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 .
Field evaluation of a random forest activity classifier for wrist-worn accelerometer data.
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
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 .