Objective smartphone data as a potential diagnostic marker of bipolar disorder

Capturing the College Experience: A Four-Year Mobile Sensing Study of Mental Health, Resilience and Behavior of College Students during the Pandemic

Understanding the dynamics of mental health among undergraduate students across the college years is of critical importance, particularly during a global pandemic. In our study, we track two cohorts of first-year students at Dartmouth College for four years, both on and off campus, creating the longest longitudinal mobile sensing study to date. Using passive sensor data, surveys, and interviews, we capture changing behaviors before, during, and after the COVID-19 pandemic subsides. Our findings reveal the pandemic's impact on students' mental health, gender based behavioral differences, impact of changing living conditions and evidence of persistent behavioral patterns as the pandemic subsides. We observe that while some behaviors return to normal, others remain elevated. Tracking over 200 undergraduate students from high school to graduation, our study provides invaluable insights into changing behaviors, resilience and mental health in college life. Conducting a long-term study with frequent phone OS updates poses significant challenges for mobile sensing apps, data completeness and compliance. Our results offer new insights for Human-Computer Interaction researchers, educators and administrators regarding college life pressures. We also detail the public release of the de-identified College Experience Study dataset used in this paper and discuss a number of open research questions that could be studied using the public dataset.

Remote sensing mental health: A systematic review of factors essential to clinical translation from validation research

Background

Mental illness remains a major global health challenge largely due to the absence of definitive biomarkers applicable to diagnostics and care processes. Although remote sensing technologies, embedded in devices such as smartphones and wearables, offer a promising avenue for improved mental health assessments, their clinical integration has been slow.

Objective

This scoping review, following preferred reporting items for systematic reviews and meta-analyses guidelines, explores validation studies of remote sensing in clinical mental health populations, aiming to identify critical factors for clinical translation.

Methods

Comprehensive searches were conducted in six databases. The analysis, using narrative synthesis, examined clinical and socio-demographic characteristics of the populations studied, sensing purposes, temporal considerations and reference mental health assessments used for validation.

Results

The narrative synthesis of 50 included studies indicates that ten different sensor types have been studied for tracking and diagnosing mental illnesses, primarily focusing on physical activity and sleep patterns. There were many variations in the sensor methodologies used that may affect data quality and participant burden. Observation durations, and thus data resolution, varied by patient diagnosis. Currently, reference assessments predominantly rely on deficit focussed self-reports, and socio-demographic information is underreported, therefore representativeness of the general population is uncertain.

Conclusion

To fully harness the potential of remote sensing in mental health, issues such as reliance on self-reported assessments, and lack of socio-demographic context pertaining to generalizability need to be addressed. Striking a balance between resolution, data quality, and participant burden whilst clearly reporting limitations, will ensure effective technology use. The scant reporting on participants' socio-demographic data suggests a knowledge gap in understanding the effectiveness of passive sensing techniques in disadvantaged populations.

Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review

BACKGROUND

Smartphones and wearable biosensors can continuously and passively measure aspects of behavior and physiology while also collecting data that require user input. These devices can potentially be used to monitor symptom burden; estimate diagnosis and risk for relapse; predict treatment response; and deliver digital interventions in patients with obsessive-compulsive disorder (OCD), a prevalent and disabling psychiatric condition that often follows a chronic and fluctuating course and may uniquely benefit from these technologies.

OBJECTIVE

Given the speed at which mobile and wearable technologies are being developed and implemented in clinical settings, a continual reappraisal of this field is needed. In this scoping review, we map the literature on the use of wearable devices and smartphone-based devices or apps in the assessment, monitoring, or treatment of OCD.

METHODS

In July 2022 and April 2023, we conducted an initial search and an updated search, respectively, of multiple databases, including PubMed, Embase, APA PsycINFO, and Web of Science, with no restriction on publication period, using the following search strategy: ("OCD" OR "obsessive" OR "obsessive-compulsive") AND ("smartphone" OR "phone" OR "wearable" OR "sensing" OR "biofeedback" OR "neurofeedback" OR "neuro feedback" OR "digital" OR "phenotyping" OR "mobile" OR "heart rate variability" OR "actigraphy" OR "actimetry" OR "biosignals" OR "biomarker" OR "signals" OR "mobile health").

RESULTS

We analyzed 2748 articles, reviewed the full text of 77 articles, and extracted data from the 25 articles included in this review. We divided our review into the following three parts: studies without digital or mobile intervention and with passive data collection, studies without digital or mobile intervention and with active or mixed data collection, and studies with a digital or mobile intervention.

CONCLUSIONS

Use of mobile and wearable technologies for OCD has developed primarily in the past 15 years, with an increasing pace of related publications. Passive measures from actigraphy generally match subjective reports. Ecological momentary assessment is well tolerated for the naturalistic assessment of symptoms, may capture novel OCD symptoms, and may also document lower symptom burden than retrospective recall. Digital or mobile treatments are diverse; however, they generally provide some improvement in OCD symptom burden. Finally, ongoing work is needed for a safe and trusted uptake of technology by patients and providers.

Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review

BACKGROUND

Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity.

OBJECTIVE

This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers.

METHODS

This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed.

RESULTS

This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials.

CONCLUSION

mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.

Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study

BACKGROUND

Lack of quantifiable biomarkers is a major obstacle in diagnosing and treating depression. In adolescents, increasing suicidality during antidepressant treatment further complicates the problem.

OBJECTIVE

We sought to evaluate digital biomarkers for the diagnosis and treatment response of depression in adolescents through a newly developed smartphone app.

METHODS

We developed the Smart Healthcare System for Teens At Risk for Depression and Suicide app for Android-based smartphones. This app passively collected data reflecting the social and behavioral activities of adolescents, such as their smartphone usage time, physical movement distance, and the number of phone calls and text messages during the study period. Our study consisted of 24 adolescents (mean age 15.4 [SD 1.4] years, 17 girls) with major depressive disorder (MDD) diagnosed with Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version and 10 healthy controls (mean age 13.8 [SD 0.6] years, 5 girls). After 1 week's baseline data collection, adolescents with MDD were treated with escitalopram in an 8-week, open-label trial. Participants were monitored for 5 weeks, including the baseline data collection period. Their psychiatric status was measured every week. Depression severity was measured using the Children's Depression Rating Scale-Revised and Clinical Global Impressions-Severity. The Columbia Suicide Severity Rating Scale was administered in order to assess suicide severity. We applied the deep learning approach for the analysis of the data. Deep neural network was employed for diagnosis classification, and neural network with weighted fuzzy membership functions was used for feature selection.

RESULTS

We could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of the 24 adolescents with MDD, 10 responded to antidepressant treatments. We predicted the treatment response of adolescents with MDD with training accuracy of 94.2% and 3-fold validation accuracy of 76%. Adolescents with MDD tended to move longer distances and use smartphones for longer periods of time compared to controls. The deep learning analysis showed that smartphone usage time was the most important feature in distinguishing adolescents with MDD from controls. Prominent differences were not observed in the pattern of each feature between the treatment responders and nonresponders. The deep learning analysis revealed that the total length of calls received as the most important feature predicting antidepressant response in adolescents with MDD.

CONCLUSIONS

Our smartphone app demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict the treatment response of adolescents with MDD by examining smartphone-based objective data with deep learning approaches.

A digital self-report survey of mood for bipolar disorder

OBJECTIVES

Bipolar disorder (BP) is commonly researched in digital settings. As a result, standardized digital tools are needed to measure mood. We sought to validate a new survey that is brief, validated in digital form, and able to separately measure manic and depressive severity.

METHODS

We introduce a 6-item digital survey, called digiBP, for measuring mood in BP. It has three depressive items (depressed mood, fidgeting, fatigue), two manic items (increased energy, rapid speech), and one mixed item (irritability); and recovers two scores (m and d) to measure manic and depressive severity. In a secondary analysis of individuals with BP who monitored their symptoms over 6 weeks (n = 43), we perform a series of analyses to validate the digiBP survey internally, externally, and as a longitudinal measure.

RESULTS

We first verify a conceptual model for the survey in which items load onto two factors ("manic" and "depressive"). We then show weekly averages of m and d scores from digiBP can explain significant variation in weekly scores from the Young Mania Rating Scale (R²  = 0.47) and SIGH-D (R²  = 0.58). Lastly, we examine the utility of the survey as a longitudinal measure by predicting an individual's future m and d scores from their past m and d scores.

CONCLUSIONS

While further validation is warranted in larger, diverse populations, these validation analyses should encourage researchers to consider digiBP for their next digital study of BP.

Ecological momentary assessment of mood and movement with bipolar disorder over time: Participant recruitment and efficacy of study methods

OBJECTIVES

Mobile technology and ambulatory research tools enable the study of human experience in vivo, when and where it occurs. This includes cognitive processes that cannot be directly measured or observed (e.g., emotion) but can be reported in the moment when prompted.

METHODS

For the Bipolar Affective Disorder and older Adults (BADAS) Study, 50 participants were randomly prompted twice daily to complete brief smartphone questionnaires. This included the Bipolar Disorder Symptom Scale which was developed to briefly measure symptoms of both depression (cognitive and somatic) and hypo/mania (affrontive symptoms and elation/loss of insight). Participants could also submit voluntary or unsolicited app responses anytime; all were time- and GPS-stamped. Herein, we describe BADAS study methods that enabled effective recruitment, adherence and retention.

RESULTS

We collected 9600 app responses over 2 year, for an average response rate of 1.4×/day. Over an average of 145 consecutive days (range 2-435 days), BADAS participants reported depression and hypo/mania symptom levels (a.m. and p.m.), sleep quality (a.m.), medication adherence (a.m.) and any significant events of the day (p.m.). They received $1/day for the first 90 days after submitting both a.m. and p.m. questionnaires.

CONCLUSION

BADAS study methods demonstrates the utility of ecological momentary assessment in longitudinal psychiatric research.

mHealth technology to assess, monitor and treat daily functioning difficulties in people with severe mental illness: A systematic review

Severe mental illness (SMI) is associated with poor daily functioning; however available interventions currently under-deliver on their recovery prospect. Mobile digital health (mHealth) interventions are increasingly being developed and evaluated, and have the potential to support recovery. This review evaluates the use of mHealth technology to assess, monitor and reduce functioning difficulties in people with SMI. Studies were systematically searched on multiple databases. Study quality was assessed and double-rated independently. Findings were organised using a narrative synthesis and results were summarised according to the mHealth device purpose, i.e., assessment and monitoring or intervention. Thirty-eight studies comprised of 2262 participants met the inclusion criteria. Smartphones were the most popular mHealth device; personal digital assistants, wearables and tablets were also used. mHealth was widely found to be acceptable and feasible, with preliminary findings suggesting it can support functional recovery by augmenting an intervention, simplifying the assessment, increasing monitoring frequency and/or providing more detailed information. Considerations for overcoming barriers to implementation, recommendations for future research to establish effectiveness, personalisation and specification of mHealth devices and methodologies are discussed. The value of mHealth for remote delivery of recovery based interventions is also considered.

Ecological Momentary Assessment of Bipolar Disorder Symptoms and Partner Affect: Longitudinal Pilot Study

BACKGROUND

The World Health Organization ranks bipolar disorder (BD) as the 7th leading cause of disability. Although the effects on those with BD are well described, less is reported on the impact of BD on cohabiting partners or any interactions between the two; this requires in vivo data collection measured each day over several months.

OBJECTIVE

We set out to demonstrate the utility of ecological momentary assessment with BD couples measured using yoked smartphone apps. When randomly prompted over time, we assumed distinct patterns of association would emerge between BD symptoms (both depression and hypo/mania) and partner mood (positive and negative affect).

METHODS

For this pilot study, we recruited an international sample of young and older adults with BD and their cohabiting partners where available. Both participants and partners downloaded separate apps onto their respective smartphones. Within self-specified "windows of general availability," participants with BD were randomly prompted to briefly report symptoms of depression and hypo/mania (ie, BDS_x), positive and negative mood (ie, POMS-15; partners), and any important events of the day (both). The partner app was yoked to the participant app so that the former was prompted roughly 30 minutes after the participant with BD or the next morning if outside the partner's specified availability.

RESULTS

Four couples provided 312 matched BD symptom and partner mood responses over an average of 123 days (range 65-221 days). Both were GPS- and time-stamped (mean 3:11 hrs between questionnaires, SD 4:51 hrs). Total depression had a small but significant association with positive (r=-.14; P=.02) and negative partner affect (r=.15; P=.01]. Yet total hypo/mania appeared to have no association with positive partner affect (r=-.01; P=.87); instead, negative partner affect was significantly correlated with total hypo/mania (r=.26; P=.01). However, when we look specifically at BD factors, we see that negative partner affect is associated only with affrontive symptoms of hypo/mania (r=.38; P=.01); elation or loss of insight appears unrelated to either positive (r=.10; P=.09) or negative partner affect (r=.02; P=.71). Yet affrontive symptoms of hypo/mania were significantly correlated with negative affect, but only when couples were together (r=.41; P=.01), not when apart (r=.22; P=.12). That is, these angry interpersonal symptoms of hypo/mania appear to be experienced most negatively by spouses when couples are together.

CONCLUSIONS

These initial findings demonstrate the utility of in vivo ambulatory data collection in longitudinal mental health research. Preliminary analyses suggest different BD symptoms are associated with negative and positive partner mood. These negative effects appear greater for hypo/mania than depressive symptoms, but proximity to the person with BD is important.

Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II

BACKGROUND

Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals.

OBJECTIVE

By combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue.

METHODS

Behaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models.

RESULTS

Linear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P<.001) were significantly associated with interest in COVID fatigue. Notably, these behavioral and mental health changes are consistent with those observed around the initial implementation of COVID-19 lockdowns in the spring of 2020.

CONCLUSIONS

In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic.

Patient electronic communication data in clinical care: what is known and what is needed

The novel coronavirus (COVID-19) and physical distancing guidelines around the world have resulted in unprecedented changes to normal routine and increased smartphone use to maintain social relationships and support. Reports of depressive and anxiety symptom are on the rise, contributing to suffering among people-especially adolescents and young adults-with pre-existing mental health conditions. Psychiatric care has shifted primarily to telehealth limiting the important patient nonverbal communication that has been part of in-person clinical sessions. Supplementing clinical care with patient electronic communication (EC) data may provide valuable information and influence treatment decision making. Research in the impact of patient EC data on managing psychiatric symptoms is in its infancy. This review aims to identify how patient EC has been used in clinical care and its benefits in psychiatry and research. We discuss smartphone applications used to gather different types of EC data, how data have been integrated into clinical care, and implications for clinical care and research.

Reducing the rate of psychiatric re-admissions in bipolar disorder using smartphones-The RADMIS trial

OBJECTIVES

The MONARCA I and II trials were negative but suggested that smartphone-based monitoring may increase quality of life and reduce perceived stress in bipolar disorder (BD). The present trial was the first to investigate the effect of smartphone-based monitoring on the rate and duration of readmissions in BD.

METHODS

This was a randomized controlled single-blind parallel-group trial. Patients with BD (ICD-10) discharged from hospitalization in the Mental Health Services, Capital Region of Denmark were randomized 1:1 to daily smartphone-based monitoring including a feedback loop (+ standard treatment) or to standard treatment for 6 months. Primary outcomes: the rate and duration of psychiatric readmissions.

RESULTS

We included 98 patients with BD. In ITT analyses, there was no statistically significant difference in rates (hazard rate: 1.05, 95% CI: 0.54; 1.91, p = 0.88) or duration of readmission between the two groups (B: 3.67, 95% CI: -4.77; 12.11, p = 0.39). There was no difference in scores on the Hamilton Depression Rating Scale (B = -0.11, 95% CI: -2.50; 2.29, p = 0.93). The intervention group had higher scores on the Young Mania Rating Scale (B: 1.89, 95% CI: 0.0078; 3.78, p = 0.050). The intervention group reported lower levels of perceived stress (B: -7.18, 95% CI: -13.50; -0.86, p = 0.026) and lower levels of rumination (B: -6.09, 95% CI: -11.19; -1.00, p = 0.019).

CONCLUSIONS

Smartphone-based monitoring did not reduce rate and duration of readmissions. There was no difference in levels of depressive symptoms. The intervention group had higher levels of manic symptoms, but lower perceived stress and rumination compared with the control group.

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data

Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS.

A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.

Automatically Generated Smartphone Data in Young Patients With Newly Diagnosed Bipolar Disorder and Healthy Controls

Background: Smartphones may facilitate continuous and fine-grained monitoring of behavioral activities via automatically generated data and could prove to be especially valuable in monitoring illness activity in young patients with bipolar disorder (BD), who often present with rapid changes in mood and related symptoms. The present pilot study in young patients with newly diagnosed BD and healthy controls (HC) aimed to (1) validate automatically generated smartphone data reflecting physical and social activity and phone usage against validated clinical rating scales and questionnaires; (2) investigate differences in automatically generated smartphone data between young patients with newly diagnosed BD and HC; and (3) investigate associations between automatically generated smartphone data and smartphone-based self-monitored mood and activity in young patients with newly diagnosed BD. Methods: A total of 40 young patients with newly diagnosed BD and 21 HC aged 15-25 years provided daily automatically generated smartphone data for 3-779 days [median (IQR) = 140 (11.5-268.5)], in addition to daily smartphone-based self-monitoring of activity and mood. All participants were assessed with clinical rating scales. Results: (1) The number of outgoing phone calls was positively associated with scores on the Young Mania Rating Scale and subitems concerning activity and speech. The number of missed calls (p = 0.015) and the number of outgoing text messages (p = 0.017) were positively associated with the level of psychomotor agitation according to the Hamilton Depression Rating scale subitem 9. (2) Young patients with newly diagnosed BD had a higher number of incoming calls compared with HC (BD: mean = 1.419, 95% CI: 1.162, 1.677; HC: mean = 0.972, 95% CI: 0.637, 1.308; p = 0.043) and lower self-monitored mood and activity (p's < 0.001). (3) Smartphone-based self-monitored mood and activity were positively associated with step counts and the number of outgoing calls, respectively (p's < 0.001). Conclusion: Automatically generated data on physical and social activity and phone usage seem to reflect symptoms. These data differ between young patients with newly diagnosed BD and HC and reflect changes in illness activity in young patients with BD. Automatically generated smartphone-based data could be a useful clinical tool in diagnosing and monitoring illness activity in young patients with BD.

Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder

Background: Smartphones comprise a promising tool for symptom monitoring in patients with unipolar depressive disorder (UD) collected as either patient-reportings or possibly as automatically generated smartphone data. However, only limited research has been conducted in clinical populations. We investigated the association between smartphone-collected monitoring data and validated psychiatric ratings and questionnaires in a well-characterized clinical sample of patients diagnosed with UD. Methods: Smartphone data, clinical ratings, and questionnaires from patients with UD were collected 6 months following discharge from psychiatric hospitalization as part of a randomized controlled study. Smartphone data were collected daily, and clinical ratings (i.e., Hamilton Depression Rating Scale 17-item) were conducted three times during the study. We investigated associations between (1) smartphone-based patient-reported mood and activity and clinical ratings and questionnaires; (2) automatically generated smartphone data resembling physical activity, social activity, and phone usage and clinical ratings; and (3) automatically generated smartphone data and same-day smartphone-based patient-reported mood and activity. Results: A total of 74 patients provided 11,368 days of smartphone data, 196 ratings, and 147 questionnaires. We found that: (1) patient-reported mood and activity were associated with clinical ratings and questionnaires (p < 0.001), so that higher symptom scores were associated with lower patient-reported mood and activity, (2) Out of 30 investigated associations on automatically generated data and clinical ratings of depression, only four showed statistical significance. Further, lower psychosocial functioning was associated with fewer daily steps (p = 0.036) and increased number of incoming (p = 0.032), outgoing (p = 0.015) and missed calls (p = 0.007), and longer phone calls (p = 0.012); (3) Out of 20 investigated associations between automatically generated data and daily patient-reported mood and activity, 12 showed statistical significance. For example, lower patient-reported activity was associated with fewer daily steps, shorter distance traveled, increased incoming and missed calls, and increased screen-time. Conclusion: Smartphone-based self-monitoring is feasible and associated with clinical ratings in UD. Some automatically generated data on behavior may reflect clinical features and psychosocial functioning, but these should be more clearly identified in future studies, potentially combining patient-reported and smartphone-generated data.

Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations

Background: Remote monitoring and digital phenotyping harbor potential to aid clinical diagnosis, predict episode course and recognize early signs of mental health crises. Digital communication metrics, such as phone call and short message service (SMS) use may represent novel biomarkers of mood and diagnosis in Bipolar Disorder (BD) and Borderline Personality Disorder (BPD). Materials and Methods: BD (n = 17), BPD (n = 17) and Healthy Control (HC, n = 21) participants used a smartphone application which monitored phone calls and SMS messaging, alongside self-reported mood. Linear mixed-effects regression models were used to assess the association between digital communications and mood symptoms, mood state, trait-impulsivity, diagnosis and the interaction effect between mood and diagnosis. Results: Transdiagnostically, self-rated manic symptoms and manic state were positively associated with total and outgoing call frequency and cumulative total, incoming and outgoing call duration. Manic symptoms were also associated with total and outgoing SMS frequency. Transdiagnostic depressive symptoms were associated with increased mean incoming call duration. For the different diagnostic groups, BD was associated with increased total call frequency and BPD with increased total and outgoing SMS frequency and length compared to HC. Depression in BD, but not BPD, was associated with decreased total and outgoing call frequency, mean total and outgoing call duration and total and outgoing SMS frequency. Finally, trait-impulsivity was positively associated with total call frequency, total and outgoing SMS frequency and cumulative total and outgoing SMS length. Conclusion: These results identify a general increase in phone call and SMS communications associated with self-reported manic symptoms and a diagnosis-moderated decrease in communications associated with depression in BD, but not BPD, participants. These findings may inform the development of clinical tools to aid diagnosis and remote symptom monitoring, as well as informing understanding of differential psychopathologies in BD and BPD.

Wearable Computing Technology for Assessment of Cognitive Functioning of Bipolar Patients and Healthy Controls

Mobile cognitive tests have been emerged to first, bring the assessments outside the clinics and second, frequently measure individuals' cognitive performance in their free-living environment. Patients with Bipolar Disorder (BD) suffer from cognitive impairments and poor sleep quality negatively affects their cognitive performance. Wearables are capable of unobtrusively collecting multivariate data including activity and sleep features. In this study, we analyzed daily attention, working memory, and executive functions of patients with BD and healthy controls by using a smartwatch-based tool called UbiCAT to 1) investigate its concurrent validity and feasibility, 2) identify digital phenotypes of mental health using cognitive and mobile sensor data, and 3) classify patients and healthy controls on the basis of their daily cognitive and mobile data. Our findings demonstrated that UbiCAT is feasible with valid measures for in-the-wild cognitive assessments. The analysis showed that the patients responded more slowly during the attention task than the healthy controls, which could indicate a lower alertness of this group. Furthermore, sleep duration correlated positively with participants' working memory performance the next day. Statistical analysis showed that features including cognitive measures of attention and executive functions, sleep duration, time in bed, awakening frequency and duration, and step counts are the digital phenotypes of mental health diagnosis. Supervised learning models was used to classify individuals' mental health diagnosis using their daily observations. Overall, we achieved accuracy of approximately 74% using K-Nearest Neighbour (KNN) method.

Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability

Increased stability in one's daily routine is associated with well-being in the general population and often a goal of behavioral interventions for people with serious mental illnesses like schizophrenia. Assessing behavioral stability has been limited in clinical research by the use of retrospective scales, which are susceptible to reporting biases and memory inaccuracies. Mobile passive sensors, which are less susceptible to these sources of error, have emerged as tools to assess behavioral patterns in a range of populations. The present study developed and examined a metric of behavioral stability from data generated by a passive sensing system carried by 61 individuals with schizophrenia for one year. This metric-the Stability Index-appeared orthogonal from existing measures drawn from passive sensors and matched the predictive performance of state-of-the-art features. Specifically, greater stability in social activity (e.g., calls and messages) were associated with lower symptoms, and greater stability in physical activity (e.g., being still) appeared associated with elevated symptoms. This study provides additional support for the predictive value of individualized over population-level data in psychiatric populations. The Stability Index offers also a promising tool for generating insights about the impact of behavioral stability in schizophrenia-spectrum disorders.

Smartphone-based activity measurements in patients with newly diagnosed bipolar disorder, unaffected relatives and control individuals

BACKGROUND

In DSM-5 activity is a core criterion for diagnosing hypomania and mania. However, there are no guidelines for quantifying changes in activity. The objectives of the study were (1) to investigate daily smartphone-based self-reported and automatically-generated activity, respectively, against validated measurements of activity; (2) to validate daily smartphone-based self-reported activity and automatically-generated activity against each other; (3) to investigate differences in daily self-reported and automatically-generated smartphone-based activity between patients with bipolar disorder (BD), unaffected relatives (UR) and healthy control individuals (HC).

METHODS

A total of 203 patients with BD, 54 UR, and 109 HC were included. On a smartphone-based app, the participants daily reported their activity level on a scale from -3 to + 3. Additionally, participants owning an android smartphone provided automatically-generated data, including step counts, screen on/off logs, and call- and text-logs. Smartphone-based activity was validated against an activity questionnaire the International Physical Activity Questionnaire (IPAQ) and activity items on observer-based rating scales of depression using the Hamilton Depression Rating scale (HAMD), mania using Young Mania Rating scale (YMRS) and functioning using the Functional Assessment Short Test (FAST). In these analyses, we calculated averages of smartphone-based activity measurements reported in the period corresponding to the days assessed by the questionnaires and rating scales.

RESULTS

(1) Smartphone-based self-reported activity was a valid measure according to scores on the IPAQ and activity items on the HAMD and YMRS, and was associated with FAST scores, whereas the majority of automatically-generated smartphone-based activity measurements were not. (2) Daily smartphone-based self-reported and automatically-generated activity correlated with each other with nearly all measurements. (3) Patients with BD had decreased daily self-reported activity compared with HC. Patients with BD had decreased physical (number of steps) and social activity (more missed calls) but a longer call duration compared with HC. UR also had decreased physical activity compared with HC but did not differ on daily self-reported activity or social activity.

CONCLUSION

Daily self-reported activity measured via smartphone represents overall activity and correlates with measurements of automatically generated smartphone-based activity. Detecting activity levels using smartphones may be clinically helpful in diagnosis and illness monitoring in patients with bipolar disorder. Trial registration clinicaltrials.gov NCT02888262.

Can digital data diagnose mental health problems? A sociological exploration of 'digital phenotyping'

This paper critically explores the research and development of 'digital phenotyping', which broadly refers to the idea that digital data can measure and predict people's mental health as well as their potential risk for mental ill health. Despite increasing research and efforts to digitally track and predict ill mental health, there has been little sociological and critical engagement with this field. This paper aims to fill this gap by introducing digital phenotyping to the social sciences. We explore the origins of digital phenotyping, the concept of the digital phenotype and its potential for benefit, linking these to broader developments within the field of 'mental health sensing'. We then critically discuss the technology, offering three critiques. First, that there may be assumptions of normality and bias present in the use of algorithms; second, we critique the idea that digital data can act as a proxy for social life; and third that the often biological language employed in this field risks reifying mental health problems. Our aim is not to discredit the scientific work in this area, but rather to call for scientists to remain reflexive in their work, and for more social science engagement in this area.

Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?

Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the 'moment-by-moment quantification of the individual-level human phenotype in its own environment', represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians' capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders.

The Therapeutic Alliance in Digital Mental Health Interventions for Serious Mental Illnesses: Narrative Review

BACKGROUND

Digital mental health interventions offer unique advantages, and research indicates that these interventions are effective for a range of mental health concerns. Although these interventions are less established for individuals with serious mental illnesses, they demonstrate significant promise. A central consideration in traditional face-to-face therapies is the therapeutic alliance, whereas the nature of a digital therapeutic alliance and its relationship with outcomes requires further attention, particularly for individuals with serious mental illnesses.

OBJECTIVE

This narrative review aims to encourage further consideration and critical evaluation of the therapeutic alliance in digital mental health, specifically for individuals with serious mental illnesses.

METHODS

A narrative review was conducted by combining 3 main areas of the literature: the first examining the evidence for digital mental health interventions for serious mental illnesses, the second illuminating the nature and role of the therapeutic alliance in digital interventions, and the third surrounding practical considerations to enhance a digital therapeutic alliance.

RESULTS

Results indicated that a therapeutic alliance can be cultivated in digital interventions for those with serious mental illnesses, but that it may have unique, yet-to-be-confirmed characteristics in digital contexts. In addition, a therapeutic alliance appears to be less directly associated with outcomes in digital interventions than with those in face-to-face therapies. One possibility is that the digital therapeutic alliance is associated with increased engagement and adherence to digital interventions, through which it appears to influence outcomes. A number of design and implementation considerations may enhance the digital therapeutic alliance, including human support and technological features.

CONCLUSIONS

More research is required to further understand the nature and specific role of a therapeutic alliance in digital interventions for serious mental illnesses, particularly in informing their design. This review revealed several key research priorities to advance the therapeutic alliance in digital interventions.

Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

Internet- and mobile-based approaches have become increasingly significant to psychological research in the field of bipolar disorders. While research suggests that emotional aspects of bipolar disorders are substantially related to the social and global functioning or the suicidality of patients, these aspects have so far not sufficiently been considered within the context of mobile-based disease management approaches. As a multiprofessional research team, we have developed a new and emotion-sensitive assistance system, which we have adapted to the needs of patients with bipolar disorder. Next to the analysis of self-assessments, third-party assessments, and sensor data, the new assistance system analyzes audio and video data of these patients regarding their emotional content or the presence of emotional cues. In this viewpoint, we describe the theoretical and technological basis of our emotion-sensitive approach and do not present empirical data or a proof of concept. To our knowledge, the new assistance system incorporates the first mobile-based approach to analyze emotional expressions of patients with bipolar disorder. As a next step, the validity and feasibility of our emotion-sensitive approach must be evaluated. In the future, it might benefit diagnostic, prognostic, or even therapeutic purposes and complement existing systems with the help of new and intuitive interaction models.

Digital solutions for shaping mood and behavior among individuals with mood disorders

Mood disorders present on-going challenges to the medical field, with difficulties ranging from establishing effective treatments to understanding complexities of one's mood. One solution is the use of mobile apps and wearables for measuring physiological symptoms and real-time mood in order to shape mood and behavior. Current digital research is focused on increasing engagement in monitoring mood, uncovering mood dynamics, predicting mood, and providing digital microinterventions. This review discusses the importance and risks of user engagement, as well as barriers to improving it. Research on mood dynamics highlights the possibility to reveal data-driven computational phenotypes that could guide treatment. Mobile apps are being used to track voice patterns, GPS, and phone usage for predicting mood and treatment response. Future directions include utilizing mobile apps to deliver and evaluate microinterventions. To continue these advances, standardized reporting and study designs should be considered to improve digital solutions for mood disorders.

Automatically generated smartphone data and subjective stress in healthy individuals - a pilot study

Background: Most people will also experience symptoms of stress at some point. Smartphone use has increased during the last decade and may be a new way of monitoring stress. Thus, it is of interest to investigate whether automatically generated smartphone data reflecting smartphone use is associated with subjective stress in healthy individuals.Aims: to investigate whether automatically generated smartphone data (e.g. the number of outgoing sms/day) was associated with (1) smartphone-based subjectively reported perceived stress, (2) perceived stress (Cohen's Perceived Stress Scale (PSS)) (3) functioning (Functioning Assessment Short Test (FAST)) and (4) non-clinical depressive symptoms (Hamilton Depression Rating Scale 17-items (HDRS)).Methods: A cohort of 40 healthy blood donors used an app for daily self-assessment of stress for 16 weeks. At baseline participants filled out the PSS and were clinically evaluated using the FAST and the HDRS. The PSS assessment was repeated at the end of the study. Associations were estimated with linear mixed effect regression and linear regression models.Results: There were no statistically significant associations between automatically generated smartphone data and perceived stress, functioning or severity of depressive symptoms, respectively (e.g. the number of outgoing text messages/day and self-assessed stress (B = 0.30, 95% CI: -0.40; 0.99, p = .40).Conclusions: Participants presented with low levels of stress during the study. Automatically generated smartphone data was not able to catch potential subjective stress among healthy individuals in the present study. Due to the small sample and low levels of stress the results should be interpreted with caution.

The effect of smartphone-based monitoring on illness activity in bipolar disorder: the MONARCA II randomized controlled single-blinded trial

BACKGROUND

Recently, the MONARCA I randomized controlled trial (RCT) was the first to investigate the effect of smartphone-based monitoring in bipolar disorder (BD). Findings suggested that smartphone-based monitoring sustained depressive but reduced manic symptoms. The present RCT investigated the effect of a new smartphone-based system on the severity of depressive and manic symptoms in BD.

METHODS

Randomized controlled single-blind parallel-group trial. Patients with BD, previously treated at The Copenhagen Clinic for Affective Disorder, Denmark and currently treated at community psychiatric centres, private psychiatrists or GPs were randomized to the use of a smartphone-based system or to standard treatment for 9 months. Primary outcomes: differences in depressive and manic symptoms between the groups.

RESULTS

A total of 129 patients with BD (ICD-10) were included. Intention-to-treat analyses showed no statistically significant effect of smartphone-based monitoring on depressive (B = 0.61, 95% CI -0.77 to 2.00, p = 0.38) and manic (B = -0.25, 95% CI -1.1 to 0.59, p = 0.56) symptoms. The intervention group reported higher quality of life and lower perceived stress compared with the control group. In sub-analyses, the intervention group had higher risk of depressive episodes, but lower risk of manic episodes compared with the control group.

CONCLUSIONS

There was no effect of smartphone-based monitoring. In patient-reported outcomes, patients in the intervention group reported improved quality of life and reduced perceived stress. Patients in the intervention group had higher risk of depressive episodes and reduced risk of manic episodes. Despite the widespread use and excitement of electronic monitoring, few studies have investigated possible effects. Further studies are needed.

Biosensors for Personal Mobile Health: A System Architecture Perspective

Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor-based sensors are enabling measurement of vital signs, and microfluidic-based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it will become critical for researchers to design biosensors - with appropriate functionalities and specifications - to work seamlessly with accompanying connected hardware and software. This article describes recent research in biosensors, as well as current mobile health devices in use, as classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. We also discuss the path forward for integrating biosensors into smartphone-based mobile health devices.

Acoustic Feature Selection with Fuzzy Clustering, Self Organizing Maps and Psychiatric Assessments

Acoustic features about phone calls are promising markers for prediction of bipolar disorder episodes. Smartphones enable collection of voice signal on a daily basis, and thus, the amount of data available for analysis is quickly growing. At the same time, even though the collected data are crisp, there is a lot of imprecision related to the extraction of acoustic features, as well as to the assessment of patients’ mental state. In this paper, we address this problem and perform an advanced approach to feature selection. We start from the recursive feature elimination, then two alternative approaches to clustering (fuzzy clustering and self organizing maps) are performed. Finally, taking advantage of the partially assumed labels about the state of a patient derived from psychiatric assessments, we calculate the degree of agreement between clusters and labels aiming at selection of most adequate subset of acoustic parameters. The proposed method is preliminary validated on the real-life data gathered from smartphones of bipolar disorder patients.

Clinical or gimmickal: The use and effectiveness of mobile mental health apps for treating anxiety and depression

OBJECTIVES

The increase in ownership of smartphones and tablet devices has seen a worldwide government push, championed by the World Health Organization, towards digital healthcare services generally. Mental health has been a strong presence in the digitisation of healthcare because of the potential to solve some of the difficulties in accessing face-to-face services. This review summarises the recent history of e-mental health services and illuminates two very different paths. The first is the considerable amount of research that has proven the effectiveness of many online mental health programmes for personal computers and laptops, resulting in widespread acceptance of their ability to make a contribution in an individual's recovery from anxiety and depression. The second is associated with the more recent development of apps for smartphones and tablet devices and the contrasting paucity of research that has accompanied this burgeoning area of e-mental health. This review also outlines the current state of play for research into the effectiveness of mobile mental health apps for anxiety and depression, including issues associated with methodology, and offers sources of practical advice for clinicians wanting more information about these new digital tools.

CONCLUSION

Research into the effectiveness of mental health apps is lacking, and the majority have no evidence of efficacy. Clinicians need to be aware of what apps have such evidence and should exercise caution when recommending apps to patients. Suggestions are offered on the direction of future research, including an appeal to further include clinicians in the development and efficacy testing of mental health apps.

[Ambulatory monitoring and digital phenotyping in the diagnostics and treatment of bipolar disorders]

BACKGROUND

Reliable and valid diagnostics and treatment of bipolar disorders and affective episodes are subject to extensive, especially methodological limitations in the clinical practice.

OBJECTIVE

The use of smartphones and mobile sensor technology for improvement in diagnostics and treatment of bipolar disorders.

METHODS

Critical discussion of current research on the use of ambulatory monitoring and digital phenotyping with bipolar disorders.

RESULTS

In many studies the observation periods were too short and the sensors applied were too inaccurate to enable reliable and valid detection of behavioral changes in the context of affective episodes.

CONCLUSION

The clarification and operationalization of psychopathological constructs to allow for the measurement of objectively observable and ascertainable behavioral changes during depressive and (hypo)manic states are essential for the successful application of modern mobile technologies in the diagnostics and treatment of bipolar disorders.

Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety

The use of data generated passively by personal electronic devices, such as smartphones, to measure human function in health and disease has generated significant research interest. Particularly in psychiatry, objective, continuous quantitation using patients' own devices may result in clinically useful markers that can be used to refine diagnostic processes, tailor treatment choices, improve condition monitoring for actionable outcomes, such as early signs of relapse, and develop new intervention models. If a principal goal for digital phenotyping is clinical improvement, research needs to attend now to factors that will help or hinder future clinical adoption. We identify four opportunities for research directed toward this goal: exploring intermediate outcomes and underlying disease mechanisms; focusing on purposes that are likely to be used in clinical practice; anticipating quality and safety barriers to adoption; and exploring the potential for digital personalized medicine arising from the integration of digital phenotyping and digital interventions. Clinical relevance also means explicitly addressing consumer needs, preferences, and acceptability as the ultimate users of digital phenotyping interventions. There is a risk that, without such considerations, the potential benefits of digital phenotyping are delayed or not realized because approaches that are feasible for application in healthcare, and the evidence required to support clinical commissioning, are not developed. Practical steps to accelerate this research agenda include the further development of digital phenotyping technology platforms focusing on scalability and equity, establishing shared data repositories and common data standards, and fostering multidisciplinary collaborations between clinical stakeholders (including patients), computer scientists, and researchers.

The Validity of Daily Self-Assessed Perceived Stress Measured Using Smartphones in Healthy Individuals: Cohort Study

Background

Smartphones may offer a new and easy tool to assess stress, but the validity has never been investigated.

Objective

This study aimed to investigate (1) the validity of smartphone-based self-assessed stress compared with Cohen Perceived Stress Scale (PSS) and (2) whether smartphone-based self-assessed stress correlates with neuroticism (Eysenck Personality Questionnaire-Neuroticism, EPQ-N), psychosocial functioning (Functioning Assessment Short Test, FAST), and prior stressful life events (Kendler Questionnaire for Stressful Life Events, SLE).

Methods

A cohort of 40 healthy blood donors with no history of personal or first-generation family history of psychiatric illness and who used an Android smartphone were instructed to self-assess their stress level daily (on a scale from 0 to 2; beta values reflect this scale) for 4 months. At baseline, participants were assessed with the FAST rater-blinded and filled out the EPQ, the PSS, and the SLE. The PSS assessment was repeated after 4 months.

Results

In linear mixed-effect regression and linear regression models, there were statistically significant positive correlations between self-assessed stress and the PSS (beta=.0167; 95% CI 0.0070-0.0026; P=.001), the EPQ-N (beta=.0174; 95% CI 0.0023-0.0325; P=.02), and the FAST (beta=.0329; 95% CI 0.0036-0.0622; P=.03). No correlation was found between smartphone-based self-assessed stress and the SLE.

Conclusions

Daily smartphone-based self-assessed stress seems to be a valid measure of perceived stress. Our study contains a modest sample of 40 healthy participants and adds knowledge to a new but growing field of research. Smartphone-based self-assessed stress is a promising tool for measuring stress in real time in future studies of stress and stress-related behavior.

Reporting guidelines on remotely collected electronic mood data in mood disorder (eMOOD)-recommendations

Prospective monitoring of mood was started by Kraepelin who made and recorded frequent observations of his patients. During the last decade, the number of research studies using remotely collected electronic mood data has increased markedly. However, standardized measures and methods to collect, analyze and report electronic mood data are lacking. To get better understanding of the nature, correlates and implications of mood and mood instability, and to standardize this process, we propose guidelines for reporting of electronic mood data (eMOOD). This paper provides an overview of remotely collected electronic mood data in mood disorders and discusses why standardized reporting is necessary to evaluate and inform mood research in Psychiatry. Adherence to these guidelines will improve interpretation, reproducibility and future meta-analyses of mood monitoring in mood disorder research.

Relationships between smartphone social behavior and relapse in schizophrenia: A preliminary report

Social dysfunction is a hallmark of schizophrenia. Social isolation may increase individuals' risk for psychotic symptom exacerbation and relapse. Monitoring and timely detection of shifts in social functioning are hampered by the limitations of traditional clinic-based assessment strategies. Ubiquitous mobile technologies such as smartphones introduce new opportunities to capture objective digital indicators of social behavior. The goal of this study was to evaluate whether smartphone-collected digital measures of social behavior can provide early indication of relapse events among individuals with schizophrenia. Sixty-one individuals with schizophrenia with elevated risk for relapse were given smartphones with the CrossCheck behavioral sensing system for a year of remote monitoring. CrossCheck leveraged the device's microphone, call record, and text messaging log to capture digital socialization data. Relapse events including psychiatric hospitalizations, suicidal ideation, and significant psychiatric symptom exacerbations were recorded by trained assessors. Exploratory mixed effects models examined relationships of social behavior to relapse, finding that reductions in number and duration of outgoing calls, as well as number of text messages were associated with relapses. Number and duration of incoming phone calls and in-person conversations were not. Smartphone enabled social activity may provide an important metric in determining relapse risk in schizophrenia and provide access to sensitive, meaningful and ecologically valid data streams never before available in routine care.