Journal of Affective Disorders Reports最新文献

{"title":"Not just ‘Asian’ but Japanese, Korean, Filipino, Chinese, and mixed Asian American: Examining depression in an ethnically heterogenous sample of Asian American young adults","authors":"Rupa Jose ,&nbsp;Anthony Rodriguez ,&nbsp;Elizabeth J. D’Amico","doi":"10.1016/j.jadr.2026.101101","DOIUrl":"10.1016/j.jadr.2026.101101","url":null,"abstract":"<div><div>To date, most research focused on depression prevalence is with adolescents or adults and focuses mainly on risk factors, with few studies assessing factors related to depression for Asian American young adults. Using longitudinal data collected from web-based surveys administered over a 5-year period (wave 10: 2017–2018; wave 14: 2021–2022; and wave 15:2022–2023) on 460 young adults (22–28 years old at wave 15) who identify as Japanese, Korean, Filipino, Chinese, and mixed Asian American, we examined associations between risk <em>and</em> protective factors, such as discrimination, prior depression, social cohesion, neighborhood Asian concentration, and recent depression using a structural equation modeling (SEM) framework. Comparing SEM results from a combined Asian American model to our multi-group Asian American model, we find that except for prior depression, results vary by Asian ethnic origins. Differences by Asian origins were found for female sex, educational attainment, discrimination, and social cohesion; we also found differences in association direction with greater Asian concentration in the neighborhood positively related to depression scores for Japanese American but negatively related to depression scores for Chinese American (and to a lesser degree Korean American) young adults. Findings highlight the importance of understanding how risk and protective factors contribute to depression among different Asian American groups to better address depression vulnerability in prevention programming.</div></div>","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101101"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148349440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating experimental paradigms for inducing loneliness in older adults: An examination of emotional responses","authors":"Natasha C. Taylor,&nbsp;Natasha L. Gallant","doi":"10.1016/j.jadr.2026.101126","DOIUrl":"10.1016/j.jadr.2026.101126","url":null,"abstract":"<div><div>Loneliness in older adults is a prevalent social and public health issue that can lead to a variety of serious physical and psychological consequences in the long-term, yet experimental methods for studying its immediate emotional impacts remain understudied among older adults. This study investigated the effectiveness of two loneliness-induction techniques—video stimuli and autobiographical recall—in evoking loneliness and related emotional states (e.g., sadness, negative affect) in older adults. It also examined the roles of gender, trait loneliness, depressive symptoms, and social connection on loneliness. Results indicated that both methods increased loneliness and negative affect, while reducing positive affect, with video stimuli producing the strongest and most consistent emotional responses. Autobiographical recall was less effective, potentially due to memory reconstruction biases and age-related changes in autobiographical specificity. Trait loneliness and depression were associated with elevated baseline distress and reduced emotional reactivity. These findings support the use of these experimental methods of loneliness induction among older adults and underscore the importance of individual differences. Given the severe consequences of loneliness, confirming the use of experimental methods of loneliness induction offers future researchers the opportunity to examine its cognitive, physiological, and behavioural components in controlled settings, ultimately informing intervention development.</div></div>","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101126"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148582639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital phenotyping of perinatal mental health: A systematic review of machine learning and wearable sensor approaches","authors":"Emma Ricci-De Lucca,&nbsp;Katelyn Heavey,&nbsp;Elizabeth Murnane","doi":"10.1016/j.jadr.2026.101111","DOIUrl":"10.1016/j.jadr.2026.101111","url":null,"abstract":"<div><div>Pregnancy and the postpartum period are marked by significant psychosocial transition and elevated mental health vulnerability. When mental health needs go unmet, they can manifest as perinatal mood and anxiety disorders, which affect approximately 1 in 8 women and carry significant consequences for mother and child. While machine learning (ML) applied to wearable data shows promise for mental health detection, its application to the perinatal population remains underexplored and methodologically limited. We conducted a systematic literature review of articles focusing on ML and artificial intelligence applications for detecting perinatal mental health states using wearable-derived data. We searched PubMed, Scopus, ACM, IEEE, and ScienceDirect. We screened a total of 484 records and excluded 472 after abstract review and duplicate removal. We assessed 12 full-text articles, ultimately including 11 studies. ML models trained on wearable-derived physiological and behavioral features demonstrated moderate to strong predictive performance for classifying or predicting perinatal mental health states (e.g., stress, anxiety, postpartum depression, loneliness), with most AUC values exceeding 0.693. Heart rate and heart rate variability-derived features were the most consistently studied and frequently reported important predictors across included studies, and Random Forest and Support Vector Machine were the most frequently applied algorithms. However, most studies relied on internal validation, and substantial methodological limitations were identified: homogeneous samples, non-validated outcome measures, and limited real-world testing. While wearable-based ML shows promise for perinatal mental health detection, the evidence base remains early-stage and rigorous validation and feasibility studies are essential before clinical implementation.</div></div>","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101111"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148582581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting weekly instability in depressive symptoms among individuals diagnosed with Major Depressive Disorder using deep learning and passively-collected movement data","authors":"Anna M. Langener,&nbsp;George D. Price,&nbsp;Dawson Haddox,&nbsp;Michael V. Heinz,&nbsp;Daniel M. Mackin,&nbsp;Matthew D. Nemesure,&nbsp;Amanda C. Collins,&nbsp;Damien Lekkas,&nbsp;Tess Z. Griffin,&nbsp;Arvind Pillai,&nbsp;Subigya Nepal,&nbsp;Andrew T. Campbell,&nbsp;Nicholas C. Jacobson","doi":"10.1016/j.jadr.2026.101102","DOIUrl":"10.1016/j.jadr.2026.101102","url":null,"abstract":"<div><div>Major Depressive Disorder (MDD) is a common mental health condition marked by persistent low mood, reduced motivation, and low energy. Persons with MDD often experience large fluctuations in their symptoms over hours and days, which can offer valuable clinical insights, highlighting potential targets for treatment and intervention. Yet, it remains unclear how accurately these acute symptom changes can be predicted. In this preregistered study, we examined whether deep learning models could predict short-term fluctuations in depressive symptoms among individuals diagnosed with MDD. Passively-collected accelerometry data, a ubiquitous, privacy-preserving data stream with empirical ties to MDD symptomatology, was used to predict instability in depressive symptoms. Our sample contains clinically-depressed individuals per a structured clinical interview (<em>N</em> = 179, Mean Age = 38.2 ± 10.0, Women = 81.6%). Participants completed depressive symptom assessments three times daily for 90 days, resulting in an average of 214.49 EMA questionnaires (median: 237, min: 77, max: 270, SD: 54.27) per person. Although our results suggest a small-moderate correspondence between the predictions and observed outcomes of passively-collected movement alone in predicting future high/low depression instability (AUC<em>_Test</em> = 0.59 ± 0.06, Sensitivity<em>_Test</em> = 0.56 ± 0.21, Specificity<em>_Test</em> = 0.59 ± 0.23), our model performed worse than a random intercept only baseline model (AUC<em>_Test</em> = 0.83) and predicted intraindividual variability of instability in depressive symptoms below chance (AUC<em>_Test</em> = 0.46 ± 0.21). Thus, there is a need to improve predictive performance to enhance clinical utility. This may require the inclusion of additional passive sensing modalities in future research.</div></div>","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101102"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148582585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing Trust and Distrust in AI Therapists: A Grounded Theory Study of Public Online Narratives","authors":"Mahdi Naeim,&nbsp;Mohammad Narimani,&nbsp;Seifollah Aghajani,&nbsp;Mitra Naeim","doi":"10.1016/j.jadr.2026.101109","DOIUrl":"10.1016/j.jadr.2026.101109","url":null,"abstract":"<div><div>The global demand for mental health services is increasing, yet access to professional therapists remains limited. In this context, artificial intelligence (AI)-based solutions, especially therapeutic chatbots, are gaining attention as complementary tools. However, user adoption is hindered by trust issues. This study aimed to develop a grounded theory model to explore how trust in AI-driven therapeutic systems is built, maintained, and potentially lost. Trust is conceptualized as a multi-dimensional construct, incorporating system reliability (from human-computer interaction), healthcare credibility (including safety and ethics), and relational trust reflecting therapeutic alliance elements like empathy. A qualitative grounded theory approach was used, analyzing 557 public text units from social media, mental health forums, and related online platforms. Through open, axial, and selective coding, a mid-level conceptual framework was developed, resulting in the \"Credential Reappraisal\" model. This model outlines three stages: initial evidence appraisal, interactional testing based on empathy and accuracy, and trust stabilization or collapse. Social feedback and the system’s error management were found to be central to strengthening or undermining trust. The study reveals that trust in AI-based therapists is not static but dynamic, continuously renegotiated through interactions with the system and feedback from others. Transparency, accuracy, and error management are key factors in influencing trust. The study emphasizes the need for trust-by-design in developing AI-driven therapeutic systems, ensuring transparency, explainable responses, and active user feedback mechanisms for continuous improvement.</div></div>","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101109"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148582589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of repetitive transcranial magnetic stimulation in a patient with borderline personality disorder: A case report","authors":"Christiane Licht,&nbsp;Nihal Neda Aslan,&nbsp;Dorothee Maria Gescher,&nbsp;Irene Neuner","doi":"10.1016/j.jadr.2026.101130","DOIUrl":"10.1016/j.jadr.2026.101130","url":null,"abstract":"<div><h3>Background</h3><div>Borderline personality disorder (BPD) is characterized by emotional dysregulation, high impulsivity, inconsistent identity, and disturbed interpersonal functioning. Neuroimaging studies have elicited functional disruptions in the prefrontal-limbic network, including dala hyperactivation, leading to interferences with cognitive processes in BPD, and decreased activation in prefrontal regions such as the dorsomedial prefrontal cortex (DMPFC).</div></div><div><h3>Methods</h3><div>This case report describes a female patient with BPD undergoing a multi-week, intermittent theta burst stimulation (iTBS) protocol targeting the DMPFC. Repeated assessments of borderline symptom severity, including domains related to emotional regulation and behavioral dyscontrol, as well as depressive symptoms, tools. We aimed to explore the clinical effects of DMPFC-targeted iTBS, with tolerability inferred from patient report and the absence of clinically relevant adverse events.</div><div>The aim was to explore the clinical effects of DMPFC-targeted iTBS, with tolerability inferred from patient report and the absence of clinically relevant adverse events.</div></div><div><h3>Results</h3><div>Over the course of treatment, borderline symptom severity decreased markedly on self-rated BPD scales, with improvements particularly noted in domains related to emotion regulation, impulsivity-related and self-harm-associated behaviors,. Depressive symptoms showed only modest improvement. The intervention was well tolerated based on spontaneous patient report, and no significant adverse events were observed.</div></div><div><h3>Conclusion</h3><div>The results indicate that DMPFC-targeted rTMS could be a promising neuromodulation method for influencing core domains of BPD symptomatology, particularly emotional dysregulation and related behavioral features.</div><div>Controlled studies using domain-specific outcome measures and systematic tolerability assessments are needed to further evaluate efficacy, underlying mechanisms, and optimal stimulation parameters.</div></div>","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101130"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148640547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated bioinformatics analysis of ac4C RNA modification and macrophages reveals common molecular mechanisms in ulcerative colitis and major depressive disorder","authors":"Yanlin Zhu,&nbsp;Xianzhe Wu,&nbsp;Baiqing Xian,&nbsp;Dan Chen","doi":"10.1016/j.jadr.2026.101123","DOIUrl":"10.1016/j.jadr.2026.101123","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Ulcerative colitis (UC) and major depressive disorder (MDD) are two chronic diseases with high comorbidity rates, and existing therapies have limitations such as insufficient efficacy and significant side effects. This study aims to reveal the common mechanisms of ac4C RNA modification and macrophages in both diseases by integrating multi-omics data. In this research, we identified 346 differentially expressed genes between UC and MDD, with functional enrichment analysis primarily focusing on immune and inflammatory response signaling pathways. We intersected these with ac4C RNA modification-related genes to obtain 39 co-expressed genes, whose functional enrichment analysis mainly concentrated on redox reactions, extracellular vesicle biology, and metabolic signaling pathways. Additionally, we analyzed the relationship between UC and MDD and macrophage infiltration, conducting differential analysis of macrophage infiltration values between the disease group and the normal group. Furthermore, we intersected the 39 co-expressed genes obtained from the intersection of both diseases with the WGCNA key module genes of the two diseases, resulting in three key genes. Four machine learning algorithms were employed to predict the diagnostic performance of the models constructed from the key genes of both diseases, with the highest diagnostic performance being RF, and AUC values all above 0.9. Subsequently, differential expression validation of the key genes was conducted in the training and validation sets of both diseases, and ROC curve analysis of the obtained key genes was performed, with AUC values all greater than 0.6. A further correlation analysis of key gene expression levels and key macrophage infiltration values confirmed a significant positive correlation between the key genes of both diseases and the macrophage infiltration values under the ssGSEA algorithm. In addition, the relationship between key genes and gut microbiota and metabolites was analyzed, along with the GSEA enrichment of key genes, and potential microRNAs and TFs regulating the key genes were predicted. Reverse pharmacology analysis, molecular docking, and molecular dynamics simulations were used to predict therapeutic drugs for UC and MDD. Finally, single-cell sequencing analysis was conducted on both diseases, yielding relevant cell clustering results, and the expression of key genes was analyzed in the single-cell datasets of both diseases. Ultimately, FKBP5 was identified as a common biomarker of ac4C RNA modification and macrophages in both diseases. Five potential most significantly related Chinese Materia Medica for treating UC and MDD were identified. Based on molecular docking and molecular dynamics simulation analysis, isoacteoside targeting FKBP5 was predicted to be the most effective compound. These findings greatly enhance our understanding of the regulatory mechanisms of ac4C modification and macrophages in brain-gut axis diseases. Key biomarkers have be","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101123"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148656360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preventive interventions for paternal perinatal depression from pregnancy through one year after childbirth: A scoping review","authors":"Hiroko Iwata,&nbsp;Yui Ishii,&nbsp;Mai Kosaka,&nbsp;Eriko Terasawa,&nbsp;Asana Kakehashi,&nbsp;Yuki Kanazawa,&nbsp;Kunie Maehara,&nbsp;Sayaka Abe,&nbsp;Marika Seki,&nbsp;Kayoko Kimura,&nbsp;Fusae Toyama,&nbsp;Emi Mori","doi":"10.1016/j.jadr.2026.101139","DOIUrl":"10.1016/j.jadr.2026.101139","url":null,"abstract":"<div><h3>Background</h3><div>Depression affects both fathers and mothers during the perinatal period; however, preventive interventions targeting paternal perinatal depression remain under-researched. This scoping review aimed to systematically map the literature on preventive interventions for paternal perinatal depression from pregnancy through one year after childbirth.</div></div><div><h3>Methods</h3><div>This review followed JBI methodology. We included studies of primary and secondary preventive interventions for paternal perinatal depression from pregnancy through one year after childbirth. Primary preventive interventions promoting mental health were included when paternal depression was reported as an outcome; studies targeting individuals with a diagnosed depression were excluded. MEDLINE, CINAHL, APA PsycINFO, CENTRAL, and Ichushi-Web were searched for published literature, and Google Scholar and ProQuest Health and Medical Collection for grey literature. Two reviewers independently screened titles/abstracts and full texts. Data were extracted using a standardized form and analyzed according to intervention characteristics, including target population, outcomes, aims, content, delivery mode, timing, provider, theoretical basis, and level of intervention.</div></div><div><h3>Results</h3><div>Forty-seven studies (2012–2023), primarily from the United States and Australia, were included, with randomized controlled trials as the predominant design. Interventions showed substantial heterogeneity across target populations, aims, outcomes, content, delivery strategies, timing, and providers. Many adopted a couple-based approach and aimed to promote overall mental health rather than focusing solely on fathers or depressive symptoms.</div></div><div><h3>Conclusions</h3><div>Given the interdependence of maternal and paternal mental health, couple-based interventions may provide a more comprehensive understanding of their effects on preventing paternal depression and promoting overall mental health.</div></div>","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101139"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148656457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The psychobiological sequelae of COVID-19 diagnosis: A longitudinal study including pre-pandemic baseline measurements","authors":"Adrian Hase,&nbsp;Andreas Buchmann,&nbsp;Ruth Tuura,&nbsp;Christopher Ritter,&nbsp;Yusuf Isikdogan,&nbsp;Sylvia Huber,&nbsp;Sabrina Theresia Müller,&nbsp;Carmen Ghisleni,&nbsp;Melanie Haynes,&nbsp;Yasser Morsy,&nbsp;Michael Scharl,&nbsp;Gregor Hasler","doi":"10.1016/j.jadr.2026.101142","DOIUrl":"10.1016/j.jadr.2026.101142","url":null,"abstract":"<div><div>The COVID-19 pandemic presented a huge challenge for the world because of the disease COVID-19 itself, as well as the psychosocial consequences of the pandemic. To explore the long-term effects of COVID-19, we used data from an ongoing prospective study, including comprehensive pre-pandemic baseline measures, to study the biological and psychiatric effects of both receiving a medical COVID-19 diagnosis at any point during the pandemic (labeled “COVID diagnosis”) and the perceived stress in the subjectively worst month of the pandemic (labeled “pandemic stress”). We included 129 Swiss young adults (regardless of COVID-19 status) who participated before (T1) and after the onset of the COVID-19 pandemic (T2). We measured depression, anxiety, and stress symptoms using clinical interviews and psychological questionnaires and further included heart rate variability, blood-based hormone, and gut microbiome measurements as physical health outcomes. Individuals reporting a medical COVID-19 diagnosis (<em>N</em> = 59) exhibited more expert-rated depression symptoms and a marginally worse expert-rated depression trajectory throughout the pandemic than those without a diagnosis (<em>N</em> = 70). In addition, they exhibited a significant restructuring of the gut microbiome and dehydroepiandrosterone changes throughout the pandemic. Pandemic stress was positively related with T2 observer-rated depression symptoms and sleep problems. These findings show the lasting differential effects of COVID-19 illness and pandemic stress on depression-related psychological and physiological outcomes.</div></div>","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101142"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148640550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy of an internet–based intervention for loneliness in older adults: An RCT","authors":"David J. Eberle,&nbsp;Andreas Maercker,&nbsp;Eva Heim,&nbsp;Jun Wen,&nbsp;Christine Dworschak","doi":"10.1016/j.jadr.2026.101124","DOIUrl":"10.1016/j.jadr.2026.101124","url":null,"abstract":"<div><h3>Background</h3><div>Loneliness is an increasing issue in modern society and leads to a wide range of health problems. Due to various reasons, older adults are particularly affected by loneliness and often not effectively supported by standard interventions. The goal of this study is to investigate the efficacy of a new internet–based loneliness intervention for elderly individuals.</div></div><div><h3>Methods</h3><div>N = 117 individuals participated in the study. This randomized controlled trial (RCT) includes an intervention group and a waitlist control group. The intervention condition is characterized by 7 therapeutic modules to address loneliness and related psychological problems. A baseline, post–intervention, and 6 months follow–up assessment was administered to measure the efficacy of the program.</div></div><div><h3>Results</h3><div>Findings showed no significant loneliness differences across participants at post–intervention and follow–up. However, in the intervention group, male individuals were characterized by lower loneliness levels compared to the control group at post–intervention. At post–intervention, depressive symptoms were also significantly lower in the intervention group compared to the control group across all participants.</div></div><div><h3>Limitations</h3><div>The present study did not monitor to what extent participants engaged with the therapeutic program and its modules. It is also not known which individual modules of the program contributed most or least to the observed results.</div></div><div><h3>Conclusions</h3><div>The findings demonstrate the importance of sociodemographic factors in the treatment of loneliness. The intervention showed partial efficacy and is most suitable for lonely elderly individuals with depressive symptoms.</div></div>","PeriodicalId":52768,"journal":{"name":"Journal of Affective Disorders Reports","volume":"25 ","pages":"Article 101124"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148641208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}