Tag: Sleep and arousal regulation

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Evaluating Wearable Devices for Remote Monitoring in Psychosis: Pilot Study Nested Within the CONNECT Cohort Study

Background: Digital remote monitoring technologies, including smartphones and wearables, offer promising avenues for early detection of psychosis relapse. However, selecting devices that are acceptable to participants and produce high-quality data remains challenging. Objective: The aim of this nested pilot study was to assess the acceptability and data quality of 3 commercially available wearable devices in people with psychosis recruited to the CONNECT cohort study. Methods: Participants recruited to the CONNECT study before July 31, 2024, were included in the pilot study and selected 1 of 3 wearable devices: a Fitbit Charge 5, Samsung Galaxy Watch 5, or Apple Watch SE. Baseline demographics were compared between device groups. Acceptability of devices to participants was assessed through a Wearable Device Satisfaction Questionnaire after 3 months of use, with the proportion of positive responses to each question calculated and compared. Data completeness was also assessed by calculating the number (and percentage) of valid days of step count, heart rate, and sleep data, and comparing between groups. Data quality was assessed through summarizing the amount of troubleshooting required, additional metrics available from the wearables, and continuity of data completeness by calculating the proportion of participants with at least 3 days of heart rate data per week for the first 20 weeks of follow-up. Predefined criteria were used to determine the next steps for the wider CONNECT study: if one device was superior, this would be selected; if none were found to be superior and the Fitbit was found to be noninferior, then Fitbit would be retained. Results: Of the first 107 participants recruited to CONNECT, 105 were included in the pilot study evaluation. The Samsung Galaxy Watch was selected most frequently by participants (46/105, 43.8%), followed by the Apple Watch (27/105, 25.7%), and Fitbit Charge (23/105, 21.9%). Differences in participant demographics were observed across device groups. Self-reported acceptability after use did not differ substantially between devices. However, in terms of data completeness, the median proportion of valid heart rate data days was significantly lower for Samsung Galaxy (median 31.2%, IQR 8.5%-46.0%) compared to Fitbit (median 80.1%, IQR 26.7%-95.0%; =.003) and Apple Watch (median 49.3%, IQR 21.5%-86.0%; =.02). There was no significant difference between Fitbit and Apple Watch. Similar patterns were observed for step count and sleep data. The Samsung Galaxy Watch required more frequent troubleshooting for data flow issues and lacked additional physiological metrics, available from the other devices. Conclusions: Due to comparatively lower data quality and technical performance, the Samsung Galaxy Watch was discontinued for use in the subsequent phase of the CONNECT study. The study highlights the importance of incorporating nested evaluations of devices in long-term research.
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A dual-branch network with brain region-constrained attention for EEG emotion recognition

IntroductionElectroencephalography (EEG)-based emotion recognition provides an objective avenue for affective computing. However, the complexity of EEG signals across temporal, frequency, and spatial domains makes any single dimension inadequate.MethodsTo overcome these limitations, we propose the Brain Region-Constrained Attention Dual-Branch Network (BRAD-Net). This network adopts a parallel Spatio-Temporal and Spectral-Spatial dual-branch architecture to achieve synergistic multi-domain EEG feature learning. Within the spatio-temporal branch, we introduce a novel Brain Region-Constrained Attention mechanism, which strictly confines self-attention computation to channels belonging to the same brain region. This design not only suppresses irrelevant cross-region interference but also incorporates neuroanatomical priors of brain parcellation, thereby enabling effective and interpretable representation learning.ResultsIn subject-dependent experiments using 10-fold cross-validation on DEAP and DREAMER datasets, BRAD-Net achieves high accuracies of 97.44%, 97.70%, and 97.97% for valence, arousal, and dominance on DEAP, and 99.66%, 99.78%, and 99.80% on DREAMER, respectively. Leave-one-subject-out validation on DREAMER dataset achieves accuracies of 72.80% and 75.66% for arousal and dominance, respectively. Additionally, the BRAD-Net demonstrates strong cross-paradigm adaptability, achieving 98.21% accuracy on a depression classification dataset.ConclusionsThese findings confirm that integrating neuroanatomical priors into a dual-branch multi-dimensional learning framework effectively extracts robust and interpretable neural representations. BRAD-Net not only advances high-performance EEG emotion recognition but also provides a novel, biologically-constrained design paradigm for developing more interpretable brain-computer interface models. By demonstrating that restricting attention to within-brain-region interactions suffices for accurate emotion recognition, our work offers a new theoretical perspective on the application of brain parcellation knowledge in classification models.
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Suvorexant 3 – PE-PC

Conditions: PTSD – Post Traumatic Stress Disorder; Insomnia; TBI (Traumatic Brain Injury)

Interventions: Drug: Suvorexant; Drug: Placebo; Behavioral: Prolonged Exposure Therapy for Primary Care (PE-PC)

Sponsors: Northern California Institute of Research and Education; Lowcountry Center for Veterans Research

Not yet recruiting
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Mining Association Rules From a Multimodal Dataset of a Digital Therapeutics Application for Sleep Improvement Through a Healthy Lifestyle: Quantitative Study

Background: The demand for sleep interventions is high and steadily growing. Digital therapeutics (DTx) can help individuals improve their sleep remotely, over an extended period, and with less effort from medical professionals. Obstructive sleep apnea (OSA), one of the most prevalent and consequential sleep disorders, can be treated with health-supporting behavior changes, such as physical exercise and weight loss, and, therefore, acts as a promising application for DTx. Objective: The study aimed to analyze a digital intervention from both medical and technological perspectives by moving beyond clinical markers and exploring more deeply how the DTx application was used. This study aimed to propose a novel way in which association rules can function as an exploratory tool to analyze the sleep, behavior, and engagement of participants with the DTx application on a day-to-day level. Methods: A lifestyle intervention study (N=192) targeted at adults with mild-to-moderate OSA aimed to reduce their OSA severity using a DTx application and an exercise program over a study period of 12 weeks. The participants’ OSA severity was assessed through polysomnography at the beginning and at the end of the study period, and the participants tracked their sleep with a digital sleep diary and a smartwatch over the course of the entire study. The DTx application provided data on when and how the participants pursued the proposed lifestyle interventions. These heterogeneous data sources were combined into one multimodal dataset, which was explored through descriptive statistics. Ultimately, the data were turned into a transaction-based format, and association rules were derived using the Apriori algorithm. Results: Analyzing the participants’ interaction with the application revealed the lifestyle interventions they pursued and how their behavior and sleep patterns changed over time. The Apriori algorithm generated a set of association rules with lift and confidence scores that were significantly higher than those for the co-occurrence of items through random chance. The rules show co-occurrence of missions and items from the sleep diary, as well as items derived from the watch measurements. Conclusions: The study showed the richness of the various data sources provided by a digital intervention using wearables and how they can be used to get an in-depth understanding of the study. The generated association rules showed the presence of significant co-occurrences across the different data modalities and highlighted their effectiveness as an exploratory tool for multimodal health data.
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Opinion: The smart way to regulate the peptide boom

Americans are using peptide compounds (short chains of amino acids promoted for recovery, sleep, performance, metabolic health, and longevity) in large and growing numbers. Many obtain them from unregulated online sellers and informal markets, often without medical supervision, reliable quality controls, or accurate dosing information. Whether we like it or not, this is a mainstream reality, and it creates the very risks regulators seek to avoid.

With Kyle Diamantas now at the helm of the Food and Drug Administration as acting commissioner, the agency has a timely opportunity. Rather than choosing between unrestricted gray-market access or a blanket crackdown that simply drives use underground, the agency can chart a third way on peptides. 

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Access to an mHealth Tool for Symptom Management in Pediatric Oncology Care: Triangulation Study

Background: Digital health offers opportunities to facilitate symptom assessments and communication for children with cancer, particularly after discharge. However, access to these tools must be established to ensure that they effectively support the user. PicPecc (Pictorial Support in Person-Centered Care for Children) is a mobile health tool developed to enable children to remotely assess symptoms and communicate with health care professionals. Understanding access to PicPecc is essential for evaluating its use in pediatric oncology. Objective: The aim was to test a digital intervention with PicPecc in pediatric oncology care through the lens of access to technology. Methods: This study uses a triangulation approach to determine access to digital technology through an intervention, PicPecc outside hospital. Fourteen children (6‐17 y), 5 parents, and 6 nurses from 2 pediatric oncology units in Sweden participated. Children were encouraged to use PicPecc for 2 weeks (achieving a median of 14, IQR 9.75-16 days) following hospital discharge to assess pain, nausea, sleep disturbances, and feelings using an assessment scale, pictures, personal notes, and a chat function. Nurses monitored assessments and responded via the administrative interface. Access was analyzed through interviews and an instrument, and by recording the consumption of PicPecc. Data analysis was based on the 5 dimensions of access (availability, accessibility, accommodation, affordability, and acceptability). Results: The intervention, PicPecc outside hospital, supported availability by enabling children to communicate symptoms in a safe and structured way. Children and parents mentioned feeling safe when they were discharged from the hospital, and nurses perceived it as a valuable complement to follow-up after discharge. PicPecc outside hospital was generally accessible, although initial challenges with log-in procedures related to the PIN code were common. Barriers related to accommodation included interpreting the scale and obtaining an overview of assessments. Affordability was high, as internet access and device availability were not barriers; however, children’s motivation varied depending on symptom burden. Acceptability was strong among children up to 12 years of age, who appreciated the design and gaming function, while the older children found the visual design less age-appropriate. Conclusions: Access to the mobile health tool, PicPecc outside hospital, appears promising for supporting remote symptom assessment in pediatric oncology, particularly among children up to 12 years of age. However, identified barriers, such as motivational factors and integration into the health care system, need to be addressed.
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Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing

Background: Depression in older adults is often underdiagnosed due to atypical symptom presentation and generational stigma, leading to delayed intervention. Early identification of individuals at risk of developing elevated depressive symptoms is therefore critical, but traditional approaches show limited predictive accuracy. To date, no study has applied machine learning (ML) models to predict clinically significant depressive symptoms at 2-year follow-up in older adults in the United Kingdom using data from the English Longitudinal Study of Ageing (ELSA). Moreover, the impact of encoding strategies for categorical health care variables has not been examined. Objective: This study aimed to develop and evaluate ML models to predict the clinically significant depressive symptoms at 2-year follow-up in older adults using ELSA data. We further compared ordinal and one-hot encoding strategies across different ML architectures and identified key predictors of depressive symptoms at follow-up. Methods: Data were drawn from 4 consecutive waves of ELSA, including participants aged ≥50 years without significant depressive symptoms at the baseline wave (waves 6‐9). Clinically significant depressive symptoms were defined as 8-item Center for Epidemiologic Studies Depression Scale (CES-D 8) scores of ≥4 at the subsequent wave (waves 7‐10). Over 120 features spanning sociodemographic, psychological, and health-related domains were analyzed. Eight ML models were applied, including tree-based ensembles, deep learning architectures for tabular data, distance-based methods, probabilistic methods, and linear methods. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and -score. Model interpretability was examined using Shapley additive explanations (SHAP). Sensitivity analyses assessed the robustness of results across alternative CES-D 8 thresholds (≥3, ≥4, and ≥5) and encoding strategies. Results: Across waves, the best-performing models achieved mean AUROC scores of 0.72‐0.73, with a peak of 0.75 in the highest-performing wave. Ordinal encoding consistently outperformed one-hot encoding across all ML models, yielding improvements in AUROCs and -scores, with the greatest increase in tree-based methods. SHAP consistently identified loneliness, sleep disturbances, and low social engagement as strong predictors of elevated depressive symptoms at follow-up. Sensitivity analyses across CES-D 8 thresholds demonstrated robust feature importance, with AUROCs ranging from 0.67 to 0.82. Traditional ML models (random forest, extreme gradient boosting, and support vector machines) generally achieved higher performance than the deep learning models for this task. Conclusions: Our findings demonstrate the feasibility of predicting clinically significant depressive symptoms at 2-year follow-up in UK older adults, with moderate accuracy. Ordinal encoding demonstrates superior performance for health care datasets with inherently ordered categorical features. The identification of consistent risk factors highlights opportunities for developing targeted clinical screening tools and preventive interventions. This study provides new evidence on depressive symptom prediction in the UK context, leveraging longitudinal data from ELSA, and contributes to advancing digital mental health research for aging populations.
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