Tag: Mood and emotional regulation

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Modeling Short-Term Symptom Changes and Behavioral Subtypes of Depression and Anxiety in the General Population: Observational Study Using Smartphone Data

Background: Smartphone-based digital phenotyping has emerged as a promising approach for monitoring mental health using passive behavioral data. Prior studies have linked smartphone-derived features to depression and anxiety severity; however, knowledge regarding whether short-term changes in symptoms can be captured using passive smartphone data in general population samples remains limited, as does the understanding of how such findings should be interpreted vis-à-vis behavioral patterns and demographic variability. Objective: This study aimed to model short-term changes in depression and anxiety severity using passive smartphone data, examine model performance across demographic subgroups, and identify behavioral patterns associated with symptom changes. Methods: We collected 2 weeks of smartphone usage data from 95 adults in the general population and assessed depressive and anxiety symptoms using the clinician-rated Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale, respectively. Behavioral features—including physical activity, app use, and screen usage metrics—were extracted and compressed using an autoencoder and principal component analysis. The resulting features—along with age, sex, and baseline Hamilton scores—were used to train random forest classifiers predicting symptom score changes (increase, decrease, or unchanged). Additionally, we examined whether model performance differed across demographic subgroups and whether models excluding baseline scores retained predictive performance, as baseline severity was expected to be a strong predictor. To add explanatory value beyond prediction, behavioral subtypes associated with symptom changes were identified by applying unsupervised clustering. Results: The model exhibited moderate performance in predicting changes in the Hamilton Depression Rating Scale (mean accuracy=0.70, mean area under the receiver operating characteristic curve=0.74) and Hamilton Anxiety Rating Scale (mean accuracy=0.65, mean area under the receiver operating characteristic curve=0.69) scores. Performance varied according to demographics, with reduced accuracy among younger adults and females, although these differences were not significant in permutation tests. Excluding baseline Hamilton scores diminished performance substantially, suggesting that baseline symptom severity accounted for a substantial proportion of the predictive performance. Clustering revealed 4 distinct behavioral subtypes according to smartphone usage patterns. A cluster characterized by structured, daytime-focused smartphone use and lower temporal entropy demonstrated greater improvement in depressive symptoms, whereas clusters with lower and irregular usage patterns exhibited minimal improvement or worsening. Conclusions: Passive smartphone-derived behavioral data demonstrated moderate ability to model short-term symptom changes in this predominantly nonclinical sample. However, a substantial proportion of the predictive performance was attributable to baseline symptom severity, underscoring that passive smartphone data may provide modest supplementary information rather than robust stand-alone predictive value. Nevertheless, clustering analyses indicated that passive data may still assist in identifying behaviorally distinct subtypes associated with different depressive symptom trajectories. These findings reflect a practical contribution to digital phenotyping research by elucidating both the potential and constraints of passive smartphone data for short-term symptom monitoring in small general population samples.
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Circadian–immune crosstalk in insomnia disorder: mechanisms and therapeutic implications

Insomnia disorder (ID) is a common sleep–wake disorder characterized by persistent difficulty initiating or maintaining sleep, early-morning awakening, or non-restorative sleep, accompanied by daytime functional impairment. ID has traditionally been explained by the hyperarousal model, which emphasizes cognitive, emotional, cortical, neuroendocrine, and autonomic overactivation. However, this model alone does not fully account for the chronic persistence, relapse tendency, and multisystem associations of ID. Emerging evidence suggests that circadian rhythm disruption, impaired melatonin signaling, hypothalamic–pituitary–adrenal (HPA) axis activation, autonomic imbalance, and low-grade inflammation may also contribute to the development and maintenance of ID. Available evidence indicates that sleep disturbance is more consistently associated with selected inflammatory markers, particularly C-reactive protein (CRP) and interleukin-6 (IL-6), whereas findings for tumor necrosis factor-alpha (TNF-α) remain less consistent. The circadian system regulates sleep, endocrine function, metabolism, and immune-inflammatory activity through the suprachiasmatic nucleus, melatonin and cortisol rhythms, peripheral clock genes, and rhythmic immune-cell responses. Disruption of this temporal network may alter melatonin secretion, inflammatory rhythmicity, and stress-related neuroendocrine responses, thereby contributing to the persistence of insomnia symptoms. Compared with previous reviews that have separately discussed hyperarousal, circadian rhythm disruption, melatonin signaling, or sleep-related inflammation, this review integrates these processes into a circadian–immune perspective for understanding ID. We summarize alterations in sleep–wake rhythms, melatonin signaling, HPA-axis activity, autonomic regulation, and immune-inflammatory responses in ID, and discuss potential intervention strategies, including light management, melatonin and melatonin receptor agonists, cognitive behavioral therapy for insomnia (CBT-I), physical activity, time-restricted eating, and stress management. This review aims to provide a mechanistic basis for understanding the chronicity and heterogeneity of ID and for developing individualized intervention strategies.
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Coping styles and mental health outcomes in partners who have experienced a perinatal loss: a longitudinal study

Perinatal loss is common, but little is known about its impact on partners during the grieving process. This study examined psychological outcomes and coping strategies among recently bereaved partners (≤6 months post-loss; N = 73) at baseline (T1) and six-month follow-up (T2) via online survey. Participants were predominantly male (78%) and typically aged 25–44 (90%), and had experienced a range of perinatal losses (<20 weeks’ gestation to 28 days of life). Grief and depression symptoms were assessed using the Perinatal Grief Scale (PGS) and PHQ-9 respectively, and multivariate regression analyses examined the role of coping styles, demographic characteristics, and loss-related factors. Participants reported varying levels of grief and depression symptoms, with 38% displaying moderate-to-severe depression symptoms at follow-up. Across the cohort, participants reported using a range of coping strategies; however, avoidant coping was uniquely associated with higher grief and depression scores at baseline (PHQ-9: β = .30, p = .007; PGS: β = .49, p <.001). Avoidant coping also predicted poorer grief and depression outcomes at follow-up, although these relationships were no longer significant after controlling for baseline symptoms. Additionally, stillbirth, female gender, and younger age were associated with greater psychological distress at baseline across outcomes (β = .21–.32, p ≤.05). Associations between gender, stillbirth, and psychological outcomes remained at follow-up, although these did not reach conventional levels of statistical significance (β = .24–.40, p ≤.073). These findings highlight the psychological impact of perinatal loss on partners and underscore the importance of improving access to support for this group. Avoidant coping may represent a key target for intervention. Future research should further investigate factors influencing grief trajectories and identify effective forms of support for bereaved partners.
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<![CDATA[Where can vagus nerve stimulation fit into treating depression?]]>
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Effect of Psilocybin and Structured Integrated Reframing Therapy on Gut-Brain Axis Biomarkers and Depression in Major Depressive Disorder

Conditions: Major Depressive Disorder; Post-Traumatic Stress Disorder (PTSD)

Interventions: Drug: Psilocybin; Behavioral: Structured Integrated Reframing Therapy (SIRT); Drug: Selective Serotonin Reuptake Inhibitor (SSRI)

Sponsors: Khyber Medical University Peshawar; Hayatabad Medical Complex; KMU Institute of Health Science, Islamabad

Recruiting
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A Web-Based Self-Management Intervention for Return-to-Work Among Persons With Common Mental Disorders on Sick Leave: Case Study of mWorks

Background: mWorks is a co-designed, web-based self-management intervention developed to empower persons with common mental disorders who are on sick leave during the return-to-work process. However, limited knowledge of how mWorks is delivered and engaged with in real-world settings constrains further development and implementation. In line with the Medical Research Council framework for complex intervention evaluation, such an approach is required to examine (1) contextual factors influencing implementation, (2) fidelity and variation in delivery, and (3) how service users and professionals experience and respond to the intervention. Objective: This study aimed to evaluate the process of implementing mWorks, specifically focusing on assessing the intervention’s delivery in relation to the context, implementation process, and mechanisms of impact. Methods: This single-case study was bounded by the delivery period of 10 weeks in a primary and specialist mental health service context. During this period, return-to-work professionals (n=2) and service users (n=6) collaborated to initiate mWorks usage. Both qualitative and quantitative methods were used to triangulate multiple data sources. Results: The pandemic and mental health problems posed contextual barriers, particularly during recruitment. However, perceptions of mWorks as a credible and relevant intervention facilitated its implementation. The delivery was performed according to plan, with minimal adaptations. All users adhered to the intervention, and dialogue meetings were highly valued. mWorks was used flexibly according to users’ needs, both during sick leave and at work. The potential impacts included a transformative process for users, fostering acceptance, self-esteem, self-compassion, and a sense of control. It also had the potential to prevent mental ill health, transform negatives into positives, facilitate disclosure of mental health, and support goal setting. The use of quantitative measures for empowerment, engagement, self-efficacy, depression stigma, and quality of life proved feasible and supported the assumptions and direction of results. Conclusions: The recruitment stage of the implementation program encountered significant contextual barriers. However, once the delivery stage began, the implementation of mWorks proved to be feasible. Despite the limited scope of this study, with its small number of participants, the triangulation of data suggests that both users and professionals benefited from mWorks.
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Perspectives on Remote Monitoring via Smartphones and Wearables Among Individuals With Lived Experience or at Risk of Eating Disorders (“This Could Go Very, Very Wrong”): Qualitative Interview Study

Background: Remote measurement technology (RMT) is increasingly used in health research to collect real-world data relevant to clinical states (eg, sleep, activity, and stress). Concerns exist about the impact of remote tracking via personal devices and wearables on individuals with or at risk of eating disorders (EDs) by promoting a focus on exercise, diet, and appearance. There is a lack of research applying RMT to EDs. Objective: This study aimed to explore how smartphone- and wearable-based RMTs influence eating-, exercise-, and weight-related experiences among individuals with a history of or at risk of EDs and to identify perceived benefits, harms, and recommendations for their use in this population. Methods: In total, 14 semistructured interviews were conducted with former participants of Remote Assessment of Disease and Relapse: Major Depressive Disorder, a 2-year digital health study tracking depression outcomes via RMTs. Participants were included in this follow-up if they had disclosed a history of a comorbid ED or were within the at-risk age range (18-30 years) for EDs during Remote Assessment of Disease and Relapse: Major Depressive Disorder and displayed subclinical ED symptoms (Eating Disorder Diagnostic Scale). Interviews explored the impact of app engagement and wearables (Fitbits) on food, activity, and weight-related behaviors and attitudes. Template analysis was adopted to capture themes guided by the focus on ED-relevant domains. Results: In total, 6 themes captured participants’ experiences with RMTs across clinical status and presentation. Participants broadly appreciated the convenience and reflective potential, while some described emotional strain linked to constant self-tracking. Health data impacted participants’ eating and exercise habits through a dynamic process from awareness to cognition to action, fostering healthy routines or obsessive patterns, depending on emotional state, ED presentation, and recovery stage. Self-tracking appeared to mirror illness stage, supporting ED recovery among those with greater distance from illness, but risking reinforcement of compulsive patterns among those with residual or emerging symptoms. Participants’ recommendations for future studies in EDs stressed balancing autonomy with safeguards for vulnerable individuals. Conclusions: These exploratory findings, drawn from individuals with lived ED experience and young people at subclinical risk, suggest that RMT use was shaped by recovery stage and contextual factors, rather than being inherently beneficial or harmful. While findings should not be interpreted as evidence of RMT safety or acceptability in ED cohorts broadly, they raise important questions about ethical RMT design, including the selection of wearables, access to data, and researcher communication with participants.
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