Respondents of health survey powered by the innovative NURO app exhibit correlations between exercise frequencies and diet habits, and between stress levels and sleep wellness
Nurosene's NURO app ( nurosene.com ) is an innovative smartphone application that gathers and analyzes active self-report metrics from users, empowering them with data-driven health machine intelligence. We present the data collected and analyzed from the initial round of participants who respo...
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Published in | Frontiers in psychiatry Vol. 13; p. 945780 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Abstract | Nurosene's NURO app (
nurosene.com
) is an innovative smartphone application that gathers and analyzes active self-report metrics from users, empowering them with data-driven health machine intelligence. We present the data collected and analyzed from the initial round of participants who responded to a 12-question survey on their life-style and health status. Exploratory results using a variational autoencoder (VAE) suggested that much of the variability of the 12 dimensional data could be accounted for by two approximately uncorrelated latent variables: one pertaining to stress and sleep, and the other pertaining to exercise and diet. Subsequent modeling of the data using exploratory and confirmatory factor analyses (EFAs and CFAs) found that optimal data fits consisted of four factors, namely exercise, diet, stress, and sleep. Covariance values were high between exercise and diet, and between stress and sleep, but much lower between other pairings of non-identical factors. Both EFAs and CFAs provided extra contexts to and quantified the more preliminary VAE observations. Overall, our results significantly reduce the apparent complexity of the response data. This reduction allows for more efficient future stratification and analyses of participants based on simpler latent variables. Our discovery of novel relationships between stress and sleep, and between exercise and diet suggests the possibility of applying predictive analytics in future efforts. |
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AbstractList | Nurosene's NURO app (
nurosene.com
) is an innovative smartphone application that gathers and analyzes active self-report metrics from users, empowering them with data-driven health machine intelligence. We present the data collected and analyzed from the initial round of participants who responded to a 12-question survey on their life-style and health status. Exploratory results using a variational autoencoder (VAE) suggested that much of the variability of the 12 dimensional data could be accounted for by two approximately uncorrelated latent variables: one pertaining to stress and sleep, and the other pertaining to exercise and diet. Subsequent modeling of the data using exploratory and confirmatory factor analyses (EFAs and CFAs) found that optimal data fits consisted of four factors, namely exercise, diet, stress, and sleep. Covariance values were high between exercise and diet, and between stress and sleep, but much lower between other pairings of non-identical factors. Both EFAs and CFAs provided extra contexts to and quantified the more preliminary VAE observations. Overall, our results significantly reduce the apparent complexity of the response data. This reduction allows for more efficient future stratification and analyses of participants based on simpler latent variables. Our discovery of novel relationships between stress and sleep, and between exercise and diet suggests the possibility of applying predictive analytics in future efforts. Nurosene's NURO app (nurosene.com) is an innovative smartphone application that gathers and analyzes active self-report metrics from users, empowering them with data-driven health machine intelligence. We present the data collected and analyzed from the initial round of participants who responded to a 12-question survey on their life-style and health status. Exploratory results using a variational autoencoder (VAE) suggested that much of the variability of the 12 dimensional data could be accounted for by two approximately uncorrelated latent variables: one pertaining to stress and sleep, and the other pertaining to exercise and diet. Subsequent modeling of the data using exploratory and confirmatory factor analyses (EFAs and CFAs) found that optimal data fits consisted of four factors, namely exercise, diet, stress, and sleep. Covariance values were high between exercise and diet, and between stress and sleep, but much lower between other pairings of non-identical factors. Both EFAs and CFAs provided extra contexts to and quantified the more preliminary VAE observations. Overall, our results significantly reduce the apparent complexity of the response data. This reduction allows for more efficient future stratification and analyses of participants based on simpler latent variables. Our discovery of novel relationships between stress and sleep, and between exercise and diet suggests the possibility of applying predictive analytics in future efforts. |
Author | Ho, Ernest C. Y. Gallucci, Daniel Loren, Joseph Pani, Luca Geraci, Joseph |
AuthorAffiliation | 3 Center for Biotechnology and Genomics Medicine, Medical College of Georgia , Augusta, GA , United States 1 Nurosene Health Inc. , Toronto, ON , Canada 2 Department of Molecular Medicine, Queen's University , Kingston, ON , Canada 4 Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia , Modena , Italy 5 Department of Psychiatry and Behavioral Sciences, Leonard M. Miller School of Medicine, University of Miami , Miami, FL , United States |
AuthorAffiliation_xml | – name: 2 Department of Molecular Medicine, Queen's University , Kingston, ON , Canada – name: 3 Center for Biotechnology and Genomics Medicine, Medical College of Georgia , Augusta, GA , United States – name: 4 Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia , Modena , Italy – name: 1 Nurosene Health Inc. , Toronto, ON , Canada – name: 5 Department of Psychiatry and Behavioral Sciences, Leonard M. Miller School of Medicine, University of Miami , Miami, FL , United States |
Author_xml | – sequence: 1 givenname: Daniel surname: Gallucci fullname: Gallucci, Daniel – sequence: 2 givenname: Ernest C. Y. surname: Ho fullname: Ho, Ernest C. Y. – sequence: 3 givenname: Joseph surname: Geraci fullname: Geraci, Joseph – sequence: 4 givenname: Joseph surname: Loren fullname: Loren, Joseph – sequence: 5 givenname: Luca surname: Pani fullname: Pani, Luca |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Luca Pani orcid.org/0000-0001-9273-2839 ORCID: Joseph Geraci orcid.org/0000-0003-0967-2164 These authors have contributed equally to this work and share first authorship Edited by: Marijn Lijffijt, Baylor College of Medicine, United States This article was submitted to Psychopharmacology, a section of the journal Frontiers in Psychiatry Reviewed by: Haitham Jahrami, Arabian Gulf University, Bahrain; Karl Schweizer, Goethe University Frankfurt, Germany |
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Snippet | Nurosene's NURO app (
nurosene.com
) is an innovative smartphone application that gathers and analyzes active self-report metrics from users, empowering them... Nurosene's NURO app (nurosene.com) is an innovative smartphone application that gathers and analyzes active self-report metrics from users, empowering them... |
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SubjectTerms | digital health technology machine learning mental and physical wellbeing Psychiatry smartphone application survey questionnaire variational auto encoder |
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Title | Respondents of health survey powered by the innovative NURO app exhibit correlations between exercise frequencies and diet habits, and between stress levels and sleep wellness |
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