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The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.

and circadian function are leading candidate markers for early relapse identification in MDD. Consumer-grade wearable devices may offer opportunity for remote and real-time examination of dynamic changes in sleep. Objective: We used FitBit data from individuals with recurrent MDD to describe longitudinal associations of sleep duration, quality, and regularity with subsequent depressive relapse and depression severity.Design: Data were collected as part of a longitudinal remote measurement technologies (RMT) cohort study in people with recurrent MDD. Participants: A total of 623 people with MDD wore a FitBit and completed regular outcome assessments via email for a median follow-up of 541 days. Multivariable regression models tested for associations between sleep features and depression outcomes. We considered two samples of people with at least one assessment of relapse (n=213) or at least one assessment of depression severity (n=390). Results: Increased intra-individual variability in total sleep time, greater sleep fragmentation, and later sleep mid-points were associated with worse depression outcomes. Adjusted Population Attributable Fractions (PAFs) suggested that an intervention to increase sleep consistency in adults with MDD could reduce the population risk for depression by up to 18-37%. Conclusion: We found consistent associations between wearable-derived sleep features and the probability of depressive relapse and increased depressive symptom severity. Disordered sleep is prevalent and disruptive, and challenging to capture longitudinally via conventional laboratory sleep assessments. Our study demonstrates a role for consumer-grade activity trackers to predict relapse risk and depression severity in people with recurrent MDD.

Recent growth in remote studies has shown the effectiveness of digital health technologies in recruiting and monitoring the health and behavior of large and diverse populations of interest in real-world settings. However, retaining and engaging participants to monitor their long-term health trajectories has remained a significant challenge. Uneven participant engagement combined with attrition over the course of the study could lead to imbalanced study cohort and data collection, which may severely impact the generalizability of real-world evidence.We report findings from long-term participant retention and engagement patterns in a multinational remote digital depression study with up to two years of real-world behavior monitoring. In total, real-world engagement data from 614 participants with 14,964 surveys and 135,014 days of phone passive and wearable (Fitbit) data were analyzed using survival and unsupervised clustering methods. A considerable proportion of participants (N=415; 67.6%) were retained during the first 43 weeks of the study. Clustering participants’ long-term usage data of study apps and wearables showed three distinct subgroups with different engagement levels (most, middle, and least). Notable findings comparing participants' characteristics across these subgroups were: 1.) Participants (33.2%; N= 204) with the highest baseline depression severity (4 points higher PHQ8 score, p < .01) were in the least engaged group (median bi-weekly surveys completed = 4) compared to the most engaged group (37.6%; N = 231) that on average completed 20 bi-weekly surveys. 2.) A considerable proportion (44.6%; N = 91) of participants in the least engaged group still contributed wearable data for up to 10 months. 3.) The participants in the least engaged group also took significantly longer in responding to surveys in naturalistic settings (3.8 hours more, p < .001) and were younger (age difference = 5 years, p < .01) in comparison to participants in the most engaged group. Our findings show various factors such as socio-demographics, app usage behavior, and depression severity can be linked to the long-term retention and density of real-world data collected in remote digital research studies. Finally, passive data gathered from wearables without additional participant burden showed advantages over active survey data for long-term monitoring, providing greater contiguity and duration of data collected. Together these findings could inform the design of future remote digital health research studies to enable equitable and balanced health data collection from diverse target populations.

This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of 95 . 3% , a sensitivity of 100% and a specificity of 90 . 6% , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate. (c) 2021 Elsevier Ltd. All rights reserved.

BACKGROUND: Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics. METHODS: The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables. RESULTS: A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance. LIMITATIONS: Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions. CONCLUSIONS: These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment. ispartof: JOURNAL OF AFFECTIVE DISORDERS vol:310 pages:106-115 ispartof: location:Netherlands status: published

Although the benefits of exercise on Major Depressive Disorder (MDD) are well established, longitudinal studies of objectively measured activity in clinical populations are needed to establish specific guidelines for exercise by persons with moderate-to-severe depression. This study examines the association between objectively assessed daily step count and depressive symptoms over a 24-week follow- up period in outpatients receiving treatment for moderate-to-severe depression.Participants were US Veterans with MDD enrolled in the Precision Medicine in Mental Health Care study (PRIME Care), a pragmatic, multi-site, randomized, controlled trial that examines the utility of genetic testing in the context of pharmacotherapy for MDD. Participants were a subset (N = 66) enrolled in actigraphy (using GT9X ActiGraph) monitoring component of the trial. Daily steps were examined as a predictor of depressive symptoms over 4-, 8-, 12-, 18-, and 24-weeks.On average, participants took 3,460 (±1,768) steps per day. In generalized linear mixed models, an increase in 1,000 steps per day was associated with a 0.6-point decrease in depressive symptom severity at the subsequent follow-up assessment.Activity monitoring was observational and causal inferences cannot be made between daily steps and subsequent depressive symptom severity. Results may not generalize to non-treatment-seeking populations.Study findings provide an initial metric for persons with clinically significant MDD, of whom most do not get sufficient daily activity. The findings can inform future trials aimed at determining how much daily activity is needed to improve symptoms in individuals with MDD.

Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. Methods Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. Results Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. Conclusions RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

Most smartphones and wearables are nowadays equipped with location sensing (using Global Positioning System and mobile network information) that enable continuous location tracking of their users. Several studies have reported that the amount of time an individual experiencing symptoms of Major Depressive Disorder (MDD) spends at home a day (i.e., home stay), as well as various mobility related metrics, are associated with symptom severity in MDD. Due to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with the MDD symptoms. In the present study, we examined the relationship between overall severity of the depressive symptoms, as assessed by the eight-item Patient Health Questionnaire (PHQ-8), and median daily home stay over the two weeks preceding the completion of a questionnaire in individuals with MDD. We used questionnaire and geolocation data of 164 participants collected in the observational Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study. Participant age and severity of the MDD symptoms were found to be significantly related to home stay, with older and more severely affected individuals spending more time at home. The association between home stay and symptom severity appeared to be stronger on weekdays than on weekends. Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay. Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future MDD studies.

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C16 remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70-0.78 and 0.72-0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by χ2 test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data.

Intrauterine growth restriction (IUGR) is a fetal condition characterized by growth-rate reduction. Afflicted fetuses tend to display abnormalities in heart rate.To study the differences in the heart-rate variability of low-risk fetuses and IUGR fetuses during different behavioral states.A total of 40 fetal magnetocardiograms were analyzed from 20 low-risk and 20 IUGR fetuses recorded using a 151-sensor SQUID-array system. The maternal cardiac signals were attenuated using signal-space projection. Fetal R waves were identified using an adaptive Hilbert transform approach and fetal heart rate was calculated. In each three-minute window, the heart rate was classified into patterns reflective of quiet sleep (pattern A) and active sleep (pattern B) using the criteria of Nijhuis. Two adjacent 3-min windows exhibiting the same pattern were selected for analysis from every dataset. Heart-rate variability in that 6-min window was characterized using three measures, standard deviation of normal to normal (SDNN), root mean square of successive differences (RMSSD) and phase plane area (PPA).All three measures tended to be lower in the IUGR group compared to the low-risk group. However, when the measures were analyzed in patterns, only PPA showed significant difference between the risk groups in pattern A, whereas both PPA and SDNN showed highly significant risk-group differences in pattern B. RMSSD did not show any significant risk-group difference.The result signifies that the heart-rate variability of IUGR fetuses is different from that of low-risk fetuses, and only PPA was able to capture the HRV differences in both quiet and active states. The difference between these two groups of fetuses shows that the fetal-activity states are potential confounders when characterizing heart-rate variability.

Fetal magnetoencephalography (fMEG) recordings are contaminated by maternal and fetal magnetocardiography (MCG) signals and by other biological and environmental interference. Currently, all methods for the attenuation of these signals are based on a time-domain approach. We have developed and tested a frequency dependent procedure for removal of MCG and other interference from the fMEG recordings. The method uses a set of reference channels and performs subtraction of interference in the frequency domain (SUBTR). The interference-free frequency domain signals are converted back to the time domain. We compare the performance of the frequency dependent approach with our present approach for MCG attenuation based on orthogonal projection (OP). SUBTR has an advantage over OP and similar template approaches because it removes not only the MCG but also other small amplitude biological interference, avoids the difficulties with inaccurate determination of the OP operator, provides more consistent and stable fMEG results, does not cause signal redistribution, and if references are selected judiciously, it does not reduce fMEG signal amplitude. SUBTR was found to perform well in simulations and on real fMEG recordings, and has a potential to improve the detection of fetal brain signals. The SUBTR removes interference without the need for a model of the individual interference sources. The method may be of interest for any sensor array noise reduction application where signal-free reference channels are available.

The fetal brain remains inaccessible to neurophysiological studies. Magnetoencephalography (MEG) is being assessed to fill this gap. We performed 40 fetal MEG (fMEG) recordings with gestational ages (GA) ranging from 30 to 37 weeks. The data from each recording were divided into 15 second epochs which in turn were classified as continuous (CO), discontinuous (DC), or artifact. The fetal behavioral state, quiet or active sleep, was determined using previously defined criteria based on fetal movements and heart rate variability. We studied the correlation between the fetal state, the GA and the percentage of CO and DC epochs. We also analyzed the spectral edge frequency (SEF) and studied its relation with state and GA. We found that the odds of a DC epoch decreased by 6% per week as the GA increased (P = 0.0036). This decrease was mainly generated by changes during quiet sleep, which showed 52% DC epochs before a 35 week GA versus 38% after 35 weeks (P = 0.0006). Active sleep did not show a significant change in DC epochs with GA. When both states were compared for MEG patterns within each GA group (before and after 35 weeks), the early group was found to have more DC epochs in quiet sleep (54%) compared to active sleep (42%) (P = 0.036). No significant difference in DC epochs between the two states was noted in the late GA group. Analysis of SEF showed a significant difference (P = 0.0014) before and after a 35 week GA, with higher SEF noted at late GA. However, when both quiet and active sleep states were compared within each GA group, the SEF did not show a significant difference. We conclude that fMEG shows reproducible variations in gross features and frequency content, depending on GA and behavioral state. Fetal MEG is a promising tool to investigate fetal brain physiology and maturation.

To identify quantitative MEG indices of spontaneous brain activity for fetal neurological maturation in normal pregnancies and examine the effect of fetal state on these indices.Spontaneous MEG brain activity was examined in 22 low-risk fetal recordings with gestational age (GA) ranging from 30 to 37 weeks. As major quantitative characteristics of spontaneous activity, burst duration (BD) and interburst interval (IBI) were studied in correlation with GA and fetal state.IBI showed a decrease with gestational age (-0.21 s/week, P=0.0031). This trend was only maintained in the quiet-sleep state. With respect to BD, no significant trends were detected with GA and state.IBI can be quantified as a fetal brain maturational parameter. The decrease in IBI over gestation was similar to the trend reported in the preterm neonatal EEG studies. Quiet sleep could be the optimal state to study such MEG maturational indices.With further investigation, indices extracted from spontaneous fetal brain activity may serve as an early warning for fetal neurological distress.

Acute lung injury (ALI) is a devastating complication of acute illness and one of the leading causes of multiple organ failure and mortality in the intensive care unit (ICU). The detection of this syndrome is limited due to the complexity of the disease, insufficient understanding of its development and progression, and the large amount of risk factors and modifiers. In this preliminary study, we present a novel mathematical model for ALI detection. It is constructed based on clinical and research knowledge using three complementary techniques: rule-based fuzzy inference systems, Bayesian networks, and finite state machines. The model is developed in Matlab(®)'s Simulink environment and takes as input pre-ICU and ICU data feeds of critically ill patients. Results of the simulation model were validated against actual patient data from an epidemiologic study. By appropriately combining all three techniques the performance attained is in the range of 71.7-92.6% sensitivity and 60.3-78.4% specificity.

Changes in fetal magnetocardiographic (fMCG) signals are indicators for fetal body movement. We propose a novel approach to reliably extract fetal body movements based on the field strength of the fMCG signal independent of its frequency. After attenuating the maternal MCG, we use a Hilbert transform approach to identify the R-wave. At each R-wave, we compute the center-of-gravity (cog) of the coordinate positions of MCG sensors, each weighted by the magnitude of the R-wave amplitude recorded at the corresponding sensor. We then define actogram as the distance between the cog computed at each R-wave and the average of the cog from all the R-waves in a 3-min duration. By applying a linear de-trending approach to the actogram we identify the fetal body movement and compare this with the synchronous occurrence of the acceleration in the fetal heart rate. Finally, we apply this approach to the fMCG recorded simultaneously with ultrasound from a single subject and show its improved performance over the QRS-amplitude based approach in the visually verified movements. This technique could be applied to transform the detection of fetal body movement into an objective measure of fetal health and enhance the predictive value of prevalent clinical testing for fetal wellbeing.

The fetal magnetoencephalogram (fMEG) is measured in the presence of large interference from maternal and fetal magnetocardiograms (mMCG and fMCG). These cardiac interferences can be attenuated by orthogonal projection (OP) technique of the corresponding spatial vectors. However, the OP technique redistributes the fMEG signal among the channels and also leaves some cardiac residuals (partially attenuated mMCG and fMCG) due to loss of stationarity in the signal. In this paper, we propose a novel way to extract and localize the neonatal and fetal spontaneous brain activity by using independent component analysis (ICA) technique. In this approach, we perform ICA on a small subset of sensors for 1-min duration. The independent components obtained are further investigated for the presence of discontinuous patterns as identified by the Hilbert phase analysis and are used as decision criteria for localizing the spontaneous brain activity. In order to locate the region of highest spontaneous brain activity content, this analysis is performed on the sensor subsets, which are traversed across the entire sensor space. The region of the spontaneous brain activity as identified by the proposed approach correlated well with the neonatal and fetal head location. In addition, the burst duration and the inter-burst interval computed for the identified discontinuous brain patterns are in agreement with the reported values.

We propose a novel approach based on Hilbert phase to identify the burst in the uterine myometrial activity. We apply this approach to 24 serial magnetomyographic signals recorded from four pregnant women using a 151 SQUID array system. The bursts identified with this approach are evaluated for duration and are correlated with the gestational age. In all four subjects, we find a decrease in the duration of burst as the subject approaches active labor. As was shown in animal studies, this result indicates a faster conduction time between the muscle cells which activate a larger number of muscle units in a synchronous manner.

We propose a novel method to characterize the spontaneous brain signals using Hilbert phases. The Hilbert phase of a signal exhibits phase slips when the magnitude of the successive phase difference exceeds pi. To this end we use standard deviation (sigmaDeltatau) of the time (Deltatau) between successive phase slips to characterize the signals. We demonstrate the application of this approach to neonatal and fetal magnetoencephalographic signals recorded using a 151-sensor array to identify the sensors containing the neonatal and fetal brain signals. To this end we propose a spatial filter using sigma(Deltatau) as weights to reconstruct the brain signals.

The Hilbert phase phi(t) of a signal x(t) exhibits slips when the magnitude of their successive phase difference |phi(t(i+1))-phi(t(i))| exceeds pi. By applying this approach to periodic, uncorrelated, and long-range correlated data, we show that the standard deviation of the time difference between the successive phase slips Deltatau normalized by the percentage of slips in the data is characteristic of the correlation in the data. We consider a 50x50 square lattice and model each lattice point by a second-order autoregressive (AR2) process. Further, we model a subregion of the lattice using a different set of AR2 parameters compared to the rest. By applying the proposed approach to the lattice model, we show that the two distinct parameter regions introduced in the lattice are clearly distinguishable. Finally, we demonstrate the application of this approach to spatiotemporal neonatal and fetal magnetoencephalography signals recorded using 151 superconducting quantum interference device sensors to identify the sensors containing the neonatal and fetal brain signals and discuss the improved performance of this approach over the traditionally used spectral approach.

The discontinuous patterns in neonatal magnetoencephalographic (MEG) data are quantified with a novel Hilbert phase (HP) based approach. The expert neurologists' scores were used as the gold standard. The performance of this approach was analyzed using a receiver operating characteristic (ROC) curve, and it was compared with two other approaches, namely spectral ratio (SR) and discrete wavelet transform (DWT) that have been proposed for the detection of discontinuous patterns in neonatal EEG. The area under the ROC curve (AUC) was used as a performance measure. AUCs obtained for SR, HP, and DWT were 0.87, 0.80, and 0.56, respectively. Although the performance of HP was lower than SR, it carries information about the frequency content of the signal that helps to distinguish brain patterns from artifacts such as cardiac residuals. Based on this property, the HP approach was extended to fetal MEG data. Further, using the frequency property of the HP approach, burst duration and interburst interval were computed for the discontinuous patterns detected and they are in agreement with reported values.

Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of daily walking in real-world scenarios and their relationships with depression are yet to be fully explored. This study aimed to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. In this study, we used two ambulatory datasets: a public dataset with 71 elder adults' 3-day acceleration signals collected by a wearable device, and a subset of an EU longitudinal depression study with 215 participants and their phone-collected acceleration signals (average 463 hours per participant). We detected participants' gait cycles and force from acceleration signals and extracted 20 statistics-based daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period corresponding to the self-reported depression score. The gait cadence of faster steps (75th percentile) over a long-term period has a significant negative association with the depression symptom severity of this period in both datasets. Daily-life gait features could significantly improve the goodness of fit of evaluating depression severity relative to laboratory gait patterns and demographics, which was assessed by likelihood-ratio tests in both datasets. This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The gait cadence of faster steps in daily-life walking has the potential to be a biomarker for evaluating depression severity, which may contribute to clinical tools to remotely monitor mental health in real-world settings.