Smartphone Screen Time Characteristics in People With Suicidal Thoughts: Retrospective Observational Data Analysis Study.

Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.

Family happiness and college students' smartphone addiction control: the chain mediation effect of emotion regulation and self-control.

Background: More and more college students use smartphones for a long time every day. The problem of smartphone addiction has become a hot topic of social concern. It not only affects the physical and mental health of college students but also affects the development of families and society. To investigate the effect of family happiness on college students' smartphone addiction, we conducted a questionnaire survey and analysis. Methods: In this study, 214 college students were investigated using the adolescent family happiness questionnaire, college students' smartphone addiction scale, self-control scale, and emotion regulation scale. The data were analyzed by Amos and SPSS26.0 software. Results: The results showed that: (1) Family happiness, emotion regulation, and self-control were significantly inversely correlated with smartphone addiction; (2) Emotion regulation and self-control served as partial mediators in the linkage between family happiness and smartphone addiction, and the chain mediation effect of emotion regulation and self-control was significant. Conclusion: Family happiness, emotion regulation, and self-control are key factors that significantly influence college students' smartphone addiction behavior. Family happiness not only has a direct effect on college students' smartphone addiction behavior but also has an indirect effect on it through the chain mediation effect of emotion regulation and self-control.

Smartphone Addiction among Adolescents in Southern Italy and Correlation with Other Risky Behaviors.

BACKGROUND: Addiction to smartphone is defined as the disability to cease using the device despite the physical, psychological, or social consequences. The diagnosis of this disorder is increasing exponentially among teenagers, even more so after Corona Virus Disease 2019 (COVID-19) pandemic. This study aims to analyze the risk of mobile phone addiction among teenagers in Italy and its relationship with other risk behaviors, such as drug consumption and emotional well-being, following the COVID-19 pandemic. METHODS: A cross-sectional observational study was conducted in Catania, Sicily, during 2022. The study sample comprised 342 students from a secondary education center that answered to a validated Italian version of the Smartphone Addiction Scale Short Version (SAS-SV) accessing through computers and mobile devices. Subjective Wellbeing was measured with the Personal Wellbeing Index-School Children (PWI-SC). Also, they completed the NIAAA Brief Alcohol Use Screener. RESULTS: Our results showed that 30.1% of students who participated in the survey were classified with a smartphone addiction. Besides, most of the participants used the Smartphone for social networks (86.0%). Regarding the other aspects measured, the mean self-perceived well-being score measured with the PWI-SC was 55.5, and 36.2% of participants were categorized as "high risk" for depression. Finally, four out of ten participants were at "moderate or high risk" of an alcohol use disorder. CONCLUSIONS: Our results suggest that the use of mobile phones is increasing in adolescents, an age group also exposed to social and health problems.

Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder: Algorithm Development and Validation.

BACKGROUND: Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders. OBJECTIVE: This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model. METHODS: Eligible study participants completed a virtual screening visit where all eligible participants were instructed to download the custom-built BiAffect smartphone keyboard (University of Illinois). The BiAffect keyboard unobtrusively captures keystroke dynamics. All eligible and consenting participants were instructed to use this keyboard exclusively for up to 4 weeks of the study in real life, and participants' compliance was checked at the 2 follow-up visits at week 2 and week 4. As part of the research protocol, every study participant underwent evaluations by a study psychiatrist during each visit. RESULTS: We found that derived phenotypes were associated with not only the diagnoses and severity of depression and mania but also specific individual symptoms. Using a linear mixed-effects model with random intercepts accounting for the nested data structure from daily data, the backspace rates on the continuous scale did not differ between participants in the healthy control and in the mood disorders groups (P=.11). The 3-class model had mean backspace rates of 0.112, 0.180, and 0.268, respectively, with a SD of 0.048. In total, 3 classes, respectively, were estimated to comprise 37.5% (n=47), 54.4% (n=72), and 8.1% (n=9) of the sample. We grouped individuals into Low, Medium, and High backspace rate groups. Individuals with unipolar mood disorder were predominantly in the Medium group (n=54), with some in the Low group (n=27) and a few in the High group (n=6). The Medium group, compared with the Low group, had significantly higher ratings of depression (b=2.32, P=.008). The High group was not associated with ratings of depression with (P=.88) or without (P=.27) adjustment for medication and diagnoses. The High group, compared with the Low group, was associated with both nonzero ratings (b=1.91, P=.02) and higher ratings of mania (b=1.46, P<.001). The High group, compared with the Low group, showed significantly higher odds of elevated mood (P=.03), motor activity (P=.04), and irritability (P<.05). CONCLUSIONS: This study demonstrates the promise of mobile typing kinematics in mood disorder research and practice. Monitoring a single mobile typing kinematic feature, that is, backspace rates, through passive sensing imposes a low burden on the participants. Based on real-life keystroke data, our derived digital phenotypes from this single feature can be useful for researchers and practitioners to distinguish between individuals with and those without mood disorder symptoms.

Smartphone addiction, depression, distress, eustress, loneliness, and sleep deprivation in adolescents: a latent profile and network analysis approach.

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

Smartphone Apps for Pulmonary Hypertension: Systematic Search and Content Evaluation.

Background: Pulmonary hypertension (PH) is a chronic and complex condition, requiring consistent management and education. The widespread use of smartphones has opened possibilities for mobile health apps to support both patients and health care professionals in monitoring and managing PH more effectively. Objective: This study aimed to identify and assess the quality of free smartphone apps for PH targeted at either patients or health care professionals. Methods: A systematic search was conducted on freely available apps for patients with PH and health care professionals, accessed from a Spanish IP address, on Android (Google Play) and iOS (App Store) platforms. Searches were performed in October 2022 and 2023. Apps were independently analyzed by two reviewers, focusing on general characteristics. Quality assessment was based on the Mobile Application Rating Scale (MARS) framework, and Mann-Whitney U tests compared mean MARS scores against specific variables. Results: In the overall study, 21 apps were identified. In the 2022 search, 19 apps were listed (9 iOS, 7 Android, 3 available on both platforms). In the subsequent 2023 search, 16 apps were identified (6 Android, 7 iOS, 3 available on both platforms). Of those identified in 2022, 14 remained available in 2023, with only 7 updated since 2022. In addition, 12 apps targeted patients or the general population, while 9 targeted health care professionals; none involved patients in the development or design. Conversely, 13 apps involving health care professionals were identified. There were 10 apps that received pharmaceutical industry funding. The primary goal for 81% (17/21) of the apps was to disseminate general information about PH. The overall mean MARS quality was acceptable in 2022 and 2023, with mean ratings of 3.1 (SD 0.6) and 3.3 (SD 0.5), respectively. The functionality category achieved the highest scores in both years, indicating ease of use and intuitive navigation. In contrast, the subjective quality domain consistently received the lowest ratings in the MARS assessment across both years. None of the apps underwent clinical testing themselves; however, 2 incorporated tools or algorithms derived from trials. The overall quality of iOS apps statistically outperformed that of Android apps in both years (P<.05). Furthermore, the involvement of health care professionals in app development was associated with enhanced quality, a trend observed in both years (P=.003 for both years). Conclusions: This review of mobile health apps for PH reveals their emergent development stage, with generally acceptable quality but lacking refinement. It highlights the critical role of health care professionals in app development, as they contribute significantly to quality and reliability. Despite this, a notable stagnation in app quality and functionality improvement over 2 years points to a need for continuous innovation and clinical validation for effective clinical integration. This research advocates for future app developers to actively engage with health care professionals, integrate patient insights, and mandate rigorous clinical validation for PH management.

Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults.

BACKGROUND: Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal and spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations of these studies by using intensive data collection schemes that take advantage of the widespread use of smartphones while allowing for less burdensome data collection and longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors and psychological well-being. OBJECTIVE: The aim of this study was to investigate the feasibility and scalability of embedding smartphone-based digital phenotyping in large epidemiological cohorts by examining participant adherence to a smartphone-based data collection protocol in 2 ongoing nationwide prospective cohort studies. METHODS: Participants (N=2394) of the Beiwe Substudy of the Nurses' Health Study 3 and Growing Up Today Study were followed over 1 year. During this time, they completed questionnaires every 10 days delivered via the Beiwe smartphone app covering topics such as emotions, stress and enjoyment, physical activity, access to green spaces, pets, diet (vegetables, meats, beverages, nuts and dairy, and fruits), sleep, and sitting. These questionnaires aimed to measure participants' key health behaviors to combine them with objectively assessed high-resolution GPS and accelerometer data provided by participants during the same period. RESULTS: Between July 2021 and June 2023, we received 11.1 TB of GPS and accelerometer data from 2394 participants and 23,682 survey responses. The average follow-up time for each participant was 214 (SD 148) days. During this period, participants provided an average of 14.8 (SD 5.9) valid hours of GPS data and 13.2 (SD 4.8) valid hours of accelerometer data. Using a 10-hour cutoff, we found that 51.46% (1232/2394) and 53.23% (1274/2394) of participants had >50% of valid data collection days for GPS and accelerometer data, respectively. In addition, each participant submitted an average of 10 (SD 11) surveys during the same period, with a mean response rate of 36% across all surveys (SD 17%; median 41%). After initial processing of GPS and accelerometer data, we also found that participants spent an average of 14.6 (SD 7.5) hours per day at home and 1.6 (SD 1.6) hours per day on trips. We also recorded an average of 1046 (SD 1029) steps per day. CONCLUSIONS: In this study, smartphone-based digital phenotyping was used to collect intensive longitudinal data on lifestyle and behavioral factors in 2 well-established prospective cohorts. Our assessment of adherence to smartphone-based data collection protocols over 1 year suggests that adherence in our study was either higher or similar to most previous studies with shorter follow-up periods and smaller sample sizes. Our efforts resulted in a large dataset on health behaviors that can be linked to spatial datasets to examine environmental and behavioral drivers of chronic disease.

Examining the relationship between smartphone characteristics and the prevalence of hand discomfort among university students.

BACKGROUND: Students are among the groups that use smartphones for long periods throughout the day and night. Therefore, this study aimed to examine the relationship between smartphone characteristics and the prevalence of hand discomfort among university students. METHODS: This study included 204 university students, selected based on their willingness to participate and inclusion criteria. Participants reported hand pain and discomfort by completing the Cornell Hand Discomfort Questionnaire (CHDQ). Personal information was collected through a demographic questionnaire. Smartphone characteristics were obtained from the Internet based on the smartphone model self-reported by students. RESULTS: According to the Cornell questionnaire, 59.3% of students reported experiencing discomfort in their right hand, while 38.2% reported discomfort in their left hand due to smartphone use. Furthermore, 36.3% of students reported experiencing pain in two or more regions on their right hand, while 20.1% reported pain in two or more areas on their left hand. More than half of the students in the right hand (53.5%) and more than one-third (33.3%) in the left hand obtained pain scores of more than 1.5. The chi-square test indicated a statistically significant relationship between the weight of the smartphone and the prevalence of discomfort in the right hand (χ2 = 4.80, p = 0.03). Furthermore, a statistically significant relationship was found between the discomfort or pain scores experienced in both hands and the number of painful areas in those hands (right hand: χ2 = 219.04, p = 0.00; left hand: χ2 = 213.13, p = 0.00). CONCLUSIONS: Smartphone use can cause discomfort and pain in the hands of university students. The physical characteristics of the smartphone, such as its weight, play a significant role in contributing to right-hand-related pain among students. It is important to consider ergonomic factors in smartphone design and usage to reduce musculoskeletal problems among users, especially students.

Associations between smartphone addiction, parenting styles, and mental well-being among adolescents aged 15-19 years in Gujarat, India.

BACKGROUND: The pervasive use of smartphones among adolescents has raised concerns about addiction and its impact on mental well-being. This study investigates the prevalence of smartphone addiction and its associations with socio-demographic factors, parenting styles, and mental health among Indian adolescents. METHODS: A cross-sectional study was conducted among 560 school-going adolescents (aged 15-19) in Gujarat, India, from January to October 2023. Data was collected using validated scales: the Smartphone Addiction Scale-Short Version (SAS-SV), the Parenting Styles and Dimensions Questionnaire (PSDQ), and the Depression Anxiety Stress Scale-21 (DASS-21). Bivariate and multivariate logistic regression analyses were performed to identify factors associated with smartphone addiction. RESULTS: The prevalence of smartphone addiction was 64.6%. Urban residence (AOR: 2.4, 95% CI: 1.8-3.3), higher parental education (AOR: 3.3, 95% CI: 1.7-4.3 for graduate fathers), longer smartphone use (AOR: 2.08, 95% CI: 1.7-3.6 for > 3 years), and higher socioeconomic status (AOR: 1.9, 95% CI: 1.5-3.51) were associated with increased odds of addiction. Authoritarian and permissive parenting styles in both parents were positively associated with smartphone addiction, while authoritative parenting was negatively associated. Smartphone addiction was strongly associated with mental health issues, particularly with severe stress (AOR: 10.82, 95% CI: 5.11-22.88, p < 0.001). CONCLUSION: Smartphone addiction is highly prevalent among Indian adolescents and is significantly associated with urban living, higher socioeconomic status, non-authoritative parenting styles, and poor mental health. These findings underscore the need for digital literacy programs, parenting interventions promoting authoritative styles, and mental health support to foster healthy smartphone use among adolescents.

Smartphone addiction in Iranian schoolchildren: a population-based study.

Smartphone addiction is a public health problem and is increasing globally. It is important to know the latest status of this phenomenon in different region of the world. This study aimed to investigate smartphone addiction in an Iranian student population. The participants were a random sample of Shahroud Schoolchildren Eye Cohort study in 2018. Smartphone addiction was measured by the Smartphone Addiction Scale Short Version (SAS-sv) questionnaire. The prevalence and mean estimates were reported with 95% confidence intervals. The analysis of variance, t-test and multiple logistic regression were used for analysis of data. The mean age of 2682 participating students was 13.5 year with an age range of 10 to 15 year, and 1197 (44.6%) of them were girls. The mean SAS-sv score was 27.96 (95% CI: 27.36-28.58) for boys and 26.10 (95% CI: 25.34-26.80) for girls. This score follows a U-shaped pattern with a lower mean score observed at age 8. The Prevalence of smartphone addiction was 29.8% (95% CI: 28.1-31.5) in studied population. Compared to 10 year old students, smartphone addiction was higher in 14 (Odds Ratio (OR) = 1.7, 95% CI: 1.1-2.7) and 15 (OR = 2.4, 95% CI: 1.5-3.8) years old students. Smartphone addiction was higher in boys (OR = 1.9; 95% CI, 1.5-3.0; P value < 0.001). Smartphone addiction was not different in urban and rural students after adjusting for age and gender (P = 0.089). Compared to European countries the prevalence of smartphone addiction in Iranian students is relatively high and need prompt intervention especially in boys and older adolescents.

Exploring the relationship between depressive symptoms, problematic smartphone use, and cumulative risk factors in nursing students: A cross sectional study.

BACKGROUND: No single risk factor is decisive in shaping an individual's healthy development. This study aimed to analyze the relationship between depressive symptoms and a cumulative risk index comprising individual, family, and social variables among nursing students. METHODS: We enrolled 1716 Chinese nursing students from three universities in a paperless survey that assessed a range of individual, family, and social risk factors associated with depressive symptoms. Multiple risk analysis was conducted to create a composite risk score for each individual. A test for trend was employed to assess the relationship between the multiple risk classification and depressive symptoms individually. Additionally, a 2-step cluster analysis and χ2 tests were used to examine the relationship between the different clusters and the level of depressive symptoms. RESULTS: The mean scores of depressive symptoms increased significantly as the number of risk factors increased, regardless of their combination. As the number of risk factors increased, the proportion of nursing students in the normal group decreased, while the proportion in the group with depressive symptoms of varying severity tended to increase (P < 0.001). A high-risk cluster characterized by poor sleep quality combined with problematic smartphone use was associated with higher levels of depressive symptoms (P < 0.001). CONCLUSION: Based on these findings that cumulative exposure to multiple risk factors is more harmful than cumulative exposure to fewer risk factors, then interventions that isolate only one risk factor are less likely to be effective than those that are multifaceted.

Quantifying urban park use in the USA at scale: empirical estimates of realised park usage using smartphone location data.

BACKGROUND: A large body of evidence connects access to greenspace with substantial benefits to physical and mental health. In urban settings where access to greenspace can be limited, park access and use have been associated with higher levels of physical activity, improved physical health, and lower levels of markers of mental distress. Despite the potential health benefits of urban parks, little is known about how park usage varies across locations (between or within cities) or over time. METHODS: We estimated park usage among urban residents (identified as residents of urban census tracts) in 498 US cities from 2019 to 2021 from aggregated and anonymised opted-in smartphone location history data. We used descriptive statistics to quantify differences in park usage over time, between cities, and across census tracts within cities, and used generalised linear models to estimate the associations between park usage and census tract level descriptors. FINDINGS: In spring (March 1 to May 31) 2019, 18·9% of urban residents visited a park at least once per week, with average use higher in northwest and southwest USA, and lowest in the southeast. Park usage varied substantially both within and between cities; was unequally distributed across census tract-level markers of race, ethnicity, income, and social vulnerability; and was only moderately correlated with established markers of census tract greenspace. In spring 2019, a doubling of walking time to parks was associated with a 10·1% (95% CI 5·6-14·3) lower average weekly park usage, adjusting for city and social vulnerability index. The median decline in park usage from spring 2019 to spring 2020 was 38·0% (IQR 28·4-46·5), coincident with the onset of physical distancing policies across much of the country. We estimated that the COVID-19-related decline in park usage was more pronounced for those living further from a park and those living in areas of higher social vulnerability. INTERPRETATION: These estimates provide novel insights into the patterns and correlates of park use and could enable new studies of the health benefits of urban greenspace. In addition, the availability of an empirical park usage metric that varies over time could be a useful tool for assessing the effectiveness of policies intended to increase such activities. FUNDING: Google.

Nomophobia, loneliness and depressive symptom levels of adults living in a district of Türkiye.

This study was conducted to evaluate whether smart cell phones detach people from social life, make them lonely, cause depressive symptoms and whether there is a relationship between them. This is a cross-sectional study using face-to-face survey method. The sample was determined as 376 participants, with 95% confidence interval, 50% probability depending on the probability of having a smartphone or not. Participants in the sample were determined by systematic random sampling from adults aged 20 and over in Çiftlik district of Nigde province. The NMP-Q, DASS-42 and UCLA-LS were used. The median age of the 376 participants (192 male, 184 female) was 30 and the mean age was 32.0 (±10.94) years. Participants that had social media accounts were 68.9% of the population. Over 70% of the participants had mild, moderate or severe nomophobia. Nomophobia status of the participants was affected by the mean duration of the first-time smartphone use (P = .017), the mean daily smartphone usage time of the participants (P < .001), the mean number of smartphone users in the participants' families (P = .003), the mean depression and stress scores of the participants (P < .001), having social media account (P = .001), and declaration of smart phone dependency (P = .005). Nomophobia status was not affected by participants' gender, mean age, educational level, employment status, economic status and marital status (P > .05). The duration of daily smartphone use, the duration of first-time smartphone use, and having multiple smartphone users in family increased nomophobia. Also, having active social media accounts and self-reported smartphone addiction found correlated to nomophobia. Smartphone addiction increases depression, anxiety, and stress scores of the participants.

Prevalence, associated factors and consequence of problematic smartphone use among adolescents and young adults in Bangladesh: A cross-sectional study.

BACKGROUND: Problematic smartphone use (PSU) and attention deficit hyperactivity disorder (ADHD) in children, adolescents, and young adults are of major concern to parents. However, the prevalence and associated factors related to these issues in Bangladeshi adolescents and young adults remain unclear to the best of our knowledge. The aim of this study is to assess PSU and ADHD in the context of adolescent and young adult age groups in Bangladesh. METHODS: The present study collected data from diverse geographical locations in Bangladesh via face-to-face surveys using stratified random sampling methods. We considered age, sex, and geographic location stratification criteria. A total of 578 respondents participated in the survey initially. From this, we discarded 36 responses after screening because the information provided was insufficient or incomplete response. In the end, 542 replies were incorporated into the final analysis. PSU and ADHD depend on several factors, including the individual's demographic background. RESULTS: The prevalence of PSU and ADHD symptoms in adolescents and young adults in Bangladesh is 61.44% and 37.45%, respectively based on our findings. The symptoms of PSU are correlated with age, education level, family type (nuclear/joint), sleeping pattern, physical exercise, and residence area. ADHD symptoms are correlated with age, education level, living with family, smoking habit, physical disability, sleeping pattern, physical exercise, residence area, and PSU. Also, we observed that ADHD and PSU symptoms are positively correlated with each other. CONCLUSION: A large proportion of young adults and adolescents reported PSU and ADHD symptoms. The present findings have practical implications in clinical psychology, psychotherapy, and related policy considerations. We propose to develop an inclusive interventional strategy and community-based programs to address PSU and ADHD-related issues.

Trends and Influencing Factors in Problematic Smartphone Use Prevalence (2012-2022): A Systematic Review and Meta-Analysis.

Given that problematic smartphone use (PSU) has been researched for a long time, it becomes necessary to assess how the trends of PSU prevalence have evolved over time. In total, 106 articles from 2012 to 2022 with 109 studies covering 97,748 individuals were included in this systematic review and meta-analysis. The estimation of the global pooled PSU prevalence was 37.1% (95% confidence interval, 33.5 - 40.8%) and the PSU prevalence is growing over time in total. Although the PSU prevalence exhibited wide variation between regions and measurement scales, the increasing trend remained. In addition, the regression slope of the PSU prevalence trend in males was higher than that in females. Males initially had a lower prevalence of PSU than females, but this steadily changed over time until it was greater in males than in females. Moreover, the prevalence of PSU showed an increasing trend across all age groups, with a faster growth rate in the older age group. Considering the aforementioned results, it's necessary to implement proper interventions to do with this phenomenon. In addition, considering the differences in prevalence brought by regions, and measurement scales, in practice and research, we should not only take into account the disparities of PSU between countries or cultures, but also to concern about establishing unified standard and measurement tools to confirm it.

Child and adolescent exposure to unhealthy food marketing across digital platforms in Canada.

BACKGROUND: Children and adolescents are exposed to a high volume of unhealthy food marketing across digital media. No previous Canadian data has estimated child exposure to food marketing across digital media platforms. This study aimed to compare the frequency, healthfulness and power of food marketing viewed by children and adolescents across all digital platforms in Canada. METHODS: For this cross-sectional study, a quota sample of 100 youth aged 6-17 years old (50 children, 50 adolescents distributed equally by sex) were recruited online and in-person in Canada in 2022. Each participant completed the WHO screen capture protocol where they were recorded using their smartphone or tablet for 30-min in an online Zoom session. Research assistants identified all instances of food marketing in the captured video footage. A content analysis of each marketing instance was then completed to examine the use of marketing techniques. Nutritional data were collected on each product viewed and healthfulness was determined using Health Canada's 2018 Nutrient Profile Model. Estimated daily and yearly exposure to food marketing was calculated using self-reported device usage data. RESULTS: 51% of youth were exposed to food marketing. On average, we estimated that children are exposed to 1.96 marketing instances/child/30-min (4067 marketing instances/child/year) and adolescents are exposed to 2.56 marketing instances/adolescent/30-min (8301 marketing instances/adolescent/year). Both children and adolescents were most exposed on social media platforms (83%), followed by mobile games (13%). Both age groups were most exposed to fast food (22% of marketing instances) compared to other food categories. Nearly 90% of all marketing instances were considered less healthy according to Health Canada's proposed 2018 Nutrient Profile Model, and youth-appealing marketing techniques such as graphic effects and music were used frequently. CONCLUSIONS: Using the WHO screen capture protocol, we were able to determine that child and adolescent exposure to the marketing of unhealthy foods across digital media platforms is likely high. Government regulation to protect these vulnerable populations from the negative effects of this marketing is warranted.

'There's more to life than staring at a small screen': a mixed methods cohort study of problematic smartphone use and the relationship to anxiety, depression and sleep in students aged 13-16 years old in the UK.

BACKGROUND: Depression and anxiety are common in adolescents and have increased over the last decade. During that period, smartphone usage has become ubiquitous. OBJECTIVES: The study aim was to assess the association between problematic smartphone usage (PSU) and anxiety. METHODS: Using a prospective mixed methods cohort study design, students aged 13-16 year old from two schools were enrolled regarding their smartphone use, mood and sleep via a semistructured questionnaire at baseline and week 4. The primary outcome was symptoms of anxiety (Generalised Anxiety Disorder Questionnaire, GAD-7) and exposure was PSU (Smartphone Addiction Scale Short Version). A linear regression was fitted to assess the change in anxiety. Thematic analysis of free-text responses was conducted. FINDINGS: The sample included 69 participants that were enrolled and followed up between 28 March and 3 June 2022. Of those with PSU, 44.4% exhibited symptoms of moderate to severe anxiety compared with 26.4% of those without PSU. There was a linear association between change in symptoms of anxiety and PSU ß=0.18 (95% CI 0.04 to 0.32, p=0.013). Several themes were found: both positive and negative effects of smartphones on relationships; negative effects on school performance and productivity; mixed effects on mood; a desire to reduce the amount of time spent on smartphones. CONCLUSIONS: Increased anxiety, depression and inability to sleep were seen in participants as their PSU score increased over time. Participants reported both positive and negative effects of smartphones and almost all used strategies to reduce use. CLINICAL IMPLICATIONS: Interventions need to be developed and evaluated for those seeking support.

Internet addiction among adolescents during the COVID-19 pandemic: Associations with sociodemographic and psychological distress.

Background: The COVID-19 pandemic has had tremendous implications for billions of adolescents worldwide due to school closures, forcing students to embrace internet usage for daily tasks. Uncontrolled use of the internet among adolescents makes them vulnerable to internet addiction (IA). This study aims to determine the prevalence of IA among adolescents and assess its association with sociodemographic factors, smartphone use, and psychological distress during the pandemic. Method: A cross-sectional self-administered online survey was conducted among students aged 13-17 from May 15th, 2021, until May 30th, 2021, using the Malay version of the Internet Addiction Test (MVIAT), the Depression, Anxiety, and Stress Scale (DASS-21), and the Coronavirus Impacts Questionnaires, as well as a sociodemographic information form. The data was analyzed with IBM SPSS Statistics version 23. Results: A total of 420 adolescents participated in the survey. The majority of them (70.7%) were female, with a mean age of 15.47 years (±1.49 years old). About 45.5% of the respondents were classified as internet addicted users. The Chi-square test analysis showed that age (p = 0.002), smartphone usage (p = 0.010), rate of midnight use (p < 0.001), frequency (p < 0.001), duration (p < 0.001) of device usage, and presence of depression, anxiety, and stress (p < 0.001) were all significantly associated with IA. Multiple logistic regression showed age (aOR = 1.16, 95% CI [1.00-1.35], p = 0.048), smartphone usage (aOR =3.52, 95% CI [1.43-8.67], p = 0.006), mild or moderate depression (aOR = 2.43, 95% CI [1.36-4.34], p = 0.003), severe or extremely severe stress (aOR = 6.41, 95% CI [2.18-18.82], p = 0.001) were significantly related to IA. Conclusions: Late adolescence, the use of smartphones, and the presence of psychological distress like depression, and stress were potentially associated with IA. Wise use of smartphones and early identification of any psychological distress among adolescents are warranted, especially during the pandemic.

Randomised pilot study comparing a coach to SMARTPhone reminders to aid the management of heart failure (HF) patients: humans or machines.

Ambulatory management of congestive heart failure (HF) continues to be a challenging clinical problem. Recent studies have focused on the role of HF clinics, nurse practitioners and disease management programmes to reduce HF readmissions. This pilot study is a pragmatic factorial study comparing a coach intervention, a SMARTPHONE REMINDER system intervention and BOTH interventions combined to Treatment as USUAL (TAU). We determined that both modalities were acceptable to patients prior to randomisation. Fifty-four patients were randomised to the four groups. The COACH group had no readmissions for HF 6 months after enrolment compared with 18% for the SMARTPHONE REMINDER Group, 8% for the BOTH intervention group and 13% for TAU. Medium-to-high medication adherence was maintained in all four groups although sodium consumption was lower at 3 months for the COACH and combined (BOTH) groups. This pilot study suggests a beneficial effect on rehospitalisation with the use of support measures including coaches and telephone reminders that needs confirmation in a larger trial.

Effect of a Smartphone App (S-Check) on Actual and Intended Help-Seeking and Motivation to Change Methamphetamine Use Among Adult Consumers of Methamphetamine in Australia: Randomized Waitlist-Controlled Trial.

BACKGROUND: Interventions are required that address delays in treatment-seeking and low treatment coverage among people consuming methamphetamine. OBJECTIVE: We aim to determine whether a self-administered smartphone-based intervention, the "S-Check app" can increase help-seeking and motivation to change methamphetamine use, and determine factors associated with app engagement. METHODS: This study is a randomized, 28-day waitlist-controlled trial. Consenting adults residing in Australia who reported using methamphetamine at least once in the last month were eligible to download the app for free from Android or iOS app stores. Those randomized to the intervention group had immediate access to the S-Check app, the control group was wait-listed for 28 days before gaining access, and then all had access until day 56. Actual help-seeking and intention to seek help were assessed by the modified Actual Help Seeking Questionnaire (mAHSQ), modified General Help Seeking Questionnaire, and motivation to change methamphetamine use by the modified readiness ruler. χ2 comparisons of the proportion of positive responses to the mAHSQ, modified General Help Seeking Questionnaire, and modified readiness ruler were conducted between the 2 groups. Logistic regression models compared the odds of actual help-seeking, intention to seek help, and motivation to change at day 28 between the 2 groups. Secondary outcomes were the most commonly accessed features of the app, methamphetamine use, feasibility and acceptability of the app, and associations between S-Check app engagement and participant demographic and methamphetamine use characteristics. RESULTS: In total, 560 participants downloaded the app; 259 (46.3%) completed eConsent and baseline; and 84 (32.4%) provided data on day 28. Participants in the immediate access group were more likely to seek professional help (mAHSQ) at day 28 than those in the control group (n=15, 45.5% vs n=12, 23.5%; χ21=4.42, P=.04). There was no significant difference in the odds of actual help-seeking, intention to seek help, or motivation to change methamphetamine use between the 2 groups on the primary logistic regression analyses, while in the ancillary analyses, the imputed data set showed a significant difference in the odds of seeking professional help between participants in the immediate access group compared to the waitlist control group (adjusted odds ratio 2.64, 95% CI 1.19-5.83, P=.02). For participants not seeking help at baseline, each minute in the app increased the likelihood of seeking professional help by day 28 by 8% (ratio 1.08, 95% CI 1.02-1.22, P=.04). Among the intervention group, a 10-minute increase in app engagement time was associated with a decrease in days of methamphetamine use by 0.4 days (regression coefficient [ß] -0.04, P=.02). CONCLUSIONS: The S-Check app is a feasible low-resource self-administered intervention for adults in Australia who consume methamphetamine. Study attrition was high and, while common in mobile health interventions, warrants larger studies of the S-Check app. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12619000534189; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377288&isReview=true.