Faith Matcham

Pain, anxiety, and depression are common among young people, frequently co-occur with each other, and may be longitudinally related. This systematic review and meta-analysis aimed to synthesise the direction and strength of the temporal associations between pain and anxiety/depression among young people. PsycINFO, Web of Science, Scopus, PubMed, Proquest Dissertations and Theses, Google Scholar, and OSF Preprints were searched from inception until 3 July 2026. Additional studies were identified through citation searching and references of existing systematic reviews. Longitudinal, prospective, population-or community-based studies examining temporal associations between pain and anxiety/depression among young people (0-24 years) were included. Two independent reviewers screened studies, extracted data, assessed risk of bias using ROBINS-E, and evaluated certainty of evidence using GRADE. Data were synthesised using three-level meta-analysis and narrative synthesis. Forty-two studies from 31 longitudinal cohorts were included. Anxiety, depression, and combined anxiety/depression were positively associated with subsequent pain. However, after adjustment for confounders, only depression remained significantly associated. Pain was positively associated with subsequent anxiety, depression, and combined anxiety/depression both before and after confounder adjustment. Heterogeneity was moderate to considerable and was partly explained by sex, baseline age, follow-up duration, pain type, pain informant, pain and anxiety/depression operationalisation, and risk of bias. Two of 14 outcomes had high quality of evidence, whereas the remainder ranged from very low to moderate quality. These findings highlight longitudinal associations between pain and anxiety/depression among young people and should be considered in the screening, treatment, and public health management of these problems.

BACKGROUND Many individuals seeking support for mental health difficulties face challenges identifying appropriate services and navigating complex systems of care. In response, a growing range of digital guidance tools have emerged to support service discovery, navigation, triage and referral. However, the landscape of these technologies remains poorly characterised, and distinctions between signposting, navigation, triage and referral tools are often unclear. OBJECTIVE To identify, classify and map publicly available digital guidance tools designed to support navigation of mental health services and resources, and to examine how these tools are positioned within mental health care pathways. METHODS An environmental scan of publicly available digital guidance tools was conducted using a structured search and screening process informed by established scoping review methodology. Eligible tools were required to provide guidance, navigation, signposting, triage, referral or decision-support functions related to mental health support. Two reviewers independently screened identified tools and extracted data on provider characteristics, functionality, personalisation, human involvement and pathway positioning. Data were synthesised descriptively and conceptually. RESULTS A total of 149 digital guidance tools were identified. Most tools were delivered by NHS organisations (44.3%, n = 66), government or local authority providers (26.2%, n = 39), or charities and non-profit organisations (16.8%, n = 25). Signposting was the most common functionality (92.6%, n = 138), followed by crisis escalation (59.1%, n = 88), referral guidance (53.7%, n = 80) and service directories (53.0%, n = 79). Functions involving greater levels of assessment or personalisation were less common, including service matching (38.9%, n = 58), triage (37.6%, n = 56), needs assessment (34.9%, n = 52) and symptom screening (16.8%, n = 25). Stepped-care guidance was rare (1.3%, n = 2). Personalisation varied across tools, with 36.9% (n = 55) providing no clear personalisation, 35.6% (n = 53) incorporating needs-based personalisation, 18.1% (n = 27) using location-based approaches and 7.4% (n = 11) employing multi-factor personalisation. Most tools operated as fully self-guided resources (69.8%, n = 104), while 21.5% (n = 32) incorporated direct human support and 5.4% (n = 8) used hybrid models. Mapping these functions onto stages of the mental health care pathway showed that tools were more likely to support awareness, service discovery, referral and crisis escalation than to support identification of need or selection between care options, with most tools (65.1%, n = 97) spanning four or more pathway stages. CONCLUSIONS The UK digital mental health guidance landscape is extensive and predominantly delivered through public-sector and third-sector organisations. Most tools focus on helping users find and reach services, with relatively few supporting identification of need, service selection or sophisticated personalised navigation. The findings suggest that digital guidance tools currently function primarily as access and navigation resources rather than integrated pathway-management systems. These findings further suggest that evidence-based pathway guidance underpinning mental health care is rarely operationalised within publicly available digital navigation tools, highlighting a potential gap in evidence translation. CLINICALTRIAL Not applicable

BACKGROUND Hazardous alcohol use imposes a substantial global health burden, yet access to effective face-to-face interventions is constrained by cost, availability, and logistical barriers. Digital health interventions offer scalable alternatives, and conversational artificial intelligence (AI) platforms may extend their reach by delivering adaptive, speech-based therapeutic content. However, the feasibility and acceptability of speech-to-speech AI-facilitated cognitive behavioral therapy (CBT) for alcohol reduction have not previously been evaluated. OBJECTIVE This study aimed to evaluate the feasibility, acceptability, and preliminary engagement profile of the Curb Health App, a speech-to-speech AI-facilitated CBT platform for adults seeking to reduce their alcohol consumption; to characterize the clinical severity and motivational readiness of the sample attracted to the platform under real-world recruitment conditions; and to generate participant-informed development priorities to inform a future controlled trial. METHODS A feasibility design was employed, combining quantitative survey and behavioral engagement data with structured participant feedback. Adults currently consuming alcohol and interested in reducing their intake were recruited via a university participant pool and via a community alcohol reduction service, and received 30-day access to the app. The quantitative arm assessed baseline drinking severity using the Alcohol Use Disorders Identification Test (AUDIT), motivational stage using the Readiness to Change Questionnaire, Treatment Version (RCQ-TV), and behavioral engagement using mean session duration derived from server-side usage logs. Pearson correlations were computed with Holm-Bonferroni correction. Feedback from two semistructured participant sessions was examined against a codebook grounded in the Theory of Planned Behaviour (TPB) and is reported here as illustrative user experience. RESULTS The sample (N = 55) presented with substantial baseline severity (mean AUDIT score 19.5, SD 8.96), with 92.7% (51/55) of participants scoring at or above the hazardous drinking threshold (AUDIT ≥8) and 58% (32/55) classified in the Action stage of change. AUDIT severity correlated positively with age (r=0.44; P<.01) and Contemplation scores (r=0.50; P<.01) and negatively with Precontemplation scores (r=−0.45; P<.01) Mean session duration among participants with available data (n=23) was 4.17 (SD 3.14) minutes, substantially below published engagement benchmarks for comparable alcohol reduction apps. Feedbacks from the two participants indicated broadly favorable acceptability of the AI agent. One participant perceived the agent as warm and therapeutically engaging, whereas the other experienced the voice as robotic and the conversational turns as overly long. Both expressed a residual preference for human support. Both participants convergently identified technical barriers, including audio instability, connectivity-related session loss, and absent conversation persistence, rather than resistance to the therapeutic content, as the primary drivers of disengagement. Reported behavioral signals included stopping after a second drink in a high-pressure social context, attributed directly to drink refusal content, and the cessation of solitary drinking during the intervention period. CONCLUSIONS Speech-to-speech AI-facilitated CBT appears acceptable to adults with hazardous alcohol use, but critically low engagement driven by technical implementation barriers currently limits its feasibility. Audio stability, conversation persistence, and re-engagement prompts represent modifiable priorities that must be resolved before a randomized pilot trial with an active comparator is needed. CLINICALTRIAL https://osf.io/v9ubq/overview?view_only=c319aae214984c77bc172154e18f4999

BACKGROUND Remote measurement technologies (RMTs) offer a potential solution for monitoring depression symptoms in situ while reducing reliance on self-report assessments. Sleep and physical activity measures derived from wearable devices may capture behavioural patterns relevant to depressions severity, but their incremental value for predicting current depression severity remains unclear. OBJECTIVE To evaluate the predictive value of wearable-derived sleep and physical activity measures for current depression severity, and to determine whether RMT-derived behavioural subtypes improve prediction compared with individual RMT features and baseline sociodemographic and clinical characteristics. METHODS Longitudinal data from the Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD) cohort were used to predict three-monthly depression severity, at three to 36-months of follow-up, measured using the Inventory of Depressive Symptomatology- Self Report. Elastic net and random forest models were compared across different combinations of baseline individual characteristics, individual sleep and physical activity RMT features, and RMT-derived behavioural subtypes. RMT features were derived from data collected during the seven days preceding each IDS-SR assessment. Behavioural subtypes were previously derived from these longitudinal RMT data using a Hidden Markov model, which estimated participants’ subtype membership probabilities at each timepoint. Prediction models were evaluated using nested group five-fold cross-validation and assessed using root mean square error, R2 and mean absolute error. RESULTS Models including baseline depression severity achieved R2 values of approximately 0.6, with RMSE of approximately 10 and MAE of 8. Adding individual RMT features of RMT-derived subtypes provided little improvement in predictive performance. Excluding baseline depression severity reduced R2 to approximately 0.1, while RMSE increased to approximately 15 and MAE to 12, demonstrating the substantial contribution of baseline depression severity to predictive performance. CONCLUSIONS Wearable-derived sleep and physical activity measures and their behavioural subtypes provided limited additional predictive value for current depression severity beyond baseline individual characteristics, particularly baseline depression severity. Although these measures did not substantially improve prediction in the present models, they may still capture complementary behavioural information relevant to understanding depression.

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.

Background: Efforts 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. Objective: This 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. Methods: We 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. Results: Three 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. Conclusions: The 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.

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 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: Loneliness 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. Objective: This 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. Methods: Building 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. Results: Quantitative 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. Conclusions: The 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.

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.

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.