Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of...

Area of Science:

• Chronobiology and behavioral science
• Human-computer interaction
• Sleep science

Background:

• Human biological rhythms are typically assessed via physical activity (PA), but mental activity may provide a more accurate reflection.
• Existing methods for measuring mental activity are limited.

Purpose of the Study:

• To propose and validate a novel approach using human-smartphone interaction to quantify general mental activity (GMA) and working mental activity (WMA).
• To investigate the relationship between these mental activities, physical activity, and sleep patterns.

Main Methods:

• 24 healthcare professionals wore actigraphy devices and used a smartphone app for over 457 person-days.
• Physical activity (PA) was measured by actigraphy; GMA and WMA were derived from smartphone interaction patterns.
• Machine learning models (XGBoost, CNNs) were used to model WMA, incorporating GPS-defined work hours. Circadian rhythms and phase differences were analyzed using signal processing techniques.
• Multilevel modeling examined associations between sleep indicators (total sleep time, midpoint of sleep) and next-day activity levels.

Main Results:

• Working mental activity (WMA) occurred approximately 1.08 hours earlier than PA on workdays, while general mental activity (GMA) commenced 1.22 hours later than PA.
• A significant negative correlation was found between WMA and the previous night's midpoint of sleep (later sleep times linked to reduced WMA).
• No significant correlations were observed between PA or GMA and sleep indicators, nor between WMA and total sleep time.

Conclusions:

• General mental activity (GMA) and working mental activity (WMA), derived from smartphone interactions, are valuable indicators of human biological rhythms.
• Mental activities are intricately linked to sleep patterns, offering new avenues for behavioral and health research.
• This approach provides novel insights into the complex interplay between mental exertion, physical activity, and sleep.