Adolescents' problematic mobile phone use (PMPU) has become increasingly severe. This study examined the changes in relationships between dimensions of PMPU, protective (positive psychological capital, i.e., optimism, hope, resilience, core self-evaluation) and risk (psychological distress, i.e., anxiety, depression, loneliness, and stress) factors within the network, aiming to identify the most influential factors over time and find the longitudinal predictive relationships between the factors and PMPU. A total of 1,170 Chinese adolescents participated over three waves (T1: January 2023, T2: August 2023, T3: February 2024). Cross-section network analysis showed that "core self-evaluation", "depression", "hope", "loss of control", and "stress_P" were the central nodes. "Stress_N" (sense of losing control and negative affective reactions) in risk factors and "affect control" (ability to regulate emotions) in protective factors were the bridge symptoms in the network across three timepoints. As shown in network comparison, the global strength of the network remained stable from T1 to T2 but increased from T2 to T3. The edge strength between "family support", "anxiety" and the nodes of PMPU weakened across the time. While, correlations between "loneliness", "goal planning", "positive thinking", "affect control" and PMPU nodes strengthened. The relationship between "Stress_N" and PMPU initially increasing before decreasing. Longitudinal cross-lagged network analysis revealed that "negative life consequence" and "craving" in PMPU strongly predicted protective/risk factors, while "hope," "affect control," and "core self-evaluation" were most susceptible to prediction. The findings highlight the significant role of "core self-evaluation" and "stress_N" in the development of adolescents' PMPU and the negative results of PMPU. Additionally, the changes in the network over time suggest that the factors influencing PMPU evolve, with various protective/risk factors gaining or losing significance at different stages. The results of CLPN emphasize the negative outcome of PMPU. Therefore, targeting interventions on the internalized symptoms may help alleviate the severity of PMPU among adolescents.

Despite the remarkable contribution of smartphones in improving our lives, concerns have been raised about their uncontrolled usage, emphasizing its consequences on individual sleep and well-being. Therefore, we investigated the relationship between problematic smartphone use (PSU) and both positive and negative dimensions of subjective well-being (SWB) - subjective happiness and depressive symptoms, as well as the mediating role of sleep quality. A sample of 384 Bangladeshi youths (mean age = 18.99; 49.3 % female) were recruited through a convenience sampling technique and interviewed using a structured questionnaire that assessed PSU, sleep quality, subjective happiness, and depressive symptoms. The mediation analysis results indicated that sleep quality mediated the association between PSU and subjective happiness and depressive symptoms. The results also showed that subjective sleep quality, sleep disturbances, and daytime dysfunction were particularly responsible for the mediation effect. Thus, the findings highlight the necessity of designing sleep quality-enhancing interventions for youth to subside PSU's detrimental effects on subjective well-being.

With the increasing rate of smartphone use among adolescents, smartphone addiction has become a social issue, causing reduced attention. This may make adolescents more susceptible to traumatic dental injuries (TDIs).

To assess the association between smartphone use and TDIs among Korean adolescents.

This study used open-source data from the 2023 Korea Youth Risk Behavior Survey. This study included responses from 52 875 Korean adolescents. Information on demographics, weekly smartphone use, weekly exercise days, and tooth fracture experiences in the past year was collected and analyzed.

The mean smartphone use per week was 2146.91 ± 1276.60 min in all participants, and the frequency of tooth fractures was 9.3% in extremely low users, 10.1% in low users, 11.1% in excessive users, and 13.6% in extremely excessive users. Logistic regression analysis revealed that higher smartphone usage time correlated with a greater risk of tooth fracture by increasing odd ratios for low, excessive, and extremely excessive users compared to excessively low users.

Within the limitations of this study, increased smartphone use was associated with higher rates of tooth fracture in Korean adolescents. This finding suggests the potential role of smartphone use in contributing to TDIs.

Smartphone addiction is increasingly prevalent, particularly among younger individuals, and has been linked to poor sleep quality. This study aims to assess the effects of smartphone addiction, usage time, socio-demographic factors, relationship with parents, parents' smartphone usage duration, and earthquake-related experiences on sleep quality among adolescents in the earthquake-affected Kahta district of Adıyaman, Turkey.

A cross-sectional study was conducted between June 1st and July 31st 2024 with 394 adolescents aged 12 to 18. Data were collected using a questionnaire that included socio-demographic variables, the Pittsburgh Sleep Quality Index (PSQI), and the Smartphone Addiction Scale.

The prevalence of smartphone addiction was 57.9%, while 66.2% of participants had poor sleep quality. Adolescents using smartphones for more than two hours daily had a higher frequency of poor sleep quality (p = 0.024). Parental influence was significant: participants whose mothers used smartphones for more than one hour daily had a 2.03 times higher risk of poor sleep quality (p = 0.022). Adolescents with poor relationships with parents had a significantly higher risk of sleep disturbances (p < 0.001). Additionally, those who experienced intense fear during the earthquake or lost loved ones showed significantly worse sleep quality (p = 0.007).

Smartphone addiction and prolonged usage are strongly associated with poor sleep quality among adolescents. Maternal smartphone use, affects adolescent sleep. Moreover, traumatic experiences related to the earthquake, such as fear and loss, significantly worsen sleep quality. These findings highlight the need for interventions addressing both smartphone addiction and family dynamics to improve sleep health in disaster-affected adolescents.

Not applicable.

Problematic smartphone use (PSU) has emerged as a growing public health concern, with increasing evidence linking it to mental health issues such as anxiety, depression, and sleep disturbances. While smartphones offer numerous benefits, excessive use can contribute to cognitive overload, emotional distress, and behavioral addiction. This review aims to explore the burden of PSU, its psychological and neurobiological mediating factors, and potential prevention and intervention strategies.

A narrative literature review was conducted using relevant studies from PubMed, Scopus, and Google Scholar. Articles were selected based on their relevance to PSU and its associations with mental health conditions, including depression, anxiety, and insomnia. Both theoretical and empirical studies were included to provide a broad perspective on the topic.

Findings indicate that PSU is associated with increased stress, anxiety, depression, and sleep disruptions. The mechanisms underlying these associations include dopaminergic dysregulation, social comparison, Fear of Missing Out, cognitive overload, and poor sleep hygiene. Various interventions, such as cognitive-behavioral therapy, digital detox programs, and smartphone usage tracking applications, have been proposed to mitigate the negative impact of PSU.

PSU is a significant risk factor for mental health disturbances, particularly among adolescents and young adults. Addressing PSU requires a multi-faceted approach involving behavioral interventions, public awareness initiatives, and policy regulations. Further research is needed to assess the long-term effectiveness of existing interventions and to develop tailored strategies for reducing smartphone overuse and its psychological consequences.

Mobile phone addiction is increasingly prevalent among college students and can lead to various physical, psychological, and social problems. This study aimed to investigate the relationship between physical activity and mobile phone addiction among Chinese college students and to examine the potential chain mediating effects of social anxiety and loneliness in this relationship.

A cross-sectional survey was conducted with 268 Chinese college students (62.7% male, mean age = 20.36 years, SD = 1.73) who completed standardized measures including the Physical Activity Rating Scale-3, Social Phobia Inventory, UCLA Loneliness Scale, and Mobile Phone Addiction Tendency Scale. Data were analyzed using Pearson's correlations and PROCESS macro for mediation analyses with bootstrapping.

Physical activity had a significant negative effect on mobile phone addiction (β = -0.11, p < 0.05) with a total effect of β = -0.28 (p < 0.001). Social anxiety [β = -0.12, 95% CI (-0.18, -0.06)] and loneliness [β = -0.03, 95% CI (-0.06, -0.01)] independently mediated this relationship, while a significant chain mediation through both variables was also found [β = -0.02, 95% CI (-0.03, -0.01)], with all indirect effects accounting for 59.68% of the total effect.

These findings extend previous research by identifying the psychological mechanisms through which physical activity protects against mobile phone addiction, providing new theoretical insights into the sequential pathway from physical activity to reduced addiction, and practical implications for developing targeted interventions that combine physical activity promotion with strategies to reduce social anxiety and loneliness among college students.

The use of electronic screens has become ubiquitous in modern society, but the specific relationship between hours of electronic screen exposure and mental health outcomes needs to be further investigated. Hence, this study aims to examine the detailed relationship between electronic screen exposure time and mental health status.

A sample of 2896 university students were recruited and their baseline characteristics, including demographics, lifestyle habits, and mental health status, were collected. The association between electronic screen exposure time and mental health status was investigated using linear regression analysis. The predictive performance of electronic screen exposure time for predicting mental health status, including severe anxiety and depression was, also analyzed using the area under the curve (AUC) analysis.

The time of exposure to electronic screen was significantly associated with anxiety (p < 0.001) and depression (p < 0.001). The AUC value was 0.641 (95% CI: 0.571-0.712) for severe anxiety and 0.665 (95% CI: 0.613-0.716) for severe depression. The optimal cut-off value for predicting anxiety and depression based on electronic screen exposure time was found to be both 6.5 hours. A subgroup analysis confirmed that participants with higher exposure time to electronic screens had significantly higher anxiety (p < 0.001) and depression scores (p < 0.001). The association between the hours of exposure to electronic screen and severe anxiety (p = 0.042) and severe depression (p < 0.001) remained significant after controlling for demographics, exercises, and other lifestyle factors.

The findings suggest that excessive electronic screen exposure time is associated with increased risk of anxiety and depression among university students. These findings highlight the need for interventions targeting electronic screen use to improve mental health outcomes, and less than 6.5 hours per day of exposure to electronic screen are recommended among university students.

Anxiety symptoms are common among university students in China, posing challenges to mental health. Physical activity may reduce anxiety, but the mechanisms are not fully understood. This study examines how smartphone addiction acts as a mediator and sleep quality as a moderator in the relationship between physical activity and anxiety, aiming to offer theoretical insights and practical strategies for mental health interventions.

This cross-sectional study was conducted in September 2023 at Guangxi University. A stratified sampling method was used to approach 719 students from diverse physical education classes to distribute questionnaires, and 527 valid questionnaires were returned. Validated instruments included the International Physical Activity Questionnaire-Short Form (IPAQ-SF), Smartphone Addiction Scale-Short Version (SAS-SV), Pittsburgh Sleep Quality Index (PSQI), and Self-Rating Anxiety Scale (SAS). Data analysis involved standardization, descriptive statistics, Pearson correlation, normality testing, mediation and moderation analyses, and Bootstrap validation.

(1) Physical activity was negatively correlated with smartphone addiction (r = -0.13, p < 0.01). (2) Smartphone addiction was positively correlated with poor sleep quality (r = 0.40, p < 0.01) and anxiety (r = 0.43, p < 0.01). (3) Poor sleep quality and anxiety were significantly correlated (r = 0.57, p < 0.01). (4) A masking effect occurred as the non-significant positive direct effect (β = 0.062) was nearly canceled out by the mediation of smartphone addiction (β = -0.058), inducing total effect near-zero. (5) Sleep quality significantly influenced the link between smartphone addiction and anxiety, especially in those with poorer sleep, where the impact of smartphone addiction on anxiety was stronger (β = 0.061, p = 0.036).

This study revealed a more complex relationship between physical activity and anxiety than initially hypothesized. Our findings further revealed the relationship between physical activity and university students' anxiety, and considered the mediating role of smartphone addiction between the two, as well as the moderating role of sleep quality in the relationship between mobile phone addiction and university students' anxiety.

Smartphone addiction is one of the major social issues among young people these days. The current study aims to identify the determinants of smartphone addiction and examine the association between smartphone addiction and multiple behavioral/psychological problems. The study sample consisted of 1105 university students from Singapore. Students in the high-risk smartphone use group reported higher levels of smartphone addiction, cybervictimization, and depressive and anxiety symptoms. Depressive and anxiety symptoms showed a positive association with smartphone addiction. Additionally, both depressive and anxiety symptoms had significant indirect effects when assessing two separate simple mediation models. When testing the parallel mediation model, the indirect effect of cybervictimization on smartphone addiction occurred through anxiety but not depression. Based on these findings, the current study proposed the implementation of routine screening and the provision of multi-level services in education settings. Nevertheless, the study has limitations related to the study population and the use of self-reported questionnaires.

Nomophobia is a recent medical term; it is a combination of "no-mobile" and "phobia". Nomophobia encompasses feelings of fear, anxiety, and discomfort stemming from the absence of a mobile device or the inability to access one, when necessary, as well as the apprehension of disconnection from the digital realm. It's correlated with many psychological problems.

This study aimed to explore the impact of nomophobia and its psychological correlates, particularly stress, loneliness, and depression, among adolescents.

Explanatory sequential mixed-methods design was used. A sample of 180 students were participated. They completed the Nomophobia Questionnaire, Perceived Stress Scale, Beck Depression Inventory, and UCLA Loneliness Scale, followed by semi-structured interviews with 30 students exhibiting high levels of nomophobia.

The study found that the majority of students (76.7%) exhibited high levels of nomophobia, with a higher prevalence among females (78.72%) compared to males (69.76%). Students reported high levels of stress (mean = 32.96, SD = 1.35) and social loneliness (mean = 67.9, SD = 4.17), while depression levels were low (mean = 7.03, SD = 4.1). Female students experienced higher levels of stress and loneliness than their male counterparts. Stress and social loneliness were identified as significant predictors of nomophobia, with positive associations (coefficients: 1.64 and 1.20, respectively) and strong correlations (R² = 0.93 and 0.98, p = 0.01). Depression showed a negative but non-significant association with nomophobia (-0.43, p = 0.06). The qualitative results reveal four major themes: compulsive phone behaviors, emotional issues, health concerns, and behavioral problems.

Nomophobia significantly affects the emotional, physical, and social health of students, highlighting the necessity for specific strategies aimed at addressing smartphone addiction and fostering more positive digital behaviors within this age group.

This study explores the relationship between smartphone addiction and sleep quality, and to further assess the mediating role of loneliness and depression.

A survey of 200 older adults individuals was conducted using the Smartphone Addiction Scale-Short Version (SAS-SV) for smartphone addiction, Pittsburgh Sleep Quality Index (PSQI) for sleep quality, Geriatric Depression Scale-15 (GDS-15) for depression, and DeJong Gierveld Loneliness Scale (DGLS) for loneliness. Linear regression and mediation analysis were performed for statistical analysis.

A negative causal relationship was observed between smartphone addiction and sleep quality in the older adults (p < 0.05). Depression and loneliness in the older adults may potentially exacerbate the effect of smartphone addiction on sleep quality (p < 0.05).

Individuals with smartphone addiction appeared to have poorer sleep quality, psychological factors play a mediating role in the association between smartphone addiction and sleep quality in the older adults. This emphasizes the importance of care for the mental health of the older adults and recognizes the negative impact of smartphone addiction on their quality of life.

This study aims to explore the association between mobile phone involvement, body mass index (BMI) levels, and the sleep quality of Chinese university students.

Using a cluster sampling method, we selected 17,085 university students from three universities in eastern, central, and western China as the study subjects. Demographic information such as age and sex were collected. The Pittsburgh Sleep Quality Index (PSQI) and the Mobile Phone Involvement Questionnaire (MPIQ) were utilized to measure their sleep quality scores and mobile phone involvement scores, respectively. Pearson correlation analysis, two-way ANOVA, and multiple linear regression were employed to examine the relationship between BMI levels, mobile phone involvement, and sleep quality.

The results show that 15.87% (2,712 participants) are classified as overweight, and 18.45% (3,151 participants) are classified as obese. Additionally, 35.87% (6,125 participants) exhibit mobile phone involvement, while 57.94% (9,899 participants) reported poor sleep quality. Pearson correlation analysis indicates a significant negative correlation (p < 0.01) between sleep quality and both BMI levels and mobile phone involvement. Two-way ANOVA shows the significant effect of BMI levels (p < 0.001) and mobile phone involvement (p < 0.001) on sleep quality, and there is no interaction effect between the two. Additionally, the sleep quality of overweight and obese individuals is significantly poorer than that of those with normal weight (p < 0.05), while the sleep quality of overweight individuals is significantly lower than that of obese individuals (p < 0.05). Multiple linear regression analysis indicates that, after controlling for age and gender, both BMI (β = -2.69) levels and mobile phone involvement (β = -1.34) are significantly negatively associated with sleep quality (p < 0.001), accounting for 19% of the variance in poor sleep quality.

This study found that BMI levels and mobile phone involvement are both independently associated with sleep quality among Chinese university students. However, among individuals with excess BMI, although their sleep quality is worse than individuals with normal weight, overweight individuals may have poorer sleep quality than obese individuals. This study also revealed high rates of overweight and obesity, with over half of participants reporting poor sleep quality, highlighting the need for targeted interventions to address weight management and mobile phone usage to improve sleep health in this population.

The enigma of smartphone addiction (SA) has plagued academics for the last decade, now scholars believed this behaviour might affect physical and mental wellbeing. SA has become a complex problem, yet to date, there is limited research investigating the predictors of SA and its effect on "health-related quality of life (HRQoL)". This study aimed to address this gap. The data was gathered from a convenience sample of 440 young adults completed between July 2021 and February 2022 through online survey in Bangladesh. On Logistic regression, after controlling for socio-demographic variables; friend support, process, social and compulsive usage were determined as significant predictors of SA. Those who were smartphone addicted were more presumably to have a lower quality of life. This study has significant implications for designing prevention pro-grams and policy development in relation to predictors of SA and its effect on HRQoL.

nursing students face serious problems related to smartphone addiction. Anxiety is among the problems associated with this addiction. In Morocco, this relationship still needs to be investigated. The objective of this study was to examine the relationship between smartphone addiction and anxiety and analyze the factors related among Moroccan nursing students.

this is a cross-sectional study. The data was collected using a self-administered questionnaire that included items on socio-demographic characteristics, smartphone use, and lifestyle behaviors. The smartphone addiction scale short version and the Beck Anxiety Inventory were used.

three hundred and thirty-eight (308) students took part in the study. The prevalence of smartphone addiction was 41.2%, 42.4% for females, and 39.5% for males without significant association. For anxiety, 34.1% have severe anxiety and 21.1% have moderate anxiety. In the multivariate analysis, anxiety was found to be a factor associated with smartphone addiction (OR=1.33; 95% CI: 1.04, 1.70; p=0.02). Two other independent factors were also found to be associated with smartphone addiction, which is residence with parents (OR=1.68; 95% CI: 1.01, 2.78; p=0.05) and smartphone consultation in the morning (OR=0.80; 95% CI: 0.61, 0.96; p=0.02). The participants with high levels of anxiety have the highest smartphone addiction scores with a statistically significant association (p=0.000).

this study showed significant levels of smartphone addiction and anxiety among the participants. A relationship between these two variables has been highlighted. It is essential to educate these young students in terms of the rational use of smartphones to avoid any negative influence on their daily lives.

The increasing prevalence of problematic smartphone use (PSU) among university students is raising concerns, particularly as excessive smartphone engagement is linked to negative outcomes such as mental health issues, academic underperformance, and sleep disruption. Despite the severity of PSU, its association with behaviors such as physical activity, mobility, and sociability has received limited research attention. Ecological momentary assessment (EMA), including passive data collection through digital phenotyping indicators, offers an objective approach to explore these behavioral patterns.

This study aimed to examine associations between self-reported psychosocial measures; app-based EMA data, including daily behavioral indicators from GPS location tracking; and PSU in university students during the examination period.

A 6-week observational study involving 243 university students was conducted using app-based EMA on personal smartphones to collect data on daily behaviors and psychosocial factors related to smartphone overuse. PSU was assessed using the Korean Smartphone Addiction Proneness Scale. Data collected from the Big4+ app, including self-reports on mood, sleep, and appetite, as well as passive sensor data (GPS location, acceleration, and steps) were used to evaluate overall health. Logistic regression analysis was conducted to identify factors that significantly influenced smartphone overuse, providing insights into daily behavior and mental health patterns.

In total, 23% (56/243) of the students exhibited PSU. The regression analysis revealed significant positive associations between PSU and several factors, including depression (Patient Health Questionnaire-9; odds ratio [OR] 8.48, 95% CI 1.95-36.87; P=.004), social interaction anxiety (Social Interaction Anxiety Scale; OR 4.40, 95% CI 1.59-12.15; P=.004), sleep disturbances (General Sleep Disturbance Scale; OR 3.44, 95% CI 1.15-10.30; P=.03), and longer sleep duration (OR 3.11, 95% CI 1.14-8.48; P=.03). Conversely, a significant negative association was found between PSU and time spent at home (OR 0.35, 95% CI 0.13-0.94; P=.04).

This study suggests that negative self-perceptions of mood and sleep, along with patterns of increased mobility identified through GPS data, increase the risk of PSU, particularly during periods of academic stress. Combining psychosocial assessments with EMA data offers valuable insights for managing PSU during high-stress periods, such as examinations, and provides new directions for future research.

College students are disproportionately affected by smartphone addiction, which has been linked to various health impairments, including sleep disorders. This study explores the relationship between smartphone addiction and sleep disorders, with a focus on negative emotions as a mediator and gender as a moderator.

Cluster sampling was used to survey 1056 Chinese college students. The Mobile Phone Addiction Index, Negative Affect Scale, and Sleep Disorder Scale were administered. Versions of SPSS 27.0 and PROCESS macro 4.0 were used for data analysis, employing Models 15 and 4 to examine moderation and mediation, respectively.

A strong direct correlation was identified between smartphone addiction and sleep disorder, with negative emotions mediating this relationship, accounting for 33.7% of the effect. Gender significantly moderated the mediation process, with females demonstrating a greater correlation than males between sleep disorders and negative emotions.

Negative emotions partially mediated smartphone addiction's association with sleep disorder, whereas gender significantly moderated this mediation. These findings underscore the complex dynamics among smartphone addiction, emotional well-being, and sleep among college students.

The aim of the study was to explore the subjective perception of their own sleep and daytime habits in (pre-)adolescents with chronic diseases.

Self-administered questionnaires exploring daytime and nighttime habits, health behavior, daytime sleepiness, depression and anxiety were fulfilled by the (pre-)adolescents.

Hundred sixty-one patients with a chronic disease, aged 14.3±2.6 years old, participated to the study. Mean total time in bed was 8h52±1h09 (range 5h00-11h30) on school days (TIBS) and 9h59±1h28 (range 6h00-14h00) on non-school days (TIBN), with 11 (7%) adolescents reporting sleeping ≤7 hours during school days. The mean sleep time difference between TIBS and TIBN was 67±95 minutes (range: 210-330 min), with 33 patients (20%) having a sleep debt>2 h, and 38% reporting sleep initiating problems. Patients with cystic fibrosis had the lowest mean TIBS, the highest percentage (37%) of patients with sleep debt >2 h. Obese patients were the sleepiest (33%) with 8% having sleep debt. Anxiety and severe depression were observed in 22% and 20% of the patients, respectively, and correlated with fatigue at wake up and daytime sleepiness.

In these (pre-)adolescents with a chronic disease, 20% had sleep debt but sleep duration was reasonable with acceptable respect of sleep hygiene rules.

The relationship between mobile phone addiction and negative affect among adolescents is characterized by intricate interconnections. These factors have an impact on the emotional and psychological well-being of young people. While previous studies have provided preliminary insights into this relationship, further in-depth exploration and research is required to fully understand the complex mechanisms behind these relationships and to identify any group differences.

This study collected questionnaire data from 493 adolescents aged 17-22 years old in Jiangxi Province. The data were analyzed using Pearson correlation analysis, restricted cubic spline (RCS), and structural equation modelling (SEM) with the aim of exploring the mechanisms and differences in the effects of mobile phone addiction on negative emotions of different youth groups.

The findings of the study indicated notable discrepancies in mobile phone addiction by gender and place of residence. Secondly, mobile phone addiction among adolescents positively drives negative emotions. The findings revealed that negative emotions triggered by mobile phone addiction were significantly higher among rural adolescents than urban adolescents; male adolescents exhibited higher levels than female adolescents in the same place of residence. Additionally, family factors, physical exercise and psychological resilience can directly or indirectly inhibit mobile phone addiction among adolescents. Finally, adolescents' psychological resilience can significantly alleviate the negative emotions associated with mobile phone addiction. Physical exercise and family factors can reduce the negative emotional impact of mobile phone addiction by increasing psychological resilience.

This study employs the relationship between mobile phone addiction and negative emotions in adolescents, reveals the complex mechanisms and group differences behind these relationships, and provides a fresh perspective for understanding the impact of mobile phone addiction on adolescents' mental health, as well as a scientific basis for the development of effective interventions.

Insomnia is a prevalent sleep disorder affecting millions worldwide, with significant impacts on daily functioning and quality of life. While traditionally assessed through subjective measures such as the Insomnia Severity Index (ISI), the advent of wearable technology has enabled continuous, objective sleep monitoring in natural environments. However, the relationship between subjective insomnia severity and objective sleep parameters remains unclear.

This study aims to (1) explore the relationship between subjective insomnia severity, as measured by ISI scores, and activity-based objective sleep parameters obtained through wearable devices; (2) determine whether subjective perceptions of insomnia align with objective measures of sleep; and (3) identify key psychological and physiological factors contributing to the severity of subjective insomnia complaints.

A total of 250 participants, including both individuals with and without insomnia aged 19-70 years, were recruited from March 2023 to November 2023. Participants were grouped based on ISI scores: no insomnia, mild, moderate, and severe insomnia. Data collection involved subjective assessments through self-reported questionnaires and objective measurements using wearable devices (Fitbit Inspire 3) that monitored sleep parameters, physical activity, and heart rate. The participants also used a smartphone app for ecological momentary assessment, recording daily alcohol consumption, caffeine intake, exercise, and stress. Statistical analyses were used to compare groups on subjective and objective measures.

Results indicated no significant differences in general sleep structure (eg, total sleep time, rapid eye movement sleep time, and light sleep time) among the insomnia groups (mild, moderate, and severe) as classified by ISI scores (all P>.05). Interestingly, the no insomnia group had longer total awake times and lower sleep quality compared with the insomnia groups. Among the insomnia groups, no significant differences were observed regarding sleep structure (all P>.05), suggesting similar sleep patterns regardless of subjective insomnia severity. There were significant differences among the insomnia groups in stress levels, dysfunctional beliefs about sleep, and symptoms of restless leg syndrome (all P≤.001), with higher severity associated with higher scores in these factors. Contrary to expectations, no significant differences were observed in caffeine intake (P=.42) and alcohol consumption (P=.07) between the groups.

The findings demonstrate a discrepancy between subjective perceptions of insomnia severity and activity-based objective sleep parameters, suggesting that factors beyond sleep duration and quality may contribute to subjective sleep complaints. Psychological factors, such as stress, dysfunctional sleep beliefs, and symptoms of restless legs syndrome, appear to play significant roles in the perception of insomnia severity. These results highlight the importance of considering both subjective and objective assessments in the evaluation and treatment of insomnia and suggest potential avenues for personalized treatment strategies that address both psychological and physiological aspects of sleep disturbances.

Clinical Research Information Service KCT0009175; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=26133.

The relationships between body satisfaction, problematic smartphone use, sleep deprivation and psychological well-being have been examined with cross-sectional methods. However, the lack of any longitudinal study in which these variables were examined together made it necessary to conduct this study. In this study, we tested whether problematic smartphone use and sleep deprivation serially mediate the relationship between body satisfaction and psychological well-being. After matching the data obtained from the first (T1) and second waves (T2) of the study, the final sample consisted of 222 Turkish adolescents (M = 14.08 years, SD = 0.70). For this purpose, a study was conducted in two separate time waves and structural equation modeling was used to test longitudinal serial mediation between variables. According to the results, problematic smartphone use and sleep deprivation play a serial mediating role in the longitudinal relationship between body satisfaction and psychological well-being.

Smartphone use and addiction among adolescents is an emerging public health concern. This study aimed to assess smartphone use, addiction, patterns of use, and associated factors among adolescents.

An institutional-based mixed-method study was conducted among 560 adolescents aged 15-19 years. Data were collected on smartphone use, and addiction assessed by a Short Version of the Smartphone Addiction Scale (SAS-SV), patterns of use, and correlations with demographic factors, physical health, and psychological symptoms. Statistical analysis included percentages, logistic regression, and tests of significance. A P value of < 0.05 is considered significant.

Smartphone use prevalence was 89.8% [95% CI (87.09%-92.49%)], and addiction prevalence was 64% [95% CI (62.66%-72.34%)] among users. Factors associated with smartphone use are statistically significant based on adjusted odds ratios (AOR), which were urban residence [2.5 (1.8-3.8)], use of handsfree kit [2.04 (1.56-2.95)], illiterate father's education [2.7 (1.9-7.8)], illiterate [2.38 (1.98-5.7)], primary/secondary educated [2.15 (1.06-4.3)] mother's education, and addiction [3.2 (1.7-4.3)]. Addicted users showed higher use for gaming, videos, and social media (P < 0.05). No significant physical or psychological health differences were found between addicted and non-addicted groups.

Smartphone addiction (SA) was highly prevalent among adolescents. Use patterns, such as gaming and social media, as well as perceptions of harm, were implicated in addiction risk. Tailored interventions are required to address this emerging public health problem among adolescents.

Numerous studies have investigated the relationship between social support and problematic mobile phone use among adolescents, yet a definitive consensus remains elusive. The high prevalence of problematic mobile phone use among children and adolescents requires urgent clarity on this issue. However, previous meta-analyses on this topic have primarily focused on college students, overlooking this association in younger age groups. The present study thus concentrated on children and adolescents, conducting a three-level meta-analysis to combine existing research findings and analyze various moderators to identify sources of research heterogeneity. A systematic literature search retrieved a total of 33 studies with 135 effect sizes for this meta-analysis, and 25,537 students (53.83% female, age range 7-19, grades range 3rd-12th) were included. The results showed a negative correlation (r = -0.139) between social support and problematic mobile phone use in children and adolescents. Age, social support measurement, sources of social support, and symptoms of problematic mobile phone use were found to have a significant moderating influence. Specifically, social support showed a stronger negative correlation with problematic mobile phone use in older adolescents compared to their younger counterparts. The correlation was more pronounced when using the Multidimensional Scale of Perceived Social Support than other scales. Family support exhibited a stronger negative correlation with problematic mobile phone use compared to other sources of support. Among the symptoms of problematic mobile phone use, the inability to control craving has the strongest negative correlation with social support. This meta-analysis suggested that providing more social support, particularly in the form of family support, during the development of children and adolescents may help alleviate problematic mobile phone use.

Clinical guidelines recommend nonpharmacological treatment (nPHT) as the primary intervention for subthreshold depression management. Counseling (CS) and electroacupuncture (EA) are two promising nonpharmacological approaches for improving both depression and sleep disturbance. However, the intrinsic neuroimaging mechanisms underlying the antidepressant effects of these nPHTs are not yet fully understood.

We analyzed longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) data from a randomized, single-blind clinical trial involving 96 first-episode, drug-naïve college students with subclinical depression and sleep disorders (sDSD; mean age 20.43 ± 2.72 years; 66.7% female) and 90 healthy controls (HCs; mean age 21.02 ± 2.68 years; 61.1% female). Participants with sDSD were randomly assigned to receive either scalp EA (n = 47) or CS (n = 49) for six weeks. The regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) before and after nPHT were calculated. Correlation and mediation analyses were performed to investigate the complex relationships between fMRI indicators and clinical symptoms.

The ALFF in the left paracentral lobule in sDSD patients presented an interaction effect between group and time following six weeks of nPHT. In the CS group, the ALFF in the left paracentral lobule decreased (p < 0.001), and in the EA group, it increased (p < 0.05). Compared with HCs, the baseline sDSD has many abnormal brain regions in terms of ALFF and ReHo. The whole-brain average ReHo was negatively correlated with depression scores (r = -0.26, p < 0.001) and sleep quality scores (r = -0.25, p < 0.001) and mediated the association between depression and sleep disorders [β = 0.2857, p < 0.001, 95% CI (0.23, 0.35)].

Nonpharmacological therapies provide different therapeutic outcomes in terms of the same rs-fMRI indicator. ALFF in the left paracentral lobule could be used as an imaging biomarker in nPHT selection. Rs-fMRI indicators are promising for understanding the neural basis of the complex relationship between subclinical depression and insomnia comorbidities in young adults.

Anxiety and problematic smartphone use (PSPU) are prevalent issues among college students, and traditional research has tended to focus on cross-sectional data and grounded only in overall levels, thereby ignoring the complex interactions between the two over time. The development of network analysis methods has provided a new perspective for in-depth exploration of the relationship. This study aimed to explore the complex longitudinal interactions and specific pathways between problematic smartphone use and anxiety among Chinese college students from an internal specific symptom perspective. This study constructed a cross-lagged network model using longitudinal data on problematic smartphone use and anxiety symptoms in two waves of college students collected from 2022 to 2023 (N=741, Mage = 18.49, SD=0.81, 45.6 % male). The study found that anxiety symptoms and problematic smartphone use interacted with each other and had a vicious cycle of symptoms over time, with the effects of anxiety symptoms being more pronounced. "Feeling afraid" and "Uncontrollable worrying" had the most significant effects on the other symptoms, with "Impatient without the phone" and "Can't stand not having a phone" were more likely to be influenced by other symptoms. "Feeling afraid" may be a bridge symptom in the network to connect the anxiety and problematic smartphone use communities. The findings suggest that accurately intervening in the intrinsic link between problematic smartphone use and anxiety symptoms can combat the exacerbation of both problems simultaneously, resulting in more effective and comprehensive treatment.

To investigate the sleep quality of incoming college freshmen who have completed college entrance exams but have not yet entered college, to identify associated factors, and the mediating role of siesta characteristics on the relationship between siesta duration and sleep quality. This study included 6,519 incoming freshmen from two colleges in Xuzhou city, China. Weighted multiple linear regression (MLR) and binary logistic regression (BLR) were employed to investigate the factors associated with sleep quality. Mediation analysis was conducted to examine the influence of siesta characteristics on sleep quality and siesta duration. Stratified analysis was conducted based on gender and college level to examine the correlation between sleep quality and siesta durations (1-29 min, 30-50 min, ≥ 60 min). Both regression models found that longer siesta duration was a risk factor for poor sleep quality. Mediation analysis demonstrated that sleep position, the interval between lunch and the siesta, and the subjective rating of sleepiness 30 min after the siesta mediated this relationship. Male students with 30-59 min siestas, junior college freshmen with 1-29 min siestas, and undergraduate freshmen with ≥ 60 min siestas exhibited an increased risk of poor sleep quality compared to their counterparts without siestas. Furthermore, lifestyle behaviors and physical activity were also found to impact sleep quality. Longer siesta duration has been identified as a risk factor for sleep quality, especially in incoming undergraduate and male students. Some siesta characteristics mediate the relationship between them.

The psychological and physiological health of undergraduates was correlated with the sleep quality, which can be improved through increasing physical activity. However, the correlations between physical activity and sleep quality are subject to various factors. In this study, we investigated the effects of self-control and mobile phone addiction on the correlations between physical activity on undergraduates' sleep quality at the psychological and behavioral levels. Data was collected through a survey with a convenient sample of 2,274 students in China. The study utilized scales of physical activity, sleep quality, self-control, and mobile phone addiction to quantitatively evaluate the impact of physical activity on the sleep quality of undergraduates. The correlations were analyzed using SPSS 26.0, including descriptive statistics, confidence tests, common method bias tests, correlation analysis, and hypothesis tests. Pearson correlation analysis shows that physical activity was significantly correlated with sleep quality (r = -0.541, p < 0.001), and that physical activity and sleep quality were significantly correlated with self-control and mobile phone addiction. Regression analysis shows that physical activity had a significant positive regression effect on self-control (standardized regression coefficient β = 0.234, p < 0.001), a significant negative regression effect on mobile phone addiction (β = -0.286, p < 0.001), and a significant negative regression effect on sleep quality (β = -0.351, p < 0.001). Further, a chain mediation model of physical activity → self-control → mobile phone addiction → sleep quality was proposed. The findings provide basic data for college students to promote physical activity and improve sleep quality.

Despite the many benefits of smartphones, researchers have raised concerns over problematic smartphone use (PSU) and its negative effects on physical and psychological well-being. Studies examining PSU and its impact among adults remain limited. Hence, we aim to examine the prevalence of PSU among adults in Singapore, and explore its associations with smartphone activities, sleep quality, and psychological well-being, as well as age and gender-related differences in these associations.

A household survey (n = 1200) was conducted among multi-ethnic Singapore adults aged 21 to 60. The survey employed a proportionate stratified random sampling approach. The Smartphone Addiction Scale-Short Version was used to determine risk of PSU. Adjusted multivariable logistic regressions, age-stratified (21-30, and above 30) analyses and sensitivity analyses were performed.

The survey response rate was 45.7%. PSU prevalence rate was estimated to be 34.0%. Adults at risk were younger (OR = 3.72, p < 0.001), had poor sleep quality (OR = 2.94), reported depressive (OR = 2.84, p = 0.001) or anxiety symptoms (OR = 2.44, p < 0.001), tend to use smartphones for social media (OR = 2.81, p = 0.002) or entertainment (OR = 2.72, p < 0.001). Protective factors include higher levels of social support (OR = 0.76, p = 0.007), using smartphones for calling family (OR = 0.39, p = 0.003) and friends (OR = 0.53, p = 0.030), and spending four hours or less of smartphone usage duration (OR = 0.40, p < 0.001). Sensitivity analyses confirmed these findings. Associations between PSU and poor sleep quality (OR = 3.72, p < 0.001), depressive (OR = 3.83, p < 0.001), and anxiety symptoms (OR = 2.59, p = 0.004) and social media usage (OR = 3.46, p < 0.001) were more pronounced in adults over 30. PSU was more prevalent among females in those aged 21-30 (OR = 2.60, p = 0.022). Social support appears to be a protective factor for adults over 30 (OR = 0.64, p < 0.001) but was not observed in those aged 21-30. Among males, younger age (21-30 years), poor sleep quality, depressive symptoms, and anxiety symptoms, and using social media and entertainment apps were significantly associated with PSU. Females showed similar associations. Social support appears to be a protective factor for females (OR = 0.70, p = 0.018), but this association was not observed for males. Shorter smartphone usage times were inversely associated with PSU in both genders.

A substantial proportion of adults exhibited PSU. Findings highlight the differential associations between PSU and psychological well-being, social support, interactions with technology, and sleep quality. These associations are influenced by age which has implications for preventive efforts.

Adolescent sleep disorders have emerged as a significant global public health issue. This study aims to investigate the relationship between physical activity and sleep disorders in adolescents, and to further analyze the chain mediating effects of anxiety and mobile phone dependence, with the goal of providing empirical evidence for related clinical research.

This cross-sectional study collected data through an offline questionnaire survey conducted in 2024. The survey assessed physical activity, sleep disorders, anxiety, and mobile phone dependence. Descriptive statistics and correlation analyses were utilized to construct a chain mediation model.

A total of 495 valid samples were obtained, with 227 girls (45.9%) and 268 boys (54.1%). The average age of the participants was 13.37 years (SD = 1.05). The results indicated significant negative correlations between physical activity and adolescent anxiety, mobile phone dependence, and sleep disorders. Anxiety and mobile phone dependence fully mediated the relationship between physical activity and sleep disorders.

This study provides further insight into the underlying mechanisms linking physical activity and sleep disorders in adolescents. Physical activity not only has a direct negative effect on sleep disorders but also indirectly predicts sleep disorders through its impact on anxiety and mobile phone dependence. It is recommended that families and schools encourage increased physical activity among adolescents, as it can reduce anxiety and mobile phone dependence, thereby alleviating sleep disorders.

Previous studies indicate an increasing prevalence of depression among university students worldwide. Besides, university students are more likely to excessively use smartphones, making them more susceptible to smartphone addiction. Pandemic conditions can also have negative effects on mental health. Thus, this study aims to investigate the frequency of depression among university students during COVID-19 pandemic.

The study population for this mixed-method research, which includes both qualitative and quantitative components, consists of students studying health sciences at a state university in Istanbul, Türkiye. No sample was selected for the quantitative data collection; instead, it was aimed to reach the entire population. Sociodemographic characteristics, the 10-item Smartphone Addiction Scale-Short Version (SAS-SV), and the 21-item Beck Depression Inventory (BDI) were used. In the qualitative part of our study, semi-structured online interviews were conducted with 12 students. Statistical significance level was set at p < 0.05.

The study, involving 819 students, found that 25.2% had moderate depression and 5.7% had severe depression. A statistically significant relationship was found between self-perceived smartphone addiction and the presence of moderate-severe depression (p < 0.001). Participants who spent more than 4 h a day on their smartphones, those who checked their smartphones more than 30 times a day, those who perceived themselves as smartphone addicts, and those who experienced smartphone-related sleep problems obtained statistically significantly higher scores from BDI compared to others (p < 0.05). According to our qualitative results, some participants thought that smartphone use could cause a depressive mood by isolating people, while others believed it could do so through the negative effects of social media. All participants reported that the quarantine period increased their smartphone usage.

Our results suggest a potential interaction between smartphone addiction and depression. This indicates the potential benefit of assessing and addressing both conditions simultaneously.

The theoretical model of smartphone addiction highlights the role of emotional factors in fostering addictive behaviors. However, most research has focused on long-term emotional states and pathologies, often overlooking the immediate effects of daily emotional fluctuations on smartphone usage and their mechanisms.

Our study employed an online survey and a moderated parallel mediation model to explore how daily emotional experiences influence smartphone addiction among college students. We analyzed the mediating roles of anxiety, stress, and depression, and the moderating effect of gender.

Our findings indicate that daily negative emotional experiences were positively correlated with smartphone addiction, with stress serving as a significant mediator in the relationship between both positive and negative emotional experiences and addiction. Interestingly, positive emotional experiences directly increased smartphone addiction risk among female students, but they also significantly reduced stress and depression, especially pronounced in women. Further analysis indicated that positive emotions primarily mitigate addiction through reducing stress, a pathway especially significant in females.

The study not only confirms the substantial impact of emotional experiences on addiction but also deepens our understanding of their mechanisms, underlining the importance of considering the nature of emotional experiences and gender-specific effects in devising prevention and intervention strategies.

Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual's own environment may improve self-monitoring and clinical management for people with MS (pwMS).

We present a machine learning approach that enables longitudinal monitoring of clinically relevant patient-reported symptoms for pwMS by harnessing passively collected data from sensors in smartphones and fitness trackers.

We divide the collected data into discrete periods for each patient. For each prediction period, we first extract patient-level behavioral features from the current period (action features) and the previous period (context features). Then, we apply a machine learning (ML) approach based on Support Vector Machine with Radial Bias Function Kernel and AdaBoost to predict the presence of depressive symptoms (every two weeks) and high global MS symptom burden, severe fatigue, and poor sleep quality (every four weeks).

Between November 16, 2019, and January 24, 2021, 104 pwMS (84.6% women, 93.3% non-Hispanic White, 44.0±11.8 years mean±SD age) from a clinic-based MS cohort completed 12-weeks of data collection, including a subset of 44 pwMS (88.6% women, 95.5% non-Hispanic White, 45.7±11.2 years) who completed 24-weeks of data collection. In total, we collected approximately 12,500 days of passive sensor and behavioral health data from the participants. Among the best-performing models with the least sensor data requirement, ML algorithm predicts depressive symptoms with an accuracy of 80.6% (35.5% improvement over baseline; F1-score: 0.76), high global MS symptom burden with an accuracy of 77.3% (51.3% improvement over baseline; F1-score: 0.77), severe fatigue with an accuracy of 73.8% (45.0% improvement over baseline; F1-score: 0.74), and poor sleep quality with an accuracy of 72.0% (28.1% improvement over baseline; F1-score: 0.70). Further, sensor data were largely sufficient for predicting symptom severity, while the prediction of depressive symptoms benefited from minimal active patient input in the form of response to two brief questions on the day before the prediction point.

Our digital phenotyping approach using passive sensors on smartphones and fitness trackers may help patients with real-world, continuous, self-monitoring of common symptoms in their own environment and assist clinicians with better triage of patient needs for timely interventions in MS (and potentially other chronic neurological disorders).

Nomophobia becoming widespread can have negative effects on the psychology of nursing students, who are the nurses of the future. Yet, the psychological well-being of the nursing group dealing with human health is essential for psychologically supporting the individuals they care for and reducing the potential for errors in nursing care. Therefore, it is necessary to determine nomophobia in nursing students and examine its relationship with psychological states.

This research was conducted to determine the effect of nomophobia levels on depression, anxiety and stress levels of nursing students.

The descriptive and correlational research was completed with 544 nursing students. Data were collected using the Data Collection Form, Nomophobia Scale, Depression, Anxiety and Stress Scale (DASS-21) and analysed using descriptive statistics, Kolmogorov-Smirnov, Shapiro-Wilk, Spearman, Mann-Whitney U and Kruskal-Wallis tests. p < 0.05 was considered statistically significant.

The students' mean age was 21.10 ± 1.32. Their mean nomophobia score was 102.51 ± 27.06. 65.6% had depression, 66.2% had anxiety, and 35.8% had stress at different levels. A relationship was found between the students' nomophobia mean scores and their depression, anxiety and stress subscale scores (p < 0.001).

The students' nomophobia levels were high. As students' nomophobia levels increased, their depression, anxiety and stress levels increased. Our recommendation is to inform nurses about nomophobia, support conscious phone use, and direct students affected by nomophobia to relevant units. Our recommendation is to inform nursing students about nomophobia, direct students affected by nomophobia to relevant units, and support their participation in activities that will raise awareness among students.

Based on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study aimed to explore the relationship between autistic traits and problematic smartphone use (PSU) among Chinese adolescents and to examine the serial mediation effect of anxiety and executive dysfunction in the association between autistic traits and PSU.

The Autism-Spectrum Quotient, Smartphone Addiction Scale, the trait version of the State-Trait Anxiety Inventory, and Dysexecutive Questionnaire were administered to a sample comprising 412 senior high school students (average age = 17.05 years, SD = 0.65). Structural equation models were utilized to explore the simple and serial mediating role of anxiety and executive dysfunction played in the association between autistic traits and PSU.

This study found that social rather than non-social autistic traits were positively associated with anxiety, executive dysfunction, and PSU. Furthermore, after controlling for gender, anxiety and executive dysfunction acted as sequential mediators in the connection between social autistic trait and PSU. However, non-social autistic trait did not predict anxiety, executive dysfunction, or PSU.

This study supports the I-PACE model and deepens understanding of PSU formation. Furthermore, the findings underscore the importance of addressing social challenges faced by adolescents with high autistic traits, providing a viable potential intervention pathway to promote healthy smartphone use in this population.

Addressing the complex pattern of digital behaviors and interactions among youth, this research introduces a novel comprehensive scale, the Problematic Smartphone and Social Network Use Scale (PSSNUS), meticulously developed and validated across five studies.

Using a mixed-methods approach across five stages, including focus groups (Study 1; n = 31), cognitive interviews (Study 2; n = 16), exploratory factor analysis (Study 3; n = 316), and expert panel (Study 4; n = 4), this procedure reduced 58 initial items to an 18-item scale. Subsequently, a confirmatory factor analysis and further analyses (Study 5; n = 355) examined the factorial structure's replicability, reliability, and validity of the scale.

The PSSNUS manifested as a reliable (ω ranging 0.70-0.89) instrument, comprising a structure with five key factors (x2 = 173.994, df = 125, p = 0.002, CFI = 0.994, CFI = 0.993, SRMR = 0.039, and RMSEA = 0.027) that capture both individual and social aspects of this construct: Psychological dependence (PD), Online interaction preference (OIP), Digital recognition desire (DRD), Social networks control seeking (SNCS), and Productivity loss (PL). Minor gender differences in both the DRD factor and in the overall score were found, with females scoring higher. The PSSNUS showed convergent and divergent validity through significant but modest correlations with daily smartphone usage hours, procrastination, emotional intelligence and mental health symptomatology (anxiety, depression and stress). This measure further exhibited incremental validity, controlling for other possible predictors, regarding anxiety, depression, and stress (6.5, 11.5 and 7.5% of additional variance explained), highlighting the DRD dimension's unique predictive power.

Emerging as a solid instrument, the PSSNUS broadens the concept of problematic smartphone use among young Spaniards to include social functioning aspects, serving as an interesting tool for those aiming to explore further the adverse influence of digital media on youth.

The impact of mobile phone addiction (MPA) on college students' sleep quality (SQ) has received increasing attention in recent years. On this basis, the mediating roles of rumination and FOMO (fear of missing out) on MPA and SQ were examined in this study. In a cross-sectional study, preliminary data were gathered via questionnaires from 1401 college students (19.35 ± 0.81) from 5 universities in Jiangxi Province, China. Mobile Phone Addiction Tendency Scale, Rumination Response Scale, Fear of Missing Out Scale and Sleep Quality Scale were employed in the research. The mediation model was tested using SPSS 26.0, with FOMO and rumination acting as the mediating variables. Between MPA and SQ, rumination and FOMO each had their own unique mediation effect. Furthermore, rumination and FOMO function as a chain of mediators between college students' MPA and SQ. This study provided confirmation and clarification that in college students, the association between MPA and SQ was mediated by rumination and FOMO. This study improves our understanding of MPA, SQ and how MPA may affect SQ in college students. It also shows that the impact of MPA and SQ of college students should be given more consideration by educators, parents, and college students themselves.

Premenstrual dysphoric disorder (PMDD), a more severe manifestation of premenstrual syndrome (PMS), is characterized by emotional, behavioral, and physical symptoms that begin in the mid-to-late luteal phase of the menstrual cycle, when estradiol and progesterone levels precipitously decline, and remit after the onset of menses. Remotely monitoring physiologic variables associated with PMDD depression symptoms, such as heart rate variability (HRV), sleep, and physical activity, holds promise for developing an affective state prediction model. Switching into and out of depressive states is associated with an increased risk of suicide, and therefore, monitoring periods of affective switching may help mitigate risk. Management of other chronic health conditions, including cardiovascular disease and diabetes, has benefited from remote digital monitoring paradigms that enable patients and physicians to monitor symptoms in real-time and make behavioral and medication adjustments. PMDD is a chronic condition that may benefit from real-time, remote monitoring. However, clinical practice has not advanced to monitoring affective states in real-time. Identifying remote monitoring paradigms that can detect within-person affective state change may help facilitate later research on timely and efficacious interventions for individuals with PMDD. This narrative review synthesizes the current literature on behavioral and physiological correlates of PMDD suitable for remote monitoring during the menstrual cycle. The reliable measurement of heart rate variability (HRV), sleep, and physical activity, with existing wearable technology, suggests the potential of a remote monitoring paradigm in PMDD and other depressive disorders.

The prevalence of smartphone usage is steadily rising, leading to the potential development of addiction due to problematic use. This study examined the relationship between smartphone addiction, self-perceived attention deficit and hyperactivity symptoms, and sleep quality among 443 university students at Bingöl University.

Participants completed several questionnaires, including the Smartphone Addiction Scale-Short Version, the Pittsburgh Sleep Quality Index, and the Adult ADHD Self-Report Scale.

This study examined 443 participants, with a mean age of 20.97 ± 3.29, of whom 72.7% (n = 322) were female. Results showed that the majority of participants primarily used smartphones (94.8%, n = 420) for social media browsing (49.9%, n = 221). Factors such as smoking, preference for smartphone screens, and prolonged screen exposure significantly affected sleep quality. Smartphone addiction rates were notable, with 50.4% (n = 61) of males and 47.2% (n = 152) of females being affected; this addiction was associated with poorer sleep quality. Correlations were found between age, sleep duration, and scores on smartphone addiction, sleep quality, and attention deficit scales. Linear regression analysis revealed that age, attention deficit scores, and sleep quality scores significantly influenced levels of smartphone addiction.

These findings contribute valuable insights into the impact of smartphone addiction on attention and sleep in university students.

Previous research on adolescent smartphone addiction has tended to focus on general populations that are assumed to be homogeneous, overlooking latent profiles. Furthermore, previous research has not focused on potentially important differences in the latent profiles of adolescent smartphone addiction in networks. The present study aimed to reveal the latent profiles of smartphone addiction, depression, stress, eustress, loneliness, and sleep deprivation in adolescents, and general and latent profiles were examined in the network.

The study group consisted of 436 (222 boys and 214 girls) adolescents, aged between 13 and 18 years. The findings of the present study were provided using Pearson correlation, ANOVA, latent profile analysis and network analysis. The data were analyzed using SPSS, JASP, and Mplus.

The results of the study showed solutions with three latent profiles. The non-addicted group constituted 20.87%, the addicted group covered 29.82% and the risky group included 49.31% of the study group. Although the general profile and the addicted latent group had similar characteristics, the differences in the risky and non-addicted groups contributed to the current literature by providing a further and remarkable perspective on smartphone addiction, depression, distress, eustress, loneliness, and sleep deprivation in adolescents.

The theoretical and practical implications of the present results will provide contributions to researchers and practitioners in understanding smartphone addiction.

With the increased availability and sophistication of digital devices in the last decade, young people have become mainstream mobile phone users. Heavy mobile phone dependence causes affective problems (depression, anxiety) and loss of attention on current activities, leading to more cluttered thoughts. Problematic mobile phone use has been found to increase the occurrence of mind wandering, but the neural mechanism underlying this relationship remains unclear. The current study aims to investigate the neural mechanism between mobile phone use and mind wandering. University students from datasets (ongoing research project named Gene-Brain-Behavior project, GBB) completed psychological assessments of mobile phone addiction and mind wandering and underwent resting-state functional connectivity (FC) scanning. FC matrix was constructed to further conduct correlation and mediation analyses. Students with high mobile phone addiction scores were more likely to have high mind wandering scores. FC among the default mode, motor, frontoparietal, basal ganglia, limbic, medial frontal, visual association, and cerebellar networks formed the neural basis of mind wandering. FC between the frontoparietal and motor networks, between the default mode network and cerebellar network, and within the cerebellar network mediated the relationship between mobile phone addiction and mind wandering. The findings confirm that mobile phone addiction is a risk factor for increased mind wandering and reveal that FC in several brain networks underlies this relationship. They contribute to research on behavioral addiction, education, and mental health among young adults.

Network modeling has been suggested as an effective method to explore intricate relationships among antecedents, mediators, and symptoms. In this study, we aimed to investigate whether the severity of depressive symptoms in college students affects the multivariate relationships among anhedonia, smartphone addiction, and mediating factors.

A survey was conducted among 1347 Chinese college students (587 female) to assess depressive symptoms, anhedonia, addictive behaviors, anxiety, and insomnia. The participants were categorized the non-depressive symptom (NDS) and depressive symptom (DS) groups based on a cut-off score of 5 on the 9-item Patient Health Questionnaire-9. Network analysis was performed to investigate the symptom-to-symptom influences of symptoms in these two groups.

The network of the DS group was more densely connected than that of the NDS group. Social anticipatory anhedonia was a central factor for DS, while withdraw/escape (one factor of smartphone addiction) was a central factor for NDS. The DS group exhibited greater strength between the PHQ9 score and social anticipatory anhedonia, as well as between the PHQ9 score and alcohol misuse score, compared to the NDS group. On the other hand, the NDS group had higher strength between anxiety and feeling lost, as well as between anxiety and withdraw/escape, compared to the DS group.

The findings suggest that there is a close relationship between social anhedonia, smartphone addiction, and alcohol consumption in the DS group. Addressing on ameliorating social anhedonia and smartphone addiction may be effective in preventing and managing depression in college students.

This study evaluates the potential mechanisms through which stress affects sleep quality and examines the mediating roles of rumination, social anxiety, emotion-focused coping strategies, and smartphone dependence in the relationship between stress and sleep quality.

From October 2023 to April 2024, we conducted three surveys with 426 university students and utilized structural equation modeling to explore the mechanisms by which stress impacts sleep quality.

Stress not only significantly predicts sleep quality but also significantly affects sleep quality through rumination, emotion-focused coping strategies, and smartphone dependence as independent mediators. Additionally, stress influences sleep quality through both dual-mediation and triple-mediation paths.

Stress has a significant direct and indirect impact on sleep quality. This study reveals the complex mechanisms through which stress affects sleep quality. Improving individuals' sleep quality requires not only considering the influence of real-life stressors but also examining the intersecting effects between stress and factors such as ruminative thinking, social anxiety, smartphone dependency, and emotion-focused coping strategies. The intense social competition in Chinese society exacerbates the decline in sleep quality, highlighting the need for the government to implement more policies aimed at maintaining the physical and mental health of the population to alleviate the increasingly severe sleep issues and mental health crisis.

Smartphone-based monitoring in natural settings provides opportunities to monitor mental health behaviors, including suicidal thoughts and behaviors. To date, most suicidal thoughts and behaviors research using smartphones has primarily relied on collecting so-called "active" data, requiring participants to engage by completing surveys. Data collected passively from smartphone sensors and logs may offer an objectively measured representation of an individual's behavior, including smartphone screen time.

This study aims to present methods for identifying screen-on bouts and deriving screen time characteristics from passively collected smartphone state logs and to estimate daily smartphone screen time in people with suicidal thinking, providing a more reliable alternative to traditional self-report.

Participants (N=126; median age 22, IQR 16-33 years) installed the Beiwe app (Harvard University) on their smartphones, which passively collected phone state logs for up to 6 months after discharge from an inpatient psychiatric unit (adolescents) or emergency department visit (adults). We derived daily screen time measures from these logs, including screen-on time, screen-on bout duration, screen-off bout duration, and screen-on bout count. We estimated the mean of these measures across age subgroups (adults and adolescents), phone operating systems (Android and iOS), and monitoring stages after the discharge (first 4 weeks vs subsequent weeks). We evaluated the sensitivity of daily screen time measures to changes in the parameters of the screen-on bout identification method. Additionally, we estimated the impact of a daylight time change on minute-level screen time using function-on-scalar generalized linear mixed-effects regression.

The median monitoring period was 169 (IQR 42-169) days. For adolescents and adults, mean daily screen-on time was 254.6 (95% CI 231.4-277.7) and 271.0 (95% CI 252.2-289.8) minutes, mean daily screen-on bout duration was 4.233 (95% CI 3.565-4.902) and 4.998 (95% CI 4.455-5.541) minutes, mean daily screen-off bout duration was 25.90 (95% CI 20.09-31.71) and 26.90 (95% CI 22.18-31.66) minutes, and mean daily screen-on bout count (natural logarithm transformed) was 4.192 (95% CI 4.041-4.343) and 4.090 (95% CI 3.968-4.213), respectively; there were no significant differences between smartphone operating systems (all P values were >.05). The daily measures were not significantly different for the first 4 weeks compared to the fifth week onward (all P values were >.05), except average screen-on bout in adults (P value = .018). Our sensitivity analysis indicated that in the screen-on bout identification method, the cap on an individual screen-on bout duration has a substantial effect on the resulting daily screen time measures. We observed time windows with a statistically significant effect of daylight time change on screen-on time (based on 95% joint confidence intervals bands), plausibly attributable to sleep time adjustments related to clock changes.

Passively collected phone logs offer an alternative to self-report measures for studying smartphone screen time characteristics in people with suicidal thinking. Our work demonstrates the feasibility of this approach, opening doors for further research on the associations between daily screen time, mental health, and other factors.