Actigraph measures discriminate pediatric bipolar disorder from attention-deficit/hyperactivity disorder and typically developing controls

What types of objective measures have been used to assess core ADHD symptoms in children and young people in naturalistic settings? A scoping review

OBJECTIVES

We described the range and types of objective measures of attention-deficit/hyperactivity disorder (ADHD) in children and young people (CYP) reported in research that can be applied in naturalistic settings.

DESIGN

Scoping review using best practice methods.

DATA SOURCES

MEDLINE, APA PsycINFO, Embase, (via OVID); British Education Index, Education Resources Information Centre, Education Abstracts, Education Research Complete, Child Development and Adolescent Papers, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Psychology and Behavioural Sciences Collection (via EBSCO) were searched between 1 December 2021 and 28 February 2022.

ELIGIBILITY CRITERIA

Papers reported an objective measure of ADHD traits in CYP in naturalistic settings written in English.

DATA EXTRACTION AND SYNTHESIS

2802 papers were identified; titles and abstracts were screened by two reviewers. 454 full-text papers were obtained and screened. 128 papers were eligible and included in the review. Data were extracted by the lead author, with 10% checked by a second team member. Descriptive statistics and narrative synthesis were used.

RESULTS

Of the 128 papers, 112 were primary studies and 16 were reviews. 87% were conducted in the USA, and only 0.8% originated from the Global South, with China as the sole representative. 83 objective measures were identified (64 observational and 19 acceleration-sensitive measures). Notably, the Behaviour Observation System for Schools (BOSS), a behavioural observation, emerged as one of the predominant measures. 59% of papers reported on aspects of the reliability of the measure (n=76). The highest inter-rater reliability was found in an unnamed measure (% agreement=1), Scope Classroom Observation Checklist (% agreement=0.989) and BOSS (% agreement=0.985). 11 papers reported on aspects of validity. 12.5% of papers reported on their method of data collection (eg, pen and paper, on an iPad). Of the 47 papers that reported observer training, 5 reported the length of time the training took ranging from 3 hours to 1 year. Despite recommendations to integrate objective measures alongside conventional assessments, use remains limited, potentially due to inconsistent psychometric properties across studies.

CONCLUSIONS

Many objective measures of ADHD have been developed and described, with the majority of these being direct behavioural observations. There is a lack of reporting of psychometric properties and guidance for researchers administering these measures in practice and in future studies. Methodological transparency is needed. Encouragingly, recent papers begin to address these issues.

Optimal interval and feature selection in activity data for detecting attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurobehavioral disorder that is common in children and adolescents. Inattention, impulsivity, and hyperactivity are the key symptoms of ADHD patients. Traditional clinical assessments delay ADHD diagnosis and increase undiagnosed cases and costs, as well. The use of deep learning (DL) and machine learning (ML)-based objective techniques for diagnosing ADHD has grown exponentially in recent years as the efficiency of early diagnosis has improved. This research highlights the significance of utilizing feature selection techniques before constructing machine learning models on activity datasets. It also explores the distinctions between specific time-interval activity data and broader interval activity data in identifying ADHD patients from the clinical control group. Five ML models were developed and tested to assess the performance of two sets of features and different categories of activity data in predicting ADHD. The study concludes with the following findings: (i) the model's performance showed a notable improvement of 0.11 in accuracy with the adoption of a precise feature selection process; (ii) activity data recorded in the morning and at night are more significant predictors of ADHD compared to other times; (iii) the utilization of specific time interval data is crucial for ADHD prediction; (iv) the random forest performs better than the other machine learning models used in the study, with 84% accuracy, 79% precision, 85% F1-score, and 92% recall. As we move into an era where early disease prediction is possible, combining artificial intelligence methods with activity data could create a strong framework for helping doctors make decisions that can be used far beyond hospitals.

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales.

METHODS

This approach is mainly based on movement quantization through analysis of variance in patients' skeletons detected in outpatient videos. The patients' skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations.

RESULTS

The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor "thigh angle", with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39; accuracy, 91.03%; sensitivity, 90.25%; specificity, 91.86%; and AUC, 94.00%).

CONCLUSIONS

The proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automatically and can assist physicians in diagnosing ADHD.

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.

Exploring an objective measure of overactivity in children with rare genetic syndromes

BACKGROUND

Overactivity is prevalent in several rare genetic neurodevelopmental syndromes, including Smith-Magenis syndrome, Angelman syndrome, and tuberous sclerosis complex, although has been predominantly assessed using questionnaire techniques. Threats to the precision and validity of questionnaire data may undermine existing insights into this behaviour. Previous research indicates objective measures, namely actigraphy, can effectively differentiate non-overactive children from those with attention-deficit hyperactivity disorder. This study is the first to examine the sensitivity of actigraphy to overactivity across rare genetic syndromes associated with intellectual disability, through comparisons with typically-developing peers and questionnaire overactivity estimates.

METHODS

A secondary analysis of actigraphy data and overactivity estimates from The Activity Questionnaire (TAQ) was conducted for children aged 4-15 years with Smith-Magenis syndrome (N=20), Angelman syndrome (N=26), tuberous sclerosis complex (N=16), and typically-developing children (N=61). Actigraphy data were summarized using the M10 non-parametric circadian rhythm variable, and 24-hour activity profiles were modelled via functional linear modelling. Associations between actigraphy data and TAQ overactivity estimates were explored. Differences in actigraphy-defined activity were also examined between syndrome and typically-developing groups, and between children with high and low TAQ overactivity scores within syndromes.

RESULTS

M10 and TAQ overactivity scores were strongly positively correlated for children with Angelman syndrome and Smith-Magenis syndrome. M10 did not substantially differ between the syndrome and typically-developing groups. Higher early morning activity and lower evening activity was observed across all syndrome groups relative to typically-developing peers. High and low TAQ group comparisons revealed syndrome-specific profiles of overactivity, persisting throughout the day in Angelman syndrome, occurring during the early morning and early afternoon in Smith-Magenis syndrome, and manifesting briefly in the evening in tuberous sclerosis complex.

DISCUSSION

These findings provide some support for the sensitivity of actigraphy to overactivity in children with rare genetic syndromes, and offer syndrome-specific temporal descriptions of overactivity. The findings advance existing descriptions of overactivity, provided by questionnaire techniques, in children with rare genetic syndromes and have implications for the measurement of overactivity. Future studies should examine the impact of syndrome-related characteristics on actigraphy-defined activity and overactivity estimates from actigraphy and questionnaire techniques.

Hierarchical organization of human physical activity

Human physical activity (HPA), a fundamental physiological signal characteristic of bodily motion is of rapidly growing interest in multidisciplinary research. Here we report the existence of hitherto unidentified hierarchical levels in the temporal organization of HPA on the ultradian scale: on the minute's scale, passive periods are followed by activity bursts of similar intensity ('quanta') that are organized into superstructures on the hours- and on the daily scale. The time course of HPA can be considered a stochastic, quasi-binary process, where quanta, assigned to task-oriented actions are organized into work packages on higher levels of hierarchy. In order to grasp the essence of this complex dynamic behaviour, we established a stochastic mathematical model which could reproduce the main statistical features of real activity time series. The results are expected to provide important data for developing novel behavioural models and advancing the diagnostics of neurological or psychiatric diseases.

General spectral characteristics of human activity and its inherent scale-free fluctuations

The scale-free nature of daily human activity has been observed in different aspects; however, the description of its spectral characteristics is incomplete. General findings are complicated by the fact that-although actigraphy is commonly used in many research areas-the activity calculation methods are not standardized; therefore, activity signals can be different. The presence of 1/f noise in activity or acceleration signals was mostly analysed for short time windows, and the complete spectral characteristic has only been examined in the case of certain types of them. To explore the general spectral nature of human activity in greater detail, we have performed Power Spectral Density (PSD) based examination and Detrended Fluctuation Analysis (DFA) on several-day-long, triaxial actigraphic acceleration signals of 42 healthy, free-living individuals. We generated different types of activity signals from these, using different acceleration preprocessing techniques and activity metrics. We revealed that the spectra of different types of activity signals generally follow a universal characteristic including 1/f noise over frequencies above the circadian rhythmicity. Moreover, we discovered that the PSD of the raw acceleration signal has the same characteristic. Our findings prove that the spectral scale-free nature is generally inherent to the motor activity of healthy, free-living humans, and is not limited to any particular activity calculation method.

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health

Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1-Data From Wearable Devices

OBJECTIVES

With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to wearable data in HEOR.

METHODS

We identified studies published in PubMed between January 2016 and March 2021. Studies that included at least 1 HEOR-related Medical Subject Headings term, applied an ML, and used wearable data were eligible for inclusion. Two reviewers abstracted information including ML application types and data on which ML was applied and analyzed them using descriptive analyses.

RESULTS

A total of 148 studies were identified from PubMed, among which 32 studies met the inclusion criteria. There has been an increase over time in the number of ML studies using wearable data. ML has been more frequently used for monitoring events in real time (78%) than to predict future events (22%). There has been a wide range of outcomes examined, ranging from general physical or mental health (24%) to more disease-specific outcomes (eg, disease incidence [19%] and progression [13%]) and treatment-related outcomes (eg, treatment adherence [9%] and outcomes [9%]). Data for ML models were more often derived from wearable devices with specific medical purposes (60%) than those without (40%).

CONCLUSION

There has been a wide range of applications of ML to wearable data. Both medical and nonmedical wearable devices have been used as a data source, showing the potential for providing rich data for ML studies in HEOR.

Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol

Introduction

Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices.

Methods and analysis

Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set.

Discussion

Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device.

Clinical trial registration

[https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].

Clock Genes Profiles as Diagnostic Tool in (Childhood) ADHD—A Pilot Study

Attention deficit hyperactivity disorder (ADHD) is a very common disorder in children and adults. A connection with sleep disorders, and above all, disorders of the circadian rhythm are the subject of research and debate. The circadian system can be represented on different levels. There have been a variety of studies examining 24-h rhythms at the behavioral and endocrine level. At the molecular level, these rhythms are based on a series of feedback loops of core clock genes and proteins. In this paper, we compared the circadian rhythms at the behavioral, endocrine, and molecular levels between children with ADHD and age- and BMI-matched controls, complementing the previous data in adults. In a minimally invasive setting, sleep was assessed via a questionnaire, actigraphy was used to determine the motor activity and light exposure, saliva samples were taken to assess the 24-h profiles of cortisol and melatonin, and buccal mucosa swaps were taken to assess the expression of the clock genes BMAL1 and PER2. We found significant group differences in sleep onset and sleep duration, cortisol secretion profiles, and in the expression of both clock genes. Our data suggest that the analysis of circadian molecular rhythms may provide a new approach for diagnosing ADHD in children and adults.

Sleep disturbances, circadian activity, and nocturnal light exposure characterize high risk for and current depression in adolescence

Study Objectives

Major depressive disorder (MDD) in adolescence is associated with irregularities in circadian rhythms and sleep. The characterization of such impairment may be critical to design effective interventions to prevent development of depression among adolescents. This study aimed to examine self-reported and actimetry-based circadian rhythms and sleep–wake behavior associated with current MDD and high risk (HR) for MDD among adolescents.

Methods

Ninety-six adolescents who took part in the IDEA-RiSCo study were recruited using an empirically developed depression-risk stratification method: 26 classified as low risk (LR), 31 as HR, and 39 as a current depressive episode (MDD). We collected self-report data on insomnia, chronotype, sleep schedule, sleep hygiene as well as objective data on sleep, rest-activity, and light exposure rhythms using actimetry for 10 days.

Results

Adolescents with MDD exhibited more severe insomnia, shorter sleep duration, higher social jetlag (SJL), lower relative amplitude (RA) of activity, and higher exposure to artificial light at night (ALAN) compared with the other groups. They also presented poorer sleep hygiene compared with the LR group. The HR group also showed higher insomnia, lower RA, higher exposure to ALAN, and higher SJL compared with the LR group.

Conclusions

HR adolescents shared sleep and rhythm alterations with the MDD group, which may constitute early signs of depression, suggesting that preventive strategies targeting sleep should be examined in future studies. Furthermore, we highlight that actimetry-based parameters of motor activity (particularly RA) and light exposure are promising constructs to be explored as tools for assessment of depression in adolescence.

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.

Accurate Identification of ADHD among Adults Using Real-Time Activity Data

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopment disorder that affects millions of children and typically persists into adulthood. It must be diagnosed efficiently and consistently to receive adequate treatment, otherwise, it can have a detrimental impact on the patient’s professional performance, mental health, and relationships. In this work, motor activity data of adults suffering from ADHD and clinical controls has been preprocessed to obtain 788 activity-related statistical features. Afterwards, principal component analysis has been carried out to obtain significant features for accurate classification. These features are then fed into six different machine learning algorithms for classification, which include C4.5, kNN, Random Forest, LogitBoost, SVM, and Naive Bayes. The detailed evaluation of the results through 10-fold cross-validation reveals that SVM outperforms other classifiers with an accuracy of 98.43%, F-measure of 98.42%, sensitivity of 98.33%, specificity of 98.56% and AUC of 0.983. Thus, a PCA-based SVM approach appears to be an effective choice for accurate identification of ADHD patients among other clinical controls using real-time analysis of activity data.

Machine Learning in Health Promotion and Behavioral Change: Scoping Review

BACKGROUND

Despite health behavioral change interventions targeting modifiable lifestyle factors underlying chronic diseases, dropouts and nonadherence of individuals have remained high. The rapid development of machine learning (ML) in recent years, alongside its ability to provide readily available personalized experience for users, holds much potential for success in health promotion and behavioral change interventions.

OBJECTIVE

The aim of this paper is to provide an overview of the existing research on ML applications and harness their potential in health promotion and behavioral change interventions.

METHODS

A scoping review was conducted based on the 5-stage framework by Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews) guidelines. A total of 9 databases (the Cochrane Library, CINAHL, Embase, Ovid, ProQuest, PsycInfo, PubMed, Scopus, and Web of Science) were searched from inception to February 2021, without limits on the dates and types of publications. Studies were included in the review if they had incorporated ML in any health promotion or behavioral change interventions, had studied at least one group of participants, and had been published in English. Publication-related information (author, year, aim, and findings), area of health promotion, user data analyzed, type of ML used, challenges encountered, and future research were extracted from each study.

RESULTS

A total of 29 articles were included in this review. Three themes were generated, which are as follows: (1) enablers, which is the adoption of information technology for optimizing systemic operation; (2) challenges, which comprises the various hurdles and limitations presented in the articles; and (3) future directions, which explores prospective strategies in health promotion through ML.

CONCLUSIONS

The challenges pertained to not only the time- and resource-consuming nature of ML-based applications, but also the burden on users for data input and the degree of personalization. Future works may consider designs that correspondingly mitigate these challenges in areas that receive limited attention, such as smoking and mental health.

Actigraphically measured psychomotor slowing in depression: systematic review and meta-analysis

Psychomotor slowing is a key feature of depressive disorders. Despite its great clinical importance, the pathophysiology and prevalence across different diagnoses and mood states are still poorly understood. Actigraphy allows unbiased, objective, and naturalistic assessment of physical activity as a marker of psychomotor slowing. Yet, the true effect-sizes remain unclear as recent, large systematic reviews are missing. We conducted a novel meta-analysis on actigraphically measured slowing in depression with strict inclusion and exclusion criteria for diagnosis ascertainment and sample duplications. Medline/PubMed and Web-of-Science were searched with terms combining mood-keywords and actigraphy-keywords until September 2021. Original research measuring actigraphy for ⩾24 h in at least two groups of depressed, remitted, or healthy participants and applying operationalized diagnosis was included. Studies in somatically ill patients, N < 10 participants/group, and studies using consumer-devices were excluded. Activity-levels between groups were compared using random-effects models with standardized-mean-differences and several moderators were examined. In total, 34 studies (n = 1804 patients) were included. Patients had lower activity than controls [standardized mean difference (s.m.d.) = -0.78, 95% confidence interval (CI) -0.99 to -0.57]. Compared to controls, patients with unipolar and bipolar disorder had lower activity than controls whether in depressed (unipolar: s.m.d. = -0.82, 95% CI -1.07 to -0.56; bipolar: s.m.d. = -0.94, 95% CI -1.41 to -0.46), or remitted/euthymic mood (unipolar: s.m.d. = -0.28, 95% CI -0.56 to 0.0; bipolar: s.m.d. = -0.92, 95% CI -1.36 to -0.47). None of the examined moderators had any significant effect. To date, this is the largest meta-analysis on actigraphically measured slowing in mood disorders. They are associated with lower activity, even in the remitted/euthymic mood-state. Studying objective motor behavior via actigraphy holds promise for informing screening and staging of affective disorders.

Annual Research Review: Translational machine learning for child and adolescent psychiatry

Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.

A systematic review and meta-analysis of sleep and circadian rhythms disturbances in individuals at high-risk of developing or with early onset of bipolar disorders

Sleep and circadian rhythms disturbances (SCRD) in young people at high risk or with early onset of bipolar disorders (BD) are poorly understood. We systematically searched for studies of self, observer or objective estimates of SCRD in asymptomatic or symptomatic offspring of parents with BD (OSBD), individuals with presentations meeting recognized BD-at-risk criteria (BAR) and youth with recent onset of full-threshold BD (FT-BD). Of 76 studies eligible for systematic review, 35 (46%) were included in random effects meta-analyses. Pooled analyses of self-ratings related to circadian rhythms demonstrated greater preference for eveningness and more dysregulation of social rhythms in BAR and FT-BD groups; analyses of actigraphy provided some support for these findings. Meta-analysis of prospective studies showed that pre-existing SCRD were associated with a 40% increased risk of onset of BD, but heterogeneity in assessments was a significant concern. Overall, we identified longer total sleep time (Hedges g: 0.34; 95% confidence intervals:.1,.57), especially in OSBD and FT-BD and meta-regression analysis indicated the effect sizes was moderated by the proportion of any sample manifesting psychopathology or receiving psychotropic medications. This evolving field of research would benefit from greater attention to circadian rhythm as well as sleep quality measures.

Motor activity patterns can distinguish between interepisode bipolar disorder patients and healthy controls

BACKGROUND

Bipolar disorder (BD) is linked to circadian rhythm disruptions resulting in aberrant motor activity patterns. We aimed to explore whether motor activity alone, as assessed by longitudinal actigraphy, can be used to classify accurately BD patients and healthy controls (HCs) into their respective groups.

METHODS

Ninety-day actigraphy records from 25 interepisode BD patients (ie, Montgomery-Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) < 15) and 25 sex- and age-matched HCs were used in order to identify latent actigraphic biomarkers capable of discriminating between BD patients and HCs. Mean values and time variations of a set of standard actigraphy features were analyzed and further validated using the random forest classifier.

RESULTS

Using all actigraphy features, this method correctly assigned 88% (sensitivity = 85%, specificity = 91%) of BD patients and HCs to their respective group. The classification success may be confounded by differences in employment between BD patients and HCs. When motor activity features resistant to the employment status were used (the strongest feature being time variation of intradaily variability, Cohen's d = 1.33), 79% of the subjects (sensitivity = 76%, specificity = 81%) were correctly classified.

CONCLUSION

A machine-learning actigraphy-based model was capable of distinguishing between interepisode BD patients and HCs solely on the basis of motor activity. The classification remained valid even when features influenced by employment status were omitted. The findings suggest that temporal variability of actigraphic parameters may provide discriminative power for differentiating between BD patients and HCs while being less affected by employment status.

Do Children of Patients with Bipolar Disorder have a Worse Perception of Sleep Quality?

INTRODUCTION

The offspring of bipolar parents (BO) is a high-risk population for inheriting the bipolar disorder (BD) and other early clinical manifestations, such as sleep disturbances.

OBJECTIVE

To compare the presence of psychiatric disorders and sleep disturbances of BO versus offspring of control parents (OCP).

METHODS

A cross-sectional analytical study was conducted that compared BO versus OCP. The participants were assessed using valid tools to determine the presence of psychiatric symptoms or disorders. The "Sleep Evaluation Questionnaire" and "School Sleep Habits Survey" were used to determine sleep characteristics and associated factors. Sleep records (7-21 days) were also obtained by using an actigraphy watch.

RESULTS

A sample of 42 participants (18 BO and 24 OCP) was recruited. Differences were found in the presentation of the psychiatric disorder. The BO group showed a higher frequency of major depression disorder (MDD; P = .04) and Disruptive Mood Dysregulation Disorder (DMDD; P = .04). The OCP group showed a higher frequency of Attention Deficit and Hyperactivity Disorder (ADHD; P = .65), and Separation Anxiety Disorder (SAD; P = .46). Differences were also found in sleep by using subjective measurements. Compared to the OCP group, BO had a worse perception of quality of sleep (P = .02), a higher frequency of nightmares (P = .01), a shorter total sleep time, and a higher sleep latency. Nevertheless, no differences were found between groups in the actigraphy measurements.

CONCLUSIONS

The BO group had a higher frequency of Mood Disorders, and at the same time a higher number of sleep disturbances in the subjective measurements. It is possible that there is an association between mood symptoms, sleep disturbances, and coffee intake. No differences were found in the sleep profile by using actigraphy.

Detailed analysis and comparison of different activity metrics

Actigraphic measurements are an important part of research in different disciplines, yet the procedure of determining activity values is unexpectedly not standardized in the literature. Although the measured raw acceleration signal can be diversely processed, and then the activity values can be calculated by different activity calculation methods, the documentations of them are generally incomplete or vary by manufacturer. These numerous activity metrics may require different types of preprocessing of the acceleration signal. For example, digital filtering of the acceleration signals can have various parameters; moreover, both the filter and the activity metrics can also be applied per axis or on the magnitudes of the acceleration vector. Level crossing-based activity metrics also depend on threshold level values, yet the determination of their exact values is unclear as well. Due to the serious inconsistency of determining activity values, we created a detailed and comprehensive comparison of the different available activity calculation procedures because, up to the present, it was lacking in the literature. We assessed the different methods by analysing the triaxial acceleration signals measured during a 10-day movement of 42 subjects. We calculated 148 different activity signals for each subject’s movement using the combinations of various types of preprocessing and 7 different activity metrics applied on both axial and magnitude data. We determined the strength of the linear relationship between the metrics by correlation analysis, while we also examined the effects of the preprocessing steps. Moreover, we established that the standard deviation of the data series can be used as an appropriate, adaptive and generalized threshold level for the level intersection-based metrics. On the basis of these results, our work also serves as a general guide on how to proceed if one wants to determine activity from the raw acceleration data. All of the analysed raw acceleration signals are also publicly available.

Cardiovascular reactivity as a measure of irritability in a transdiagnostic sample of youth: Preliminary associations

OBJECTIVES

Irritability is a transdiagnostic symptom in developmental psychopathology, conceptualized as a low threshold for frustration and increased proneness to anger. While central to emotion regulation, there is a vital need for empirical studies to explore the relationship between irritability and underlying physiological mechanisms of cardiovascular arousal.

METHODS

We examined the relationship between irritability and cardiovascular arousal (i.e., heart rate [HR] and heart rate variability [HRV]) in a transdiagnostic sample of 51 youth (M = 12.63 years, SD = 2.25; 62.7% male). Data was collected using the Empatica E4 during a laboratory stop-signal task. In addition, the impact of motion activity, age, medication, and sleep on cardiovascular responses was explored.

RESULTS

Main findings showed that irritability was associated with increased HR and decreased HRV during task performance.

CONCLUSIONS

Findings support the role of peripheral physiological dysregulation in youth with emotion regulation problems and suggest the potential use of available wearable consumer electronics as an objective measure of irritability and physiological arousal in a transdiagnostic sample of youth.

Symptoms and Characteristics of Youth Hospitalized for Depression: Subthreshold Manic Symptoms Can Help Differentiate Bipolar from Unipolar Depression

Background: Most people who have major depressive disorder (MDD) or bipolar disorder (BD) will have their first episode of depression in adolescence. However, in the absence of significant [hypo]manic symptoms, there are no clear guidelines for distinguishing bipolar from unipolar depression, which can lead to misdiagnosis and ineffective/harmful treatment. We aimed to compare phenomenological differences among youth with MDD or BD hospitalized for an acute episode of depression. Methods: A retrospective electronic chart review of adolescents hospitalized in an acute care inpatient unit who had a discharge diagnosis of MDD, MDD with mixed or psychotic features (MDD+), BD-I-current episode depressed, or BD-II-current episode depressed, was performed. Results: Altogether, 598 patients (mean age = 15.1 ± 1.5 years, female = 71%, and White = 46%) met study inclusion criteria, i.e., BD-I: n = 39, BD-II: n = 84, MDD: n = 422, and MDD+: n = 53 patients. The admission Hamilton Depression Rating Scale (HAMD) total score was significantly higher in the BD-I (29.3 ± 9.1) and MDD+ (31.2 ± 9.3) groups versus the MDD group (24.3 ± 9.7) (p < 0.05). Although there were some group differences in the severity of individual depression symptoms, these did not line up neatly across BD and MDD groups. At admission, Young Mania Rating Scale (YMRS) total scores were significantly higher in the BD-I (14.4 ± 7.4), BD-II (13.8 ± 6.5), and MDD+ groups (14.3 ± 6.6) versus the MDD group (8.2 ± 4.6, p < 0.05). Additionally, 9 of 11 and 4 of 11 YMRS items scored significantly higher in the BD-II and BD-I groups versus the MDD group, respectively. The motor activity and hypersexuality items, in particular, were scored consistently higher in the BD groups than MDD groups. Limitations: All diagnoses were made based on a clinical interview and not a structured diagnostic interview, and some of the subgroup sample sizes were relatively modest, limiting the power for group comparisons. Conclusion: The presence of subsyndromal manic symptoms during an episode of MDD currently offers the clearest way by which to differentiate bipolar depression from unipolar depression.

Generalizing the Prediction of Bipolar Disorder Onset Across High-Risk Populations

OBJECTIVE

Risk calculators (RC) to predict clinical outcomes are gaining interest. An RC to estimate risk of bipolar spectrum disorders (BPSD) could help reduce the duration of undiagnosed BPSD and improve outcomes. Our objective was to adapt an RC previously validated in the Pittsburgh Bipolar Offspring Study (BIOS) sample to achieve adequate predictive ability in both familial high-risk and clinical high-risk youths.

METHOD

Participants (aged 6-12 years at baseline) from the Longitudinal Assessment of Manic Symptoms (LAMS) study (N = 473) were evaluated semi-annually. Evaluations included a Kiddie Schedule for Affective Disorders (K-SADS) interview. After testing an RC that closely approximated the original, we made modifications to improve model prediction. Models were trained in the BIOS data, which included biennial K-SADS assessments, and tested in LAMS. The final model was then trained in LAMS participants, including family history of BPSD as a predictor, and tested in the familial high-risk sample.

RESULTS

Over follow-up, 65 youths newly met criteria for BPSD. The original RC identified youths who developed BPSD only moderately well (area under the curve [AUC] = 0.67). Eliminating predictors other than the K-SADS screening items for mania and depression improved accuracy (AUC = 0.73) and generalizability. The model trained in LAMS, including family history as a predictor, performed well in the BIOS sample (AUC = 0.74).

CONCLUSION

The clinical circumstances under which the assessment of symptoms occurs affects RC accuracy; focusing on symptoms related to the onset of BPSD improved generalizability. Validation of the RC under clinically realistic circumstances will be an important next step.

Effect of lithium on circadian rhythm in bipolar disorder: A systematic review and meta-analysis

OBJECTIVES

Circadian rhythm disruption is commonly reported in patients with bipolar disorder. Lithium has been suggested to have effects on the circadian clock, the biological basis of the circadian rhythm. The objective of the current review was to review systematically the existing studies on the effect of lithium on circadian rhythm in patients with bipolar disorder.

METHODS

We systematically searched the scientific literature up to September 2020 for experimental or observational studies which measured circadian rhythm in bipolar patients taking lithium (in comparison with placebo or other active treatments) and carried out a meta-analysis. Circadian rest-activity was our primary outcome, but we also collected data about sleep quality and chronotype (Morningness-Eveningness). The protocol was registered in PROSPERO (CRD42018109790).

RESULTS

Four observational studies (n = 668) and one experimental study (n = 29) were included. Results from the meta-analysis suggest a potential association between lithium and shifts towards morningness (standardized mean difference [SMD]: 0.42, 95% confidence interval [CI]: -0.05 to 0.90). One cohort study with 21 days of follow-up found that patients treated with lithium had significantly larger amplitude (0.68, 0.01 to 1.36) when compared to anticonvulsants.

CONCLUSION

This review highlights the insufficient evidence to inform us about the effect of lithium on circadian rhythm. However, we found that chronotype can be a potential target for further exploration of biomarkers or biosignatures of lithium treatment in patients with bipolar disorder. Further studies with prospective and longitudinal study design, adopting actigraphy to monitor daily circadian rest-activity changes are needed.

Internet of things issues related to psychiatry

Background

Internet of Things (IoT) devices for remote monitoring, diagnosis, and treatment are widely viewed as an important future direction for medicine, including for bipolar disorder and other mental illness. The number of smart, connected devices is expanding rapidly. IoT devices are being introduced in all aspects of everyday life, including devices in the home and wearables on the body. IoT devices are increasingly used in psychiatric research, and in the future may help to detect emotional reactions, mood states, stress, and cognitive abilities. This narrative review discusses some of the important fundamental issues related to the rapid growth of IoT devices.

Main body

Articles were searched between December 2019 and February 2020. Topics discussed include background on the growth of IoT, the security, safety and privacy issues related to IoT devices, and the new roles in the IoT economy for manufacturers, patients, and healthcare organizations.

Conclusions

The use of IoT devices will increase throughout psychiatry. The scale, complexity and passive nature of data collection with IoT devices presents unique challenges related to security, privacy and personal safety. While the IoT offers many potential benefits, there are risks associated with IoT devices, and from the connectivity between patients, healthcare providers, and device makers. Security, privacy and personal safety issues related to IoT devices are changing the roles of manufacturers, patients, physicians and healthcare IT organizations. Effective and safe use of IoT devices in psychiatry requires an understanding of these changes.

Real-time mobile monitoring of bipolar disorder: a review of evidence and future directions

Rapidly accumulating data from mobile assessments are facilitating our ability to track patterns of emotions, behaviors, biologic rhythms, and their contextual influences in real time. These approaches have been widely applied to study the core features, traits, changes in states, and the impact of treatments in bipolar disorder (BD). This paper reviews recent evidence on the application of both passive and active mobile technologies to gain insight into the role of the circadian system and patterns of sleep and motor activity in people with BD. Findings of more than two dozen studies converge in demonstrating a broad range of sleep disturbances, particularly longer duration and variability of sleep patterns, lower average and greater variability of motor activity, and a shift to later peak activity and sleep midpoint, indicative of greater evening orientation among people with BD. The strong associations across the domains tapped by real-time monitoring suggest that future research should shift focus on sleep, physical/motor activity, or circadian patterns to identify common biologic pathways that influence their interrelations. The development of novel data-driven functional analytic tools has enabled the derivation of individualized multilevel dynamic representations of rhythms of multiple homeostatic regulatory systems. These multimodal tools can inform clinical research through identifying heterogeneity of the manifestations of BD and provide more objective indices of treatment response in real-world settings. Collaborative efforts with common protocols for the application of multimodal sensor technology will facilitate our ability to gain deeper insight into mechanisms and multisystem dynamics, as well as environmental, physiologic, and genetic correlates of BD.

Neurobiology of bipolar disorders: a review of genetic components, signaling pathways, biochemical changes, and neuroimaging findings

Bipolar disorder (BD) is a chronic mental illness characterized by changes in mood that alternate between mania and hypomania or between depression and mixed states, often associated with functional impairment. Although effective pharmacological and non-pharmacological treatments are available, several patients with BD remain symptomatic. The advance in the understanding of the neurobiology underlying BD could help in the identification of new therapeutic targets as well as biomarkers for early detection, prognosis, and response to treatment in BD. In this review, we discuss genetic, epigenetic, molecular, physiological and neuroimaging findings associated with the neurobiology of BD. Despite the advances in the pathophysiological knowledge of BD, the diagnosis and management of the disease are still essentially clinical. Given the complexity of the brain and the close relationship between environmental exposure and brain function, initiatives that incorporate genetic, epigenetic, molecular, physiological, clinical, environmental data, and brain imaging are necessary to produce information that can be translated into prevention and better outcomes for patients with BD.

Measuring circadian function in bipolar disorders: Empirical and conceptual review of physiological, actigraphic, and self-report approaches

BACKGROUND

Interest in biological clock pathways in bipolar disorders (BD) continues to grow, but there has yet to be an audit of circadian measurement tools for use in BD research and practice.

PROCEDURE

The International Society for Bipolar Disorders Chronobiology Task Force conducted a critical integrative review of circadian methods that have real-world applicability. Consensus discussion led to the selection of three domains to review-melatonin assessment, actigraphy, and self-report.

RESULTS

Measurement approaches used to quantify circadian function in BD are described in sufficient detail for researchers and clinicians to make pragmatic decisions about their use. A novel integration of the measurement literature is offered in the form of a provisional taxonomy distinguishing between circadian measures (the instruments and methods used to quantify circadian function, such as dim light melatonin onset) and circadian constructs (the biobehavioral processes to be measured, such as circadian phase).

CONCLUSIONS

Circadian variables are an important target of measurement in clinical practice and biomarker research. To improve reproducibility and clinical application of circadian constructs, an informed systematic approach to measurement is required. We trust that this review will decrease ambiguity in the literature and support theory-based consideration of measurement options.

Data Handling for E-Mental Health Professionals

Digital psychiatry and e-mental health have proliferated and permeated vastly in the current landscape of mental health care provision. The COVID-19 crisis has accelerated this digital transformation, and changes that usually take many years to translate into clinical practice have been implemented in a matter of weeks. These have outpaced the checks and balances that would typically accompany such changes, which has brought into focus a need to have a proper approach for digital data handling. Health care data is sensitive, and is prone to hacking due to the lack of stringent protocols regarding its storage and access. Mental health care data need to be more secure due to the stigma associated with having a mental health condition. Thus, there is a need to emphasize proper data handling by mental health professionals, and policies to ensure safeguarding patient's privacy are required. The aim of useful, free, and fair use of mental health care data for clinical, business, and research purposes should be balanced with the need to ensure the data is accessible to only those who are authorized. Systems and policies should be in place to ensure that data storage, access, and disposal are systematic and conform to data safety norms.

Do Children of Patients with Bipolar Disorder have a Worse Perception of Sleep Quality?

INTRODUCTION

The offspring of bipolar parents (BO) is a high-risk population for inheriting the bipolar disorder (BD) and other early clinical manifestations, such as sleep disturbances.

OBJECTIVE

To compare the presence of psychiatric disorders and sleep disturbances of BO versus offspring of control parents (OCP).

METHODS

A cross-sectional analytical study was conducted that compared BO versus OCP. The participants were assessed using valid tools to determine the presence of psychiatric symptoms or disorders. The "Sleep Evaluation Questionnaire" and "School Sleep Habits Survey" were used to determine sleep characteristics and associated factors. Sleep records (7-21 days) were also obtained by using an actigraphy watch.

RESULTS

A sample of 42 participants (18 BO and 24 OCP) was recruited. Differences were found in the presentation of the psychiatric disorder. The BO group showed a higher frequency of major depression disorder (MDD; P=.04) and Disruptive Mood Dysregulation Disorder (DMDD; P=.04). The OCP group showed a higher frequency of Attention Deficit and Hyperactivity Disorder (ADHD; P=.65), and Separation Anxiety Disorder (SAD; P=.46). Differences were also found in sleep by using subjective measurements. Compared to the OCP group, BO had a worse perception of quality of sleep (P=.02), a higher frequency of nightmares (P=.01), a shorter total sleep time, and a higher sleep latency. Nevertheless, no differences were found between groups in the actigraphy measurements.

CONCLUSIONS

The BO group had a higher frequency of Mood Disorders, and at the same time a higher number of sleep disturbances in the subjective measurements. It is possible that there is an association between mood symptoms, sleep disturbances, and coffee intake. No differences were found in the sleep profile by using actigraphy.

Telemedicine Cognitive Behavioral Therapy for Anxiety After Stroke: Proof-of-Concept Randomized Controlled Trial

Supplemental Digital Content is available in the text.

Background and Purpose:

Disabling anxiety affects a quarter of stroke survivors but access to treatment is poor. We developed a telemedicine model for delivering guided self-help cognitive behavioral therapy (CBT) for anxiety after stroke (TASK-CBT). We aimed to evaluate the feasibility of TASK-CBT in a randomized controlled trial workflow that enabled all trial procedures to be carried out remotely. In addition, we explored the feasibility of wrist-worn actigraphy sensor as a way of measuring objective outcomes in this clinical trial.

Methods:

We recruited adult community-based stroke patients (n=27) and randomly allocated them to TASK-CBT (n=14) or relaxation therapy (TASK-Relax), an active comparator (n=13).

Results:

In our sample (mean age 65 [±10]; 56% men; 63% stroke, 37% transient ischemic attacks), remote self-enrolment, electronic signature, intervention delivery, and automated follow-up were feasible. All participants completed all TASK-CBT sessions (14/14). Lower levels of anxiety were observed in TASK-CBT when compared with TASK-Relax at both weeks 6 and 20. Mean actigraphy sensor wearing-time was 33 days (±15).

Conclusions:

Our preliminary feasibility data from the current study support a larger definitive clinical trial and the use of wrist-worn actigraphy sensor in anxious stroke survivors.

Registration:

URL: https://www.clinicaltrials.gov. Unique identifier: NCT03439813.

Digital Phenotyping With Mobile and Wearable Devices: Advanced Symptom Measurement in Child and Adolescent Depression

With an estimated 75% of all mental disorders beginning in the first two decades of life,¹ childhood and adolescence are crucial developmental periods to identify and intercept the unfolding of mental health problems, their relationships with physical health, and the multiple, interwoven connections to the surrounding environment.² Because an individual's mental health is best conceptualized, captured, and treated by taking into account the network of physiological and social functions that constitute the context of individual experience, accessing and analyzing data on multiple health indicators simultaneously can accelerate prediction of disease progression. With the advent of new technologies, dense and extensive amounts of biopsychosocial readouts that can be translated into clinically relevant information have become available in real time, with the potential to revolutionize the practice of medicine. However, challenges to this more ecological and comprehensive approach to mental health measurement include the actual capacity of capturing, safely storing, and analyzing dense data sets (encompassing, for example, mood, cognitions, physical activity, sleep, social interactions) from multiple synchronized sources, and identifying which among multiple indicators ultimately prove useful to improve prediction of a deterioration in symptoms and of initiating early intervention. In this Translations article, we focus on digital phenotyping (DP), which relates to the capturing of the aforementioned relevant biopsychosocial data. This concept is rapidly growing and gaining relevance to child and adolescent psychiatry, and is connected with overarching data science themes of "big data" (extremely large data sets, including data from electronic medical records, imaging, genomics, and patients' smartphones),^3,4 in addition to "machine learning" (the science of getting computers to act without being explicitly programmed)⁵ and "precision medicine" (the practice of custom tailoring treatments to a patient's disease processes),⁶ which have all received attention in this journal. We will describe principles and current applications of DP, together with its potential to facilitate improved outcomes and its limits, using depression in children and adolescents as an illustrative example.

Circadian rest-activity patterns in bipolar disorder and borderline personality disorder

Bipolar disorder (BD) and borderline personality disorder (BPD) are two psychiatric disorders with overlapping features that can be challenging to separate diagnostically. Growing evidence suggests that circadian rhythm disturbances are associated with psychiatric illness, however circadian patterns of behaviour have not been elucidated in BPD or differentiated from BD. This study compared the circadian structure and timing of rest-activity patterns in BPD with BD and healthy volunteers. Participants with BD (N = 31) and BPD (N = 21) and healthy controls (HC, N = 35) wore an actigraph on their non-dominant wrist for 28 day periods as part of the Automated Monitoring of Symptom Severity (AMoSS) study. Non-parametric circadian rhythm analysis of rest-activity patterns and cosinor analysis of distal temperature rhythms were conducted to elucidate circadian function between groups. Covariates controlled for included employment status, BMI and gender. Compared with HC and BD, individuals with BPD showed significantly delayed phase of night-time rest patterns ("L5 onset") (mean difference = 1:47 h, P < 0.001; mean difference = 1:38 h, P = 0.009, respectively), and relative to HC showed delayed daytime activity onset ("M10 onset") (mean difference = 2:13 h, P = 0.048) and delayed temperature phase (mean difference = 1:22 h, P = 0.034). These findings suggest that delayed circadian function may be a clinically important phenotype in individuals with BPD. Future work should interrogate the causality of this association and examine interventions which target delayed circadian function in the treatment of BPD.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Does childhood experience of attention-deficit hyperactivity disorder symptoms increase sleep/wake cycle disturbances as measured with actigraphy in adult patients with bipolar disorder?

Childhood attention-deficit hyperactivity disorder (ADHD) is a common precursor of adult bipolar disorders (BD). Furthermore, actigraphy studies demonstrate that each disorder may be associated with abnormalities in sleep and activity patterns. This study investigates whether the presence or absence of self-reported childhood experiences of ADHD symptoms is associated with different sleep and activity patterns in adults with BD. A sample of 115 euthymic adult patients with BD was assessed for childhood ADHD symptoms using the Wender Utah Rating Scale (WURS) and then completed 21 days of actigraphy monitoring. Actigraphic measures of sleep quantity and variability and daytime activity were compared between BD groups classified as ADHD+ (n = 24) or ADHD- (n = 91), defined according to established cutoff scores for the WURS; then we examined any associations between sleep-wake cycle parameters and ADHD dimensions (using the continuous score on the WURS). Neither approach revealed any statistically significant associations between actigraphy parameters and childhood ADHD categories or dimensions. We conclude that the sleep and activity patterns of adult patients with BD do not differ according to their self-reported history of ADHD symptoms. We discuss the implications of these findings and suggest how future studies might confirm or refute our findings.

Modeling Biobehavioral Rhythms with Passive Sensing in the Wild

Biobehavioral rhythms are associated with numerous health and life outcomes. We study the feasibility of detecting rhythms in data that is passively collected from Fitbit devices and using the obtained model parameters to predict readmission risk after pancreatic surgery. We analyze data from 49 patients who were tracked before surgery, in hospital, and after discharge. Our analysis produces a model of individual patients' rhythms for each stage of treatment that is predictive of readmission. All of the rhythm-based models outperform the traditional approaches to readmission risk stratification that uses administrative data.

Real-time Mobile Monitoring of the Dynamic Associations Among Motor Activity, Energy, Mood, and Sleep in Adults With Bipolar Disorder

IMPORTANCE

Biologic systems involved in the regulation of motor activity are intricately linked with other homeostatic systems such as sleep, feeding behavior, energy, and mood. Mobile monitoring technology (eg, actigraphy and ecological momentary assessment devices) allows the assessment of these multiple systems in real time. However, most clinical studies of mental disorders that use mobile devices have not focused on the dynamic associations between these systems.

OBJECTIVES

To examine the directional associations among motor activity, energy, mood, and sleep using mobile monitoring in a community-identified sample, and to evaluate whether these within-day associations differ between people with a history of bipolar or other mood disorders and controls without mood disorders.

DESIGN, SETTING, AND PARTICIPANTS

This study used a nested case-control design of 242 adults, a subsample of a community-based sample of adults. Probands were recruited by mail from the greater Washington, DC, metropolitan area from January 2005 to June 2013. Enrichment of the sample for mood disorders was provided by volunteers or referrals from the National Institutes of Health Clinical Center or by participants in the National Institute of Mental Health Mood and Anxiety Disorders Program. The inclusion criteria were the ability to speak English, availability to participate, and consent to contact at least 2 living first-degree relatives. Data analysis was performed from June 2013 through July 2018.

MAIN OUTCOMES AND MEASURES

Motor activity and sleep duration data were obtained from minute-to-minute activity counts from an actigraphy device worn on the nondominant wrist for 2 weeks. Mood and energy levels were assessed by subjective analogue ratings on the ecological momentary assessment (using a personal digital assistant) by participants 4 times per day for 2 weeks.

RESULTS

Of the total 242 participants, 92 (38.1%) were men and 150 (61.9%) were women, with a mean (SD) age of 48 (16.9) years. Among the participants, 54 (22.3%) had bipolar disorder (25 with bipolar I; 29 with bipolar II), 91 (37.6%) had major depressive disorder, and 97 (40.1%) were controls with no history of mood disorders. A unidirectional association was found between motor activity and subjective mood level (β = -0.018, P = .04). Bidirectional associations were observed between motor activity (β = 0.176; P = .03) and subjective energy level (β = 0.027; P = .03) as well as between motor activity (β = -0.027; P = .04) and sleep duration (β = -0.154; P = .04). Greater cross-domain reactivity was observed in bipolar disorder across all outcomes, including motor activity, sleep, mood, and energy.

CONCLUSIONS AND RELEVANCE

These findings suggest that interventions focused on motor activity and energy may have greater efficacy than current approaches that target depressed mood; both active and passive tracking of multiple regulatory systems are important in designing therapeutic targets.

Windows Into Human Health Through Wearables Data Analytics

Background

Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness- or health-related outcomes of interest for users, patients, and health care providers.

Objectives

The aim of this review is to highlight a selected group of fitness- and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators.

Methods

A systematic search of the Pubmed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), ECG algorithm (8287), continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014-2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These papers covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and pivotal studies on sensor algorithm development were summarized, and a summary table was constructed using references generated by the literature review as described (Table 1).

Conclusions

Wearable health technologies aim to collect and process raw physiological or environmental parameters into salient digital health information. Much of the current and future utility of wearables lies in the signal processing steps and algorithms used to analyze large volumes of data. Continued algorithmic development and advances in machine learning techniques will further increase analytic capabilities. In the context of these advances, our review aims to highlight a range of advances in fitness- and other health-related indicators provided by current wearable technologies.

Sleep and circadian rhythm function and trait impulsivity: An actigraphy study

We report the relationship between daily rest-activity patterns and trait impulsivity in healthy young adults. The Barratt Impulsiveness Scale was used to identify high and low impulsive individuals among a group of 51 volunteers. Participants' sleep behaviour and circadian rhythm function was assessed using week-long actigraphy. High impulsive individuals displayed phase-delayed patterns of sleep, a decreased total sleep time and sleep efficiency, and disrupted circadian function. Such outcomes were also associated with greater self-reported attention deficit hyperactivity disorder symptoms. The results highlight that sleep and circadian rhythm disturbances may be associated with impulsive traits replicating relationships described in psychiatric illnesses in which impulsivity is a core feature.

Wearables and the medical revolution

Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

Sleep and disruptive mood dysregulation disorder: A pilot actigraphy study

OBJECTIVE

To explore the clinical characteristics and motor activity profile during sleep periods of children and adolescents presenting with disruptive mood dysregulation disorder (DMDD).

METHOD

Twenty-one youths (mean age±standard deviation, 11.7±3 years) wore a wrist actigraph for 9 consecutive days (including both school days and non-school days), to measure sleep parameters: sleep latency, sleep efficiency and the number and duration of periods of wakefulness after sleep onset (WASO). We divided the night-time actigraphy recording sessions into three sections and compared the first and last thirds of the night.

RESULTS

All the study participants had a psychiatric comorbidity (primarily attention deficit hyperactivity disorder, depressive disorder or anxiety disorder). On non-school days, bedrest onset and activity onset were shifted later by about 1h. There was no significant difference between school days and non-school days with regard to the total sleep time. Sleep efficiency was significantly greater on non-school days. Sleep was fragmented on both school days and non-school days. The mean number of episodes of WASO was 24.9 for school days and 30.9 for non-school days. Relative to the first third of the night, we observed a significantly greater number of episodes of WASO during the last third of the night, a period associated with a larger proportion of rapid eye movement (REM) sleep.

DISCUSSION

Sleep appeared to be fragmented in the study population of youths with DMDD. The greater frequency of WASO in the last third of the night points to a possible impairment of the motor inhibition normally associated with REM sleep.

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.

Adolescents at clinical-high risk for psychosis: Circadian rhythm disturbances predict worsened prognosis at 1-year follow-up

BACKGROUND

Individuals with psychotic disorders experience disruptions to both the sleep and circadian components of the sleep/wake cycle. Recent evidence has supported a role of sleep disturbances in emerging psychosis. However, less is known about how circadian rhythm disruptions may relate to psychosis symptoms and prognosis for adolescents with clinical high-risk (CHR) syndromes. The present study examines circadian rest/activity rhythms in CHR and healthy control (HC) youth to clarify the relationships among circadian rhythm disturbance, psychosis symptoms, psychosocial functioning, and the longitudinal course of illness.

METHODS

Thirty-four CHR and 32 HC participants were administered a baseline evaluation, which included clinical interviews, 5days of actigraphy, and a sleep/activity diary. CHR (n=29) participants were re-administered clinical interviews at a 1-year follow-up assessment.

RESULTS

Relative to HC, CHR youth exhibited more fragmented circadian rhythms and later onset of nocturnal rest. Circadian disturbances (fragmented rhythms, low daily activity) were associated with increased psychotic symptom severity among CHR participants at baseline. Circadian disruptions (lower daily activity, rhythms that were more fragmented and/or desynchronized with the light/dark cycle) also predicted severity of psychosis symptoms and psychosocial impairment at 1-year follow-up among CHR youth.

CONCLUSIONS

Circadian rhythm disturbances may represent a potential vulnerability marker for emergence of psychosis, and thus, rest/activity rhythm stabilization has promise to inform early-identification and prevention/intervention strategies for CHR youth. Future studies with longer study designs are necessary to further examine circadian rhythms in the prodromal period and rates of conversion to psychosis among CHR teens.

Dysregulation of objectively assessed 24-hour motor activity patterns as a potential marker for bipolar I disorder: results of a community-based family study

There has been a growing number of studies that have employed actigraphy to investigate differences in motor activity in mood disorders. In general, these studies have shown that people with bipolar disorders (BPDs) tend to exhibit greater variability and less daytime motor activity than controls. The goal of this study was to examine whether patterns of motor activity differ in euthymic individuals across the full range of mood disorder subtypes (Bipolar I (BPI), Bipolar II (BPII) and major depression (MDD)) compared with unaffected controls in a community-based family study of mood spectrum disorders. Minute-to-minute activity counts derived from actigraphy were collected over a 2-week period for each participant. Prospective assessments of the level, timing and day-to-day variability of physical activity measures were compared across diagnostic groups after controlling for a comprehensive list of potential confounding factors. After adjusting for the effects of age, sex, body mass index (BMI) and medication use, the BPI group had lower median activity intensity levels across the second half of the day and greater variability in the afternoon compared with controls. Those with a history of BPII had increased variability during the night time compared with controls, indicating poorer sleep quality. No differences were found in the average intensity, variability or timing of activity in comparisons between other mood disorder subgroups and controls. Findings confirm evidence from previous studies that BPI may be a manifestation of a rhythm disturbance that is most prominent during the second half of the day. The present study is the largest study to date that included the full range of mood disorder subgroups in a nonclinical sample that increases the generalizability of our findings to the general community. The manifestations of activity patterns outside of acute episodes add to the accumulating evidence that dysregulation of patterns of activity may constitute a potential biomarker for BPD.