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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Jan 8, 2024
Date Accepted: Apr 18, 2024

The final, peer-reviewed published version of this preprint can be found here:

Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities

Chen HH, Lin C, Chang HC, Chang JH, Chuang HH, Lin YH

Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities

J Med Internet Res 2024;26:e56144

DOI: 10.2196/56144

PMID: 38885499

PMCID: 11217705

Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: A Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities

  • Hung-Hsun Chen; 
  • Chen Lin; 
  • Hsiang-Chih Chang; 
  • Jen-Ho Chang; 
  • Hai-Hua Chuang; 
  • Yu-Hsuan Lin

ABSTRACT

Background:

Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human biological rhythms.

Objective:

This study proposes a novel approach based on human-smartphone interaction to compute mental activity, encompassing general mental activity (GMA) and working mental activity (WMA).

Methods:

A total of 24 healthcare professionals wore wrist actigraphy devices and installed the "Staff Hours" app on their smartphones. The study period included a total of 457 person-days, consisting of 332 workdays and 125 non-workdays. PA was assessed using actigraphy, while GMA was derived from smartphone interaction patterns. WMA was derived from human-smartphone interaction patterns, specifically GPS-defined work hours, using a machine learning model. Activity levels and phase differences among the three activities were analyzed, along with correlations with sleep indicators.

Results:

Significant differences were found in activity levels between workdays and non-workdays for WMA. WMA exhibited an earlier phase than PA (1.08±1.22 hours, P <0.001), and PA showed an earlier phase than GMA (1.22±1.22 hours, P <0.001). However, when comparing sleep onset and wake time using PA and GMA, no significant differences were observed. A later bedtime and wake time were associated with lower activity levels in WMA the following day.

Conclusions:

The findings demonstrate differences in activity levels, phase patterns, and associations with sleep indicators for WMA, PA, and GMA, providing valuable insights into the dynamics of physical and mental activities in relation to sleep patterns.


 Citation

Please cite as:

Chen HH, Lin C, Chang HC, Chang JH, Chuang HH, Lin YH

Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities

J Med Internet Res 2024;26:e56144

DOI: 10.2196/56144

PMID: 38885499

PMCID: 11217705

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