The coronavirus disease (COVID-19) pandemic has created an urgent need for coordinated mechanisms to respond to the outbreak across health sectors, and digital health solutions have been identified as promising approaches to address this challenge. This editorial discusses the current situation regarding digital health solutions to fight COVID-19 as well as the challenges and ethical hurdles to broad and long-term implementation of these solutions. To decrease the risk of infection, telemedicine has been used as a successful health care model in both emergency and primary care. Official communication plans should promote facile and diverse channels to inform people about the pandemic and to avoid rumors and reduce threats to public health. Social media platforms such as Twitter and Google Trends analyses are highly beneficial to model pandemic trends as well as to monitor the evolution of patients’ symptoms or public reaction to the pandemic over time. However, acceptability of digital solutions may face challenges due to potential conflicts with users’ cultural, moral, and religious backgrounds. Digital tools can provide collective public health benefits; however, they may be intrusive and can erode individual freedoms or leave vulnerable populations behind. The COVID-19 pandemic has demonstrated the strong potential of various digital health solutions that have been tested during the crisis. More concerted measures should be implemented to ensure that future digital health initiatives will have a greater impact on the epidemic and meet the most strategic needs to ease the life of people who are at the forefront of the crisis.

In elderly populations, frailty is associated with higher mortality risk. Although many frailty scores (FS) have been proposed, no single score is considered the gold standard. We aimed to evaluate the agreement between a wide range of FS in the English Longitudinal Study of Ageing (ELSA). Through a literature search, we identified 35 FS that could be calculated in ELSA wave 2 (2004–2005). We examined agreement between each frailty score and the mean of 35 FS, using a modified Bland-Altman model and Cohen's kappa (κ). Missing data were imputed. Data from 5,377 participants (ages ≥60 years) were analyzed (44.7% men, 55.3% women). FS showed widely differing degrees of agreement with the mean of all scores and between each pair of scores. Frailty classification also showed a very wide range of agreement (Cohen's κ = 0.10–0.83). Agreement was highest among “accumulation of deficits”-type FS, while accuracy was highest for multidimensional FS. There is marked heterogeneity in the degree to which various FS estimate frailty and in the identification of particular individuals as frail. Different FS are based on different concepts of frailty, and most pairs cannot be assumed to be interchangeable. Research results based on different FS cannot be compared or pooled.

Frail elderly people experience elevated mortality. However, no consensus exists on the definition of frailty, and many frailty scores have been developed. The main aim of this study was to compare the association between 35 frailty scores and incident cardiovascular disease (CVD), incident cancer, and all-cause mortality. Also, we aimed to assess whether frailty scores added predictive value to basic and adjusted models for these outcomes. Through a structured literature search, we identified 35 frailty scores that could be calculated at wave 2 of the English Longitudinal Study of Ageing (ELSA), an observational cohort study. We analysed data from 5,294 participants, 44.9% men, aged 60 years and over. We studied the association between each of the scores and the incidence of CVD, cancer, and all-cause mortality during a 7-year follow-up using Cox proportional hazard models at progressive levels of adjustment. We also examined the added predictive performance of each score on top of basic models using Harrell’s C statistic. Using age of the participant as a timescale, in sex-adjusted models, hazard ratios (HRs) (95% confidence intervals) for all-cause mortality ranged from 2.4 (95% CI: 1.7–3.3) to 26.2 (95% CI: 15.4–44.5). In further adjusted models including smoking status and alcohol consumption, HR ranged from 2.3 (95% CI: 1.6–3.1) to 20.2 (95% CI: 11.8–34.5). In fully adjusted models including lifestyle and comorbidity, HR ranged from 0.9 (95% CI: 0.5–1.7) to 8.4 (95% CI: 4.9–14.4). HRs for CVD and cancer incidence in sex-adjusted models ranged from 1.2 (95% CI: 0.5–3.2) to 16.5 (95% CI: 7.8–35.0) and from 0.7 (95% CI: 0.4–1.2) to 2.4 (95% CI: 1.0–5.7), respectively. In sex- and age-adjusted models, all frailty scores showed significant added predictive performance for all-cause mortality, increasing the C statistic by up to 3%. None of the scores significantly improved basic prediction models for CVD or cancer. A source of bias could be the differences in mortality follow-up time compared to CVD/cancer, because the existence of informative censoring cannot be excluded. There is high variability in the strength of the association between frailty scores and 7-year all-cause mortality, incident CVD, and cancer. With regard to all-cause mortality, some scores give a modest improvement to the predictive ability. Our results show that certain scores clearly outperform others with regard to three important health outcomes in later life. Finally, we think that despite their limitations, the use of frailty scores to identify the elderly population at risk is still a useful measure, and the choice of a frailty score should balance feasibility with performance.

OBJECTIVE Frailty is a dynamic state of vulnerability in the elderly. We examined whether individuals with overt diabetes or higher levels of HbA_1c or fasting plasma glucose (FG) experience different frailty trajectories with aging. RESEARCH DESIGN AND METHODS Diabetes, HbA_1c, and FG were assessed at baseline, and frailty status was evaluated with a 36-item frailty index every 2 years during a 10-year follow-up among participants from the English Longitudinal Study of Ageing (ELSA). Mixed-effects models with age as time scale were used to assess whether age trajectories of frailty differed as a function of diabetes, HbA_1c, and FG. RESULTS Among 5,377 participants (median age [interquartile range] 70 [65, 77] years, 45% men), 35% were frail at baseline. In a model adjusted for sex, participants with baseline diabetes had an increased frailty index over aging compared with those without diabetes. Similar findings were observed with higher levels of HbA_1c, while FG was not associated with frailty. In a model additionally adjusted for income, social class, smoking, alcohol, and hemoglobin, only diabetes was associated with an increased frailty index. Among nonfrail participants at baseline, both diabetes and HbA_1c level were associated with a higher increased frailty index over time. CONCLUSIONS People with diabetes or higher HbA_1c levels at baseline had a higher frailty level throughout later life. Nonfrail participants with diabetes or higher HbA_1c also experienced more rapid deterioration of frailty level with aging. This observation could reflect a role of diabetes complications in frailty trajectories or earlier shared determinants that contribute to diabetes and frailty risk in later life.

Digital health interventions (DHIs) are interesting resources to improve various health conditions. However, their use in the older and frail population is still sparse. We aimed to give an overview of DHI used in the frail older population. Scoping review with PRISMA guidelines based on Population, Concept, and Context. We included original studies in English with DHI (concept) on people described as frail (population) in the clinical or community setting (context) and no limitation on date of publication. We searched 3 online databases (PubMed, Scopus, and Web of Science). We described DHI in terms of purpose, delivering, content and assessment. We also described frailty assessment and study design. We included 105 studies that fulfilled our eligibility criteria. The most frequently reported DHIs were with the purpose of monitoring (45; 43%), with a delivery method of sensor-based technologies (59; 56%), with a content of feedback to users (34; 32%), and for assessment of feasibility (57; 54%). Efficacy was reported in 31 (30%) studies and usability/feasibility in 57 (55%) studies. The most common study design was descriptive exploratory for new methodology or technology (24; 23%). There were 14 (13%) randomized controlled trials, with only 4 of 14 studies (29%) showing a low or moderate risk of bias. Frailty assessment using validated scales was reported in only 47 (45%) studies. There was much heterogeneity among frailty assessments, study designs, and evaluations of DHIs. There is now a strong need for more standardized approaches to assess frailty, well-structured randomized controlled trials, and proper evaluation and report. This work will contribute to the development of better DHIs in this vulnerable population.

Psychological complications are frequent in type 1 diabetes (T1D) but they might be difficult to distinguish one from the other in clinical practice. Our objective was to study the distinguishing characteristics, overlaps and their use in the literature between three concepts of T1D: depression, diabetes distress (DD) and diabetes burnout (DB). A scoping review (PRISMA guidelines) performed in three databases (PubMed/MEDLINE, PsycInfo, Web of Science) with the keywords: T1D, depression, diabetes and burnout, from January 1990 to June 2021. We selected original studies with participants with T1D, which reported depression, DD, or DB. We extracted information about the concepts, their sub-concepts and screening tools. Of the 4763 studies identified, 201 studies were included in the study. Seventy-three percent, 57% and 45% of sub-concepts do not overlap in depression, DD, and DB, respectively. We observed overlap between depression (27%)/DD (27%) and between DD (20%)/DB (50%). A number of sub-concepts distinguish depression and DD. Overlaps between concepts suggest that a more precise definition is still lacking. DB is still a relatively new concept and more research is needed to better understand how it can present itself differently, in order to personalize care in comparison to those having DD. This project was supported by the MSDAvenir Foundation (Project World Diabetes Distress Study).