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 .
Classifiers for Accelerometer-Measured Behaviors in Older Women
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
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 .