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

The association between objectively-measured sedentary behavior patterns and predicted 10-year ASCVD risk

This study aims to investigated the association between sedentary behavior (SB) and predicted 10-year atherosclerotic cardiovascular disease (ASCVD) risk and determine whether the associations differ by how the behavior is accumulated, in US middle-aged and older adults. Cross-sectional data were derived from national health and nutrition examination survey (NHANES) 2003-2006. Seven-day wearing of accelerometer was used to assess SB pattern, exported as total SB, bouts of 1-9, 10-29, 30-59 and ≥ 60 min SB. Predicted 10-year ASCVD risk was calculated using validated pooled cohort equations. Linear regression was used to estimate adjusted coefficients. A total of 2327 participants were enrolled with mean age of 56.9 and mean predicted 10-year ASCVD risk of 10.7%. We observed significant associations of total SB and its longer accumulated patterns with higher 10-year predicted ASCVD risk, in a linear fashion and independent of a list of covariates. A 30 min increment per day of total SB, bouts in 10-29, bouts in 30-59 and bouts in ≥ 60 min were associated with 0.14, 0.14, 0.23 and 0.12% higher multivariable-adjusted 10-year predicted ASCVD risk. There are significant associations of total SB as well as its longer accumulated patterns with higher 10-year predicted ASCVD risk, independent of a list of covariates and in a linear fashion. The result indicates that reducing total sedentary time and interrupting long duration of prolonged SB, could be meaningful to for public guideline to lessen the personal and public health burden of cardiovascular health.

Association between inflammatory biomarkers and hypertension among sedentary adults in US: NHANES 2009-2018

Our study focuses on the relationship between inflammatory biomarkers and hypertension among sedentary adults in the United States, using data from the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018. We categorized 24,614 participants into two groups based on their daily sedentary time: 9607 individuals in the sedentary group (≥7 h) and 15,007 in the non-sedentary group (<7 h). We found that the sedentary group had a significantly higher prevalence of hypertension than the non-sedentary group. Using weighted multiple logistic regression and smoothing curves, we assessed the correlation between inflammatory biomarkers and hypertension among the sedentary adults. The odds ratios for hypertension were 1.92 for the monocyte to high-density lipoprotein ratio (MHR), 1.15 for the systemic inflammation response index (SIRI), and 1.19 for the natural logarithm of the systemic immune-inflammation index (lnSII), all showing nonlinear associations. Furthermore, a significant positive correlation was found between sedentary time and inflammatory biomarkers (MHR, SIRI, and lnSII). Our findings suggest that prolonged sedentary behavior in the US significantly increases hypertension risk, likely due to marked increases in inflammation markers.

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.

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.

Prospective Associations of Accelerometer-Measured Machine-Learned Sedentary Behavior With Death Among Older Women: The OPACH Study

BACKGROUND

Sedentary behavior is a recognized mortality risk factor. The novel and validated convolutional neural network hip accelerometer posture algorithm highly accurately classifies sitting and postural changes compared with accelerometer count cut points. We examined the prospective associations of convolutional neural network hip accelerometer posture-classified total sitting time and mean sitting bout duration with all-cause and cardiovascular disease (CVD) death.

METHODS AND RESULTS

Women (n=5856; mean±SD age, 79±7 years; 33% Black women, 17% Hispanic or Latina women, 50% White women) in the Women's Health Initiative Objective Physical Activity and Cardiovascular Health (OPACH) Study wore the ActiGraph GT3X+ for ~7 days from May 2012 to April 2014 and were followed through February 19, 2022 for all-cause and CVD death. The convolutional neural network hip accelerometer posture algorithm classified total sitting time and mean sitting bout duration from GT3X+ output. Over follow-up (median, 8.4 years; range, 0.1-9.9), there were 1733 deaths (632 from CVD). Adjusted Cox regression hazard ratios (HRs) comparing women in the highest total sitting time quartile (>696 min/d) to those in the lowest (<556.0 min/d) were 1.57 (95% CI; 1.35-1.83; P-trend<0.001) for all-cause death and 1.78 (95% CI; 1.36-2.31; P-trend<0.001) for CVD death. HRs comparing women in the longest mean sitting bout duration quartile (>15 minutes) to the shortest (<9.3 minutes) were 1.43 (95% CI; 1.23-1.66; P-trend<0.001) for all-cause death and 1.52 (95% CI; 1.18-1.96; P-trend<0.001) for CVD death. Apparent nonlinear associations for total sitting time suggested higher all-cause death (P nonlinear=0.009) and CVD death (P nonlinear=0.008) risk after ~660 to 700 min/d.

CONCLUSIONS

Higher total sitting time and longer mean sitting bout duration are associated with higher all-cause and CVD mortality risk among older women. These data support interventions aimed at reducing both total sitting time and interrupting prolonged sitting.

Association between accelerometry measured patterns of sedentary behaviors and functional status in older adults

BACKGROUND

Older adults are highly sedentary, and too much sedentary behavior (SB) is associated with negative health effects, but little is known about SB patterns and their associations with functional status.

AIMS

To examine the association between objectively measured sedentary behavior time (SBT) and sedentary behavior fragmentation (SBF) and functional status in older adults using the National Health Aging Trends Study (NHATS) dataset, a nationally representative sample from 2021.

METHODS

Data from NHATS were analyzed using weighted linear regressions to examine the association between objective measures of SBT (mean hours spent in SB/day during waking hours) and SBF, and six functional variables (difficulties with activities of daily living [ADL], short physical performance battery, hand grip strength, immediate word recall, delayed word recall, and mental health), accounting for sociodemographic, body mass index, and the number of chronic conditions.

RESULTS

A total of 738 individuals from the NHATS were included. Higher SBT was associated with greater difficulties with ADL, poorer short physical performance battery and hand grip strength, lower scores in both immediate and delayed word recall, and poorer mental health. Higher SBF was associated with fewer difficulties with ADL, better short physical performance battery and hand grip strength, a higher score in immediate word recall, and better mental health.

DISCUSSIONAND CONCLUSIONS

Greater fragmentation of SB was associated with better function, and increasing SBF may be a useful strategy for mitigating the effects of SB in older adults, but prospective research is needed to support this approach.

Sedentary Behavior and Phase Angle: An Objective Assessment in Physically Active and Inactive Older Adults

The purpose of the present study was to investigate the associations of the objectively assessed total sedentary behavior (SB) amount and SB patterns with phase angle (PhA) in older populations and to conduct a comparison analysis for those who are physically active (meet 150 min/week) and inactive (less than 150 min/week). During May to August 2023, a convenience sampling was used to recruit older adults (aged ≥ 65 years) living in a community in Taiwan. The total SB amount (minutes/day), SB patterns (including SB bouts and breaks), and physical activity were assessed by a triaxial accelerometer. A multifrequency bioelectrical impedance analyzer (BIA) was used to measure PhA. Multiple linear regression analysis was employed to examine the associations between SB and PhA in the total number of participants and stratified in the physically active and inactive groups. For the whole sample (n = 166; mean age: 72.1 ± 5.5 years), the total SB amount and patterns of SB were not associated with PhA. For those physically active, the total SB amount and SB patterns were not associated with PhA. Among those physically inactive, the total SB amount was negatively related to PhA (B: -0.059, 95% CI: -0.118, 0.000). This study underscores the importance of minimizing the total SB amount in physically inactive older adults, providing evidence for future interventions targeting SB and PhA in this population.

Sedentary behavior patterns and the risk of non-communicable diseases and all-cause mortality: A systematic review and meta-analysis

BACKGROUND

The association between sedentary behavior and health-related outcomes has been well established, whereas it is inconclusive whether a sedentary behavior pattern is an additional risk factor for health-related outcomes independent of total sedentary time and physical activity.

OBJECTIVES

To determine sedentary behavior patterns and their association with risks of noncommunicable diseases and all-cause mortality and to assess whether this association is independent of total sedentary time and physical activity.

DESIGN

This was a systematic review and meta-analysis.

METHODS

Studies were obtained by searching the Web of Science Core Collection, PubMed/Medline, the Cochrane Library, Embase, CINAHL, and SPORTDiscus up to April 2023. All observational studies published in English or Chinese were included if they explored sedentary behavior patterns and their association with risks of abdominal obesity, metabolic syndrome, diabetes, cardiovascular disease, cancer, and all-cause mortality among individuals who had never experienced the outcome event before the baseline assessment. Data extraction using a standardized form and quality appraisal using two authoritative tools were then performed. All these steps were completed by two independent reviewers from December 2022 to May 2023. If data were sufficiently homogenous, meta-analyses were performed; otherwise, narrative syntheses were employed. Harvest plots were also used to visually represent the distribution of evidence.

RESULTS

Eighteen studies comprising 11 prospective cohort studies and seven cross-sectional studies were included. The findings suggested that prolonged sedentary time and usual sedentary bout duration were two metrics that reflected the nonlinear dose-response effect of prolonged sedentary behavior patterns. Only extremely high levels of prolonged sedentary behavior patterns significantly increased the risk of adverse health outcomes, independent of physical activity. Whether prolonged sitting was an additional risk factor for adverse health outcomes, independent of total sedentary time, was inconclusive due to an insufficient number of primary studies that included total sedentary time as one of the potential covariates. There was some evidence that supported a sedentary bout that significantly increased the risk of adverse health outcomes was 30-60 min. The threshold of prolonged sedentary time differed with outcomes, and future studies are needed to make this threshold more precise.

CONCLUSION

A prolonged sedentary behavior pattern was associated with increased risks of several major noncommunicable diseases and all-cause mortality. People, especially those who do not reach the recommended level of moderate-to-vigorous physical activity, are encouraged to interrupt sedentary bouts every 30 to 60 min and limit prolonged sedentary time per day as much as possible.

TWEETABLE ABSTRACT

Breaking up consecutive sedentary bouts >30 to 60 min and substituting them with brief bouts of physical activity.

Sedentary Behavior and Incident Dementia Among Older Adults

Importance

Sedentary behavior is associated with cardiometabolic disease and mortality, but its association with dementia is unclear.

Objective

To investigate whether accelerometer-assessed sedentary behavior is associated with incident dementia.

Design, Setting, and Participants

A retrospective study of prospectively collected data from the UK Biobank including 49 841 adults aged 60 years or older without a diagnosis of dementia at the time of wearing the wrist accelerometer and living in England, Scotland, or Wales. Follow-up began at the time of wearing the accelerometer (February 2013 to December 2015) and continued until September 2021 in England, July 2021 in Scotland, and February 2018 in Wales.

Exposures

Mean daily sedentary behavior time (included in the primary analysis) and mean daily sedentary bout length, maximum daily sedentary bout length, and mean number of daily sedentary bouts (included in the secondary analyses) were derived from a machine learning-based analysis of 1 week of wrist-worn accelerometer data.

Main Outcome and Measures

Incident all-cause dementia diagnosis from inpatient hospital records and death registry data. Cox proportional hazard models with linear and cubic spline terms were used to assess associations.

Results

A total of 49 841 older adults (mean age, 67.19 [SD, 4.29] years; 54.7% were female) were followed up for a mean of 6.72 years (SD, 0.95 years). During this time, 414 individuals were diagnosed with incident all-cause dementia. In the fully adjusted models, there was a significant nonlinear association between time spent in sedentary behavior and incident dementia. Relative to a median of 9.27 hours/d for sedentary behavior, the hazard ratios (HRs) for dementia were 1.08 (95% CI, 1.04-1.12, P < .001) for 10 hours/d, 1.63 (95% CI, 1.35-1.97, P < .001) for 12 hours/d, and 3.21 (95% CI, 2.05-5.04, P < .001) for 15 hours/d. The adjusted incidence rate of dementia per 1000 person-years was 7.49 (95% CI, 7.48-7.49) for 9.27 hours/d of sedentary behavior, 8.06 (95% CI, 7.76-8.36) for 10 hours/d, 12.00 (95% CI, 10.00-14.36) for 12 hours/d, and 22.74 (95% CI, 14.92-34.11) for 15 hours/d. Mean daily sedentary bout length (HR, 1.53 [95% CI, 1.03-2.27], P = .04 and 0.65 [95% CI, 0.04-1.57] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 0.48 hours) and maximum daily sedentary bout length (HR, 1.15 [95% CI, 1.02-1.31], P = .02 and 0.19 [95% CI, 0.02-0.38] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 1.95 hours) were significantly associated with higher risk of incident dementia. The number of sedentary bouts per day was not associated with higher risk of incident dementia (HR, 1.00 [95% CI, 0.99-1.01], P = .89). In the sensitivity analyses, after adjustment for time spent in sedentary behavior, the mean daily sedentary bout length and the maximum daily sedentary bout length were no longer significantly associated with incident dementia.

Conclusions and Relevance

Among older adults, more time spent in sedentary behaviors was significantly associated with higher incidence of all-cause dementia. Future research is needed to determine whether the association between sedentary behavior and risk of dementia is causal.

Device-measured physical activity and sedentary time in a national sample of Luxembourg residents: the ORISCAV-LUX 2 study

BACKGROUND

Existing information about population physical activity (PA) levels and sedentary time in Luxembourg are based on self-reported data.

METHODS

This observational study included Luxembourg residents aged 18-79y who each provided ≥4 valid days of triaxial accelerometry in 2016-18 (n=1122). Compliance with the current international PA guideline (≥150 min moderate-to-vigorous PA (MVPA) per week, irrespective of bout length) was quantified and variability in average 24h acceleration (indicative of PA volume), awake-time PA levels, sedentary time and accumulation pattern were analysed by linear regression. Data were weighted to be nationally representative.

RESULTS

Participants spent 51% of daily time sedentary (mean (95% confidence interval (CI)): 12.1 (12.0 to 12.2) h/day), 11% in light PA (2.7 (2.6 to 2.8) h/day), 6% in MVPA (1.5 (1.4 to 1.5) h/day), and remaining time asleep (7.7 (7.6 to 7.7) h/day). Adherence to the PA guideline was high (98.1%). Average 24h acceleration and light PA were higher in women than men, but men achieved higher average accelerations across the most active periods of the day. Women performed less sedentary time and shorter sedentary bouts. Older participants (aged ≥55y) registered a lower average 24h acceleration and engaged in less MVPA, more sedentary time and longer sedentary bouts. Average 24h acceleration was higher in participants of lower educational attainment, who also performed less sedentary time, shorter bouts, and fewer bouts of prolonged sedentariness. Average 24h acceleration and levels of PA were higher in participants with standing and manual occupations than a sedentary work type, but manual workers registered lower average accelerations across the most active periods of the day. Standing and manual workers accumulated less sedentary time and fewer bouts of prolonged sedentariness than sedentary workers. Active commuting to work was associated with higher average 24h acceleration and MVPA, both of which were lower in participants of poorer self-rated health and higher weight status. Obesity was associated with less light PA, more sedentary time and longer sedentary bouts.

CONCLUSIONS

Adherence to recommended PA is high in Luxembourg, but half of daily time is spent sedentary. Specific population subgroups will benefit from targeted efforts to replace sedentary time with PA.

Sedentary Behaviour and Its Relationship with Early Vascular Ageing in the General Spanish Population: A Cross-Sectional Study

Sedentary behaviour is associated with a greater predisposition to developing cardiovascular diseases. The aim of the study was to analyse the relationship between sedentary time and early vascular ageing. A total of 501 participants (49.70% men) were recruited through random sampling stratified by age group and sex. Vascular ageing was evaluated considering three criteria: (1) the vascular ageing index (VAI); (2) the carotid−femoral pulse wave velocity (cfPWV) 10th and 90th percentiles of the reference values in the European population by age; and (3) the Framingham’s heart age. The carotid intima−media thickness was measured using a Sonosite Micromaxx ultrasound, the presence of peripheral artery disease was assessed by calculating the ankle−brachial index using a VaSera VS-1500, and the cfPWV was measured with a SphygmoCor® device. Weekly sedentary hours were evaluated through a sitting time questionnaire. The average age of the population was 55.90 ± 14.24 years. The men spent more hours sitting per week (47.6 ± 16.6 vs. 36.8 ± 17.3 h/W), at work (16.7 ± 16.2 vs. 9.73 ± 14.9 h/W), and watching TV (21.6 ± 12.5 vs. 18.7 ± 11.9 h/W). In the logistic regression analysis, the individuals with early vascular aging (EVA), with respect to those with healthy vascular aging (HVA), spent more hours sitting per week (OR = 1.03 vs. OR = 1.02; p < 0.05) and watching TV (OR = 1.03 vs. OR = 1.03; p < 0.05), using the criteria of the European guideline and VAI, and more hours sitting when commuting (OR = 1.04; p < 0.05), using Framingham’s heart age to define EVA. The results of this study indicate that sedentary time is associated with early vascular ageing. Therefore, reducing sedentary time would improve vascular health.