Sedentary Bout Length Research Articles

Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model

BackgroundWrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts.MethodsWe developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL.ResultsIn OPTIMISE cohort, STEPHEN’s estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06–0.25] vs. 0.23 [0.13–0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06–0.26] vs. 0.42[0.32–0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03–0.25] vs. 0.36[0.15–0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes).ConclusionsSTEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/. The package includes trained models, but users have the flexibility to train their own models.

Abstract P209: Accelerometer-Measured Sedentary Behaviors and Venous Thromboembolism Risk Among Older Women

Introduction: Venous insufficiency, which can occur with prolonged sedentary behavior, is associated with venous thromboembolism (VTE) risk. Whether accelerometer-measured sedentary behaviors are associated with VTE risk is unknown. Hypothesis: Longer total sitting times, mean sitting bout durations, and time spent in prolonged sitting bouts are associated with a greater risk of incident VTE. Methods: We studied participants of the Women’s Health Initiative (WHI) Objective Physical Activity and Cardiovascular Health (OPACH) study without prior VTE, with adherent accelerometer wear, and with sedentary behavior data (n=5,591). Between May 2012-April 2014, participants wore the ActiGraph GT3X+ triaxial accelerometer at the hip for 7 days. Three sedentary behavior measures were classified using the Convolutional Neural Network Hip Accelerometer Posture algorithm: total sitting time, mean sitting bout duration, and time spent in prolonged (≥30 minutes) sitting bouts. VTE events were centrally adjudicated. Multivariable-adjusted (Table) Cox models estimated HRs and 95% CIs for each sedentary behavior in relation to VTE risk. Women were censored at death, loss to follow-up, or end of follow-up (February 2023). Mediation by BMI was evaluated for behaviors associated with VTE. Results: On average, participants were 79 years old. Over a mean follow-up of 8.2 years, 229 women experienced a VTE. In primary confounder-adjusted models, longer mean sitting bout durations were associated with greater incident VTE risk (HR per 5-minute increase=1.15; 95% CI: 1.04, 1.28) (Table). BMI mediated approximately 30% of this association (p&lt;0.01). We found no significant evidence that total sitting time nor time spent in prolonged sitting bouts were associated with VTE. Conclusions: Longer mean sitting bout duration was associated with greater VTE risk, with substantial mediation by BMI. Our results suggest that reducing sedentary bout length in older women may reduce VTE risk.

Sedentary Behavior and Incident Dementia Among Older Adults.

Sedentary behavior is associated with cardiometabolic disease and mortality, but its association with dementia is unclear. To investigate whether accelerometer-assessed sedentary behavior is associated with incident dementia. 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. 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. 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. 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. 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.

Accelerometer-measured sedentary behavior and risk of functional disability in older Japanese adults: a 9-year prospective cohort study

BackgroundThe associations of sedentary time and patterns with functional disability among older adults remain unclear, and few studies have accounted for the co-dependency of sedentary behavior and physical activities when modeling sedentary behavior with risk of functional disability. We aimed to examine the associations between sedentary time and patterns and risk of incident functional disability, and assess whether replacing sedentary time with light physical activity (LPA) or moderate-to-vigorous intensity physical activity (MVPA) is associated with reduced risk of functional disability in community-dwelling older adults.MethodsA total of 1,687 Japanese adults aged ≥ 65 years without functional disability at baseline were prospectively followed-up for 9 years (2011–2020). Functional disability was ascertained using the national database of Japan’s Long-term Care Insurance System. Sedentary time and patterns, LPA, and MVPA were measured using a tri-axial accelerometer secured to participants’ waists.ResultsDuring follow-up, 466 participants developed functional disability. Compared with the lowest quartile of total sedentary time, the multivariable-adjusted hazard ratios (95% confidence intervals) of functional disability for the second, third, and top quartiles were 1.21 (0.91‒1.62), 1.45 (1.10‒1.92), and 1.40 (1.05‒1.88) (p for trend = 0.01). After further adjusting for MVPA, total sedentary time was no longer significantly associated with the risk of functional disability (p for trend = 0.41). Replacing 10 min/day of sedentary time with the same amount of MVPA (but not LPA) was significantly associated with a 12% reduced risk of functional disability (hazard ratio [95% confidence interval]: 0.88 [0.84‒0.92]). No significant association was observed between sedentary bout length and functional disability.ConclusionHigher levels of total sedentary time were associated with an increased risk of incident functional disability. However, this association was not independent of MVPA. Replacing sedentary time with MVPA, but not LPA, was associated with reduced risk of functional disability in older adults.

Association of lifestyle habits and cardiovascular risk among sedentary adults.

This study aimed to analyze the association of lifestyle habits (physical activity, sleep habits, and eating habits) with cardiovascular risk (arterial stiffness and autonomic nervous system function) among sedentary adults. Sixty adults of sedentariness and physical activity were evaluated by accelerometers; sleep and eating habits were assessed by questionnaires; cardiovascular risks were assessed by pulse wave velocity (PWV), ankle-brachial index, flow mediated dilation, and heart rate variability; circulating biomarkers were also determined. Prolonged sitting (represented by longer maximum length of sedentary bouts, lower length of sedentary breaks, and more total time of sitting) were (P < .05) significantly associated with matrix metalloproteinases, neuropeptide Y, C-reactive protein, peptide Y, ghrelin, and leptin; significant associations (P < .05) were also observed of total time in physical activity with most circulating biomarkers except interleukin-6, tumor necrosis factor-α, and adiponectin. Sleep habits, especially sleep efficiency, were (P < .05) significantly associated with PWV, ankle-brachial index, and circulating biomarkers. Eating habits (including emotional overeating and enjoyment of food) were (P < .05) significantly associated with PWVs and flow mediated dilation; satiety responsiveness and enjoyment of food were (P < .05) significantly associated with low-frequency spectral component expressed in normalized units, high frequency spectral component expressed in normalized units, and ratio between low-frequency/high frequency spectral component expressed in normalized units. The findings indicated that several lifestyle habits among sedentary adults were closely associated with increased cardiovascular risk. Sedentary people were encouraged to live with sufficient physical activity, good sleep, and healthy eating habits for decreasing arterial stiffness and balancing autonomic nervous function.

Multidomain cognitive training increases physical activity in people with Parkinson's disease with mild cognitive impairment

IntroductionCognitive impairment in Parkinson's disease (PD), especially in patients with mild cognitive impairment (PD-MCI), coincides with less physical activity. Cognitive trainings (CT) have been found to promote laboratory environment-based movement. Knowledge about their effect in natural home-based environment, reflecting everyday function, is sparse. This explorative study investigated short-term effects of CT on physical activity assessed by home-based accelerometry, and its relation to change of cognitive function over time and non-cognitive outcomes in patients with PD-MCI. Cognitive and non-cognitive correlates of movement parameters at pretest were evaluated as well. MethodsEighteen patients with PD-MCI of the TrainParC study were analyzed. Those patients received either a 6-week multidomain group CT or physical training (PT). Physical activity and sedentary behavior were assessed with wearable accelerometers worn up to seven days pre- and post-training. ResultsPatients in the CT group displayed significantly greater increases in active periods after training than patients assigned to PT. In the CT group, increases in executive functioning were associated with increases in active periods and decreases in active mean bout length after training. At pretest, reduced working memory correlated with longer sedentary mean bout length, and impairment in activities of daily living (ADL) correlated with a higher number of sedentary periods. ConclusionStudy data revealed that CT can increase physical activity in patients with PD-MCI, possibly due to effects on executive functions, which needs further investigation in larger sample sizes. Lower working memory performance and ADL impairment might be associated with a more inactive lifestyle in patients with PD-MCI.

CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19 Using Biobehavioral Rhythms Derived From Wearable Physiological Data

Goal: To investigate whether a deep learning model can detect Covid-19 from disruptions in the human body's physiological (heart rate) and rest-activity rhythms (rhythmic dysregulation) caused by the SARS-CoV-2 virus. Methods: We propose CovidRhythm, a novel Gated Recurrent Unit (GRU) Network with Multi-Head Self-Attention (MHSA) that combines sensor and rhythmic features extracted from heart rate and activity (steps) data gathered passively using consumer-grade smart wearable to predict Covid-19. A total of 39 features were extracted (standard deviation, mean, min/max/avg length of sedentary and active bouts) from wearable sensor data. Biobehavioral rhythms were modeled using nine parameters (mesor, amplitude, acrophase, and intra-daily variability). These features were then input to CovidRhythm for predicting Covid-19 in the incubation phase (one day before biological symptoms manifest). Results: A combination of sensor and biobehavioral rhythm features achieved the highest AUC-ROC of 0.79 [Sensitivity = 0.69, Specificity = 0.89, F[Formula: see text] = 0.76], outperforming prior approaches in discriminating Covid-positive patients from healthy controls using 24 hours of historical wearable physiological. Rhythmic features were the most predictive of Covid-19 infection when utilized either alone or in conjunction with sensor features. Sensor features predicted healthy subjects best. Circadian rest-activity rhythms that combine 24 h activity and sleep information were the most disrupted. Conclusions: CovidRhythm demonstrates that biobehavioral rhythms derived from consumer-grade wearable data can facilitate timely Covid-19 detection. To the best of our knowledge, our work is the first to detect Covid-19 using deep learning and biobehavioral rhythms features derived from consumer-grade wearable data.

Sedentary Behavior and Lower-Extremity Physical Function across the Lifespan of Adults with Multiple Sclerosis.

One outcome of aging with multiple sclerosis (MS) involves the decline in physical function, including compromised balance, reduced walking speed, and lower-extremity strength. Sedentary behavior, the other end of the activity continuum, may be targeted for improvements in physical function among adults with MS, but less is known about the relationship between sedentary behavior and physical function with increasing age in MS. This study examined the associations between device-measured volume and pattern of sedentary behavior and physical function based on SPPB (Short Physical Performance Battery) of ambulatory persons with MS across the lifespan. We categorized participants (N = 216) into young (20–39 years), middle-aged (40–59 years), and older (60–79 years) age groups. Participants completed the SPPB during a single visit to the laboratory and wore an accelerometer for a 7-day period. The one-way analysis of variance indicated no differences in volume and pattern of sedentary behavior among the three age groups, except for average sedentary bout length. Spearman bivariate correlations indicated that pattern, but not volume, of sedentary behavior was associated with physical function in young, middle-aged, and older adults, and the magnitude of these associations varied by age group. Future interventions may reduce and break up prolonged periods of sedentary behavior for improvements in physical function and possibly other consequences in persons with MS.

Associations of Objectively-Measured Sedentary Time and Patterns with Cognitive Function in Non-Demented Japanese Older Adults: A Cross-Sectional Study.

This study aimed to investigate the cross-sectional associations of objectively-measured sedentary time and patterns with cognitive function in Japanese older adults. A total of 1681 non-demented community-dwelling older adults (aged 73 ± 6, 62.1% women) were included. Total sedentary time, prolonged sedentary time (accumulated in ≥30 min bouts) and mean sedentary bout length were assessed using a tri-axial accelerometer. Global and domain-specific cognitive functions were measured using the Montreal Cognitive Assessment. The average of total sedentary time and prolonged sedentary time were 462 ± 125 and 186 ± 111 min/day, respectively. Greater prolonged sedentary time, but not total sedentary time, was significantly associated with poorer performance in the orientation domain even after controlling for moderate-to-vigorous physical activity (p for trend = 0.002). A significant inverse association was also observed between mean sedentary bout length and the orientation domain (p for trend = 0.009). No significant associations were observed for global cognitive function or other cognitive domains. Sedentary time accumulated in prolonged bouts, but not total sedentary time, was inversely associated with orientation ability among older adults. Our results encourage further researches to confirm the role of prolonged sedentary time in changes to cognitive domains over time among older adults.

Does physical activity moderate the association between device-measured sedentary time patterns and depressive symptoms in adults?

To investigate the association between sedentary time (ST) patterns and depressive symptoms, and whether moderate-to-vigorous physical activity (MVPA) can moderate this association. This cross-sectional study included a representative sample of 243 adults (mean age 41.8±16.7 years, 56.4% women) from a city in Southeast Brazil. Depressive symptoms were estimated through the Hospital Anxiety and Depression Scale (HADS). ST patterns (i.e., number of breaks, mean length of sedentary bouts, and number of long sedentary bouts), total ST, and MVPA were assessed using accelerometers. Poisson regression models revealed associations of total ST (ß = 0.063; 95%CI 0.011 to 0.116) and number of long bouts (0.108; 0.047 to 0.171) with depressive symptoms among men. MVPA moderated the associations of breaks and longer bouts of ST with depressive symptoms, with an increase of one break/hour, the increase of one long bout, and a decrease of 1 minute in mean bout length being associated with a reduction of 0.211 and increases of 0.081 and 0.166, respectively, in the number of depressive symptoms among men with physical inactivity (breaks = -0.211; -0.360 to -0.063; mean bout length = 0.081; 0.003 to 0.158; number of long bouts = 0.166; 0.090 to 0.242). Interventions that encourage breaking up ST should be helpful to reduce depressive symptoms among people with physical inactivity.

A new measure to quantify sedentary behavior using accelerometer data: Application to the Hispanic Community Health Study/Study of Latinos.

Availability of accelerometer data has made it possible to objectively and continuously monitor sedentary behavior. Various summaries of the extensive accelerometer data have been used to understand the relationship between sedentary behavior and health. However, the widely used summary measures on sedentary bouts, average bout length or its derivatives, fail to reveal patterns of accumulated sedentary behavior over time. Studies have suggested that prolonged uninterrupted sedentary behavior can be an important metric that is related to health states. Yet existing measures to capture the prolonged sedentary patterns either rely on parametric assumptions on the underlying distribution of sedentary bout length or have to categorize sedentary bout length into somewhat arbitrary categories. Gini index was also used; however, it only measures the variability in bout lengths but not the actual length. To overcome these limitations, we proposed a non-parametric weighted survival function to characterize uninterrupted sedentary behavior over time in a continuous fashion and used the area under the survival curve as a new summary measure to quantify sedentary behavior. We showed that this measure is a weighted average of bout length and contains the information on both the mean and variability of bout lengths. We demonstrated in the simulation studies that the proposed measure could better identify prolonged uninterrupted sedentary behavior and predict health outcomes. We applied this new measure and existing sedentary measures to data from the Hispanic Community Health Study/Study of Latinos to examine the association between sedentary behavior and overweight/obesity.

Objectively Measured Patterns of Daily Physical Activity and Phenotypic Frailty.

Self-reported low physical activity is a defining feature of phenotypic frailty but does not adequately capture physical activity performed throughout the day. This study examined associations between accelerometer-derived patterns of routine daily physical activity and frailty. Wrist accelerometer and frailty data from 638 participants (mean age 77 [SD = 5.5] years; 44% women) were used to derive 5 physical activity metrics: active minutes/day, sedentary minutes/day, total activity counts/day, activity fragmentation (reciprocal of the average active bout length), and sedentary fragmentation (reciprocal of the average sedentary bout length). Robust, prefrail, and frail statuses were identified using the physical frailty phenotype defined as having 0, 1-2, or ≥3 of the following criterion: weight loss, exhaustion, slowness, self-reported low activity, and weakness. Frailty was collapsed into not frail (robust and prefrail) and frail, and each frailty criteria was dichotomized. Multiple logistic regression was used to model each accelerometer metric. Separate frailty criteria and interactions with age and sex were also examined. With higher amounts and intensity of daily activity (more active minutes, fewer sedentary minutes, higher activity counts) and lower activity fragmentation, the odds of frailty were lower compared to robust/prefrail states (p < .02 for all). For interactions, only an age by sedentary fragmentation interaction on the odds of frailty was observed (p = .01). For each separate criteria, accelerometer metrics were associated with odds of slowness, low activity, and weakness. Less favorable patterns of objectively measured daily physical activity are associated with frailty and the components of slowness, low self-reported activity, and weakness.

221Association of physical activity bout length and changing intensity with mortality in UK Biobank

Abstract Background Spending more time active (and less time sedentary) is associated with many health benefits but it is unclear whether these associations differ depending on whether time spent sedentary or in moderate-vigorous physical activity (MVPA) is accumulated in long or short bouts. We used a novel analytical approach to examine whether length of sedentary and MVPA bouts associates with all-cause mortality. Methods We used data on 79,507 participants from UK Biobank. We derived the total time participants spent in activity categories (sleep, sedentary, light activity and MVPA) and in sedentary and MVPA bouts of short (1-15 minutes), medium (16-40 minutes) and long (41+ minutes) duration, on average per day. We used Cox proportion hazards regression to estimate the association of spending 10 minutes more average daily time in one activity or bout length category, coupled with spending 10 minutes less time in another, with all-cause mortality. Results Those spending more time in MVPA had lower mortality risk, irrespective of whether this replaced time spent sleeping, sedentary or in light activity. We found little evidence to suggest that mortality risk differed depending on the length of sedentary or MVPA bouts. Conclusions We uniquely show that higher total MVPA improves health irrespective of whether it is obtained from several short bouts or fewer longer bouts, supporting recent policy changes in some countries. Key messages Our results suggest that time spent in MVPA associates with lower mortality risk irrespective of whether it is obtained from several short bouts or fewer longer bouts.

Accelerometer-measured physical activity and sedentary time are associated with maximal fat oxidation in young adults

ABSTRACT The present work aimed to examine the association between physical activity (PA) and sedentary behaviour with maximal fat oxidation (MFO) in young individuals. A total of 77 active adults (30 women; 22.8 ± 4.5 years) were included in this cross-sectional study in which PA and sedentary behaviour were measured using accelerometers for 7 consecutive days. PA was classified into different intensities (i.e. light, moderate, vigorous, and moderate-to-vigorous) and sedentary behaviour into sedentary time (i.e. time, number of bouts, and length of bouts) and sedentary breaks (i.e. time, number of breaks, and length of breaks). MFO was determined using a graded cycloergometer test through indirect calorimetry and relativized to lean mass (MFOLM) and lean leg mass (MFOLL). Positive associations were found for light and vigorous PA in relation with MFO, MFOLM and MFOLL, independently of cofounders (P ≤ 0.01). Moreover, a negative association was found between MFO and MFOLM and the length of sedentary bouts which was accentuated after adjusting by cardiorespiratory fitness (P ≤ 0.05). These results suggest that light and vigorous PA and sedentary behaviour are related to MFO during exercise. Despite this, further interventional studies are needed to clarify if increments of light and vigorous PA could enhance MFO in different populations.

Feasibility, preliminary efficacy, and acceptability of a twitter-based social support group vs Fitbit only to decrease sedentary behavior in women

BackgroundHealth behavior change interventions delivered by social media allow for real-time, dynamic interaction, peer social support, and experimenter-provided content. AimsWe tested the feasibility, acceptability, and preliminary efficacy of a novel Twitter-based walking break intervention with daily behavior change strategies and prompts for social support, combined with a Fitbit, vs. Fitbit alone. MethodsIn a 2-group pilot, 45 sedentary women from a heart clinic were randomized to Twitter + Fitbit activity tracker (Tweet4Wellness, n = 23) or Fitbit-only (control, n = 22). All received a Fitbit and 13 weeks of tailored weekly step goals. Tweet4Wellness consisted of a private Twitter support group, with daily automated behavior change “tweets” informed by behavior change theory, and encouragement to communicate within the group. Feasibility outcomes included recruitment and enrollment numbers, implementation challenges, and number and type of help requests from participants throughout the study period. Preliminary efficacy outcomes provided by Fitbit data were sedentary minutes, number of hours with >250 steps, maximum sitting bout, weighted sedentary median bout length, total steps, intensity minutes (>3.0 METS), and ratio of time spent sitting-to-moving. Acceptability outcomes included level of Twitter participation within Tweet4Wellness, and Likert scale plus open-ended survey questions on enjoyment and perceived effectiveness of intervention components. Survey data on acceptability of the features of the intervention were collected at 13 weeks (end-of-treatment [EOT]) and 22 weeks (follow-up). ResultsThe study was feasible, with addressable implementation challenges. Tweet4Wellness participants changed significantly from baseline to EOT relative to control participants on number of active hours p = .018, total steps p = .028, and ratio of sitting-to-moving, p = .014. Only sitting-to-moving was significant at follow-up (p = .047). Among Tweet4Wellness participants, each tweet sent during treatment was associated with a 0.11 increase in active hours per day (p = .04) and a 292-step increase per day (p < .001). Tweet4Wellness participants averaged 54.8 (SD = 35.4) tweets, totaling 1304 tweets, and reported liking the accountability and peer support provided by the intervention. ConclusionA Twitter-delivered intervention for promoting physical activity among inactive women from a heart clinic was feasible, acceptable, and demonstrated preliminary efficacy in increasing daily active hours, daily total steps, and the ratio of sitting-to-moving from pre to post for the intervention compared with the control. Lessons learned from this pilot suggest that the next study should expand the recruitment pool, refine the intervention to increase group engagement, and select active hours, total steps, and ratio of sitting-to-movement as primary sedentary behavior measures.

The Effect of a Physical Activity Coaching Intervention on Accelerometer-Measured Sedentary Behaviours in Insufficiently Physically Active Ambulatory Hospital Patients.

Little is known about the impact that physical activity (PA) coaching interventions have on sedentary behaviours. The aim of this study was to investigate if a coaching intervention that increases PA coincidentally influences objectively measured sedentary time in insufficiently physically active adults. We recruited 120 insufficiently physically active ambulatory hospital patients and randomized them to either receive a PA coaching intervention designed to increase objectively measured moderate-to-vigorous-intensity PA (MVPA) or be part of a control group. Participants wore an accelerometer for seven days at baseline, post-intervention (three months) and follow-up (nine months). Changes in the average length of sedentary bouts, proportion of time in sedentary behaviours and number of sedentary bouts were evaluated using mixed-model ANOVAs. At baseline, both groups undertook 67 ± 13 sedentary bouts and spent 69% ± 6% of their time in sedentary behaviours. Compared with control, the intervention group decreased the number of sedentary bouts by 24% and the proportion of time in sedentary behaviours by 7% (p < 0.001). Significant changes were not observed between the groups for average length of sedentary bouts. The PA intervention led to a decrease in the number of sedentary bouts and proportion of time in sedentary behaviours. Future research should investigate PA coaching interventions designed to target simultaneous changes in MVPA and sedentary behaviours.

Sedentary activity bout length was associated with BMI and waist circumference in Swedish children aged 5-7years.

This study examined the pattern of sedentary behaviour during the week and on weekends and associations with health outcomes among children aged 5-7years in Sweden. This cross-sectional study used data from 342 children, many of whom had at least one parent born outside the Nordic region. Physical activity and sedentary time were measured by accelerometry. A sedentary bout was defined as 1-4 and 5-9min. Diet, time in front of television or computer screen, sleep and physical activity behaviour were measured via parental reports, and anthropometric data by research staff. The number of sedentary bouts was higher on weekends than on weekdays. Compared to girls, boys had more 1-4min bouts on both weekdays and weekend days, and more 5-9min bouts on weekends. A higher number of 5-9min bouts was associated with a higher body mass index and waist circumference. This study showed an association between sedentary activity and weight status in children as young as 5-7years. Reducing time, especially longer bouts, spent in sedentary activities may encourage healthy weight development in children.

Cognitive impairment and sedentary behavior predict health-related attrition in a prospective longitudinal Parkinson's disease study

IntroductionIn Parkinson's disease (PD), the high burden of motor and non-motor symptoms, such as cognitive impairment or falls, is associated with rapid disease progression and mortality. This is often reflected by an increased drop-out rate of PD patients in longitudinal studies. Active physical behavior can impact the disease course beneficially and has an overall positive effect on health. Contrarily, sedentary behavior is associated with cognitive impairment in PD. The aim of this study was to investigate whether sedentary physical behavior assessed in the home environment and cognitive impairment can predict health-related study attrition due to sickness and death in PD. MethodsData of 45 PD patients, longitudinally assessed, were analyzed. Of those, 20 patients completed six yearly visits, 16 dropped out due to sickness or death, and nine for other reasons. All patients wore a mobile device to assess physical behavior and completed cognitive testing. ResultsLogistic regression revealed global cognition was the primary predictor for health-related drop-out in varying models (p ≤ .04). In the survival analysis, cognitive impairment (p = .005) and longer sedentary mean bout length (p = .02) were associated with drop-out due to sickness and death. The occurrence of health-related study drop-out or death was highest in patients with both impaired cognition and longer sedentary bouts. ConclusionsCognition was the primary predictor for study drop-out due to sickness and death. However, it seems that sedentary behavior might have a potential negative influence on PD patients’ health, especially those with cognitive impairment.

Accuracy of Sedentary Behavior-Triggered Ecological Momentary Assessment for Collecting Contextual Information: Development and Feasibility Study.

BackgroundSedentary behavior has received much attention in the scientific community over the past decade. There is growing evidence that sedentary behavior is negatively associated with physical and mental health. However, an in-depth understanding of the social and environmental context of sedentary behavior is missing. Information about sedentary behavior, such as how everyday sedentary behavior occurs throughout the day (eg, number and length of sedentary bouts), where, when, and with whom it takes place, and what people are doing while being sedentary, is useful to inform the development of interventions aimed at reducing sedentary time. However, examining everyday sedentary behavior requires specific methods.ObjectiveThe purpose of this paper is (1) to introduce sedentary behavior–triggered Ecological Momentary Assessment (EMA) as a methodological advancement in the field of sedentary behavior research and (2) to examine the accuracy of sedentary behavior–triggered EMA in 3 different studies in healthy adults. Moreover, we compare the accuracy of sedentary behavior–triggered EMA to simulations of random-trigger designs.MethodsSedentary behavior–triggered EMA comprises a continuous assessment of sedentary behavior via accelerometers and repeated contextual assessments via electronic diaries (ie, an application on a smartphone). More specifically, the accelerometer analyzes and transfers data regarding body position (a sitting or lying position, or an upright position) via Bluetooth Low Energy (BLE) to a smartphone in real time and triggers the deployment of questionnaires. Each time a participant spends a specified time (eg, 20 minutes) in a sedentary position, the e-diary triggers contextual assessments. To test the accuracy of this method, we calculated a percentage score for all triggered prompts in relation to the total number of bouts that could trigger a prompt.ResultsBased on the accelerometer recordings, 29.3% (5062/17278) of all sedentary bouts were classified as moderate-to-long (20-40 minutes) and long bouts (≥ 41 minutes). On average, the accuracy by participant was 82.77% (3339/4034; SD 21.01%, range 71.00-88.22%) on the study level. Compared to simulations of random prompts (every 120 minutes), the number of triggered prompts was up to 47.9% (n=704) higher through the sedentary behavior–triggered EMA approach. Nearly 40% (799/2001) of all prolonged sedentary bouts (≥ 20 minutes) occurred during work, and in 57% (1140/2001) of all bouts, the participants were not alone.ConclusionsSedentary behavior–triggered EMA is an accurate method for collecting contextual information on sedentary behavior in daily life. Given the growing interest in sedentary behavior research, this sophisticated approach offers a real advancement as it can be used to collect social and environmental contextual information or to unravel dynamic associations. Furthermore, it can be modified to develop sedentary behavior–triggered mHealth interventions.

Differences in Physical Activity and Sedentary Behavior Patterns of Postmenopausal Women With Normal vs. Low Total Hip Bone Mineral Density.

Purpose: Recent evidence suggests that sedentary behavior (SB) may be associated with bone health. This study compares free-living physical activity (PA) and SB distribution patterns of postmenopausal women with normal vs. low total hip bone mineral density (BMD).Methods: Sixty nine post-menopausal women [mean (min-max) age: 61 (46–79) years] wore ActiGraph GT3X+ activity monitors on the bilateral ankles for 7 days in free-living. Participants were split into two groups: those with normal hip BMD (T-scores ≥-1.0; N = 34) and those with low hip BMD (T-scores <-1.0; N = 35) as defined by the World Health Organization. Daily active time, step counts, sedentary time, sedentary break number, and median sedentary bout length were estimated from ankle acceleration data. The distribution and accumulation patterns of time spent in sedentary bouts, sedentary breaks, and stepping bouts, and sedentary break and stepping bout lengths' variability were also investigated. Group differences were assessed using two-sampled t-tests and Mann-Whitney U-tests with significance levels of 0.5.Results: Significant between group differences (p < 0.05) were in total daily active time [median (IQR): 257 (209–326) vs. 249 (199–299) min], step count [14,188 (10,938–18,646) vs. 13,204 (10,337–16,630) steps], sedentary time [669 (584–731) vs. 687 (615–753) min], and sedentary break number [93 (68–129) breaks vs. 88 (64–113) breaks], as well as median sedentary bout length [15.1 (11.9–22.1) vs. 15.8 (12.1–24.9) min]. Participants' sedentary bouts were found to be power law distributed with 52% of sedentary time occurring in bouts ≥20 min for the normal BMD group, and 58% for the low BMD group. Significant differences were observed between groups in sedentary bouts' and sedentary breaks' power distribution exponents (p < 0.0001) and patterns of sedentary and stepping time accumulation using the Gini index (p ≤ 0.0014). Variability was significantly lower for sedentary break and stepping bout lengths for the low BMD group (p ≤ 0.0001). Participants with lower hip BMD have longer sedentary bouts with shorter and less complex activity bouts compared to participants with normal hip BMD.Conclusion: The results suggest healthier hip BMD may be associated with PA distributed more evenly throughout the day with shorter sedentary bouts. PA distribution should be considered in exercise-based bone health management programs.