Daytime and nighttime smartphone use: A study of associations between multidimensional smartphone behaviours and sleep among 24,856 Danish adults

Tracked and self-reported nighttime smartphone use, general health, and healthcare utilization: results from the SmartSleep Study

STUDY OBJECTIVES

Nighttime smartphone use is an increasing public health concern. We investigated whether nighttime smartphone use is associated with general health and primary healthcare utilization.

METHODS

Four thousand five hundred and twenty individuals (age 35.6 ± 9.7 years, 35% male) provided self-reported information on smartphone use frequency, symptoms of depression, and general health (one-item perceived health and cross-symptom composite score). A subset of the study sample (n = 3221) tracked their nighttime smartphone use. Primary healthcare utilization, i.e. the number of weeks in which at least one service from the patient's general practitioner (GP) was billed in 2020, was extracted from Danish population registries. Statistical analysis comprised logistic and multiple linear regression, controlling for sociodemographics.

RESULTS

Three hundred and nineteen individuals (7%) reported using their smartphone almost every night or more. More frequent self-reported nighttime smartphone use was associated with poor general health across all measures. Using the smartphone almost every night or more was associated with 2.8 [95% CI: 1.9, 4.1] fold higher odds of reporting poor health and with an average of 1.4 [95% CI: 0.7, 2.1] additional GP utilizations per year compared to no use. Associations were also found for the cross-symptom composite score across all symptoms. Further adjustment for symptoms of depression attenuated some associations. Smartphone use towards the end of the sleep period (sleep-offset use) was associated with poorer self-reported general health, but not with healthcare utilization.

CONCLUSIONS

Nighttime smartphone use frequency is associated with poor general health and healthcare utilization. Further studies should investigate the underlying causal structure and nighttime smartphone use as a transdiagnostic intervention target.

Can social connections become stressful? Exploring the link between social media use and perceived stress in cross-sectional and longitudinal analyses of 25,053 adults

BACKGROUND

Social media has become a dominant part of daily lives for many, but excessive use may lead to an experience of stress. Only relatively few studies have investigated social media's influence on mental health.

AIMS

We aimed to investigate whether social media use is associated with perceived stress and changes in perceived stress over 18 months.

METHODS

The study population consisted of 25,053 adults (mean age 42.8; 62% women) from the SmartSleep Study. Self-reported frequency of social media use, of 10 specific social media platforms, and of perceived stress (the Perceived Stress Scale 4 item) was obtained at baseline and 18-months follow-up (N = 1745). The associations were evaluated at baseline and follow-up using multiple linear regression models adjusted for potential confounders.

RESULTS

Compared to non-use, high social media use (at least every second hour) was associated with a slightly higher perceived stress level at baseline. No association was found between the frequency of social media use and changes in perceived stress during follow-up. Only small differences in these associations were noted across social media platforms.

CONCLUSIONS

Further studies are needed to comprehensively explore the relationship between excessive social media use and mental health, recognizing different characteristics across social media platforms.

Microaggressions, perceptions of campus climate, mental health, and alcohol use among first-year college students of color

Depressive and anxiety symptoms are increasingly common, and problematic alcohol use remains prevalent in college. To expand on prior research on mostly white samples, we surveyed first-year students of color from our predominately white university (Southeastern US) to identify risk factors for mental health symptoms and potentially co-occurring problematic alcohol use. Results showed significant associations between microaggressions and poor campus climate (hypothetical predictors) with depressive, anxiety, somatic symptoms (hypothetical outcomes) that were indirectly linked through perceived stress, poor sleep, and academic burnout (hypothetical mediators). Poor campus climate, academic burnout, and using alcohol to cope were the most relevant to alcohol use disorder symptoms. Results support efforts to address and reduce racial microaggressions and promote a positive campus climate for all.

Self-reported and tracked nighttime smartphone use and their association with overweight and cardiometabolic risk markers

Nighttime smartphone use is associated with sleep problems, which in turn have a bidirectional association with overweight. We aim to investigate whether nighttime smartphone use and sleep are related to overweight and metabolic dysfunction in adult populations. We used data from three population samples (aged 16-89) from the SmartSleep Study, which included survey data (N = 29,838), high-resolution tracking data (N = 3446), follow-up data (N = 1768), and cardiometabolic risk markers (N = 242). Frequent self-reported nighttime smartphone use was associated with 51% higher odds (95% CI: 1.32; 1.70) of overweight compared with no use. Tracked nighttime smartphone use was also associated with overweight. Similar results were found for obesity as an outcome. No consistent associations were found between nighttime smartphone use and cardiometabolic risk markers in a small subsample of healthy young women. Poor sleep quality (vs. good sleep quality) was associated with overweight (OR = 1.19, 85% CI: 1.10; 1.28). Overall, frequent nighttime smartphone use was consistently associated with overweight and a higher BMI across diverse population samples. The bidirectional interplay between nighttime smartphone use, sleep, and overweight may create a vicious circle of metabolic dysfunction over time. Therefore, nighttime smartphone use may be a potential target point for public health interventions to reduce overweight at the population level.

Effect of short video addiction on the sleep quality of college students: chain intermediary effects of physical activity and procrastination behavior

Introduction

The purpose of this study was to examine the impact of short video addiction on college students' sleep quality and to elucidate the mechanism underlying this relationship. Thus, we examined the correlation between short video addiction and sleep quality and analyzed the roles of physical activity and procrastination.

Methods

The Short Video Addiction Scale, Pittsburgh Sleep Quality Index, Aitken Procrastination Inventory, and Physical Activity Rating Scale were administered to 337 college students. Data were analyzed using SPSS 27.0. Pearson's correlation analysis and mediation analysis using the bootstrapping test were performed for the standard method bias test.

Results

(1) Overall, 25.2% of college students had problems with sleep quality (indicated by a PSQI score ≥ 8). (2) Short video addiction score is positively correlated with college students' sleep quality score; procrastination score was positively associated with both short video addiction score and sleep quality score, and physical activity score was negatively associated with them. (3) Short video addiction significantly positive predicted sleep quality (β = 0.458, P < 0.001), a significant negative predictive effect on physical exercise (β = -0.183, P < 0.001), and a significant positive effect on procrastination behavior (β = 0.246, P < 0.001). After physical exercise and procrastination behavior were entered into the regression equation, short video addiction and procrastination were significantly positive predictors of sleep quality, and physical activity was significantly negative predictor of sleep quality. (4) After accounting for the variables of age, gender, and grade, physical activity and procrastination behaviors independently mediated the association between short-video addiction and sleep quality. Physical activity and procrastination behavior acted as chain mediators in the association between short video addiction and sleep quality, with a chain mediation effect percentage of 1.04%. Short video addiction directly affects college students' sleep quality, indirectly through physical activity and procrastination behavior.

Nighttime smartphone use, sleep quality, and mental health: investigating a complex relationship

STUDY OBJECTIVES

This study investigated the complex relationship between nighttime smartphone use, sleep, and mental health among adult populations in Denmark.

METHODS

Data from three interconnected samples (aged 16-89 years) from the SmartSleep Study included 5798 individuals with survey and register data; 4239 individuals also provided high-resolution smartphone tracking data. Logistic regression models and causal discovery algorithms, which suggest possible causal pathways consistent with the underlying data structure, were used to infer the relationship between self-reported and tracked nighttime smartphone use, self-reported sleep quality, mental health indicators, and register-based psychotropic medication use.

RESULTS

Frequent self-reported nighttime smartphone use was associated with high perceived stress (OR: 2.24, 95% CI = 1.42 to 3.55) and severe depressive symptoms (OR: 2.96, 95% CI = 2.04 to 4.28). We found no clear associations between tracked nighttime smartphone use and mental health outcomes, except for the cluster that used their smartphones repeatedly during the sleep period, which was associated with severe depressive symptoms (OR = 1.69, 95% CI = 1.24 to 2.31). Poor sleep quality (vs. good sleep quality) was associated with high perceived stress (OR = 5.07, 95% CI = 3.72 to 6.90), severe depressive symptoms (OR = 9.67, 95% CI = 7.09 to 13.19), and psychotropic medication use (OR = 2.13, 95% CI = 1.36 to 3.35). The causal discovery models suggest that nighttime smartphone use affects mental health through both problematic smartphone use and poor sleep quality.

CONCLUSION

The complex relationship between nighttime smartphone use, sleep, and poor mental health may create a vicious circle over time, and nighttime smartphone use may constitute a potential leverage point for public health interventions aimed at improving sleep and mental health.

Bidirectional relations between problematic smartphone use and bedtime procrastination among Chinese university students: Self-control as a mediator

OBJECTIVE

Based on theoretical and empirical evidence, this study examined the longitudinal relationship between problematic smartphone use and bedtime procrastination and the potential mediating role of self-control.

METHODS

Between November 2021 and November 2022, a three waves longitudinal design was employed. Wave 1 included 622 Chinese college students (46.95% male, Mage = 18.16, SD = 0.73), with 6-month intervals between waves. The data was collected using self-report assessments.

RESULTS

The results of the cross-lagged panel models show that problematic smartphone use positively predicted later bedtime procrastination, and the bedtime procrastination also positively predicted later problematic smartphone use among college students. More importantly, self-control played a mediating role in their bidirectional predictive relationship.

CONCLUSIONS

These findings add to the understanding of the causal relationship between problematic smartphone use and bedtime procrastination and reveal the core psychological mechanisms underlying their vicious cycle, with important practical implications for the prevention and intervention of problematic smartphone use and sleep problems.

Cohort profile: The SmartSleep Study, Denmark, combining evidence from survey, clinical and tracking data

PURPOSE

The SmartSleep Study is established to comprehensively assess the impact of night-time smartphone use on sleep patterns and health. An innovative combination of large-scale repeated survey information, high-resolution sensor-driven smartphone data, in-depth clinical examination and registry linkage allows for detailed investigations into multisystem physiological dysregulation and long-term health consequences associated with night-time smartphone use and sleep impairment.

PARTICIPANTS

The SmartSleep Study consists of three interconnected data samples, which combined include 30 673 individuals with information on smartphone use, sleep and health. Subsamples of the study population also include high-resolution tracking data (n=5927) collected via a customised app and deep clinical phenotypical data (n=245). A total of 7208 participants are followed in nationwide health registries with full data coverage and long-term follow-up.

FINDINGS TO DATE

We highlight previous findings on the relation between smartphone use and sleep in the SmartSleep Study, and we evaluate the interventional potential of the citizen science approach used in one of the data samples. We also present new results from an analysis in which we use 803 000 data points from the high-resolution tracking data to identify clusters of temporal trajectories of night-time smartphone use that characterise distinct use patterns. Based on these objective tracking data, we characterise four clusters of night-time smartphone use.

FUTURE PLANS

The unprecedented size and coverage of the SmartSleep Study allow for a comprehensive documentation of smartphone activity during the entire sleep span. The study has been expanded by linkage to nationwide registers, which allow for further investigations into the long-term health and social consequences of night-time smartphone use. We also plan new rounds of data collection in the coming years.

Predicting stress and depressive symptoms using high-resolution smartphone data and sleep behavior in Danish adults

Study Objectives

The early detection of mental disorders is crucial. Patterns of smartphone behavior have been suggested to predict mental disorders. The aim of this study was to develop and compare prediction models using a novel combination of smartphone and sleep behavior to predict early indicators of mental health problems, specifically high perceived stress and depressive symptoms.

Methods

The data material included two separate population samples nested within the SmartSleep Study. Prediction models were trained using information from 4522 Danish adults and tested in an independent test set comprising of 1885 adults. The prediction models utilized comprehensive information on subjective smartphone behavior, objective night-time smartphone behavior, and self-reported sleep behavior. Receiver operating characteristics area-under-the-curve (ROC AUC) values obtained in the test set were recorded as the performance metrics for each prediction model.

Results

Neither subjective nor objective smartphone behavior was found to add additional predictive information compared to basic sociodemographic factors when forecasting perceived stress or depressive symptoms. Instead, the best performance for predicting poor mental health was found in the sleep prediction model (AUC = 0.75, 95% CI: 0.72–0.78) for perceived stress and (AUC = 0.83, 95%CI: 0.80–0.85) for depressive symptoms, which included self-reported information on sleep quantity, sleep quality and the use of sleep medication.

Conclusions

Sleep behavior is an important predictor when forecasting mental health symptoms and it outperforms novel approaches using objective and subjective smartphone behavior.

Nighttime smartphone use and changes in mental health and wellbeing among young adults: a longitudinal study based on high-resolution tracking data

Frequent nighttime smartphone use can disturb healthy sleep patterns and may adversely affect mental health and wellbeing. This study aims at investigating whether nighttime smartphone use increases the risk of poor mental health, i.e. loneliness, depressive symptoms, perceived stress, and low life satisfaction among young adults. High-dimensional tracking data from the Copenhagen Network Study was used to objectively measure nighttime smartphone activity. We recorded more than 250,000 smartphone activities during self-reported sleep periods among 815 young adults (university students, mean age: 21.6 years, males: 77%) over 16 weekdays period. Mental health was measured at baseline using validated measures, and again at follow-up four months later. Associations between nighttime smartphone use and mental health were evaluated at baseline and at follow-up using multiple linear regression adjusting for potential confounding. Nighttime smartphone use was associated with a slightly higher level of perceived stress and depressive symptoms at baseline. For example, participants having 1-3 nights with smartphone use (out of 16 observed nights) had on average a 0.25 higher score (95%CI:0.08;0.41) on the Perceived stress scale ranging from 0 to 10. These differences were small and could not be replicated at follow-up. Contrary to the prevailing hypothesis, nighttime smartphone use is not strongly related to poor mental health, potentially because smartphone use is also a social phenomenon with associated benefits for mental health.

The COVID-19 infodemic at your fingertips. Reciprocal relationships between COVID-19 information FOMO, bedtime smartphone news engagement, and daytime tiredness over time

Considering that insufficient sleep has long been regarded as a significant public health challenge, the COVID-19 pandemic and its co-evolving infodemic have further aggravated many people's sleep health. People's engagement with pandemic-related news, particularly given that many people are now permanently online via smartphones, has been identified as a critical factor for sleep health, such that public health authorities have recommended limited news exposure. This two-wave panel survey, conducted with a representative sample in Austria during its first COVID-19 lockdown, examines (a) how fear of missing out on pandemic-related news (i.e., COVID-19 information FOMO) is reciprocally related to smartphone-based bedtime news engagement, as well as (b) how both bedtime news engagement and COVID-19 information FOMO predict daytime tiredness. Partial metric measurement invariant structural equation modeling revealed that COVID-19 information FOMO and bedtime news engagement are reciprocally associated over time, indicating a potentially harmful reinforcing loop. However, results further suggested that COVID-19 information FOMO may be the primary driver of daytime tiredness, not smartphone-based bedtime news engagement. These findings suggest that a perceived loss of (informational) control over the pandemic outbreak more strongly than poor sleep habits accounts for depleted energy resources during lockdown. However, given the initial evidence for a reinforcing loop, this effect pattern may change in the long term.