0269 No More Actiwatches: Can Apple Watches be a More Scalable Alternative?

Abstract Introduction The Philips Respironics Actiwatch has become a gold standard for actigraphy data collection. With the announcement of their discontinuation, there has been increased momentum to identify an alternative, particularly with consumer-based devices. One promising solution is the Apple Watch because it allows user access to raw accelerometer data, thus eliminating the long-standing problem of the “black box algorithm” with wearable technology. This study compared the activity counts derived from Apple Watch data with that from the Actiwatch. Methods Adults wore an Actiwatch and Apple Watch on the same non-dominant wrist for 7 to 14 days (mean = 9). Accelerometer data were recorded and activity counts were derived from accelerometer data (algorithm for Apple Watch from Lindert et al, 2013). Daily sleep diaries were also completed. Actigraphy activity counts were binned into 2, 5, 10, 30, and 60 minute bins to examine concordance by bin sizes. Agreement between Apple Watch and Actiwatch were quantified with Lin’s Concordance Correlation Coefficients (CCC) across bins. Usability was assessed utilizing an exit survey. Results In increasing order of bins, the CCCs were: .63 (2 mins.), .70 (5 mins.), .78 (10 mins.), .90 (30 mins.), and .92 (60 mins.). Agreement was substantial for bins of 5 minutes or greater with 30 and 60 minutes showing the strongest agreement. The y-intercepts for all bins were positive indicating that Apple Watches were more likely to detect activity counts compared to Actiwatches. In terms of usability, a large majority (88%-100%) of participants favored the Apple Watch for comfort, convenience, preference to wear in public, and preference to wear again. The only dimension where there was not a clear preference was ease of use, with 56% indicating a preference for the Apple Watch. Conclusion This is the first study supporting Apple Watch as a potentially feasible alternative to an Actiwatch. Given that the Apple Watch’s accelerometer appears to be more sensitive, use of a different algorithm for determining activity counts from raw accelerometer data may improve the concordance at smaller bins. Future research should explore the effectiveness of the Apple Watch for sleep/wake detection in clinical samples. Support (if any)