Search Results (1,474)

Objective: To evaluate the effectiveness of a pharmacist-led educational intervention in reducing premenstrual syndrome (PMS) symptoms and improving self-care practices. Methods: A mixed-design study was conducted in community pharmacies in Spain between January and June 2025. First, a cross-sectional analysis determined PMS prevalence. Second, a longitudinal pre-post study was performed with women suffering from PMS. The intervention involved personalized guidance and an evidence-based educational infographic. Primary outcomes included symptom severity (measured by a numeric rating scale) and quality of life. Results: 350 women participated in the study. The mean age of participants was 23.7 ± 6.3 years (range: 17–51 years). At the six-month follow-up, the PMS group showed a significant reduction in mean pain intensity (from 6.86 to 3.26; p < 0.001) and a smaller reduction in the control group (from 4.82 to 2.88; p < 0.001), alongside improvements in irritability, insomnia, and fatigue. The proportion of women reporting a negative impact on quality of life decreased from 97.0% to 60.8% (p < 0.001). Oral contraceptive use was identified as a protective factor (OR: 0.33; 95% CI: 0.17–0.65). Conclusions: Educational interventions led by community pharmacists are effective in significantly alleviating PMS symptoms and enhancing women’s quality of life. Practice Implications: Community pharmacists are strategically positioned to identify women with PMS and provide evidence-based education. Implementing structured protocols and visual tools in pharmacies can optimize symptom management and promote self-care. Full article

Restorative justice within the juvenile justice system has gained increasing attention as an alternative to punitive approaches, particularly in relation to the social reintegration of adolescents in conflict with the law, while complementary interventions such as music therapy are often implemented to support emotional regulation, social skills, and personal development within restorative contexts. This observational, cross-sectional study examined the associations between restorative justice practices, participation in music therapy, and indicators of social reintegration among 317 adolescents involved in restorative programs in Peru. Data were collected using a structured survey composed of ordinal-scale items assessing dimensions of restorative practices, engagement in music therapy, and perceived social reintegration, with the instrument demonstrating satisfactory internal consistency. Statistical associations were analysed using Somers’ d, a non-parametric measure appropriate for assessing ordinal associations in observational research. The results revealed statistically significant and directionally consistent associations between restorative justice practices and social reintegration outcomes, as well as positive associations between participation in music therapy and higher levels of reported social reintegration. These findings should be interpreted in light of the study’s cross-sectional design and non-probability sampling strategy, which limit causal inference and generalizability. While the results are consistent with the potential relevance of integrating music-based activities within restorative contexts, future research employing experimental or longitudinal designs is required to examine causal mechanisms and long-term effects and to further clarify the role of therapeutic interventions in supporting the social reintegration of justice-involved adolescents. Full article

Background/Objectives: The incorporation of peer support within mental health services has shown benefits for service users’ recovery and engagement, yet implementation is often hindered by role ambiguity and limited institutional recognition. The aim of this study is to explore the experiences of workers in a programme that provides peer support within a personal assistance model. The focus is on how they perceive the shaping of their professional role and their integration within care teams, rather than on evaluating service outcomes or effectiveness. Methods: An interpretive qualitative methodology with an exploratory approach was used. The study was conducted in a single organisational setting and focused on the self-reported experiences of personal assistants. Fieldwork was conducted in 2025 with ten personal assistants. Data were obtained through individual semi-structured interviews and one focus group with the same participants. A thematic content analysis combining inductive and deductive coding strategies was conducted using MAXQDA (version 24.11). Results: Findings indicate that the Personal Assistant role was perceived as reducing some of the ambiguity commonly associated with peer support, due to a clearer contractual framework and a more explicit delineation of functions. However, tensions persisted in relation to its hybrid professional identity, experiences of task overload, and ongoing gaps in coordination with traditional professional roles. Key facilitators included institutional support, accessible coordination, a supportive culture of care, and informal peer networks. Perceived benefits were reported for service users, including increased trust, hope, and autonomy, as well as for assistants, who described enhanced professional purpose and progress in their own recovery, alongside risks of emotional strain. Conclusions: Analysing the perspective of participants, the personal assistance model may represent a promising framework for the professionalisation of peer support through functional clarity, continuous supervision, and recognition of experiential knowledge. Further progress requires strengthening internal communication, expanding training opportunities, and enhancing the structural participation of personal assistants in decision-making. The study contributes an exploratory qualitative perspective to the growing literature on integrating lived-experience professionals into mental health services. Full article

Background: Evidence on the long-term impact of sapropterin in phenylketonuria (PKU) is limited. Understanding its effects on dietary restrictions, growth in children, and caregiver burden is essential to optimize PKU management. Methods: This prospective, two-year longitudinal study with a comparison group followed 33 children with PKU after sapropterin responsiveness assessment (21 responsive, 12 non-responsive). Outcomes included metabolic control, prescribed protein intake, dietary patterns, growth, psychological measures, and caregiver burden. Results: Sapropterin-responsive children increased natural protein intake from 10 g to 28 g/day at 2 years (p < 0.001), with reduced protein substitute intake (60 g [56–63] to 45 g [40–60], p < 0.05); no changes occurred in non-responsive children (p > 0.05). Animal-based foods (cheese, eggs, meat, fish) were introduced in 52% (11/21) of responsive children once tolerance exceeded approximately 25 g/day. The caregivers of responsive children reported reduced financial, familial-social, and personal burden (all p ≤ 0.05), alongside decreased food neophobia (p = 0.005) and caregiver depression (p = 0.013). In sapropterin-responsive children, weight and BMI z-scores remained stable, while height z-score increased over 24 months (p = 0.03); non-responsive children had higher weight and BMI z-scores than responsive children at 24 months (p = 0.037 and p = 0.026). Blood phenylalanine concentrations remained within recommended target ranges overall, with lower median values in responsive children at several time points. Conclusions: Sapropterin enabled more flexible, sustainable dietary management in responsive children with PKU, supporting metabolic control, growth, and improved family well-being and social participation. Equitable access to therapies and long-term dietetic support remain essential to optimize outcomes. Full article

Background/Objectives: Bereavement in childhood, adolescence, and young adulthood is associated with a range of grief responses, and a subset of bereaved individuals develop persistent or severe grief symptoms. Understanding the prevalence and risk factors of prolonged grief symptoms is important for guiding supportive care. Methods: We systematically searched PubMed, MedLine, Embase and PsycINFO for all studies comparing the prevalence and prognostic factors of prolonged grief-related symptoms among young individuals following parental or sibling death from cancer. Young individuals were defined as those not more than 25 years old before losing a parent or sibling to any cancer. Prolonged grief-related symptoms were defined as the presence of grief symptoms at least six months following the death of a parent or sibling of the bereaved person. Retrospective cross-sectional studies were included for evaluating prognostic factors affecting prolonged grief-related symptoms, but were not used for meta-analyses. Random-effects meta-analyses were conducted for the primary analysis. Results: From 1561 records identified, thirteen studies were included with five for quantitative pooling in meta-analysis. The pooled prevalence of self-reported prolonged grief-related symptoms was 48% (95% CI: 29–67%). Stratified analyses suggested a prevalence of 28% (95% CI: 7–65%) after parental death and 59% (95% CI: 45–72%) after sibling death. Factors associated with elevated prolonged grief-related symptoms included pre-existing depression, emotional difficulties, and insomnia. As no included studies conducted diagnostic clinical interviews, prolonged grief disorder according to the ICD-11 or DSM-5-TR criteria could not be assessed. Conclusions: Prolonged grief-related symptoms appear common among young individuals bereaved by loss of a parent or sibling to cancer, especially after sibling loss. However, interpretation remains limited by substantial heterogeneity, such as outcome measures, symptom thresholds, assessment time window, non-validated symptom measures, and predominance of cross-sectional studies. Future larger and methodologically rigorous studies using validated grief instruments across diverse settings are needed to clarify grief trajectories and guide developmentally appropriate intervention strategies. Full article

Background: Digital transformation in healthcare has advanced rapidly in hospitals and primary care, while long-term care facilities have often lagged behind. In nursing homes, nurses play a central role in coordinating care and accessing medical expertise, yet digital tools to support these tasks remain inconsistently implemented. The CareConnect study, funded under the German Model Program for Telecare (§ 125a SGB XI), aimed to develop and implement a multiprofessional telecare system tailored to nursing home care. Objective: This implementation study examined the feasibility, acceptability, and early adoption of a multiprofessional telecare system in nursing homes, focusing on implementation processes, contextual influences, and facilitators and barriers to integration into routine nursing workflows. Methods: A participatory implementation design was employed over 15 months (June 2024–August 2025), involving a university hospital, two nursing homes (NHs), and four medical practices in an urban region in Germany. The telecare intervention consisted of scheduled video-based teleconsultations and interdisciplinary case discussions supported by diagnostic devices (e.g., otoscopes, dermatoscopes, ECGs). The implementation strategy followed the Standards for Reporting Implementation Studies (StaRI) and was informed by the Consolidated Framework for Implementation Research (CFIR). Data sources included telecare documentation, nurse surveys, researcher observations, and structured feedback discussions. Quantitative and qualitative data were analyzed descriptively and triangulated to assess implementation outcomes and mechanisms. Results: A total of 152 documented telecare contacts were conducted with 69 participating residents. Most interactions occurred with general practitioners (48.7%) and dermatologists (23%). Across all contacts, in 79% of cases, there was no need for an in-person visit or transportation. Physicians rated most cases as suitable for digital management, as indicated by a mean of 4.09 (SD = 1.00) on a 5-point Likert scale. Nurses reported improved communication, time savings, and enhanced technical and diagnostic skills. Key challenges included delayed technical integration, interoperability issues, and varying interpretations of data protection requirements across facilities. Conclusions: This pilot study suggests that telecare can be feasibly introduced and accepted in nursing home settings when implemented through context-sensitive, participatory strategies. Implementation science approaches are essential for understanding how telecare can be sustainably embedded into routine nursing home practice. Full article

Background: Spondyloarthritis (SpA) typically develops before 40 years of age, but increasing life expectancy has led to a growing number of cases in older adults. It is well known that age at onset may influence disease presentation, comorbidities, and patient outcomes. Objectives: To assess whether age at onset influences SpA clinical presentation. Methods: We analyzed clinical, demographic, clinimetric, and imaging data in 272 SpA patients, grouped by onset age: early (≤40, n = 119), intermediate (41–59, n = 127), and late (≥60, n = 26). All patients had a minimum follow-up duration of 12 months. Their epidemiologic, clinic, and clinimetric data were collected, as well as patient-reported outcome measures (PROs) [Patient Global Assessment (PGA), Health Assessment Questionnaire (HAQ), FACIT-Fatigue (FACIT-F), SHORT-FORM 36 (SF-36), Hospital Anxiety and Depression Scale (HADS), Work Productivity and Activity Impairment Questionnaire (WPAI), CSI (Central Sensitization Inventory), and Psoriatic Arthritis Impact of Disease (PsAID) questionnaire]. In univariate analyses, differences in categorical variables across onset groups were assessed using Fisher’s exact test; for continuous variables, between-group comparisons were performed using the Mann–Whitney U test (two-tailed) or the Kruskal–Wallis test, as appropriate, with Bonferroni correction for post hoc analyses. Multivariable regression models were subsequently fitted, adjusting for sex, diagnosis, and disease duration. For binary outcomes, multivariable logistic regression models were used, while multivariable linear regression models (ANCOVA) were applied for continuous outcomes. The overall association between onset group and each outcome was formally tested using likelihood ratio tests, comparing models including the onset variable with nested models excluding it. A p-value < 0.05 was considered statistically significant. Results: Patients’ mean age was 60.0 ± 13.7 years; 55.9% of them were males; and there were 188 cases (69.1%) of psoriatic arthritis (PsA) and 84 cases (30.9%) of ankylosing spondylitis (AS). In early-onset patients, inflammatory back pain (IBP) was more frequent, whereas late-onset patients more often presented with joint swelling. A family history of SpA and psoriasis was less common in late-onset forms. Comorbidities, including osteoporosis, osteoarthritis, hypertension, hyperuricemia, and diabetes, were more prevalent in older-onset patients, resulting in a higher overall comorbidity burden in Groups 2 and 3. Patient-reported outcomes were largely similar across age groups, although work activity limitation was more pronounced in younger patients. Conclusions: Age at onset seems to influence SpA phenotypes: early-onset could favor axial involvement, while late-onset may associate with peripheral arthritis. Late-onset forms are associated with a more severe comorbidity burden, in particular for cardiovascular risk factors. Lung involvement proved to be more prevalent with respect to the general population, so it should be checked in the routinary assessment of SpA patients. These findings suggest that rheumatologists could tailor their routine assessments based on patients’ age at disease onset. Interestingly, work productivity seems more impacted in early-onset patients. All these points highlight the importance of age at disease onset in SpA, guiding toward personalized medicine in terms of follow-up, therapy, and more holistic patient management. Full article

The article reports on a bilingual and interpretable book recommendation platform for schoolchildren. This platform uses a lightweight K-Nearest Neighbors algorithm combined with gamification and learning analytics. This application has been designed for a bilingual learning environment in Kazakhstan, supporting learning in Kazakh and Russian languages, and is intended to improve reading engagement through culturally adjusted personalization. The recommendation engine combines content and collaborative filtering in that it leverages structured book data (genres, target age ranges, authors, languages, and semantics) and learner attributes (language of instruction, preferences, and learner history). A hybrid ranking function combines the similarity to the user and the item similarity to produce top-N recommendations, whereas gamification elements (points, achievements, and reading challenges) are used to foster sustained activity.Teacher dashboards show learners’ overall reading activity and progress through real-time data visualization. The initial calibration of the model was carried out using an open-source book collection consisting of 5197 items. Thereafter, the model was modified for a curated bilingual collection of 600 books intended for use in educational institutions in the Kazakh and Russian languages. The validation experiment was carried out on a pilot test involving 156 children. The experimental outcome suggests a stable level of recommendation in terms of the Precision@10 and Recall@10 values of 0.71 and 0.63 respectively. The computational complexity remained low. Moreover, the bilingual normalization technique increased the relevance of recommendations of non-majority language items by 12.4%. In conclusion, the proposed approach presents a scalable and transparent framework for AI-assisted reading personalization in bilingual e-learning systems. Future research will focus on transparent recommendation interfaces and more adaptive learner modeling. Full article

Background/Objectives: Early turnover among new graduate nurses remains challenging in South Korea. This study examined how socialisation factors—based on Scott et al.’s transition model and Herzberg’s motivation-hygiene theory—are associated with early retention at the first hospital of employment among new graduate nurses. Methods: This retrospective cross-sectional study analysed secondary data from the Graduate Occupational Mobility Survey (GOMS), a nationally representative dataset of college and university graduates in Korea, collected using a stratified multi-stage sampling method. The study included 602 new graduate nurses from the 2017–2019 datasets who had worked as nurses at their first hospital of employment. Anticipatory socialisation factors included personal and educational characteristics. Organisational socialisation factors referred to workplace-related characteristics of the first hospital, including motivational factors and hygiene factors. The outcome variable was early retention. Multiple logistic regression analyses were performed to identify factors associated with early retention. Results: A total of 68.6% of nurses remained in their first hospital employment. Retention was more likely among nurses whose high school, nursing school, and first hospital were in the same region (p = 0.019), those employed in Seoul (p < 0.001), and those working in larger hospitals (p < 0.001). Retention was also associated with satisfaction with autonomy and authority (p = 0.013). Conversely, lower retention was observed among nurses who were dissatisfied with interpersonal relationships (p < 0.001) and those who reported satisfaction with growth opportunities (p < 0.001). Conclusions: Targeted strategies that support new graduate nurses during their transition are essential. Aligning education-to-employment regions and strengthening workplace conditions may enhance early retention. Full article

Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care. Full article

Background and Objectives: Posthepatectomy liver failure (PHLF) remains a major cause of morbidity and mortality for patients undergoing major liver resections. Recent research highlights the expanding role of machine learning (ML), a crucial subfield of artificial intelligence (AI), in optimizing risk stratification. The aim of the current study was to review, elaborate on and critically analyze the available literature regarding the use of ML-driven risk prediction models for posthepatectomy liver failure. Materials and Methods: A systematic search was conducted in the PubMed/MEDLINE, Scopus and Web of Science databases. Fifteen studies that trained and validated ML models for prediction of PHLF were further included and analyzed. Results: The available literature supports the value of ML-derived models for PHLF prediction. Perioperative clinical, laboratory and imaging features have been combined in a variety of different algorithms to provided interpretable and accurate models for identifying patients at risk of PHLF. The ML-based algorithms have consistently demonstrated high area under the curve and sensitivity values, surpassing traditionally used risk scores in predictive performance. Limitations include the small sample sizes, heterogeneity in populations included, lack of external validation and a reported poor ability to distinguish between true positive and false positive cases in several studies. Conclusions: Despite the constraints, ML-driven tools, in combination with traditional scoring systems and clinical insight, may enable early and accurate PHLF risk detection, personalized surgical planning and optimization of postoperative outcomes in liver surgery. Full article

The psychosocial well-being of employees is crucial to health and productivity, and it forms the basis for organisational sustainability. Unfortunately, most studies rely on narrow indicators or small samples and thus are not generalisable. The present study aims to identify psychosocial and health-related factors that distinguish employees with high and low SWB and determine whether these effects are universal or sector-specific. A total of 1628 employees with organisations in Latvia’s public administration, healthcare, pharmaceutical and energy sectors participated by completing the Multidimensional Psychosocial Well-Being Scale for Employed Persons (MPSWEP). This instrument assesses five key work environment factors: social inclusion, professional development, work intensity, health risks and autonomy. Subjective well-being (SWB) was measured as a separate outcome variable, and additional self-reported health problems were included as an independent variable in the analysis. Higher odds of high SWB were observed with greater social inclusion (OR = 5.11; p < 0.001), whereas higher work intensity (OR = 0.51; p < 0.001) and health problems (OR = 0.25; p < 0.001) were associated with lower odds of high SWB. Model accuracy was high (AUC = 0.85–0.87), with significant differences between sectors. The results suggest that some resources universally facilitate well-being across sectors, while others exert more sector-specific effects. Full article

Psoriasis and psoriatic arthritis (PsA) occasionally coexist with antinuclear antibody (ANA) positivity, cutaneous lupus erythematosus (CLE), or systemic lupus erythematosus (SLE), creating one of the most challenging therapeutic overlap scenarios in immunodermatology. Divergent immune pathways—IL-23/Th17-driven psoriatic inflammation versus type I interferon-mediated autoimmunity—generate unique vulnerabilities when systemic treatments are used. To synthesize treatment outcomes, lupus-related safety signals, and mechanistic insights across systemic therapies in patients with psoriasis or PsA who also exhibit ANA positivity, CLE, or SLE. A systematic review following PRISMA 2020 guidelines was conducted across PubMed/MEDLINE, Embase, the Cochrane Library, Scopus, and ClinicalTrials.gov from database inception through 31 October 2025. Thirty-three eligible reports (29 unique clinical studies; 1429 patients) were included and organized into six prespecified overlap subgroups. Mechanistic and translational studies—including ustekinumab and deucravacitinib SLE trial data and reports of IL-17 inhibitor-associated CLE—were reviewed separately to provide contextual interpretation. IL-23 inhibitors were consistently associated with a favorable cross-disease safety profile, with no clear signal for CLE worsening, SLE flares, or drug-induced autoimmunity. IL-17 inhibitors maintained strong psoriatic efficacy but were associated with an increased frequency of de novo or exacerbated CLE. TNF-α inhibitors showed the strongest association with ANA seroconversion, anti-dsDNA induction, drug-induced lupus, and lupus flares. Ustekinumab demonstrated a stable safety profile across lupus-spectrum disease despite variable efficacy in formal SLE trials. TYK2 inhibition provided dual modulation of IL-23 and type I interferon pathways and showed emerging utility in psoriasis or PsA coexisting with CLE or SLE. Apremilast, methotrexate, and mycophenolate mofetil remained reliable non-biologic systemic options. Phototherapy was associated with potential risk in ANA-positive or lupus-susceptible populations and therefore requires careful consideration. Interpretation is limited by the predominantly observational nature and heterogeneity of the available evidence. IL-23 inhibition and TYK2 inhibition appear to offer a balanced profile of efficacy and lupus-related safety in psoriatic disease complicated by lupus-spectrum autoimmunity. IL-17 inhibitors and TNF-α inhibitors may be associated with higher risk in CLE- or SLE-prone patients and therefore warrant particular caution. Personalized treatment strategies should integrate the relative dominance of psoriatic versus lupus disease, ANA/ENA profile, CLE subtype, and underlying mechanistic considerations. Prospective, biomarker-driven studies are needed to guide therapy in this increasingly recognized overlap population (PROSPERO registration: CRD420251241279). Full article

Background: Severe asthma exacerbations (SAEs) significantly contribute to asthma-related morbidity, mortality, and healthcare burden. Despite therapeutic advances, a subset of patients remains exacerbation-prone. This review aims to summarize current evidence on risk factors, phenotypes, and biomarkers associated with SAEs, and explore personalized strategies for their acute management. Methods: We conducted a comprehensive literature review focusing on clinical, inflammatory, and environmental drivers of SAE. Special attention was given to Type 2 (T2) biomarkers—blood eosinophil count (BEC) and fractional exhaled nitric oxide (FeNO)—as tools for phenotyping and treatment guidance. Emerging evidence on the use of biologics during exacerbations was also analyzed. Results: SAEs are heterogeneous in etiology and inflammatory profile. Respiratory infections, allergen exposure, obesity, and comorbidities increase exacerbation risk. T2-high SAEs respond well to corticosteroids and biologics, whereas T2-low SAEs show limited treatment benefit. BEC and FeNO reliably predict exacerbation risk and corticosteroid responsiveness. Recent case reports suggest potential roles for anti-IL-5 and anti-thymic stromal lymphopoietin (TSLP) biologics in acute care. Conclusions: Biomarker-guided management of SAEs may enhance therapeutic precision and avoid overtreatment. Integrating phenotypic (observable characteristics) and endotypic (biological markers) assessment into acute care could improve patient outcomes and optimize resource use. Prospective trials are needed to confirm these approaches. Full article

This pilot study examined the feasibility, usability, and physiological effects of a high-intensity exercise program delivered through immersive virtual reality (IVR) in adults with Down syndrome (DS). Twenty participants (mean age: 29.85 ± 9.37 years) completed a 12-week intervention using the FitXR exergame on Meta Quest 3, with two sessions per week. Usability, safety, and personal experiences were assessed via the System Usability Scale (SUS), Simulator Sickness Questionnaire (SSQ), and Game Experience Questionnaire (GEQ), while body composition and strength were measured using bioelectrical impedance analysis and standardized tests (handgrip dynamometry, Five Sit-to-Stand Test). Results indicated excellent usability (SUS: 92.88–95.03/100), minimal cybersickness (SSQ: 2.12 → 1.98/48), and high adherence (90%). Positive experiences increased significantly, with no negative experiences reported. Lower-limb strength has been considered as a primary outcome, which has shown to improve significantly (p = 0.018; Cohen’s d = 0.89), whereas upper-limb strength and body composition changes were minimal. These findings suggest that IVR-based exercise is a safe, engaging, and feasible strategy for promoting physical activity and enhancing functional strength in adults with DS. Further controlled trials with longer duration and nutritional strategies are warranted to optimize body composition outcomes. Full article