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Implantable medical devices (IMDs) are medical instruments embedded inside the body. Well-informed and empowered patients living with IMDs are key players of improving IMD-related patient safety and health outcomes. However, little is known about IMD patients' epidemiology, characteristics, and current awareness levels. Our primary aim was to investigate the point and lifetime prevalence of patients living with IMDs. Patients' IMD-related knowledge and determinants of IMDs' impact on their life were also explored. An online cross-sectional survey was conducted. Respondents' IMD history, whether they received instructions for use and IMD's overall impact on life were recorded by self-reports. Patients' knowledge about living with IMDs was assessed on visual analogue scales (VAS, 0-10). Shared decision-making was analyzed by the 9-item Shared Decision Making Questionnaire (SDM-Q-9). Descriptive statistics and subgroup comparisons between IMD wearers were performed for statistical differences. Significant determinants of IMD's overall impact on life were examined in linear regression analysis. In the total sample (N = 1400, mean age 58.1 ±11.1; female 53.7%), nearly one third of respondents were living with IMD (30.9%; 433/1400). Among them, the most frequent IMDs were tooth implants (30.9%) and intraocular lens (26.8%). Mean knowledge VAS scores were similar (range: 5.5 ±3.8-6.5 ±3.2) but differences by IMD types were observed. Patients who received instructions for use or reported better impact on life indicated higher self-reported knowledge. Regression confirmed that patients' knowledge was significant predictor of IMD's impact on life, but this effect was overwritten by the SDM-Q-9. This first comprehensive epidemiological study on IMDs provides basic data for public health strategy planning alongside the implementation of MDR. Improved self-perceived outcomes were associated with higher knowledge hence education of patients receiving IMD deserves consideration. We suggest to investigate further the role of shared decision-making on IMD's overall impact on patients' life in future prospective studies.

The introduction of new medical technologies such as sensors has accelerated the process of collecting patient data for relevant clinical decisions, which has led to the introduction of a new technology known as digital biomarkers. This study aims to assess the methodological quality and quality of evidence from meta-analyses of digital biomarker-based interventions. This study follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline for reporting systematic reviews, including original English publications of systematic reviews reporting meta-analyses of clinical outcomes (efficacy and safety endpoints) of digital biomarker-based interventions compared with alternative interventions without digital biomarkers. Imaging or other technologies that do not measure objective physiological or behavioral data were excluded from this study. A literature search of PubMed and the Cochrane Library was conducted, limited to 2019-2020. The quality of the methodology and evidence synthesis of the meta-analyses were assessed using AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) and GRADE (Grading of Recommendations, Assessment, Development, and Evaluations), respectively. This study was funded by the National Research, Development and Innovation Fund of Hungary. A total of 25 studies with 91 reported outcomes were included in the final analysis; 1 (4%), 1 (4%), and 23 (92%) studies had high, low, and critically low methodologic quality, respectively. As many as 6 clinical outcomes (7%) had high-quality evidence and 80 outcomes (88%) had moderate-quality evidence; 5 outcomes (5%) were rated with a low level of certainty, mainly due to risk of bias (85/91, 93%), inconsistency (27/91, 30%), and imprecision (27/91, 30%). There is high-quality evidence of improvements in mortality, transplant risk, cardiac arrhythmia detection, and stroke incidence with cardiac devices, albeit with low reporting quality. High-quality reviews of pedometers reported moderate-quality evidence, including effects on physical activity and BMI. No reports with high-quality evidence and high methodological quality were found. Researchers in this field should consider the AMSTAR-2 criteria and GRADE to produce high-quality studies in the future. In addition, patients, clinicians, and policymakers are advised to consider the results of this study before making clinical decisions regarding digital biomarkers to be informed of the degree of certainty of the various interventions investigated in this study. The results of this study should be considered with its limitations, such as the narrow time frame. RR2-10.2196/28204.

Background The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice. Methods In November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies. Results From 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs. Conclusions Automation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.

Healthy life expectancy (HLE) is becoming an important indicator of population health. While actuarial estimates of HLE are frequently studied, there is scarcity of research on the subjective expectations of people about their HLE. The objective of this study is to compare actuarial and subjective HLE (sHLE) estimates in the ≥50-year-old Hungarian general population. Furthermore, we assessed subjective life expectancy (sLE) and explored determinants of the individual variance of sHLE and sLE. We conducted a cross-sectional online survey in 2019. Subjective health expectations were measured at 60, 70, 80 and 90 years of age via the Global Activity Limitation Indicator (GALI). Point-estimates of sLE and background variables were also recorded. sHLE was estimated from GALI and sLE responses. Actuarial estimates of life expectancy (LE) and HLE for 2019 were provided by the Central Statistical Office of Hungary. Five hundred and four respondents (female 51.6%) were included. Mean (±SD) age was 63 (±7.5) years. Median LE (81.5 years, 95%CI 81.1-81.7) and sLE (82 years, 95%CI 80-85) were similar (p = 0.142), while median sHLE (66.8 years, 95%CI 65.5-68.3) was lower than HLE (72.7 years, 95%CI 82.4-82.9) by 5.9 years (p<0.001). Despite the greater median actuarial LE of women compared to men (p<0.001), we found no gender differences between the median sLE (p = 0.930), HLE (p = 0.417) and sHLE (p = 0.403) values. With less apparent gender differences among the predictors when compared to sLE, sHLE was mainly determined by self-perceived health, age and place of residence, while self-perceived health, close relatives' longevity, social conditions, happiness and perceived lifestyle influenced sLE. Along subjective life expectancy, subjective healthy life expectancy may be a feasible indicator and provide insights to individuals' subjective expectations underlying the demographic estimates of the healthy life expectancy of the population.

Diabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine. We aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies. We searched the PubMed and Web of Science databases from 2016 to 2020. Studies were included if the use of ML was reported in children with DM aged 2 to 18 years, including studies on complications, screening studies, and in silico samples. In studies following the ML workflow of training, validation, and testing of results, reporting quality was assessed via MI-CLAIM by consensus judgments of independent reviewer pairs. Positive answers to the 17 binary items regarding sufficient reporting were qualitatively summarized and counted as a proxy measure of reporting quality. The synthesis of results included testing the association of reporting quality with publication and data type, participants (human or in silico), research goals, level of code sharing, and the scientific field of publication (medical or engineering), as well as with expert judgments of clinical impact and reproducibility. After screening 1043 records, 28 studies were included. The sample size of the training cohort ranged from 5 to 561. Six studies featured only in silico patients. The reporting quality was low, with great variation among the 21 studies assessed using MI-CLAIM. The number of items with sufficient reporting ranged from 4 to 12 (mean 7.43, SD 2.62). The items on research questions and data characterization were reported adequately most often, whereas items on patient characteristics and model examination were reported adequately least often. The representativeness of the training and test cohorts to real-world settings and the adequacy of model performance evaluation were the most difficult to judge. Reporting quality improved over time (r=0.50; P=.02); it was higher than average in prognostic biomarker and risk factor studies (P=.04) and lower in noninvasive hypoglycemia detection studies (P=.006), higher in studies published in medical versus engineering journals (P=.004), and higher in studies sharing any code of the ML pipeline versus not sharing (P=.003). The association between expert judgments and MI-CLAIM ratings was not significant. The reporting quality of ML studies in the pediatric population with DM was generally low. Important details for clinicians, such as patient characteristics; comparison with the state-of-the-art solution; and model examination for valid, unbiased, and robust results, were often the weak points of reporting. To assess their clinical utility, the reporting standards of ML studies must evolve, and algorithms for this challenging population must become more transparent and replicable.

Background COSMIN (Consensus-based Standards for the selection of health Measurement INstruments) provides a framework for selecting and validating patient-reported outcome measurements (PROMs). This study aims to validate the Hungarian version of the GOHAI and, for the first time, to assess its Standard Error of Measurement (SEM), Smallest Detectable Change (SDC), and Measurement Invariance (MI) across general and clinical populations as well as different age groups, following COSMIN guidelines. Materials and methods The translation was performed using a forward-backward process. A mixed sample ( n  = 306) was recruited in Budapest from May 2023 to February 2024, consisting of the general population (45.1%), recruited from health kiosks and a nursing home, and the clinical population (54.9%), sourced from Semmelweis University’s care units. The sample was further divided into two age groups: 18–64 years old (54.9%) and 65 + years old (45.1%). GOHAI was administered twice to 108 stable participants. For both the additive score (ADD-GOHAI) and simple count (SC-GOHAI), structural validity and measurement invariance by subgroups were assessed via Confirmatory Factor Analysis (CFA). Internal consistency was evaluated using Cronbach’s alpha, and test-retest reliability was measured using the intraclass correlation coefficient (ICC). SEM was calculated using the SEM agreement formula, and SDC using: . Convergent and known-group validity were tested against predefined hypotheses for structural validity. Results Contrary to a three factor model, a single-factor model showed good fit in all subgroups for both scoring methods, with adequate internal consistency (Cronbach 𝛼: 0.76–0.85). Four of the six hypotheses for convergent validity and all ten hypotheses for known-groups validity supported the predefined criteria. Measurement invariance between clinical and general populations, or by age, was not demonstrated, so GOHAI’s different measurement properties should be considered when comparing subpopulations. Test-retest reliability was adequate (ICC: 0.87–0.96). SDC was ≈5 points using ADD-GOHAI and 2–3 points using SC-GOHAI. Conclusion The Hungarian version of GOHAI demonstrates satisfactory psychometric properties across both general and clinical populations, as well as among both younger and older age groups. While the measurement properties of SC-GOHAI may be more stable between populations, ADD-GOHAI seems more suitable for individual follow-up. However, observed changes must be considered in relation to the measurement error associated with GOHAI.

Sensors and digital devices have revolutionized the measurement, collection, and storage of behavioral and physiological data, leading to the new term digital biomarkers. This study aimed to investigate the scope of clinical evidence covered by systematic reviews (SRs) of randomized controlled trials involving digital biomarkers. This scoping review was organized using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. With the search limited to English publications, full-text SRs of digital biomarkers included randomized controlled trials that involved a human population and reported changes in participants' health status. PubMed and the Cochrane Library were searched with time frames limited to 2019 and 2020. The World Health Organization's classification systems for diseases (International Classification of Diseases, Eleventh Revision), health interventions (International Classification of Health Interventions), and bodily functions (International Classification of Functioning, Disability, and Health [ICF]) were used to classify populations, interventions, and outcomes, respectively. A total of 31 SRs met the inclusion criteria. The majority of SRs studied patients with circulatory system diseases (19/31, 61%) and respiratory system diseases (9/31, 29%). Most of the prevalent interventions focused on physical activity behavior (16/31, 52%) and conversion of cardiac rhythm (4/31, 13%). Looking after one's health (physical activity; 15/31, 48%), walking (12/31, 39%), heart rhythm functions (8/31, 26%), and mortality (7/31, 23%) were the most commonly reported outcomes. In total, 16 physiological and behavioral data groups were identified using the ICF tool, such as looking after one's health (physical activity; 14/31, 45%), walking (11/31, 36%), heart rhythm (7/31, 23%), and weight maintenance functions (7/31, 23%). Various digital devices were also studied to collect these data in the included reviews, such as smart glasses, smartwatches, smart bracelets, smart shoes, and smart socks for measuring heart functions, gait pattern functions, and temperature. A substantial number (24/31, 77%) of digital biomarkers were used as interventions. Moreover, wearables (22/31, 71%) were the most common types of digital devices. Position sensors (21/31, 68%) and heart rate sensors and pulse rate sensors (12/31, 39%) were the most prevalent types of sensors used to acquire behavioral and physiological data in the SRs. In recent years, the clinical evidence concerning digital biomarkers has been systematically reviewed in a wide range of study populations, interventions, digital devices, and sensor technologies, with the dominance of physical activity and cardiac monitors. We used the World Health Organization's ICF tool for classifying behavioral and physiological data, which seemed to be an applicable tool to categorize the broad scope of digital biomarkers identified in this review. To understand the clinical value of digital biomarkers, the strength and quality of the evidence on their health consequences need to be systematically evaluated.

Background The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences. Methods A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist’s and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents’ electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions. Results Altogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD = 11.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first ( p  = 0.677), and a significantly higher average amount for radiologist vs. AI in the second task ( p  = 0.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen. Conclusions Individuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies.

Purpose The CarerQol instrument has been designed and validated as an instrument able to measure both the positive and the negative impacts of caregiving on the quality of life of informal caregivers (CarerQol-7D), as well as their general happiness (CarerQol-VAS). The aim of this study was to assess the construct validity of the CarerQol in the Hungarian context. Methods The CarerQol was translated into Hungarian. Subsequently, in a cross-sectional online survey, representative for the general Hungarian population ( N  = 1000), informal caregivers were identified ( N  = 149, female 51.2%, mean age 53.2). Clinical, convergent and discriminant validity of the CarerQol were evaluated in relation to the caregivers’ and care recipients’ EQ-5D-5L health status, and caregiving situation characteristics. Results Average CarerQol-7D and CarerQol-VAS scores were 76.0 (SD 16.2) and 6.8 (SD 2.3), respectively. CarerQol-7D and CarerQol-VAS scores were significantly correlated with caregiving time ( r  = − 0.257; − 0.212), caregivers’ EQ-5D-5L scores ( r  = 0.453; 0.326) and the CarerQol-7D also with care recipients’ EQ-5D-5L scores ( r  = 0.247). CarerQol-7D scores differed significantly with relevant caregiving characteristics (e.g. nature and severity of care recipients’ health status, sharing household) and both the CarerQol-7D and CarerQol-VAS with the overall care experience. Conclusion Our findings confirmed the validity of the Hungarian language version of the CarerQol and support the cross-cultural validity of the instrument. CarerQol-7D scores performed better in distinguishing caregiving situation characteristics than the general happiness measure CarerQol-VAS. Care recipients’ health status was only weakly associated with informal caregivers’ care-related quality of life and happiness. Caregivers’ own health and caregiving circumstances were more strongly associated with these scores.

Background The Versus Arthritis Musculoskeletal Health Questionnaire (MSK-HQ) measures symptom severity and health-related quality of life (HRQoL) of people with musculoskeletal (MSK) conditions. We aimed to test the psychometric properties of the MSK-HQ among the general adult population and identify the determinants of MSK-HQ states. In addition, we aimed to explore the relationship between MSK-HQ and standard well-being measurement tools. Methods The translation proccess of the MSK-HQ into Hungarian followed the standard methods provided by the developer. A cross-sectional online survey was performed in Hungary involving a population normative sample ( N  = 2004, women: 53.1%; mean age: 48.3, SD = 16.6 years). Socio-demographic characteristics and self-reported MSK disorders were recorded. Alongside the MSK-HQ, standard measures of HRQoL (EQ-5D-5L), physical functioning (HAQ-DI) and well-being (ICECAP-A/O, WHO-5, Happiness VAS) were applied. Clinical and convergent validity were assessed by subgroup comparisons (Mann–Whitney-U and Kruskal–Wallis tests) and Spearman’s rank correlations. Internal consistency was assessed by Cronbach’s alpha. Test–retest reliability ( N  = 50) was evaluated by intraclass correlation coefficient (ICC). Predictors of MSK-HQ were analysed by ordinary least square multiple regressions. Results The mean MSK-HQ index score was 44.1 (SD = 9.9). MSK-HQ scores were significantly lower in subgroups with self-reported MSK disorders. Correlations were strong between MSK-HQ and EQ-5D-5L (0.788), EQ VAS (0.644) and HAQ-DI (-0.698) and moderate with the well-being measures ( p  < 0.05). Cronbach’s alpha was 0.924 and ICC was 0.936 ( p  < 0.05). Being a man, living in the capital, having higher income and education were positively associated with MSK-HQ scores. Conclusions This is the first study to prove the validity and reliability of the MSK-HQ among the general public. The impact of socio-demographic characteristics on MSK-HQ scores deserves consideration in clinical studies.