DIGItal health literacy after COVID-19 outbreak among frail and non-frail cardiology patients: the DIGI-COVID study
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background The COVID-19 pandemic has highlighted the role of telemedicine in reducing face-to-face visits. Telemedicine requires either the use of digital support methods and a minimum technological knowledge of the patients. Digital health literacy, defined as the use of digital literacy skills to find and use health information and services, may influence the use of telemedicine in most patients, particularly in specific groups such as those with frailty. Aim To explore the association between frailty status, patients' use of digital tools and digital health literacy to determine whether it would be possible to implement control visits in patients followed in a cardiac arrhythmias outpatient clinic. Methods We prospectively enrolled consecutive patients referring to arrhythmias outpatient clinics of our department from March to September 2022. Patients were divided according to frailty status as defined by the Edmonton Frail Scale (EFS) into three subgroups: robust, pre-frail, and frail. The degree of health digital literacy was assessed through the Digital Health Literacy Instrument (DHLI) Scale. The DHLI explores 7 digital skill categories measured by 21 self-report questions. The self-report questions require participants to rate on a 4-point scale how difficult different tasks are and how frequently they encounter certain difficulties on the Internet. The total DHLI and each skill category score were calculated by summing the received scores in every single domain (3 questions per each skill category) and reported as mean and median. A multivariable logistic regression analysis was also use to evaluate the association between the non-use of the Internet and frailty status. Results A total of 300 patients were enrolled (36.3% females, median age 75 [66-84]) and stratified according to frailty status as: (i) Robust (EFS ≤ 5; n = 212, 70.7%), (ii) Pre-Frail (EFS 6-7; n = 47, 15.7%), and (iii) Frail (EFS ≥ 8; n = 41, 13.7%). Frail patients used less frequently smartphones, PC and emails and had less availability of Wi-Fi at home compared to robust patients (Table 1). At the multivariable logistic regression analysis, frailty was significantly associated with the non-use of the Internet (adjusted odds ratio, 2.58 95% confidence interval 1.92-5.61). Digital health literacy score decreased as the level of frailty increased in all the domains explored (operational skills, navigation skills, information searching, evaluating the reliability of the information, determining the relevance of online information, adding self-generated content and protecting privacy while using the internet, all p<0.001, Table 2). Conclusions Frail patients are characterized by a lower use of digital tools and access to the Internet even though these patients would benefit the most from telemedicine. Digital skills are strongly influenced by frail status highlighting the need to implement digital health literacy with specific interventions in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".