To use or not to use? Compulsive behavior and its role in smartphone addiction

Who uses food barcode scanner apps and why? Exploration of users' characteristics and development of the Food Barcode Scanner App Questionnaire

BACKGROUND

Food barcode scanner apps (FBSAs) are increasingly being used to verify food quality. By scanning a product's barcode, they can provide a range of information, including nutritional quality or information on the toxicity of food components. Although they seem to be widely used, no study has yet examined their use in the general population. The objectives of this study were therefore twofold: (a) to identify who the users of FBSA are and (b) to evaluate behaviours and cognitions associated with use of these apps through the development and validation of the Food Barcode Scanner App Questionnaire (FBSAQ).

METHOD

A total of 1626 women (average age of 37.51 years; SD = 12.67) from the general population were included in this study, with 25.7% reporting themselves as using at least one FBSA. Participants completed questionnaires assessing socio-demographic and health characteristics, the use of health apps and the FBSAQ, when relevant.

RESULTS

The users of FBSAs did not differ from nonusers in regard to key socio-demographic characteristics, but they were more likely to use healthcare services and other health apps than nonusers of FBSAs. Psychometric analyses allowed validation of the FBSAQ through three factors: pathological use, dietary concerns and exclusion of unhealthy components.

CONCLUSION

Data showed that the use of FBSAs can be beneficial for many individuals, as they help with food choices. However, some user may develop more problematic behaviours and have difficulties in not using these apps.

Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study

BACKGROUND

Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work.

OBJECTIVE

In this study, we aimed to develop a novel approach called "probability in work mode," which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours.

METHODS

To capture human-smartphone interactions and GPS locations, we used the "Staff Hours" app, developed by our team, to passively and continuously record participants' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work.

RESULTS

Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes.

CONCLUSIONS

Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being.

Exploring the Impact of Smartphone Addiction on Risk Decision-Making Behavior among College Students Based on fNIRS Technology

Smartphone Addiction is a social issue caused by excessive smartphone use, affecting decision-making processes. Current research on the risky decision-making abilities of smartphone addicts is limited. This study used the functional Near-Infrared Spectroscopy (fNIRS) brain imaging technique and a Sequential Risk-Taking Task experimental paradigm to investigate the decision-making behavior and brain activity of smartphone addicts under varying risk levels. Using a mixed experimental design, the research assessed decision-making ability and brain activation levels as dependent variables across two groups (addiction and control), two risk amounts (high and low), and two outcomes (gain and loss). The study included 42 participants, with 25 in the addiction group and 17 in the control group. Results indicated that risk level significantly impacted the decision-making ability of smartphone addicts, with high-risk levels leading to weaker decision-making ability and increased risk-taking. However, at low-risk levels, decision-making abilities between addicts and healthy individuals showed no significant difference. Furthermore, brain imaging results using fNIRS revealed stronger brain activation in the dorsolateral Prefrontal Cortex (dlPFC) region for smartphone addicts under loss outcome conditions, with no significant differences between the two groups in terms of brain activation at varying risk volumes. These findings are critical in promoting healthy smartphone use, guiding clinical treatment, and advancing brain mechanism research.

The promise of a model-based psychiatry: building computational models of mental ill health

Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.

Development of Digital Biomarkers of Mental Illness via Mobile Apps for Personalized Treatment and Diagnosis

The development of precision psychiatry is largely based on multi-module measurements from the molecular, cellular, and behavioral levels, which are integrated to assess neurocognitive performances and clinically observed psychopathology. Nevertheless, quantifying mental activities and functions accurately and continuously has been a major difficulty within this field. This article reviews the latest efforts that utilize mobile apps to collect human–smartphone interaction data and contribute towards digital biomarkers of mental illnesses. The fundamental principles underlying a behavioral analysis with mobile apps were introduced, such as ways to monitor smartphone use under different circumstances and construct long-term patterns and trend changes. Examples were also provided to illustrate the potential applications of mobile apps that gain further insights into traditional research topics in occupational health and sleep medicine. We suggest that, with an optimized study design and analytical approach that accounts for technical challenges and ethical considerations, mobile apps will enhance the systemic understanding of mental illnesses.

Exploring subtypes and correlates of internet gaming disorder severity among adolescents during COVID-19 in China: A latent class analysis

The WHO recently included Gaming Disorder as a psychiatric diagnosis. Whether there are distinct groups of adolescents who differ based on severity of gaming disorder and their relationships with other mental health and addictive behavior outcomes, including problematic smartphone use (PSU), remains unclear. The current study explored and identified subtypes of Internet Gaming Disorder (IGD) severity and estimated the association between these subtypes and other disorders. Participants completed online questionnaires assessing the severity of IGD, PSU, depression, and anxiety during COVID-19. We conducted a latent class analysis of IGD symptoms among 1,305 Chinese adolescents (mean age = 15.2; male = 58.5%) from 11 secondary schools in Macao (SAR), China. Multinomial logistic regression estimated correlates of latent class membership and PSU. A 4-class model adequately described the sample subgroups. Classes were labeled as normative gamers (30.9%), occasional gamers (42.4%), problematic gamers (22.7%), and addictive gamers (4.1%). Relative to normative gamers, PSU severity, depression, and being male were significantly higher among problematic gamers, addictive gamers, and occasional gamers. Only problematic gamers showed significant positive associations with anxiety severity compared to the other groups. The study revealed the differences in severity of gaming disorder and its association with psychopathology outcomes. Application in screening for IGD and comorbidity is discussed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12144-022-03133-8.

The impacts of academic stress on college students' problematic smartphone use and Internet gaming disorder under the background of neijuan: Hierarchical regressions with mediational analysis on escape and coping motives

With sluggish economic growth in the post-pandemic era, the phenomenon "neijuan" becomes increasingly severe in many Asian countries like China. Neijuan refers to a hypercompetitive social environment wherein individuals involuntarily get involved in inhumane work or study hours, resulting in a considerable amount of tension and stress. Previous pathology research has shown that stress can trigger the overuse of Internet-based devices and services, which can subsequently lead to problematic smartphone use (PSU) and Internet gaming disorder (IGD). Provided college students are generally deemed one of the groups most susceptible to neijuan, limited attention has been given to the stimuli and the resultant psychological and behavioral ill-beings. Our study examined the impacts of academic stress on Chinese college students' PSU and IGD problems, with the inclusion of escape and coping motives as mediators. Based upon the results of hierarchical regressions and path analysis, we found that whereas academic stress increased IGD tendency mediated through escape and coping motives, excessive use of smartphone might have developed into a habitual behavior rather than effective escape and coping instruments. Demographic and academic characteristics, such as gender and whether studying at a prestigious institution, also exerted influences on college students' IGD intensity.

Altered connectivity in the right inferior frontal gyrus associated with self-control in adolescents exhibiting problematic smartphone use: A fMRI study

BACKGROUND

With the continued spread of smartphones and development of the internet, the potential negative effects arising from problematic smartphone use (PSU) in adolescents are being reported on an increasing basis. This study aimed to investigate whether altered resting-state functional connectivity (rsFC) is related to the psychological factors underlying PSU in adolescents.

METHODS

Resting-state functional magnetic resonance images were acquired from 47 adolescents with PSU and 46 healthy control adolescents (the CON group). Seed-based functional connectivity analyses were then performed to compare the two groups with respect to rsFC in the right inferior frontal gyrus, associated with various forms of self-control, and rsFC in the left inferior frontal gyrus.

RESULTS

Compared to the CON group, the PSU group exhibited a reduction in rsFC between the right inferior frontal gyrus and limbic areas, including the bilateral parahippocampal gyrus, the left amygdala, and the right hippocampus. In addition, a reduction in fronto-limbic rsFC was associated with the severity of PSU, the degree of self-control, and the amount of time the subjects used their smartphones.

CONCLUSION

Adolescents with PSU exhibited reduced levels of fronto-limbic functional connectivity; this mechanism is involved in salience attribution and self-control, attributes that are critical to the clinical manifestation of substance and behavioral addictions. Our data provide clear evidence for alterations in brain connectivity with respect to self-control in PSU.

Smartphone addiction and depression, anxiety: The role of bedtime procrastination and self-control

BACKGROUND

Owing to the widespread use of smartphones, researchers have an increasing interest in smartphone addiction. The purpose of this study is to look into the outcomes of smartphone addiction while answering when and how smartphone addiction may predict university students' depression and anxiety.

METHODS

Primary data were collected from 355 students studying in different universities in China. Participants completed Smartphone Addiction Scale-Short Version (SAS-SV), Bedtime Procrastination Scale (BPS), Self-control Scale (SCS) and Depression-Anxiety-Stress Scale (DASS). PROCESS macros in SPSS24.0 were used to examine the moderated mediating effects.

RESULTS

Smartphone addiction Scale scores were positively correlated with depression, anxiety among university students through bedtime procrastination. Self-control was found to play the moderating role such that the mediated relationships were weak for students with high self-control.

LIMITATIONS

This study is a cross sectional study, so we cannot make causal inferences.

CONCLUSIONS

Individuals with smartphone addiction are inclined to postpone their bedtime and further experience more depression and anxiety. Self-control serves as a protective factor for bedtime procrastination, depression and anxiety.

Caudate nucleus volume mediates the link between glutamatergic neurotransmission and problematic smartphone use in youth

BACKGROUND AND AIMS

Problematic smartphone use (PSU) is growing rapidly among teens. It has similar presentations as other behavioral addictions in terms of excessive use, impulse control problems, and negative consequences. However, the underlying neurobiological mechanisms remain undiscovered. We hypothesized that structural changes in the striatum might serve as an important link between alteration in glutamate signaling and development of PSU.

METHODS

Among 88 participants, twenty (F:M, 12:8; age 16.2 ± 1.1) reported high scores in the smartphone addiction proneness scale (SAPS) with a cut-off score of 42; the other 68 (F:M, 19:49; age 15.3 ± 1.7) comprised the control group. Sociodemographic data and depression, anxiety, and impulsivity traits were measured. Striatal volumes (caudate, putamen, and nucleus accumbens) were estimated from T1 imaging data. Serum glutamate levels were estimated from peripheral blood samples. Group comparisons of each data were performed after controlling for age and gender. Mediation analyses were conducted to test the indirect effects of glutamate level alteration on PSU through striatal volumetric alteration.

RESULTS

The PSU group showed a decrease in both caudate volumes than the control group. Left caudate volume was positively correlated with serum glutamate level, and negatively with impulsivity traits and SAPS scores. The mediation model revealed a significant indirect effect of serum glutamate on SAS scores through the reduced left caudate volume.

DISCUSSION AND CONCLUSIONS

This study suggests that altered glutamatergic neurotransmission may be associated with PSU among teens, possibly through reduced left caudate volume. Current findings might support neural mechanisms of smartphone addiction.

Impulsivity-Compulsivity Axis: Evidence of Its Clinical Validity to Individually Classify Subjects on the Use/Abuse of Information and Communication Technologies

The compulsive habit model proposed by Everitt and Robbins has accumulated important empirical evidence. One of their proposals is the existence of an axis, on which each a person with a particular addiction can be located depending on the evolutionary moment of his/her addictive process. The objective of the present study is to contribute in addressing the identification of such axis, as few studies related to it have been published to date. To do so, the use/abuse of Information and Communication Technologies (ICT) was quantified on an initial sample of 807 subjects. Questionnaires were also delivered to measure impulsivity, compulsivity and symptoms of prefrontal dysfunction. Evidence of the existence of the proposed axis was obtained by means of Machine Learning techniques, thus allowing the classification of each subject along the continuum. The present study provides preliminary evidence of the existence of the Impulsivity-Compulsivity axis, as well as an IT tool so that each patient that starts getting treatment for an addiction can be statistically classified as “impulsive” or “compulsive.” This would allow the matching of each person with the most appropriate treatment depending on his/her moment in the addiction/abuse process, thus facilitating the individualized design of each therapeutic process and a possible improvement of the results of the treatment.

Two-dimensional taxonomy of internet addiction and assessment of smartphone addiction with diagnostic criteria and mobile apps

A recent review by Montag et al. raised a taxonomical argument about internet addiction. We propose a two-dimensional taxonomy of internet addiction by both the device and the content as the solution. For the assessment of smartphone addiction, measurements should be based on functional impairment and validated by diagnostic criteria rather than solely on self-reported questionnaires. We detail the potential of mobile applications (apps) to improve the assessment of smartphone addiction. App-generated indicators could fulfill the unmet need of assessment of smartphone addiction and facilitate future assessment and treatment planning of smartphone addiction.

Assessing User Retention of a Mobile App: Survival Analysis

BACKGROUND

A mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device.

OBJECTIVE

The primary aim of this study was to quantify user retention for the "Staff Hours" app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention.

METHODS

We developed an app called "Staff Hours" to automatically calculate users' work hours through GPS data (passive data). "Staff Hours" not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the "Staff Hours" database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention.

RESULTS

A total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700).

CONCLUSIONS

Our findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.

Development of the Smartphone Addiction Risk Rating Score for a Smartphone Addiction Management Application

Smartphone usage characteristics are useful for identification of the risk factors for smartphone addiction. Risk rating scores can be developed based on smartphone usage characteristics. This study aimed to investigate the smartphone addiction risk rating (SARR) score using smartphone usage characteristics. We evaluated 593 smartphone users using online surveys conducted between January 2 and January 31, 2019. We identified 102 smartphone users who were addicted to smartphones and 491 normal users based on the Korean Smartphone Addiction Proneness Scale for Adults. A multivariate logistic regression analysis was used to identify significant risk factors for smartphone addiction. The SARR score was calculated using a nomogram based on the significant risk factors. Weekend average usage time, habitual smartphone behavior, addictive smartphone behavior, social usage, and process usage were the significant risk factors associated with smartphone addiction. Furthermore, we developed the SARR score based on these factors. The SARR score ranged between 0 and 221 points, with the cut-off being 116.5 points. We developed a smartphone addiction management application using the SARR score. The SARR score provided insights for the development of monitoring, prevention, and prompt intervention services for smartphone addiction.

Examining the associations between smartphone use and mother-infant bonding and family functioning: A survey design

Excessive smartphone use has been found to be associated with dysfunctional social and family relations. While most studies of this phenomenon have focused on adolescent and adult addiction, none has yet to focus on mothers with infants. This study examined the association of excessive smartphone use with mother-infant bonding, maternal mental health, and family functioning in Jordan. The predictive value of the study variables with respect to the level of smartphone use was evaluated. A descriptive correlational cross-sectional survey design was used. A sample of 114 mothers with infants was interviewed in person and completed a web-based questionnaire. Approximately 16% reported using smartphones 5 to 14 hours per day; 6.7% described themselves as smartphone addicts. The results suggest that excessive smartphone use may be linked to unhealthy family functioning. No associations were found between smartphone use and mother-infant bonding or maternal mental health. Raising awareness of this linkage and limiting smartphone use are recommended as precautionary measures. Although this study failed to find any association between smartphone use and mother-infant bonding, further studies using empirical methods might have better success.

Passive objective measures in the assessment of problematic smartphone use: A systematic review

Research focussing on problematic smartphone use has predominantly employed psychometric tests which cannot capture the automatic processes and behaviours associated with problematic use. The present review aimed to identify passive objective measures that have been used or developed to assess problematic smartphone use. A systematic search was conducted using Web of Science, Scopus, PsychInfo and PubMed databases to identify passive objective measures that have been employed to assess problematic smartphone use, resulting in 18 studies meeting the inclusion criteria. Objective data that were monitored predominantly focussed on general screen usage time and checking patterns. Findings demonstrate that passive monitoring can enable smartphone usage patterns to be inferred within a relatively short timeframe and provide ecologically valid data on smartphone behaviour. Challenges and recommendations of employing passive objective measures in smartphone-based research are discussed.

Exercise as an Alternative Approach for Treating Smartphone Addiction: A Systematic Review and Meta-Analysis of Random Controlled Trials

BACKGROUND

With the emergence of electronic products, smartphones have become an indispensable tool in our daily life. On the other hand, smartphone addiction has become a public health issue. To help reduce smartphone addiction, cost-effective interventions such as exercise are encouraged.

PURPOSE

We therefore performed a systematic review and meta-analysis evaluating existing literature on the rehabilitative effects of exercise interventions for individuals with a smartphone addiction.

METHODS

We searched PubMed, Web of Science, Scopus, CNKI, and Wanfang from inception to September 2019. Nine eligible randomized controlled trials (RCT) were finally included for meta-analysis (SMD represents the magnitude of effect of exercise) and their methodological quality were assessed using the PEDro scale.

RESULTS

We found significant positive effects of exercise interventions (Taichi, basketball, badminton, dance, run, and bicycle) on reducing the total score (SMD = -1.30, 95% CI -1.53 to -1.07, p < 0.005, I² = 62%) of smartphone addiction level and its four subscales (withdrawal symptom: SMD = -1.40, 95% CI -1.73 to -1.07, p < 0.001, I² = 81%; highlight behavior: SMD = -1.95, 95% CI -2.99 to -1.66, p < 0.001, I² = 79%; social comfort: SMD = -0.99, 95% CI -1.18 to -0.81, p = 0.27, I² = 21%; mood change: SMD = -0.50, 95% CI 0.31 to 0.69, p = 0.25, I² = 25%). Furthermore, we found that individuals with severe addiction level (SMD = -1.19, I² = 0%, 95%CI:-1.19 to -0.98) benefited more from exercise engagement, as compared to those with mild to moderate addiction levels (SMD = - 0.98, I² = 50%, 95%CI:-1.31 to -0.66); individuals with smartphone addiction who participated in exercise programs of 12 weeks and above showed significantly greater reduction on the total score (SMD = -1.70, I² = 31.2%, 95% CI -2.04 to -1.36, p = 0.03), as compared to those who participated in less than 12 weeks of exercise intervention (SMD = -1.18, I² = 0%, 95% CI-1.35 to -1.02, p < 0.00001). In addition, individuals with smartphone addiction who participated in exercise of closed motor skills showed significantly greater reduction on the total score (SMD = -1.22, I² = 0 %, 95% CI -1.41 to -1.02, p = 0.56), as compared to those who participated in exercise of open motor skills (SMD = -1.17, I² = 44%, 95% CI-1.47 to -0.0.87, p = 0.03).

CONCLUSIONS

Exercise interventions may have positive effects on treating smartphone addiction and longer intervention durations may produce greater intervention effects.

Validation of the Mobile App-Recorded Circadian Rhythm by a Digital Footprint

Background

Modern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app.

Objective

This study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST), as well as the timing of sleep onset and wake time, and to validate the app-recorded circadian rhythm with the corresponding 30-day self-reported midpoint of sleep and the consistency of social jetlag.

Methods

The mobile app, Rhythm, recorded parameters and these parameters were hypothesized to be used to infer a relative long-term pattern of the circadian rhythm. In total, 28 volunteers downloaded the app, and 30 days of automatically recorded data along with self-reported sleep measures were collected.

Results

No significant difference was noted between app-recorded and self-reported midpoint of sleep time and between app-recorded and self-reported social jetlag. The overall correlation coefficient of app-recorded and self-reported midpoint of sleep time was .87.

Conclusions

The circadian rhythm for 1 month, daily TST, and timing of sleep onset could be automatically calculated by the app and algorithm.

Temporal Stability of Smartphone Use Data: Determining Fundamental Time Unit and Independent Cycle

Background

Assessing human behaviors via smartphone for monitoring the pattern of daily behaviors has become a crucial issue in this century. Thus, a more accurate and structured methodology is needed for smartphone use research.

Objective

The study aimed to investigate the duration of data collection needed to establish a reliable pattern of use, how long a smartphone use cycle could perpetuate by assessing maximum time intervals between 2 smartphone periods, and to validate smartphone use and use/nonuse reciprocity parameters.

Methods

Using the Know Addiction database, we selected 33 participants and passively recorded their smartphone usage patterns for at least 8 weeks. We generated 4 parameters on the basis of smartphone use episodes, including total use frequency, total use duration, proactive use frequency, and proactive use duration. A total of 3 additional parameters (root mean square of successive differences, Control Index, and Similarity Index) were calculated to reflect impaired control and compulsive use.

Results

Our findings included (1) proactive use duration correlated with subjective smartphone addiction scores, (2) a 2-week period of data collection is required to infer a 2-month period of smartphone use, and (3) smartphone use cycles with a time gap of 4 weeks between them are highly likely independent cycles.

Conclusions

This study validated temporal stability for smartphone use patterns recorded by a mobile app. The results may provide researchers an opportunity to investigate human behaviors with more structured methods.

Development of a mobile application (App) to delineate "digital chronotype" and the effects of delayed chronotype by bedtime smartphone use

The widespread use and deep reach of smartphones motivate the use of mobile applications to continuously monitor the relationship between circadian system, individual sleep patterns, and environmental effects. We selected 61 adults with 14-day data from the "Know Addiction" database. We developed an algorithm to identify the "sleep time" based on the smartphone behaviors. The total daily smartphone use duration and smartphone use duration prior to sleep onset were identified respectively. We applied mediation analysis to investigate the effects of total daily smartphone use on sleep through pre-sleep use (PS). The results showed participants' averaged pre-sleep episodes within 1 h prior to sleep are 2.58. The duration of three pre-sleep uses (PS_1∼3) maybe a more representative index for smartphone use before sleep. Both total daily duration and the duration of the last three uses prior to sleep of smartphone use significantly delayed sleep onset, midpoint of sleep and reduced total sleep time. One hour of increased smartphone use daily, delays the circadian rhythm by 3.5 min, and reduced 5.5 min of total sleep time (TST). One hour of increased pre-sleep smartphone use delayed circadian rhythm by 1.7 min, and reduced 39 s of TST. The mediation effects of PS_1∼3 significantly impacted on these three sleep indicators. PS_1∼3 accounted for 14.3% of total daily duration, but the proportion mediated of delayed circadian rhythm was 44.0%. We presented "digital chronotype" with an automatic system that can collect high temporal resolution data from naturalistic settings with high ecological validity. Smartphone screen time, mainly mediated by pre-sleep use, delayed the circadian rhythm and reduced the total sleep time.

Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure

The purpose of this study was to carry out a review of observational studies that consider links between mobile phone use and mental health from a psychological or behavioral perspective. Systematic literature searches in PubMed and PsycINFO for articles published until 2017 were done. Exclusion criteria included: papers that considered radiofrequency fields, attention, safety, relational consequences, sexual behavior, cyberbullying, and reviews, qualitative, and case or experimental studies. A total of 4738 papers were screened by title and abstract, 404 were retrieved in full text, and 290 were included. Only 5% had any longitudinal design. Self-reporting was the dominating method of measurement. One third of the studies included children or youth. A majority of adult populations consisted of university students and/or self-selected participants. The main research results included associations between frequent mobile phone use and mental health outcomes, such as depressive symptoms and sleep problems. Mobile phone use at bedtime was associated with, e.g., shorter sleep duration and lower sleep quality. "Problematic use" (dependency) was associated with several negative outcomes. In conclusion, associations between mobile phone use and adverse mental health outcomes are found in studies that take a psychological or behavioral perspective on the exposure. However, more studies of high quality are needed in order to draw valid conclusions about the mechanisms and causal directions of associations.

Psychometric Properties and Factor Structures of Chinese Smartphone Addiction Inventory: Test of Two Models

There has been a growing concern of excessive smartphone use that interferes with people’s daily functioning, most notably among youngsters. The Smartphone Addiction Inventory (SPAI) was constructed to assess this type of information technology addiction. Although the SPAI was developed in a Taiwanese adolescent sample, this measure has not been validated on Chinese youngsters in other regions. Moreover, the initial evidence yielded a four-factor structure, but recent findings obtained an alternative five-factor structure. As no studies have systematically compared these two factor structures, which of the models fits the data better remained unknown. This study aimed to evaluate the empirical validity of both the four- and five-factor structures of the SPAI in a sample of university students from Mainland China (n = 463). Four psychometric properties of the SPAI were examined. First, the structural validity of both factor models was evaluated with confirmatory factor analysis. Satisfactory fit was found for both the five-factor model (RMSEA = 0.06, SRMR = 0.05, CFI = 0.99, TLI = 0.99) and the four-factor model (RMSEA = 0.07, SRMR = 0.06, CFI = 0.98, TLI = 0.98), but the five-factor model demonstrated an overall better model fit. Second, the five-factor model yielded good internal consistencies (all Cronbach α’s > 0.70). Third, concurrent validity of the SPAI was supported by its moderately strong correlations with four widely adopted criterion variables (i.e., loneliness, social anxiety, depression, and impulsivity). Lastly, the convergent validity of the SPAI was demonstrated by its strong, positive correlation with a popular, validated measure of Internet addiction. This study is the first to demonstrate the validity of the newly proposed five-factor model of the SPAI in a sample of Mainland Chinese youngsters.