💰The « 1 Million Dollar Question » of the day: What is a #DigitalBiomarker?
Welll, no one really seems to agree… That is a bummer!
We need to do better for such a critical definition. We need official guidance and a consensus on the definition.
See this new systematic mapping and review
⚠️ Not yet peer-reviewed ⚠️.
The preprint @medrxivpreprint https://www.medrxiv.org/content/10.1101/2023.09.01.23294897v1
#DigitalBiomarkers #Research #DigitalHealth #AI #HealthTech



Definitions of digital biomarkers: a systematic mapping of the biomedical literature
Background Technological devices such as smartphones, wearables, sensors, or virtual assistants allow to collect data on health and disease processes and are thus increasingly considered a useful digital alternative to conventional biomarkers. We aimed to provide a systematic overview of the emerging literature on "digital biomarkers" with their definitions, features, and citations in biomedical research. Methods We analyzed all articles in PubMed that used the term "digital biomarker(s)" in title or abstract, considering any study involving humans and any review, editorial, perspective, or other opinion-based article up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of "digital biomarkers" mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorizations of definitions considering the FDA BEST framework (i.e., data type, data collection method, purpose of biomarker), analysing the structural similarity of definitions by performing text analyses (hierarchical clustering on the distance-matrix) and citation analyses (based on citation metrics obtained from OpenAlex via Local Citation Network; last search 26 June 2023). Results We identified 415 articles prominently using the term "digital biomarker". They were published between 2014 and 2023 (median 2021), mostly describing primary research (283 articles; 68%). Most articles did not provide a definition of a digital biomarker (n=287; 69%). The 128 articles providing a definition of a digital biomarker reported 127 different definitions. Of these 127 definitions, 78 considered data collection, 56 data type, 50 the purpose, and 23 were based on all three key components. The 128 articles with a definition were cited a median of 6 times (interquartile range 2-20) with up to 517 citations. Of the ten most frequently cited articles using a definition, all used a different one. Conclusions The most frequently used definitions for digital biomarkers are highly different and there is no consensus about what this emerging term means. Our overview highlights key defining characteristics of digital biomarkers which can inform the development of a harmonized and more widely accepted definition.
### Competing Interest Statement
RC2NB (Research Center for Clinical Neuroimmunology and Neuroscience Basel) is supported by Foundation Clinical Neuroimmunology and Neuroscience Basel. All authors declare no competing interests.
### Funding Statement
No specific funding.
### Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
All data that has been analyzed is provided in the supplementary material S2.
medRxivIf you're interested in participating in #ColiveVoice, find more details at https://www.colivevoice.org.
Should you want to partner with Colive Voice and aid in recruiting participants (open to anyone above the age of 15, regardless of health status and conditions), please contact us https://www.colivevoice.org/en/contact/
#Diabetes #Cancer #MentalHealth #RespiratoryHealth #Research #Science #DigitalHealth #DigitalEpidemiology #DigitalBiomarkers #VocalBiomarkers
I am spending my vacation in #Guadeloupe with my family-in-law, so I took this opportunity to visit our friends at the Diabetology Department of CHU de la Guadeloupe.
Like many other partners, they assist the Luxembourg Institute of Health / Deep Digital Phenotyping research unit in recruiting participants for the Colive Voice research program.
#Research #DigitalHealth #DigitalEpidemiology #VocalBiomarkers #DigitalBiomarkers #ColiveVoice #Diabetes #Cancer #MentalHealth
Audio Digital Biomarkers Start-Up Raises $3.7M in Seed Funds
A new company developing techniques to analyze internal physiological sounds captured by #stethoscopes as #health indicators is raising $3.7 million in seed funds.
https://sciencebusiness.technewslit.com/?p=44321
#News #Science #Business #MedicalDevice #DigitalBiomarkers #Engineering #StartUp #Entrepreneurs #TheNetherlands #Finland #FDA #Finance #VentureCapital #SeedFunds

Audio Digital Biomarkers Start-Up Raises $3.7M in Seed Funds
A new company developing techniques to analyze internal physiological sounds captured by stethoscopes as health indicators is raising $3.7 million in seed funds.
Science and EnterpriseWe are looking for a PhD student or postdoc to work with us on characterizing cognitive changes in early Alzheimer's disease using smartphone-based cognitive tests. Please get in touch for questions and apply via the link in the ad
https://jobs.dzne.de/de/jobs/101092/phd-student-fmx-or-postdoctoral-researcher-fmx-4059202212 #DigitalBiomarkers #EMA #AmbulatoryAssessment #Alz
PhD student (f/m/x) or Postdoctoral Researcher (f/m/x) 4059/2022/12
I will soon advertise a PhD student/postdoc position to work with us on characterizing cognitive change in early Alzheimer’s disease using smartphone-based high-frequency testing. Please get in touch if interested!
#DigitalBiomarkers #EMA #AmbulatoryAssessment#DigitalHealth #DigitalBiomarkers #VocalBiomarkers #COVID19
This study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice.
Some limitations were identified 👇
5/
#DigitalHealth #DigitalBiomarkers #VocalBiomarkers #COVID19
We used pre-trained models that were further fine-tuned on our dataset 👇
3/
#DigitalHealth #DigitalBiomarkers #VocalBiomarkers #COVID19
This work has been led by the great @[email protected] and our @[email protected] @[email protected]. Thanks to the coauthors & the @[email protected] & the Losch Foundation for funding the Predi-Cohort study 🙏
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Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study
Objective To develop a vocal biomarker for fatigue monitoring in people with COVID-19.
Design Prospective cohort study.
Setting Predi-COVID data between May 2020 and May 2021.
Participants A total of 1772 voice recordings were used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone’s operating system (Android/iOS). The recordings were collected from 296 participants tracked for 2 weeks following SARS-CoV-2 infection.
Primary and secondary outcome measures Four machine learning algorithms (logistic regression, k-nearest neighbours, support vector machine and soft voting classifier) were used to train and derive the fatigue vocal biomarker. The models were evaluated based on the following metrics: area under the curve (AUC), accuracy, F1-score, precision and recall. The Brier score was also used to evaluate the models’ calibrations.
Results The final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (p<0.001). We developed four models for Android female, Android male, iOS female and iOS male users with a weighted AUC of 86%, 82%, 79%, 85% and a mean Brier Score of 0.15, 0.12, 0.17, 0.12, respectively. The vocal biomarker derived from the prediction models successfully discriminated COVID-19 participants with and without fatigue.
Conclusions This study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID.
Trial registration number [NCT04380987][1].
Data are available in a public, open access repository. Audio data, datasets and source code used in this study are publicly available. Audio data available in Zenodo repository, (DOI: 10.5281/zenodo.5937844]Datasets and source code available in Github, ([https://github.com/LIHVOICE/Predi\_COVID\_Fatigue\_Vocal\_Biomarker][2]).
[1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT04380987&atom=%2Fbmjopen%2F12%2F11%2Fe062463.atom
[2]: https://github.com/LIHVOICE/Predi_COVID_Fatigue_Vocal_Biomarker
BMJ Open