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 thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults
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
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 .