Validity of Two Awake Wear-Time Classification Algorithms for activPAL in Youth, Adults, and Older Adults

Abstract

Background: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. Methods: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. Results: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while <11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0–15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods. Conclusion: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5–2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.

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“…The activPAL has previously been validated in children [ 33 ]. Briefly, activPAL data were downloaded using the manufacturer software (V8.10.8.32, PAL technologies, Glasgow, UK) and the subsequent event.csv files were processed in PAL-V1.1 (Leicester, UK) with a validated algorithm that identified waking hours, prolonged non-wear time (≥ 5 h) and invalid data [ 34 , 35 ]. Diary-reported non-wear periods deemed plausible were removed.…”

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“…ActiGraph non-wear was determined by the Choi algorithm (90-min window, 30-min streamframe, and 2-min tolerance) [ 29 , 30 ]. activPAL non-wear and sleep were determined by ProcessingPAL using default settings that were shown to have good validity in this population [ 31 – 33 ]. There were no additional wear time criteria employed for the training and model selection datasets.…”

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“…Although the validity remains unclear, these algorithms have been used in other studies. Little evidence is currently available regarding the validity of these algorithms for this age-group, they are being used in similar research ( 24 ), and validation in youth and adults is promising ( 25 ).…”