Defining Activity Thresholds Triggering a ‘Stand Hour’ for Apple Watch Users: A Cross-Sectional Study

Katy Lyons (Lead / Corresponding author), Alison Hau Hei Man, David Booth, Graham Rena

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Abstract

Background:
Sedentary behavior (SB) is one of the largest contributing factors increasing the risk of developing noncommunicable diseases, including cardiovascular disease and type 2 diabetes. Guidelines from the World Health Organization for physical activity suggest the substitution of SB with light physical activity. The Apple Watch contains a health metric known as the stand hour (SH). The SH is intended to record standing with movement for at least 1 minute per hour; however, the activity measured during the determination of the SH is unclear.
Objective:
In this cross-sectional study, we analyzed the algorithm used to determine time spent standing per hour. To do this, we investigated activity measurements also recorded on Apple Watches that influence the recording of an SH. We also aimed to estimate the values of any significant SH predictors in the recording of a SH.
Methods:
The cross-sectional study used anonymized data obtained in August 2022 from 20 healthy individuals gathered via convenience sampling. Apple Watch data were extracted from the Apple Health app through the use of a third-party app. Appropriate statistical models were fitted to analyze SH predictors.
Results:
Our findings show that active energy (AE) and step count (SC) measurements influence the recording of an SH. Comparing when an SH is recorded with when an SH is not recorded, we found a significant difference in the mean and median AE and SC. Above a threshold of 97.5 steps or 100 kJ of energy, it became much more likely that an SH would be recorded when each predictor was analyzed as a separate entity.
Conclusions:
The findings of this study reveal the pivotal role of AE and SC measurements in the algorithm underlying the SH recording; however, our findings also suggest that a recording of an SH is influenced by more than one factor. Irrespective of the internal validity of the SH metric, it is representative of light physical activity and might, therefore, have use in encouraging individuals through various means, for example, notifications, to reduce their levels of SB.
Original languageEnglish
Article numbere53806
Number of pages11
JournalJMIR Formative Research
Volume8
DOIs
Publication statusPublished - 10 Jun 2024

Keywords

  • stand hour
  • Apple Watch
  • sedentary behavior
  • light physical activity
  • cardiovascular disease
  • type 2 diabetes
  • data collection
  • wearable
  • wearables
  • watch
  • smartwatch
  • stand
  • standing
  • sedentary
  • physical activity
  • exercise
  • movement
  • algorithm
  • algorithms
  • predict
  • predictive
  • predictor
  • predictors
  • prediction
  • machine learning

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