Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach
The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical exam...
Gespeichert in:
| Veröffentlicht in: | Frontiers in digital health Jg. 4; S. 861808 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Switzerland
Frontiers Media S.A
14.04.2022
|
| Schlagworte: | |
| ISSN: | 2673-253X, 2673-253X |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (
SD
= 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown. |
|---|---|
| AbstractList | The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown.The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown. The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 ( SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown. The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown. The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 ( = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown. |
| Author | Saito, Tomoki Kishi, Akifumi Suzuki, Hikaru |
| AuthorAffiliation | 1 JMDC Inc. , Tokyo , Japan 2 Graduate School of Education, The University of Tokyo , Tokyo , Japan |
| AuthorAffiliation_xml | – name: 1 JMDC Inc. , Tokyo , Japan – name: 2 Graduate School of Education, The University of Tokyo , Tokyo , Japan |
| Author_xml | – sequence: 1 givenname: Tomoki surname: Saito fullname: Saito, Tomoki – sequence: 2 givenname: Hikaru surname: Suzuki fullname: Suzuki, Hikaru – sequence: 3 givenname: Akifumi surname: Kishi fullname: Kishi, Akifumi |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35493532$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kk1vEzEQhleoiH7QH8AF-cglwR_r3TUHpKotEClROVDBzZrYs4krxw72JgJ-fZ2kVC0HTrZm3vcZy_OeVkchBqyqN4yOhejU-94uhuWYU87HXcM62r2oTnjTihGX4sfRk_txdZ7zHaWUS8Y5la-qYyFrJaTgJ9WfrwmtM4PbIplFi96FBYk9mWEYwJOJ9wFzJjch40Bu8677HSHB3CO5wq0zmAkEW_SFUgzXv2DlAgwuBnIFA3wgMzBLF5BMiy3s_BfrdYql-Lp62YPPeP5wnlW3n66_XX4ZTW8-Ty4vpiMjBRtGKC0IOu9boH1rqGBqrqwCi8jqtpXU0rlCZRtgDAXwxiBnLYWGYidbJZg4qyYHro1wp9fJrSD91hGc3hdiWmhIgzMetW0lyKbrueFQ15wCdEaJWlHBuezAFtbHA2u9ma_QmvJLCfwz6PNOcEu9iFutaN00qimAdw-AFH9uMA965bJB7yFg3GTNG9k1ZTk1LdK3T2c9Dvm7vCJgB4FJMeeE_aOEUb3LiN5nRO8yog8ZKZ72H49xw35d5bnO_8d5Dx1bwyE |
| CitedBy_id | crossref_primary_10_2196_59660 crossref_primary_10_1016_j_ajp_2024_104168 crossref_primary_10_1371_journal_pone_0304932 crossref_primary_10_3389_fpsyt_2024_1291299 crossref_primary_10_1126_sciadv_adv2643 crossref_primary_10_1038_s41746_023_00890_z crossref_primary_10_1109_ACCESS_2025_3577968 crossref_primary_10_1109_JSEN_2023_3303436 crossref_primary_10_1371_journal_pdig_0000473 crossref_primary_10_3389_fpubh_2023_1190464 crossref_primary_10_2196_77066 crossref_primary_10_1142_S0218126625504328 crossref_primary_10_18267_j_aip_243 crossref_primary_10_1192_bja_2025_10133 crossref_primary_10_3390_ijerph191911913 |
| Cites_doi | 10.1016/S2215-0366(20)30136-X 10.1073/pnas.1312114110 10.1001/jama.262.11.1479 10.1016/S0140-6736(13)61611-6 10.1145/1882471.1882479 10.1016/j.bpsc.2017.11.007 10.1038/s41591-018-0300-7 10.1016/j.cub.2016.04.011 10.2196/16273 10.1016/j.biopsych.2004.04.005 10.1016/j.jad.2020.09.118 10.1002/jgf2.367 10.1016/j.jad.2018.11.077 10.1016/j.jad.2013.12.007 10.1080/07420520500545979 10.2196/18694 10.1109/MSMC.2018.2806565 10.1093/sleep/zsz254 10.1111/jcmm.14170 10.1007/s11920-018-0925-8 10.1093/sleep/zsaa291 10.1016/0006-3223(95)00188-3 10.5665/sleep.1396 10.1111/jsr.12931 10.1001/jamaneurol.2020.2108 10.1093/ije/dyu038 10.1109/ICDM.2003.1250950 10.1176/appi.ajp.162.10.1785 10.1098/rspb.2017.0882 10.1001/jamainternmed.2019.0142 10.1080/07420528.2017.1413578 10.1016/j.jad.2017.04.021 10.15420/ecr.2019.02 10.1016/j.maturitas.2018.03.016 10.1016/j.smrv.2017.12.002 10.1016/j.jad.2012.07.001 10.2196/jmir.9410 10.1093/sleep/zsaa141 10.1109/JBHI.2015.2440764 10.1371/journal.pone.0165267 10.1186/s12966-015-0314-1 10.1038/s41746-020-0226-6 10.1146/annurev-clinpsy-032816-044949 10.1109/RBME.2018.2811735 10.2196/10527 10.1038/nature08227 10.1176/ajp.157.1.81 10.1145/3422821 10.1371/journal.pone.0043539 10.1152/ajpregu.00925.2007 10.1016/j.jad.2017.04.030 10.1177/0748730402239679 10.1038/s41598-018-32402-510.1038/s41598-020-59762-1 10.1152/ajpregu.00349.2016 10.1249/MSS.0000000000001947 10.1016/j.sleep.2019.03.009 10.1016/j.jpsychores.2019.109822 10.2196/1102910.2196/15966 10.1080/07420528.2020.1746796 10.1038/s41598-017-03171-4 10.1109/ACCESS.2020.3010715 10.1103/PhysRevLett.99.138103 10.1038/s41746-021-00400-z 10.1145/2939672.2939785 10.1037/t02254-000 10.1146/annurev-clinpsy-032816-045037 10.1111/jsr.12944 10.3109/09540261.2014.911149 10.3390/ijerph17010331 10.1145/3398069 10.1002/phy2.152 10.1093/sleep/zsz180 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Saito, Suzuki and Kishi. Copyright © 2022 Saito, Suzuki and Kishi. 2022 Saito, Suzuki and Kishi |
| Copyright_xml | – notice: Copyright © 2022 Saito, Suzuki and Kishi. – notice: Copyright © 2022 Saito, Suzuki and Kishi. 2022 Saito, Suzuki and Kishi |
| DBID | AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.3389/fdgth.2022.861808 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2673-253X |
| ExternalDocumentID | oai_doaj_org_article_d75a568f2c2a4420aa8c9349032258ad PMC9046696 35493532 10_3389_fdgth_2022_861808 |
| Genre | Journal Article |
| GroupedDBID | 53G 9T4 AAFWJ AAYXX AFPKN ALMA_UNASSIGNED_HOLDINGS CITATION GROUPED_DOAJ M~E OK1 PGMZT RPM ACXDI NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c531t-e5da30bf7a0f7c0319b9d9adee147750d0b9e9d6a11e3a26ce2170a60e8579313 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 17 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001030200700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2673-253X |
| IngestDate | Fri Oct 03 12:44:01 EDT 2025 Tue Sep 30 16:51:36 EDT 2025 Thu Sep 04 17:13:18 EDT 2025 Thu Jan 02 22:54:07 EST 2025 Tue Nov 18 22:25:17 EST 2025 Sat Nov 29 03:17:42 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | predictive detection sleep mental illness wearable data mHealth machine learning medical examination physical activity |
| Language | English |
| License | Copyright © 2022 Saito, Suzuki and Kishi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c531t-e5da30bf7a0f7c0319b9d9adee147750d0b9e9d6a11e3a26ce2170a60e8579313 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Personalized Medicine, a section of the journal Frontiers in Digital Health Reviewed by: Ming Huang, Nara Institute of Science and Technology (NAIST), Japan; Raffaele Gravina, University of Calabria, Italy Edited by: Akane Sano, Rice University, United States |
| OpenAccessLink | https://doaj.org/article/d75a568f2c2a4420aa8c9349032258ad |
| PMID | 35493532 |
| PQID | 2658649340 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_d75a568f2c2a4420aa8c9349032258ad pubmedcentral_primary_oai_pubmedcentral_nih_gov_9046696 proquest_miscellaneous_2658649340 pubmed_primary_35493532 crossref_primary_10_3389_fdgth_2022_861808 crossref_citationtrail_10_3389_fdgth_2022_861808 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-04-14 |
| PublicationDateYYYYMMDD | 2022-04-14 |
| PublicationDate_xml | – month: 04 year: 2022 text: 2022-04-14 day: 14 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Frontiers in digital health |
| PublicationTitleAlternate | Front Digit Health |
| PublicationYear | 2022 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | Wright (B44) 2017; 312 Thieme (B11) 2020; 27 Kessler (B1) 2007; 6 Leary (B67) 2020; 77 van de Leemput (B72) 2014; 111 Horne (B28) 1976; 4 Bent (B40) 2020; 3 Kim (B16) 2015; 19 Phillips (B26) 2017; 7 Ojio (B32) 2020; 37 Brooks (B68) 2020; 17 Murray (B69) 2019; 58 Walch (B43) 2019; 42 Sano (B15) 2012; 7 Cho (B17) 2019; 21 Cook (B45) 2017; 217 Kishi (B63) 2008; 294 de Zambotti (B46) 2018; 35 Fang (B13) 2021; 4 Dwyer (B8) 2018; 14 Straiton (B52) 2018; 112 Haghayegh (B54) 2019; 21 Forman (B36) 2010; 12 Nakamura (B14) 2007; 99 Ford (B57) 1989; 262 Hamill (B48) 2020; 29 Foo (B73) 2017; 284 Arfken (B62) 2014; 156 Chen (B33) 2016 Chikersal (B12) 2021; 28 Steel (B2) 2014; 43 Freeman (B61) 2020; 7 Feehan (B53) 2018; 6 Sano (B18) 2018; 20 Zheng (B6) 2018; 4 Mohr (B7) 2017; 13 Wittmann (B23) 2006; 23 Kahawage (B47) 2020; 29 Pesonen (B66) 2019; 245 Roenneberg (B25) 2016; 26 Svensson (B39) 2019; 126 Baron (B24) 2014; 26 Taylor (B31) 2018; 20 Setoyama (B20) 2021; 279 Perkins (B5) 2005; 162 Kishi (B64) 2011; 34 Evenson (B22) 2015; 12 de Zambotti (B42) 2019; 51 Burton (B70) 2013; 145 Roenneberg (B29) 2003; 18 Au (B30) 2017; 218 Gholamiangonabadi (B35) 2020; 8 Whiteford (B3) 2013; 382 Lunsford-Avery (B27) 2020; 10 Depner (B38) 2020; 43 Kishi (B65) 2013; 1 Chinoy (B50) 2021; 44 Fuller (B55) 2020; 8 Scheffer (B71) 2009; 461 Bliwise (B41) 2021; 44 Roberts (B59) 2000; 157 Topol (B10) 2019; 25 Breslau (B58) 1996; 39 Rush (B4) 2004; 56 Nagai (B21) 2020; 21 Massoomi (B51) 2019; 14 Baron (B37) 2018; 40 Setoyama (B19) 2016; 11 Zadrozny (B34) 2003 Roomkham (B49) 2018; 11 Bzdok (B9) 2018; 3 Fang (B60) 2019; 23 Greenland (B56) 2019; 179 |
| References_xml | – volume: 7 start-page: 628 year: 2020 ident: B61 article-title: Sleep disturbance and psychiatric disorders publication-title: Lancet Psychiatry. doi: 10.1016/S2215-0366(20)30136-X – volume: 111 start-page: 87 year: 2014 ident: B72 article-title: Critical slowing down as early warning for the onset and termination of depression publication-title: Proc Natl Acad Sci U S A. doi: 10.1073/pnas.1312114110 – volume: 262 start-page: 1479 year: 1989 ident: B57 article-title: Epidemiologic study of sleep disturbances and psychiatric disorders. An opportunity for prevention? publication-title: JAMA. doi: 10.1001/jama.262.11.1479 – volume: 382 start-page: 1575 year: 2013 ident: B3 article-title: Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010 publication-title: Lancet. doi: 10.1016/S0140-6736(13)61611-6 – volume: 12 start-page: 49 year: 2010 ident: B36 article-title: Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement publication-title: ACM SIGKDD Explor Newsl. doi: 10.1145/1882471.1882479 – volume: 3 start-page: 223 year: 2018 ident: B9 article-title: Machine learning for precision psychiatry: opportunities and challenges publication-title: Biol Psychiatry Cogn Neurosci Neuroimaging. doi: 10.1016/j.bpsc.2017.11.007 – volume: 25 start-page: 44 year: 2019 ident: B10 article-title: High-performance medicine: the convergence of human and artificial intelligence publication-title: Nat Med. doi: 10.1038/s41591-018-0300-7 – volume: 26 start-page: R432 year: 2016 ident: B25 article-title: The circadian clock and human health publication-title: Curr Biol. doi: 10.1016/j.cub.2016.04.011 – volume: 21 start-page: e16273 year: 2019 ident: B54 article-title: Accuracy of wristband Fitbit models in assessing sleep: systematic review and meta-analysis publication-title: J Med Internet Res. doi: 10.2196/16273 – volume: 56 start-page: 46 year: 2004 ident: B4 article-title: One-year clinical outcomes of depressed public sector outpatients: a benchmark for subsequent studies publication-title: Biol Psychiat. doi: 10.1016/j.biopsych.2004.04.005 – volume: 279 start-page: 20 year: 2021 ident: B20 article-title: Personality classification enhances blood metabolome analysis and biotyping for major depressive disorders: two-species investigation publication-title: J Affect Disord. doi: 10.1016/j.jad.2020.09.118 – volume: 21 start-page: 211 year: 2020 ident: B21 article-title: Data resource profile: JMDC claims databases sourced from medical institutions publication-title: J Gen Fam Med. doi: 10.1002/jgf2.367 – volume: 245 start-page: 757 year: 2019 ident: B66 article-title: REM sleep fragmentation associated with depressive symptoms and genetic risk for depression in a community-based sample of adolescents publication-title: J Affect Disord. doi: 10.1016/j.jad.2018.11.077 – volume: 156 start-page: 36 year: 2014 ident: B62 article-title: The status of sleep abnormalities as a diagnostic test for major depressive disorder publication-title: J Affect Disord. doi: 10.1016/j.jad.2013.12.007 – volume: 23 start-page: 497 year: 2006 ident: B23 article-title: Social jetlag: misalignment of biological and social time publication-title: Chronobiol Int. doi: 10.1080/07420520500545979 – volume: 8 start-page: e18694 year: 2020 ident: B55 article-title: Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review publication-title: JMIR mHealth uHealth. doi: 10.2196/18694 – volume: 4 start-page: 6 year: 2018 ident: B6 article-title: An emerging wearable world: new gadgetry produces a rising tide of changes and challenges publication-title: IEEE Syst Man Cybern Mag. doi: 10.1109/MSMC.2018.2806565 – volume: 43 start-page: zsz254 year: 2020 ident: B38 article-title: Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions publication-title: Sleep doi: 10.1093/sleep/zsz254 – volume: 23 start-page: 2324 year: 2019 ident: B60 article-title: Depression in sleep disturbance: a review on a bidirectional relationship, mechanisms and treatment publication-title: J Cell Mol Med. doi: 10.1111/jcmm.14170 – volume: 20 start-page: 59 year: 2018 ident: B31 article-title: Chronotype and mental health: recent advances publication-title: Curr Psychiatry Rep. doi: 10.1007/s11920-018-0925-8 – volume: 44 start-page: zsaa291 year: 2021 ident: B50 article-title: Performance of seven consumer sleep-tracking devices compared with polysomnography publication-title: Sleep doi: 10.1093/sleep/zsaa291 – volume: 39 start-page: 411 year: 1996 ident: B58 article-title: Sleep disturbance and psychiatric disorders: a longitudinal epidemiological study of young adults publication-title: Biol Psychiatry. doi: 10.1016/0006-3223(95)00188-3 – volume: 34 start-page: 1551 year: 2011 ident: B64 article-title: Sleep-stage dynamics in patients with chronic fatigue syndrome with or without fibromyalgia publication-title: Sleep. doi: 10.5665/sleep.1396 – volume: 29 start-page: e12931 year: 2020 ident: B47 article-title: Validity, potential clinical utility, and comparison of consumer and research-grade activity trackers in Insomnia Disorder I: In-lab validation against polysomnography publication-title: J Sleep Res. doi: 10.1111/jsr.12931 – volume: 77 start-page: 1241 year: 2020 ident: B67 article-title: Association of rapid eye movement sleep with mortality in middle-aged and older adults publication-title: JAMA Neurol. doi: 10.1001/jamaneurol.2020.2108 – volume: 43 start-page: 476 year: 2014 ident: B2 article-title: The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013 publication-title: Int J Epidemiol. doi: 10.1093/ije/dyu038 – start-page: 435 volume-title: Proceedings of the Third IEEE International Conference on Data Mining year: 2003 ident: B34 article-title: Cost-sensitive learning by cost-proportionate example weighting doi: 10.1109/ICDM.2003.1250950 – volume: 162 start-page: 1785 year: 2005 ident: B5 article-title: Relationship between duration of untreated psychosis and outcome in first-episode schizophrenia: a critical review and meta-analysis publication-title: Am J Psychiat. doi: 10.1176/appi.ajp.162.10.1785 – volume: 284 start-page: 20170882 year: 2017 ident: B73 article-title: Dynamical state transitions into addictive behavior and their early-warning signals publication-title: Proc R Soc B. doi: 10.1098/rspb.2017.0882 – volume: 179 start-page: 605 year: 2019 ident: B56 article-title: Precision preventive medicine–ready for prime time? publication-title: JAMA Int Med. doi: 10.1001/jamainternmed.2019.0142 – volume: 35 start-page: 465 year: 2018 ident: B46 article-title: A validation study of Fitbit Charge 2™ compared with polysomnography in adults publication-title: Chronobiol Int. doi: 10.1080/07420528.2017.1413578 – volume: 218 start-page: 93 year: 2017 ident: B30 article-title: The relationship between chronotype and depressive symptoms: a meta-analysis publication-title: J Affect Disord. doi: 10.1016/j.jad.2017.04.021 – volume: 14 start-page: 181 year: 2019 ident: B51 article-title: Increasing and evolving role of smart devices in modern medicine publication-title: Eur Cardiol. doi: 10.15420/ecr.2019.02 – volume: 6 start-page: 168 year: 2007 ident: B1 article-title: Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization's World Mental Health Survey Initiative publication-title: World Psychiatry. – volume: 112 start-page: 85 year: 2018 ident: B52 article-title: The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: a systematic review publication-title: Maturitas. doi: 10.1016/j.maturitas.2018.03.016 – volume: 40 start-page: 151 year: 2018 ident: B37 article-title: Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep publication-title: Sleep Med Rev. doi: 10.1016/j.smrv.2017.12.002 – volume: 145 start-page: 21 year: 2013 ident: B70 article-title: Activity monitoring in patients with depression: a systematic review publication-title: J Affect Disord. doi: 10.1016/j.jad.2012.07.001 – volume: 20 start-page: e210 year: 2018 ident: B18 article-title: Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: observational study publication-title: J Med Internet Res. doi: 10.2196/jmir.9410 – volume: 44 start-page: zsaa141 year: 2021 ident: B41 article-title: A multitrait, multimethod matrix approach for a consumer-grade wrist-worn watch measuring sleep duration and continuity publication-title: Sleep doi: 10.1093/sleep/zsaa141 – volume: 19 start-page: 1347 year: 2015 ident: B16 article-title: Covariation of depressive mood and spontaneous physical activity in major depressive disorder: toward continuous monitoring of depressive mood publication-title: IEEE J Biomed Health Inform. doi: 10.1109/JBHI.2015.2440764 – volume: 11 start-page: e0165267 year: 2016 ident: B19 article-title: Plasma metabolites predict severity of depression and suicidal ideation in psychiatric patients–a multicenter pilot analysis publication-title: PLoS ONE. doi: 10.1371/journal.pone.0165267 – volume: 12 start-page: 159 year: 2015 ident: B22 article-title: Systematic review of the validity and reliability of consumer-wearable activity trackers publication-title: Int J Behav Nutr Phys Act. doi: 10.1186/s12966-015-0314-1 – volume: 3 start-page: 18 year: 2020 ident: B40 article-title: Investigating sources of inaccuracy in wearable optical heart rate sensors publication-title: NPJ Dig Med. doi: 10.1038/s41746-020-0226-6 – volume: 13 start-page: 23 year: 2017 ident: B7 article-title: Personal sensing: understanding mental health using ubiquitous sensors and machine learning publication-title: Annu Rev Clin Psychol. doi: 10.1146/annurev-clinpsy-032816-044949 – volume: 11 start-page: 53 year: 2018 ident: B49 article-title: Promises and challenges in the use of consumer-grade devices for sleep monitoring publication-title: IEEE Rev Biomed Eng. doi: 10.1109/RBME.2018.2811735 – volume: 6 start-page: e10527 year: 2018 ident: B53 article-title: Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data publication-title: JMIR mHealth uHealth. doi: 10.2196/10527 – volume: 461 start-page: 53 year: 2009 ident: B71 article-title: Early-warning signals for critical transitions publication-title: Nature. doi: 10.1038/nature08227 – volume: 157 start-page: 81 year: 2000 ident: B59 article-title: Sleep complaints and depression in an aging cohort: a prospective perspective publication-title: Am J Psychiatry. doi: 10.1176/ajp.157.1.81 – volume: 28 start-page: 1 year: 2021 ident: B12 article-title: Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing: a machine learning approach with robust feature selection publication-title: ACM Trans Comput–Hum Interact. doi: 10.1145/3422821 – volume: 7 start-page: e43539 year: 2012 ident: B15 article-title: Enhanced persistency of resting and active periods of locomotor activity in schizophrenia publication-title: PLoS ONE. doi: 10.1371/journal.pone.0043539 – volume: 294 start-page: R1980 year: 2008 ident: B63 article-title: Dynamics of sleep stage transitions in healthy humans and patients with chronic fatigue syndrome publication-title: Am J Physiol Regul Integr Comp Physiol. doi: 10.1152/ajpregu.00925.2007 – volume: 217 start-page: 299 year: 2017 ident: B45 article-title: Utility of the Fitbit Flex to evaluate sleep in major depressive disorder: a comparison against polysomnography and wrist-worn actigraphy publication-title: J Affect Disord. doi: 10.1016/j.jad.2017.04.030 – volume: 18 start-page: 80 year: 2003 ident: B29 article-title: Life between clocks: daily temporal patterns of human chronotypes publication-title: J Biol Rhythms. doi: 10.1177/0748730402239679 – volume: 10 start-page: 2993 year: 2020 ident: B27 article-title: Validation of the sleep regularity index in older adults and associations with cardiometabolic risk publication-title: Sci Rep doi: 10.1038/s41598-018-32402-510.1038/s41598-020-59762-1 – volume: 312 start-page: R358 year: 2017 ident: B44 article-title: How consumer physical activity monitors could transform human physiology research publication-title: Am J Physiol Regul Integr Comp Physiol. doi: 10.1152/ajpregu.00349.2016 – volume: 51 start-page: 1538 year: 2019 ident: B42 article-title: Wearable sleep technology in clinical and research settings publication-title: Med Sci Sports Exerc. doi: 10.1249/MSS.0000000000001947 – volume: 58 start-page: 93 year: 2019 ident: B69 article-title: Delayed Sleep on Melatonin (DelSoM) Study Group Sleep regularity is associated with sleep-wake and circadian timing, and mediates daytime function in Delayed Sleep-Wake Phase Disorder publication-title: Sleep Med. doi: 10.1016/j.sleep.2019.03.009 – volume: 126 start-page: 109822 year: 2019 ident: B39 article-title: A validation study of a consumer wearable sleep tracker compared to a portable EEG system in naturalistic conditions publication-title: J Psychosom Res. doi: 10.1016/j.jpsychores.2019.109822 – volume: 21 start-page: e15966 year: 2019 ident: B17 article-title: Mood prediction of patients with mood disorders by machine learning using passive digital phenotypes based on the circadian rhythm: prospective observational cohort study publication-title: J Med Internet Res. doi: 10.2196/1102910.2196/15966 – volume: 37 start-page: 877 year: 2020 ident: B32 article-title: Association of depressive symptoms with habitual sleep duration and sleep timing in junior high school students publication-title: Chronobiol Int. doi: 10.1080/07420528.2020.1746796 – volume: 7 start-page: 3216 year: 2017 ident: B26 article-title: Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing publication-title: Sci Rep. doi: 10.1038/s41598-017-03171-4 – volume: 8 start-page: 133982 year: 2020 ident: B35 article-title: Deep neural networks for human activity recognition with wearable sensors: leave-one-subject-out cross-validation for model selection publication-title: IEEE Access. doi: 10.1109/ACCESS.2020.3010715 – volume: 99 start-page: 138103 year: 2007 ident: B14 article-title: Universal scaling law in human behavioral organization publication-title: Phys Rev Lett. doi: 10.1103/PhysRevLett.99.138103 – volume: 4 start-page: 28 year: 2021 ident: B13 article-title: Day-to-day variability in sleep parameters and depression risk: a prospective cohort study of training physicians publication-title: NPJ Digit Med. doi: 10.1038/s41746-021-00400-z – start-page: 785 volume-title: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining year: 2016 ident: B33 article-title: Xgboost: a scalable tree boosting system doi: 10.1145/2939672.2939785 – volume: 4 start-page: 97 year: 1976 ident: B28 article-title: A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms publication-title: Int J Chronobiol. doi: 10.1037/t02254-000 – volume: 14 start-page: 91 year: 2018 ident: B8 article-title: Machine learning approaches for clinical psychology and psychiatry publication-title: Annu Rev Clin Psychol. doi: 10.1146/annurev-clinpsy-032816-045037 – volume: 29 start-page: e12944 year: 2020 ident: B48 article-title: Validity, potential clinical utility and comparison of a consumer activity tracker and a research-grade activity tracker in insomnia disorder II: outside the laboratory publication-title: J Sleep Res. doi: 10.1111/jsr.12944 – volume: 26 start-page: 139 year: 2014 ident: B24 article-title: Circadian misalignment and health publication-title: Int Rev Psychiatry. doi: 10.3109/09540261.2014.911149 – volume: 17 start-page: 331 year: 2020 ident: B68 article-title: Sleep regularity index in patients with alcohol dependence: daytime napping and mood disorders as correlates of interest publication-title: Int J Environ Res Public Health. doi: 10.3390/ijerph17010331 – volume: 27 start-page: 1 year: 2020 ident: B11 article-title: Machine learning in mental health: a systematic review of the HCI literature to support the development of effective and implementable ML systems publication-title: ACM Trans Comput–Hum Interact. doi: 10.1145/3398069 – volume: 1 start-page: e00152 year: 2013 ident: B65 article-title: The effects of exercise on dynamic sleep morphology in healthy controls and patients with chronic fatigue syndrome publication-title: Physiol Rep. doi: 10.1002/phy2.152 – volume: 42 start-page: zsz180 year: 2019 ident: B43 article-title: Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device publication-title: Sleep doi: 10.1093/sleep/zsz180 |
| SSID | ssj0002512205 |
| Score | 2.3233254 |
| Snippet | The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 861808 |
| SubjectTerms | Digital Health machine learning medical examination mental illness mHealth physical activity predictive detection |
| Title | Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35493532 https://www.proquest.com/docview/2658649340 https://pubmed.ncbi.nlm.nih.gov/PMC9046696 https://doaj.org/article/d75a568f2c2a4420aa8c9349032258ad |
| Volume | 4 |
| WOSCitedRecordID | wos001030200700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2673-253X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002512205 issn: 2673-253X databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2673-253X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002512205 issn: 2673-253X databaseCode: M~E dateStart: 20190101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELagQogLKu_wqIzECSnUr_jRW6FbwWFLDyD2Fk38aCutsmg3RaiH_nY8SbraRQguXKIocRTL8_B8mvE3hLyRXngDPJZKc58BiuJlU2lVpgS6idFKDqlvNmFOTuxs5k43Wn1hTdhADzws3H4wFVTaJuEFKCUYgPVOKsdQEy0E9L7MuA0whT4Yd23BqiGNmVGY20_hrMPkgxDvrOYW20lubEQ9X_-fgszfayU3Np_jXXJ_jBrp4TDbB-RWbB-Su9MxL_6IXJ0u8R5dF8XuZnjGnC4SHRh66Kf5HD0a_dyuYkf7KgH6Las4HpuiR7F3FhTaQMe0DZ38BCyRQaHRI-jggE77ostIRz7WM3o4kpE_Jl-PJ18-fCzHrgqlz_bWlbEKIFmTDLBkPB5ialxwEGLkyuT4IbDGRRc0cB4lCOwYxg0DzaKtsjFz-YTstIs2PiM0gyEvkos57LMZVjaQoxEhktQhqOiSKgi7WeLaj5Tj2PliXmfogVKpe6nUKJV6kEpB3q4_-T7wbfxt8HuU23ogUmX3D7IC1aMC1f9SoIK8vpF6nU0L8yXQxsXlqhY5OtMqj2UFeTpowfpXMuNqWUlRELOlH1tz2X7TXpz39N2OKa2dfv4_Jv-C3MP1wPQWVy_JTre8jK_IHf-ju1gt98htM7N7vWXk6_R68gv5ehOy |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Predictive+Modeling+of+Mental+Illness+Onset+Using+Wearable+Devices+and+Medical+Examination+Data%3A+Machine+Learning+Approach&rft.jtitle=Frontiers+in+digital+health&rft.au=Saito%2C+Tomoki&rft.au=Suzuki%2C+Hikaru&rft.au=Kishi%2C+Akifumi&rft.date=2022-04-14&rft.issn=2673-253X&rft.eissn=2673-253X&rft.volume=4&rft.spage=861808&rft_id=info:doi/10.3389%2Ffdgth.2022.861808&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2673-253X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2673-253X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2673-253X&client=summon |