Data were available from 2,449 men and women aged 40–75 years who participated in The Maastricht Study from 2010 to 2013. Participants were classified into the four groups according to obesity (BMI≥30kg/m²) and metabolic syndrome (ATPIII definition). Daily activity was measured for 7 days with the activPAL physical activity monitor and classified as time spent sitting, standing, and stepping.

Results

In our study population, 562 individuals were obese. 19.4% of the obese individuals and 72.7% of the non-obese individuals was metabolically healthy. After adjustments for age, sex, educational level, smoking, alcohol use, waking time, T2DM, history of CVD and mobility limitation, MHO (n = 107) spent, per day, more time stepping (118.2 versus 105.2 min; p<0.01) and less time sedentary (563.5 versus 593.0 min., p = 0.02) than MUO (n = 440). In parallel, MHNO (n = 1384) spent more time stepping (125.0 versus 115.4 min; p<0.01) and less time sedentary (553.3 versus 576.6 min., p<0.01) than MUNO (n = 518).

Conclusion

Overall, the metabolically healthy groups were less sedentary and more physically active than the metabolically unhealthy groups. Therefore, physical activity and sedentary time may partly explain the presence of the metabolic syndrome in obese as well as non-obese individuals.

Associations of total amount and patterns of sedentary behaviour with type 2 diabetes and the metabolic syndrome: The Maastricht Study

AIMS/HYPOTHESIS

The study investigated cross-sectional associations of total amount and patterns of sedentary behaviour with glucose metabolism status and the metabolic syndrome.

METHODS

We included 2,497 participants (mean age 60.0 ± 8.1 years, 52% men) from The Maastricht Study who were asked to wear an activPAL accelerometer 24 h/day for 8 consecutive days. We calculated the daily amount of sedentary time, daily number of sedentary breaks and prolonged sedentary bouts (≥30 min), and the average duration of the sedentary bouts. To determine glucose metabolism status, participants underwent an oral glucose tolerance test. Associations of sedentary behaviour variables with glucose metabolism status and the metabolic syndrome were examined using multinomial logistic regression analyses.

RESULTS

Overall, 1,395 (55.9%) participants had normal glucose metabolism, 388 (15.5%) had impaired glucose metabolism and 714 (28.6%) had type 2 diabetes. The odds ratio per additional hour of sedentary time was 1.22 (95% CI 1.13, 1.32) for type 2 diabetes and 1.39 (1.27, 1.53) for the metabolic syndrome. No significant or only weak associations were seen for the number of sedentary breaks, number of prolonged sedentary bouts or average bout duration with either glucose metabolism status or the metabolic syndrome.

CONCLUSIONS/INTERPRETATION

An extra hour of sedentary time was associated with a 22% increased odds for type 2 diabetes and a 39% increased odds for the metabolic syndrome. The pattern in which sedentary time was accumulated was weakly associated with the presence of the metabolic syndrome. These results suggest that sedentary behaviour may play a significant role in the development and prevention of type 2 diabetes, although longitudinal studies are needed to confirm our findings.