Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning

Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges

In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children.

StresSense: Real-Time detection of stress-displaying behaviors

BACKGROUND

Wrist-worn gadgets like smartphones are ideal for unobtrusively gathering user data, in various fields such as health and fitness monitoring, communication, and productivity enhancement. They seamlessly integrate into users' daily lives, providing valuable insights and features without the need for constant attention or disruption. In sensitive domains like mental health, these devices provide user-friendly, privacy-protected means of diagnosis and treatment, offering a secure and cost-effective avenue for seeking help.

OBJECTIVES

This study addresses the limitations of traditional mental health assessment techniques, such as intrusive sensing and subjective self-reporting, by harnessing the unobtrusive data collection capabilities of smartphones. Equipped with accelerometers and other sensors, these devices offer a novel approach to mental health research. Our objective was to develop methods for real-time detection of stress and boredom behavior markers using smart devices and machine learning algorithms.

METHODOLOGY

By leveraging data from accelerometers (A), gyroscopes (G), and magnetometers (M), we compiled a dataset indicative of stress-related behaviors and trained various machine-learning models for predictive accuracy. The methodology involved collecting data from motion sensors (A, G, and M) on the dominant arm's wrist-worn smartphone, followed by data preprocessing, transformation from time series format, and training a Deep Neural Network (DNN) model for activity recognition.

FINDINGS

Remarkably, the DNN achieved an accuracy of 93.50% on test data, outperforming traditional and ensemble machine learning methods across different window sizes, and demonstrated real-time accuracy of 77.78%, validating its practical application.

CONCLUSION

In conclusion, this research presents a novel dataset for detecting stress and boredom behaviors using smartphones, reducing reliance on costly devices and offering a more objective assessment. It also proposes a DNN-based method for wrist-worn devices to accurately identify complex activities associated with stress and boredom, with benefits in terms of privacy and user convenience. This advancement represents a significant contribution to the field of mental health research, providing a less intrusive and more user-friendly approach to monitoring mental well-being.

Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review

This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

Evaluation of the Michigan Clinical Consultation and Care Program: An Evidence-Based Approach to Perinatal Mental Healthcare

Mood and anxiety disorders affect pregnant individuals and their families at increased rates throughout the perinatal period. Geographic, financial, and social barriers often preclude adequate diagnosis and treatment. The aim of this manuscript is to describe the consultation and care arms of the Michigan Clinical Consultation and Care (MC3) program, a statewide program designed to facilitate access to perinatal mental healthcare for OB/Gyn patients, and to describe the participants engaged in the program, examine the predictors of participant retention, and provide preliminary data regarding participants' mental health outcomes. We enrolled 209 participants to the clinical care arm, of which 48 were lost to follow-up, while 107 remained enrolled at the time of data analysis. A total of 54 participants met their treatment goals. A total of 97% of participants asserted they were satisfied with the services they received. Black race and public insurance predicted faster attrition from the care arm treatment; risks for interpersonal violence exposure and substance use were unrelated to attrition. Preliminary mental health outcomes showed significant decreases in anxiety and depression, with the most dramatic decreases in the first month of treatment. Overall, the MC3 clinical care arm shows promising rates of adherence, excellent program satisfaction, and a positive impact on perinatal mental health, supporting continued program implementation and ongoing evaluation.

Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning

Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health.Clinical Relevance- This work advances the use of wearables for detecting childhood mental health disorders.

Wearable devices for anxiety & depression: A scoping review

Background

The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices.

Objective

This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression.

Methods

Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized.

Results

From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (n = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (n = 8 or 14%). Finally, n = 26 or 46% of studies highlighted the smartphone as a wearable device host device.

Conclusion

The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

BACKGROUND

Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services.

OBJECTIVE

This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues.

METHODS

We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis.

RESULTS

Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine.

CONCLUSIONS

Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies' results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.

How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.

UVM KID Study: Identifying Multimodal Features and Optimizing Wearable Instrumentation to Detect Child Anxiety

Anxiety and depression, collectively known as internalizing disorders, begin as early as the preschool years and impact nearly 1 out of every 5 children. Left undiagnosed and untreated, childhood internalizing disorders predict later health problems including substance abuse, development of comorbid psychopathology, increased risk for suicide, and substantial functional impairment. Current diagnostic procedures require access to clinical experts, take considerable time to complete, and inherently assume that child symptoms are observable by caregivers. Multi-modal wearable sensors may enable development of rapid point-of-care diagnostics that address these challenges. Building on our prior work, here we present an assessment battery for the development of a digital phenotype for internalizing disorders in young children and an early feasibility case study of multi-modal wearable sensor data from two participants, one of whom has been clinically diagnosed with an internalizing disorder. Results lend support that sacral movement responses and R-R interval during a short stress-induction task may facilitate child diagnosis. Multi-modal sensors measuring movement and surface biopotentials of the chest and trapezius are also shown to have significant redundancy, introducing the potential for sensor optimization moving forward. Future work aims to further optimize sensor placement, signals, features, and assessments to enable deployment in clinical practice. Clinical Relevance- This work considers the development and optimization of technologies for improving the identification of children with internalizing disorders.

Machine learning-based classification of the movements of children with profound or severe intellectual or multiple disabilities using environment data features

Communication interventions have broadened from dialogical meaning-making, assessment approaches, to remote-controlled interactive objects. Yet, interpretation of the mostly pre-or protosymbolic, distinctive, and idiosyncratic movements of children with intellectual disabilities (IDs) or profound intellectual and multiple disabilities (PIMD) using computer-based assistive technology (AT), machine learning (ML), and environment data (ED: location, weather indices and time) remain insufficiently unexplored. We introduce a novel behavior inference computer-based communication-aid AT system structured on machine learning (ML) framework to interpret the movements of children with PIMD/IDs using ED. To establish a stable system, our study aimed to train, cross-validate (10-fold), test and compare the classification accuracy performance of ML classifiers (eXtreme gradient boosting [XGB], support vector machine [SVM], random forest [RF], and neural network [NN]) on classifying the 676 movements to 2, 3, or 7 behavior outcome classes using our proposed dataset recalibration (adding ED to movement datasets) with or without Boruta feature selection (53 child characteristics and movements, and ED-related features). Natural-child-caregiver-dyadic interactions observed in 105 single-dyad video-recorded (30-hour) sessions targeted caregiver-interpreted facial, body, and limb movements of 20 8-to 16-year-old children with PIMD/IDs and simultaneously app-and-sensor-collected ED. Classification accuracy variances and the influences of and the interaction among recalibrated dataset, feature selection, classifiers, and classes on the pooled classification accuracy rates were evaluated using three-way ANOVA. Results revealed that Boruta and NN-trained dataset in class 2 and the non-Boruta SVM-trained dataset in class 3 had >76% accuracy rates. Statistically significant effects indicating high classification rates (>60%) were found among movement datasets: with ED, non-Boruta, class 3, SVM, RF, and NN. Similar trends (>69%) were found in class 2, NN, Boruta-trained movement dataset with ED, and SVM and RF, and non-Boruta-trained movement dataset with ED in class 3. These results support our hypotheses that adding environment data to movement datasets, selecting important features using Boruta, using NN, SVM and RF classifiers, and classifying movements to 2 and 3 behavior outcomes can provide >73.3% accuracy rates, a promising performance for a stable ML-based behavior inference communication-aid AT system for children with PIMD/IDs.

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

Features of wearable devices used for Anxiety & Depression: A scoping review (Preprint).. [DOI: 10.2196/preprints.33287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

The rates of mental health disorders such as anxiety and depression are at an all time high and the need for readily available digital health care solutions has never been greater. Wearable devices (WD) have seen a steady rise in the usage of sensors previously reserved for hospital settings. The availibity of features that make use of WDs for anxiety and depression is in its infancy, but we are seeing the potential for consumers to self monitor moods and behaviours with everyday commercially available devices and the ability to self-regulate their health needs.

OBJECTIVE

This study aims to explore features of wearable devices (WDs) used for anxiety and depression

METHODS

We have searched the following six bibliographic databases while conducting this review: MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar. Two reviewers independently performed study selection and data extraction; two other individual reviewers justified cross-checking of extracted data. We utilized a narrative approach for synthesizing the data.

RESULTS

From an initial 2,408 studies we assess and report the features in 58 studies that were highlighted according to our inclusion criteria. Wrist worn devices were identified in the bulk of our studies (n=42 or 71%). Depression was assessed in most of the studies (n=27 or 47%), whereas anxiety was assessed in n=15 or 25% of studies. More than a quarter (n=16 or 27%) of the included studies assessed both mental disorders. Finally n=26 or 46% of studies highlighted the wearable device host device as a smartphone.

CONCLUSIONS

The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies such as anxiety and depression. We see WDs having real potential in aiding with self-care and with purposefully designed WDs that combine the expertise of technologists and clinical experts WDs could play a key role in self-care monitoring and diagnosis.

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.

Digital Phenotype for Childhood Internalizing Disorders: Less Positive Play and Promise for a Brief Assessment Battery

Childhood internalizing disorders, like anxiety and depression, are common, impairing, and difficult to detect. Universal childhood mental health screening has been recommended, but new technologies are needed to provide objective detection. Instrumented mood induction tasks, designed to press children for specific behavioral responses, have emerged as means for detecting childhood internalizing psychopathology. In our previous work, we leveraged machine learning to identify digital phenotypes of childhood internalizing psychopathology from movement and voice data collected during negative valence tasks (pressing for anxiety and fear). In this work, we develop a digital phenotype for childhood internalizing disorders based on wearable inertial sensor data recorded from a Positive Valence task during which a child plays with bubbles. We find that a phenotype derived from features that capture reward responsiveness is able to accurately detect children with underlying internalizing psychopathology (AUC = 0.81). In so doing, we explore the impact of a variety of feature sets computed from wearable sensors deployed to two body locations on phenotype performance across two phases of the task. We further consider this novel digital phenotype in the context of our previous Negative Valence digital phenotypes and find that each task brings unique information to the problem of detecting childhood internalizing psychopathology, capturing different problems and disorder subtypes. Collectively, these results provide preliminary evidence for a mood induction task battery to develop a novel diagnostic for childhood internalizing disorders.

A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression

Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception. Results: Our data analysis was organized by technology - to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.

Feasibility of using "SMARTER" methodology for monitoring precipitating conditions of pediatric migraine episodes

OBJECTIVE

To evaluate the feasibility in children of an intensive prospective data monitoring methodology for identifying precipitating conditions for migraine occurrence.

BACKGROUND

Migraine headaches are a common pain condition in childhood and can become increasingly chronic and disabling with repeated episodes. Identifying conditions that forecast when a child's migraine is likely to occur may facilitate next-generation adaptive treatments to prevent future migraine attacks.

METHODS

In this cohort study of a sample of 30 youth (ages 10-17) with migraine recruited through a pediatric headache clinic, smartphones supplemented with wearable biosensors were used over a period of 28 days to collect contextual data thought to be potentially relevant to headache occurrence. Self-reported data on headache occurrence, lifestyle, and perceptions of the environment were collected in 4 epochs per day using custom real-time reporting software. Data derived from the wearable biosensor included information on autonomic arousal and physical activity. Built-in sensors on participants' own phones also were used to indicate location and to quantify the sensory environment (e.g., ambient noise and light levels). Data fidelity was monitored to evaluate feasibility of the methods, and participant acceptability was assessed via an end-of-study survey.

RESULTS

Self-report data were obtained on a mean of 88.9% (24.9/28) of assigned days (SD = 22.4%) and at a mean of 68.9% (77.2/112) of assigned moments (SD = 24.5%). Data from the wearable biosensor were obtained for a mean of 18.7 hours per day worn (SD = 2.3 hours), with participants on average wearing the sensor on 20.3 days (SD = 9.9). Fidelity of obtaining objective data from phone sensors on the sensory environment and other environmental conditions was highly variable, with these data obtainable from 5 to 22/30 (16.7%-73.3%) of participants' own phones. Most participants (63.3%-100%) responded with at least "somewhat agree" to questions about acceptability of the study methods. However, 5 to 7/30 (16.7%-23.3%) patients indicated difficulties with burden and remembering to wear the sensor. Almost all participants (29/30, 96.7%) agreed that they would want information about when a migraine might occur.

CONCLUSIONS

A contemporary data sampling approach comprising ambulatory sensors and real-time reporting appears to be acceptable to most youth with migraine in this study. Reliability of acquiring some data sources from participants' own phones, however, was suboptimal. Further refining these data sampling methods may enable a novel means of predicting and preventing recurrences of migraine episodes in youth.

Inertial Sensors-Applications and Challenges in a Nutshell

This editorial provides a concise introduction to the methods and applications of inertial sensors. We briefly describe the main characteristics of inertial sensors and highlight the broad range of applications as well as the methodological challenges. Finally, for the reader’s guidance, we give a succinct overview of the papers included in this special issue.

The Interaction of Adverse Childhood Experiences and Resiliency on the Outcome of Depression Among Children and Youth, 8-17 year olds

BACKGROUND

Adverse childhood experiences (ACEs) are common among children. Little is known on how resilience factors and positive childhood experiences (PCEs) may moderate the relationship between ACEs and childhood depression.

OBJECTIVE

Our study fills this gap by providing recent, nationally representative estimates of ACE and PCE exposure for ages 8-17 and examines the associations between ACE exposure and PCEs on the outcome of depression.

PARTICIPANTS AND SETTING

Data were drawn from the nationally representative 2016-2017 National Survey of Children's Health (NSCH) and included a total sample of 40,302 children and adolescents.

METHODS

Chi square analysis and multivariate logistic regressions were performed to assess associations of depression with 9 ACE and 6 PCE exposures. Additive and multiplicative interactions were examined between ACE exposure and PCEs (resiliency measures) on depression. Survey sampling weights and SAS survey procedures were used.

RESULTS

Our study found that 4% of children had current depression and those with an ACE count greater than four had increased odds (aOR: 2.29; CI: 1.74-3.02). Multivariate regressions demonstrated associations between depression and low resiliency as well as significant interactions between ACE exposure and three PCEs. Children who were exposed to greater than four ACEs and did not exhibit resilience had 8.75 higher odds of depression (CI: 5.23-14.65) compared to those with less than four ACEs and some resilience.

CONCLUSIONS

These findings illustrate the need for the promotion of PCEs and the building of resiliency for combatting depression and reducing the impact of trauma in children and adolescents.

The Association of Adverse Childhood Experiences With Anxiety and Depression for Children and Youth, 8 to 17 Years of Age

OBJECTIVE

To determine the prevalence of anxiety and depression and examine their association with adverse childhood experiences (ACEs) among children and adolescents ages 8 to 17 years old.

METHODS

Using data from the 2016-2017 National Survey of Children's Health, we conducted a cross-sectional study design with a total sample of 39,929. Our exposure and outcome variables included caregiver report of 9 ACE exposures and current anxiety or current depression. Survey sampling weights and SAS survey procedures were implemented to produce nationally representative results.

RESULTS

Our study found that 9% of children had current anxiety while 4% had current depression. Multivariate analysis concluded that all ACE measures were associated with significantly higher odds of both anxiety and depression. Children exposed to 4 or more ACEs had higher odds of anxiety (adjusted odds ratio [aOR] = 1.7; 95% confidence interval [CI], 1.4-2.1) and depression (aOR = 2.2; 95% CI, 1.7-2.9) than children with exposure to fewer than four ACEs. Assessment of the outcomes of anxiety and depression separately showed differential impacts of ACE exposures as associations were stronger with depression for almost all ACE categories.

CONCLUSIONS

Our study demonstrates a differential association between ACEs and anxiety and depression. This highlights the importance of assessing the impact of ACEs on internalizing behaviors separately. These findings are significant for pediatric providers as diagnosis and treatment for mental health disorders are vital components of pediatric care and further support the American Academy of Pediatrics' recommendation to screen for ACEs.

Mobile and wearable technology for monitoring depressive symptoms in children and adolescents: A scoping review

BACKGROUND

There has been rapid growth of mobile and wearable tools that may help to overcome challenges in the diagnosis and prediction of Major Depressive Disorder in children and adolescents, tasks that rely on clinical reporting that is inherently based on retrospective recall of symptoms and associated features. This article reviews more objective ways of measuring and monitoring mood within this population.

METHODS

A scoping review of peer-reviewed studies examined published research that employs mobile and wearable tools to characterize depression in children and/or adolescents. Our search strategy included the following terms: (1) monitoring or prediction (2) depression (3) mobile apps or wearables and (4) children and youth (including adolescents), and was applied to five databases.

RESULTS

Our search produced 829 citations (2008- Feb 2019), of which 30 (journal articles, conference papers and abstracts) were included in the analysis, and 2 reviews included in our discussion. The majority of the evidence involved smartphone apps, with very few studies using actigraphy. Mobile and wearables captured a variety of data including unobtrusive passive analytics, movement and light data, plus physical and mental health data, including depressive symptom monitoring. Most studies also examined feasibility.

LIMITATIONS

This review was limited to published research in the English language. The review criteria excluded any apps that were mainly treatment focused, therefore there was not much of a focus on clinical outcomes.

CONCLUSIONS

This scoping review yielded a variety of studies with heterogeneous populations, research methods and study objectives, which limited our ability to address our research objectives cohesively. Certain mobile technologies, however, have demonstrated feasibility for tracking depression that could inform models for predicting relapse.

Open-Source Remote Gait Analysis: A Post-Surgery Patient Monitoring Application

Critical to digital medicine is the promise of improved patient monitoring to allow assessment and personalized intervention to occur in real-time. Wearable sensor-enabled observation of physiological data in free-living conditions is integral to this vision. However, few open-source algorithms have been developed for analyzing and interpreting these data which slows development and the realization of digital medicine. There is clear need for open-source tools that analyze free-living wearable sensor data and particularly for gait analysis, which provides important biomarkers in multiple clinical populations. We present an open-source analytical platform for automated free-living gait analysis and use it to investigate a novel, multi-domain (accelerometer and electromyography) asymmetry measure for quantifying rehabilitation progress in patients recovering from surgical reconstruction of the anterior cruciate ligament (ACL). Asymmetry indices extracted from 41,893 strides were more strongly correlated (r = −0.87, p < 0.01) with recovery time than standard step counts (r = 0.25, p = 0.52) and significantly differed between patients 2- and 17-weeks post-op (p < 0.01, effect size: 2.20–2.96), and controls (p < 0.01, effect size: 1.74–4.20). Results point toward future use of this open-source platform for capturing rehabilitation progress and, more broadly, for free-living gait analysis.

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

Tracing Physical Behavior in Virtual Reality: A Narrative Review of Applications to Social Psychology

Virtual reality (VR) offers unique benefits to social psychological research, including a high degree of experimental control alongside strong ecological validity, a capacity to manipulate any variable of interest, and an ability to trace the physical, nonverbal behavior of the user in a very fine-grained and automated manner. VR improves upon traditional behavioral measurement techniques (e.g., observation and coding) on several fronts as data collection is covert, continuous, passive, and occurs within a controlled context. The current review synthesizes extant methods for tracing physical behavior in VR, such as gaze tracking and interpersonal distance measurement, and describes how researchers have applied these methods to understand important phenomena within the context of social psychology. To date, primary areas of application have included the assessment of social approach and avoidance, social evaluation and bias, and engagement. The limitations of behavioral tracing methods in VR, as well as future directions for their continued application and extension, are discussed. This narrative review equips readers with a thorough understanding of behavioral tracing methods that can be implemented in VR, their benefits and drawbacks, the insight they may offer into social processes, and future avenues of work for applying emergent technologies to research questions in social psychology.

Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood

Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying young children with internalizing disorders using a 3-min speech task. We show that machine learning analysis of audio data from the task can be used to identify children with an internalizing disorder with 80% accuracy (54% sensitivity, 93% specificity). The speech features most discriminative of internalizing disorder are analyzed in detail, showing that affected children exhibit especially low-pitch voices, with repeatable speech inflections and content, and high-pitched response to surprising stimuli relative to controls. This new tool is shown to outperform clinical thresholds on parent-reported child symptoms, which identify children with an internalizing disorder with lower accuracy (67-77% versus 80%), and similar specificity (85-100% versus 93%), and sensitivity (0-58% versus 54%) in this sample. These results point toward the future use of this approach for screening children for internalizing disorders so that interventions can be deployed when they have the highest chance for long-term success.