This study aimed to systematically identify, synthesize, and evaluate measurement properties of patient-reported outcome measures (PROMs) of eHL in adult populations. A systematic review was conducted, considering studies reporting the development or validation of eHL instruments for adult populations. Four databases and grey literature were searched from January 2000 to 2024, with additional website searches up to 2022. Quality assessment, data analysis and synthesis followed the COSMIN methodology and findings were reported according to PRISMA 2020 guidelines. The GRADE framework was used to assess evidence quality. A total of 8558 citations were identified. Seven instruments, 89 articles and 3 reports were included in this review. The HL19-DIGI, DHLI, TeHLI, eHLQ, eHLA, and Lisane demonstrated sufficient ratings for aspects of content validity, albeit with varying levels of evidence, ranging from very low to high. Five instruments showed sufficient ratings for structural validity and internal consistency, but evidence on their reliability was insufficient. No information on responsiveness was mentioned in articles. The HL19-DIGI, DHLI, eHEALS, and eHLQ were the most frequently investigated instruments. This review identified 17 eHL instruments, of which seven demonstrated adequate content validity. However, insufficient evidence exists regarding psychometric properties for widespread implementation. It is strongly recommended that the content of these instruments be updated to reflect patients’ evolving use of eHealth services, and that further psychometrics evaluations be conducted systematically. PROSPERO CRD42021232765.
Cannabinoid hyperemesis syndrome (CHS) is a condition that affects approximately one-third of cannabis users. Symptoms include recurrent episodes of vomiting, nausea, and abdominal pain. Affected individuals often find temporary relief by taking hot baths or showers. This article provides an overview of the typical manifestations of CHS and an update on the current knowledge surrounding its cause and treatment, illustrated by a clinical case.
Artificial intelligence (AI) is no longer just a passing trend. It is slowly becoming an integral part of our daily lives and our medical practice. AI opens new avenues for improving the cardiovascular prevention field. This article describes two viewpoints: a) how AI helps the patient to manage his/her cardiovascular risk factors and to educate himself/herself and b) how AI helps clinicians to improve their clinical practice. The day-to-day clinical practice already integrates smartwatches and large language models. Meanwhile, risk scores using machine learning and genetic data are still in the validation process.
BACKGROUND:Frailty is a major public health concern in aging populations. Socioeconomic disadvantage increases the risk of frailty, yet the mechanisms underlying this association remain unclear. OBJECTIVES:To examine the mediating role of chronic diseases in the longitudinal association between socioeconomic disadvantage and frailty. DESIGN:Population-based cohort study. SETTING:Lausanne, Switzerland. PARTICIPANTS:4731 community-dwelling adults aged 65-70 years at recruitment (2004, 2010, and 2014), followed for up to 16 years, as part of the Lausanne Cohort 65+. INTERVENTION:None. MEASUREMENTS:Socioeconomic disadvantage was assessed using indicators of education, occupation, income, health insurance subsidy, and financial strain. Frailty was measured using the Fried phenotype (unintentional weight loss, exhaustion, low physical activity, weakness, and slow walking speed). Chronic conditions (obesity, diabetes, hypertension, cardiovascular and respiratory disease, and multimorbidity [≥2 conditions]) were assessed at baseline using standardized self-reported physician diagnoses. Counterfactual mediation using Cox proportional hazards models estimated the proportion of the socioeconomic disadvantage-frailty association mediated by each condition. RESULTS:Socioeconomic disadvantage was associated with a 1.5-2.5-fold higher risk of incident frailty. Obesity mediated 13-55% of this association, diabetes 11-22%, and multimorbidity 21-39%, whereas hypertension, cardiovascular, and respiratory disease showed minimal or no mediation. CONCLUSIONS:Chronic diseases-particularly obesity and diabetes-partly explain the long-term impact of socioeconomic disadvantage on frailty, underscoring stark inequities in healthy aging. Early detection and management of these conditions in socioeconomically vulnerable older adults, alongside population-level prevention and efforts to address adverse socioeconomic conditions as root causes, could help reduce these inequalities.
