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 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 .
Performance of Activity Classification Algorithms in Free-Living Older Adults.
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
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 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 .