The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age

Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	John Zulueta [verfasserIn] Alexander Pantelis Demos [verfasserIn] Claudia Vesel [verfasserIn] Mindy Ross [verfasserIn] Andrea Piscitello [verfasserIn] Faraz Hussain [verfasserIn] Scott A. Langenecker [verfasserIn] Melvin McInnis [verfasserIn] Peter Nelson [verfasserIn] Kelly Ryan [verfasserIn] Alex Leow [verfasserIn] Olusola Ajilore [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	digital biomarkers bipolar disorder brain age estimation smartphone digital phenotyping

Übergeordnetes Werk:	In: Frontiers in Psychiatry - Frontiers Media S.A., 2010, 12(2021)
Übergeordnetes Werk:	volume:12 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fpsyt.2021.739022

Katalog-ID:	DOAJ016512421

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520			\|a Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker.
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allfields	10.3389/fpsyt.2021.739022 doi (DE-627)DOAJ016512421 (DE-599)DOAJc37291c7946340d48648f8c91d99219f DE-627 ger DE-627 rakwb eng RC435-571 John Zulueta verfasserin aut The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker. digital biomarkers bipolar disorder brain age estimation smartphone digital phenotyping Psychiatry Alexander Pantelis Demos verfasserin aut Claudia Vesel verfasserin aut Mindy Ross verfasserin aut Andrea Piscitello verfasserin aut Faraz Hussain verfasserin aut Scott A. Langenecker verfasserin aut Melvin McInnis verfasserin aut Peter Nelson verfasserin aut Kelly Ryan verfasserin aut Alex Leow verfasserin aut Alex Leow verfasserin aut Olusola Ajilore verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 12(2021) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:12 year:2021 https://doi.org/10.3389/fpsyt.2021.739022 kostenfrei https://doaj.org/article/c37291c7946340d48648f8c91d99219f kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2021.739022/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
spelling	10.3389/fpsyt.2021.739022 doi (DE-627)DOAJ016512421 (DE-599)DOAJc37291c7946340d48648f8c91d99219f DE-627 ger DE-627 rakwb eng RC435-571 John Zulueta verfasserin aut The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker. digital biomarkers bipolar disorder brain age estimation smartphone digital phenotyping Psychiatry Alexander Pantelis Demos verfasserin aut Claudia Vesel verfasserin aut Mindy Ross verfasserin aut Andrea Piscitello verfasserin aut Faraz Hussain verfasserin aut Scott A. Langenecker verfasserin aut Melvin McInnis verfasserin aut Peter Nelson verfasserin aut Kelly Ryan verfasserin aut Alex Leow verfasserin aut Alex Leow verfasserin aut Olusola Ajilore verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 12(2021) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:12 year:2021 https://doi.org/10.3389/fpsyt.2021.739022 kostenfrei https://doaj.org/article/c37291c7946340d48648f8c91d99219f kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2021.739022/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
allfields_unstemmed	10.3389/fpsyt.2021.739022 doi (DE-627)DOAJ016512421 (DE-599)DOAJc37291c7946340d48648f8c91d99219f DE-627 ger DE-627 rakwb eng RC435-571 John Zulueta verfasserin aut The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker. digital biomarkers bipolar disorder brain age estimation smartphone digital phenotyping Psychiatry Alexander Pantelis Demos verfasserin aut Claudia Vesel verfasserin aut Mindy Ross verfasserin aut Andrea Piscitello verfasserin aut Faraz Hussain verfasserin aut Scott A. Langenecker verfasserin aut Melvin McInnis verfasserin aut Peter Nelson verfasserin aut Kelly Ryan verfasserin aut Alex Leow verfasserin aut Alex Leow verfasserin aut Olusola Ajilore verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 12(2021) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:12 year:2021 https://doi.org/10.3389/fpsyt.2021.739022 kostenfrei https://doaj.org/article/c37291c7946340d48648f8c91d99219f kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2021.739022/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
allfieldsGer	10.3389/fpsyt.2021.739022 doi (DE-627)DOAJ016512421 (DE-599)DOAJc37291c7946340d48648f8c91d99219f DE-627 ger DE-627 rakwb eng RC435-571 John Zulueta verfasserin aut The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker. digital biomarkers bipolar disorder brain age estimation smartphone digital phenotyping Psychiatry Alexander Pantelis Demos verfasserin aut Claudia Vesel verfasserin aut Mindy Ross verfasserin aut Andrea Piscitello verfasserin aut Faraz Hussain verfasserin aut Scott A. Langenecker verfasserin aut Melvin McInnis verfasserin aut Peter Nelson verfasserin aut Kelly Ryan verfasserin aut Alex Leow verfasserin aut Alex Leow verfasserin aut Olusola Ajilore verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 12(2021) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:12 year:2021 https://doi.org/10.3389/fpsyt.2021.739022 kostenfrei https://doaj.org/article/c37291c7946340d48648f8c91d99219f kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2021.739022/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
allfieldsSound	10.3389/fpsyt.2021.739022 doi (DE-627)DOAJ016512421 (DE-599)DOAJc37291c7946340d48648f8c91d99219f DE-627 ger DE-627 rakwb eng RC435-571 John Zulueta verfasserin aut The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker. digital biomarkers bipolar disorder brain age estimation smartphone digital phenotyping Psychiatry Alexander Pantelis Demos verfasserin aut Claudia Vesel verfasserin aut Mindy Ross verfasserin aut Andrea Piscitello verfasserin aut Faraz Hussain verfasserin aut Scott A. Langenecker verfasserin aut Melvin McInnis verfasserin aut Peter Nelson verfasserin aut Kelly Ryan verfasserin aut Alex Leow verfasserin aut Alex Leow verfasserin aut Olusola Ajilore verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 12(2021) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:12 year:2021 https://doi.org/10.3389/fpsyt.2021.739022 kostenfrei https://doaj.org/article/c37291c7946340d48648f8c91d99219f kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2021.739022/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
language	English
source	In Frontiers in Psychiatry 12(2021) volume:12 year:2021
sourceStr	In Frontiers in Psychiatry 12(2021) volume:12 year:2021
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	digital biomarkers bipolar disorder brain age estimation smartphone digital phenotyping Psychiatry
isfreeaccess_bool	true
container_title	Frontiers in Psychiatry
authorswithroles_txt_mv	John Zulueta @@aut@@ Alexander Pantelis Demos @@aut@@ Claudia Vesel @@aut@@ Mindy Ross @@aut@@ Andrea Piscitello @@aut@@ Faraz Hussain @@aut@@ Scott A. Langenecker @@aut@@ Melvin McInnis @@aut@@ Peter Nelson @@aut@@ Kelly Ryan @@aut@@ Alex Leow @@aut@@ Olusola Ajilore @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	631498796
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callnumber-first	R - Medicine
author	John Zulueta
spellingShingle	John Zulueta misc RC435-571 misc digital biomarkers misc bipolar disorder misc brain age estimation misc smartphone misc digital phenotyping misc Psychiatry The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age
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topic_title	RC435-571 The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age digital biomarkers bipolar disorder brain age estimation smartphone digital phenotyping
topic	misc RC435-571 misc digital biomarkers misc bipolar disorder misc brain age estimation misc smartphone misc digital phenotyping misc Psychiatry
topic_unstemmed	misc RC435-571 misc digital biomarkers misc bipolar disorder misc brain age estimation misc smartphone misc digital phenotyping misc Psychiatry
topic_browse	misc RC435-571 misc digital biomarkers misc bipolar disorder misc brain age estimation misc smartphone misc digital phenotyping misc Psychiatry
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title	The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age
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title_full	The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age
author_sort	John Zulueta
journal	Frontiers in Psychiatry
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author_browse	John Zulueta Alexander Pantelis Demos Claudia Vesel Mindy Ross Andrea Piscitello Faraz Hussain Scott A. Langenecker Melvin McInnis Peter Nelson Kelly Ryan Alex Leow Olusola Ajilore
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author-letter	John Zulueta
doi_str_mv	10.3389/fpsyt.2021.739022
author2-role	verfasserin
title_sort	effects of bipolar disorder risk on a mobile phone keystroke dynamics based biomarker of brain age
callnumber	RC435-571
title_auth	The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age
abstract	Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker.
abstractGer	Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker.
abstract_unstemmed	Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker.
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title_short	The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age
url	https://doi.org/10.3389/fpsyt.2021.739022 https://doaj.org/article/c37291c7946340d48648f8c91d99219f https://www.frontiersin.org/articles/10.3389/fpsyt.2021.739022/full https://doaj.org/toc/1664-0640
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author2	Alexander Pantelis Demos Claudia Vesel Mindy Ross Andrea Piscitello Faraz Hussain Scott A. Langenecker Melvin McInnis Peter Nelson Kelly Ryan Alex Leow Olusola Ajilore
author2Str	Alexander Pantelis Demos Claudia Vesel Mindy Ross Andrea Piscitello Faraz Hussain Scott A. Langenecker Melvin McInnis Peter Nelson Kelly Ryan Alex Leow Olusola Ajilore
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up_date	2024-07-03T21:28:32.650Z
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