Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review

BACKGROUND

There has been an increased interest in understanding social anxiety (SA) and SA disorder (SAD) antecedents and consequences as they occur in real time, resulting in a proliferation of studies using ambulatory assessment (AA). Despite the exponential growth of research in this area, these studies have not been synthesized yet.

OBJECTIVE

This review aimed to identify and describe the latest advances in the understanding of SA and SAD through the use of AA.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature search was conducted in Scopus, PubMed, and Web of Science.

RESULTS

A total of 70 articles met the inclusion criteria. The qualitative synthesis of these studies showed that AA permitted the exploration of the emotional, cognitive, and behavioral dynamics associated with the experience of SA and SAD. In line with the available models of SA and SAD, emotion regulation, perseverative cognition, cognitive factors, substance use, and interactional patterns were the principal topics of the included studies. In addition, the incorporation of AA to study psychological interventions, multimodal assessment using sensors and biosensors, and transcultural differences were some of the identified emerging topics.

CONCLUSIONS

AA constitutes a very powerful methodology to grasp SA from a complementary perspective to laboratory experiments and usual self-report measures, shedding light on the cognitive, emotional, and behavioral antecedents and consequences of SA and the development and maintenance of SAD as a mental disorder.

A collaborative realist review of remote measurement technologies for depression in young people

Digital mental health is becoming increasingly common. This includes use of smartphones and wearables to collect data in real time during day-to-day life (remote measurement technologies, RMT). Such data could capture changes relevant to depression for use in objective screening, symptom management and relapse prevention. This approach may be particularly accessible to young people of today as the smartphone generation. However, there is limited research on how such a complex intervention would work in the real world. We conducted a collaborative realist review of RMT for depression in young people. Here we describe how, why, for whom and in what contexts RMT appear to work or not work for depression in young people and make recommendations for future research and practice. Ethical, data protection and methodological issues need to be resolved and standardized; without this, RMT may be currently best used for self-monitoring and feedback to the healthcare professional where possible, to increase emotional self-awareness, enhance the therapeutic relationship and monitor the effectiveness of other interventions.

The relationship between adverse childhood experiences and depressive symptoms in rural left-behind adolescents: A cross-sectional survey

Objective

We assessed the current status of depressive symptoms and the associated factors in rural left-behind adolescents. Moreover, we investigated the relationship between adverse childhood experiences and depressive symptoms.

Methods

Students from two rural junior high schools in Huaihua City were enrolled from July to September 2022. Before distributing the questionnaires, guardians of the students were contacted, and consent was obtained from the students themselves. The questionnaires were filled out anonymously and collected on-site.

Results

The prevalence of depressive symptoms among the 325 left-behind teenagers was 23.40%; the rate of emotional abuse in adverse childhood experiences was 17.50%, physical abuse was 15.70%, sexual abuse was 9.50%, emotional neglect was 24.60%, while physical neglect was 27.70%. The five dimensions of adverse childhood experiences were associated with depressive symptoms (r = 0.597, 0.395, 0.410, 0.498, 0.741, p < 0.01).

Conclusions

Depressive symptoms were common among rural left-behind adolescents. Adverse childhood experiences were associated with depressive symptoms in rural left-behind adolescents. Occurrence of adverse childhood experiences should be reduced to improve on depressive symptoms.

Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety

Background

Prior literature links passively sensed information about a person's location, movement, and communication with social anxiety. These findings hold promise for identifying novel treatment targets, informing clinical care, and personalizing digital mental health interventions. However, social anxiety symptoms are heterogeneous; to identify more precise targets and tailor treatments, there is a need for personal sensing studies aimed at understanding differential predictors of the distinct subdomains of social anxiety. Our objective was to conduct a large-scale smartphone-based sensing study of fear, avoidance, and physiological symptoms in the context of trait social anxiety over time.

Methods

Participants (n = 1013; 74.6 % female; M age = 40.9) downloaded the LifeSense app, which collected continuous passive data (e.g., GPS, communication, app and device use) over 16 weeks. We tested a series of multilevel linear regression models to understand within- and between-person associations of 2-week windows of passively sensed smartphone data with fear, avoidance, and physiological distress on the self-reported Social Phobia Inventory (SPIN). A shifting sensor lag was applied to examine how smartphone features related to SPIN subdomains 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction).

Results

A decrease in time visiting novel places was a strong between-person predictor of social avoidance over time (distal β = -0.886, p = .002; medial β = -0.647, p = .029; proximal β = -0.818, p = .007). Reductions in call- and text-based communications were associated with social avoidance at both the between- (distal β = -0.882, p = .002; medial β = -0.932, p = .001; proximal β = -0.918, p = .001) and within- (distal β = -0.191, p = .046; medial β = -0.213, p = .028) person levels, as well as between-person fear of social situations (distal β = -0.860, p < .001; medial β = -0.892, p < .001; proximal β = -0.886, p < .001) over time. There were fewer significant associations of sensed data with physiological distress. Across the three subscales, smartphone data explained 9-12 % of the variance in social anxiety.

Conclusion

Findings have implications for understanding how social anxiety manifests in daily life, and for personalizing treatments. For example, a signal that someone is likely to begin avoiding social situations may suggest a need for alternative types of exposure-based interventions compared to a signal that someone is likely to begin experiencing increased physiological distress. Our results suggest that as a prophylactic means of targeting social avoidance, it may be helpful to deploy interventions involving social exposures in response to decreases in time spent visiting novel places.

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks. We posit that passively detecting social contexts can be particularly useful for social anxiety research, as it may ultimately help identify changes in social anxiety status and patterns of social avoidance and withdrawal. To this end, we recruited a sample of highly socially anxious undergraduate students (N=46) to examine whether we could detect the presence of experimentally manipulated virtual social contexts via wristband sensors. Using a multitask machine learning pipeline, we leveraged passively sensed biobehavioral streams to detect contexts relevant to social anxiety, including (1) whether people were in a social situation, (2) size of the social group, (3) degree of social evaluation, and (4) phase of social situation (anticipating, actively experiencing, or had just participated in an experience). Results demonstrated the feasibility of detecting most virtual social contexts, with stronger predictive accuracy when detecting whether individuals were in a social situation or not and the phase of the situation, and weaker predictive accuracy when detecting the level of social evaluation. They also indicated that sensing streams are differentially important to prediction based on the context being predicted. Our findings also provide useful information regarding design elements relevant to passive context detection, including optimal sensing duration, the utility of different sensing modalities, and the need for personalization. We discuss implications of these findings for future work on context detection (e.g., just-in-time adaptive intervention development).

Smartphone-Based Passive Sensing in Monitoring Patients With Cancer: A Systematic Review

PURPOSE

Patients with cancer are prone to frequent unplanned hospital visits because of disease or treatment complications. Smartphone-based passive sensing (SBPS) comprises data collection using smartphone sensors or device usage patterns, which may be an affordable and burdenless technique for remote monitoring of patients with cancer and timely detection of safety events. The aim of this article was to systematically review the published literature to identify the current state of SBPS in oncology care and research.

METHODS

A literature search was done with cutoff date July 29, 2022, using six different databases. Articles were included if they reported original studies using SBPS in patients with cancer or cancer survivors. Data extracted from studies included type of sensors used, cancer type, study objectives, and main findings.

RESULTS

Twelve studies were included, the oldest report being from 2017. The most frequent of the nine analyzed sensors and smartphone analytics was the accelerometer (eight studies) and geolocation (eight studies), followed by call logs (two studies). Breast cancer was the most studied cancer type (eight studies with 111 patients), followed by GI cancers (six studies with 133 patients). All studies aiming for feasibility concluded that SBPS in oncology was feasible (seven studies). SBPS was used as a monitoring tool, with passively sensed data being correlated with adverse events, symptom burden, cancer-related fatigue, decision conflict, recovery trends after surgery, or psychosocial impact. SBPS was also used in one study as a predictive tool for health deterioration.

CONCLUSION

SBPS shows early promise in oncology, although it cannot yet replace traditional tools to monitor quality of life and clinical outcomes. For this, validation of SBPS will be required. Therefore, further research is warranted with this developing technique.

A Systematic Review of Location Data for Depression Prediction

Depression contributes to a wide range of maladjustment problems. With the development of technology, objective measurement for behavior and functional indicators of depression has become possible through the passive sensing technology of digital devices. Focusing on location data, we systematically reviewed the relationship between depression and location data. We searched Scopus, PubMed, and Web of Science databases by combining terms related to passive sensing and location data with depression. Thirty-one studies were included in this review. Location data demonstrated promising predictive power for depression. Studies examining the relationship between individual location data variables and depression, homestay, entropy, and the normalized entropy variable of entropy dimension showed the most consistent and significant correlations. Furthermore, variables of distance, irregularity, and location showed significant associations in some studies. However, semantic location showed inconsistent results. This suggests that the process of geographical movement is more related to mood changes than to semantic location. Future research must converge across studies on location-data measurement methods.

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.

Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students

Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results: In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions: Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.

Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study

BACKGROUND

The experience sampling methodology (ESM) has long been considered as the gold standard for gathering data in everyday life. In contrast, current smartphone technology enables us to acquire data that are much richer, more continuous, and unobtrusive than is possible via ESM. Although data obtained from smartphones, known as mobile sensing, can provide useful information, its stand-alone usefulness is limited when not combined with other sources of information such as data from ESM studies. Currently, there are few mobile apps available that allow researchers to combine the simultaneous collection of ESM and mobile sensing data. Furthermore, such apps focus mostly on passive data collection with only limited functionality for ESM data collection.

OBJECTIVE

In this paper, we presented and evaluated the performance of m-Path Sense, a novel, full-fledged, and secure ESM platform with background mobile sensing capabilities.

METHODS

To create an app with both ESM and mobile sensing capabilities, we combined m-Path, a versatile and user-friendly platform for ESM, with the Copenhagen Research Platform Mobile Sensing framework, a reactive cross-platform framework for digital phenotyping. We also developed an R package, named mpathsenser, which extracts raw data to an SQLite database and allows the user to link and inspect data from both sources. We conducted a 3-week pilot study in which we delivered ESM questionnaires while collecting mobile sensing data to evaluate the app's sampling reliability and perceived user experience. As m-Path is already widely used, the ease of use of the ESM system was not investigated.

RESULTS

Data from m-Path Sense were submitted by 104 participants, totaling 69.51 GB (430.43 GB after decompression) or approximately 37.50 files or 31.10 MB per participant per day. After binning accelerometer and gyroscope data to 1 value per second using summary statistics, the entire SQLite database contained 84,299,462 observations and was 18.30 GB in size. The reliability of sampling frequency in the pilot study was satisfactory for most sensors, based on the absolute number of collected observations. However, the relative coverage rate-the ratio between the actual and expected number of measurements-was below its target value. This could mostly be ascribed to gaps in the data caused by the operating system pushing away apps running in the background, which is a well-known issue in mobile sensing. Finally, some participants reported mild battery drain, which was not considered problematic for the assessed participants' perceived user experience.

CONCLUSIONS

To better study behavior in everyday life, we developed m-Path Sense, a fusion of both m-Path for ESM and Copenhagen Research Platform Mobile Sensing. Although reliable passive data collection with mobile phones remains challenging, it is a promising approach toward digital phenotyping when combined with ESM.

The (un)learning of social functions and its significance for mental health

Social interactions are dynamic, context-dependent, and reciprocal events that influence prospective strategies and require constant practice and adaptation. This complexity of social interactions creates several research challenges. We propose a new framework encouraging future research to investigate not only individual differences in capacities relevant for social functioning and their underlying mechanisms, but also the flexibility to adapt or update one's social abilities. We suggest three key capacities relevant for social functioning: (1) social perception, (2) sharing emotions or empathizing, and (3) mentalizing. We elaborate on how adaptations in these capacities may be investigated on behavioral and neural levels. Research on these flexible adaptations of one's social behavior is needed to specify how humans actually "learn to be social". Learning to adapt implies plasticity of the relevant brain networks involved in the underlying social processes, indicating that social abilities are malleable for different contexts. To quantify such measures, researchers need to find ways to investigate learning through dynamic changes in adaptable social paradigms and examine several factors influencing social functioning within the three aformentioned social key capacities. This framework furthers insight concerning individual differences, provides a holistic approach to social functioning, and may improve interventions for ameliorating social abilities in patients.

The Influence of Occupational Therapy on College Students' Home Physical Exercise Behavior and Mental Health Status under the Artificial Intelligence Technology

The core of occupational therapy is to help patients with mental illness recover their social work, give play to their self-worth, obtain financial resources, and improve their self-confidence. Occupational therapy can help patients relieve symptoms and restore social function, reduce disease recurrence, and improve the reemployment rate and the overall health level of patients. In order to deeply excavate the inner connection between the mental health status and physical exercise status of college students, the physical exercise behavior of college students during home isolation is studied. First, the "physical exercise behavior questionnaire" and "symptom self-assessment scale" were used to investigate the physical exercise behavior and mental health status of college students. Second, descriptive statistics, correlation analysis, independent sample t-test, and variance analysis were carried out on the survey results using mathematical statistics methods and big data technology. The survey results show high reliability, and the Cronbach's α coefficients were all greater than 0.9. There was a positive correlation between physical exercise methods and mental health in general, and the difference in the degree of exercise is significantly different from the mental health of students (p < 0.05). With the increase of exercise intensity, the score of "symptom self-assessment scale" first decreased and then increased, and the exercise intensity of medium and high intensity showed the best psychological state. And the correlation dimension of depression was the highest. This indicated that the students who liked family physical exercise were less likely to suffer from depression. In addition, depression was the most relevant dimension with self demand physical exercise, and interpersonal sensitivity was the most relevant dimension with social expansion physical exercise. The conclusion shows that the more active the students participate in family physical exercise, the healthier their mental state is. Occupational therapy has obvious curative effect on depression, which can improve patients' negative symptoms, their living ability, and social function. Meanwhile, analyzing data through big data technology reduces human workload and improves data processing efficiency and accuracy. The scheme proposed here provides some ideas for the application of big data technology in occupational therapy.

Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping

BACKGROUND

The COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS).

OBJECTIVE

We presented a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated stay-at-home period due to a global pandemic.

METHODS

First, we extracted features that capture behavior changes due to the stay-at-home order. Then, we adapted and applied an existing algorithm to these behavior-change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the stay-at-home period.

RESULTS

Using data collected between November 2019 and May 2020, the algorithm detected depression with an accuracy of 82.5% (65% improvement over baseline; F₁-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; F₁-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; F₁-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; F₁-score: 0.84).

CONCLUSIONS

Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics, which would cause drastic behavior changes.

Changes in Internalizing Symptoms and Anxiety Sensitivity Among College Students During the COVID-19 Pandemic

The early months of the COVID-19 pandemic saw significant increases in symptoms of anxiety and depression, particularly among college students. However, research has not examined how internalizing symptoms in this population have changed as the pandemic has continued into its second year. Further, there has yet to be an examination of potential changes in transdiagnostic vulnerability factors. Therefore, the purpose of the current repeated cross-sectional study was to examine differences by term in undergraduates’ symptoms of depression, anxiety, worry, social anxiety, and anxiety sensitivity in the Spring 2020 (n = 251), Fall 2020 (n = 427), and Spring 2021 (n = 256) semesters. Results indicated that there were significant increases in depression, anxiety, worry, and anxiety sensitivity from Spring 2020 to Fall 2020 that were maintained through the Spring 2021 semester, and levels of social anxiety were significantly higher in Spring 2021 compared to Spring 2020. These findings suggest that the mental health impacts of the COVID-19 pandemic on college students have continued beyond the initial months, and colleges and universities will need to develop comprehensive plans to adequately address college students’ mental health needs.

Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

BACKGROUND

This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively.

AIMS

Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology.

RESULTS

Overall, 118 studies were assessed as eligible. Considering the terms employed, "EMA", "ESM", and "DP" were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps' information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales.

CONCLUSIONS

Findings suggest links between a person's digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person's broader contextual and developmental circumstances in relation to their digital data/records.

Digital phenotyping in psychiatry: A scoping review

BACKGROUND

Digital phenotyping has been defined as the moment-by-moment assessment of an illness state through digital means, promising objective, quantifiable data on psychiatric patients' conditions, and could potentially improve diagnosis and management of mental illness. As it is a rapidly growing field, it is to be expected that new literature is being published frequently.

OBJECTIVE

We conducted this scoping review to assess the current state of literature on digital phenotyping and offer some discussion on the current trends and future direction of this area of research.

METHODS

We searched four databases, PubMed, Ovid MEDLINE, PsycINFO and Web of Science, from inception to August 25th, 2021. We included studies written in English that 1) investigated or applied their findings to diagnose psychiatric disorders and 2) utilized passive sensing for management or diagnosis. Protocols were excluded. A narrative synthesis approach was used, due to the heterogeneity and variability in outcomes and outcome types reported.

RESULTS

Of 10506 unique records identified, we included a total of 107 articles. The number of published studies has increased over tenfold from 2 in 2014 to 28 in 2020, illustrating the field's rapid growth. However, a significant proportion of these (49% of all studies and 87% of primary studies) were proof of concept, pilot or correlational studies examining digital phenotyping's potential. Most (62%) of the primary studies published evaluated individuals with depression (21%), BD (18%) and SZ (23%) (Appendix 1).

CONCLUSION

There is promise shown in certain domains of data and their clinical relevance, which have yet to be fully elucidated. A consensus has yet to be reached on the best methods of data collection and processing, and more multidisciplinary collaboration between physicians and other fields is needed to unlock the full potential of digital phenotyping and allow for statistically powerful clinical trials to prove clinical utility.

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study

BACKGROUND

The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored.

OBJECTIVE

We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time.

METHODS

Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse-Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants' location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility.

RESULTS

This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=-0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=-0.07, P<.001) the subsequent periodicity of mobility.

CONCLUSIONS

Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.

Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments

Smartphones are capable of passively capturing persons' social interactions, movement patterns, physiological activation, and physical environment. Nevertheless, little research has examined whether momentary anxiety symptoms can be accurately assessed using these methodologies. In this research, we utilize smartphone sensors and personalized deep learning models to predict future anxiety symptoms among a sample reporting clinical anxiety disorder symptoms. Participants (N = 32) with generalized anxiety disorder and/or social anxiety disorder (based on self-report) installed a smartphone application and completed ecological momentary assessment symptoms assessing their anxiety and avoidance symptoms hourly for the course of one week (T = 2007 assessments). During the same period, the smartphone app collected information about physiological activation (heart rate and heart rate variability), exposure to light, social contact, and GPS location. GPS locations were coded to reveal the type of location and the weather information. Personalized deep learning models using the smartphone sensor data were capable of predicting the majority of total variation in anxiety symptoms (R² = 0.748) and predicting a large proportion of within-person variation at the hour-by-hour level (mean R² = 0.385). These results suggest that personalized deep learning models using smartphone sensor data are capable of accurately predicting future anxiety disorder symptom changes.

The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones

BACKGROUND

Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms.

OBJECTIVE

The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD.

METHODS

We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse-Major Depressive Disorder study. The participants were recruited from three study sites: King's College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables.

RESULTS

Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI -0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day).

CONCLUSIONS

Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD.

Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review

The ability to regulate emotions in response to stress is central to healthy development. While early research in emotion regulation predominantly employed static, self-report measurement, the past decade has seen a shift in focus toward understanding the dynamic nature of regulation processes. This is reflected in recent refinements in the definition of emotion regulation, which emphasize the importance of the ability to flexibly adapt regulation efforts across contexts. The latest proliferation of digital technologies employed in mental health research offers the opportunity to capture the state- and context-sensitive nature of emotion regulation. In this conceptual review, we examine the use of digital technologies (ecological momentary assessment; wearable and smartphone technology, physical activity, acoustic data, visual data, and geo-location; smart home technology; virtual reality; social media) in the assessment of emotion regulation and describe their application to interventions. We also discuss challenges and ethical considerations, and outline areas for future research.

Clustering and Feature Analysis of Smartphone Data for Depression Monitoring

Modern advancements have allowed society to be at the most innovative stages of technology which involves the possibility of multimodal data collection. Dartmouth dataset is a rich dataset collected over 10 weeks from 60 participants. The dataset includes different types of data but this paper focuses on 10 different smartphone sensor data and a Patient Health Questionnaire (PHQ) 9 survey that monitors the severity of depression. This paper extracts key features from smartphone data to identify depression. A multi-view bi-clustering (MVBC) algorithm is applied to categorize homogeneous behaviour subgroups. MVBC takes multiple views of sensing data as input. The algorithm inputs three views: average, trend, and location views. MVBC categorizes the subjects to low, medium and high PHQ-9 scores. Real-world data collection may have fewer sensors, allowing for less features to be extracted. This creates a focus on prioritization of features. In this body of work, minimum redundancy maximum relevance (mRMR) is applied to the sensing features to prioritize the features that better distinguish the different groups. The resulting MVBC are compared to literature to support the categorized clusters. Decision Tree (DT) 10-fold cross validation shows that our method can classify individuals into the correct subgroups using a reduced number of features to achieve an overall accuracy of 94.7±1.62%. Achieving high accuracies with reduced features allows for focus on low power analysis and edge computing applications for long-term mental health monitoring using a smartphone.

A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study

Background

Internalizing disorders are the most common psychiatric problems observed among youth in Canada. Sadly, youth with internalizing disorders often avoid seeking clinical help and rarely receive adequate treatment. Current methods of assessing internalizing disorders usually rely on subjective symptom ratings, but internalizing symptoms are frequently underreported, which creates a barrier to the accurate assessment of these symptoms in youth. Therefore, novel assessment tools that use objective data need to be developed to meet the highest standards of reliability, feasibility, scalability, and affordability. Mobile sensing technologies, which unobtrusively record aspects of youth behaviors in their daily lives with the potential to make inferences about their mental health states, offer a possible method of addressing this assessment barrier.

Objective

This study aims to explore whether passively collected smartphone sensor data can be used to predict internalizing symptoms among youth in Canada.

Methods

In this study, the youth participants (N=122) completed self-report assessments of symptoms of anxiety, depression, and attention-deficit hyperactivity disorder. Next, the participants installed an app, which passively collected data about their mobility, screen time, sleep, and social interactions over 2 weeks. Then, we tested whether these passive sensor data could be used to predict internalizing symptoms among these youth participants.

Results

More severe depressive symptoms correlated with more time spent stationary (r=0.293; P=.003), less mobility (r=0.271; P=.006), higher light intensity during the night (r=0.227; P=.02), and fewer outgoing calls (r=−0.244; P=.03). In contrast, more severe anxiety symptoms correlated with less time spent stationary (r=−0.249; P=.01) and greater mobility (r=0.234; P=.02). In addition, youths with higher anxiety scores spent more time on the screen (r=0.203; P=.049). Finally, adding passively collected smartphone sensor data to the prediction models of internalizing symptoms significantly improved their fit.

Conclusions

Passively collected smartphone sensor data provide a useful way to monitor internalizing symptoms among youth. Although the results replicated findings from adult populations, to ensure clinical utility, they still need to be replicated in larger samples of youth. The work also highlights intervention opportunities via mobile technology to reduce the burden of internalizing symptoms early on.

A Mobile App to Identify Lifestyle Indicators Related to Undergraduate Mental Health (Smart Healthy Campus): Observational App-Based Ecological Momentary Assessment

BACKGROUND

Undergraduate studies are challenging, and mental health issues can frequently occur in undergraduate students, straining campus resources that are already in demand for somatic problems. Cost-effective measures with ubiquitous devices, such as smartphones, offer the potential to deliver targeted interventions to monitor and affect lifestyle, which may result in improvements to student mental health. However, the avenues by which this can be done are not particularly well understood, especially in the Canadian context.

OBJECTIVE

The aim of this study is to deploy an initial version of the Smart Healthy Campus app at Western University, Canada, and to analyze corresponding data for associations between psychosocial factors (measured by a questionnaire) and behaviors associated with lifestyle (measured by smartphone sensors).

METHODS

This preliminary study was conducted as an observational app-based ecological momentary assessment. Undergraduate students were recruited over email, and sampling using a custom 7-item questionnaire occurred on a weekly basis.

RESULTS

First, the 7-item Smart Healthy Campus questionnaire, derived from fully validated questionnaires-such as the Brief Resilience Scale; General Anxiety Disorder-7; and Depression, Anxiety, and Stress Scale-21-was shown to significantly correlate with the mental health domains of these validated questionnaires, illustrating that it is a viable tool for a momentary assessment of an overview of undergraduate mental health. Second, data collected through the app were analyzed. There were 312 weekly responses and 813 sensor samples from 139 participants from March 2019 to March 2020; data collection concluded when COVID-19 was declared a pandemic. Demographic information was not collected in this preliminary study because of technical limitations. Approximately 69.8% (97/139) of participants only completed one survey, possibly because of the absence of any incentive. Given the limited amount of data, analysis was not conducted with respect to time, so all data were analyzed as a single collection. On the basis of mean rank, students showing more positive mental health through higher questionnaire scores tended to spend more time completing questionnaires, showed more signs of physical activity based on pedometers, and had their devices running less and plugged in charging less when sampled. In addition, based on mean rank, students on campus tended to report more positive mental health through higher questionnaire scores compared with those who were sampled off campus. Some data from students found in or near residences were also briefly examined.

CONCLUSIONS

Given these limited data, participants tended to report a more positive overview of mental health when on campus and when showing signs of higher levels of physical activity. These early findings suggest that device sensors related to physical activity and location are useful for monitoring undergraduate students and designing interventions. However, much more sensor data are needed going forward, especially given the sweeping changes in undergraduate studies due to COVID-19.

Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study

BACKGROUND

The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort.

OBJECTIVE

This study aims to evaluate whether changes in phone sensor-derived behavioral features were associated with subsequent changes in mental health symptoms.

METHODS

This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms.

RESULTS

A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (Time: r=-0.23, P=.02; Locations: r=-0.36, P<.001), exercise duration (r=0.39; P=.03) and use of active apps (r=-0.31; P<.001). Among the depression and anxiety groups, changes in depression were preceded by changes in GPS features for Locations (r=-0.20; P=.03) and Transitions (r=-0.21; P=.03). Depression changes were not related to subsequent sensor-derived features. The minimal symptoms group showed no significant relationships. There were no associations between sensor-based features and anxiety and minimal associations between sensor-based features and social anxiety.

CONCLUSIONS

Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms.

Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study

Background

The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals’ behaviors to infer their mental states and therefore screen for anxiety disorders and depression.

Objective

The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression.

Methods

An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated.

Results

Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression.

Conclusions

We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries.

Cognitive bias modification for threat interpretations: using passive Mobile Sensing to detect intervention effects in daily life

BACKGROUND

Social anxiety disorder is associated with distinct mobility patterns (e.g., increased time spent at home compared to non-anxious individuals), but we know little about if these patterns change following interventions. The ubiquity of GPS-enabled smartphones offers new opportunities to assess the benefits of mental health interventions beyond self-reported data.

OBJECTIVES

This pre-registered study (https://osf.io/em4vn/?view_only=b97da9ef22df41189f1302870fdc9dfe) assesses the impact of a brief, online cognitive training intervention for threat interpretations using passively-collected mobile sensing data.

DESIGN

Ninety-eight participants scoring high on a measure of trait social anxiety completed five weeks of mobile phone monitoring, with 49 participants randomly assigned to receive the intervention halfway through the monitoring period.

RESULTS

The brief intervention was not reliably associated with changes to participant mobility patterns.

CONCLUSIONS

Despite the lack of significant findings, this paper offers a framework within which to test future intervention effects using GPS data. We present a template for combining clinical theory and empirical GPS findings to derive testable hypotheses, outline data processing steps, and provide human-readable data processing scripts to guide future research. This manuscript illustrates how data processing steps common in engineering can be harnessed to extend our understanding of the impact of mental health interventions in daily life.

Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

BACKGROUND

Research in mental health has found associations between depression and individuals' behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones.

OBJECTIVE

This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8).

METHODS

The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features.

RESULTS

A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547).

CONCLUSIONS

Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals' behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.

Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples

Depression is one of the most common mental health issues in the United States, affecting the lives of millions of people suffering from it as well as those close to them. Recent advances in research on mobile sensing technologies and machine learning have suggested that a person's depression can be passively measured by observing patterns in people's mobility behaviors. However, the majority of work in this area has relied on highly homogeneous samples, most frequently college students. In this study, we analyse over 57 million GPS data points to show that the same procedure that leads to high prediction accuracy in a homogeneous student sample (N = 57; AUC = 0.82), leads to accuracies only slightly higher than chance in a U.S.-wide sample that is heterogeneous in its socio-demographic composition as well as mobility patterns (N = 5,262; AUC = 0.57). This pattern holds across three different modelling approaches which consider both linear and non-linear relationships. Further analyses suggest that the prediction accuracy is low across different socio-demographic groups, and that training the models on more homogeneous subsamples does not substantially improve prediction accuracy. Overall, the findings highlight the challenge of applying mobility-based predictions of depression at scale.

Fusing Location Data for Depression Prediction

Recent studies have demonstrated that geographic location features collected using smartphones can be a powerful predictor for depression. While location information can be conveniently gathered by GPS, typical datasets suffer from significant periods of missing data due to various factors (e.g., phone power dynamics, limitations of GPS). A common approach is to remove the time periods with significant missing data before data analysis. In this paper, we develop an approach that fuses location data collected from two sources: GPS and WiFi association records, on smartphones, and evaluate its performance using a dataset collected from 79 college students. Our evaluation demonstrates that our data fusion approach leads to significantly more complete data. In addition, the features extracted from the more complete data present stronger correlation with self-report depression scores, and lead to depression prediction with much higher F ₁ scores (up to 0.76 compared to 0.5 before data fusion). We further investigate the scenerio when including an additional data source, i.e., the data collected from a WiFi network infrastructure. Our results show that, while the additional data source leads to even more complete data, the resultant F ₁ scores are similar to those when only using the location data (i.e., GPS and WiFi association records) from the phones.

Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability

BACKGROUND

Passive sensor data from mobile devices can shed light on daily activities, social behavior, and maternal-child interactions to improve maternal and child health services including mental healthcare. We assessed feasibility and acceptability of the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) platform. The StandStrong passive data collection platform was piloted with adolescent and young mothers, including mothers experiencing postpartum depression, in Nepal.

METHODS

Mothers (15-25 years old) with infants (< 12 months old) were recruited in person from vaccination clinics in rural Nepal. They were provided with an Android smartphone and a Bluetooth beacon to collect data in four domains: the mother's location using the Global Positioning System (GPS), physical activity using the phone's accelerometer, auditory environment using episodic audio recording on the phone, and mother-infant proximity measured with the Bluetooth beacon attached to the infant's clothing. Feasibility and acceptability were evaluated based on the amount of passive sensing data collected compared to the total amount that could be collected in a 2-week period. Endline qualitative interviews were conducted to understand mothers' experiences and perceptions of passive data collection.

RESULTS

Of the 782 women approached, 320 met eligibility criteria and 38 mothers (11 depressed, 27 non-depressed) were enrolled. 38 mothers (11 depressed, 27 non-depressed) were enrolled. Across all participants, 5,579 of the hour-long data collection windows had at least one audio recording [mean (M) = 57.4% of the total possible hour-long recording windows per participant; median (Mdn) = 62.6%], 5,001 activity readings (M = 50.6%; Mdn = 63.2%), 4,168 proximity readings (M = 41.1%; Mdn = 47.6%), and 3,482 GPS readings (M = 35.4%; Mdn = 39.2%). Feasibility challenges were phone battery charging, data usage exceeding prepaid limits, and burden of carrying mobile phones. Acceptability challenges were privacy concerns and lack of family involvement. Overall, families' understanding of passive sensing and families' awareness of potential benefits to mothers and infants were the major modifiable factors increasing acceptability and reducing gaps in data collection.

CONCLUSION

Per sensor type, approximately half of the hour-long collection windows had at least one reading. Feasibility challenges for passive sensing on mobile devices can be addressed by providing alternative phone charging options, reverse billing for the app, and replacing mobile phones with smartwatches. Enhancing acceptability will require greater family involvement and improved communication regarding benefits of passive sensing for psychological interventions and other health services. Registration International Registered Report Identifier (IRRID): DERR1-10.2196/14734.

Association between Bullying Victimization and Symptoms of Depression among Adolescents: A Moderated Mediation Analysis

BACKGROUND

Bullying victimization and its effect on symptoms of depression have received attention from researchers, but few studies have considered the potential mechanism. The aim of this study was to examine a moderated mediation model for the association between bullying victimization and depressive symptoms in terms of it being mediated by social anxiety, and investigated whether sleep duration would show moderating effects in this relationship.

METHODS

In this study, there were 2956 students, who completed three questionnaires, including a bullying victimization scale, as well as a social anxiety and epidemiologic studies depression scale.

RESULTS

Bullying victimization's effects on depressive symptoms were mediated by social anxiety. Furthermore, sleep duration moderated the relationship between bullying victimization and depressive symptoms.

CONCLUSIONS

The research contributes by clarifying the mechanisms underlying the relationship between bullying victimization and depressive symptoms.

A randomized control trial to test a peer support group approach for reducing social isolation and depression among female Mexican immigrants

BACKGROUND

Female Mexican Immigrants (FMIs) experience high rates of depression compared with other populations. For this population, depression is often exacerbated by social isolation associated with the experience of immigration. Aim 1. To measure whether a culturally situated peer group intervention will reduce depression and stress associated with the experience of immigration. Aim 2. To test whether an intervention using a "women's funds of knowledge" approach results in improved resilience, knowledge and empowerment. Aim 3. To investigate whether a culturally situated peer group intervention using a women's funds of knowledge approach can give participants a sense and experience of social and physical connection ("emplacement") that is lost in the process of immigration.

METHODS

This mixed-methods study will implement "Tertulias" ("conversational gatherings" in Spanish), a peer support group intervention designed to improve health outcomes for FMI participants in Albuquerque, New Mexico. We will document results of the intervention on our primary hypotheses of a decrease in depression, and increases in resilience and social support, as well as on our secondary hypotheses of decreased stress (including testing of hair cortisol as a biomarker for chronic stress), and an increase in social connectedness and positive assessment of knowledge and empowerment.

DISCUSSION

This project will address mental health disparities in an underserved population that experiences high rates of social isolation. Successful completion of this project will demonstrate that health challenges that may appear too complex and too hard to address can be using a multi-level, holistic approach. Our use of hair samples to test for the 3-month average levels of systemic cortisol will contribute to the literature on an emerging biomarker for analyzing chronic stress.

TRIAL REGISTRATION

This study was registered with ClinicalTrials.gov on 2/3/20, Identifier # NCT04254198 .

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals

Traditionally, mental health specialists monitor their patients' social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson's correlation coefficient >70%) with individuals' social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.

Reduced positive affect on days with stress exposure predicts depression, anxiety disorders, and low trait positive affect 7 years later

Positive emotions serve important functions for mental health. Susceptibility to reduced positive emotions in the context of stress may increase risk for poor mental health outcomes, including anxiety and depressive disorders and low overall levels of positive emotion. In an 8-day daily diary study within a larger panel study (N = 1,517), we tested whether degree of reduction in time spent experiencing positive affect on days of stress exposure predicted lower levels of positive affect and elevated risk for major depressive and anxiety disorders (generalized anxiety disorder or panic disorder) 7 years later. Bayesian multilevel structural equation modeling controlling for overall levels of affect, stress exposure, leisure time, sex, age, and past year diagnoses of depression and anxiety disorders was conducted. Participants, on average, reported less time experiencing positive affect on days with stressors compared to days without stressors. In addition, participants varied in the extent to which their time spent experiencing positive affect differed across days with and without stressors. Those who reported an especially reduced proportion of the day experiencing positive affect on days with stressors also experienced lower positive affect and greater risk for major depressive disorder and anxiety disorders 7 years later. These prospective associations suggest that between-person differences in the within-person association between stress and positive emotions have implications for mental health years later. The efficacy of preventive interventions could be improved by fostering resilience of positive emotions during common stressful events. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Digital phenotyping for mental health of college students: a clinical review

Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help-the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students.

Automatic Depression Prediction Using Internet Traffic Characteristics on Smartphones

Depression is a serious mental health problem. Recently, researchers have proposed novel approaches that use sensing data collected passively on smartphones for automatic depression screening. While these studies have explored several types of sensing data (e.g., location, activity, conversation), none of them has leveraged Internet traffic of smartphones, which can be collected with little energy consumption and the data is insensitive to phone hardware. In this paper, we explore using coarse-grained meta-data of Internet traffic on smartphones for depression screening. We develop techniques to identify Internet usage sessions (i.e., time periods when a user is online) and extract a novel set of features based on usage sessions from the Internet traffic meta-data. Our results demonstrate that Internet usage features can reflect the different behavioral characteristics between depressed and non-depressed participants, confirming findings in psychological sciences, which have relied on surveys or questionnaires instead of real Internet traffic as in our study. Furthermore, we develop machine learning based prediction models that use these features to predict depression. Our evaluation shows that Internet usage features can be used for effective depression prediction, leading to F 1 score as high as 0.80.

Social context and the real-world consequences of social anxiety

BACKGROUND

Social anxiety lies on a continuum, and young adults with elevated symptoms are at risk for developing a range of psychiatric disorders. Yet relatively little is known about the factors that govern the hour-by-hour experience and expression of social anxiety in the real world.

METHODS

Here we used smartphone-based ecological momentary assessment (EMA) to intensively sample emotional experience across different social contexts in the daily lives of 228 young adults selectively recruited to represent a broad spectrum of social anxiety symptoms.

RESULTS

Leveraging data from over 11 000 real-world assessments, our results highlight the central role of close friends, family members, and romantic partners. The presence of such close companions was associated with enhanced mood, yet socially anxious individuals had fewer confidants and spent less time with the close companions that they do have. Although higher levels of social anxiety were associated with a general worsening of mood, socially anxious individuals appear to derive larger benefits - lower levels of negative affect, anxiety, and depression - from their close companions. In contrast, variation in social anxiety was unrelated to the amount of time spent with strangers, co-workers, and acquaintances; and we uncovered no evidence of emotional hypersensitivity to these less-familiar individuals.

CONCLUSIONS

These findings provide a framework for understanding the deleterious consequences of social anxiety in emerging adulthood and set the stage for developing improved intervention strategies.

The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study

BACKGROUND

Objective and continuous severity measures of anxiety and depression are highly valuable and would have many applications in psychiatry and psychology. A collective source of data for objective measures are the sensors in a person's smartphone, and a particularly rich source is the microphone that can be used to sample the audio environment. This may give broad insight into activity, sleep, and social interaction, which may be associated with quality of life and severity of anxiety and depression.

OBJECTIVE

This study aimed to explore the properties of passively recorded environmental audio from a subject's smartphone to find potential correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment.

METHODS

An Android app was designed, together with a centralized server system, to collect periodic measurements of the volume of sounds in the environment and to detect the presence or absence of English-speaking voices. Subjects were recruited into a 2-week observational study during which the app was run on their personal smartphone to collect audio data. Subjects also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the environmental audio of 84 participants with sufficient data, and correlations were measured between the 4 audio features and the 4 self-report measures.

RESULTS

The regularity in daily patterns of activity and inactivity inferred from the environmental audio volume was correlated with the severity of depression (r=-0.37; P<.001). A measure of sleep disturbance inferred from the environmental audio volume was also correlated with the severity of depression (r=0.23; P=.03). A proxy measure of social interaction based on the detection of speaking voices in the environmental audio was correlated with depression (r=-0.37; P<.001) and functional impairment (r=-0.29; P=.01). None of the 4 environmental audio-based features tested showed significant correlations with the measures of generalized anxiety or social anxiety.

CONCLUSIONS

In this study group, the environmental audio was shown to contain signals that were associated with the severity of depression and functional impairment. Associations with the severity of social anxiety disorder and generalized anxiety disorder were much weaker in comparison and not statistically significant at the 5% significance level. This work also confirmed previous work showing that the presence of voices is associated with depression. Furthermore, this study suggests that sparsely sampled audio volume could provide potentially relevant insight into subjects' mental health.

Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones

Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life.

Mental health ubiquitous monitoring supported by social situation awareness: A systematic review

Traditionally, the process of monitoring and evaluating social behavior related to mental health has based on self-reported information, which is limited by the subjective character of responses and various cognitive biases. Today, however, there is a growing amount of studies that have provided methods to objectively monitor social behavior through ubiquitous devices and have used this information to support mental health services. In this paper, we present a Systematic Literature Review (SLR) to identify, analyze and characterize the state of the art about the use of ubiquitous devices to monitor users' social behavior focused on mental health. For this purpose, we performed an exhaustive literature search on the six main digital libraries. A screening process was conducted on 160 peer-reviewed publications by applying suitable selection criteria to define the appropriate studies to the scope of this SLR. Next, 20 selected studies were forwarded to the data extraction phase. From an analysis of the selected studies, we recognized the types of social situations identified, the process of transforming contextual data into social situations, the use of social situation awareness to support mental health monitoring, and the methods used to evaluate proposed solutions. Additionally, we identified the main trends presented by this research area, as well as open questions and perspectives for future research. Results of this SLR showed that social situation-aware ubiquitous systems represent promising assistance tools for patients and mental health professionals. However, studies still present limitations in methodological rigor and restrictions in experiments, and solutions proposed by them have limitations to be overcome.

Optimizing Smartphone-Delivered Cognitive Behavioral Therapy for Body Dysmorphic Disorder Using Passive Smartphone Data: Initial Insights From an Open Pilot Trial

Background

Smartphone-delivered cognitive behavioral therapy (CBT) is becoming more common, but research on the topic remains in its infancy. Little is known about how people typically engage with smartphone CBT or which engagement and mobility patterns may optimize treatment. Passive smartphone data offer a unique opportunity to gain insight into these knowledge gaps.

Objective

This study aimed to examine passive smartphone data across a pilot course of smartphone CBT for body dysmorphic disorder (BDD), a psychiatric illness characterized by a preoccupation with a perceived defect in physical appearance, to inform hypothesis generation and the design of subsequent, larger trials.

Methods

A total of 10 adults with primary diagnoses of BDD were recruited nationally and completed telehealth clinician assessments with a reliable evaluator. In a 12-week open pilot trial of smartphone CBT, we initially characterized natural patterns of engagement with the treatment and tested how engagement and mobility patterns across treatment corresponded with treatment response.

Results

Most participants interacted briefly and frequently with smartphone-delivered treatment. More frequent app usage (r=–0.57), as opposed to greater usage duration (r=–0.084), correlated strongly with response. GPS-detected time at home, a potential digital marker of avoidance, decreased across treatment and correlated moderately with BDD severity (r=0.49).

Conclusions

The sample was small in this pilot study; thus, results should be used to inform the hypotheses and design of subsequent trials. The results provide initial evidence that frequent (even if brief) practice of CBT skills may optimize response to smartphone CBT and that mobility patterns may serve as useful passive markers of symptom severity. This is one of the first studies to examine the value that passively collected sensor data may contribute to understanding and optimizing users’ response to smartphone CBT. With further validation, the results can inform how to enhance smartphone CBT design.

Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health

Background

Loneliness has drawn increasing attention over the past few decades due to rising recognition of its close connection with serious health issues, like dementia. Yet, researchers are failing to find solutions to alleviate the globally experienced burden of loneliness.

Purpose

This review aims to shed light on possible reasons for why interventions have been ineffective. We suggest new directions for research on loneliness as it relates to precision health, emerging technologies, digital phenotyping, and machine learning.

Results

Current loneliness interventions are unsuccessful due to (i) their inconsideration of loneliness as a heterogeneous construct and (ii) not being targeted at individuals' needs and contexts. We propose a model for how loneliness interventions can move towards finding the right solution for the right person at the right time. Taking a precision health approach, we explore how transdisciplinary research can contribute to creating a more holistic picture of loneliness and shift interventions from treatment to prevention.

Conclusions

We urge the field to rethink metrics to account for diverse intra-individual experiences and trajectories of loneliness. Big data sharing and evolving technologies that emphasize human connection raise hope for realizing our model of precision health applied to loneliness. There is an urgent need for precise, integrated, and theory-driven interventions that focus on individuals' needs and the subjective burden of loneliness in the ageing context.

Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

BACKGROUND

Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier.

OBJECTIVE

This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset.

METHODS

In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants' social anxiety symptom severity.

RESULTS

The results suggested that these passive sensor data could be utilized to accurately predict participants' social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect.

CONCLUSIONS

These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.

Life within a limited radius: Investigating activity space in women with a history of child abuse using global positioning system tracking

Early experiences of childhood sexual or physical abuse are often associated with functional impairments, reduced well-being and interpersonal problems in adulthood. Prior studies have addressed whether the traumatic experience itself or adult psychopathology is linked to these limitations. To approach this question, individuals with posttraumatic stress disorder (PTSD) and healthy individuals with and without a history of child abuse were investigated. We used global positioning system (GPS) tracking to study temporal and spatial limitations in the participants’ real-life activity space over the course of one week. The sample consisted of 228 female participants: 150 women with PTSD and emotional instability with a history of child abuse, 35 mentally healthy women with a history of child abuse (healthy trauma controls, HTC) and 43 mentally healthy women without any traumatic experiences in their past (healthy controls, HC). Both traumatized groups—i.e. the PTSD and the HTC group—had smaller movement radii than the HC group on the weekends, but neither spent significantly less time away from home than HC. Some differences between PTSD and HC in movement radius seem to be related to correlates of PTSD psychopathology, like depression and physical health. Yet group differences between HTC and HC in movement radius remained even when contextual and individual health variables were included in the model, indicating specific effects of traumatic experiences on activity space. Experiences of child abuse could limit activity space later in life, regardless of whether PTSD develops.