A comparison of passive and active estimates of sleep in a cohort with schizophrenia

Utility of Digital Phenotyping Based on Wrist Wearables and Smartphones in Psychosis: Observational Study

Background

Digital phenotyping provides insights into an individual's digital behaviors and has potential clinical utility.

Objective

In this observational study, we explored digital biomarkers collected from wrist-wearable devices and smartphones and their associations with clinical symptoms and functioning in patients with schizophrenia.

Methods

We recruited 100 outpatients with schizophrenia spectrum disorder, and we collected various digital data from commercially available wrist wearables and smartphones over a 6-month period. In this report, we analyzed the first week of digital data on heart rate, sleep, and physical activity from the wrist wearables and travel distance, sociability, touchscreen tapping speed, and screen time from the smartphones. We analyzed the relationships between these digital measures and patient baseline measurements of clinical symptoms assessed with the Positive and Negative Syndrome Scale, Brief Negative Symptoms Scale, and Calgary Depression Scale for Schizophrenia, as well as functioning as assessed with the Social and Occupational Functioning Assessment Scale. Linear regression was performed for each digital and clinical measure independently, with the digital measures being treated as predictors.

Results

Digital data were successfully collected from both the wearables and smartphones throughout the study, with 91% of the total possible data successfully collected from the wearables and 82% from the smartphones during the first week of the trial-the period under analysis in this report. Among the clinical outcomes, negative symptoms were associated with the greatest number of digital measures (10 of the 12 studied here), followed by overall measures of psychopathology symptoms, functioning, and positive symptoms, which were each associated with at least 3 digital measures. Cognition and cognitive/disorganization symptoms were each associated with 1 or 2 digital measures.

Conclusions

We found significant associations between nearly all digital measures and a wide range of symptoms and functioning in a community sample of individuals with schizophrenia. These findings provide insights into the digital behaviors of individuals with schizophrenia and highlight the potential of using commercially available wrist wearables and smartphones for passive monitoring in schizophrenia.

Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study

BACKGROUND

Although significant research has explored the digital phenotype in mood disorders, the time-lagged and bidirectional relationship between mood and global positioning system (GPS) mobility remains relatively unexplored. Leveraging the widespread use of smartphones, we examined correlations between mood and behavioral changes, which could inform future scalable interventions and personalized mental health monitoring.

OBJECTIVE

This study aims to investigate the bidirectional time lag relationships between passive GPS data and active ecological momentary assessment (EMA) data collected via smartphone app technology.

METHODS

Between March 2020 and May 2022, we recruited 45 participants (mean age 42.3 years, SD 12.1 years) who were followed up for 6 months: 35 individuals diagnosed with mood disorders referred by psychiatrists and 10 healthy control participants. This resulted in a total of 5248 person-days of data. Over 6 months, we collected 2 types of smartphone data: passive data on movement patterns with nearly 100,000 GPS data points per individual and active data through EMA capturing daily mood levels, including fatigue, irritability, depressed, and manic mood. Our study is limited to Android users due to operating system constraints.

RESULTS

Our findings revealed a significant negative correlation between normalized entropy (r=-0.353; P=.04) and weekly depressed mood as well as between location variance (r=-0.364; P=.03) and depressed mood. In participants with mood disorders, we observed bidirectional time-lagged associations. Specifically, changes in homestay were positively associated with fatigue (β=0.256; P=.03), depressed mood (β=0.235; P=.01), and irritability (β=0.149; P=.03). A decrease in location variance was significantly associated with higher depressed mood the following day (β=-0.015; P=.009). Conversely, an increase in depressed mood was significantly associated with reduced location variance the next day (β=-0.869; P<.001). These findings suggest a dynamic interplay between mood symptoms and mobility patterns.

CONCLUSIONS

This study demonstrates the potential of utilizing active EMA data to assess mood levels and passive GPS data to analyze mobility behaviors, with implications for managing disease progression in patients. Monitoring location variance and homestay can provide valuable insights into this process. The daily use of smartphones has proven to be a convenient method for monitoring patients' conditions. Interventions should prioritize promoting physical movement while discouraging prolonged periods of staying at home.

Associations between disturbed sleep and attenuated psychotic experiences in people at clinical high risk for psychosis

BACKGROUND

Pre-diagnostic stages of psychotic illnesses, including 'clinical high risk' (CHR), are marked by sleep disturbances. These sleep disturbances appear to represent a key aspect in the etiology and maintenance of psychotic disorders. We aimed to examine the relationship between self-reported sleep dysfunction and attenuated psychotic symptoms (APS) on a day-to-day basis.

METHODS

Seventy-six CHR young people completed the Experience Sampling Methodology (ESM) component of the European Union Gene-Environment Interaction Study, collected through PsyMate® devices, prompting sleep and symptom questionnaires 10 times daily for 6 days. Bayesian multilevel mixed linear regression analyses were performed on time-variant ESM data using the brms package in R. We investigated the day-to-day associations between sleep and psychotic experiences bidirectionally on an item level. Sleep items included sleep onset latency, fragmentation, and quality. Psychosis items assessed a range of perceptual, cognitive, and bizarre thought content common in the CHR population.

RESULTS

Two of the seven psychosis variables were unidirectionally predicted by previous night's number of awakenings: every unit increase in number of nightly awakenings predicted a 0.27 and 0.28 unit increase in feeling unreal or paranoid the next day, respectively. No other sleep variables credibly predicted next-day psychotic symptoms or vice-versa.

CONCLUSION

In this study, the relationship between sleep disturbance and APS appears specific to the item in question. However, some APS, including perceptual disturbances, had low levels of endorsement amongst this sample. Nonetheless, these results provide evidence for a unidirectional relationship between sleep and some APS in this population.

Smartphone sensor data estimate alcohol craving in a cohort of patients with alcohol-associated liver disease and alcohol use disorder

BACKGROUND

Sensors within smartphones, such as accelerometer and location, can describe longitudinal markers of behavior as represented through devices in a method called digital phenotyping. This study aimed to assess the feasibility of digital phenotyping for patients with alcohol-associated liver disease and alcohol use disorder, determine correlations between smartphone data and alcohol craving, and establish power assessment for future studies to prognosticate clinical outcomes.

METHODS

A total of 24 individuals with alcohol-associated liver disease and alcohol use disorder were instructed to download the AWARE application to collect continuous sensor data and complete daily ecological momentary assessments on alcohol craving and mood for up to 30 days. Data from sensor streams were processed into features like accelerometer magnitude, number of calls, and location entropy, which were used for statistical analysis. We used repeated measures correlation for longitudinal data to evaluate associations between sensors and ecological momentary assessments and standard Pearson correlation to evaluate within-individual relationships between sensors and craving.

RESULTS

Alcohol craving significantly correlated with mood obtained from ecological momentary assessments. Across all sensors, features associated with craving were also significantly correlated with all moods (eg, loneliness and stress) except boredom. Individual-level analysis revealed significant relationships between craving and features of location entropy and average accelerometer magnitude.

CONCLUSIONS

Smartphone sensors may serve as markers for alcohol craving and mood in alcohol-associated liver disease and alcohol use disorder. Findings suggest that location-based and accelerometer-based features may be associated with alcohol craving. However, data missingness and low participant retention remain challenges. Future studies are needed for further digital phenotyping of relapse risk and progression of liver disease.

Mobile sensing to advance tumor modeling in cancer patients: A conceptual framework

As mobile and wearable devices continue to grow in popularity, there is strong yet unrealized potential to harness people's mobile sensing data to improve our understanding of their cellular and biologically-based diseases. Breakthrough technical innovations in tumor modeling, such as the three dimensional tumor microenvironment system (TMES), allow researchers to study the behavior of tumor cells in a controlled environment that closely mimics the human body. Although patients' health behaviors are known to impact their tumor growth through circulating hormones (cortisol, melatonin), capturing this process is a challenge to rendering realistic tumor models in the TMES or similar tumor modeling systems. The goal of this paper is to propose a conceptual framework that unifies researchers from digital health, data science, oncology, and cellular signaling, in a common cause to improve cancer patients' treatment outcomes through mobile sensing. In support of our framework, existing studies indicate that it is feasible to use people's mobile sensing data to approximate their underlying hormone levels. Further, it was found that when cortisol is cycled through the TMES based on actual patients' cortisol levels, there is a significant increase in pancreatic tumor cell growth compared to when cortisol levels are at normal healthy levels. Taken together, findings from these studies indicate that continuous monitoring of people's hormone levels through mobile sensing may improve experimentation in the TMES, by informing how hormones should be introduced. We hope our framework inspires digital health researchers in the psychosocial sciences to consider how their expertise can be applied to advancing outcomes across levels of inquiry, from behavioral to cellular.

Innovations in Technology and Mechanisms of Change in Behavioral Interventions

The purpose of this manuscript is to provide an overview of, and rationale for, the increasing adoption of a wide range of cutting-edge technological methods in assessment and intervention which are relevant for treatment. First, we review traditional approaches to measuring and monitoring affect, behavior, and cognition in behavior and cognitive-behavioral therapy. Second, we describe evolving active and passive technology-enabled approaches to behavior assessment including emerging applications of digital phenotyping facilitated through fitness trackers, smartwatches, and social media. Third, we describe ways that these emerging technologies may be used for intervention, focusing on novel applications for the use of technology in intervention efforts. Importantly, though some of the methods and approaches we describe here warrant future testing, many aspects of technology can already be easily incorporated within an established treatment framework.

Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health

The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.

Multi-ancestry genome-wide analysis identifies shared genetic effects and common genetic variants for self-reported sleep duration

Both short (≤6 h per night) and long sleep duration (≥9 h per night) are associated with increased risk of chronic diseases. Despite evidence linking habitual sleep duration and risk of disease, the genetic determinants of sleep duration in the general population are poorly understood, especially outside of European (EUR) populations. Here, we report that a polygenic score of 78 European ancestry sleep duration single-nucleotide polymorphisms (SNPs) is associated with sleep duration in an African (n = 7288; P = 0.003), an East Asian (n = 13 618; P = 6 × 10-4) and a South Asian (n = 7485; P = 0.025) genetic ancestry cohort, but not in a Hispanic/Latino cohort (n = 8726; P = 0.71). Furthermore, in a pan-ancestry (N = 483 235) meta-analysis of genome-wide association studies (GWAS) for habitual sleep duration, 73 loci are associated with genome-wide statistical significance. Follow-up of five loci (near HACD2, COG5, PRR12, SH3RF1 and KCNQ5) identified expression-quantitative trait loci for PRR12 and COG5 in brain tissues and pleiotropic associations with cardiovascular and neuropsychiatric traits. Overall, our results suggest that the genetic basis of sleep duration is at least partially shared across diverse ancestry groups.

Linguistic and non-linguistic markers of disorganization in psychotic illness

BACKGROUND

Disorganization, presenting as impairment in thought, language and goal-directed behavior, is a core multidimensional syndrome of psychotic disorders. This study examined whether scalable computational measures of spoken language, and smartphone usage pattern, could serve as digital biomarkers of clinical disorganization symptoms.

METHODS

We examined in a longitudinal cohort of adults with a psychotic disorder, the associations between clinical measures of disorganization and computational measures of 1) spoken language derived from monthly, semi-structured, recorded clinical interviews; and 2) smartphone usage pattern derived via passive sensing technologies over the month prior to the interview. The language features included speech quantity, rate, fluency, and semantic regularity. The smartphone features included data missingness and phone usage during sleep time. The clinical measures consisted of the Positive and Negative Symptom Scale (PANSS) conceptual disorganization, difficulty in abstract thinking, and poor attention, items. Mixed linear regression analyses were used to estimate both fixed and random effects.

RESULTS

Greater severity of clinical symptoms of conceptual disorganization was associated with greater verbosity and more disfluent speech. Greater severity of conceptual disorganization was also associated with greater missingness of smartphone data, and greater smartphone usage during sleep time. While the observed associations were significant across the group, there was also significant variation between individuals.

CONCLUSIONS

The findings suggest that digital measures of speech disfluency may serve as scalable markers of conceptual disorganization. The findings warrant further investigation into the use of recorded interviews and passive sensing technologies to assist in the characterization and tracking of psychotic illness.

Using digital phenotyping to understand health-related outcomes: A scoping review

BACKGROUND

Digital phenotyping may detect changes in health outcomes and potentially lead to proactive measures to mitigate health declines and avoid major medical events. While health-related outcomes have traditionally been acquired through self-report measures, those approaches have numerous limitations, such as recall bias, and social desirability bias. Digital phenotyping may offer a potential solution to these limitations.

OBJECTIVES

The purpose of this scoping review was to identify and summarize how passive smartphone data are processed and evaluated analytically, including the relationship between these data and health-related outcomes.

METHODS

A search of PubMed, Scopus, Compendex, and HTA databases was conducted for all articles in April 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines.

RESULTS

A total of 40 articles were included and went through an analysis based on data collection approaches, feature extraction, data analytics, behavioral markers, and health-related outcomes. This review demonstrated a layer of features derived from raw sensor data that can then be integrated to estimate and predict behaviors, emotions, and health-related outcomes. Most studies collected data from a combination of sensors. GPS was the most used digital phenotyping data. Feature types included physical activity, location, mobility, social activity, sleep, and in-phone activity. Studies involved a broad range of the features used: data preprocessing, analysis approaches, analytic techniques, and algorithms tested. 55% of the studies (n = 22) focused on mental health-related outcomes.

CONCLUSION

This scoping review catalogued in detail the research to date regarding the approaches to using passive smartphone sensor data to derive behavioral markers to correlate with or predict health-related outcomes. Findings will serve as a central resource for researchers to survey the field of research designs and approaches performed to date and move this emerging domain of research forward towards ultimately providing clinical utility in patient care.

Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research

The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following: phone usage, messages usage, visits, device usage, and ambient light. We compared data from these five new sensors from SensorKit to our current digital phenotyping data collection sensors to assess similarity and differences in both raw and processed data. We present sample data from all five of these new sensors. We also present sample data from current digital phenotyping sources and compare these data to SensorKit sensors when applicable. SensorKit offers great potential for health research. Many SensorKit sensors improve upon previously accessible features and produce data that appears clinically relevant. SensorKit sensors will likely play a substantial role in digital phenotyping. However, using these data requires advanced health app infrastructure and the ability to securely store high-frequency data.

Technology and Mental Health: State of the Art for Assessment and Treatment

Technology is ubiquitous in society and is now being extensively used in mental health applications. Both assessment and treatment strategies are being developed and deployed at a rapid pace. The authors review the current domains of technology utilization, describe standards for quality evaluation, and forecast future developments. This review examines technology-based assessments of cognition, emotion, functional capacity and everyday functioning, virtual reality approaches to assessment and treatment, ecological momentary assessment, passive measurement strategies including geolocation, movement, and physiological parameters, and technology-based cognitive and functional skills training. There are many technology-based approaches that are evidence based and are supported through the results of systematic reviews and meta-analyses. Other strategies are less well supported by high-quality evidence at present, but there are evaluation standards that are well articulated at this time. There are some clear challenges in selection of applications for specific conditions, but in several areas, including cognitive training, randomized clinical trials are available to support these interventions. Some of these technology-based interventions have been approved by the U.S. Food and Drug administration, which has clear standards for which types of applications, and which claims about them, need to be reviewed by the agency and which are exempt.

Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating

BACKGROUND

Binge eating is a subjective loss of control while eating, which leads to the consumption of large amounts of food. It can cause significant emotional distress and is often accompanied by purging behaviors (eg, meal skipping, overexercising, or vomiting).

OBJECTIVE

The aim of this study was to explore the potential of mobile sensing to detect indicators of binge-eating episodes, with a view toward informing the design of future context-aware mobile interventions.

METHODS

This study was conducted in 2 stages. The first involved the development of the DeMMI (Detecting Mental health behaviors using Mobile Interactions) app. As part of this, we conducted a consultation session to explore whether the types of sensor data we were proposing to capture were useful and appropriate, as well as to gather feedback on some specific app features relating to self-reporting. The second stage involved conducting a 6-week period of data collection with 10 participants experiencing binge eating (logging both their mood and episodes of binge eating) and 10 comparison participants (logging only mood). An optional interview was conducted after the study, which discussed their experience using the app, and 8 participants (n=3, 38% binge eating and n=5, 63% comparisons) consented.

RESULTS

The findings showed unique differences in the types of sensor data that were triangulated with the individuals' episodes (with nearby Bluetooth devices, screen and app use features, mobility features, and mood scores showing relevance). Participants had a largely positive opinion about the app, its unobtrusive role, and its ease of use. Interacting with the app increased participants' awareness of and reflection on their mood and phone usage patterns. Moreover, they expressed no privacy concerns as these were alleviated by the study information sheet.

CONCLUSIONS

This study contributes a series of recommendations for future studies wishing to scale our approach and for the design of bespoke mobile interventions to support this population.

Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World

There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership.

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.

Validation of an ecological momentary assessment to measure processing speed and executive function in schizophrenia

Cognitive impairments are a core feature of schizophrenia that have negative impacts on functional outcomes. However, it remains challenging to assess these impairments in clinical settings. Smartphone apps provide the opportunity to measure cognitive impairments in an accessible way; however, more research is needed to validate these cognitive assessments in schizophrenia. We assessed the initial accessibility, validity, and reliability of a smartphone-based cognitive test to measure cognition in schizophrenia. A total of 29 individuals with schizophrenia and 34 controls were included in the analyses. Participants completed the standard pen-and-paper Trail Making Tests (TMT) A and B, and smartphone-based versions, Jewels Trail Tests (JTT) A and B, at the single in-lab visit. Participants were asked to complete the JTT remotely once per week for three months. We also investigated how subjective sleep quality and mood may affect cognitive performance longitudinally. In-lab and remote JTT scores moderately and positively correlated with in-lab TMT scores. Moderate test-retest reliability was observed across the in-lab, first remote, and last remote completion times of the JTT. Additionally, individuals with schizophrenia had significantly lower performance compared to controls on both the in-lab JTT and TMT. Self-reported mood had a significant effect on JTT A performance over time but no other significant relationships were found remotely. Our results support the initial accessibility, validity and reliability of using the JTT to measure cognition in schizophrenia. Future research to develop additional smartphone-based cognitive tests as well as with larger samples and in other psychiatric populations are warranted.

Feasibility and acceptability of the mobile application for the prevention of suicide (MAPS)

Rates of Veteran suicide continue to be unacceptably high. Suicidal ideation and behavior are contextually and situationally based, limiting the ability of traditional prevention and assessment strategies to prevent acute crises. The Mobile Application for the Prevention of Suicide (MAPS) is a novel, smartphone-based intervention strategy that utilizes ecological momentary assessment to identify suicide risk in the moment and delivers treatment strategies in real-time. The app is personalized to each patient, utilizes empirically intervention strategies, and is delivered adjunctively to Veterans Affairs (VA) treatment as usual. This article outlines the MAPS intervention and presents results of an open trial to assess its feasibility and acceptability. Eight Veterans were recruited from aVeterans Affairs Medical Center (VAMC) psychiatric inpatient unit following hospitalization for either a suicide ideation or attempt. Veterans received MAPS for 2 weeks post-hospitalization. Veterans reported high levels of satisfaction with MAPS and all opted to extend their use of MAPS beyond the 2-week trial period. MAPS may be a useful adjunctive to treatment as usual for high-risk Veterans by allowing patients and their providers to better track suicide risk and deploy intervention strategies when risk is detected.

Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation

BACKGROUND

Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep.

OBJECTIVE

This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest.

METHODS

The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward-sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each.

RESULTS

Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data.

CONCLUSIONS

We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments.

Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses

BACKGROUND

Digital phenotyping is the use of data from smartphones and wearables collected in situ for capturing a digital expression of human behaviors. Digital phenotyping techniques can be used to analyze both passively (e.g., sensor) and actively (e.g., survey) collected data. Machine learning offers a possible predictive bridge between digital phenotyping and future clinical state. This review examines passive digital phenotyping across the schizophrenia spectrum and bipolar disorders, with a focus on machine-learning studies.

METHODS

A systematic review of passive digital phenotyping literature was conducted using keywords related to severe mental illnesses, data-collection devices (e.g., smartphones, wearables, actigraphy devices), and streams of data collected. Searches of five databases initially yielded 3312 unique publications. Fifty-one studies were selected for inclusion, with 16 using machine-learning techniques.

RESULTS

All studies differed in features used, data pre-processing, analytical techniques, algorithms tested, and performance metrics reported. Across all studies, the data streams and other study factors reported also varied widely. Machine-learning studies focused on random forest, support vector, and neural net approaches, and almost exclusively on bipolar disorder.

DISCUSSION

Many machine-learning techniques have been applied to passively collected digital phenotyping data in schizophrenia and bipolar disorder. Larger studies, and with improved data quality, are needed, as is further research on the application of machine learning to passive digital phenotyping data in early diagnosis and treatment of psychosis. In order to achieve greater comparability of studies, common data elements are identified for inclusion in future studies.

Sociodemographic characteristics of missing data in digital phenotyping

The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for "digital phenotyping," the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.

Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study

Background

Ecological momentary assessment (EMA) tools appear to be useful interventions for collecting real-time data on patients’ behavior and functioning. However, concerns have been voiced regarding the acceptability of EMA among patients with schizophrenia and the factors influencing EMA acceptability.

Objective

The aim of this study was to investigate the acceptability of a passive smartphone-based EMA app, evidence-based behavior (eB2), among patients with schizophrenia spectrum disorders and the putative variables underlying their acceptance.

Methods

The participants in this study were from an ongoing randomized controlled trial (RCT) of metacognitive training, consisting of outpatients with schizophrenia spectrum disorders (F20-29 of 10th revision of the International Statistical Classification of Diseases and Related Health Problems), aged 18-64 years, none of whom received any financial compensation. Those who consented to installation of the eB2 app (users) were compared with those who did not (nonusers) in sociodemographic, clinical, premorbid adjustment, neurocognitive, psychopathological, insight, and metacognitive variables. A multivariable binary logistic regression tested the influence of the above (independent) variables on “being user versus nonuser” (acceptability), which was the main outcome measure.

Results

Out of the 77 RCT participants, 24 (31%) consented to installing eB2, which remained installed till the end of the study (median follow-up 14.50 weeks) in 14 participants (70%). Users were younger and had a higher education level, better premorbid adjustment, better executive function (according to the Trail Making Test), and higher cognitive insight levels (measured with the Beck Cognitive Insight Scale) than nonusers (univariate analyses) although only age (OR 0.93, 95% CI 0.86-0.99; P=.048) and early adolescence premorbid adjustment (OR 0.75, 95% CI 0.61-0.93; P=.01) survived the multivariable regression model, thus predicting eB2 acceptability.

Conclusions

Acceptability of a passive smartphone-based EMA app among participants with schizophrenia spectrum disorders in this RCT where no participant received financial compensation was, as expected, relatively low, and linked with being young and good premorbid adjustment. Further research should examine how to increase EMA acceptability in patients with schizophrenia spectrum disorders, in particular, older participants and those with poor premorbid adjustment.

Trial Registration

ClinicalTrials.gov NCT04104347; https://clinicaltrials.gov/ct2/show/NCT04104347

Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data

Using polysomnography over multiple weeks to characterize an individual’s habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities. Agreement in bed and wake time estimates across modalities was high (rho = 0.81–0.92) and were adrift of one another for an average of 4 min, providing redundant sleep measurement. On the ~23% of nights where discrepancies between modalities exceeded 1 h, k-means clustering revealed three patterns, each consistently expressed within a given individual. The three corresponding groups that emerged differed systematically in age, sleep timing, time in bed, and peri-sleep phone usage. Hence, contrary to being problematic, discrepant data across measurement modalities facilitated the identification of stable interindividual differences in sleep behavior, underscoring its utility to characterizing population sleep and peri-sleep behavior.

Digital Phenotyping of Emotion Dysregulation Across Lifespan Transitions to Better Understand Psychopathology Risk

Ethical and consensual digital phenotyping through smartphone activity (i. e., passive behavior monitoring) permits measurement of temporal risk trajectories unlike ever before. This data collection modality may be particularly well-suited for capturing emotion dysregulation, a transdiagnostic risk factor for psychopathology, across lifespan transitions. Adolescence, emerging adulthood, and perinatal transitions are particularly sensitive developmental periods, often marked by increased distress. These participant groups are typically assessed with laboratory-based methods that can be costly and burdensome. Passive monitoring presents a relatively cost-effective and unobtrusive way to gather rich and objective information about emotion dysregulation and risk behaviors. We first discuss key theoretically-driven concepts pertaining to emotion dysregulation and passive monitoring. We then identify variables that can be measured passively and hold promise for better understanding emotion dysregulation. For example, two strong markers of emotion dysregulation are sleep disturbance and problematic use of Internet/social media (i.e., use that prompts negative emotions/outcomes). Variables related to mobility are also potentially useful markers, though these variables should be tailored to fit unique features of each developmental stage. Finally, we offer our perspective on candidate digital variables that may prove useful for each developmental transition. Smartphone-based passive monitoring is a rigorous method that can elucidate psychopathology risk across human development. Nonetheless, its use requires researchers to weigh unique ethical considerations, examine relevant theory, and consider developmentally-specific lifespan features that may affect implementation.

Deriving symptom networks from digital phenotyping data in serious mental illness

BACKGROUND

Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.

AIMS

In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.

METHOD

Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains.

RESULTS

The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups.

CONCLUSIONS

Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Daily self-reported and automatically generated smartphone-based sleep measurements in patients with newly diagnosed bipolar disorder, unaffected first-degree relatives and healthy control individuals

OBJECTIVES

(1) To investigate daily smartphone-based self-reported and automatically generated sleep measurements, respectively, against validated rating scales; (2) to investigate if daily smartphone-based self-reported sleep measurements reflected automatically generated sleep measurements and (3) to investigate the differences in smartphone-based sleep measurements between patients with bipolar disorder (BD), unaffected first-degree relatives (UR) and healthy control individuals (HC).

METHODS

We included 203 patients with BD, 54 UR and 109 HC in this study. To investigate whether smartphone-based sleep calculated from self-reported bedtime, wake-up time and screen on/off time reflected validated rating scales, we used the Pittsburgh Sleep Quality Index (PSQI) and sleep items on the Hamilton Depression Rating Scale 17-item (HAMD-17) and the Young Mania Rating Scale (YMRS).

FINDINGS

(1) Self-reported smartphone-based sleep was associated with the PSQI and sleep items of the HAMD and the YMRS. (2) Automatically generated smartphone-based sleep measurements were associated with daily self-reports of hours slept between 12:00 midnight and 06:00. (3) According to smartphone-based sleep, patients with BD slept less between 12:00 midnight and 06:00, with more interruption and daily variability compared with HC. However, differences in automatically generated smartphone-based sleep were not statistically significant.

CONCLUSION

Smartphone-based data may represent measurements of sleep patterns that discriminate between patients with BD and HC and potentially between UR and HC.

CLINICAL IMPLICATION

Detecting sleep disturbances and daily variability in sleep duration using smartphones may be helpful for both patients and clinicians for monitoring illness activity.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov (NCT02888262).

The Future of Shift Work: Circadian Biology Meets Personalised Medicine and Behavioural Science

Shift work is commonplace in modern societies, and shift workers are predisposed to the development of numerous chronic diseases. Disruptions to the circadian systems of shift workers are considered important contributors to the biological dysfunction these people frequently experience. Because of this, understanding how to alter shift work and zeitgeber (time cue) schedules to enhance circadian system function is likely to be key to improving the health of shift workers. While light exposure is the most important zeitgeber for the central clock in the circadian system, diet and exercise are plausible zeitgebers for circadian clocks in many tissues. We know little about how different zeitgebers interact and how to tailor zeitgeber schedules to the needs of individuals; however, in this review we share some guidelines to help shift workers adapt to their work schedules based on our current understanding of circadian biology. We focus in particular on the importance of diet timing and composition. Going forward, developments in phenotyping and "envirotyping" methods may be important to understanding how to optimise shift work. Non-invasive, multimodal, comprehensive phenotyping using multiple sources of time-stamped data may yield insights that are critical to the care of shift workers. Finally, the impact of these advances will be reduced without modifications to work environments to make it easier for shift workers to engage in behaviours conducive to their health. Integrating findings from behavioural science and ergonomics may help shift workers make healthier choices, thereby amplifying the beneficial effects of improved lifestyle prescriptions for these people.

Towards clinically actionable digital phenotyping targets in schizophrenia

Digital phenotyping has potential to quantify the lived experience of mental illness and generate real-time, actionable results related to recovery, such as the case of social rhythms in individuals with bipolar disorder. However, passive data features for social rhythm clinical targets in individuals with schizophrenia have yet to be studied. In this paper, we explore the relationship between active and passive data by focusing on temporal stability and variance at an individual level as well as large-scale associations on a population level to gain clinically actionable information regarding social rhythms. From individual data clustering, we found a 19% cluster overlap between specific active and passive data features for participants with schizophrenia. In the same clinical population, two passive data features in particular associated with social rhythms, "Circadian Routine" and "Weekend Day Routine," and were negatively associated with symptoms of anxiety, depression, psychosis, and poor sleep (Spearman ρ ranged from -0.23 to -0.30, p < 0.001). Conversely, in healthy controls, more stable social rhythms were positively correlated with symptomatology (Spearman ρ ranged from 0.20 to 0.44, p < 0.05). Our results suggest that digital phenotyping in schizophrenia may offer clinically relevant information for understanding how daily routines affect symptomatology. Specifically, negative correlations between smartphone reported anxiety, depression, psychosis, and poor sleep in individuals with schizophrenia, but not in healthy controls, offer an actionable clinical target and area for further investigation.

The HOPE Pilot Study: Harnessing Patient-Reported Outcomes and Biometric Data to Enhance Cancer Care

PURPOSE

Integrating patient-reported outcomes (PROs) into clinical practice is an increasingly promising strategy for improving patients' symptoms, communication, and clinical outcomes. The objective of the current study was to assess the feasibility, acceptability, and perceived effectiveness of a mobile health intervention that was designed to collect PROs and activity data as a measure of health status.

PATIENTS AND METHODS

This work was a pilot intervention with 10 patients with gynecologic cancers who received palliative chemotherapy. The HOPE (Helping Our Patients Excel) study used wearable accelerometers to assess physical activity and the Beiwe research platform to collect PROs, stratify patient responses by risk, provide tailored symptom management, and notify patients and clinicians of high-risk symptoms. Feasibility and acceptability were assessed through enrollment and adherence rates, and perceived effectiveness was evaluated by patients and oncologists at study completion.

RESULTS

The approach-to-consent rate was 100%, and participants were 90% and 70% adherent to the wearable accelerometers and smartphone surveys, respectively. Participants' mean daily step count was 3,973 (standard deviation [SD], 2,305 steps) and increased from week 1 (mean, 3,520 steps; SD, 1,937 steps) to week 3 (mean, 4,136 steps; SD, 1,578 steps). Active monitoring of participants' heart rates, daily steps, and PROs throughout the study identified anomalies in participants' behavior patterns that suggested poor health for two patients (20%). Patients and clinicians indicated that the intervention improved physical activity, communication, and symptom management.

CONCLUSION

A mobile health intervention that collects PROs and activity data as a measure of health status is feasible, acceptable, and was perceived to be effective in improving symptom management in patients with advanced gynecologic cancers. A larger, multisite, randomized clinical trial to assess the efficacy of the HOPE intervention on patients' symptoms, health-related quality of life, clinical outcomes, and health care use is warranted.

Assessing the potential of longitudinal smartphone based cognitive assessment in schizophrenia: A naturalistic pilot study

BACKGROUND

Although cognition is a core symptom of schizophrenia and associated with functional impairment, the degree of training for and time associated with its assessment makes it difficult to routinely monitor in clinic care.Smartphone based cognitive assessments could serve as a tool to measure cognition in real time as well as being easily scalable for broad use.Combined with other data gathered from smartphone sensors such as steps, sleep, and self-reported symptoms - capturing 'cognition in context' could provide a powerful new tool for assessing the functional burden of disease in schizophrenia.

METHODS

18 participants with schizophrenia and 17 healthy controls completed novel cognitive assessments on their personal smartphones over the course of 12 weeks while also capturing self-reported surveys and step count. No payment or incentives were offered for engaging with the smartphone app. Differing levels of difficulty in cognitive tasks were tested and the results were modeled using a modified Cox proportional hazard model.

RESULTS

On the smartphone cognitive assessments that involved on simple patterns, both controls and those with schizophrenia achieved similar scores. On the more complex assessment that added task switching in addition to pattern recognition, those with schizophrenia achieved scores lower than controls. Collecting other forms of data such as surveys and steps was also feasible using the same smartphone platform.

DISCUSSION

It is feasible for those with schizophrenia to use their own smartphones to complete cognitive assessments and other measures related to their mental health. While we did not investigate the correlations between these cognitive assessments and other smartphone captured metrics like step count or self-reported symptoms, the potential to longitudinally assess cognition in the context of patients' environments outside of the clinic presents unique opportunities for characterizing cognitive burden in schizophrenia.

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

BACKGROUND

Technological advancements, together with the decrease in both price and size of a large variety of sensors, has expanded the role and capabilities of regular mobile phones, turning them into powerful yet ubiquitous monitoring systems. At present, smartphones have the potential to continuously collect information about the users, monitor their activities and behaviors in real time, and provide them with feedback and recommendations.

OBJECTIVE

This systematic review aimed to identify recent scientific studies that explored the passive use of smartphones for generating health- and well-being-related outcomes. In addition, it explores users' engagement and possible challenges in using such self-monitoring systems.

METHODS

A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, to identify recent publications that explore the use of smartphones as ubiquitous health monitoring systems. We ran reproducible search queries on PubMed, IEEE Xplore, ACM Digital Library, and Scopus online databases and aimed to find answers to the following questions: (1) What is the study focus of the selected papers? (2) What smartphone sensing technologies and data are used to gather health-related input? (3) How are the developed systems validated? and (4) What are the limitations and challenges when using such sensing systems?

RESULTS

Our bibliographic research returned 7404 unique publications. Of these, 118 met the predefined inclusion criteria, which considered publication dates from 2014 onward, English language, and relevance for the topic of this review. The selected papers highlight that smartphones are already being used in multiple health-related scenarios. Of those, physical activity (29.6%; 35/118) and mental health (27.9; 33/118) are 2 of the most studied applications. Accelerometers (57.7%; 67/118) and global positioning systems (GPS; 40.6%; 48/118) are 2 of the most used sensors in smartphones for collecting data from which the health status or well-being of its users can be inferred.

CONCLUSIONS

One relevant outcome of this systematic review is that although smartphones present many advantages for the passive monitoring of users' health and well-being, there is a lack of correlation between smartphone-generated outcomes and clinical knowledge. Moreover, user engagement and motivation are not always modeled as prerequisites, which directly affects user adherence and full validation of such systems.

Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders

PURPOSE OF REVIEW

Sleep is an important feature in mental illness. Smartphones can be used to assess and monitor sleep, yet there is little prior application of this approach in depressive, anxiety, or psychotic disorders. We review uses of smartphones and wearable devices for sleep research in patients with these conditions.

RECENT FINDINGS

To date, most studies consist of pilot evaluations demonstrating feasibility and acceptability of monitoring sleep using smartphones and wearable devices among individuals with psychiatric disorders. Promising findings show early associations between behaviors and sleep parameters and agreement between clinic-based assessments, active smartphone data capture, and passively collected data. Few studies report improvement in sleep or mental health outcomes. Success of smartphone-based sleep assessments and interventions requires emphasis on promoting long-term adherence, exploring possibilities of adaptive and personalized systems to predict risk/relapse, and determining impact of sleep monitoring on improving patients' quality of life and clinically meaningful outcomes.

Caregiver Daily Reporting of Symptoms in Autism Spectrum Disorder: Observational Study Using Web and Mobile Apps

BACKGROUND

Currently, no medications are approved to treat core symptoms of autism spectrum disorder (ASD). One barrier to ASD medication development is the lack of validated outcome measures able to detect symptom change. Current ASD interventions are often evaluated using retrospective caregiver reports that describe general clinical presentation but often require recall of specific behaviors weeks after they occur, potentially reducing accuracy of the ratings. My JAKE, a mobile and Web-based mobile health (mHealth) app that is part of the Janssen Autism Knowledge Engine-a dynamically updated clinical research system-was designed to help caregivers of individuals with ASD to continuously log symptoms, record treatments, and track progress, to mitigate difficulties associated with retrospective reporting.

OBJECTIVE

My JAKE was deployed in an exploratory, noninterventional clinical trial to evaluate its utility and acceptability to monitor clinical outcomes in ASD. Hypotheses regarding relationships among daily tracking of symptoms, behavior, and retrospective caregiver reports were tested.

METHODS

Caregivers of individuals with ASD aged 6 years to adults (N=144) used the My JAKE app to make daily reports on their child's sleep quality, affect, and other self-selected specific behaviors across the 8- to 10-week observational study. The results were compared with commonly used paper-and-pencil scales acquired over a concurrent period at regular 4-week intervals.

RESULTS

Caregiver reporting of behaviors in real time was successfully captured by My JAKE. On average, caregivers made reports 2-3 days per week across the study period. Caregivers were positive about their use of the system, with over 50% indicating that they would like to use My JAKE to track behavior outside of a clinical trial. More positive average daily reporting of overall type of day was correlated with 4 weekly reports of lower caregiver burden made at 4-week intervals (r=-0.27, P=.006, n=88) and with ASD symptoms (r=-0.42, P<.001, n=112).

CONCLUSIONS

My JAKE reporting aligned with retrospective Web-based or paper-and-pencil scales. Use of mHealth apps, such as My JAKE, has the potential to increase the validity and accuracy of caregiver-reported outcomes and could be a useful way of identifying early changes in response to intervention. Such systems may also assist caregivers in tracking symptoms and behavior outside of a clinical trial, help with personalized goal setting, and monitoring of progress, which could collectively improve understanding of and quality of life for individuals with ASD and their families.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02668991; https://clinicaltrials.gov/ct2/show/NCT02668991.

Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review

BACKGROUND

Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration.

OBJECTIVE

To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy.

METHODS

A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied.

RESULTS

Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression.

CONCLUSIONS

Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.

A new window into psychosis: The rise digital phenotyping, smartphone assessment, and mobile monitoring

This commentary piece discusses recent advances in the use of mobile technologies like smartphone and wearable sensors for schizophrenia research. By framing both the opportunities as well as challenges for the field, this piece aims to frame the both current and future research efforts.

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.

Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia

Digital phenotyping, or the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices and smartphones, in particular, holds great potential for behavioral monitoring of patients. However, realizing the potential of digital phenotyping requires understanding of the smartphone as a scientific data collection tool. In this pilot study, we detail a procedure for estimating data quality for phone sensor samples and model the relationship between data quality and future symptom-related survey responses in a cohort with schizophrenia. We find that measures of empirical coverage of collected accelerometer and GPS data, as well as survey timing and survey completion metrics, are significantly associated with future survey scores for a variety of symptom domains. We also find evidence that specific measures of data quality are indicative of domain-specific future survey outcomes. These results suggest that for smartphone-based digital phenotyping, metadata is not independent of patient-reported survey scores, and is therefore potentially useful in predicting future clinical outcomes. This work raises important questions and considerations for future studies; we explore and discuss some of these implications.

A pilot study shows that smartphone-collected data from patients with schizophrenia could be used to infer their mental-health status. Using smartphones as scientific data gathering tools holds great promise for understanding some of the behavioral features of psychiatric disorders and could provide an early indication of worsening symptoms. However, few studies have assessed the quality of the collected data, and thus the accuracy of clinical outcome prediction. Patrick Staples at the Harvard T. H. Chan School of Public Health in Boston, MA, and colleagues examined the relationship between data quality and future symptom-related survey responses in 16 patients with schizophrenia. They found that smartphone sensor data as well as phone-use metrics related to the completion of symptom-related surveys were significantly associated with survey results, highlighting the clinical relevance of this approach.