This study explored barriers and facilitators to digital self-management engagement among individuals with chronic pain. Semi-structured interviews were conducted with 24 participants, guided by a 32-item schedule informed by digital health adoption literature and the Capability, Opportunity, Motivation and Behaviour (COM-B) model. Reflexive thematic analysis identified key influences on engagement, which were then mapped onto the COM-B framework to inform intervention design. Barriers were linked to physical and cognitive limitations, information access, financial constraints, self-efficacy and individual differences. Facilitators included social connection, enhanced pain awareness, autonomy and accessibility. While themes aligned with most COM-B components, no clear influences were mapped to Automatic Motivation. Findings provide nuanced insights into the behavioural and contextual factors shaping engagement with app-based interventions. By applying the COM-B model, this study offers a theoretically grounded understanding of digital self-management uptake, supporting the development of more responsive and accessible interventions for people living with chronic pain.

BackgroundRemote 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. ObjectiveThis 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. MethodsIn 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. ResultsIn 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. ConclusionsThese 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.

Cognitive impairment represents a core feature of major depressive disorder (MDD), often persisting after mood symptoms remit and not addressed by usual antidepressant treatments. Despite its relevance, cognition is typically assessed with infrequent tests in clinical settings, overlooking its contextual nature. Smartphones and wearables enable ecologically valid, repeated measurements of cognition and daily life behaviors that may impact it. We examined whether sleep duration, step count, and smartphone screen time are associated with cognitive functioning in MDD. We conducted secondary analyses of RADAR-MDD, a multicenter study following individuals with recurrent MDD. Cognitive functioning - self-reported and performance-based - was assessed with the THINC-it® app. Sleep duration and step count were measured with Fitbit devices, and screen time with the RADAR-Base app. Cognitive assessments (outcomes) were linked to behavioral measures (predictors) from the day of and the day preceding each assessment. Two-level multilevel models estimated between-person (differences in participant means) and within-person (deviations from participant means) effects. The sample included 502 participants, further subdivided by behavior-cognitive outcome pair. For performance-based cognitive assessments, positive associations at the between-person level were found for step count (β = 0.104, SE = 0.031, p < 0.001) and screen time (β = 0.075, SE = 0.036, p = 0.038), and sleep duration showed a quadratic negative effect (β = -0.080, SE = 0.018, p < 0.001). No within-person effects were detected. For self-reported cognitive functioning, step count showed positive associations both between (β = 0.161, SE = 0.037, p < 0.001) and within persons (β = 0.027, SE = 0.010, p = 0.005), while screen time was negatively associated within persons (β = -0.033, SE = 0.011, p = 0.002). Our findings illustrate that smartphones and wearables can collect meaningful daily life data of MDD patients that can be used to support cognitive health. Step count emerges as a promising behavioral target as it is simple to track and is correlated with better cognitive outcomes.

As mental health difficulties are of increasing concern and people are encouraged to communicate their feelings, loneliness has become a critical issue for the community, with the number of people identifying as lonely being higher than ever. Loneliness can lead to conditions ranging from simple changes in social behaviors to adverse health situations, with depression and anxiety being the dominant ones. These conditions are known to trigger a cascade of emotions then translated into alterations of physiological signals which can be detected and monitored with digital means, such as wearable sensors. The purpose of this work is to review the smart wearable sensing systems that have been used for the detection of these conditions and the recent advances in the development of these sensors. We first review the physiological signals associated with each one of the main loneliness manifestations, then we present the real-life studies performed involving participants and the use of wearable sensors and subsequently we analyze the main categories of sensors used in the studies, the technological advances and the remaining drawbacks for each one of them. Through this review we managed to examine thoroughly the current smart wearable sensing systems on digital monitoring of loneliness and to identify the most promising fields for development.

STUDY OBJECTIVES:To evaluate the efficacy of three categories of standalone, audio-based sleep interventions (Bedtime Stories, Sleep Sounds, Sleep Skills) delivered via mental health application (MHapp) in improving sleep among working adults with sleep disturbance. METHODS:A multi-arm, parallel randomized controlled trial was conducted. Adults with self-reported sleep disturbances were recruited online and randomly allocated to Bedtime Stories, Sleep Sounds, Sleep Skills, or digital control. Participants completed self-report questionnaires on sleep disturbance and other related outcomes at baseline (t0) and after the 4-week intervention (t1). The primary analysis followed an intention-to-treat approach using mixed-effects models. RESULTS:A total of 495 working adults (mean age = 32.7 years; 55.8% female) were randomized. For sleep disturbance (primary outcome), the between-group Hedges' g effect sizes were very small and not statistically significant (Bedtimes stories vs. control: g = 0.12, 95% CI -0.13 to 0.37, Sleep Sounds vs. control: g = 0.14, 95% CI -0.11 to 0.39, Sleep Skills 0.07, 95% CI -0.07 to 0.29), with slightly greater reductions in sleep disturbance for the intervention groups than control. The same pattern was observed for sleep-related impairment, mental health, well-being, and pre-sleep arousal. CONCLUSION:Audio-based sleep interventions delivered via a MHapp did not demonstrate superior efficacy over a digital control condition in reducing self-reported sleep disturbance among working adults. Although safe and well-tolerated, their use as standalone treatments for sleep disturbance is not supported by these findings. Future research should explore effectiveness in real-world settings, including user content choice across categories, and use objective sleep measures. CLINICAL TRIAL REGISTRATION:Registered at https://www.isrctn.com/ under "Evaluating the efficacy of audio-based digital tools to improve sleep on the Unmind workplace well-being platform"; https://www.isrctn.com/ISRCTN13426045; registration number: 13426045.

OBJECTIVES:Self-management is central in chronic pain care, and mobile health (mHealth) applications (apps) offer scalable tools to support symptom monitoring and management. Although promising, these apps vary in quality, adaptability, and integration of evidence-based behaviour change techniques (BCTs). Many remain unregulated and under-evaluated, leaving their benefits for pain management unclear. We systematically evaluated the quality of commercially available pain management apps in the United Kingdom and examined the prevalence of pain-related BCTs and adaptive features. DESIGN AND METHODS:Freely available English-language apps from the 'Health and Fitness' or 'Medical' categories in the Apple® and Google Play® stores were screened and assessed for quality using the Mobile App Rating Scale (MARS; 1 = inadequate, 5 = excellent) and coded for BCTs and adaptive features. RESULTS:Twenty-three apps were included, with a mean MARS score of 3.03 (range = 1.8-4.6). Five scored >4.0, while 39% scored 3.0-3.9, indicating moderate quality. Apps included a mean of 3.3 BCTs, most commonly self-monitoring (87%), instruction (61%), and behaviour-health links (52%). Social support (13%) and goal setting (17%) were rare. An average of 2.3 adaptive mechanisms were identified, with proximal outcomes in all apps and intervention options in 70%, but decision points and tailoring variables were infrequent. CONCLUSION:Commercially available pain apps in the United Kingdom are generally of moderate quality, with limited integration of social, goal-setting, and adaptive features. Greater personalization is needed to strengthen engagement and clinical impact in digital pain self-management.

BackgroundLoneliness is a critical issue among older adults and constitutes a significant risk factor for a range of physical and mental health conditions. However, current assessment methods primarily rely on self-report questionnaires and clinical evaluations, which are susceptible to recall bias and social desirability bias, highlighting the need for more objective and continuous assessment approaches. Recent studies have reported associations between physiological and behavioral indicators and the experience of loneliness in older adults. While these technologies have demonstrated correlations between physiological and behavioral sensor data and the experience of loneliness, their implementation has been limited. Most systems rely on fixed-location sensors or smartphone apps, with little attention given to the integration of these tools into users’ daily routines. To date, no published studies have applied smart textile technology, which integrates sensing capabilities directly into garments or furniture, as a medium for loneliness detection. This study addresses that gap by exploring the usability, experiential acceptability, and ethical considerations of smart textile-based monitoring systems. ObjectiveThis study aims to assess the perceived usability, acceptability, and emotional resonance of a smart loneliness monitoring system integrating sensing garments, furniture, and a mobile app and identify design implications to guide future improvement and promote sustained engagement among older adults. MethodsBuilding on earlier conceptual research, a functional prototype system was developed and evaluated through 2 immersive in-person workshops with older adults (N=10). A mixed methods approach was applied, combining structured questionnaires, sensory ethnographic observations, focus group discussions, and experience-based co-design. Quantitative data were analyzed descriptively, and qualitative data were analyzed thematically to explore user perceptions related to system usability, emotional response, lifestyle compatibility, and ethical considerations. ResultsQuantitative data indicated high user satisfaction in dimensions such as comfort, ease of use, and feedback clarity. However, trust in long-term monitoring and willingness to use the system regularly varied. Thematic analysis revealed 4 main areas influencing acceptance, including wearability, usability, and daily integration; trust, privacy, and data control; perceptions of loneliness and the limits of detection; and adoption, applicability, and ethical futures. Participants emphasized the need for discretion, personalization, and human oversight in system feedback and data-sharing mechanisms. ConclusionsThe resulting prototype was positively received, demonstrating the potential of smart systems for passive and personalized loneliness monitoring among older adults. However, adoption is influenced by perceptions of autonomy, emotional sensitivity, and contextual integration. Future development should focus on modularity, transparency, and integration within care infrastructures to ensure ethical and sustainable deployment.

Digital self-monitoring tools are increasingly used as health interventions to support goal achievement. However, many mobile health (mHealth) applications prioritise quantitative metrics, overlooking qualitative dimensions critical for understanding user engagement and behavioral change. This study explored students’ perceptions of a habit-tracking app’s usability and acceptability, and its effectiveness in facilitating behavior change over five weeks. Twelve university students (aged 18–32; 11 female, 1 non-binary) across UK undergraduate, postgraduate, and doctoral students participated. Using a within-person repeated-assessments qualitative design, data were collected through two methods: Think-Aloud protocols for initial impressions and semi-structured focus groups after five weeks of app use. Thematic analysis was applied, following Standards for Reporting Qualitative Research (SRQR) guidelines. Think-Aloud sessions yielded three themes: usability, acceptability, and challenges. Focus groups identified four themes: design, engagement, individual differences, and mechanisms of effect. The app was most effective for short-term habit tracking but limited in sustaining long-term habits. Features such as reminders and gamification enhanced engagement and supported positive behavioral change. Future development should prioritize goal-relevant, customizable features that foster autonomy, competence, and sustained behavior change.

Loneliness is a significant psychosocial factor that negatively impacting the health and quality of life of individuals, with social isolation recognized as a major risk factor. This paper presents a wearable, textile based multi-sensing system to continuously monitoring physiological changes associated with isolation related affective states which toward future loneliness detection. The system integrates flexible, non-invasive textile sensors for electrocardiogram (ECG), electromyography (EMG), respiration, skin temperature, and galvanic skin response (GSR) within textile, enabling continuous physiological monitoring and analyses for analyzing loneliness resulting from isolation. In our pilot study, physiological signals are recorded dunder three controlled affective arousal conditions: Low emotional arousal induced by quiet sitting, moderate emotional arousal associated with social conversation, and high emotional arousal elicited by positive emotional arousal. Experimental results demonstrate distinct physiological patterns across conditions, including reduced heart rate variability, more regular respiration, lower skin conductance activity, and decreased muscular engagement under low emotional arousal compared with socially interactive and highly arousing states. This scalable and cost-effective solution supports proactive monitoring and assessment of loneliness for users, offering potential for timely interventions by caregivers, and clinicians. The work advances emotion-aware wearable systems for geriatric care and mental health support.

BackgroundEfforts to advance our understanding of depression have long been constrained by the disorder’s vast symptom heterogeneity and by the reliance on self-report, which offers only a partial view of phenotypic expression. Digital phenotyping provides an opportunity to address these core challenges by generating real-time, objective data on behavior and physiology, offering new perspectives on understanding depression phenotypes. Yet, prior efforts to identify such objectively derived subtypes have relied on predefined diagnostic labels or supervised models, limiting discovery to existing clinical categories. ObjectiveThis study aimed to identify subtypes of depression based on objective sleep and activity data using an unsupervised learning method and to explore how participants transition between these subtypes over time. MethodsWe analyzed longitudinal Fitbit data from 623 participants with recurrent depression enrolled in the Remote Assessment of Disease and Relapse in Major Depressive Disorder study. To identify our subtypes, we applied Gaussian mixture models and hidden Markov models, incorporating a thorough model selection approach that combined grouped cross-validation and seed selection to ensure robustness. ResultsThree activity subtypes (high, light, and low activity) and 4 sleep subtypes (efficient early sleepers, efficient late sleepers, disrupted sleepers, and variable late sleepers) were consistently identified. These subtypes align with known associations between depression and behavioral patterns. Transition modeling revealed stability within individuals over follow-up, further suggesting the presence of behavioral phenotypes rather than momentary fluctuations. ConclusionsThe results demonstrate that wearable-derived features can identify reproducible and clinically relevant behavioral subtypes of sleep and activity in individuals with major depressive disorder. These subtypes reflect known behavioral correlates of depression and may offer a data-driven framework for reducing phenotypic heterogeneity, improving research stratification, and supporting personalized patient monitoring. Further work is needed to validate these findings in independent cohorts and evaluate their potential use in reducing noise when using sleep or activity data to predict depression outcomes.

Aims: Alcohol use in the workplace is typically addressed through impairment-focused policies or reactive disciplinary responses. Yet national data suggest a deeper, systemic relationship between work-related stressors and higher-risk alcohol consumption. The aim of this work is to quantify the prevalence of higher-risk alcohol use among full-time workers and investigate the role of work-related stressors and workplace-specific barriers in shaping drinking behaviour and help-seeking patterns. Methods: Data from a population survey of 2,037 UK adults (mean age=48.77 years, standard deviation=17.74; 1,066 female [52.33%], 971 male [47.67%]) were analysed to examine the prevalence of higher-risk drinking among full-time workers, demographic distribution, attribution of drinking to life stressors, and work-specific barriers that inhibit help-seeking. Results: Higher-risk alcohol use was substantially elevated among full-time workers (30.8%), second only to students, and distributed across all income levels, including 40% earning £50,000+. Only 9.6% of this group self-identified as heavy drinkers. Work-related pressures emerged as a core driver: 25.7% of higher-risk workers cited work stress, nearly double the rate among all drinkers (14.3%), with additional attribution to cost-of-living pressures, loneliness, and remote working (2.4× more common than in the general sample).These findings support a model in which alcohol use reflects a stress-response pattern rather than individual pathology. Workplace structures also shaped barriers to accessing support: concerns about career impact (17.8%) and difficulty taking time off work (17.1%) featured prominently among higher-risk adults, indicating that traditional clinic-based services are poorly aligned with the needs of working populations. Conclusion: Workforce alcohol harm in the UK is widespread, socially invisible, and tightly linked to occupational and economic stressors. Reliance on self-disclosure, performance deterioration, or manager-led referral will systematically miss the majority of affected workers. These findings emphasise the importance of incorporating AUDIT-C screening into occupational health processes and routine workplace wellbeing assessments, ensuring that alcohol-related risk is identified proactively rather than reactively. Alcohol use should be recognised as a meaningful indicator of occupational mental health, considered alongside other markers such as workplace stress, sleep disruption, and burnout. It is also essential to commission flexible, digital, or remote care pathways that minimise career-related stigma and reduce the need for employees to take time away from work in order to access support. Finally, workplace alcohol interventions should be reframed away from an emphasis on individual responsibility and instead approached as part of a broader organisational mental health strategy, acknowledging the role of workplace conditions in shaping alcohol-related risk.

BACKGROUND:Hyperactivity is a core symptom of childhood attention deficit hyperactivity disorder (ADHD), yet no population-based studies have compared objective and subjective hyperactivity measures across the school day. METHODS:This study used data from N = 6,518 seven-year-olds in the Millennium Cohort Study. Physical activity was objectively measured using actigraphy every 15 min (Monday-Friday), and hyperactivity was assessed via parent and teacher Strengths and Difficulties Questionnaire ratings, analysed both as continuous (0-10) and binary (≥7; possible ADHD) scores. The association between actigraphy and hyperactivity were assessed via repeated ANOVAs throughout the day, accounting for sex and linear regressions during three school lessons (Lesson 1: 9.00-9.45 am Lesson 2: 11.00-11.45 am and Lesson 3: 1.15-2.00 pm). The effect sizes and standard errors were compared between morning and afternoon lessons. RESULTS:Continuous ratings from both parents and teachers were positively associated with objective activity across the school day (effect sizes = .015 and .008, respectively, both pFDR < .001). For teacher ratings, associations with continuous hyperactivity were significantly stronger in Lessons 2 and 3 than in Lesson 1 (Z = 3.24, pFDR = .005; Z = 2.80, pFDR = .020, respectively). CONCLUSIONS:These results illustrate how objective activity measurement can augment subjective assessments by capturing richer behavioural patterns in naturalistic educational settings.

To inform community-based stress monitoring tools and supportive interventions, this study aimed to understand caregiver stress as experienced by a diverse group of informal caregivers guided by the Pearlin's stress process model. We used a qualitative descriptive design conducting semistructured interviews with informal caregivers (>= 18 years) currently or previously caring for an adult with health issues at home. Data were analysed using the framework approach. We recruited 27 caregivers (19 current and 8 former) from various geographic locations within the United Kingdom. In terms of background and context, poor caregiver health increased stress whereas prior employment in health or social care, and access to trusted supports and resources reduced stress. Common primary stressors included rapidly changing or palliative care care-recipient needs, loneliness and loss (i.e., loss of their normal life, or of the life and future plans they had expected). Family conflict, occupational/economic strains and social/recreational life constraints were important secondary stressors. Guilt contributed to intrapsychic strain resulting in low self-esteem and feelings of role captivity. Few participants discussed positive elements of caregiving such as mastery or gain. Stress mediators included coping strategies such as taking control, humour, taking brief respite, social activities, access to peer and other forms of social support, and trusted support for caring. Common outcomes of stress included exhaustion, physical injuries, weight loss, difficulty sleeping, depression and anxiety. Despite growing recognition of issues facing informal caregivers and policies or services put in place to support them, our data indicate key stressors remain. Future supportive initiatives should reflect dynamic and individual caregiver needs, thereby enabling caregivers to prioritise their mental and physical wellbeing and receive brief respite from caregiving responsibilities. Stress monitoring tools and accompanying supportive interventions, if codesigned with caregivers with lived experience, offer the potential to identify high-stress periods, enable timely interventions and guide more efficient resource allocation.

Background Loneliness among older adults has become a major public health concern associated with cognitive decline, depression, and increased health care use. The advancement of digital health technologies such as wearable devices, smart home systems, and mobile health apps provides new opportunities to monitor and mitigate loneliness through continuous physiological and behavioral assessment. However, the effectiveness of such technologies largely depends on user interface design, ensuring that older adults can understand, trust, and comfortably engage with the technology. Existing research on interactive platforms and user interfaces for psychological and emotional monitoring has mainly focused on usability testing and technology feasibility, with limited attention to structured design frameworks that integrate psychological, emotional, and accessibility dimensions for older adults. Furthermore, most studies rely on qualitative assessments and lack quantitative prioritization of design indicators. Objective This study aimed to build on the DELONELINESS (Design for Healthy Ageing: a Smart System to Decrease Loneliness for Older People) system to develop a user-centered hierarchical framework of interface design indicators for older adults. Methods A mixed methods design was applied, integrating literature search, qualitative focus group analysis, and expert consultation to build an initial indicator pool. A hierarchical indicator structure with 7 first-level and 26 second-level indicators was developed. The analytic hierarchy process was used to assign indicator weights through surveys of 20 experts with academic or professional experience in human-computer interaction, digital health, gerontology, and health informatics. Based on the weighted results, 3 interface design solutions were developed and comparatively evaluated using the Technique for Order Preference by Similarity to Ideal Solution. Results All expert judgment matrices satisfied the analytic hierarchy process consistency requirement (consistency ratio <0.1). The Kendall coefficient of concordance indicated good agreement among experts for both first-level indicators (W=0.313; P<.001) and second-level indicators (W=0.156; P<.001). Among the 7 first-level indicators, trust and safety (weight=0.206), ease of use (weight=0.187), and accessibility (weight=0.167) received the highest weights, indicating their importance in enhancing user confidence and engagement. Technique for Order Preference by Similarity to Ideal Solution evaluation results showed that design solution 2 achieved the highest overall performance score (relative closeness coefficient C=0.877), emphasizing clear interaction pathways, visual clarity, and guided feedback as key factors for optimal usability. Conclusions This study developed a user-centered framework for interface design in loneliness monitoring among older adults by integrating user insights, literature-derived indicators, and expert consensus, and providing a structured data-driven approach to prioritizing design requirements. The proposed framework bridges subjective user experience with objective evaluation, offering practical guidance for developing empathetic, inclusive, and trustworthy digital mental health technologies for older adults.

Background Elevated night resting heart rate (HR) has been associated with increased depression severity, yet the underlying mechanisms remain elusive. This study aimed to investigate the mediating role of sleep disturbance and the influence of anxiety on the relationship between night resting HR and depression severity. Methods This is a secondary data analysis of data collected in the Remote Assessment of Disease and Relapse (RADAR) Major Depressive Disorder (MDD) longitudinal mobile health study, encompassing 461 participants (1774 observations) across three national centers (Netherlands, Spain, and the UK). Depression severity, anxiety, and sleep disturbance were assessed every three months. Night resting HR parameters in the 2 weeks preceding assessments were measured using a wrist-worn Fitbit device. Linear mixed models and causal mediation analysis were employed to examine the impact of sleep disturbance and anxiety on night resting HR on depression severity. Covariates included age, sex, BMI, smoking, alcohol consumption, antidepressant use, and comorbidities with other medical conditions. Results Higher night resting HR was linked to subsequent depressive severity, through the mediation of sleep disturbance. Anxiety contributed to an exacerbated level of sleep disturbance, subsequently intensifying depression severity. Anxiety exhibited no direct effect on night resting HR. Conclusions Our findings underscore the mediating role of sleep disturbance in the effect of night resting HR on depression severity, and anxiety on depression severity. This insight has potential implications for early identification of indicators signalling worsening depression symptoms, enabling clinicians to initiate timely and responsive treatment measures.

Cognitive difficulties are prevalent in depression and are linked to various negative life outcomes such as psychosocial impairment, absenteeism, lower chance of recovery or remission, and overall poor quality of life. Thus, assessing cognitive functioning over time is key to expanding our understanding of depression. Recent methodological advances and the ubiquity of smartphones enable remote assessment of cognitive functioning through smartphone-based tasks and surveys. However, the association of smartphone-based assessments of cognitive functioning to depression severity remains underexplored. Using a dedicated mobile application for assessing cognitive functioning (THINC-it), we investigate within- and between-person associations between performance-based (attention, working memory, processing speed, attention switching) and self-report measures of cognitive functioning with depression severity in 475 participants from the RADAR-MDD (Remote Assessment of Disease and Relapse-Major Depressive Disorder) cohort study (t = 2036 observations over an average of 14 months of follow-up). At the between-person level, we found stronger negative associations between the self-reported cognitive functioning measure and depression severity (β = -0.649, p  < 0.001) than between the performance-based measures and depression severity (βs = -0.220 to -0.349, p s  < 0.001). At the within-person level, we found negative associations between depression severity and the self-reported measure (β = -0.223, p  < 0.001), processing speed (β = -0.026, p=0.032) and attention (β = -0.037, p=0.003). These findings suggest that although THINC-it could adequately and remotely detect poorer cognitive performance in people with higher depressive symptoms, it was not capable of tracking within-person change over time. Nonetheless, repeatedly measuring self-reports of cognitive functioning showed more potential in tracking within-person changes in depression severity, underscoring their relevance for patient monitoring.

Objectives:This article aims to evaluate the feasibility and acceptability of intensive assessment of symptoms in RA patients starting a new biologic treatment. Methods:Participant symptoms and experiences were collected six times a day for 14 days and once a day for 16 days in a single cohort. Wearable devices were also given to participants to track sleep and physical activity. Qualitative interviews were conducted to provide feedback regarding the acceptability of methods. Recruitment and completion rates were used to test for feasibility. The mean and variability of data for each day were calculated to reflect on data quality. Qualitative interview data were analysed by deductive thematic analysis. Results:Of the 110 patients approached, 27 (15.5%) could not be contacted and 12 (14.5%) were excluded due to meeting exclusion criteria. Of 71 contactable and eligible participants, 31 (43.7%) joined the study. The survey completion rate was 74.6% (1943/2604) for the first 14 days, ranging from 10.7 to 100%. Completion rates for days 15-30 ranged from 60 to 83%. Mean levels of severity of symptoms (pain, fatigue, joint stiffness) showed a decrease after treatment, as expected. Qualitative interviews demonstrated that participants reported a positive experience that was not overly burdensome. Surveys were described as quick and easy to complete, but repetitive for some participants. Discussion:Recruitment and completion rates were acceptable and comparable to similar studies in the field. Qualitative analysis showed largely positive reviews from participants with feedback mainly focusing on survey timings.

Recent advancements in Large Language Models (LLMs) present promising opportunities for applying these technologies to aid the detection and monitoring of Major Depressive Disorder. However, demographic biases in LLMs may present challenges in the extraction of key information, where concerns persist about whether these models perform equally well across diverse populations. This study investigates how demographic factors, specifically age and gender affect the performance of LLMs in classifying depression symptom severity across multilingual datasets. By systematically balancing and evaluating datasets in English, Spanish, and Dutch, we aim to uncover performance disparities linked to demographic representation and linguistic diversity. The findings from this work can directly inform the design and deployment of more equitable LLM-based screening systems. Gender had varying effects across models, whereas age consistently produced more pronounced differences in performance. Additionally, model accuracy varied noticeably across languages. This study emphasizes the need to incorporate demographic-aware models in health-related analyses. It raises awareness of the biases that may affect their application in mental health and suggests further research on methods to mitigate these biases and enhance model generalization.

Sleep disturbances are prevalent in the general population, coinciding with a surge in the availability and use of digital sleep aids. Among these, standalone audio-based tools, termed Sonic Sleep Aids (SSA), such as sleep music, ambient sounds, bedtime stories, and sleep skills (e.g. guided meditation, positive psychology techniques), have gained popularity. This perspective piece examines the phenomenon of SSA by discussing the existing evidence and highlighting the different levels of empirical support across SSA types. Music-based relaxation has demonstrated efficacy in improving sleep quality, whereas findings on ambient sounds (e.g. white, pink noise) are inconclusive. Empirical support for narrated content as a sleep aid remains limited. Guided practices like mindfulness and self-compassion show potential, yet further research is needed to support their effectiveness, particularly when limited to bedtime practice. In the broader context, the widespread use of app-based SSA raises questions about their alignment with sleep hygiene recommendations, which typically discourage bedtime screen use. This concern is compounded by a paucity of randomized controlled trials testing their effectiveness against well-matched controls, alongside the risk of increased dependency on technology and altered relationships with rest and introspection. Against these concerns, potential benefits include accessibility and reduced reliance on pharmacological aids. A research agenda is proposed to investigate the efficacy of digitally delivered SSA in naturalistic settings, their mechanisms of action, and their impact across different populations. Understanding these factors is crucial to determine whether SSA serve as beneficial tools or divert individuals from more effective, evidence-based approaches to sleep.