title: STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT Reproducibility of Accelerometer-Assessed Physical Activity and Sedentary Time. Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study Field evaluation of a random forest activity classifier for wrist-worn accelerometer data. Sitting-time and 9-year all-cause mortality in older women Performance of Activity Classification Algorithms in Free-Living Older Adults. Comparison of accelerometer cut points for predicting activity intensity in youth. Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults Classifiers for Accelerometer-Measured Behaviors in Older Women
abstract: In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old . Such investigations are often analysed inappropriately , notably by using correlation coefficients . The use of correlation is misleading . An alternative approach , based on graphical techniques and simple calculations , is described , together with the relation between this analysis and the assessment of repeatability 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 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 OBJECTIVES Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance . Previous work has generally relied on choreographed activity trials to train and test classification models . However , validity in free-living context s is starting to emerge . Study aims were : ( 1 ) train and test a r and om forest activity classifier for wrist accelerometer data ; and ( 2 ) determine if models trained on laboratory data perform well under free-living conditions . DESIGN Twenty-one participants ( mean age=27.6±6.2 ) completed seven lab-based activity trials and a 24h free-living trial ( N=16 ) . METHODS Participants wore a GENEActiv monitor on the non-dominant wrist . Classification models recognising four activity classes ( sedentary , stationary+ , walking , and running ) were trained using time and frequency domain features extracted from 10-s non-overlapping windows . Model performance was evaluated using leave-one-out-cross-validation . Models were implemented using the r and omForest package within R. Classifier accuracy during the 24h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors . RESULTS Overall classification accuracy for the r and om forest algorithm was 92.7 % . Recognition accuracy for sedentary , stationary+ , walking , and running was 80.1 % , 95.7 % , 91.7 % , and 93.7 % , respectively for the laboratory protocol . Agreement with the activPAL data ( stepping vs. non-stepping ) during the 24h free-living trial was excellent and , on average , exceeded 90 % . The ICC for stepping time was 0.92 ( 95 % CI=0.75 - 0.97 ) . However , sensitivity and positive predictive values were modest . Mean bias was 10.3min/d ( 95 % LOA=-46.0 to 25.4min/d ) . CONCLUSIONS The r and om forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions , but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous field-based evaluations using observation as a criterion measure Background Studies of mid-aged adults provide evidence of a relationship between  sitting-time  and all-cause mortality , but evidence in older adults is limited . The aim is to examine the relationship between  total sitting-time  and all-cause mortality  in older women . Methods The prospect i ve cohort design involved 6656 participants in the Australian Longitudinal Study on Women 's Health who were followed for up to 9 years ( 2002 , age 76–81 , to 2011 , age 85–90 ) .  Self-reported total sitting-time was linked to all-cause mortality data from the National Death Index from 2002 to 2011 . Cox proportional hazard models were used to examine the relationship between  sitting-time and all-cause mortality , with adjustment for potential sociodemographic , behavioural and health confounders . Results There were 2003 ( 30.1 % ) deaths during a median follow-up of 6 years . Compared with participants who sat < 4 h/day , those who sat 8–11 h/day had a 1.45 times higher risk of death and those who sat ≥11 h/day had a 1.65 times higher risk of death . These risks remained after adding sociodemographic and behavioural covariates , but were attenuated after adjustment for health covariates . A significant interaction ( p=0.02 ) was found between sitting-time and physical activity ( PA )  , with increased mortality risk for prolonged sitting only among participants not meeting PA guidelines ( HR for sitting ≥8 h/day : 1.31 , 95 % CI 1.07 to 1.61 ) ; HR for sitting ≥11 h/day : 1.47 , CI 1.15 to 1.93 ) . Conclusions  Prolonged sitting-time  was positively associated with all-cause mortality . Women who reported sitting for more than 8 h/day and did not meet PA guidelines had an increased risk of dying within the next 9 years PURPOSE The objective of this study is to compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults . METHODS Thirty-five older adults ( 21 females and 14 males , 70.8 ± 4.9 yr ) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors  ( in the dominant hip , wrist , and ankle ; ActiGraph , LLC , Pensacola , FL ) . Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz . Fifteen of the participants also wore GT3X+ in free-living setting s and were directly observed for 2 - 3 h. Time- and frequency-domain features from acceleration signals of each monitor were used to train r and om forest ( RF ) and support vector machine ( SVM ) models to classify five activity types : sedentary , st and ing , household , locomotion , and recreational activities . All algorithms were trained on laboratory data ( RFLab and SVMLab ) and free-living data ( RFFL and SVMFL ) using 20-s signal sampling windows . Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique . RESULTS Overall classification accuracy rates for the algorithms developed from laboratory data were between 49 % ( wrist ) and 55 % ( ankle ) for the SVMLab algorithms and 49 % ( wrist ) to 54 % ( ankle ) for the RFLab algorithms . The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58 % ( wrist ) to 69 % ( ankle ) and from 61 % ( wrist ) to 67 % ( ankle ) , respectively . CONCLUSIONS Our algorithms developed on free-living accelerometer data were more accurate in classifying the activity type in free-living older adults than those on our algorithms developed on laboratory accelerometer data . Future studies should consider using free-living accelerometer data to train machine learning algorithms in older adults 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 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 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
target: These studies indicate that analysis of the data using 60-s epochs and a vertical magnitude cut-point < 200 cpm or using 30- or 60-s epochs with a machine learning algorithm provides the most valid estimates of SB .
  Conclusions Few studies have examined the reliability and validity of accelerometers for measuring SB  in older adults .
  Studies to date suggest that the criteria research ers use for classifying an epoch as sedentary instead of as non-wear time ( e.g. , the non-wear algorithm used ) may need to be different for older adults than for younger adults .
  The required number of hours and days of wear for valid estimates of SB in older adults was not clear from studies to date .
background: Background The evidence showing the ill health effects of prolonged sedentary behaviour ( SB ) is growing .
  Most studies of  SB in older adults have relied on self-report measures of SB .
  However , SB is difficult for older adults to recall and objective measures that combine accelerometry with inclinometry are now available for more accurately assessing SB .
  The aim of this systematic review was to assess the validity and reliability of these accelerometers for the assessment of  SB in older adults .