Reproducibility of Accelerometer-Assessed Physical Activity and Sedentary Time. Accelerometer-measured sedentary time and physical activity-A 15 year follow-up of mortality in a Swedish population-based cohort. Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults. Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study Objective vs. Self-Reported Physical Activity and Sedentary Time: Effects of Measurement Method on Relationships with Risk Biomarkers Classifiers for Accelerometer-Measured Behaviors in Older Women Objectively measured physical activity and sedentary time: cross-sectional and prospective associations with adiposity in the Millennium Cohort Study Impact of accelerometer wear time on physical activity data: a NHANES semisimulation data approach A novel method for using accelerometer data to predict energy expenditure. "Go4Life" exercise counseling, accelerometer feedback, and activity levels in older people. Calibration and cross-validation of a wrist-worn Actigraph in young preschoolers. Development of a questionnaire to assess sedentary time in older persons – a comparative study using accelerometry Quantification of Outdoor Mobility by Use of Accelerometer-Measured Physical Behaviour Comparison of accelerometer cut points for predicting activity intensity in youth. Promoting Activity in Geriatric Rehabilitation: A Randomized Controlled Trial of Accelerometry Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults
INTRODUCTION Accelerometers are used increasingly in large epidemiologic studies , but , given logistic and cost constraints , most studies are restricted to a single , 7-day accelerometer monitoring period . It is unknown how well a 7-day accelerometer monitoring period estimates longer-term patterns of behavior , which is critical for interpreting , and potentially improving , disease risk estimates in etiologic studies . METHODS A subset of participants from the Women 's Health Study ( N=209 ; mean age , 70.6 [ SD=5.7 ] years ) completed at least two 7-day accelerometer administrations ( ActiGraph GT3X+ ) within a period of 2 - 3 years . Monitor output was translated into total counts , steps , and time spent in sedentary , light-intensity , and moderate to vigorous-intensity activity ( MVPA ) and bouted-MVPA ( i.e. , 10-minute bouts ) . For each metric , intraclass correlations ( ICCs ) and 95 % CIs were calculated using linear-mixed models and adjusted for wear time , age , BMI , and season . The data were collected in 2011 - 2015 and analyzed in 2015 - 2016 . RESULTS The ICCs ranged from 0.67 ( 95 % CI=0.60 , 0.73 ) for bouted-MVPA to 0.82 ( 95 % CI=0.77 , 0.85 ) for total daily counts and were similar across age , BMI , and for less and more active women . For all metrics , classification accuracy within 1 quartile was > 90 % . CONCLUSIONS These data provide reassurance that a 7-day accelerometer- assessment protocol provides a reproducible ( and practical ) measure of physical activity and sedentary time . However , ICCs varied by metric ; therefore , future prospect i ve studies of chronic diseases might benefit from existing methods to adjust risk estimates for within-person variability in activity to get a better estimate of the true strength of association OBJECTIVES To investigate the associations of objective ly assessed sedentary time , light intensity physical activity ( PA ) , moderate to vigorous intensity PA ( MVPA ) , and total PA with all-cause mortality and mortality from cardiovascular disease ( CVD ) or cancer in a Swedish population -based cohort with 15 years follow-up time . DESIGN Longitudinal prospect i ve cohort study . METHODS Data from 851 persons ( 56 % women ) ≥35 years at baseline were included . Primary exposure variables were  time ( min/day ) spent sedentary , in light intensity PA and in MVPA , and total counts from an Actigraph 7164 accelerometer . Data on all-cause mortality and mortality from CVD or cancer were obtained from Swedish registers . Cox proportional hazards models estimated hazard ratios ( HR ) of mortality with 95 % confidence intervals ( CI ) . RESULTS Compared with the least sedentary participants , those in the most sedentary tertile had an increased risk of all-cause mortality , HR : 2.7 ( 1.4 , 5.3 ) , CVD mortality , HR : 5.5 ( 1.4 , 21.2 ) and cancer mortality , HR : 4.3 ( 1.2 , 16.0 ) . For all-cause mortality , those in the highest light intensity PA tertile had a HR 0.34 ( 0.17 , 0.67 ) compared with the lowest tertile . A similar pattern was found for CVD and cancer mortality . More time spent in MVPA was associated with the largest risk reduction for CVD mortality , with an almost 90 % lower risk in the tertile with the most time in MVPA . CONCLUSIONS This study confirms a strong inverse relationship between MVPA and mortality , and adds new insight for the underst and ing of the associations between sedentary time  and light intensity PA  and mortality Background Physical activity has not been objective ly measured in prospect i ve cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes . Technological advances now make this possible . We describe the methods used to collect and analyse accelerometer measured physical activity  in over 100,000 participants of the UK Biobank study , and report variation by age , sex , day , time of day , and season . Methods Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them . Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration , removal of gravity and sensor noise , and identification of wear / non-wear episodes . We report age- and sex-specific wear-time compliance and accelerometer measured physical activity , overall and by hour-of-day , week-weekend day and season . Results 103,712 data sets were received ( 44.8 % response ) , with a median wear-time of 6.9 days ( IQR:6.5–7.0 ) . 96,600 participants ( 93.3 % ) provided valid data for physical activity analyses . Vector magnitude , a proxy for overall physical activity , was 7.5 % ( 2.35 mg ) lower per decade of age ( Cohen ’s d = 0.9 ) . Women had a higher vector magnitude than men , apart from those aged 45 - 54yrs . There were major differences in vector magnitude by time of day ( d = 0.66 ) . Vector magnitude differences between week and weekend days ( d = 0.12 for men , d = 0.09 for women ) and between seasons ( d = 0.27 for men , d = 0.15 for women ) were small . Conclusions It is feasible to collect and analyse objective physical activity data in large studies . The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening . This work lays the foundation for studies of physical activity and its health consequences . Our summary variables are part of the UK Biobank data set and can be used by research ers as exposures , confounding factors or outcome variables in future analyses BACKGROUND Moderate-to-vigorous-intensity physical activity is recommended to maintain and improve health , but the mortality benefits of light activity and risk for sedentary time remain uncertain . OBJECTIVES Using accelerometer-based measures , we 1 ) described the mortality dose-response for sedentary time and light- and moderate-to-vigorous-intensity activity using restricted cubic splines , and 2 ) estimated the mortality benefits associated with replacing sedentary time with physical activity , accounting for total activity . DESIGN US adults ( n = 4840 ) from NHANES ( 2003 - 2006 ) wore an accelerometer for ≤7 d and were followed prospect ively for mortality . Proportional hazards models were used to estimate adjusted HRs and 95 % CIs for mortality associations with time spent sedentary and in light- and moderate-to-vigorous-intensity physical activity . Splines were used to graphically present behavior-mortality relation . Isotemporal models estimated replacement associations for sedentary time , and separate models were fit for low- ( < 5.8 h total activity/d ) and high-active participants to account for nonlinear associations . RESULTS Over a mean of 6.6 y , 700 deaths occurred . Compared with less-sedentary adults ( 6 sedentary h/d ) , those who spent 10 sedentary h/d had 29 % greater risk ( HR : 1.29 ; 95 % CI : 1.1 , 1.5 ) . Compared with those who did less light activity ( 3 h/d ) , those who did 5 h of light activity/d had 23 % lower risk ( HR : 0.77 ; 95 % CI : 0.6 , 1.0 ) . There was no association with mortality for sedentary time or light or moderate-to-vigorous activity in highly active adults . In less-active adults , replacing 1 h of sedentary time with either light- or moderate-to-vigorous-intensity activity was associated with 18 % and 42 % lower mortality , respectively . CONCLUSIONS Health promotion efforts for physical activity have mostly focused on moderate-to-vigorous activity . However , our findings derived from accelerometer-based measurements suggest that increasing light-intensity activity and reducing sedentary time are also important , particularly for inactive adults Background Accelerometers objective ly assess physical activity ( PA ) and are currently used in several large-scale epidemiological studies , but there is no consensus for processing the data . This study compared the impact of wear-time assessment methods and using either vertical (V)-axis or vector magnitude ( VM ) cut-points on accelerometer output . Methods Participants ( 7,650 women , mean age 71.4 y ) were mailed an accelerometer ( ActiGraph GT3X+ ) , instructed to wear it for 7 days , record date s and times the monitor was worn on a log , and return the monitor and log via mail . Data were processed using three wear-time methods ( logs , Troiano or Choi algorithms ) and V-axis or VM cut-points . Results Using algorithms alone result ed in " mail-days " incorrectly identified as " wear-days " ( 27 - 79 % of subjects had > 7-days of valid data ) . Using only date s from the log and the Choi algorithm yielded : 1 ) larger sample s with valid data than using log date s and times , 2 ) similar wear-times as using log date s and times , 3 ) more wear-time ( V , 48.1 min more ; VM , 29.5 min more ) than only log date s and Troiano algorithm . Wear-time algorithm impacted sedentary time ( ~30 - 60 min lower for Troiano vs. Choi ) but not moderate-to-vigorous ( MV ) PA time . Using V-axis cut-points yielded ~60 min more sedentary time and ~10 min less MVPA time than using VM cut-points . Conclusions Combining log- date s and the Choi algorithm was optimal , minimizing missing data and research er burden . Estimates of time in physical activity and sedentary behavior are not directly comparable between V-axis and VM cut-points . These findings will inform consensus development for accelerometer data processing in ongoing epidemiologic studies Purpose Imprecise measurement of physical activity variables might attenuate estimates of the beneficial effects of activity on health-related outcomes . We aim ed to compare the cardiometabolic risk factor dose-response relationships for physical activity and sedentary behaviour between accelerometer- and question naire-based activity measures . Methods  Physical activity and sedentary behaviour  were assessed in 317 adults by 7-day accelerometry and International Physical Activity Question naire ( IPAQ ) . Fasting blood was taken to determine insulin , glucose , triglyceride and total , LDL and HDL cholesterol concentrations and homeostasis model-estimated insulin resistance ( HOMAIR ) . Waist circumference , BMI , body fat percentage and blood pressure were also measured . Results For both accelerometer-derived sedentary time ( < 100 counts.min−1 ) and IPAQ-reported sitting time significant positive ( negative for HDL cholesterol ) relationships were observed with all measured risk factors – i.e. increased sedentary behaviour was associated with increased risk ( all p≤0.01 ) . However , for HOMAIR and insulin the regression coefficients were > 50 % lower for the IPAQ-reported compared to the accelerometer-derived measure ( p<0.0001 for both interactions ) . The relationships for moderate-to-vigorous physical activity ( MVPA ) and risk factors were less strong than those observed for sedentary behaviours , but significant negative relationships were observed for both accelerometer and IPAQ MVPA measures with glucose , and insulin and HOMAIR values ( all p<0.05 ) . For accelerometer-derived MVPA only , additional negative relationships were seen with triglyceride , total cholesterol and LDL cholesterol concentrations , BMI , waist circumference and percentage body fat , and a positive relationship was evident with HDL cholesterol ( p = 0.0002 ) . Regression coefficients for HOMAIR , insulin and triglyceride were 43–50 % lower for the IPAQ-reported compared to the accelerometer-derived MVPA measure ( all p≤0.01 ) . Conclusion Using the IPAQ to determine sitting time and MVPA reveals some , but not all , relationships between these activity measures and metabolic and vascular disease risk factors . Using this self-report method to quantify activity can therefore underestimate the strength of some relationships with risk factors Purpose Machine learning methods could better improve the detection of specific types of physical activities and sedentary behaviors from accelerometer data . No studies in older population s have developed and tested algorithms for walking and sedentary time in free-living daily life . Our goal was to rectify this gap by leveraging access to data from two studies in older women . Methods In study 1 , algorithms were developed and tested in a sample of older women ( N = 39 , age range = 55–96 yr ) in the field . Women wore accelerometers and SenseCam ( ground truth annotation ) devices for 7 d , yielding 3191 h and 320 d of data . Images were annotated and time matched to accelerometer data , and r and om forest classifiers labeled behaviors ( sitting , riding in a vehicle , st and ing still , st and ing moving , and walking/running ) . In study 2 , we examined the concurrent validity of the algorithms using  accelerometer data from an observed 400-m walk test ( 2983 min of data available ) and 6 d of wearing both accelerometers and global positioning systems devices in a sample of 222 women ( age range = 67–100 ; 313,290 min of data available ) . Analyses included sensitivity , specificity balanced accuracy , and precision , as appropriate , averaged over each test participant at the minute level for each behavior . Results In study 1 , the algorithms had 82.2 % balanced accuracy . In study 2 , the classifier had 87.9 % accuracy for predicting walking . Overall machine learning classifiers and global positioning systems had 88.6 % agreement . Conclusions Free-living algorithms for walking and sedentary time yielded high levels of accuracy and concurrent validity and can be applied to existing accelerometer data from older women Objective To examine whether physical activity ( PA ) and sedentary time ( ST ) in primary school-aged children are associated with adiposity at the start of secondary school , and whether these associations differ by sex or ethnic group . Design Nationally representative prospect i ve cohort study . Setting Children born across the UK , between 2000 and 2002 . Participants 6497 singleton children . Outcome measures Measures of adiposity ( body mass index ( BMI ) , fat mass index ( FMI ) and fat free mass index (FFMI))—obtained at 7 and 11 years . Explanatory measures  Total daily PA ( mean counts per minute ( cpm ) ) ; minutes of moderate-to-vigorous PA ( MVPA ) ; and ST .  All assessed at 7 years using accelerometers . Results In cross-sectional analyses , total PA was inversely associated with FMI ( 3.7 % ( 95 % CI 2.7 % to 4.7 % ) reduction per 150 cpm increase ) , as was MVPA ( 4.2 % ( CI 3.2 % to 5.2 % ) reduction per 20 min/day increase ) . Associations were stronger in black and South Asian ethnic groups . Total PA and MVPA were not associated with FFMI . ST was positively associated with FMI ( 1.3 % ( CI 0.2 % to 2.3 % ) increase per 50 min/day increase ) and inversely associated with FFMI ( 0.5 % ( CI 0.2 % to 0.7 % ) reduction per 50 min/day increase ) . Longitudinally , MVPA at age 7 years remained inversely associated with FMI at age 11 years ( 1.5 % ( CI 0.4 % to 2.6 % ) reduction per 20 min/day increase ) . No association was found between total PA and ST and any of the later adiposity measures . Conclusions 7-year-old children who are more physically active are less likely to be obese at that age and at age 11 years . These associations were particularly evident in children from black or South Asian ethnicity at age 7 years and in boys at age 11 years . Measurements of fat mass provide valuable insights into ethnic differences in associations between adiposity and activity Background Current research practice employs wide-ranging accelerometer wear time criteria to identify a valid day of  physical activity ( PA ) measurement . Objective To evaluate the effects of varying amounts of daily accelerometer wear time on PA data . Methods A total of 1000 days of accelerometer data from 1000 participants ( age=38.7±14.3 years ; body mass index=28.2±6.7 kg/m2 ) were selected from the 2005–2006 National Health and Nutrition Examination Study data set . A reference data set was created using 200 r and om days with 14 h/day of wear time . Four additional sample s of 200 days were r and omly selected with a wear time of 10 , 11 , 12 and 13 h/day1 . These data sets were used in day-to-day comparison to create four semisimulation data sets ( 10 , 11 , 12 , 13 h/day ) from the reference data set . Differences in step count and time spent in inactivity ( < 100 cts/min ) , light ( 100–1951 cts/min ) , moderate ( 1952–5724 cts/min ) and vigorous ( ≥5725 cts/min ) intensity PA were assessed using repeated- measures analysis of variance and absolute percent error ( APE ) . Results There were significant differences for moderate intensity PA between the reference data set and semisimulation data sets of 10 and 11 h/day . Differences were observed in 10–13 h/day1 for inactivity and light intensity PA , and 10–12 h/day for steps ( all p values < 0.05 ) . APE increased with shorter wear time ( 13 h/day=3.9–14.1 % ; 12 h/day=9.9–15.2 % , 11 h/day=17.1–35.5 % ; 10 h/day=24.6–40.3 % ) . Discussion These data suggest that using accelerometer wear time criteria of 12 h/day or less may underestimate step count and time spent in various PA levels The purpose of this study was to develop a new two-regression model relating Actigraph activity counts to energy expenditure over a wide range of physical activities . Forty-eight participants [ age 35 yr ( 11.4 ) ] performed various activities chosen to represent sedentary , light , moderate , and vigorous intensities . Eighteen activities were split into three routines with each routine being performed by 20 individuals , for a total of 60 tests . Forty-five tests were r and omly selected for the development of the new equation , and 15 tests were used to cross-vali date the new equation and compare it against already existing equations . During each routine , the participant wore an Actigraph accelerometer on the hip , and oxygen consumption was simultaneously measured by a portable metabolic system . For each activity , the coefficient of variation ( CV ) for the counts per 10 s was calculated to determine whether the activity was walking/running or some other activity . If the CV was < or=10 , then a walk/run regression equation was used , whereas if the CV was > 10 , a lifestyle/leisure time physical activity regression was used . In the cross-validation group , the mean estimates using the new algorithm ( 2-regression model with an inactivity threshold ) were within 0.75 metabolic equivalents ( METs ) of measured METs for each of the activities performed ( P > or= 0.05 ) , which was a substantial improvement over the single-regression models . The new algorithm is more accurate for the prediction of energy expenditure than currently published regression equations using the Actigraph accelerometer Older people are more sedentary than other age groups . We sought to determine if providing an accelerometer with feedback about activity and counseling older subjects using Go4Life educational material would increase activity levels . Participants were recruited from independent living areas within assisted living facilities and the general public in the Rochester , MN area . 49 persons aged 65 - 95 ( 79.5±7.0 years ) who were ambulatory but sedentary and overweight participated in this r and omized controlled crossover trial for one year . After a baseline period of 2 weeks , group 1 received an accelerometer and counseling using Go4Life educational material ( www.Go4Life.nia.nih.gov ) for 24 weeks and accelerometer alone for the next 24 weeks . Group 2 had no intervention for the first 24 weeks and then received an accelerometer and Go4Life based counseling for 24 weeks . There were no significant baseline differences between the two groups . The intervention was not associated with a significant change in activity , body weight , % body fat , or blood parameters ( p>0.05 ) . Older ( 80 - 93 ) subjects were less active than younger ( 65 - 79 ) subjects ( p=0.003 ) . Over the course of the 48 week study , an increase in activity level was associated with a decline in % body fat ( p=0.008 ) . Increasing activity levels benefits older patients . However , providing an accelerometer and a Go4Life based exercise counseling program did not result in a 15 % improvement in activity levels in this elderly population . Alternate approaches to exercise counseling may be needed in elderly people of this age range OBJECTIVE To calibrate the Actigraph GT3X+ accelerometer for wrist-worn placement in young preschoolers by developing intensity thresholds for sedentary , low- and high-intensity physical activity . Furthermore , to cross-vali date the developed thresholds in young preschoolers . METHODS Actigraph GT3X+ was used to measure physical activity during structured activities and free play in 38 children ( 15 - 36 months ) . Activity was video recorded and scored into  sedentary  , low- and  high-intensity  physical activity based on Children 's Activity Rating Scale ( CARS ) and combined with accelerometer data using a 5 s epoch . Receiver operating characteristic analysis was used to develop intensity thresholds in 26 r and omly selected children . The remaining 12 children were used for cross-validation . RESULTS Intensity thresholds for sedentary were ≤89 vertical counts ( Y ) and ≤221 vector magnitude ( VM ) counts per 5 s and ≥440 Y counts and ≥730 VM counts per 5 s for high-intensity physical activity . Sensitivity and specificity were 60 - 100 % for the developed intensity thresholds . Strong correlations ( Spearman rank correlation 0.69 - 0.91 ) were found in the cross-validation sample between the developed thresholds for the accelerometer and CARS scoring time in all intensity categories . CONCLUSION The developed intensity thresholds appear valid to categorize sedentary behaviour and physical activity intensity categories in children 2 years of age Background There is currently no vali date d question naire available to assess total sedentary time  in older adults . Most studies only used TV viewing time as an indicator of sedentary time . The first aim of our study was to investigate the self-reported time spent by older persons on a set of sedentary activities , and to compare this with objective sedentary time measured by accelerometry . The second aim was to determine what set of self-reported sedentary activities should be used to validly rank people ’s total sedentary time . Finally we tested the reliability of our newly developed question naire using the best performing set of sedentary activities . Methods The study sample included 83 men and women aged 65–92 y , a r and om sample of Longitudinal Aging Study Amsterdam participants , who completed a question naire including ten sedentary activities and wore an Actigraph GT3X accelerometer for 8 days . Spearman correlation coefficients were calculated to examine the association between self-reported time and objective sedentary time . The test-retest reliability was calculated using the intraclass correlation coefficient ( ICC ) . Results Mean total self-reported sedentary time was 10.4 ( SD 3.5 ) h/d and was not significantly different from mean total objective sedentary time ( 10.2 ( 1.2 ) h/d , p = 0.63 ) . Total self-reported sedentary time on an average day ( sum of ten activities ) correlated moderately ( Spearman ’s r = 0.35 , p < 0.01 ) with total objective sedentary time . The correlation improved when using the sum of six activities ( r = 0.46 , p < 0.01 ) , and was much higher than when using TV watching only ( r = 0.22 , p = 0.05 ) . The test-retest reliability of the sum of six sedentary activities was 0.71 ( 95 % CI 0.57 - 0.81 ) . Conclusions A question naire including six sedentary activities was moderately associated with accelerometry-derived sedentary time and can be used to reliably rank sedentary time in older persons Hip fractures in older persons are associated with both low levels of daily physical activity and loss of outdoor mobility . The aim was to investigate if accelerometer-based measures of physical behaviour can be used to determine if people undertake outdoor walking and to provide reference values for physical behaviour outcomes related to outdoor mobility . Older persons ( n = 245 ) , ≥70 years , one year after hip fracture , participated .  Six objective measures of physical behaviour collected by an activity monitor were compared with self-reported outdoor mobility assessed with the Nottingham Extended ADL scale . All measures of time and length in upright periods were significantly lower in participants who reported not walking outdoors ( p < 0.001 ) . A set of cut-off points for the different physical behaviour variables was generated . Maximum length of upright events discriminated best between groups , with 31 minutes as a threshold to determine if a person is more likely to report that they walk outdoors ( sensitivity : 0.805 , specificity : 0.704 , and AUC : 0.871 ) or 41 minutes or more to determine if a person is more likely to report outdoor walking on their own ( AUC : 0.891 ) . Physical behaviour  variables from activity monitoring can provide information about patterns of physical behaviour related to outdoor activity performance UNLABELLED The absence of comparative validity studies has prevented research ers from reaching consensus regarding the application of intensity-related accelerometer cut points for children and adolescents . PURPOSE This study aim ed to evaluate the classification accuracy of five sets of independently developed ActiGraph cut points using energy expenditure , measured by indirect calorimetry , as a criterion reference st and ard . METHODS A total of 206 participants between the ages of 5 and 15 yr completed 12 st and ardized activity trials . Trials consisted of sedentary activities ( lying down , writing , computer game ) , lifestyle activities ( sweeping , laundry , throw and catch , aerobics , basketball ) , and ambulatory activities ( comfortable walk , brisk walk , brisk treadmill walk , running ) . During each trial , participants wore an ActiGraph GT1 M , and V˙O2 was measured breath-by-breath using the Oxycon Mobile portable metabolic system . Physical activity intensity was estimated using five independently developed cut points : Freedson/Trost ( FT ) , Puyau ( PU ) , Treuth ( TR ) , Mattocks ( MT ) , and Evenson ( EV ) . Classification accuracy was evaluated via weighted κ statistics and area under the receiver operating characteristic curve ( ROC-AUC ) . RESULTS Across all four intensity levels , the EV ( κ=0.68 ) and FT ( κ=0.66 ) cut points exhibited significantly better agreement than TR ( κ=0.62 ) , MT ( κ=0.54 ) , and PU ( κ=0.36 ) . The EV and FT cut points exhibited significantly better classification accuracy for moderate- to vigorous-intensity physical activity ( ROC-AUC=0.90 ) than TR , PU , or MT cut points ( ROC-AUC=0.77 - 0.85 ) . Only the EV cut points provided acceptable classification accuracy for all four levels of physical activity intensity and performed well among children of all ages . The widely applied sedentary cut point of 100 counts per minute exhibited excellent classification accuracy ( ROC-AUC=0.90 ) . CONCLUSIONS On the basis of these findings , we recommend that research ers use the EV ActiGraph cut points to estimate time spent in sedentary , light- , moderate- , and vigorous-intensity activity in children and adolescents Background Low activity levels in inpatient rehabilitation are associated with adverse outcomes . The study aim ed to test whether activity levels can be increased by the provision of monitored activity data to patients and clinicians in the context of explicit goal setting . Methods A r and omized controlled trial in three sites in Australia included 255 in patients aged 60 and older who had a rehabilitation goal to become ambulant . The primary outcome was patients ’ walking time measured by  accelerometers  during the rehabilitation admission .  Walking times from accelerometry were made available daily to treating therapists and intervention participants to motivate patients to improve incidental activity levels and reach set goals . For the control group , ‘ usual care ’ was followed , including the setting of mobility goals ; however , for this group , neither staff nor patients received data on walking times to aid the setting of daily walking time targets . Results The median daily walking time in the intervention group increased from 10.3 minutes at baseline to 32.1 minutes at day 28 , compared with an increase from 9.5 to 26.5 minutes per day in the control group . Subjects in the intervention group had significantly higher non-therapy walking time by about 7 minutes [ mean ( 95 % CI ) : 24.6 ( 21.7 , 27.4 ) ] compared to those in the control group [ mean(95 % CI ) : 17.3 ( 14.4 , 20.3 ) ] ( p = 0.001 ) . Conclusions Daily feedback to patients and therapists using an accelerometer increased walking times during rehabilitation admissions . The results of this study suggest objective monitoring of activity levels could provide clinicians with information on clinical ly important , mobility-related activities to assist goal setting . Trial Registration Australian New Zeal and Clinical Trials Registry ACTRN12611000034932 Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing . To date , accelerometers using cut-off point models are most preferred for this , however , machine learning seems a highly promising future alternative . Hence , the current study compared between cut-off point and machine learning algorithms , for optimal quantification of sedentary behaviour and physical activity intensities in the elderly . Thus , in a heterogeneous sample of forty participants ( aged ≥60 years , 50 % female ) energy expenditure during laboratory-based activities ( ranging from sedentary behaviour through to moderate-to-vigorous physical activity ) was estimated by indirect calorimetry , whilst wearing triaxial thigh-mounted accelerometers . Three cut-off point algorithms and a R and om Forest machine learning model were developed and cross-vali date d using the collected data . Detailed analyses were performed to check algorithm robustness , and examine and benchmark both overall and participant-specific balanced accuracies . This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity . Nevertheless , the machine learning algorithm outperformed the cut-off point models by being robust for all individual ’s physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities . Therefore , we propose that R and om Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry
There was consistent evidence from cross-sectional data of an unfavourable association between total sedentary time and insulin sensitivity .
There was also some evidence that total sedentary time was unfavourably associated with fasting insulin , insulin resistance and triglycerides .
Furthermore , there was some evidence from cross-sectional data of a favourable association between breaks in sedentary time and triglycerides .
CONCLUSION Total sedentary time was consistently shown to be associated with poorer insulin sensitivity , even after adjusting for time spent in physical activity .
OBJECTIVE We conducted a systematic review to investigate the cross-sectional and prospect i ve associations of accelerometer-measured total sedentary time and breaks in sedentary time with individual cardiometabolic biomarkers in adults ≥18years of age .