Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Holter Monitor: 24-Hour Monitoring

Holter Monitor: 24-Hour Monitoring

Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...

Cross-Sectional Research

Cross-Sectional Research

In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Prospective Physical Activity, Sitting and Sleep consortium (ProPASS): addressing methodological and geographical barriers to inform global public health guidelines, interventions and precision medicine.

British journal of sports medicine·2026

Same author

The CFIm25/FDPs axis orchestrates protective mitophagy in macrophages to attenuate myocardial infarction.

Biochimica et biophysica acta. Molecular basis of disease·2026

Same author

Human milk oligosaccharide composition, concentrations and association with maternal factors in the multi-ethnic Asian GUSTO cohort.

Frontiers in nutrition·2026

Same author

Complement system-mediated immunomodulation in epilepsy: From pathogenesis to therapeutic opportunities.

Neurochemistry international·2026

Same author

Screen viewing time from age 1 to 8 years and subsequent academic performance and working memory.

World journal of pediatrics : WJP·2026

Same author

Corrigendum to "Impact of the national steps challenge physical activity programme on obesity and type 2 diabetes prevalence in Singapore to 2050: simulation-based forecasting analysis" [The Lancet Regional Health - Western Pacific, Volume 68, March 2026, 101831].

The Lancet regional health. Western Pacific·2026

Search research articles

Related Experiment Video

Updated: May 31, 2026

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

24-h smartphone usage patterns in university students using high-granularity tracking.

Mingyue Chen¹, Xin Hui Chua¹, Natarajan Padmapriya^1,2

¹Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.

Scientific Reports

|May 28, 2026

Summary

This summary is machine-generated.

Objective smartphone tracking reveals university students average 6.1 hours of screen time daily, with usage patterns varying by sex and day. Self-reported screen time (SST) showed poor agreement with objective measures.

Keywords:

Behavioral patterns Digital determinants Digital health monitoring Digital phenotyping Mobile technology Temporal analysis

More Related Videos

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 31, 2026

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

Digital Health
Behavioral Science
Human-Computer Interaction

Background:

Excessive smartphone use negatively impacts student academic performance, social life, and well-being.
Current research often relies on inaccurate self-reported screen time (SST).
Objective, granular data on student smartphone usage patterns are lacking.

Purpose of the Study:

To objectively track and characterize university students' daily smartphone screen time (SST), frequency, and app usage.
To analyze 24-hour temporal patterns of smartphone use, examining differences by sex and day type (weekday/weekend).
To assess the agreement between objective SST data and self-reported measures.

Main Methods:

A micro-longitudinal study involving 100 undergraduates in Singapore.
Objective, high-granularity smartphone tracking of screen time, frequency, and app usage over 7 days.
Statistical analysis using zero-inflated Gaussian generalized linear mixed models (ZIG GLMMs) and zero-inflated negative binomial generalized linear mixed models (ZINB GLMMs).

Main Results:

Average daily SST was 6.1 ± 2.8 hours, with sustained use and a nighttime peak.
Smartphone use frequency was higher on weekdays, while weekends showed prolonged leisure use.
A weak agreement (Cohen's Kappa ≈ 0) was observed between self-reported and objective SST.

Conclusions:

Objective smartphone tracking is a feasible method for capturing granular digital behaviors with minimal participant burden.
Findings provide insights into temporal patterns of student smartphone use, highlighting discrepancies with self-reported data.
This methodology can inform future digital health research and intervention development for problematic smartphone use.