Background: Loneliness is a prevalent concern across the United Kingdom. While validated scales exist to quantify the severity of loneliness across populations, there remains a gap in understanding how loneliness manifests and is addressed within therapeutic practice. Given the associated stigma surrounding loneliness, practitioner perspectives offer crucial insights into how clients express loneliness within digital therapeutic environments. These insights can inform more nuanced conceptualizations of loneliness. Objective: This study aimed to gather the practitioners’ perspectives on loneliness within a digital therapeutic context and were defined as follows: (1) understand how practitioners identify loneliness concerns, (2) identify how loneliness is elicited in digital mental health interventions, and (3) identify co-occurring themes (such as grief, shame, and social disconnection) that signal loneliness concerns in client communications within digital therapeutic environments. Methods: Semistructured interviews were conducted with 9 practitioners. Participants included specialists in grief counseling, lesbian, gay, bisexual, transgender, and queer or questioning plus support; and digital mental health therapists. Interview transcripts were analyzed using thematic analysis, using an inductive, data-driven approach to allow themes to emerge from participant accounts rather than fitting data to preexisting theoretical frameworks. Results: The following four themes were identified: (1) Conceptualizing Loneliness: practitioners distinguished between social contact and meaningful connection; (2) Contextual Causes: loneliness emerged from life transitions, stigmatized identities, and resource reduction (eg, youth services closures and social support); (3) Expressions and Language: clients rarely expressed loneliness directly, instead using proxy terms, with disclosure patterns varying by age; and (4) Mental Health Co-occurrence: severe mental health conditions created bidirectional cycles of loneliness, exacerbated by symptoms of mental health difficulties. Practitioners reported that many clients experienced loneliness concerns, yet direct disclosure was absent across all participants’ experiences. Conclusions: Practitioners identified multiple stigmatizing experiences as contextual drivers of loneliness, particularly demonstrating how loneliness emerges not only from individual experiences but from broader patterns of social exclusion and marginalization. For therapeutic practice, these insights suggest that practitioners can use awareness of stigmatizing experiences as potential indicators when assessing loneliness risk. The presence of contextual patterns was consistent across practitioners’ experiences, providing a foundation for developing more targeted interventions to address both the emotional experience of loneliness and the underlying social drivers.
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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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AI Reveals Hidden Brain Lesions in Multiple Sclerosis MRI
It has long been known that brain gray matter plays a key role in multiple sclerosis (MS) disease progression and cognitive impairment, but because magnetic resonance imaging (MRI) has only been able to detect lesions in white matter, neither clinicians nor researchers have had a way to detect or monitor gray matter (cortical) lesions. And while many new drugs developed in the past decade can slow disease progression significantly, they primarily work on reducing white matter lesions.
A University at Buffalo (UB)-led team now reports that it has found a way to use artificial intelligence to reveal these otherwise invisible cortical lesions by reviewing existing MRI scans. The researchers say the significance of finally being able to see what has been known as one of the most important indicators in MS disease progression cannot be overstated.
“Detecting previously invisible cortical lesions on conventional legacy MRI scans has major implications for MS research and clinical care,” commented Robert Zivadinov, MD, PhD, SUNY distinguished professor in the Department of Neurology and director of the Buffalo Neuroimaging Analysis Center (BNAC) in the Jacobs School of Medicine and Biomedical Sciences at UB. “The ability to see for the first time these previously hidden indicators of MS disease progression, including cognitive impairment and disability, is an important advance.”
Added Michael G. Dwyer, PhD, associate professor of neurology and biomedical informatics in the Jacobs School and a researcher with BNAC, “What this collaboration has been able to accomplish is a real success story for applying AI in the medical arena. We now have access to these incredibly useful data on MRI scans that were there but you couldn’t see them without using AI to pull them out. The computational methods are finally at the point where we can do this.”
Zivadinov is senior author, Dwyer first and corresponding author of the team’s published paper in Communications Medicine, titled “Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post- processing and deep learning.”
“Multiple sclerosis (MS) affects both the inner, connectivity-oriented portions of the brain (white matter) and the outer layer of the brain (the cortex),” the authors explained. While the involvement of cortical lesions in MS has been known almost since the identification of MS in the late 19th century, they weren’t included on diagnostic criteria until the 21st century. And even when they were included, it was noted that their use would be greatly limited due to the current capabilities of clinical MRI.
“Historically, research and clinical care in MS have focused on white matter, where focal demyelinating lesions are a hallmark of the disease,” they continued. And although there are now many therapies that can almost completely halt the incidence of new white-matter lesions in individuals with MS, they haven’t had the same impact on clinical progression, the team continued.
Over more recent decades it’s been found that gray matter is affected from the earliest MS disease stages, and it’s become evident that gray matter pathology is more than secondary to white matter damage. “From a clinical perspective, cortical lesions are strongly associated with clinical disability and cognitive impairment,” the authors stated. “They may also have more prognostic value than white matter lesions for disability and disease course.”
There’s an urgent need for in vivo imaging methods that can show gray matter lesions, they stressed. Dwyer added, “We have all been very frustrated, knowing that these cortical lesions were there but not being able to see them. There’s a lot of ongoing damage that continues to happen in MS that you won’t see with conventional MRI, but that histopathologists have been clearly demonstrating for decades on postmortem tissue.”
For their newly reported study the team applied advanced image processing techniques, including artificial intelligence, to standard MRI scans from a large MS clinical trial. “Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data,” they noted. “These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects.”
The AI approaches the researchers used, building on work from co-authors from the Netherlands, were designed to extrapolate vital information from the relationships between multiple images that can’t be seen on a single image.
The researchers combined multiple image-processing techniques, including a new one they developed called MMCLE, or multimodal cortical lesion enhancement. They then applied these techniques to MRI scans from the large, phase III FDA regulatory ORATORIO clinical trial, a study of the MS drug Ocrelizumab that included more than 700 participants.
They found that while individual images of a patient’s brain revealed mostly white matter lesions, once they applied the AI-based image processing methods to multiple different contrast images, they were able to see anywhere from 15 to 20 cortical lesions for each patient, more than 11,000 for the whole dataset. “We confirmed that cortical lesions can be clearly visualized and quantified with these methods,” they stated. “Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.”
Dwyer explained further, “If you look on the original scans, you generally can’t see the cortical lesions, but generative AI is very powerful because it can look between the scans and detect tiny differences between them. Because it sees those minor discrepancies, AI can reveal that there’s something going wrong there, that the tissue is not behaving like healthy tissue. The trained models can view multiple MRI images together and synthesize them and synthesize what had been missing.”
Zivadinov added “This work, which has revealed that there is so much invisible pathology in the brain, will have tremendous impact for reviewing data from past clinical trials and also for those going forward,” he says.
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Self-esteem and inner strengths: a network study in Thai university students with borderline personality disorder symptoms
IntroductionSelf-esteem is widely regarded as an important construct in the psychological functioning of individuals with borderline personality disorder (BPD) symptoms. During emerging adulthood, fluctuations in self-esteem are often linked to emotional dysregulation and maladaptive adjustment; however, self-esteem has rarely been examined within a broader system of culturally relevant psychological resources. Drawing on the Theravāda Buddhist framework of the Ten Pāramīs, inner strengths may represent protective resources that can be examined alongside self-esteem to clarify how these strengths co-occur among students experiencing BPD symptoms.MethodsThe present study employed a regularized psychological network approach to investigate partial associations between self-esteem and Ten Pāramī–based inner strengths among Thai university students screening positive for BPD symptoms. Participants were 346 Thai university students (25.4% male, 74.6% female; mean age = 21.60 ± 2.24 years) identified using a standardized BPD screening instrument. Inner strengths included Truthfulness, Perseverance, Wisdom, Generosity, adherence to the Five Precepts, Meditation, Tolerance, Equanimity, Determination, and Loving-kindness.ResultThe estimated network showed a predominantly positive pattern of partial associations among strengths. The strongest edge was observed between Generosity and Loving-kindness, and links between self-esteem and Determination (and Equanimity) were among the most consistently estimated associations (based on bootstrap confidence intervals). Centrality indices were examined descriptively; however, case-dropping bootstrap results indicated very limited stability of centrality estimates (CS(cor = 0.7) = 0.13 for strength and expected influence), and centrality rankings were therefore treated as strictly exploratory. Discussion: Although some negative partial associations were estimated (e.g., involving Truthfulness, Perseverance, and Equanimity), their precision was limited and such patterns should be treated as exploratory. Exploratory gender-stratified analyses suggested that the strongest edges were similar in the female subsample, whereas the male subsample yielded a sparse/near-empty regularized network, limiting inference regarding gender differences. Given the cross-sectional design, all associations are interpreted as conditional co-occurrence rather than directional or causal effects.ConclusionOverall, these findings highlight a small set of robust co-occurring inner strengths linked to self-esteem in Thai university students with BPD symptoms and provide a culturally informed basis for hypothesis generation regarding strengths-based skills cultivation and supportive interventions in university settings.
Research progress on addictive features and reward circuit mechanisms in non-suicidal self-injury and the feasibility of precision neuromodulation
Non-Suicidal Self-Injury (NSSI) presents a significant public health challenge; however, its underlying neurobiological mechanisms remain insufficiently understood, limiting the development of targeted interventions. Emerging evidence suggests that NSSI exhibits core addictive features, such as compulsive urges and tolerance, which may be driven by dysfunctions in the brain’s reward circuitry. This review synthesizes current research on the neural overlaps between NSSI and addiction, specifically focusing on the dysregulation of the ventral striatum and prefrontal cortex. Based on this mechanistic framework, we propose the potential of Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT)—a high-dose, functional connectivity-guided transcranial magnetic stimulation protocol—as a precision treatment for NSSI. By targeting specific reward network deficits, SAINT may offer a novel, rapid-acting therapeutic strategy for patients who do not respond to conventional pharmacological or psychological interventions.
Hair cortisol as psychotherapy process parameter – an inpatient pediatric psychosomatic study
IntroductionPediatric-psychosomatic inpatient therapy is an essential part of the German health care system for the treatment of mental disorders in children and adolescents. However, empirical research in this field remains scarce and limited to psychological parameters. This longitudinal naturalistic study aimed to evaluate the efficacy and sustainability of inpatient psychosomatic therapy in children and adolescents by examining both psychological outcomes and biological markers.MethodsA total of 58 patients were assessed at seven time points before, during, and after treatment. Hair cortisol concentration (HCC) was measured as a neuroendocrine parameter of long-term stress regulation. Psychometric data were collected using five validated questionnaires.ResultsFindings indicated significant improvements in perceived stress, depressive and anxiety symptoms, family functioning and internalizing symptoms in the course of inpatient treatment. Overall, these effects remained stable at three- and six-month follow-ups, with only transient increases in depressive symptoms and family problems. HCC showed a significant decrease from admission to discharge and remained stable across follow-ups.DiscussionThese results support the efficacy of inpatient pediatric psychosomatic interventions on both psychological outcomes and neuroendocrine stress regulation and highlight the value of integrating biological markers into psychotherapy research.
Technology-Enhanced Peer Support for Depression in Older Adults: Single-Arm Mixed Methods Feasibility Study
<strong>Background:</strong> Depression in late life is often compounded by social isolation and barriers to care. There is limited study of technology-enhanced peer support for depression among older adults. <strong>Objective:</strong> This study aimed to assess the feasibility and acceptability of a technology-enhanced peer support intervention to decrease depression among older adults. <strong>Methods:</strong> We used a mixed methods pilot study among adults aged 50 years and older with depression who received a peer support intervention called Peers+. The intervention consisted of 8 weekly video chats and unidirectional texts focused on increasing depression self-care and coping. Data obtained from screening, baseline, postintervention, and 3-month follow-up were used in the analysis to assess preliminary outcomes of the intervention. Mixed effects longitudinal models were used to assess change in depression, and qualitative data were collected and analyzed to identify key themes related to participant experiences. <strong>Results:</strong> A total of 34 older adults with a mean age of 67 (SD 9.57) years participated in the study, and 82.4% (28/34) of participants finished all 8 intervention meetings. Depressive symptoms declined over the course of the study of 35 weeks (<i>F</i><sub>1, 88.8</sub>=26.0; <i>β</i>=–.14, 95% CI –0.20 to 0.09; <i>P</i><.001). Emotional well-being (<i>β</i>=.48, 95% CI 0.26-0.70; <i>P</i><.001), social functioning (<i>β</i>=.71, 95% CI 0.33-1.09; <i>P</i><.001), self-efficacy (<i>β</i>=2.29, 95% CI 0.83-3.75; <i>P</i><.001), and coping (<i>β</i>=2.90, 95% CI 0.24-5.55; <i>P</i><.001) improved throughout the study period. Participants perceived supportive texts as reinforcing trust between peer coaches, using coping strategies, increasing social connection, and providing accountability for improving self-care. Peer coaches and older adults needed technology support for participation in the study. <strong>Conclusions:</strong> This study demonstrated the feasibility and acceptability of a peer support intervention enhanced by video chats and texts, delivered by older adult peer coaches to an ethnically diverse group of older adults with depression. Study findings indicate that ongoing and accessible technology support contributed to older adult participation and engagement.
Fatherhood and addictive disorders: experiences and support needs – results of a qualitative interview study
ObjectivesParental addictive disorders are a major public health problem. Despite already known negative effects, the parental role of men seeking help is rarely acknowledged within addiction support services. This study aims to identify existing relations between fatherhood and addiction, and the resulting support needs of affected fathers.MethodsFor data collection 15 fathers with addictive disorders were interviewed by using qualitative, guided interviews. The data material was analyzed using qualitative content analysis.ResultsThe effects of addictive disorders on fatherhood are predominantly unfavorable. Conversely, fatherhood can have consumption-reducing as well as consumption-increasing effects. In this context, five topics were identified in which fathers expressed specific support needs. Particularly fathers with substance use disorders expressed their need for further support, while fathers with behavioral addictions expressed a lesser need.ConclusionsPaying attention to fatherhood in the context of addiction services provides a starting point for increasing the motivation to change. Individual support needs should be considered when implementing father-specific programs. Not only the fathers themselves, but also their children can benefit from successful treatment and expected changes in fatherhood.
Targeted stool metabolomics suggests exploratory catecholamine- and tryptophan-linked metabolic features in autism spectrum disorder
BackgroundGut-brain axis dysregulation and microbiome-linked metabolic alterations have been implicated in autism spectrum disorder (ASD), but the contribution of gut-derived neuroactive metabolites remains incompletely characterized.MethodsWe conducted a cross-sectional case-control study of 59 participants (32 ASD, 27 controls) and quantified 18 stool metabolites related to catecholamine synthesis, inhibitory neurotransmission, and tryptophan-linked NAD+-precursor metabolism using targeted liquid chromatography-tandem mass spectrometry. Group differences were assessed using fold-change analysis and linear models adjusted for age and sex. Random forest models evaluated classification performance, and within-group Spearman correlations were used to examine metabolic relationships.ResultsNorepinephrine showed the largest increase in ASD, whereas dopamine and tetrahydrobiopterin exhibited nominal group differences that did not remain significant after correction for multiple testing. A three-metabolite panel comprising tetrahydrobiopterin, γ-aminobutyric acid, and kynurenine showed exploratory discrimination between groups (area under the receiver operating characteristic curve = 0.750, 95% confidence interval 0.622–0.878), but this performance requires external validation. Correlation analysis revealed conserved bile acid coupling in both groups. In controls, tryptophan was positively associated with kynurenine, whereas this relationship was not observed in ASD. Instead, ASD samples showed broader associations between tryptophan and metabolites linked to neurotransmission and NAD+-precursor metabolism.ConclusionStool metabolite profiling revealed altered organization of tryptophan- and catecholamine-linked metabolic associations in ASD and identified a small metabolite panel with exploratory discriminative potential. These findings provide a foundation for future studies examining gut-derived neuroactive metabolites in ASD and their relationship to gut-brain axis biology.
Mental health stigma among nursing students: current status and intervention strategies— an integrative review
ObjectiveAims to synthesize the current evidence on the prevalence of mental health stigma among nursing students and to summarize the interventions that have been developed and evaluated to reduce such stigma.MethodsA comprehensive search was conducted across PubMed, CINAHL, and Web of Science databases for peer-reviewed studies published in English between January 2018 and June 2024.ResultsA total of 25 studies were included. The prevalence of mental health stigma among nursing students was found to be moderate to high. Stigma manifested in various forms, including negative attitudes toward patients, internalized shame, and public stigma directed at nursing students themselves. Interventions such as mental health training, empathy enhancement programs, and clinical internships demonstrated varying degrees of effectiveness in reducing stigma, with multi-component interventions showing the most promise.ConclusionMental health stigma among nursing students is a persistent and multifaceted issue that requires targeted strategies to address. Enhancing mental health education, increasing clinical exposure, and implementing comprehensive intervention programs are essential steps to reduce stigma and support the development of compassionate, skilled nursing professionals.