A study of wrist-worn activity measurement as a potential real-world biomarker for late-life depression

Depressive symptom screening in elderly by passive sensing data of smartphones or smartwatches: A systematic review

BACKGROUND

The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine screening questionnaires. Passive sensing data have been promoted as a tool for depressive symptoms detection though there is still limited evidence on its usage in the elderly. Therefore, this study aims to review current knowledge on the use of passive sensing data via smartphones and smartwatches in depressive symptom screening for the elderly.

METHOD

The search of literature was performed in PubMed, IEEE Xplore digital library, and PsycINFO. Literature investigating the use of passive sensing data to screen, monitor, and/or predict depressive symptoms in the elderly (aged 60 and above) via smartphones and/or wrist-worn wearables was included for initial screening. Studies in English from international journals published between January 2012 to September 2022 were included. The reviewed studies were further analyzed by a narrative analysis.

RESULTS

The majority of 21 included studies were conducted in Western countries with a few in Asia and Australia. Most studies adopted a cohort study design (n = 12), followed by cross-sectional design (n = 7) and a case-control design (n = 2). The most popular passive sensing data was related to sleep and physical activity using an actigraphy. Sleep characteristics, such as prolonged wakefulness after sleep onset, along with lower levels of physical activity, exhibited a significant association with depression. However, cohort studies expressed concerns regarding data quality stemming from incomplete follow-up and potential confounding effects.

CONCLUSION

Passive sensing data, such as sleep, and physical activity parameters should be promoted for depressive symptoms detection. However, the validity, reliability, feasibility, and privacy concerns still need further exploration.

Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol

INTRODUCTION

Despite the high prevalence of major depressive disorder (MDD) among the elderly population, the rate of treatment is low due to stigmas and barriers to medical access. Wearable devices such as smartphones and smartwatches can help to screen MDD symptoms earlier in a natural setting while forgoing these concerns. However, previous research using wearable devices has mostly targeted the younger population. By collecting longitudinal data using wearable devices from the elderly population, this research aims to produce prediction algorithms for late-life depression and to develop strategies that strengthen medical access in community care systems.

METHODS AND ANALYSIS

The current cohort study recruited a subsample of 685 elderly people from the Korean Genome and Epidemiology Study-Cardiovascular Disease Association Study, a national large cohort established in 2004. The current study has been conducted over a 3-year period to explore the development patterns of late-life depression. Participants have completed three annual face-to-face interviews (baseline, the first follow-up and the second follow-up) and 2 years of app-based surveys and passive sensing data collection. All the data collection will end at the second follow-up interview. The collected self-report, observational and passive sensing data will be primarily analysed by machine learning.

ETHICS AND DISSEMINATION

This study protocol has been reviewed and approved by the Yonsei University Mirae Campus Institutional Review Board (1041849-2 02 111 SB-180-06) in South Korea. All participants provided written informed consent. The findings of this research will be disseminated by academic publications and conference presentations.

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.

Step away from depression-results from a multicenter randomized clinical trial with a pedometer intervention during and after inpatient treatment of depression

Evidence for the effectiveness of physical activity (PA) in the treatment of depression prevails for outpatients with mild and moderate symptom levels. For inpatient treatment of severe depression, evidence-based effectiveness exists only for structured and supervised group PA interventions. The Step Away from Depression (SAD) study investigated the effectiveness of an individual pedometer intervention (PI) combined with an activity diary added to inpatient treatment as usual (TAU). In this multicenter randomized controlled trial, 192 patients were randomized to TAU or TAU plus PI. The two primary outcomes at discharge were depression-blindly rated with the Montgomery-Åsberg Depression Rating Scale (MADRS)-and average number of daily steps measured by accelerometers. Secondary outcomes were self-rated depression and PA, anxiety, remission and response rates. Multivariate analysis of variance (MANOVA) revealed no significant difference between both groups for depression and daily steps. Mean MADRS scores at baseline were 29.5 (SD = 8.3) for PI + TAU and 28.8 (SD = 8.1) for TAU and 16.4 (SD = 10.3) and 17.2 (SD = 9.9) at discharge, respectively. Daily steps rose from 6285 (SD = 2321) for PI + TAU and 6182 (SD = 2290) for TAU to 7248 (SD = 2939) and 7325 (SD = 3357). No differences emerged between groups in secondary outcomes. For severely depressed inpatients, a PI without supervision or further psychological interventions is not effective. Monitoring, social reinforcement and motivational strategies should be incorporated in PA interventions for this population to reach effectiveness.

The effects of mobile technology-based support on young women with depressive symptoms: A block randomized controlled trial

BACKGROUND

The current body of knowledge highlights the potential role of mobile technology as a medium to deliver support for psychological and physical health. This study evaluated the influence of mobile technology support on depressive symptoms and physical activity in female university students.

METHODS

A block randomized controlled trial design with a single site was used. Ninety-nine participants were block-randomized into 3 arms: Experimental Group 1 (emotional and informational support group), Experimental Group 2 (informational support group), and the control group. Interventions were delivered via mobile technology for 2 weeks. Data on depressive symptoms and physical activity were collected from 84 participants at baseline and on Days 8 and 15. Data analyses included descriptive statistics, t tests, one-way analysis of variance, and repeated-measures analysis of variance.

RESULTS

This study showed no interaction effect of time and group on depressive symptom scores and physical activity, considering the emotional and informational support from mobile technology. However, Experimental Group 1 exhibited a significant reduction in depressive symptoms during the first week of the study compared to Experimental Group 2 and the control group. While physical activity in Experimental Group 2 and control group increased only during the first week of the study and subsequently decreased, Experimental Group 1 showed an initial increase during the first week that was sustained into the second week.

CONCLUSIONS

Since informational and emotional support showed a strong effect over a short period of time, mobile technology offering emotional support could be used to provide crisis interventions for depression among young women when a short-term impact is required.

Remediation of cognitive and motor functions in Tunisian elderly patients with mild Alzheimer's disease: implications of music therapy and/or physical rehabilitation

The purpose of this study was to compare the effects of music therapy (MT) and/or physical rehabilitation (PR) on cognitive and motor function in elderly Tunisian male and female patients with mild Alzheimer's disease (AD). Male patients (N: 16; age: 74.19 ± 4.27 years; weight: 76.71 ± 5.22 kg) and female patients (N: 12; age: 71.46 ± 3.36 years; weight: 67.47 ± 4.31 kg) with mild AD were randomly assigned into 4 groups including control group (Co), PR group participated in physical rehabilitation, MT group received music therapy and MT + PR received both music therapy and physical rehabilitation. Participants were required to engage in the study for four months with three 60-min sessions per week. We found all scores of cognitive (MMSE, ADAS-Cog Total and the ADAS-Cog Memory subscale) and motor functions (step length, walking speed, 6MVT and BBS score) evaluated were the greatest in MT + PR compared to the other groups. Our study also demonstrated that MT has a greater effect on cognitive function, while PR has a more pronounced effect on motor function. Changes in MMSE scores were significantly positively correlated in the PR, MT and MT + PR groups with improvements in all motor functions including step length (r = 0.77), walking speed (r = 0.73), 6MVT (r = 0.75) and BBS scores (r = 0.78) in AD patients. In conclusion, the combination of MT and PR seems to be an appropriate intervention approach that needs consideration as a treatment strategy for elderly male and female patients with mild AD.

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.

Applying Gamification Principles and Therapeutic Movement Sequences to Design an Interactive Physical Activity Game: Development Study

BACKGROUND

Depression is a severe illness that has accelerated with the spread of COVID-19 and associated lockdowns. As a result, reported physical activity has substantially decreased, further increasing depressive symptoms.

OBJECTIVE

This study aims to explain the use of gamification principles to develop content for an interactive physical activity game for depression based on clinically proven depression diagnostic criteria.

METHODS

We discuss related work in this field, the game design framework, the users' depression severity, how we customize the contents accordingly, the gradual progression of the game to match exercise principles, and user flow optimization.

RESULTS

We provide a brief description of each of the games developed, including instructions on how to play and design aspects for flow, audio, and visual feedback methods. Exergames (interactive physical activity-based games) stimulate certain physical fitness factors such as improving reaction time, endurance, cardiovascular fitness, and flexibility. In addition, the game difficulty progresses based on various factors, such as the user's performance for successful completion, reaction time, movement speed, and stimulated larger joint range of motions. Cognitive aspects are included, as the user has to memorize particular movement sequences.

CONCLUSIONS

Mental health issues are linked to behavior and movement; therefore, future physical activity-based interactive games may provide excellent stimulation for inducing user flow, while physical activity can help train various physical fitness factors linked to depression.

Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being

Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual’s holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing. We present the core components of MHN that are premised on the importance of addressing an individual’s personal mental health state. Moreover, we provide an overview of the existing physical health navigation systems and highlight the requirements and challenges of deploying the navigational approach to the mental health domain.

A scoping review on monitoring mental health using smart wearable devices

With the continuous development of the times, social competition is becoming increasingly fierce, people are facing enormous pressure and mental health problems have become common. Long-term and persistent mental health problems can lead to severe mental disorders and even death in individuals. The real-time and accurate prediction of individual mental health has become an effective method to prevent the occurrence of mental health disorders. In recent years, smart wearable devices have been widely used for monitoring mental health and have played an important role. This paper provides a comprehensive review of the application fields, application mechanisms, common signals, common techniques and results of smart wearable devices for the detection of mental health problems, aiming to achieve more efficient and accurate prediction for individual mental health, and to achieve early identification, early prevention and early intervention to provide a reference for improving the level of individual mental health.

Marfusion: An Attention-Based Multimodal Fusion Model for Human Activity Recognition in Real-World Scenarios

Human Activity Recognition(HAR) plays an important role in the field of ubiquitous computing, which can benefit various human-centric applications such as smart homes, health monitoring, and aging systems. Human Activity Recognition mainly leverages smartphones and wearable devices to collect sensory signals labeled with activity annotations and train machine learning models to recognize individuals’ activity automatically. In order to deploy the Human Activity Recognition model in real-world scenarios, however, there are two major barriers. Firstly, sensor data and activity labels are traditionally collected using special experimental equipment in a controlled environment, which means fitting models trained with these datasets may result in poor generalization to real-life scenarios. Secondly, existing studies focus on single or a few modalities of sensor readings, which neglect useful information and its relations existing in multimodal sensor data. To tackle these issues, we propose a novel activity recognition model for multimodal sensory data fusion: Marfusion, and an experimental data collection platform for HAR tasks in real-world scenarios: MarSense. Specifically, Marfusion extensively uses a convolution structure to extract sensory features for each modality of the smartphone sensor and then fuse the multimodal features using the attention mechanism. MarSense can automatically collect a large amount of smartphone sensor data via smartphones among multiple users in their natural-used conditions and environment. To evaluate our proposed platform and model, we conduct a data collection experiment in real-life among university students and then compare our Marfusion model with several other state-of-the-art models on the collected datasets. Experimental Results do not only indicate that the proposed platform collected Human Activity Recognition data in the real-world scenario successfully, but also verify the advantages of the Marfusion model compared to existing models in Human Activity Recognition.

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.

Motor abnormalities, depression risk, and clinical course in adolescence

Background

Motor abnormalities, such as psychomotor agitation and retardation, are widely recognized as core features of depression. However, it is not currently known if motor abnormalities connote risk for depression.

Methods

Using data from the Adolescent Brain Cognitive Development (ABCD) Study, a nationally representative sample of youth (n=10,835, 9-11 years old), the present paper examines whether motor abnormalities are associated with (a) depression symptoms in early adolescence, (b) familial risk for depression (familial risk loading), and (c) future depression symptoms. Motor abnormalities measures included traditional (DSM) motor signs such as psychomotor agitation and retardation as well as other motor domains such as developmental motor delays and dyscoordination.

Results

Traditional motor abnormalities were less prevalent (agitation=3.2%, retardation=0.3%) than non-traditional domains (delays=13.79%, coordination=35.5%) among adolescents. Motor dysfunction was associated with depression symptoms (Cohen's ds=0.02 to 0.12). Familial risk for depression was related to motor abnormalities (Cohen's ds=0.08 to 0.27), with the exception of motor retardation. Family vulnerability varied in sensitivity to depression risk (e.g., retardation: .53%; dyscoordination: 32.05%). Baseline endorsement of motor abnormalities predicted future depression symptoms at one-year follow-up.

Conclusions

These findings suggest that motor signs reflect a novel, promising future direction for examining vulnerability to depression risk in early adolescence.

Handedness and depression: A meta-analysis across 87 studies

Alterations in functional brain lateralization, often indicated by an increased prevalence of left- and/or mixed-handedness, have been demonstrated in several psychiatric and neurodevelopmental disorders like schizophrenia or autism spectrum disorder. For depression, however, this relationship is largely unclear. While a few studies found evidence that handedness and depression are associated, both the effect size and the direction of this association remain elusive. Here, we collected data from 87 studies totaling 35,501 individuals to provide a precise estimate of differences in left-, mixed- and non-right-handedness between depressed and healthy samples and computed odds ratios (ORs) between these groups. Here, an OR > 1 signifies higher rates of atypical handedness in depressed compared to healthy samples. We found no differences in left- (OR = 1.04, 95% CI = [0.95, 1.15], p = .384), mixed- (OR = 1.64, 95% CI = [0.98, 2.74], p = .060) or non-right-handedness (OR = 1.05, 95% CI = [0.96, 1.15], p = .309) between the two groups. We could thus find no link between handedness and depression on the meta-analytical level.

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.

Deep learning-based automated speech detection as a marker of social functioning in late-life depression

BACKGROUND

Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device to measure speech that participants encounter as an indicator of social interaction.

METHODS

Twenty nine participants with LLD and 29 age-matched controls wore a wrist-worn device continuously for seven days, which recorded their acoustic environment. Acoustic data were automatically analysed using deep learning models that had been developed and validated on an independent speech dataset. Total speech activity and the proportion of speech produced by the device wearer were both detected whilst maintaining participants' privacy. Participants underwent a neuropsychological test battery and clinical and self-report scales to measure severity of depression, general and social functioning.

RESULTS

Compared to controls, participants with LLD showed poorer self-reported social and general functioning. Total speech activity was much lower for participants with LLD than controls, with no overlap between groups. The proportion of speech produced by the participants was smaller for LLD than controls. In LLD, both speech measures correlated with attention and psychomotor speed performance but not with depression severity or self-reported social functioning.

CONCLUSIONS

Using this device, LLD was associated with lower levels of speech than controls and speech activity was related to psychomotor retardation. We have demonstrated that speech activity measured by wearable technology differentiated LLD from controls with high precision and, in this study, provided an objective measure of an aspect of real-world social functioning in LLD.

Evaluation of the Process of Acute Treatment for Depression in Terms of Monitoring Activity and Sleep Efficiency with Actigraphy

Background

This study aimed to evaluate and follow-up the process of acute treatment for depression in terms of activity and sleep efficiency using actigraphy, and thus increase the opportunities for objective measurement in the monitoring of treatment.

Methods

A total of 20 patients with depression, and 22 and age- and gender-matched healthy volunteers were included in the study. All subjects were evaluated using a sociodemographic data form, the Hamilton Depression Rating Scale (HDRS), and actigraphy for measurement of motor activity and sleep efficiency.

Results

The activity levels and sleep efficiency of the controls were significantly higher than the pre-and post-treatment activity levels and sleep efficiency of the patients. After the treatment process, both motor activity and sleep efficiency were found to be significantly increased in the patients. A highly significant negative correlation was found between the HDRS scores and average activity counts for active intervals (r = -0.779, P < .001), and between the HDRS scores and sleep efficiency (r = -0.616, P < .001). On the other hand, a significant negative effect was found between depression and average activity counts for active intervals (RR:0.880; 95% CI:0.782-0.991).

Conclusions

Actigraphy is a useful technique for quantifying physical activities and sleep efficiency in depressed patients. Furthermore, it may provide objective follow-up data in assessing the effects of treatment for depression.

Current Advances in Wearable Devices and Their Sensors in Patients With Depression

In this study, a literature survey was conducted of research into the development and use of wearable devices and sensors in patients with depression. We collected 18 studies that had investigated wearable devices for assessment, monitoring, or prediction of depression. In this report, we examine the sensors of the various types of wearable devices (e.g., actigraphy units, wristbands, fitness trackers, and smartwatches) and parameters measured through sensors in people with depression. In addition, we discuss future trends, referring to research in other areas employing wearable devices, and suggest the challenges of using wearable devices in the field of depression. Real-time objective monitoring of symptoms and novel approaches for diagnosis and treatment using wearable devices will lead to changes in management of patients with depression. During the process, it is necessary to overcome several issues, including limited types of collected data, reliability, user adherence, and privacy concerns.

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods. Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety. Methods: A total of N = 60 adults (ages 24-68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study. Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = -0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression. Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.

Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge

Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphy. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Participants were recruited from the acute psychiatric inpatient ward at Hospital Rey Juan Carlos (Madrid, Spain). They wore miniature wrist wireless inertial sensors (actigraphs) throughout the admission. We modeled activity levels against the normalized length of admission-'Progress Towards Discharge' (PTD)-using a Hierarchical Generalized Linear Regression Model. The estimated date of hospital discharge based on early measures of motor activity and the actual hospital discharge date were compared by a Hierarchical Gaussian Process model. Twenty-three depressed patients (14 females, age: 50.17 ± 12.72 years) were recruited. Activity levels increased during the admission (mean slope of the linear function: 0.12 ± 0.13). For n = 18 inpatients (78.26%) hospitalised for at least 7 days, the mean error of Prediction of Hospital Discharge Date at day 7 was 0.231 ± 22.98 days (95% CI 14.222-14.684). These n = 18 patients were predicted to need, on average, 7 more days in hospital (for a total length of stay of 14 days) (PTD = 0.53). Motor activity increased during the admission in this sample of depressed patients and early patterns of actigraphically recorded activity allowed for accurate prediction of hospital discharge date.

Depression and Objectively Measured Physical Activity: A Systematic Review and Meta-Analysis

Depression is a major contributor to the overall global burden of disease, with high prevalence and relapse rate. Several factors have been considered in order to reduce the depression burden. Among them, physical activity (PA) showed a potential protective role. However, evidence is contrasting probably because of the differences in PA measurement. The aim of this systematic review with meta-analysis is to assess the association between objectively measured PA and incident and prevalent depression. The systematic review was conducted according to methods recommended by the Cochrane Collaboration and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Relevant papers published through 31 August 2019 were identified searching through the electronic databases PubMed/MEDLINE, Excerpta Medica dataBASE (Embase), PsycINFO, Scopus, Web of Science (WoS), and the Cochrane Library. All analyses were conducted using ProMeta3. Finally, 42 studies met inclusion criteria. The overall Effect size (ES) of depression for the highest vs. the lowest level of PA was −1.16 [(95% CI = −1.41; −0.91), p-value < 0.001] based on 37,408 participants. The results of the meta-analysis showed a potential protective effect of PA on prevalent and incident depression.

An Examination of Psychomotor Disturbance in Current and Remitted MDD: An RDoC Study

Major depressive disorder (MDD) is a serious public health problem that has, at best, modest treatment response—potentially due to its heterogeneous clinical presentation. One way to parse the heterogeneity is to investigate the role of particular features of MDD, an endeavor that can also help identify novel and focal targets for treatment and prevention efforts. Our R01 focuses on the feature of psychomotor disturbance (e.g., psychomotor agitation (PmA) and retardation (PmR)), a particularly pernicious feature of MDD, that has not been examined extensively in MDD. Aim 1 is comparing three groups of individuals—those with current MDD (n = 100), remitted MDD (n = 100), and controls (n = 50)—on multiple measures of PmR and PmA (assessed both in the lab and in the subjects’ natural environment). Aim 2 is examining the structural (diffusion MRI) and functional (resting state fMRI) connectivity of motor circuitry of the three groups as well as the relation between motor circuitry and the proposed indicators of PmR and PmA. Aim 3 is following up with subjects three times over 18 months to evaluate whether motor symptoms change in tandem with overall depressive symptoms and functioning over time and/or whether baseline PmR/PmA predicts course of depression and functioning. Aim 3 is particularly clinically significant. Finding that motor functioning and overall depression severity co-vary over time, or that motor variables predict subsequent change in overall depression severity, would support the potential clinical utility of these novel, reliable, and easily administered motor assessments.

Attention, working-memory control, working-memory capacity, and sport performance: The moderating role of athletic expertise

The aim of this research was to detangle the association between attention, working-memory (focusing on both control and capacity functions), and sport performance across athletic expertise. Specifically, the mediating effect of working-memory-control and working-memory-capacity on the attention and performance relationship will be investigated, and whether this effect differs across athlete expertise. A sample of 359 athletes (M_age = 18.91 ± SD = 1.01; 54.87% male) with a range of athletic expertise (novice n = 99, amateur n = 92, elite n = 87, and super-elite n = 81) completed a battery of neurocognitive tasks assessing attention, working-memory-control, working-memory-capacity, and a cognitively engaging motor task (e.g. basketball free-throw task). Athletes with more expertise performed better on tasks of attention, working-memory-control and working-memory-capacity. Results of structural equation modelling indicated a positive association between the cognitive measures and sport performance. Specifically, working-memory-control and working-memory-capacity mediated the attention and sport performance relationship. Additionally, invariance testing indicated larger effects for those with more athletic expertise. These findings provide a better understanding of how attention and the control and capacity functions of working-memory interact to predict performance. Theoretical and practical implications of these results are discussed.

Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors

Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors. Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included 8 weeks of behavioral and physiological monitoring through smartphone and wristband sensors and six in-person clinical interviews during which depression was assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Results: Participants wore the right and left wrist sensors 92 and 94% of the time respectively. Three machine-learning models estimating depressive symptom severity were developed-one combining features from smartphone and wearable sensors, one including only features from the smartphones, and one including features from wrist sensors-and evaluated in two different scenarios. Correlations between the models' estimate of HDRS scores and clinician-rated HDRS ranged from moderate to high (0.46 [CI: 0.42, 0.74] to 0.7 [CI: 0.66, 0.74]) and had moderate accuracy with Mean Absolute Error ranging between 3.88 ± 0.18 and 4.74 ± 1.24. The time-split scenario of the model including only features from the smartphones performed the best. The ten most predictive features in the model combining physiological and mobile features were related to mobile phone engagement, activity level, skin conductance, and heart rate variability. Conclusion: Monitoring of MDD patients through smartphones and wrist sensors following a clinician-rated HDRS assessment is feasible and may provide an estimate of changes in depressive symptom severity. Future studies should further examine the best features to estimate depressive symptoms and strategies to further enhance accuracy.

Actigraphy for evaluation of mood disorders: A systematic review and meta-analysis

BACKGROUND

Actigraphy has enabled consecutive observation of individual health conditions such as sleep or daily activity. This study aimed to examine the usefulness of actigraphy in evaluating depressive and/or bipolar disorder symptoms.

METHOD

A systematic review and meta-analysis was conducted. We selected studies that used actigraphy to compare either patients vs. healthy controls, or pre- vs. post-treatment data from the same patient group. Common actigraphy measurements, namely daily activity and sleep-related data, were extracted and synthesized.

RESULTS

Thirty-eight studies (n = 3,758) were included in the analysis. Compared with healthy controls, depressive patients were less active (standardized mean difference; SMD=1.27, 95%CI=[0.97, 1.57], P<0.001) and had longer wake after sleep onset (SMD= - 0.729, 95%CI=[- 1.20, - 0.25], p = 0.003). Total sleep time (SMD= - 0.33, 95%CI=[- 0.55, - 0.11], P = 0.004), sleep latency (SMD= - 0.22, 95%CI=[- 0.42, - 0.02], P = 0.032), and wake after sleep onset (SMD= - 0.22, 95%CI=[- 0.39, - 0.04], P = 0.015) were longer in euthymic/remitted patients compared to healthy controls. In pre- and post-treatment comparisons, sleep latency (SMD=- 0.85, 95%CI=[- 1.53, - 0.17], P = 0.015), wake after sleep onset (SMD= - 0.65, 95%CI=[- 1.20, - 0.10], P = 0.022), and sleep efficiency (SMD=0.77, 95%CI=[0.29, 1.24], P = 0.002) showed significant improvement.

LIMITATION

The sample sizes for each outcome were small. The type of actigraphy devices and patients' illness severity differed across studies. It is possible that hospitalizations and medication influenced the outcomes.

CONCLUSION

We found significant differences between healthy controls and mood disorders patients for some actigraphy-measured modalities. Specific measurement patterns characterizing each mood disorder/status were also found. Additional actigraphy data linked to severity and/or treatment could enhance the clinical utility of actigraphy.

Abnormal Voxel-Wise Degree Centrality in Patients With Late-Life Depression: A Resting-State Functional Magnetic Resonance Imaging Study

OBJECTIVES

Late-life depression (LLD) has negative impacts on somatic, emotional and cognitive domains of the lives of patients. Elucidating the abnormality in the brain networks of LLD patients could help to strengthen the understanding of LLD pathophysiology, however, the studies exploring the spontaneous brain activity in LLD during the resting state remain limited. This study aimed at identifying the voxel-level whole-brain functional connectivity changes in LLD patients.

METHODS

Fifty patients with late-life depression (LLD) and 33 healthy controls were recruited. All participants underwent a resting-state functional magnetic resonance imaging scan to assess the voxel-wise degree centrality (DC) changes in the patients. Furthermore, DC was compared between two patient subgroups, the late-onset depression (LOD) and the early-onset depression (EOD).

RESULTS

Compared with the healthy controls, LLD patients showed increased DC in the inferior parietal lobule, parahippocampal gyrus, brainstem and cerebellum (p < 0.05, AlphaSim-corrected). LLD patients also showed decreased DC in the somatosensory and motor cortices and cerebellum (p < 0.05, AlphaSim-corrected). Compared with EOD patients, LOD patients showed increased centrality in the superior and middle temporal gyrus and decreased centrality in the occipital region (p < 0.05, AlphaSim-corrected). No significant correlation was found between the DC value and the symptom severity or disease duration in the patients after the correction for multiple comparisons.

CONCLUSIONS

These findings indicate that the intrinsic abnormality of network centrality exists in a wide range of brain areas in LLD patients. LOD patients differ with EOD patients in cortical network centrality. Our study might help to strengthen the understanding of the pathophysiology of LLD and the potential neural substrates underlie related emotional and cognitive impairments observed in the patients.

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Background

Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published.

Objective

The objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies.

Methods

We conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017.

Results

A total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity).

Conclusions

Several studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.

Mapping Movement: Applying Motion Measurement Technologies to the Psychiatric Care of Older Adults

PURPOSE OF REVIEW

Recent advances in technology have changed the landscape of treatment for adults with mental illness. This review highlights technological innovations that may improve care for older adults with mental illness and neurocognitive disorders through the measurement and assessment of physical motion. These technologies include wearable sensors (such as smart watches and Fitbits), passive motion sensors, and smart home models that incorporate both active and passive motion technologies.

RECENT FINDINGS

Clinicians have evaluated motion measurement technologies in older adults with depression, dementia, anxiety, and schizophrenia. Results from studies in dementia populations suggest that motion measurement technologies can assist clinicians in diagnosing dementia earlier through the evaluation of gait, balance, and postural kinematics. Motion detection technologies can also be used to identify mood episodes at an earlier stage by detecting subtle behavioral changes. Clinicians may use the objective data provided by technologies such as accelerometers to identify illnesses earlier, which may inform treatment decisions. The data may be used as a suitable surrogate marker for detecting depression in older adults, predicting the likelihood of falls, or quantifying physical activity in older adults with chronic mental illnesses or anxiety. Motion-based technologies also have the potential to detect physical activity for older adults residing in nursing homes. Wearable technologies are generally well tolerated in older adults, although the use of new technology and electronic health data could involve privacy and security concerns among this vulnerable population.

Clinical response to treatment in inpatients with depression correlates with changes in activity levels and psychomotor speed

BACKGROUND

Monitoring clinical response to treatment in depressed inpatients, particularly identifying early improvement, may be sub-optimal. This may impact adversely on patients through longer admissions and sub-optimal pharmacotherapy. Psychomotor speed is a prominent neuropsychological function which changes as recovery occurs. This study examines simple techniques used to quantify psychomotor change and their potential to contribute to monitoring recovery.

METHODS

Activity levels were continuously monitored in patients diagnosed with a major depressive episode from four acute psychiatric wards using two actigraphs (commercial and scientific) for 3 weeks and linear regression used to calculate a gradient to express rate of change. Psychomotor speed was assessed using the simple Coin Rotation Task. Mood and functioning were rated using the Quick Inventory of Depressive Symptoms, Clinical Global Impression Scale and Functioning Assessment Short Test. The assessments were completed at baseline and follow-up (3 weeks), and correlations were calculated for all change measures.

RESULTS

In all, 24 inpatients were recruited but not all completed baseline and follow-up measures. Change in activity count ( N = 16) and psychomotor speed ( N = 13) correlated significantly with improvement in clinical measures of depressive symptoms. Actigraphs were acceptable to hospital inpatients.

LIMITATIONS

The limited size of this pilot study precludes the analysis of predictive power or the influence of other variables such as depression subtypes, age, gender or variations related to medications.

CONCLUSION

Early change in simple activity and psychomotor speed warrant further investigation for utility in measuring treatment response in depressed inpatients.

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.

A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation

BACKGROUND

Unfortunately, global efforts to promote "how much" physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexamining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and with whom) with a view to identifying promising intervention targets.

OBJECTIVE

The aim of this study was to develop and implement an innovative algorithm to determine "where" physical activity occurs using proximity sensors coupled with a widely used physical activity monitor.

METHODS

A total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters.

RESULTS

Linear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R²=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R²=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer.

CONCLUSIONS

This study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of "context sensing" will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow, patient-clinician interaction).

The revolution of personalized psychiatry: will technology make it happen sooner?

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.