Stress and Mood Associations With Smartphone Use in University Students: A 12-Week Longitudinal Study

Screen viewing patterns and their association with mental wellbeing and psychological distress: a cross-sectional study amongst university students

BACKGROUND

Dependency on screen viewing (SV) has reached unprecedented levels, and mental health issues are becoming a major public health concern. However, the associations between SV, including variations in purposes, devices, and timing, and mental health remain unclear. This study aims to provide insights into these associations among university students.

METHODS

This analysis used baseline data from a longitudinal cohort study among first-year university students matriculating in the 2021-2022 academic year. Self-reported data on sociodemographics, health behaviors and mental health outcomes alongside anthropometric measurements were collected. Unadjusted and adjusted logistic regression analyses were conducted.

RESULTS

The average age of the 997 valid students was 20.2 years, with 59.6% being female and 41.4% male. Students spent 14.3 h daily on SV, with females reporting higher SV than males. Daily SV was predominant for study purposes (7.6 h/day). Computer usage was the highest (7.0 h/day), while TV usage was the lowest (1.7 h/day). Poor mental wellbeing was reported by 33.6% of students, while 13.9% experienced psychological distress. Compared to students with low total SV, those with high levels were more likely to have poor mental wellbeing [OR (95% CI): 1.40 (0.99, 1.98)] and psychological distress [1.56 (1.00, 2.44)]. High levels of recreational and study related SV were significantly associated with poor mental wellbeing [1.81 (1.27, 2.56)] and psychological distress [1.75 (1.11, 2.83)], respectively. Those with high levels of computer time were more likely to have poor mental wellbeing [1.44 (1.01, 2.06)], and high weekend day SV was associated with greater odds of psychological distress [2.16 (1.17, 4.06)].

CONCLUSIONS

SV among university students was high, as was the high prevalence of poor mental wellbeing and psychological distress. Greater SV was associated with poor mental wellbeing and psychological distress. Differences according to purpose of SV were noted. Although recreational SV was associated with poor mental wellbeing, study related SV was associated with psychological distress. Variations across different devices and timing were also noted. This highlights the need for further longitudinal research to understand the impact of SV on mental health and to guide interventions for promoting mental health of university students globally.

Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices

Puberty is a phase in which individuals often test the boundaries of themselves and surrounding others and further define their identity - and thus their uniqueness compared to other individuals. Similarly, as Computational Social Science (CSS) grows up, it must strike a balance between its own practices and those of neighboring disciplines to achieve scientific rigor and refine its identity. However, there are certain areas within CSS that are reluctant to adopt rigorous scientific practices from other fields, which can be observed through an overreliance on passively collected data (e.g., through digital traces, wearables) without questioning the validity of such data. This paper argues that CSS should embrace the potential of combining both passive and active measurement practices to capitalize on the strengths of each approach, including objectivity and psychological quality. Additionally, the paper suggests that CSS would benefit from integrating practices and knowledge from other established disciplines, such as measurement validation, theoretical embedding, and open science practices. Based on this argument, the paper provides ten recommendations for CSS to mature as an interdisciplinary field of research.

Links Between Objectively-Measured Hourly Smartphone Use and Adolescent Wake Events Across Two Weeks

PURPOSE

Psychosocial and bioregulatory pressures threaten sleep during adolescence. Although recent work suggests that the ubiquity of smartphone use throughout adolescence may also relate to poorer sleep outcomes, most existing research relies upon self-report and retrospective measures. This study drew upon objective measures of smartphone use and sleep at the hourly level to understand how smartphone use was associated with the duration of wake events during sleeping hours.

METHODS

Across a 14-day daily study, 59 racially and ethnically diverse adolescents ages 15 to 18 had their sleep assessed via Fitbit Inspire 2 devices and uploaded screenshots of their screen time, pickups, and notifications as logged by their iPhone's iOS. Multi-level modeling was performed to assess hourly level associations between adolescent smartphone use and wake-events during their sleep sessions (N = 4,287 hourly cases).

RESULTS

In hours during adolescents' sleep session with more screen time or pickups, adolescents had longer wake event duration. More notifications in a given hour were not associated with wake event duration in the same hour.

CONCLUSIONS

Using objectively measured smartphone and sleep data collected at the hourly level, we found that during sleeping hours, when adolescents are actively engaging with their smartphones, their sleep is disrupted, such that their wake events are longer in that hour.

Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers

Introduction

Intelligent ambulatory tracking can assist in the automatic detection of psychological and emotional states relevant to the mental health changes of professionals with high-stakes job responsibilities, such as healthcare workers. However, well-known differences in the variability of ambulatory data across individuals challenge many existing automated approaches seeking to learn a generalizable means of well-being estimation. This paper proposes a novel metric learning technique that improves the accuracy and generalizability of automated well-being estimation by reducing inter-individual variability while preserving the variability pertaining to the behavioral construct.

Methods

The metric learning technique implemented in this paper entails learning a transformed multimodal feature space from pairwise similarity information between (dis)similar samples per participant via a Siamese neural network. Improved accuracy via personalization is further achieved by considering the trait characteristics of each individual as additional input to the metric learning models, as well as individual trait base cluster criteria to group participants followed by training a metric learning model for each group.

Results

The outcomes of the proposed models demonstrate significant improvement over the other inter-individual variability reduction and deep neural baseline methods for stress, anxiety, positive affect, and negative affect.

Discussion

This study lays the foundation for accurate estimation of psychological and emotional states in realistic and ambulatory environments leading to early diagnosis of mental health changes and enabling just-in-time adaptive interventions.