Effect of an Internet-Delivered Cognitive Behavioral Therapy-Based Sleep Improvement App for Shift Workers at High Risk of Sleep Disorder: Single-Arm, Nonrandomized Trial.

BACKGROUND: Shift workers are at high risk of developing sleep disorders such as shift worker sleep disorder or chronic insomnia. Cognitive behavioral therapy (CBT) is the first-line treatment for insomnia, and emerging evidence shows that internet-based CBT is highly effective with additional features such as continuous tracking and personalization. However, there are limited studies on internet-based CBT for shift workers with sleep disorders. OBJECTIVE: This study aimed to evaluate the impact of a 4-week, physician-assisted, internet-delivered CBT program incorporating machine learning-based well-being prediction on the sleep duration of shift workers at high risk of sleep disorders. We evaluated these outcomes using an internet-delivered CBT app and fitness trackers in the intensive care unit. METHODS: A convenience sample of 61 shift workers (mean age 32.9, SD 8.3 years) from the intensive care unit or emergency department participated in the study. Eligible participants were on a 3-shift schedule and had a Pittsburgh Sleep Quality Index score ≥5. The study comprised a 1-week baseline period, followed by a 4-week intervention period. Before the study, the participants completed questionnaires regarding the subjective evaluation of sleep, burnout syndrome, and mental health. Participants were asked to wear a commercial fitness tracker to track their daily activities, heart rate, and sleep for 5 weeks. The internet-delivered CBT program included well-being prediction, activity and sleep chart, and sleep advice. A job-based multitask and multilabel convolutional neural network-based model was used for well-being prediction. Participant-specific sleep advice was provided by sleep physicians based on daily surveys and fitness tracker data. The primary end point of this study was sleep duration. For continuous measurements (sleep duration, steps, etc), the mean baseline and week-4 intervention data were compared. The 2-tailed paired t test or Wilcoxon signed rank test was performed depending on the distribution of the data. RESULTS: In the fourth week of intervention, the mean daily sleep duration for 7 days (6.06, SD 1.30 hours) showed a statistically significant increase compared with the baseline (5.54, SD 1.36 hours; P=.02). Subjective sleep quality, as measured by the Pittsburgh Sleep Quality Index, also showed statistically significant improvement from baseline (9.10) to after the intervention (7.84; P=.001). However, no significant improvement was found in the subjective well-being scores (all P>.05). Feature importance analysis for all 45 variables in the prediction model showed that sleep duration had the highest importance. CONCLUSIONS: The physician-assisted internet-delivered CBT program targeting shift workers with a high risk of sleep disorders showed a statistically significant increase in sleep duration as measured by wearable sensors along with subjective sleep quality. This study shows that sleep improvement programs using an app and wearable sensors are feasible and may play an important role in preventing shift work-related sleep disorders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/24799.

Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint.

The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.

Activity-induced secretion of semaphorin 3A mediates learning.

The semaphorin family is a well-characterized family of secreted or membrane-bound proteins that are involved in activity-independent neurodevelopmental processes, such as axon guidance, cell migration, and immune functions. Although semaphorins have recently been demonstrated to regulate activity-dependent synaptic scaling, their roles in Hebbian synaptic plasticity as well as learning and memory remain poorly understood. Here, using a rodent model, we found that an inhibitory avoidance task, a hippocampus-dependent contextual learning paradigm, increased secretion of semaphorin 3A in the hippocampus. Furthermore, the secreted semaphorin 3A in the hippocampus mediated contextual memory formation likely by driving AMPA receptors into hippocampal synapses via the neuropilin1-plexin A4-semaphorin receptor complex. This signaling process involves alteration of the phosphorylation status of collapsin response mediator protein 2, which has been characterized as a downstream molecule in semaphorin signaling. These findings implicate semaphorin family as a regulator of Hebbian synaptic plasticity and learning.

Robust stability of melatonin circadian phase, sleep metrics, and chronotype across months in young adults living in real-world settings.

Appropriate synchronization of the timing of behaviors with the circadian clock and adequate sleep are both important for almost every physiological process. The timing of the circadian clock relative to social (ie, local) clock time and the timing of sleep can vary greatly among individuals. Whether the timing of these processes is stable within an individual is not well-understood. We examined the stability of circadian-controlled melatonin timing, sleep timing, and their interaction across ~ 100 days in 15 students at a single university. At three time points ~ 35-days apart, circadian timing was determined from the dim-light melatonin onset (DLMO). Sleep behaviors (timing and duration) and chronotype (ie, mid-sleep time on free days corrected for sleep loss on school/work days) were determined via actigraphy and analyzed in ~ 1-month bins. Melatonin timing was stable, with an almost perfect relationship strength as determined via intraclass correlation coefficients ([ICC]=0.85); average DLMO timing across all participants only changed from the first month by 21 minutes in month 2 and 5 minutes in month 3. Sleep behaviors also demonstrated high stability, with ICC relationship strengths ranging from substantial to almost perfect (ICCs = 0.65-0.85). Average DLMO was significantly associated with average chronotype (r2  = 0.53, P <.01), with chronotype displaying substantial stability across months (ICC = 0.61). These findings of a robust stability in melatonin timing and sleep behaviors in young adults living in real-world settings holds promise for a better understanding of the reliability of previous cross-sectional reports and for the future individualized strategies to combat circadian-associated disease and impaired safety (ie, "chronomedicine").

A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data.

The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time and resource-intensive. Numerous studies have attempted to estimate circadian phase from actigraphy data, but most of these studies have involved individuals on controlled and stable sleep-wake schedules, with mean errors reported between 0.5 and 1 hour. We found that such algorithms are less successful in estimating DLMO in a population of college students with more irregular schedules: Mean errors in estimating the time of DLMO are approximately 1.5-1.6 hours. We reframed the problem as a classification problem and estimated whether an individual's current phase was before or after DLMO. Using a neural network, we found high classification accuracy of about 90%, which decreased the mean error in DLMO estimation-identifying the time at which the switch in classification occurs-to approximately 1.3 hours. To test whether this classification approach was valid when activity and circadian rhythms are decoupled, we applied the same neural network to data from inpatient forced desynchrony studies in which participants are scheduled to sleep and wake at all circadian phases (rather than their habitual schedules). In participants on forced desynchrony protocols, overall classification accuracy dropped to 55%-65% with a range of 20%-80% for a given day; this accuracy was highly dependent upon the phase angle (ie, time) between DLMO and sleep onset, with the highest accuracy at phase angles associated with nighttime sleep. Circadian patterns in activity, therefore, should be included when developing and testing actigraphy-based approaches to circadian phase estimation. Our novel algorithm may be a promising approach for estimating the onset of melatonin in some conditions and could be generalized to other hormones.

Neonatal isolation augments social dominance by altering actin dynamics in the medial prefrontal cortex.

Social separation early in life can lead to the development of impaired interpersonal relationships and profound social disorders. However, the underlying cellular and molecular mechanisms involved are largely unknown. Here, we found that isolation of neonatal rats induced glucocorticoid-dependent social dominance over nonisolated control rats in juveniles from the same litter. Furthermore, neonatal isolation inactivated the actin-depolymerizing factor (ADF)/cofilin in the juvenile medial prefrontal cortex (mPFC). Isolation-induced inactivation of ADF/cofilin increased stable actin fractions at dendritic spines in the juvenile mPFC, decreasing glutamate synaptic AMPA receptors. Expression of constitutively active ADF/cofilin in the mPFC rescued the effect of isolation on social dominance. Thus, neonatal isolation affects spines in the mPFC by reducing actin dynamics, leading to altered social behavior later in life.

Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study.

BACKGROUND: Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. OBJECTIVE: We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. METHODS: We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. RESULTS: We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. CONCLUSIONS: New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.

QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform.

Smartphones and wearable sensors have enabled unprecedented data collection, with many products now providing feedback to users about recommended step counts or sleep durations. However, these recommendations do not provide personalized insights that have been shown to be best suited for a specific individual. A scientific way to find individualized recommendations and causal links is to conduct experiments using single-case experimental design; however, properly designed single-case experiments are not easy to conduct on oneself. We designed, developed, and evaluated a novel platform, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. We provide software for the platform that we used (available for free on GitHub), which provides the methodological elements to run many kinds of customized studies. In this work, we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep duration and regularity, activity, and leisure time affect personal happiness, stress, productivity, and sleep efficiency. We conducted a six-week pilot study (N = 13) to evaluate QuantifyMe. We describe the lessons learned developing the platform and recommendations for its improvement, as well as its potential for enabling personalized insights to be scientifically evaluated in many individuals, reducing the high administrative cost for advancing human health and wellbeing.

Nogo Receptor Signaling Restricts Adult Neural Plasticity by Limiting Synaptic AMPA Receptor Delivery.

Experience-dependent plasticity is limited in the adult brain, and its molecular and cellular mechanisms are poorly understood. Removal of the myelin-inhibiting signaling protein, Nogo receptor (NgR1), restores adult neural plasticity. Here we found that, in NgR1-deficient mice, whisker experience-driven synaptic α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) insertion in the barrel cortex, which is normally complete by 2 weeks after birth, lasts into adulthood. In vivo live imaging by two-photon microscopy revealed more AMPAR on the surface of spines in the adult barrel cortex of NgR1-deficient than on those of wild-type (WT) mice. Furthermore, we observed that whisker stimulation produced new spines in the adult barrel cortex of mutant but not WT mice, and that the newly synthesized spines contained surface AMPAR. These results suggest that Nogo signaling limits plasticity by restricting synaptic AMPAR delivery in coordination with anatomical plasticity.

Contextual learning requires synaptic AMPA receptor delivery in the hippocampus.

The hippocampus plays a central role in learning and memory. Although synaptic delivery of AMPA-type glutamate receptors (AMPARs) contributes to experience-dependent synaptic strengthening, its role in hippocampus-dependent learning remains elusive. By combining viral-mediated in vivo gene delivery with in vitro patch-clamp recordings, we found that the inhibitory avoidance task, a hippocampus-dependent contextual fear-learning paradigm, delivered GluR1-containing AMPARs into CA3-CA1 synapses of the dorsal hippocampus. To block the synaptic delivery of endogenous AMPARs, we expressed a fragment of the GluR1-cytoplasmic tail (the 14-aa GluR1 membrane-proximal region with two serines mutated to phospho-mimicking aspartates: MPR-DD). MPR-DD prevented learning-driven synaptic AMPAR delivery in CA1 neurons. Bilateral expression of MPR-DD in the CA1 region of the rat impaired inhibitory avoidance learning, indicating that synaptic GluR1 trafficking in the CA1 region of the hippocampus is required for encoding contextual fear memories. The fraction of CA1 neurons that underwent synaptic strengthening positively correlated with the performance in the inhibitory avoidance fear memory task. These data suggest that the robustness of a contextual memory depends on the number of hippocampal neurons that participate in the encoding of a memory trace.

Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study.

BACKGROUND: As mobile health (mHealth) studies become increasingly productive owing to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants' responses when implementing new survey delivery methods. OBJECTIVE: This study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants' reported emotional state. We examine the factors that affect participants' receptivity to EMAs in a 10-day wearable and EMA-based emotional state-sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs. METHODS: We collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity. RESULTS: Our findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions. CONCLUSIONS: Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points.

Social isolation perturbs experience-driven synaptic glutamate receptor subunit 4 delivery in the developing rat barrel cortex.

In neonates, the stress of social isolation can alter developing neural circuits and cause mental illness. However, the molecular and cellular bases for these effects are poorly understood. Experience-driven synaptic AMPA receptor delivery is crucial for circuit organisation during development. In the rat, whisker experience drives the delivery of glutamate receptor subunit 4 (GluA4) but not glutamate receptor subunit 1 (GluA1) to layer 4-2/3 pyramidal synapses in the barrel cortex during postnatal day (P)8-10, whereas GluA1 but not GluA4 is delivered to these synapses during P12-14. We recently reported that early social isolation disrupts experience-driven GluA1 delivery to layer 4-2/3 pyramidal synapses during P12-14. Here, we report that neonatal isolation affects even earlier stages of development by preventing experience-dependent synaptic GluA4 delivery. Thus, social isolation severely affects synaptic maturation throughout early postnatal development.

Exploiting social graph networks for emotion prediction.

Emotion prediction plays an essential role in mental healthcare and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person's physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported happiness and stress levels. In addition to a person's physiology, we also incorporate the environment's impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all users. The construction of social networks does not incur additional costs in terms of ecological momentary assessments or data collection from users and does not raise privacy concerns. We propose an architecture that automates the integration of the user's social network in affect prediction and is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. The extensive evaluation highlights the prediction performance improvement provided by the integration of social networks. We further investigate the impact of graph topology on the model's performance.

Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model.

Mobile sensing-based modeling of behavioral changes could predict an oncoming psychotic relapse in schizophrenia patients for timely interventions. Deep learning models could complement existing non-deep learning models for relapse prediction by modeling the latent behavioral features relevant to prediction. However, given the inter-individual behavioral differences, model personalization might be required. In this work, we propose RelapsePredNet, a Long Short-Term Memory (LSTM) neural network-based model for relapse prediction. The model is personalized for a particular patient by using data from patients most similar to the given patient based on their demographics or baseline mental health scores. RelapsePredNet was compared with a deep learning-based anomaly detection model for relapse prediction. Additionally, we investigated if RelapsePredNet could complement ClusterRFModel (a random forest model leveraging clustering and template features proposed in prior work) in a fusion model. The CrossCheck dataset consisting of continuous mobile sensing data obtained from 63 schizophrenia patients, each monitored for up to a year, was used for our evaluations. RelapsePredNet outperformed the deep learning-based anomaly detection for relapse prediction with an F2 score of 0.21 and 0.52 in the full test set and the Relapse Test Set (consisting of data from patients who have had relapse only), respectively, representing a 29.4% and 38.8% improvement. Patients' social functioning scale (SFS) score was found to be the best personalization metric to define patient similarity. RelapsePredNet complemented the ClusterRFModel as it improved the F2 score by 26.1% with a fusion model, resulting in an F2 score of 0.30 in the full test set.

AMPAR receptor inhibitors suppress proliferation of human small cell lung cancer cell lines.

BACKGROUND: Small cell lung cancer (SCLC) is a neuroendocrine tumor with poor prognosis. Neuroendocrine tumors possess characteristics of both nerve cells and hormone-secreting cells; therefore, targeting the neuronal properties of these tumors may lead to the development of new therapeutic options. Among the endogenous signaling pathways in the nervous system, targeting the glutamate pathway may be a useful strategy for glioblastoma treatment. Perampanel, an antagonist of the synaptic glutamate α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR), has been reported to be effective in patients with glioblastoma. In this study, we aimed to investigate the antitumor effects of AMPAR antagonists in human SCLC cell lines. METHODS: We performed to examine the expression of AMPAR using Western blot and immunohistochemical analysis. The antitumor effects of AMPAR antagonists on human SCLC cell lines were investigated in vitro and in vivo. We also analyzed the signaling pathway of AMPAR antagonists in SCLC cell lines. Statistical analysis was performed by the GraphPad Prism 6 software. RESULTS: We first examined the expression of endogenous AMPAR in six human SCLC cell lines, detecting AMPAR proteins in all of them. Next, we tested the anti-proliferative effect of two AMPAR antagonists, talampanel and cyanquixaline, using SCLC cells in vitro and in vivo. Both AMPAR antagonists inhibited cell proliferation and mitogen-activated protein kinase (MAPK) phosphorylation in SCLC cells in vitro. Further, we observed reduced proliferation of implanted cell lines in an in vivo setting, assessed by Ki-67 immunohistochemistry. Additionally, using immunohistochemical analysis we confirmed AMPAR protein expression in human SCLC samples. CONCLUSION: AMPAR may be a potential therapeutic target for SCLC.

The organization of sleep-wake patterns around daily schedules in college students.

Time is a zero-sum game, and consequently, sleep is often sacrificed for waking activities. For college students, daily activities, comprised of scheduled classes, work, study, social and other extracurricular events, are major contributors to insufficient and poor-quality sleep. We investigated the impact of daily schedules on sleep-wake timing in 223 undergraduate students (age: 18-27 years, 37% females) from a United States (U.S.) university, monitored for approximately 30 days. Sleep-wake timing and daily recorded activities (attendance at academic, studying, exercise-based and/or extracurricular activities) were captured by a twice-daily internet-based diary. Wrist-worn actigraphy was conducted to confirm sleep-wake timing. Linear mixed models were used to quantify associations between daily schedule and sleep-wake timing at between-person and within-person levels. Later schedule start time predicted later sleep onset (between and within: p<.001), longer sleep duration on the previous night (within: p<.001), and later wake time (between and within: p<.001). Later schedule end time predicted later sleep onset (between: p<.05, within: p<.001) and shorter sleep duration that night (within: p<.001). For every 1 hour that recorded activities extended beyond 10pm, sleep onset was delayed by 15 minutes at the within-person level and 45 minutes at the between-person level, and sleep duration was shortened by 5 and 23 minutes, respectively. Increased daily documented total activity time predicted earlier wake (between and within: p<.001), later sleep onset that night (within: p<.05), and shorter sleep duration (within: p<.001). These results indicate that daily schedules are an important factor in shaping sleep timing and duration in college students.

Dynamics of AMPA receptors regulate epileptogenesis in patients with epilepsy.

The excitatory glutamate α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPARs) contribute to epileptogenesis. Thirty patients with epilepsy and 31 healthy controls are scanned using positron emission tomography with our recently developed radiotracer for AMPARs, [11C]K-2, which measures the density of cell-surface AMPARs. In patients with focal-onset seizures, an increase in AMPAR trafficking augments the amplitude of abnormal gamma activity detected by electroencephalography. In contrast, patients with generalized-onset seizures exhibit a decrease in AMPARs coupled with increased amplitude of abnormal gamma activity. Patients with epilepsy had reduced AMPAR levels compared with healthy controls, and AMPARs are reduced in larger areas of the cortex in patients with generalized-onset seizures compared with those with focal-onset seizures. Thus, epileptic brain function can be regulated by the enhanced trafficking of AMPAR due to Hebbian plasticity with increased simultaneous neuronal firing and compensational downregulation of cell-surface AMPARs by the synaptic scaling.

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis.

BACKGROUND: Behavioral representations obtained from mobile sensing data can be helpful for the prediction of an oncoming psychotic relapse in patients with schizophrenia and the delivery of timely interventions to mitigate such relapse. OBJECTIVE: In this study, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data for relapse prediction tasks. The identified clusters can represent different routine behavioral trends related to daily living of patients and atypical behavioral trends associated with impending relapse. METHODS: We used the mobile sensing data obtained from the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (ambient light, sound, conversation, acceleration, etc) obtained from 63 patients with schizophrenia, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian mixture model (GMM) and partition around medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data, and thus, provide different behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using balanced random forest. The personalization was performed by identifying optimal features for a given patient based on a personalization subset consisting of other patients of similar age. RESULTS: The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active days but with low communication, etc). Although GMM-based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread, likely indicating heterogeneous behavioral characterizations. On the other hand, PAM model-based clusters had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were observed in the obtained behavioral representation features from the clustering models. The clustering model-based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.23 for the relapse prediction task in a leave-one-patient-out evaluation setting. The obtained F2 score was significantly higher than that of a random classification baseline with an average F2 score of 0.042. CONCLUSIONS: Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine and atypical behavioral trends. In this study, we used GMM-based and PAM-based cluster models to obtain behavioral trends in patients with schizophrenia. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful in enabling timely interventions.

Turning data into better mental health: Past, present, and future.

In this mini-review, we discuss the fundamentals of using technology in mental health diagnosis and tracking. We highlight those principles using two clinical concepts: (1) cravings and relapse in the context of addictive disorders and (2) anhedonia in the context of depression. This manuscript is useful for both clinicians wanting to understand the scope of technology use in psychiatry and for computer scientists and engineers wishing to assess psychiatric frameworks useful for diagnosis and treatment. The increase in smartphone ownership and internet connectivity, as well as the accelerated development of wearable devices, have made the observation and analysis of human behavior patterns possible. This has, in turn, paved the way to understand mental health conditions better. These technologies have immense potential in facilitating the diagnosis and tracking of mental health conditions; they also allow the implementation of existing behavioral treatments in new contexts (e.g., remotely, online, and in rural/underserved areas), and the possibility to develop new treatments based on new understanding of behavior patterns. The path to understand how to best use technology in mental health includes the need to match interdisciplinary frameworks from engineering/computer sciences and psychiatry. Thus, we start our review by introducing bio-behavioral sensing, the types of information available, and what behavioral patterns they may reflect and be related to in psychiatric diagnostic frameworks. This information is linked to the use of functional imaging, highlighting how imaging modalities can be considered "ground truth" for mental health/psychiatric dimensions, given the heterogeneity of clinical presentations, and the difficulty of determining what symptom corresponds to what disease. We then discuss how mental health/psychiatric dimensions overlap, yet differ from, psychiatric diagnoses. Using two clinical examples, we highlight the potential agreement areas in assessment/management of anhedonia and cravings. These two dimensions were chosen because of their link to two very prevalent diseases worldwide: depression and addiction. Anhedonia is a core symptom of depression, which is one of the leading causes of disability worldwide. Cravings, the urge to use a substance or perform an action (e.g., shopping, internet), is the leading step before relapse. Lastly, through the manuscript, we discuss potential mental health dimensions.

More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors.

Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common choice in many applications, but may not always be feasible in real-world scenarios. For example, although combining biosignals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context. To solve the challenges, we propose an effective more to less (M2L) learning framework to improve testing performance with reduced sensors through leveraging the complementary information of multiple modalities during training. More specifically, different sensors may carry different but complementary information, and our model is designed to enforce collaborations among different modalities, where positive knowledge transfer is encouraged and negative knowledge transfer is suppressed, so that better representation is learned for individual modalities. Our experimental results show that our framework achieves comparable performance when compared with the full modalities. Our code and results will be available at https://github.com/comp-well-org/More2Less.git.