Creation of a pandemic memory by tracing COVID-19 infections and immunity in Luxembourg (CON-VINCE)

BACKGROUND

During the COVID-19 pandemic swift implementation of research cohorts was key. While many studies focused exclusively on infected individuals, population based cohorts are essential for the follow-up of SARS-CoV-2 impact on public health. Here we present the CON-VINCE cohort, estimate the point and period prevalence of the SARS-CoV-2 infection, reflect on the spread within the Luxembourgish population, examine immune responses to SARS-CoV-2 infection and vaccination, and ascertain the impact of the pandemic on population psychological wellbeing at a nationwide level.

METHODS

A representative sample of the adult Luxembourgish population was enrolled. The cohort was followed-up for twelve months. SARS-CoV-2 RT-qPCR and serology were conducted at each sampling visit. The surveys included detailed epidemiological, clinical, socio-economic, and psychological data.

RESULTS

One thousand eight hundred sixty-five individuals were followed over seven visits (April 2020-June 2021) with the final weighted period prevalence of SARS-CoV-2 infection of 15%. The participants had similar risks of being infected regardless of their gender, age, employment status and education level. Vaccination increased the chances of IgG-S positivity in infected individuals. Depression, anxiety, loneliness and stress levels increased at a point of study when there were strict containment measures, returning to baseline afterwards.

CONCLUSION

The data collected in CON-VINCE study allowed obtaining insights into the infection spread in Luxembourg, immunity build-up and the impact of the pandemic on psychological wellbeing of the population. Moreover, the study holds great translational potential, as samples stored at the biobank, together with self-reported questionnaire information, can be exploited in further research.

TRIAL REGISTRATION

Trial registration number: NCT04379297, 10 April 2020.

Luxembourg Parkinson's study -comprehensive baseline analysis of Parkinson's disease and atypical parkinsonism

Background

Deep phenotyping of Parkinson's disease (PD) is essential to investigate this fastest-growing neurodegenerative disorder. Since 2015, over 800 individuals with PD and atypical parkinsonism along with more than 800 control subjects have been recruited in the frame of the observational, monocentric, nation-wide, longitudinal-prospective Luxembourg Parkinson's study.

Objective

To profile the baseline dataset and to explore risk factors, comorbidities and clinical profiles associated with PD, atypical parkinsonism and controls.

Methods

Epidemiological and clinical characteristics of all 1,648 participants divided in disease and control groups were investigated. Then, a cross-sectional group comparison was performed between the three largest groups: PD, progressive supranuclear palsy (PSP) and controls. Subsequently, multiple linear and logistic regression models were fitted adjusting for confounders.

Results

The mean (SD) age at onset (AAO) of PD was 62.3 (11.8) years with 15% early onset (AAO < 50 years), mean disease duration 4.90 (5.16) years, male sex 66.5% and mean MDS-UPDRS III 35.2 (16.3). For PSP, the respective values were: 67.6 (8.2) years, all PSP with AAO > 50 years, 2.80 (2.62) years, 62.7% and 53.3 (19.5). The highest frequency of hyposmia was detected in PD followed by PSP and controls (72.9%; 53.2%; 14.7%), challenging the use of hyposmia as discriminating feature in PD vs. PSP. Alcohol abstinence was significantly higher in PD than controls (17.6 vs. 12.9%, p = 0.003).

Conclusion

Luxembourg Parkinson's study constitutes a valuable resource to strengthen the understanding of complex traits in the aforementioned neurodegenerative disorders. It corroborated several previously observed clinical profiles, and provided insight on frequency of hyposmia in PSP and dietary habits, such as alcohol abstinence in PD.Clinical trial registration: clinicaltrials.gov, NCT05266872.

Mixed-methods study protocol to identify expectations of people with type 1 diabetes and their caregivers about voice-based digital health solutions to support the management of diabetes distress: the PsyVoice study

INTRODUCTION

Type 1 diabetes (T1D) requires continuous management to obtain a good metabolic control and prevent acute complications. This often affects psychological well-being. People with T1D frequently report diabetes distress (DD). Psychological issues can negatively affect metabolic control and well-being. New technologies can improve quality of life, reduce the treatment burden and improve glycaemic control. Voice technology may serve as an innovative and inexpensive remote monitoring device to evaluate psychological well-being. Tailoring digital health interventions according to the ability and interest of their intended 'end-users' increases the acceptability of the intervention itself. PsyVoice explores the perspectives and needs of people with T1D on voice-based digital health interventions to manage DD.

METHODS AND ANALYSIS

PsyVoice is a mixed-methods study with qualitative and quantitative data sources. For the qualitative part, the researchers will invite 20 people with a T1D or caregivers of children with T1D to participate in in-depth semi-structured interviews. They will be invited as well to answer three questionnaires to assess socio-demographics, diabetes management, e-Health literacy and diabetes distress. Information from questionnaires will be integrated with themes developed in the qualitative analysis of the interviews. People with T1D will be invited to participate in the protocol and give feedback on interview guides, questionnaires, information sheets and informed consent.

ETHICS AND DISSEMINATION

PsyVoice received ethical approval from Luxembourg's National Research Ethics Committee. Participants will receive information about the purpose, risks and strategies to ensure the confidentiality and anonymity of the study. The results of PsyVoice will guide the selection and development of voice-based technological interventions for managing DD. The outcome will be disseminated to academic and non-academic stakeholders through peer-reviewed open-access journals and a lay public report.

TRIAL REGISTRATION NUMBER

This study is registered on ClinicalTrials.gov with the number NCT05517772.

Co-Design of a Voice-Based Digital Health Solution to Monitor Persisting Symptoms Related to COVID-19 (UpcomingVoice Study): Protocol for a Mixed Methods Study

BACKGROUND

Between 10% and 20% of people with a COVID-19 infection will develop the so-called long COVID syndrome, which is characterized by fluctuating symptoms. Long COVID has a high impact on the quality of life of affected people, who often feel abandoned by the health care system and are demanding new tools to help them manage their symptoms. New digital monitoring solutions could allow them to visualize the evolution of their symptoms and could be tools to communicate with health care professionals (HCPs). The use of voice and vocal biomarkers could facilitate the accurate and objective monitoring of persisting and fluctuating symptoms. However, to assess the needs and ensure acceptance of this innovative approach by its potential users-people with persisting COVID-19-related symptoms, with or without a long COVID diagnosis, and HCPs involved in long COVID care-it is crucial to include them in the entire development process.

OBJECTIVE

In the UpcomingVoice study, we aimed to define the most relevant aspects of daily life that people with long COVID would like to be improved, assess how the use of voice and vocal biomarkers could be a potential solution to help them, and determine the general specifications and specific items of a digital health solution to monitor long COVID symptoms using vocal biomarkers with its end users.

METHODS

UpcomingVoice is a cross-sectional mixed methods study and consists of a quantitative web-based survey followed by a qualitative phase based on semistructured individual interviews and focus groups. People with long COVID and HCPs in charge of patients with long COVID will be invited to participate in this fully web-based study. The quantitative data collected from the survey will be analyzed using descriptive statistics. Qualitative data from the individual interviews and the focus groups will be transcribed and analyzed using a thematic analysis approach.

RESULTS

The study was approved by the National Research Ethics Committee of Luxembourg (number 202208/04) in August 2022 and started in October 2022 with the launch of the web-based survey. Data collection will be completed in September 2023, and the results will be published in 2024.

CONCLUSIONS

This mixed methods study will identify the needs of people affected by long COVID in their daily lives and describe the main symptoms or problems that would need to be monitored and improved. We will determine how using voice and vocal biomarkers could meet these needs and codevelop a tailored voice-based digital health solution with its future end users. This project will contribute to improving the quality of life and care of people with long COVID. The potential transferability to other diseases will be explored, which will contribute to the deployment of vocal biomarkers in general.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05546918; https://clinicaltrials.gov/ct2/show/NCT05546918.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/46103.

Validation of a Parkinson's disease questionnaire-39-based functional mobility composite score (FMCS) in people with Parkinson's disease

INTRODUCTION

Functional mobility is an important outcome for people with Parkinson's disease (PwP). Despite this, there is no established patient-reported outcome measure that serves as a gold standard for assessing patient-reported functional mobility in PwP. We aimed to validate the algorithm calculating the Parkinson's Disease Questionnaire-39 (PDQ-39) based Functional Mobility Composite Score (FMCS).

METHODS

We designed a count-based algorithm to measure patient-reported functional mobility in PwP from items of the PDQ-39 subscales mobility and activities of daily living. Convergent validity of the algorithm calculating the PDQ-39-based FMCS was assessed using the objective Timed Up and Go (n = 253) and discriminative validity was assessed by comparing the FMCS with patient-reported (MDS-UPDRS II) and clinician-assessed (MDS-UPDRS III) motor symptoms as well as between disease stages (H&Y) and PIGD phenotypes (n = 736). Participants were between 22 and 92 years old, with a disease duration from 0 to 32 years and 64.9% in a H&Y 1-2 ranging from 1 to 5.

RESULTS

Spearman correlation coefficients (r_s) ranging from -0.45 to -0.77 (p < 0.001) indicated convergent validity. Hence, a t-test suggested sufficient ability of the FMCS to discriminate (p < 0.001) between patient-reported and clinician-assessed motor symptoms. More specifically, FMCS was more strongly associated with patient-reported MDS-UPDRS II (r_s = -0.77) than clinician-reported MDS-UPDRS III (r_s = -0.45) and can discriminate between disease stages as between PIGD phenotypes (p < 0.001).

CONCLUSION

The FMCS is a valid composite score to assess functional mobility through patient reports in PwP for studying functional mobility in studies using the PDQ-39.

Substituting device-measured sedentary time with alternative 24-hour movement behaviours: compositional associations with adiposity and cardiometabolic risk in the ORISCAV-LUX 2 study

BACKGROUND

There is a considerable burden of sedentary time in European adults. We aimed to quantify the differences in adiposity and cardiometabolic health associated with theoretically exchanging sedentary time for alternative 24 h movement behaviours.

METHODS

This observational cross-sectional study included Luxembourg residents aged 18-79 years who each provided  ≥ 4 valid days of triaxial accelerometry (n = 1046). Covariable adjusted compositional isotemporal substitution models were used to examine if statistically replacing device-measured sedentary time with more time in the sleep period, light physical activity (PA), or moderate-to-vigorous PA (MVPA) was associated with adiposity and cardiometabolic health markers. We further investigated the cardiometabolic properties of replacing sedentary time which was accumulated in prolonged (≥ 30 min) with non-prolonged (< 30 min) bouts.

RESULTS

Replacing sedentary time with MVPA was favourably associated with adiposity, high-density lipoprotein cholesterol, fasting glucose, insulin, and clustered cardiometabolic risk. Substituting sedentary time with light PA was associated with lower total body fat, fasting insulin, and was the only time-exchange to predict lower triglycerides and a lower apolipoprotein B/A1 ratio. Exchanging sedentary time with more time in the sleep period was associated with lower fasting insulin, and with lower adiposity in short sleepers. There was no significant evidence that replacing prolonged with non-prolonged sedentary time was related to outcomes.

CONCLUSIONS

Artificial time-use substitutions indicate that replacing sedentary time with MVPA is beneficially associated with the widest range of cardiometabolic risk factors. Light PA confers some additional and unique metabolic benefit. Extending sleep, by substituting sedentary time with more time in the sleep period, may lower obesity risk in short sleepers.

Association between use of psychotropic medications prior to SARS-COV-2 infection and trajectories of COVID-19 recovery: Findings from the prospective Predi-COVID cohort study

Psychological disturbances are frequent following COVID-19. However, there is not much information about whether pre-existing psychological disorders are associated with the severity and evolution of COVID-19. We aimed to explore the associations between regular psychotropic medication use (PM) before infection as a proxy for mood or anxiety disorders with COVID-19 recovery trajectories. We used data from the Predi-COVID study. We followed adults, tested positive for SARS-CoV-2 and collected demographics, clinical characteristics, comorbidities and daily symptoms 14 days after inclusion. We calculated a score based on 16 symptoms and modeled latent class trajectories. We performed polynomial logistic regression with PM as primary exposure and the different trajectories as outcome. We included 791 participants, 51% were men, and 5.3% reported regular PM before infection. We identified four trajectories characterizing recovery dynamics: "Almost asymptomatic," "Quick recovery," "Slow recovery," and "Persisting symptoms". With a fully adjusted model for age, sex, socioeconomic, lifestyle and comorbidity, we observed associations between PM with the risks of being in more severe trajectories than "Almost Asymptomatic": "Quick recovery" (relative risk (95% confidence intervals) 3.1 (2.7, 3.4), "Slow recovery" 5.2 (3.0, 9.2), and "Persisting symptoms"11.7 (6.9, 19.6) trajectories. We observed a gradient of risk between PM before the infection and the risk of slow or no recovery in the first 14 days. These results suggest that a pre-existing psychological condition increases the risk of a poorer evolution of COVID-19 and may increase the risk of Long COVID. Our findings can help to personalize the care of people with COVID-19.

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

BACKGROUND

In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population.

METHODS

We performed a longitudinal analysis with participants of the English Longitudinal Study of Ageing. We included men and women with no NDs at baseline, aged 60 years and older, assessed every 2 years from 2004 to 2005 (wave2) to 2016-2017 (wave 8). The features were a set of 91 epidemiological and clinical baseline variables. The outcome was new events of Parkinson's, Alzheimer or dementia. After applying multiple imputations, we trained three DNN algorithms: Feedforward, TabTransformer, and Dense Convolutional (Densenet). In addition, we trained two algorithms based on Cox models: Elastic Net regularization (CoxEn) and selected features (CoxSf).

RESULTS

5433 participants were included in wave 2. During follow-up, 12.7% participants developed NDs. Although the five models predicted NDs events, the discriminative ability was superior using TabTransformer (Uno's C-statistic (coefficient (95% confidence intervals)) 0.757 (0.702, 0.805). TabTransformer showed superior time-dependent balanced accuracy (0.834 (0.779, 0.889)) and specificity (0.855 (0.0.773, 0.909)) than the other models. With the CoxSf (hazard ratio (95% confidence intervals)), age (10.0 (6.9, 14.7)), poor hearing (1.3 (1.1, 1.5)) and weight loss 1.3 (1.1, 1.6)) were associated with a higher DNN risk. In contrast, executive function (0.3 (0.2, 0.6)), memory (0, 0, 0.1)), increased gait speed (0.2, (0.1, 0.4)), vigorous physical activity (0.7, 0.6, 0.9)) and higher BMI (0.4 (0.2, 0.8)) were associated with a lower DNN risk.

CONCLUSION

TabTransformer is promising for prediction of NDs with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for NDs.

Device-measured physical activity and sedentary time in a national sample of Luxembourg residents: the ORISCAV-LUX 2 study

BACKGROUND

Existing information about population physical activity (PA) levels and sedentary time in Luxembourg are based on self-reported data.

METHODS

This observational study included Luxembourg residents aged 18-79y who each provided ≥4 valid days of triaxial accelerometry in 2016-18 (n=1122). Compliance with the current international PA guideline (≥150 min moderate-to-vigorous PA (MVPA) per week, irrespective of bout length) was quantified and variability in average 24h acceleration (indicative of PA volume), awake-time PA levels, sedentary time and accumulation pattern were analysed by linear regression. Data were weighted to be nationally representative.

RESULTS

Participants spent 51% of daily time sedentary (mean (95% confidence interval (CI)): 12.1 (12.0 to 12.2) h/day), 11% in light PA (2.7 (2.6 to 2.8) h/day), 6% in MVPA (1.5 (1.4 to 1.5) h/day), and remaining time asleep (7.7 (7.6 to 7.7) h/day). Adherence to the PA guideline was high (98.1%). Average 24h acceleration and light PA were higher in women than men, but men achieved higher average accelerations across the most active periods of the day. Women performed less sedentary time and shorter sedentary bouts. Older participants (aged ≥55y) registered a lower average 24h acceleration and engaged in less MVPA, more sedentary time and longer sedentary bouts. Average 24h acceleration was higher in participants of lower educational attainment, who also performed less sedentary time, shorter bouts, and fewer bouts of prolonged sedentariness. Average 24h acceleration and levels of PA were higher in participants with standing and manual occupations than a sedentary work type, but manual workers registered lower average accelerations across the most active periods of the day. Standing and manual workers accumulated less sedentary time and fewer bouts of prolonged sedentariness than sedentary workers. Active commuting to work was associated with higher average 24h acceleration and MVPA, both of which were lower in participants of poorer self-rated health and higher weight status. Obesity was associated with less light PA, more sedentary time and longer sedentary bouts.

CONCLUSIONS

Adherence to recommended PA is high in Luxembourg, but half of daily time is spent sedentary. Specific population subgroups will benefit from targeted efforts to replace sedentary time with PA.

Discovery and Analytical Validation of a Vocal Biomarker to Monitor Anosmia and Ageusia in Patients With COVID-19: Cross-sectional Study

Background

The COVID-19 disease has multiple symptoms, with anosmia and ageusia being the most prevalent, varying from 75% to 95% and from 50% to 80% of infected patients, respectively. An automatic assessment tool for these symptoms will help monitor the disease in a fast and noninvasive manner.

Objective

We hypothesized that people with COVID-19 experiencing anosmia and ageusia had different voice features than those without such symptoms. Our objective was to develop an artificial intelligence pipeline to identify and internally validate a vocal biomarker of these symptoms for remotely monitoring them.

Methods

This study used population-based data. Participants were assessed daily through a web-based questionnaire and asked to register 2 different types of voice recordings. They were adults (aged >18 years) who were confirmed by a polymerase chain reaction test to be positive for COVID-19 in Luxembourg and met the inclusion criteria. Statistical methods such as recursive feature elimination for dimensionality reduction, multiple statistical learning methods, and hypothesis tests were used throughout this study. The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Prediction Model Development checklist was used to structure the research.

Results

This study included 259 participants. Younger (aged <35 years) and female participants showed higher rates of ageusia and anosmia. Participants were aged 41 (SD 13) years on average, and the data set was balanced for sex (female: 134/259, 51.7%; male: 125/259, 48.3%). The analyzed symptom was present in 94 (36.3%) out of 259 participants and in 450 (27.5%) out of 1636 audio recordings. In all, 2 machine learning models were built, one for Android and one for iOS devices, and both had high accuracy—88% for Android and 85% for iOS. The final biomarker was then calculated using these models and internally validated.

Conclusions

This study demonstrates that people with COVID-19 who have anosmia and ageusia have different voice features from those without these symptoms. Upon further validation, these vocal biomarkers could be nested in digital devices to improve symptom assessment in clinical practice and enhance the telemonitoring of COVID-19–related symptoms.

Trial Registration

Clinicaltrials.gov NCT04380987; https://clinicaltrials.gov/ct2/show/NCT04380987

A voice-based biomarker for monitoring symptom resolution in adults with COVID-19: Findings from the prospective Predi-COVID cohort study

People with COVID-19 can experience impairing symptoms that require enhanced surveillance. Our objective was to train an artificial intelligence-based model to predict the presence of COVID-19 symptoms and derive a digital vocal biomarker for easily and quantitatively monitoring symptom resolution. We used data from 272 participants in the prospective Predi-COVID cohort study recruited between May 2020 and May 2021. A total of 6473 voice features were derived from recordings of participants reading a standardized pre-specified text. Models were trained separately for Android devices and iOS devices. A binary outcome (symptomatic versus asymptomatic) was considered, based on a list of 14 frequent COVID-19 related symptoms. A total of 1775 audio recordings were analyzed (6.5 recordings per participant on average), including 1049 corresponding to symptomatic cases and 726 to asymptomatic ones. The best performances were obtained from Support Vector Machine models for both audio formats. We observed an elevated predictive capacity for both Android (AUC = 0.92, balanced accuracy = 0.83) and iOS (AUC = 0.85, balanced accuracy = 0.77) as well as low Brier scores (0.11 and 0.16 respectively for Android and iOS when assessing calibration. The vocal biomarker derived from the predictive models accurately discriminated asymptomatic from symptomatic individuals with COVID-19 (t-test P-values<0.001). In this prospective cohort study, we have demonstrated that using a simple, reproducible task of reading a standardized pre-specified text of 25 seconds enabled us to derive a vocal biomarker for monitoring the resolution of COVID-19 related symptoms with high accuracy and calibration.

Intermittent Scanning Glucose Monitoring or Predicted Low Suspend Pump Treatment: Does It Impact Time in Glucose Target and Treatment Preference? The QUEST Randomized Crossover Study

OBJECTIVE

To compare glycemic control and treatment preference in children with type 1 diabetes (T1D) using sensor augmented pump (SAP) with predictive low glucose suspend (SmartGuard^®) or pump with independent intermittent scanning continuous glucose monitoring (iscCGM, Freestyle libre ^®).

METHODS

In this open label, cross-over study, children 6 to 14 years of age, treated with insulin pump for at least 6 months, were randomized to insulin pump and iscCGM (A) or SAP with SmartGuard^® (B) for 5 weeks followed by 5 additional weeks. The difference in percentages of time in glucose target (TIT), (3.9 - 8.0 mmol/l), <3 mmol/l, > 8 and 10 mmol/l, were analyzed using linear mixed models during the final week of each arm and were measured by blinded CGM (IPro2^®).

RESULTS

31 children (15 girls) finished the study. With sensor compliance > 60%, no difference in TIT was found, TIT: A 37.86%; 95% CI [33.21; 42.51]; B 37.20%; 95% CI [32.59; 41.82]; < 3 mmol/l A 2.27% 95% CI [0.71; 3.84] B 1.42% 95% CI [-0.13; 2.97]; > 8 mmol/l A 0.60% 95% CI [0.56, 0.67]; B 0.63% [0.56; 0.70]. One year after the study all participants were on CGM compared to 80.7% prior to the study, with a shift of 13/25 participants from iscCGM to SAP.

CONCLUSIONS

In this study, no significant difference in glycemic control was found whether treated with SAP (SmartGuard^®) or pump with iscCGM. The decision of all families to continue with CGM after the study suggests a positive impact, with preference for SmartGuard^®.

CLINICAL TRIAL REGISTRATION

[clinicaltrials.gov], identifier NCT03103867.

Methods to Generate Innovative Research Ideas and Improve Patient and Public Involvement in Modern Epidemiological Research: Review, Patient Viewpoint, and Guidelines for Implementation of a Digital Cohort Study

Background

Patient and public involvement (PPI) in research aims to increase the quality and relevance of research by incorporating the perspective of those ultimately affected by the research. Despite these potential benefits, PPI is rarely included in epidemiology protocols.

Objective

The aim of this study is to provide an overview of methods used for PPI and offer practical recommendations for its efficient implementation in epidemiological research.

Methods

We conducted a review on PPI methods. We mirrored it with a patient advocate’s viewpoint about PPI. We then identified key steps to optimize PPI in epidemiological research based on our review and the viewpoint of the patient advocate, taking into account the identification of barriers to, and facilitators of, PPI. From these, we provided practical recommendations to launch a patient-centered cohort study. We used the implementation of a new digital cohort study as an exemplary use case.

Results

We analyzed data from 97 studies, of which 58 (60%) were performed in the United Kingdom. The most common methods were workshops (47/97, 48%); surveys (33/97, 34%); meetings, events, or conferences (28/97, 29%); focus groups (25/97, 26%); interviews (23/97, 24%); consensus techniques (8/97, 8%); James Lind Alliance consensus technique (7/97, 7%); social media analysis (6/97, 6%); and experience-based co-design (3/97, 3%). The viewpoint of a patient advocate showed a strong interest in participating in research. The most usual PPI modalities were research ideas (60/97, 62%), co-design (42/97, 43%), defining priorities (31/97, 32%), and participation in data analysis (25/97, 26%). We identified 9 general recommendations and 32 key PPI-related steps that can serve as guidelines to increase the relevance of epidemiological studies.

Conclusions

PPI is a project within a project that contributes to improving knowledge and increasing the relevance of research. PPI methods are mainly used for idea generation. On the basis of our review and case study, we recommend that PPI be included at an early stage and throughout the research cycle and that methods be combined for generation of new ideas. For e-cohorts, the use of digital tools is essential to scale up PPI. We encourage investigators to rely on our practical recommendations to extend PPI in future epidemiological studies.

Unraveling the concepts of distress, burnout, and depression in type 1 diabetes: A scoping review

BACKGROUND

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).

METHODS

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.

FINDINGS

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%).

INTERPRETATION

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.

Objective and subjective sleep measures are associated with HbA1c and insulin sensitivity in the general population: Findings from the ORISCAV-LUX-2 study

AIM

To analyze the association of objective and subjective sleep measures with HbA1c and insulin sensitivity in the general population.

METHODS

Using a cross-sectional design, data from 1028 participants in the ORISCAV-LUX-2 study from the general population in Luxembourg were analyzed. Objective sleep measures were assessed using accelerometers whereas subjective measures were assessed using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Sleep measures were defined as predictors, while HbA1c and quantitative insulin sensitivity check index (QUICKI) scores were considered outcomes. Linear and spline regression models were fitted by progressively adjusting for demographic and lifestyle variables in the total sample population as well as by stratified analyses using gender, obesity status, depressive symptoms and diabetes status.

RESULTS

In fully adjusted models, total and deep sleep durations were associated with lower HbA1c (mmol/mol) levels, whereas sleep coefficients of variation (%) and poor sleep efficiency, as measured by PSQI scores (units), were associated with higher HbA1c levels. In stratified models, such associations were observed mainly in men, and in subjects who had depressive symptoms, were overweight and no diabetes. In addition, total sleep, deep sleep, coefficients of variation and poor sleep efficiency as measured by PSQI revealed non-linear associations. Similarly, greater insulin sensitivity was associated with longer total sleep time and with PSQI-6 (use of sleep medication).

CONCLUSION

Associations were more frequently observed between sleep characteristics and glycaemic control with the use of objective sleep measures. Also, such associations varied within subgroups of the population. Our results highlight the relevance of measuring sleep patterns as key factors in the prevention of diabetes.

Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach: The example of Chile

BACKGROUND

There is a need to identify priority zones for cardiometabolic prevention. Disease mapping in countries with high heterogeneity in the geographic distribution of the population is challenging. Our goal was to map the cardiometabolic health and identify hotspots of disease using data from a national health survey.

METHODS

Using Chile as a case study, we applied a Bayesian hierarchical modelling. We performed a cross-sectional analysis of the 2009-2010 Chilean Health Survey. Outcomes were diabetes (all types), obesity, hypertension, and high LDL cholesterol. To estimate prevalence, we used individual and aggregated data by province. We identified hotspots defined as prevalence in provinces significantly greater than the national prevalence. Models were adjusted for age, sex, their interaction, and sampling weight. We imputed missing data. We applied a joint outcome modelling approach to capture the association between the four outcomes.

RESULTS

We analysed data from 4,780 participants (mean age (SD) 46 (19) years; 60% women). The national prevalence (percentage (95% credible intervals) for diabetes, obesity, hypertension and high LDL cholesterol were 10.9 (4.5, 19.2), 30.0 (17.7, 45.3), 36.4 (16.4, 57.6), and 13.7 (3.4, 32.2) respectively. Prevalence of diabetes was lower in the far south. Prevalence of obesity and hypertension increased from north to far south. Prevalence of high LDL cholesterol was higher in the north and south. A hotspot for diabetes was located in the centre. Hotspots for obesity were mainly situated in the south and far south, for hypertension in the centre, south and far south and for high LDL cholesterol in the far south.

CONCLUSIONS

The distribution of cardiometabolic risk factors in Chile has a characteristic pattern with a general trend to a north-south gradient. Our approach is reproducible and demonstrates that the Bayesian approach enables the accurate identification of hotspots and mapping of disease, allowing the identification of areas for cardiometabolic prevention.

Digital Health Strategies to Fight COVID-19 Worldwide: Challenges, Recommendations, and a Call for Papers

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.

Prospective Association Among Diabetes Diagnosis, HbA1c, Glycemia, and Frailty Trajectories in an Elderly Population

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.

Effect of predicted low suspend pump treatment on improving glycaemic control and quality of sleep in children with type 1 diabetes and their caregivers: the QUEST randomized crossover study

Background

In attempting to achieve optimal metabolic control, the day-to-day management is challenging for a child with type 1 diabetes (T1D) and his family and can have a major negative impact on their quality of life. Augmenting an insulin pump with glucose sensor information leads to improved outcomes: decreased haemoglobin A1c levels, increased time in glucose target and less hypoglycaemia. Fear of nocturnal hypoglycaemia remains pervasive amongst parents, leading to chronic sleep interruption and lack of sleep for the parents and their children.

The QUEST study, an open-label, single-centre randomized crossover study, aims to evaluate the impact on time in target, in hypoglycaemia and hyperglycaemia and the effect on sleep and quality of life in children with T1D, comparing a sensor-augmented pump (SAP) with predictive low glucose suspend and alerts to the use of the same insulin pump with a flash glucose measurement (FGM) device not interacting with the pump.

Methods/design

Subjects meeting the inclusion criteria are randomized to treatment with the SAP or treatment with an insulin pump and independent FGM for 5 weeks. Following a 3-week washout period, the subjects cross over to the other study arm for 5 weeks. During the week before and in the last week of treatment, the subjects and one of their caregivers wear a sleep monitor in order to obtain sleep data. The primary endpoint is the between-arm difference in percentage of time in glucose target during the final 6 days of each treatment arm, measured by a blinded continuous glucose measurement (CGM).

Additional endpoints include comparison of quantity and quality of sleep as well as quality of life perception of the subjects and one of their caregivers in the two different treatment arms.

Recruitment started in February 2017. A total of 36 patients are planned to be randomized. The study recruitment was completed in April 2018.

Discussion

With this study we will provide more information on whether insulin pump treatment combined with more technology (SmartGuard® feature and alerts) leads to better metabolic control. The inclusion of indicators on quality of sleep with less sleep interruption, less lack of sleep and perception of quality of life in both children and their primary caregivers is essential for this study and might help to guide us to further treatment improvement.

Trial registration

ClinicalTrials.gov, NCT03103867. Registered on 6 April 2017.

Electronic supplementary material

The online version of this article (10.1186/s13063-018-3034-4) contains supplementary material, which is available to authorized users.

Agreement Between 35 Published Frailty Scores in the General Population

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.

Taste preference and psychopathology

OBJECTIVE

Excessive food intake has been linked to many factors including taste preference and the presence of psychopathology. The purpose of this study was to investigate the association between sweet and salty taste preference and psychopathology in patients with severe obesity.

METHODS

A consecutive series of patients applying for bariatric surgery was recruited for the study. Taste preference was self-reported. Psychopathology was assessed using the revised version of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). 190 patients were included in the study.

RESULTS

In comparison with patients who had salty taste preference, patients with sweet taste preference had significantly higher elevations on the depression (OD: 4.090, p = 0.010) and the hysteria (OD: 2.951, p = 0.026) clinical scales of the MMPI-2.

CONCLUSION

The results suggest the presence of an association between taste preference and psychopathology. The findings may be of interest for clinicians who are involved in the treatment of obesity. In particular, they may wish to pay increased attention to patients with sweet taste preference or who have a strong attraction for both sweet and salty foods, in order to detect psychopathology and to adapt the treatment.