The Double-edged Sword: A Mixed Methods Study of the Interplay between Bipolar Disorder and Technology Use

Human behavior is increasingly reflected or acted out through technology. This is of particular salience when it comes to changes in behavior associated with serious mental illnesses including schizophrenia and bipolar disorder. Early detection is crucial for these conditions but presently very challenging to achieve. Potentially, characteristics of these conditions\u27 traits and symptoms, at both idiosyncratic and collective levels, may be detectable through technology use patterns. In bipolar disorder specifically, initial evidence associates changes in mood with changes in technology-mediated communication patterns. However much less is known about how people with bipolar disorder use technology more generally in their lives, how they view their technology use in relation to their illness, and, perhaps most crucially, the causal relationship (if any exists) between their technology use and their disease. To address these uncertainties, we conducted a survey of people with bipolar disorder (N = 84). Our results indicate that technology use varies markedly with changes in mood and that technology use broadly may have potential as an early warning signal of mood episodes. We also find that technology for many of these participants is a double-edged sword: acting as both a culprit that can trigger or exacerbate symptoms as well as a support mechanism for recovery. These findings have implications for the design of both early warning systems and technology-mediated interventions

Associating Use of Digital Technology and Self-Reported Health Problems among College Going Students in Delhi-NCR, India

INTRODUCTION: The increased use of digital media among college students has the tendency to cause various health problems based on the duration and medium used. AIM: To assess the use of digital technology and self- reported health problems among college going students in Delhi-NCR, IndiaMATERIALS AND METHODS: Data was collected using a pre-tested and pre-validated questionnaire which was divided into three sections. The first section contained seven questions regarding demographic details, the second contained three questions regarding the device used, hours spent and the type of media assessed, while the third contained twelve questions regarding self-reported adverse events while accessing digital media. Statistical tests involved the Shapiro-Wilk test, Independent samples t-test, multivariate linear regression and the Pearson’s correlation coefficient. The analysis was done using SPSS version 19.0.RESULTS: Responses of 717 students were included in the final analysis. Most of the students were between 17-19 years (53.9%), the primary device used was smartphone (91.8%). Most students used their device for >1-4 hours (34.6%). The most common self-reported symptom was back and/or neck pain (18.4%) followed by sleep issues/ insomnia (17.7%) and headache (17.3). Multiple linear regression model revealed that good knowledge scores were significantly associated with age(p = 0.04) and the duration of device used (p = 0.02). A positive, linear, great strength of association (r: +0.747) and a significant relationship (p = 0.037) was found between self-reported health problems and the hours of device usage. CONCLUSION: It is advised that college students be advised regarding the ill effects of digital medium without taking proper precautions

Instagram photos reveal predictive markers of depression

Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness

Psychopathological symptoms and loneliness in adult internet users: a contemporary public health concern

There are different concepts that translate abusive Internet use. Almost all these concepts converge on excessive time spent online, which can trigger the emergence of problematic situations. Most of the studies reported in the literature, both nationally and internationally, focused on a young population and found negative consequences of this Internet misuse. The objective of this study consists of associating the time spent using the Internet—in years, times per week, and hours per day—with psychopathological symptoms, as well as assessing the perception of loneliness, in an adult Portuguese population. A quantitative approach, based on a survey application, was conducted in a convenience sample composed by 418 participants (64.4% female), with a mean age of 29.9 years old (SD = 9.26), ranging from 18 to 73 years. The results suggest that maladaptive patterns of Internet use found in young people seem to be replicated in the adult population. A relationship between time spent on the Internet and psychopathological symptoms, and an association between loneliness and the number of hours spent on the Internet, were also identified. In an individualized and disconnected offline world, Internet impact in individuals’ well-being results must be highlighted, since it should be understood as a public health issue. The novelty of this study lies in the target population: Portuguese Internet users over 18 years of age, for which there is no specific study on the subject, thus emphasizing the transverse nature of the problem.info:eu-repo/semantics/publishedVersio

THE RELATIONSHIP BETWEEN SOCIAL MEDIA USE AND DEPRESSION IN COLLEGE STUDENTS: A SCOPING REVIEW

Background: In 2017, 17.3 million adults in the U.S. (7.1% of all U.S. adults) have had at least one major depressive episode, with individuals ages 18-25 having the highest prevalence of major depression at 13.1%. Studies have looked at the association between social media (SM) use and depression with mixed findings. The goal of this research is to conduct a scoping review of existing evidence for the relationship between SM use and depression among college students. Methods: Searches for articles published in the scientific literature were performed on PubMed, Embase and Scopus. Articles selected followed an inclusion criteria: studied SM use and depression in college students over 18 years, were written in English, were published between October 2013 and October 2018, studied time spent on SM, addiction to SM and/or different SM behaviors and were cross-sectional, observational, longitudinal or intervention studies. Articles were screened and imported into citation manager Refworks for duplicate removal. Full-text articles were found through Pubmed, Embase, Scopus or Google Scholar for eligibility screening. Articles needing to be purchased were requested through interlibrary loan or requested from authors. Results: 14 articles met inclusion criteria; 92.9% were cross-sectional. Half of the articles measured time spent on SM (including daily hours and frequency), with mixed findings; 42.9% of articles studied SM addiction, with all studies presenting a significantly positive association with depression. Over sixty percent (64%) of the articles studied specific SM behaviors, with mixed findings. Included articles presented a larger correlation between individual characteristics (e.g. neuroticism, loneliness, suicidal ideation, self-esteem and academic stressors) and SM addiction and depression than time spent on SM and SM behaviors. The odds of SM addiction and depression were 40% higher in college students in China than in the U.S and the risk of SM addiction and depression in college students was found to be 28% higher in Hong Kong (HK)/Macau and 12% higher in Japan than in the U.S. Only one randomized controlled trial was identified in this scoping review. After 2 weeks, statistically significant reductions were observed in both SM addiction and depression through mean rank comparisons of before and after treatment. Conclusion: This review reiterated the need for longitudinal studies to access directionality and the need to standardize measures used to measure social media, depression in college students. Future studies could continue to focus on the relationship between individual characteristics (i.e. loneliness, neuroticism and self-esteem), SM addiction, SM comparison and depression and further study RCTs utilizing treatment periods longer than 2-weeks

Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions

Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification

Problematic online behaviors among adolescents and emerging adults: associations between cyberbullying perpetration, problematic social media use, and psychosocial factors

Over the past two decades, young people's engagement in online activities has grown markedly. The aim of the present study was to examine the relationship between two specific online behaviors (i.e., cyberbullying perpetration, problematic social media use) and their relationships with social connectedness, belongingness, depression, and self-esteem among high school and university students. Data were collected from two different study groups via two questionnaires that included the Cyberbullying Offending Scale, Social Media Use Questionnaire, Social Connectedness Scale, General Belongingness Scale, Short Depression-Happiness Scale, and Single Item Self-Esteem Scale. Study 1 comprised 804 high school students (48% female; mean age 16.20 years). Study 2 comprised 760 university students (60% female; mean age 21.48 years). Results indicated that problematic social media use and cyberbullying perpetration (which was stronger among high school students) were directly associated with each other. Belongingness (directly) and social connectedness (indirectly) were both associated with cyberbullying perpetration and problematic social media use. Path analysis demonstrated that while age was a significant direct predictor of problematic social media use and cyberbullying perpetration among university students, it was not significant among high school students. In both samples, depression was a direct predictor of problematic social media use and an indirect predictor of cyberbullying perpetration. However, majority of these associations were relatively weak. The present study significantly adds to the emerging body of literature concerning the associations between problematic social media use and cyberbullying perpetration

Mobile Phone and Wearable Sensor-Based mHealth Approach for Psychiatric Disorders and Symptoms : Systematic Review and Link to the m-RESIST Project

Background: Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration. Objective: To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy. Methods: A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied. Results: Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression. Conclusions: Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.Peer reviewe

Mobile Phone and Wearable Sensor-Based mHealth Approach for Psychiatric Disorders and Symptoms : Systematic Review and Link to the m-RESIST Project

Background: Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration. Objective: To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy. Methods: A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied. Results: Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression. Conclusions: Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.Peer reviewe

Loneliness and Cognitive Distortion in Adolescent Facebookers

Facebook users in Indonesia dominated by adolescents in the age range 12 - 15 years. The usage of Facebook has negative effects, such as loneliness and cognitive distortion. The study aims to determine the relationship between loneliness and cognitive distortions in early adolescence Facebook users. Using revision of UCLA Loneliness Scale (ULS) – 8 (Hays & DiMatteo, 1987) and Briere’s (2000) Cognitive Distortion Scale (CDS), data was collected from 146 early adolescents, female and male, who studied at schools in East Jakarta, South Jakarta, North Jakarta, and Depok. Based on results of data analysis using Spearman Rank correlation. A significant positive correlation was obtained between loneliness and cognitive distortion on early adolescence Facebook users (r= .271, p< .005). It was supported by a significant positive correlation between dimension of cognitive distortion (self criticism, self blame, helplessness, hopelessness, and preoccupation with danger) and loneliness with correlation rate .234 – .308