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Related Experiment Video

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An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones
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Behavioural biometrics: Using smartphone keyboard activity as a proxy for rest-activity patterns.

Gerrieke B Druijff-van de Woestijne1,2, Hannah McConchie1, Yvonne A W de Kort3

  • 1Neurocast B.V., Zeist, the Netherlands.

Journal of Sleep Research
|March 5, 2021
PubMed
Summary
This summary is machine-generated.

Smartphone keyboard activity offers a novel, unobtrusive method for monitoring rest-activity patterns. This approach reliably estimates sleep and wake times, potentially aiding in the diagnosis of sleep disturbances.

Keywords:
circadian rhythmdigital biomarkerfield assessmentmonitoringsleep diarysmartphone application

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Area of Science:

  • Sleep Science
  • Chronobiology
  • Human-Computer Interaction

Background:

  • Rest-activity patterns are crucial for healthy sleep and can be disrupted in various disorders.
  • Traditional monitoring methods like actigraphy and diaries can be burdensome.
  • There is a need for unobtrusive, long-term monitoring solutions for rest-activity patterns.

Purpose of the Study:

  • To investigate the reliability of smartphone keyboard activity for estimating rest-activity patterns.
  • To compare smartphone-derived rest-activity markers with self-reported sleep diary data.
  • To assess the feasibility of using smartphone keyboard activity for unobtrusive, long-term sleep monitoring.

Main Methods:

  • A custom smartphone keyboard was used to passively collect keyboard activity data from 51 first-year students over one week.
  • Participants also completed the Consensus Sleep Diary for daily self-reporting of sleep and rest periods.
  • Rest and activity timing were inferred from the last keyboard activity before a nightly pause and the first activity after the pause.

Main Results:

  • High correlations (r = 0.74–0.80) were found between smartphone-derived markers and self-reported onset/offset of resting periods.
  • Linear mixed models accurately estimated resting period onset and offset (R² = 0.60–0.66).
  • Chronotype and day type effects on rest-activity patterns were also investigated.

Conclusions:

  • Smartphone keyboard activity provides a reliable and unobtrusive method for estimating rest-activity patterns.
  • This method holds potential for long-term monitoring of sleep disturbances without user burden or additional devices.
  • Future research should explore this method in clinical populations with sleep-related issues or neurological disorders.