Search Results (488)

Background: Migraine is a chronic neurological disorder with a high prevalence of psychiatric comorbidity, including anxiety and depression, which compound functional impairment and reduce health-related quality of life (HRQoL). Repetitive transcranial magnetic stimulation (rTMS) is a non-pharmacological neuromodulatory intervention targeting both pain and affective circuits; however, predictors of HRQoL improvement following rTMS remain poorly characterized. Methods: In this exploratory observational study, 32 adults with migraines underwent 10–40 rTMS sessions. Quality of life was assessed using the WHOQOL-BREF and Migraine-Specific Quality of Life Questionnaire (Migraine-QoL). Psychiatric burden, headache impact, and disability were evaluated using HAMA, HAMD, HIT-6, and MIDAS at baseline and post-intervention. Paired t-tests, Spearman correlations, and linear regression identified predictors of QoL change. Results: Both WHOQOL-BREF and Migraine-QoL improved significantly following rTMS (p < 0.001). Antipsychotic use was associated with greater overall QoL improvement (p = 0.026). Given the very small subgroup size (n = 7), this finding should be interpreted with extreme caution and considered hypothesis-generating only. Higher baseline HIT-6 and HAMA correlated with greater Migraine-QoL gains (p = 0.001 and p = 0.013). In multivariate regression, higher headache severity independently predicted Migraine-QoL improvement (R² = 0.514, p < 0.001). Conclusions: rTMS produced clinically meaningful QoL improvements in migraine. Headache burden emerged as an independent predictor, while associations with anxiety severity and antipsychotic use should be considered exploratory. Full article

Objectives: To investigate clinical and imaging predictors of short- and long-term outcomes in patients with posterior circulation stroke (PCS), with particular focus on infarct topography and ischemic burden. Methods: We conducted a retrospective multicenter observational study including 251 consecutive patients with acute PCS. All patients underwent CT angiography within 24 h and follow-up CT/MRI at 48–72 h. Clinical data, vascular risk factors, stroke severity (NIHSS), and functional outcome assessed by modified Rankin Scale (mRS), were collected. Short-term outcome was defined as mRS at discharge and long-term outcome as mRS at 3 months. Favorable outcome was defined as independence, graded as mRS 0–1. Imaging analysis included pc-ASPECTS, collateral scores, and quantitative ischemic volume assessment. Multivariable logistic regression was performed to identify independent predictors of outcome. Results: Among 251 patients, 105 (41.8%) had LVO. Patients with LVO presented with higher NIHSS scores, larger infarct volumes, and more frequent multiregional involvement. Basilar artery occlusion was associated with the most severe clinical and radiological profile. Infarct location, ischemic volume, baseline NIHSS, and pre-stroke mRS were independently associated with short-term outcome. For long-term outcome, age, infarct location, diabetes, and pre-stroke mRS remained significant predictors. LVO status and treatment variables were not independently associated with outcome. Conclusions: In PCS, outcome is primarily influenced by infarct topography and clinical factors rather than LVO status alone. Multiregional involvement and baseline disability are key determinants of prognosis. These findings underscore the need for PCS-specific prognostic models and highlight the importance of detailed imaging assessment beyond vessel occlusion. Full article

Complex psychosis (CP) remains one of the most challenging conditions in mental health, characterized by persistent symptoms, cognitive impairment, functional disability, and reduced autonomy. Traditional rehabilitation approaches, although essential, are often insufficient to address the multidimensional needs of these individuals. Over the past decade, rapid advances in digital health have opened new opportunities to enhance psychosocial rehabilitation, improve engagement, and personalize treatment pathways. This narrative review synthesizes current evidence on the use of digital and immersive technologies in the rehabilitation of people with CP, including virtual reality (VR), augmented reality (AR), telerehabilitation platforms, mobile health (m-Health) applications, digital phenotyping, and AI-assisted cognitive remediation. We examine clinical trials, feasibility studies, and real-world implementations published between 2015 and 2025, highlighting the efficacy of VR-based social cognition training, remote cognitive remediation, ecological momentary interventions, and hybrid digital–in-person rehabilitation models. Mechanisms of action, transfer to real-world functioning, and predictors of engagement are described. Barriers such as digital literacy, access disparities, privacy concerns, and clinical integration are critically discussed. We also outline future directions, including adaptive algorithms, biosensor integration, and the development of multimodal digital ecosystems tailored to individual recovery trajectories. By integrating technological innovation with recovery-oriented care, digital rehabilitation tools have the potential to transform the treatment landscape for people with CP. This review offers a roadmap for clinicians, researchers, and policymakers seeking to incorporate evidence-based digital solutions into modern psychiatric rehabilitation. Full article

Background/Objectives: Bipolar disorder (BD) is one of the leading causes of disability worldwide, causing significant functional impairments in those affected. The heterogeneous course of BD renders the prediction of clinical progress and outcomes challenging, but it can be potentially enhanced with the use of artificial intelligence methods. In this systematic review, we aimed to examine the extant literature regarding the predictive accuracy of clinical functioning, illness affective state, relapse, and relevant predictors amongst patients with BD, using artificial intelligence methods. Methods: The study was guided by PRISMA and the Cochrane Handbook for Systematic Reviews. Six electronic databases were systematically searched from inception for relevant studies until July 2025 and relevant data were summarised in tables. The protocol of the review was registered on Prospero, ID: CRD42024590343. Results: Forty articles were included in this review. The area under the curve (AUC) values for clinical functioning, illness affective state, and relapse prediction were 0.59–0.72 (poor to acceptable), 0.57–0.97 (poor to outstanding), and 0.45–0.98 (poor to outstanding), respectively. Supervised, tree-based algorithms performed the best. Predictive factors included sociodemographic, clinical and psychological factors and wearable data, as well as speech and video recordings. Conclusions: Existing studies showed the potential of machine learning methods in the prediction of clinical progress and outcomes of BD (specifically functional status, affective state, and relapse) based on relevant collected variables. Longitudinal studies can further clarify and validate the associated predictive factors for earlier identification of those at risk of poorer prognosis to enhance management of BD. Full article

Background and Objectives: Acute ischemic stroke (AIS) caused by large vessel occlusion (LVO) remains a major cause of disability and mortality. Mechanical thrombectomy (MT) improves outcomes, but recovery varies. This study assessed the prognostic value of hypoperfusion intensity ratio (HIR), collateral circulation, and other clinical/imaging factors. Materials and Methods: This retrospective cohort study included 96 LVO patients treated with MT with or without intravenous thrombolysis (IVT) between 2020 and 2024 at a tertiary hospital. Inclusion required multimodal CT (CT, CTA, CTP) and clinical data (NIHSS, mRS). HIR, core volume, CBV index, mismatch ratio, and collateral status were evaluated using artificial intelligence (AI)-based software. Univariate/multivariate logistic regression identified predictors of poor outcome (mRS > 3 at 90 days). Results: Lower HIR (<0.5) and good collaterals were associated with favourable outcomes (p < 0.001). Multivariate analysis identified HIR, initial NIHSS, and procedure duration as independent predictors of poor outcome. CTP-derived core volume, cerebral blood volume index, and mismatch ratio were also significant predictors. ROC analysis showed the highest AUC for core volume (0.810). Diabetes mellitus was associated with a worse prognosis compared to other clinical factors. Conclusions: HIR and collateral status are independent predictors of functional recovery after MT. CTP-derived core volume and CBV index have strong prognostic value. AI-based perfusion analysis supports patient selection and risk stratification. Full article

Background/Objectives: Therapeutic adherence to asthma and COPD medications remains worryingly low and varies widely across patient groups, underscoring persistent challenges in chronic respiratory care. The aim of this nationwide study is to quantify real-world adherence and to identify its demographic and clinical determinants using the Belgian health care claims database of the National Institute for Health and Disability Insurance (NIHDI). Methods: Adherence was assessed using the Continuous Multiple Interval Measure of Medication Availability (CMA) among patients treated between 2020 and 2023. Mixed-effects logistic regression was applied to identify determinants of adherence. Results: Only 30.5% of patients achieved good adherence (CMA ≥ 0.8). Adherence varied substantially across pharmacological classes, ranging from 8.1% for inhaled corticosteroids to 66.4% for triple therapy. Age emerged as a major determinant, with adherence increasing steadily across age groups: only 4.0% of children and 15.7% of adolescents reached good adherence, compared with progressively higher rates in adults. Mixed-effects logistic regression confirmed age, sex, and pharmacological class as robust predictors of adherence. Conclusions: These findings highlight the magnitude of the therapeutic adherence gap in chronic respiratory diseases and clearly identify children, adolescents, and ICS or LABA + ICS users as the highest-risk groups. Recognizing these profiles has direct implications for clinical practice, as it provides concrete targets for future patient-centered interventions and guideline-concordant adherence-enhancing strategies. Full article

Background: Diabetic foot ulcer (DFU) is a major complication of type 2 diabetes (T2D), frequently resulting in disability, lower-limb amputation, and substantial healthcare burden. Early identification of patients at high risk of progressing to severe DFU is essential for timely intervention, yet evidence on associated risk factors remains limited in Bangladesh. This study aims to identify demographic, clinical, and behavioral predictors of severe DFU to support early management strategies. Methods: A cross-sectional study was conducted among 159 DFU patients attending the Rajshahi Diabetic Association General Hospital, Bangladesh. Data on demographic characteristics, clinical variables, and behavioral factors were obtained through structured questionnaires and standardized examinations. Severe DFU was defined as Wagner grades 3–5, while grades 0–2 were considered non-severe. Firth’s penalized logistic regression was used to identify determinants of severe DFU. Model performance was assessed using ROC analysis, calibration belt analysis, and decision curve analysis (DCA). Results: Among the 159 participants, 101 (63.5%) presented with severe DFU. Patients with severe DFU had significantly higher BMI (26.1 vs. 23.7 kg/m²), treatment costs (50,000 vs. 20,000 BDT), and were older (57 vs. 54 years). Severe DFU was also associated with higher prevalence of peripheral arterial disease (PAD) (29.7% vs. 3.4%), prior amputation (31.7% vs. 3.4%), peripheral neuropathy (PN) (86.1% vs. 58.6%), and poor glycemic control (71.3% vs. 30.7%) (all p < 0.05). Firth’s regression identified older age (aOR 1.08), poor glycemic control (aOR 3.90), PN (aOR 3.41), PAD (aOR 7.54), and previous amputation (aOR 13.67) as independent predictors of severe DFU. Conclusions: Older age, uncontrolled glycemia, PN, PAD, and prior amputation were significantly associated with severe stages of DFU. Early detection and targeted management of these factors are critical to reducing complications and lowering the healthcare burden. Full article

Background/Objectives: Metabolic syndrome (MetS) causes significant oral manifestations that negatively impact oral health-related quality of life (OHRQoL). This randomized controlled trial evaluated the effects of combined dental interventions and lifestyle guidance on OHRQoL in patients with MetS. Methods: In total, 82 participants with MetS were randomized into an intervention group (IG; n = 39), receiving dental treatment plus lifestyle guidance, or a control group (CG; n = 43), receiving lifestyle guidance only. OHRQoL was assessed using GOHAI and OHIP-14 at baseline, 1 month, and 3 months. Data were analyzed using repeated-measures ANOVA and multivariable ANCOVA, adjusting for age, sex, baseline OHRQoL, and waist circumference. Pearson correlations examined the relationship between metabolic changes (Δ) and OHRQoL. Results: At 3 months, the IG demonstrated significantly superior OHIP-14 scores (p = 0.020) and a large effect size in social disability (${\eta }_p^2$ = 0.148, p < 0.001) compared to the CG. Within-group analysis showed the IG achieved highly significant longitudinal improvements in pain and psychological discomfort (all p < 0.001). Subgroup analysis confirmed these gains were primarily driven by participants with missing teeth (${\eta }_p^2$ = 0.447, p < 0.001), whereas the periodontitis-only subgroup showed non-significant shifts. Multivariable analysis identified age and baseline scores as primary predictors. Notably, OHRQoL improvements significantly correlated with reductions in body weight (r = 0.355, p = 0.001) and waist circumference (r = 0.238, p = 0.031). Conclusions: Integrated dental and lifestyle interventions significantly improved OHRQoL in MetS patients by enhancing psychosocial well-being and social reintegration. Gains were functionally driven by systemic metabolic success. Addressing “nutritional barriers” through dental rehabilitation, while targeting weight loss goals, was essential for holistic MetS management. Full article

Background: Low back pain is a common musculoskeletal complaint among postpartum women due to physical changes that occur during pregnancy and delivery, which can lead to different disability levels. Therefore, the aim of this study was to evaluate the disability levels and associated factors of postpartum women within the first year after childbirth. Methods: A descriptive cross-sectional study design was used to gather data from post-partum women between 6 weeks and 12 months after childbirth using an online self-administered questionnaire. This questionnaire included demographic variables, the Nordic Musculoskeletal Symptoms Questionnaire, the Pain Intensity Numeric Rating Scale, and a back disability questionnaire. Results: Among 400 postpartum mothers, 71% reported low back pain, with 51.1% experiencing mild disability. Logistic regression showed significant predictors of disability, including cesarean delivery (6.49 times higher likelihood), having 4–5 children (1.98 times), and more than six children (3.45 times). Breastfeeding increased disability risk (2.44 times), while mixed feeding reduced it (0.52 times). The model explained 49.8% of disability variance (p < 0.001). Conclusions: Disability is a common problem among postpartum women, highlighting the importance of healthcare providers addressing these challenges. Full article

Major Depressive Disorder (MDD) is a significant global health burden that contributes to disability and reduced quality of life. Its impact extends beyond individuals, placing emotional, social, and economic strain on families and healthcare systems worldwide. Despite its prevalence, MDD remains widely misunderstood, with limited mental health literacy and persistent stigma often preventing individuals from seeking help. This research explored the prediction of MDD utilising social media data via Natural Language Processing (NLP), Machine Learning (ML), and explainable Machine Learning (xML) techniques. The research aimed at identifying depressive indicators on X (formerly Twitter) and developing interpretable models for depression risk detection. The study’s methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to ensure a systematic approach to data analysis. Data was collected via X’s API and processed using regex-based noise removal, normalisation, tokenisation, and lemmatisation. Symptoms were mapped to DSM-5-TR criteria at the post-level, with user-level MDD risk assessed based on symptom persistence over a two-week period. Risk levels were classified as No Risk, Monitor, and High Risk to facilitate early intervention. Six ML models were trained and tested, while the Synthetic Minority Over-sampling Technique (SMOTE) was applied to mitigate class imbalance. The dataset was partitioned into training and testing sets using an 80:20 split. ML models were evaluated, and the Extreme Gradient Boosting model outperformed the others. Extreme Gradient Boosting achieved an accuracy of 0.979, F1-score of 0.970, and ROC-AUC of 0.996, surpassing benchmark results reported in prior studies. Explainability techniques, such as LIME and tree-based feature importance, enhance model transparency and clinical interpretability. Depressed mood consistently emerged as the highest-weighted predictor across different models. The findings highlight the value of aligning ML models with validated diagnostic frameworks to improve trustworthiness and reduce false positives. Future research can expand beyond text-based analysis by incorporating multimodal features to broaden diagnostic depth. Full article

Background: Sleep disturbance is a well-recognized contributor to headache burden, yet the specific sleep domains associated with disability may differ between episodic migraine (EM) and episodic cluster headache (ECH). Methods: In this prospective observational study, 20 EM patients, 21 ECH patients, and 18 age-, sex-, and BMI-matched healthy controls (HCs) were evaluated during interictal periods. None of the patients were receiving prophylactic headache treatment. Sleep was assessed using the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), and Sleep Hygiene Index (SHI). Psychological status was measured with the Hospital Anxiety and Depression Scale (HADS). Headache-related disability was assessed using the Headache Impact Test-6 (HIT-6) as a continuous outcome. Separate multivariable linear regression models were constructed for each headache group. Results: Both headache groups showed significantly impaired sleep and higher anxiety and depression scores compared with controls (all p < 0.001). HIT-6 scores did not differ between EM and ECH (p = 0.770 after Bonferroni correction). In multivariable regression, excessive daytime sleepiness (ESS) independently predicted disability in EM (B = 1.633, p = 0.033; R² = 0.571). In ECH, global sleep quality (PSQI; B = 0.701, p = 0.004) and sleep hygiene (SHI; B = 0.557, p = 0.033) were independently associated with HIT-6 (R² = 0.562). No significant multicollinearity was observed (all VIF < 2.5). Conclusions: Sleep disturbance is prevalent in both EM and ECH; however, the sleep domains associated with disability differ between phenotypes. Daytime sleepiness is more relevant in EM, whereas global sleep quality and sleep hygiene are more strongly associated with disability in ECH. These findings support a phenotype-specific approach to sleep assessment in headache management. Full article

Background/Objectives: Self-rated health (SRH) is a widely used summary indicator of health and a predictor of subsequent morbidity and mortality. Among adults with severe disabilities, SRH may reflect not only chronic conditions and functional limitations but also psychological factors, particularly depressive mood. This study examined the incremental contribution of depressive mood beyond physical and functional factors to SRH among adults with severe disabilities. Methods: We analyzed data from a survey of adults with severe disabilities in Seoul, South Korea (N = 1519). SRH (higher scores indicating better health) was modeled using block-wise hierarchical linear regression with robust standard errors. Models sequentially adjusted for (1) sociodemographic factors (including living arrangement); (2) disability characteristics (disability type and multiple disability status); (3) physical and functional health factors (illness status, instrumental activities of daily living (IADL), and unmet medical need); and (4) depressive mood. Results: In the fully adjusted model (R² = 0.241), illness status (b = −0.330, p < 0.001), functional capacity (IADL; b = 0.116, p < 0.001), and depressive mood (b = −0.105, p < 0.001) were independently associated with SRH. Adding disability characteristics significantly improved model fit (ΔR² = 0.074; Wald block F(3, 1510) = 42.56, p < 0.001). Further adding illness status, IADL, and unmet medical need improved model fit (ΔR² = 0.051; Wald block F(3, 1507) = 30.85, p < 0.001), and depressive mood provided additional explanatory power (ΔR² = 0.011; Wald block F(1, 1506) = 16.86, p < 0.001). Living alone and unmet medical need were not significantly associated with SRH after adjustment. Conclusions: Depressive mood was independently associated with SRH among adults with severe disabilities, even after accounting for physical health and functional limitations. These findings suggest that attention to depressive mood may be relevant to disability-related assessment and service planning, alongside chronic disease management and functional support. The observed association reflects a short-term affective state rather than clinical depression and should be interpreted within the context of subjective health appraisal. Full article

Background/Objectives: Real-world evidence on ofatumumab (OFA) beyond 12 months remains limited in relapsing–remitting multiple sclerosis (RRMS). We assessed 24-month effectiveness and safety, compared treatment-naïve and previously treated patients, and explored predictors of failure to achieve No Evidence of Disease Activity-3 (NEDA-3). Methods: This multicenter retrospective cohort study included adult RRMS patients treated with OFA in routine clinical practice. Effectiveness analyses were restricted to patients with complete 24-month follow-up and full clinical and magnetic resonance imaging (MRI) assessment (complete-case analysis). Outcomes included relapses, MRI activity, Expanded Disability Status Scale (EDSS) progression, NEDA-3, and adverse events (AEs). Exploratory multivariable logistic regression was used to assess baseline predictors of NEDA-3 non-achievement. Results: Of 258 patients who initiated OFA, 148 had completed 24-month clinical and MRI follow-up and were evaluable for effectiveness. Over 24 months, 71.5% achieved NEDA-3; relapses occurred in 15.5% of patients, MRI activity in 15.5%, gadolinium-enhancing lesions (GELs) in 4.7%, and EDSS progression in 17.6%. Disease activity was minimal during months 12–24, with relapses in 2.7%, MRI activity in 2.0%, and no GELs. In unadjusted analyses, no statistically significant differences were observed between treatment-naïve and previously treated patients. Higher baseline EDSS was associated with failure to achieve NEDA-3. In the 24-month safety subgroup, AEs were recorded in 28.4% of patients; infections occurred in 26.4% of patients (all grade 1–2), and no serious adverse events were observed. Conclusions: In this multicenter real-world cohort, OFA was associated with low inflammatory disease activity over 24 months in RRMS patients with complete follow-up. These findings should be interpreted cautiously because the effectiveness analysis was restricted to a complete-case cohort and safety data were collected retrospectively. Full article

Background/Objectives: Early intervention is associated with improved outcomes for young children with developmental delays, yet many with mild delays are ineligible for services under the Individuals with Disabilities Education Act (IDEA). The Early Discovery (ED) Program addressed this gap by providing short-term, targeted intervention for children ages 0–5 who did not qualify for publicly funded services. This study evaluated program outcomes across intervention types. Methods: During 2024–2025, 342 families completed the ED Program, receiving one of the following: speech-language (68%), general developmental (12%), occupational (14%), or behavioral (6%) intervention across 8–20 sessions. Eligibility required Miami-Dade residency and ineligibility for IDEA-funded services. Standardized pre- and post-intervention assessments were analyzed using descriptive statistics, correlations, and group comparisons. Results: Most households reported incomes <$70,000 (71%), with many experiencing additional risk factors including prematurity (15%), public or no insurance (47%), limited English proficiency (21%), and single-caregiver households (30%). Overall, 85% of children met criteria for improvement. Improvement rates varied by child ethnicity. No statistically significant differences were observed by child age, race, gender, prematurity, insurance status, caregiver demographics, household characteristics, or intervention type. Sensitivity analyses largely confirmed the primary findings, with ethnicity no longer significant and younger age emerging as a significant predictor of improvement. Conclusions: Findings suggest short-term, targeted intervention may support developmental progress among young children with mild delays who would otherwise remain unserved. Community-based programs such as ED may play a critical role in advancing developmental equity by reaching children with developmental and socioeconomic risk factors prior to school entry. Full article

Background: The aim of this study was to derive and internally validate a prescriptive clinical prediction rule (CPR) for identifying baseline factors associated with short-term clinical improvement in patients with neck pain (NP) undergoing a manual therapy (MT)-based physiotherapy program. Methods: A prospective cohort study was conducted, including 71 patients with NP (18–65 years). Participants received six MT-based sessions over three weeks. Baseline assessments included Pain Intensity Numeric Rating Scale (PI-NRS), Neck Disability Index (NDI), Body Mass (BM), Body Mass Index (BMI), International Physical Activity Questionnaire-Short Form (IPAQ-SF), Hospital Anxiety and Depression Scale (HADS), Minnesota Satisfaction Questionnaire-Short Form (MSQ), and Craniovertebral Angle (CVA). Clinical improvement was defined using the Global Perceived Effect Scale (GPES-7). Univariate analyses, receiver operating characteristic (ROC) curve analysis, and forward stepwise logistic regression were performed to derive the predictive model. Results: Fifty-six participants (78.9%) reported moderate to complete improvement. BM ≥ 76.5 kg and MSQ score ≤ 42.5 were retained in the final regression model. When both predictors were present, the probability of clinical improvement increased to 96.43% (positive likelihood ratio = 7.58). The model demonstrated adequate fit (Nagelkerke R² = 0.247; Hosmer–Lemeshow p = 0.804). Internal validation yielded an optimism-corrected AUC of 0.741, suggesting minimal overfitting. Conclusions: Higher BM and lower MSQ score were associated with greater short-term improvement following MT in patients with NP. These findings highlight the relevance of integrating physical and psychosocial factors in prescriptive rehabilitation approaches. External validation of this CPR is required before clinical implementation. Full article