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| PMID |
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| TITLE |
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| Feasibility of Collecting and Linking Digital Phenotyping, Clinical, and Genetics Data for Mental Health Research: Pilot Observational Study. |
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| ABSTRACT |
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| BACKGROUND |
NlmCategory: UNASSIGNED |
| Digital phenotyping-the use of digital data to measure and understand behavior and internal states-shows promise for advancing predictive analytics in mental health, particularly when combined with other data sources. However, linking digital phenotyping data with sources of highly sensitive clinical or genetic data remains rare, primarily due to technical, ethical, and procedural challenges. Understanding the feasibility of collecting and linking these data types is a critical first step toward developing novel multimodal datasets. |
| OBJECTIVE |
NlmCategory: UNASSIGNED |
| Digital phenotyping-the use of digital data to measure and understand behavior and internal states-shows promise for advancing predictive analytics in mental health, particularly when combined with other data sources. However, linking digital phenotyping data with sources of highly sensitive clinical or genetic data remains rare, primarily due to technical, ethical, and procedural challenges. Understanding the feasibility of collecting and linking these data types is a critical first step toward developing novel multimodal datasets. The Mobigene Pilot Study examines the feasibility of collecting smartphone-based digital phenotyping and mental health data and linking it to genetic data from an existing cohort of adults with a history of depression (ie, the Australian Genetics of Depression Study). This paper aims to describe (1) rates of study uptake and adherence; (2) levels of adherence and engagement with daily mood assessments; (3) willingness to take part in similar research; and (4) whether feasibility indicators varied according to mental health symptoms. |
| METHODS |
NlmCategory: UNASSIGNED |
| Digital phenotyping-the use of digital data to measure and understand behavior and internal states-shows promise for advancing predictive analytics in mental health, particularly when combined with other data sources. However, linking digital phenotyping data with sources of highly sensitive clinical or genetic data remains rare, primarily due to technical, ethical, and procedural challenges. Understanding the feasibility of collecting and linking these data types is a critical first step toward developing novel multimodal datasets. The Mobigene Pilot Study examines the feasibility of collecting smartphone-based digital phenotyping and mental health data and linking it to genetic data from an existing cohort of adults with a history of depression (ie, the Australian Genetics of Depression Study). This paper aims to describe (1) rates of study uptake and adherence; (2) levels of adherence and engagement with daily mood assessments; (3) willingness to take part in similar research; and (4) whether feasibility indicators varied according to mental health symptoms. Participants aged 18-30 years with genetic data from the Australian Genetics of Depression Study were invited to participate in a two-week digital phenotyping study. They completed a baseline mental health survey and then downloaded the MindGRID digital phenotyping app. Active data from cognitive, voice, and typing tasks were collected once per day on days 1 and 11. Daily momentary assessments of self-reported mood were collected on days 2-10 (once per day for 9 days). Passive data (eg, from GPS, accelerometers) were collected throughout the two-week period. A second mental health survey was then completed after two weeks. To measure feasibility, we examined metrics of study uptake (eg, consent) and adherence (eg, proportion of completed momentary assessments), and willingness to participate in similar future research. Pearson correlations and t tests explored the relationship between feasibility indicators and mental health symptoms. |
| RESULTS |
NlmCategory: UNASSIGNED |
| Digital phenotyping-the use of digital data to measure and understand behavior and internal states-shows promise for advancing predictive analytics in mental health, particularly when combined with other data sources. However, linking digital phenotyping data with sources of highly sensitive clinical or genetic data remains rare, primarily due to technical, ethical, and procedural challenges. Understanding the feasibility of collecting and linking these data types is a critical first step toward developing novel multimodal datasets. The Mobigene Pilot Study examines the feasibility of collecting smartphone-based digital phenotyping and mental health data and linking it to genetic data from an existing cohort of adults with a history of depression (ie, the Australian Genetics of Depression Study). This paper aims to describe (1) rates of study uptake and adherence; (2) levels of adherence and engagement with daily mood assessments; (3) willingness to take part in similar research; and (4) whether feasibility indicators varied according to mental health symptoms. Participants aged 18-30 years with genetic data from the Australian Genetics of Depression Study were invited to participate in a two-week digital phenotyping study. They completed a baseline mental health survey and then downloaded the MindGRID digital phenotyping app. Active data from cognitive, voice, and typing tasks were collected once per day on days 1 and 11. Daily momentary assessments of self-reported mood were collected on days 2-10 (once per day for 9 days). Passive data (eg, from GPS, accelerometers) were collected throughout the two-week period. A second mental health survey was then completed after two weeks. To measure feasibility, we examined metrics of study uptake (eg, consent) and adherence (eg, proportion of completed momentary assessments), and willingness to participate in similar future research. Pearson correlations and t tests explored the relationship between feasibility indicators and mental health symptoms. Of 174 consenting and eligible participants, 153 (87.9%) completed the baseline mental health survey and 126 (72.4%) provided data enabling linkage of genetic, self-report, and digital data. After removal of duplicates, we found that 100 (57.5%) of these identified as unique participants and 69 (39.7%) provided complete post-study data. A small proportion of participants dropped out prior to completing the baseline survey (21/174, 12.1%) or during app-based data collection (31/174, 17.8%). Participants completed an average of 5.30 (SD 2.76) daily mood assessments. All 69 (100%) participants who completed the post-study surveys expressed willingness to participate in similar studies in the future. There was no significant association between feasibility indicators and current mental health symptoms. |
| CONCLUSIONS |
NlmCategory: UNASSIGNED |
| Digital phenotyping-the use of digital data to measure and understand behavior and internal states-shows promise for advancing predictive analytics in mental health, particularly when combined with other data sources. However, linking digital phenotyping data with sources of highly sensitive clinical or genetic data remains rare, primarily due to technical, ethical, and procedural challenges. Understanding the feasibility of collecting and linking these data types is a critical first step toward developing novel multimodal datasets. The Mobigene Pilot Study examines the feasibility of collecting smartphone-based digital phenotyping and mental health data and linking it to genetic data from an existing cohort of adults with a history of depression (ie, the Australian Genetics of Depression Study). This paper aims to describe (1) rates of study uptake and adherence; (2) levels of adherence and engagement with daily mood assessments; (3) willingness to take part in similar research; and (4) whether feasibility indicators varied according to mental health symptoms. Participants aged 18-30 years with genetic data from the Australian Genetics of Depression Study were invited to participate in a two-week digital phenotyping study. They completed a baseline mental health survey and then downloaded the MindGRID digital phenotyping app. Active data from cognitive, voice, and typing tasks were collected once per day on days 1 and 11. Daily momentary assessments of self-reported mood were collected on days 2-10 (once per day for 9 days). Passive data (eg, from GPS, accelerometers) were collected throughout the two-week period. A second mental health survey was then completed after two weeks. To measure feasibility, we examined metrics of study uptake (eg, consent) and adherence (eg, proportion of completed momentary assessments), and willingness to participate in similar future research. Pearson correlations and t tests explored the relationship between feasibility indicators and mental health symptoms. Of 174 consenting and eligible participants, 153 (87.9%) completed the baseline mental health survey and 126 (72.4%) provided data enabling linkage of genetic, self-report, and digital data. After removal of duplicates, we found that 100 (57.5%) of these identified as unique participants and 69 (39.7%) provided complete post-study data. A small proportion of participants dropped out prior to completing the baseline survey (21/174, 12.1%) or during app-based data collection (31/174, 17.8%). Participants completed an average of 5.30 (SD 2.76) daily mood assessments. All 69 (100%) participants who completed the post-study surveys expressed willingness to participate in similar studies in the future. There was no significant association between feasibility indicators and current mental health symptoms. It is feasible to collect and link multimodal datasets involving digital phenotyping, clinical, and genetic data, although there are some methodological and technical challenges. We provide recommendations for future research related to data collection platforms and compliance. |
| © Joanne R Beames, Omar Dabash, Michael J Spoelma, Artur Shvetcov, Wu Yi Zheng, Aimy Slade, Jin Han, Leonard Hoon, Joost Funke Kupper, Richard Parker, Brittany Mitchell, Nicholas G Martin, Jill M Newby, Alexis E Whitton, Helen Christensen. Originally published in JMIR Formative Research (https://formative.jmir.org). |
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| DATE PUBLISHED |
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| HISTORY |
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| PUBSTATUS |
PUBSTATUSDATE |
| received |
2025/01/26 |
| revised |
2025/04/14 |
| accepted |
2025/05/11 |
| medline |
2025/06/24 13:08 |
| pubmed |
2025/06/24 10:58 |
| entrez |
2025/06/23 11:03 |
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| AUTHORS |
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| NAME |
COLLECTIVENAME |
LASTNAME |
FORENAME |
INITIALS |
AFFILIATION |
AFFILIATIONINFO |
| Beames JR |
|
Beames |
Joanne R |
JR |
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Department of Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium. |
| Dabash O |
|
Dabash |
Omar |
O |
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Black Dog Institute, University of New South Wales, Sydney, Australia. |
| Spoelma MJ |
|
Spoelma |
Michael J |
MJ |
|
Black Dog Institute, University of New South Wales, Sydney, Australia. |
| Shvetcov A |
|
Shvetcov |
Artur |
A |
|
Black Dog Institute, University of New South Wales, Sydney, Australia. |
| Zheng WY |
|
Zheng |
Wu Yi |
WY |
|
Black Dog Institute, University of New South Wales, Sydney, Australia. |
| Slade A |
|
Slade |
Aimy |
A |
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Black Dog Institute, University of New South Wales, Sydney, Australia. |
| Han J |
|
Han |
Jin |
J |
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Division of Arts and Sciences and Centre for Global Health Equity, New York University Shanghai, Shanghai, China. |
| Hoon L |
|
Hoon |
Leonard |
L |
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Applied Artificial Intelligence Institute, Deakin University, Melbourne, Australia. |
| Kupper JF |
|
Kupper |
Joost Funke |
JF |
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Applied Artificial Intelligence Institute, Deakin University, Melbourne, Australia. |
| Parker R |
|
Parker |
Richard |
R |
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Brain and Mental Health Program, QIMR Berghofer Institute of Medical Research, Brisbane, Australia. |
| Mitchell B |
|
Mitchell |
Brittany |
B |
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Brain and Mental Health Program, QIMR Berghofer Institute of Medical Research, Brisbane, Australia. |
| Martin NG |
|
Martin |
Nicholas G |
NG |
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Brain and Mental Health Program, QIMR Berghofer Institute of Medical Research, Brisbane, Australia. |
| Newby JM |
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Newby |
Jill M |
JM |
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School of Psychology, University of New South Wales, Sydney, Australia. |
| Whitton AE |
|
Whitton |
Alexis E |
AE |
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Black Dog Institute, University of New South Wales, Hospital Road, Randwick, 2031, Australia, 61 293828507. |
| Christensen H |
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Christensen |
Helen |
H |
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Black Dog Institute, University of New South Wales, Sydney, Australia. |
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| INVESTIGATORS |
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| JOURNAL |
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| VOLUME: 9 |
| ISSUE: |
| TITLE: JMIR formative research |
| ISOABBREVIATION: JMIR Form Res |
| YEAR: 2025 |
| MONTH: Jun |
| DAY: 23 |
| MEDLINEDATE: |
| SEASON: |
| CITEDMEDIUM: Internet |
| ISSN: 2561-326X |
| ISSNTYPE: Electronic |
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| MEDLINE JOURNAL |
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| MEDLINETA: JMIR Form Res |
| COUNTRY: Canada |
| ISSNLINKING: 2561-326X |
| NLMUNIQUEID: 101726394 |
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| PUBLICATION TYPE |
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| PUBLICATIONTYPE TEXT |
| Journal Article |
| Observational Study |
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| COMMENTS AND CORRECTIONS |
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| GRANTS |
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| GENERAL NOTE |
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| KEYWORDS |
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| KEYWORD |
| anhedonia |
| anxiety |
| daily diary |
| data linkage |
| depression |
| experience sampling methodology |
| precision medicine |
| suicidal ideation |
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| MESH HEADINGS |
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| DESCRIPTORNAME |
QUALIFIERNAME |
| Humans |
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| Pilot Projects |
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| Adult |
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| Feasibility Studies |
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| Male |
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| Female |
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| Phenotype |
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| Adolescent |
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| Australia |
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| Young Adult |
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| Mental Health |
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| Depression |
genetics |
| Smartphone |
genetics |
| Data Collection |
methods |
| Mobile Applications |
methods |
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| SUPPLEMENTARY MESH |
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| GENE SYMBOLS |
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| CHEMICALS |
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| OTHER ID's |
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