scholarly journals Quantified Flu: an individual-centered approach to gaining sickness-related insights from wearable data

2021 ◽  
Author(s):  
Bastian Greshake Tzovaras ◽  
Enric Senabre Hidalgo ◽  
Karolina Alexiou ◽  
Lukaz Baldy ◽  
Basille Morane ◽  
...  
Keyword(s):  
Tracking System ◽  
Large Population ◽  
Population Based ◽  
Wearable Devices ◽  
Sensor Data ◽  
User Engagement ◽  
Online Tool ◽  
Creation Process ◽  
Iterative Development ◽  
High Level

AbstractBackgroundWearables have been used widely for monitoring health in general and recent research results show that they can be used for predicting infections based on physiological symptoms. So far the evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are comprised of people interested in learning about themselves individually using their own data, often gathered via wearable devices.ObjectiveWe explore how a co-creation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system to monitor symptoms of infection alongside wearable sensor data.MethodsWe engaged into a co-creation and design process with an existing community of personal science practitioners, jointly developing a working prototype of an online tool to perform symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis, investigating the process of how this prototype was created in a decentralized and iterative fashion.ResultsThe Quantified Flu prototype allows users to perform daily symptom reporting and is capable of visualizing those symptom reports on a timeline together with the resting heart rate, body temperature and respiratory rate as measured by wearable devices. We observe a high level of engagement, with over half of the 92 users that engaged in the symptom tracking becoming regular users, reporting over three months of data each. Furthermore, our netnographic analysis highlights how the current Quantified Flu prototype is a result of an interactive and continuous co-creation process in which new prototype releases spark further discussions of features and vice versa.ConclusionsAs shown by the high level of user engagement and iterative development, an open co-creation process can be successfully used to develop a tool that is tailored to individual needs, decreasing dropout rates.

scholarly journals Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study (Preprint)

2021 ◽  
Author(s):  
Bastian Greshake Tzovaras ◽  
Enric Senabre Hidalgo ◽  
Karolina Alexiou ◽  
Lukaz Baldy ◽  
Basile Morane ◽  
...  
Keyword(s):  
Tracking System ◽  
Large Population ◽  
Population Based ◽  
Wearable Devices ◽  
Sensor Data ◽  
User Engagement ◽  
Quantified Self ◽  
Web Based ◽  
Iterative Development ◽  
High Level

BACKGROUND Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices. OBJECTIVE This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data. METHODS We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion. RESULTS The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa. CONCLUSIONS As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates. CLINICALTRIAL

scholarly journals Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study

10.2196/28116 ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. e28116
Author(s):  
Bastian Greshake Tzovaras ◽  
Enric Senabre Hidalgo ◽  
Karolina Alexiou ◽  
Lukaz Baldy ◽  
Basile Morane ◽  
...  
Keyword(s):  
Tracking System ◽  
Large Population ◽  
Population Based ◽  
Wearable Devices ◽  
Sensor Data ◽  
User Engagement ◽  
Quantified Self ◽  
Web Based ◽  
Iterative Development ◽  
High Level

Background Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices. Objective This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data. Methods We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion. Results The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa. Conclusions As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates.

Associations between Body Composition and Mortality: A Large Population-Based Cohort Study

2020 ◽  
Author(s):  
Fu-Rong Li ◽  
Pei-Liang Chen ◽  
Xin Cheng ◽  
Hai-Lian Yang ◽  
Wen-Fang Zhong ◽  
...  
Keyword(s):  
Body Composition ◽  
Cohort Study ◽  
Large Population ◽  
Population Based

Fracture Risk in Patients with Myasthenia Gravis: A Population-Based Cohort Study

2021 ◽  
pp. 1-8
Author(s):  
Charles Kassardjian ◽  
Jessica Widdifield ◽  
J. Michael Paterson ◽  
Alexander Kopp ◽  
Chenthila Nagamuthu ◽  
...  
Keyword(s):  
Myasthenia Gravis ◽  
Lower Risk ◽  
Cox Regression ◽  
Large Population ◽  
Population Based ◽  
Osteoporosis Prevention ◽  
Inception Cohort ◽  
Hazard Ratios ◽  
Multiple Covariates ◽  
Region Of Residence

Background: Prednisone is a common treatment for myasthenia gravis (MG), and osteoporosis is a known potential risk of chronic prednisone therapy. Objective: Our aim was to evaluate the risk of serious fractures in a population-based cohort of MG patients. Methods: An inception cohort of patients with MG was identified from administrative health data in Ontario, Canada between April 1, 2002 and December 31, 2015. For each MG patient, we matched 4 general population comparators based on age, sex, and region of residence. Fractures were identified through emergency department and hospitalization data. Crude overall rates and sex-specific rates of fractures were calculated for the MG and comparator groups, as well as rates of specific fractures. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression. Results: Among 3,823 incident MG patients (followed for a mean of 5 years), 188 (4.9%) experienced a fracture compared with 741 (4.8%) fractures amongst 15,292 matched comparators. Crude fracture rates were not different between the MG cohort and matched comparators (8.71 vs. 7.98 per 1000 patient years), overall and in men and women separately. After controlling for multiple covariates, MG patients had a significantly lower risk of fracture than comparators (HR 0.74, 95% CI 0.63–0.88). Conclusions: In this large, population-based cohort of incident MG patients, MG patients were at lower risk of a major fracture than comparators. The reasons for this finding are unclear but may highlight the importance osteoporosis prevention.

scholarly journals Estimating the asymptomatic proportion of SARS-CoV-2 infection in the general population: Analysis of nationwide serosurvey data in the Netherlands

2021 ◽  
Author(s):  
Scott A. McDonald ◽  
Fuminari Miura ◽  
Eric R. A. Vos ◽  
Michiel van Boven ◽  
Hester E. de Melker ◽  
...  
Keyword(s):  
The Netherlands ◽  
Population Analysis ◽  
Large Population ◽  
Population Based ◽  
Attributable Risk ◽  
Case Definition ◽  
Aggregated Data ◽  
Age Dependent ◽  
Additive Regression ◽  
June July

Abstract Background The proportion of SARS-CoV-2 positive persons who are asymptomatic—and whether this proportion is age-dependent—are still open research questions. Because an unknown proportion of reported symptoms among SARS-CoV-2 positives will be attributable to another infection or affliction, the observed, or 'crude' proportion without symptoms may underestimate the proportion of persons without symptoms that are caused by SARS-CoV-2 infection. Methods Based on two rounds of a large population-based serological study comprising test results on seropositivity and self-reported symptom history conducted in April/May and June/July 2020 in the Netherlands (n = 7517), we estimated the proportion of reported symptoms among those persons infected with SARS-CoV-2 that is attributable to this infection, where the set of relevant symptoms fulfills the ECDC case definition of COVID-19, using inferential methods for the attributable risk (AR). Generalised additive regression modelling was used to estimate the age-dependent relative risk (RR) of reported symptoms, and the AR and asymptomatic proportion (AP) were calculated from the fitted RR. Results Using age-aggregated data, the 'crude' AP was 37% but the model-estimated AP was 65% (95% CI 63–68%). The estimated AP varied with age, from 74% (95% CI 65–90%) for < 20 years, to 61% (95% CI 57–65%) for the 50–59 years age-group. Conclusion Whereas the 'crude' AP represents a lower bound for the proportion of persons infected with SARS-CoV-2 without COVID-19 symptoms, the AP as estimated via an attributable risk approach represents an upper bound. Age-specific AP estimates can inform the implementation of public health actions such as targetted virological testing and therefore enhance containment strategies.

scholarly journals From Multiplex Serology to Serolomics—A Novel Approach to the Antibody Response against the SARS-CoV-2 Proteome

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 749
Author(s):  
Julia Butt ◽  
Rajagopal Murugan ◽  
Theresa Hippchen ◽  
Sylvia Olberg ◽  
Monique van Straaten ◽  
...  
Keyword(s):  
Antibody Response ◽  
High Throughput ◽  
Large Population ◽  
High Sensitivity ◽  
Population Based ◽  
Antibody Responses ◽  
Hek293 Cells ◽  
Novel Approach ◽  
Assay Performance ◽  
Multiplex Serology

The emerging SARS-CoV-2 pandemic entails an urgent need for specific and sensitive high-throughput serological assays to assess SARS-CoV-2 epidemiology. We, therefore, aimed at developing a fluorescent-bead based SARS-CoV-2 multiplex serology assay for detection of antibody responses to the SARS-CoV-2 proteome. Proteins of the SARS-CoV-2 proteome and protein N of SARS-CoV-1 and common cold Coronaviruses (ccCoVs) were recombinantly expressed in E. coli or HEK293 cells. Assay performance was assessed in a COVID-19 case cohort (n = 48 hospitalized patients from Heidelberg) as well as n = 85 age- and sex-matched pre-pandemic controls from the ESTHER study. Assay validation included comparison with home-made immunofluorescence and commercial enzyme-linked immunosorbent (ELISA) assays. A sensitivity of 100% (95% CI: 86–100%) was achieved in COVID-19 patients 14 days post symptom onset with dual sero-positivity to SARS-CoV-2 N and the receptor-binding domain of the spike protein. The specificity obtained with this algorithm was 100% (95% CI: 96–100%). Antibody responses to ccCoVs N were abundantly high and did not correlate with those to SARS-CoV-2 N. Inclusion of additional SARS-CoV-2 proteins as well as separate assessment of immunoglobulin (Ig) classes M, A, and G allowed for explorative analyses regarding disease progression and course of antibody response. This newly developed SARS-CoV-2 multiplex serology assay achieved high sensitivity and specificity to determine SARS-CoV-2 sero-positivity. Its high throughput ability allows epidemiologic SARS-CoV-2 research in large population-based studies. Inclusion of additional pathogens into the panel as well as separate assessment of Ig isotypes will furthermore allow addressing research questions beyond SARS-CoV-2 sero-prevalence.

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