Background/Objectives: The COVID-19 pandemic highlighted how infodemics—an excessive amount of both accurate and misleading information—undermine health responses. Artificial intelligence (AI) and digital tools have been increasingly applied to monitor, detect, and counter health misinformation online. This scoping review aims to systematically map digital and AI-based interventions, describing their applications, outcomes, ethical and equity implications, and policy frameworks. Methods: This review followed the Joanna Briggs Institute methodology and was reported according to PRISMA-ScR. The protocol was preregistered on the Open Science Framework . Searches were conducted in PubMed/MEDLINE, Scopus, Web of Science, and CINAHL (January 2017–March 2025). Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved by a third reviewer. Data extraction included study characteristics, populations, technologies, outcomes, thematic areas, and domains. Quantitative synthesis used descriptive statistics with 95% confidence intervals. Results: A total of 63 studies were included, most published between 2020 and 2024. The majority originated from the Americas (41.3%), followed by Europe (15.9%), the Western Pacific (9.5%), and other regions; 22.2% had a global scope. The most frequent thematic areas were monitoring/surveillance (54.0%) and health communication (42.9%), followed by education/training, AI/ML model development, and digital engagement tools. The domains most often addressed were applications (63.5%), responsiveness, policies/strategies, ethical concerns, and equity/accessibility. Conclusions: AI and digital tools provide significant contributions in detecting misinformation, strengthening surveillance, and promoting health literacy. However, evidence remains heterogeneous, with geographic imbalances, reliance on proxy outcomes, and limited focus on vulnerable groups. Scaling these interventions requires transparent governance, multilingual datasets, ethical safeguards, and integration into public health infrastructures.

Background/Objectives: Long COVID is a condition that was initially recognized by social support groups, and later by the scientific and medical communities. It affects COVID-19 survivors at various levels of severity, including young people, children and non-hospitalized people. Although the exact definition is unclear, the most common symptoms are fatigue and shortness of breath, which persist for months. Other symptoms include cognitive impairment, pain, palpitations, and gastrointestinal and heart problems. This study evaluated the reliability and validity of a questionnaire designed to examine the development and effects of long COVID. Methods: A questionnaire, composed of three sections, with a total of 24 items, was administered to subjects who had recovered from the COVID-19 disease in Italy. Data were collected from February to April 2025, and a statistical analysis was performed using R^® statistical software for Windows, version 4.3.3. Cronbach’s alpha was tested to check internal consistency. The questionnaire was completed voluntarily and anonymously by 250 individuals who had recovered from the SARS-CoV-2 infection. The questionnaire was self-administered and had open and structured questions. Results: The highest value of Cronbach’s alpha was found on 18 items (alpha = 0.97), which means that the questionnaire has satisfactory internal validity. Conclusions: This study highlights and confirms the continuity of symptoms manifested during the acute phase of the SARS-CoV-2 infection in the post-COVID-19 phase and the significant impact of these symptoms on daily life activities. Given its excellent reliability properties and high internal consistency, the instrument is recommended for future longitudinal studies and with large cohorts in order to carry out valid and replicable measurements of COVID-19 symptomatology.

Background/Objectives: Long COVID is a multisystemic condition persisting beyond the acute phase of SARS-CoV-2 infection. Data on young, community-dwelling adults in Italy remain limited. To describe the sociodemographic, clinical, and symptom profiles of Long-COVID patients in an Italian cohort. Methods: Cross-sectional survey (February–April 2025) on 250 adults with prior COVID-19. A validated 24-item questionnaire was administered. Descriptive statistics, 95% confidence intervals (CIs), Hedges’ g effect sizes, and exploratory subgroup analyses (sex, age ≤ 30 vs. >30) were performed. Results: Participants were 63.6% female, 56% ≤ 30 years, 4.4% with comorbidities. Acute symptoms included muscle/joint pain (2.79 ± 1.31), weakness (2.77 ± 1.28), and tiredness (2.76 ± 1.31). Persistent symptoms were excessive tiredness (2.36 ± 1.27), weakness (2.25 ± 1.29), and muscle/joint pain (2.25 ± 1.25). Acute → persistent changes were significant (p < 0.01, paired t-test) with effect sizes g = 0.31–0.42. Women reported higher persistent fatigue (mean diff = 0.40, 95% CI 0.01–0.78, p = 0.04). Conclusions: Even among young adults without comorbidities, Long COVID imposes a relevant burden. Findings highlight the need for multidisciplinary pathways, nursing-led follow-up, and targeted self-management education.

Background: The psychological well-being of university students significantly impacts their academic performance and future professional preparation. The aim of this study is to analyze the relationships between perceived stress, self-esteem, and learning strategies in university students enrolled in Nursing and Obstretics degree programs, in order to understand the impact of psychological well-being on their study abilities. Method: This study is observational cross-sectional, using non-probabilistic convenience sampling. The study involved students enrolled in Nursing and Obstetrics courses at the University of Salerno. A standardized self-reported questionnaire will be used. Results: 331 students (82.75%) participated voluntarily. Correlations show significant relationships consistent with psychological literature. Higher self-esteem correlates with lower perceived stress (r = −0.325, p < 0.01), better information elaboration (r = 0.156, p < 0.01), and higher metacognitive awareness (r = 0.123, p < 0.05), but negatively with organizational strategies (r = −0.150, p < 0.01) and self-evaluation frequency (r = −0.153, p < 0.01). Perceived stress correlates positively with organizational strategies (r = 0.180, p < 0.01) and self-evaluation frequency (r = 0.178, p < 0.01), suggesting stress may drive compensatory strategies. Multiple regression analyses showed that self-esteem was a significant positive predictor of information elaboration strategies (β = 0.49, p = 0.05). Both self-esteem (β = −0.52, p = 0.01) and perceived stress (β = −0.74, p = 0.01) were significant negative predictors of structured learning strategies, suggesting that higher levels of stress and self-esteem are associated with a reduced use of planned organizational approaches. The models explained a substantial proportion of variance, with adjusted R² values of 0.52 for elaboration and 0.63 for strategy components. Conclusions: These results emphasize the need for interventions to enhance learning strategies and stress management among students.

Background: Chronic non-communicable diseases—chiefly chronic obstructive pulmonary disease (COPD), type 2 diabetes mellitus (T2DM), and cardiovascular diseases (CVDs)—remain the leading causes of morbidity and mortality worldwide. Despite the proliferation of telehealth programs, few longitudinal studies have rigorously evaluated theory-based, nurse-led digital education across multiple chronic conditions within a unified self-care framework. PROSELF (Promotion of Self-Care through Educational Interventions in Chronic Patients) addresses this gap. Methods: Promotion of Self-Care (PROSELF) is a prospective, longitudinal, multicenter, non-interventional study in community and primary-care settings in Southern Italy. A 12-month nurse-led digital educational intervention—grounded in the Middle-Range Theory of Self-Care of Chronic Illness—targets the three core self-care dimensions (maintenance, monitoring, and management) through individualized tele-education, asynchronous reinforcement, and structured follow-up at baseline, 3, 6, and 12 months. Validated self-care, quality-of-life, and social-support measures will be used. Data will be analyzed using repeated-measures and multivariate models to evaluate longitudinal changes in self-care, adherence, and related outcomes. Participation requires digitally informed consent. Results: The PROSELF study is expected to demonstrate the effectiveness of a 12-month, nurse-led digital educational program in improving adherence and self-care behaviors among patients with chronic diseases (COPD, diabetes, and cardiovascular disease). The intervention will leverage validated self-care assessment tools and tele-educational follow-up sessions. Conclusions: Findings from this study will inform the design of scalable, evidence-based, behaviorally informed models for digital chronic care delivery and nursing education.

Chronic diseases remain one of the most pressing public health challenges in Europe, disproportionately affecting older adults and residents of rural and underserved areas. Structural barriers to healthcare access, insufficient social support networks, and fragmented service delivery models amplify health disparities in these communities. In response, proximity-based and integrated care models have emerged as promising strategies, especially under national initiatives such as Italy’s National Recovery and Resilience Plan (PNRR). The CAMP (Chronic health Assessment and Mapping of Proximity needs) study is a cross-sectional, observational, non-interventional protocol designed to identify and characterize the unmet health and social care needs of adults living with chronic conditions in rural areas of Southern Italy. Using a mixed-methods approach, the study integrates standardized quantitative tools—the SF-36, EQ-5D, Barthel Index, and MSPSS—with semi-structured interviews to assess quality of life, functional autonomy, access barriers, and perceived social support. The study population includes adults aged ≥18 years with at least one chronic condition, recruited through general practitioners and social services. Descriptive and multivariate analyses will be used to explore associations between clinical and social variables, while thematic analysis will be applied to qualitative data. Expected outcomes include a comprehensive mapping of service gaps and resource distribution, as well as feasibility assessments for implementing community hospitals and telemedicine models. The findings will inform evidence-based territorial health planning and contribute to shaping more equitable and integrated care strategies for vulnerable populations. This protocol was preregistered on the Open Science Framework on May 26, 2025 (DOI: https://doi.org/10.17605/OSF.IO/YHD87 ).