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

in Journal for the Measurement of Physical Behaviour
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  • 1 Children’s Mercy
  • | 2 University of Missouri Kansas City
  • | 3 University of California, San Diego
  • | 4 Deakin University
  • | 5 University of Kansas
  • | 6 Kaiser Permanente Washington Health Research Institute
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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.

Carlson and Tuz-Zahra are co-first authors. Carlson, Steel, and Bejarano are with the Center for Children’s Healthy Lifestyles and Nutrition, Children’s Mercy, Kansas City, MO, USA. Carlson is also with the Department of Pediatrics, Children’s Mercy and University of Missouri Kansas City, Kansas City, MO, USA. Tuz-Zahra, Bellettiere, LaCroix, and Natarajan are with the Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA. Ridgers is with the Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, Victoria, Australia. Bejarano is also with the Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA. Rosenberg and Greenwood-Hickman are with the Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA. Jankowska is with the Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA, USA.

Carlson (jacarlson@cmh.edu) is corresponding author.

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