There are no standard procedures for identifying wake wear, sleep wear and non-wear time from the 24-h accelerometer recordings. Participant diaries increase participant burden and frequently have missing values. The Choi algorithm identifies 90-min consecutive zero counts as non-wear time, allowing for 2 minutes of non-zero counts, thus possibly ignoring short non-wear bouts and over-estimating nonwear time during sleep. The ActiGraph wActiSleep-BT has a wear sensor which may offer solution for distinguishing between wear and non-wear time. The objective of this study was to compare wake and sleep wear time identified by the sensor to those identified by the Choi algorithm and by participant diaries. In the Finnish Retirement and Aging Study, 477 aging workers (mean age 62, SD 1.2, 18% male) were asked to wear wActiSleep-BT on their non-dominant wrist continuously for 7 days and 6 nights. Sleep and wake time were defined by participant diaries and divided into wear and non-wear time by three methods: participant diaries indicating start and end time of the measurement, Choi algorithm and sensor. The sensor identified significantly less wear time than the other two methods. Mean (SD) minutes of wake and sleep wear time were 804 (263) and 399 (137) by sensor, 903 (179) and 429 (99) by Choi algorithm and 922 (158) and 448 (89) by participant diaries. However, for the majority of participants the correlation of wake wear time identified by Choi algorithm and sensor was very high (Figure 1). The observed discrepancy between Choi algorithm and sensor was mostly due to underestimation of wear time by the sensor possibly owing to technical errors. In the conference we will present methods to identify those participants for whom sensor works appropriately. In addition, we will present a tool to calculate wear time for those for whom wear sensor does not work.