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Halabi R, Mulsant BH, Alda M, DeShaw A, Hintze A, Husain MI, O'Donovan C, Patterson R, Ortiz A. Not missing at random: Missing data are associated with clinical status and trajectories in an electronic monitoring longitudinal study of bipolar disorder. J Psychiatr Res 2024; 174:326-331. [PMID: 38692162 DOI: 10.1016/j.jpsychires.2024.04.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/11/2024] [Accepted: 04/18/2024] [Indexed: 05/03/2024]
Abstract
There is limited information on the association between participants' clinical status or trajectories and missing data in electronic monitoring studies of bipolar disorder (BD). We collected self-ratings scales and sensor data in 145 adults with BD. Using a new metric, Missing Data Ratio (MDR), we assessed missing self-rating data and sensor data monitoring activity and sleep. Missing data were lowest for participants in the midst of a depressive episode, intermediate for participants with subsyndromal symptoms, and highest for participants who were euthymic. Over a mean ± SD follow-up of 246 ± 181 days, missing data remained unchanged for participants whose clinical status did not change throughout the study (i.e., those who entered the study in a depressive episode and did not improve, or those who entered the study euthymic and remained euthymic). Conversely, when participants' clinical status changed during the study (e.g., those who entered the study euthymic and experienced the occurrence of a depressive episode), missing data for self-rating scales increased, but not for sensor data. Overall missing data were associated with participants' clinical status and its changes, suggesting that these are not missing at random.
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Affiliation(s)
- Ramzi Halabi
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Benoit H Mulsant
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada; National Institute of Mental Health, Klecany, Czech Republic
| | | | - Arend Hintze
- Department of MicroData Analytics, Dalarna University, Sweden
| | - Muhammad I Husain
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Claire O'Donovan
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Rachel Patterson
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Abigail Ortiz
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
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de Azevedo Cardoso T, Kochhar S, Torous J, Morton E. Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions. JMIR Ment Health 2024; 11:e58631. [PMID: 38557724 PMCID: PMC11019420 DOI: 10.2196/58631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 03/25/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.
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Affiliation(s)
- Taiane de Azevedo Cardoso
- The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Australia
- JMIR Publications, Toronto, ON, Canada
| | | | - John Torous
- Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Emma Morton
- School of Psychological Sciences, Monash University, Clayton, Australia
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Lenze E, Torous J, Arean P. Digital and precision clinical trials: innovations for testing mental health medications, devices, and psychosocial treatments. Neuropsychopharmacology 2024; 49:205-214. [PMID: 37550438 PMCID: PMC10700595 DOI: 10.1038/s41386-023-01664-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/05/2023] [Accepted: 07/10/2023] [Indexed: 08/09/2023]
Abstract
Mental health treatment advances - including neuropsychiatric medications and devices, psychotherapies, and cognitive treatments - lag behind other fields of clinical medicine such as cardiovascular care. One reason for this gap is the traditional techniques used in mental health clinical trials, which slow the pace of progress, produce inequities in care, and undermine precision medicine goals. Newer techniques and methodologies, which we term digital and precision trials, offer solutions. These techniques consist of (1) decentralized (i.e., fully-remote) trials which improve the speed and quality of clinical trials and increase equity of access to research, (2) precision measurement which improves success rate and is essential for precision medicine, and (3) digital interventions, which offer increased reach of, and equity of access to, evidence-based treatments. These techniques and their rationales are described in detail, along with challenges and solutions for their utilization. We conclude with a vignette of a depression clinical trial using these techniques.
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Affiliation(s)
- Eric Lenze
- Departments of Psychiatry and Anesthesiology, Washington University School of Medicine, St Louis, MO, USA.
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Patricia Arean
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
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Wyant K, Moshontz H, Ward SB, Fronk GE, Curtin JJ. Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study. JMIR Mhealth Uhealth 2023; 11:e41833. [PMID: 37639300 PMCID: PMC10495858 DOI: 10.2196/41833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 03/14/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
BACKGROUND Personal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized adaptive interventions. However, further development and the use of personal sensing requires a better understanding of its acceptability to people targeted for these applications. OBJECTIVE We aimed to assess the acceptability of active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. This sample was recruited as part of a larger grant-funded project to develop a machine learning algorithm to predict lapses. METHODS Participants (N=154; n=77, 50% female; mean age 41, SD 11.9 years; n=134, 87% White and n=150, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) were recruited to participate in a 3-month longitudinal study. Participants were modestly compensated for engaging with active (eg, ecological momentary assessment [EMA], audio check-in, and sleep quality) and passive (eg, geolocation, cellular communication logs, and SMS text message content) sensing methods that were selected to tap into constructs from the Relapse Prevention model by Marlatt. We assessed 3 behavioral indicators of acceptability: participants' choices about their participation in the study at various stages in the procedure, their choice to opt in to provide data for each sensing method, and their adherence to a subset of the active methods (EMA and audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method. RESULTS Of the 192 eligible individuals screened, 191 consented to personal sensing. Most of these individuals (169/191, 88.5%) also returned 1 week later to formally enroll, and 154 participated through the first month follow-up visit. All participants in our analysis sample opted in to provide data for EMA, sleep quality, geolocation, and cellular communication logs. Out of 154 participants, 1 (0.6%) did not provide SMS text message content and 3 (1.9%) did not provide any audio check-ins. The average adherence rate for the 4 times daily EMA was .80. The adherence rate for the daily audio check-in was .54. Aggregate participant ratings indicated that all personal sensing methods were significantly more acceptable (all P<.001) compared with neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active methods compared with passive methods (P=.23). However, participants reported a higher willingness to use passive (vs active) methods for 1 year (P=.04). CONCLUSIONS These results suggest that active and passive sensing methods are acceptable for people with alcohol use disorder over a longer period than has previously been assessed. Important individual differences were observed across people and methods, indicating opportunities for future improvement.
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Affiliation(s)
- Kendra Wyant
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - Hannah Moshontz
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - Stephanie B Ward
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - Gaylen E Fronk
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - John J Curtin
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
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Bayes A. Digitally acquired recreational computer programming activity and its association with bipolar disorder mood episodes. Bipolar Disord 2023; 25:433-436. [PMID: 37308318 DOI: 10.1111/bdi.13354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Adam Bayes
- Black Dog Institute, Randwick, New South Wales, Australia
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Ortiz A, Park Y, Gonzalez-Torres C, Alda M, Blumberger DM, Burnett R, Husain MI, Sanches M, Mulsant BH. Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models. Int J Bipolar Disord 2023; 11:18. [PMID: 37195477 PMCID: PMC10192477 DOI: 10.1186/s40345-023-00297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/14/2023] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Several studies have reported on the feasibility of electronic (e-)monitoring using computers or smartphones in patients with mental disorders, including bipolar disorder (BD). While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioeconomic status and use of health apps, to our knowledge, no study has examined clinical characteristics that might impact adherence with e-monitoring in patients with BD. We analyzed adherence to e-monitoring in patients with BD who participated in an ongoing e-monitoring study and evaluated whether demographic and clinical factors would predict adherence. METHODS Eighty-seven participants with BD in different phases of the illness were included. Patterns of adherence for wearable use, daily and weekly self-rating scales over 15 months were analyzed to identify adherence trajectories using growth mixture models (GMM). Multinomial logistic regression models were fitted to compute the effects of predictors on GMM classes. RESULTS Overall adherence rates were 79.5% for the wearable; 78.5% for weekly self-ratings; and 74.6% for daily self-ratings. GMM identified three latent class subgroups: participants with (i) perfect; (ii) good; and (iii) poor adherence. On average, 34.4% of participants showed "perfect" adherence; 37.1% showed "good" adherence; and 28.2% showed poor adherence to all three measures. Women, participants with a history of suicide attempt, and those with a history of inpatient admission were more likely to belong to the group with perfect adherence. CONCLUSIONS Participants with higher illness burden (e.g., history of admission to hospital, history of suicide attempts) have higher adherence rates to e-monitoring. They might see e-monitoring as a tool for better documenting symptom change and better managing their illness, thus motivating their engagement.
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Affiliation(s)
- Abigail Ortiz
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
| | - Yunkyung Park
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Christina Gonzalez-Torres
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- National Institute of Mental Health, Klecany, Czech Republic
| | - Daniel M Blumberger
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Rachael Burnett
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - M Ishrat Husain
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Marcos Sanches
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Benoit H Mulsant
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
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Sangha N, Lyall L, Wyse C, Cullen B, Whalley HC, Smith DJ. The nosological status of unipolar mania and hypomania within UK Biobank according to objective and subjective measures of diurnal rest and activity. Bipolar Disord 2022; 24:726-738. [PMID: 35656588 PMCID: PMC9796466 DOI: 10.1111/bdi.13237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
BACKGROUND There is uncertainty whether unipolar mania is a discrete sub-type of bipolar disorder. Disrupted rest/activity rhythms are a key feature of bipolar disorder (BD) but have not been well characterised in unipolar mania/hypomania (UM). We compared subjective and objective rest/activity patterns, demographic and mental health outcomes across BD, UM and control groups. METHODS UK residents aged 37-73 years were recruited into UK Biobank from 2006 to 2010. BD, UM and control groups were identified via a mental health questionnaire. Demographic, mental health and subjective sleep outcomes were self-reported. Accelerometery data were available for a subset of participants, and objective measures of sleep and activity were derived. RESULTS A greater proportion of males met UM criteria, and more females were in the BD group. Both BD and UM groups had poor mental health outcomes vs. controls. Objectively measured activity differed between all three groups: UM had highest levels of activity and BD lowest. The UM group had shorter sleep duration compared to controls. Subjective rest/activity measures showed that both mood disorder groups (compared to controls) had later chronotype preference, more disturbed sleep and increased difficulty getting up in the morning. However, the UM group were more likely to report an early chronotype compared to BD and control groups. CONCLUSIONS BD and UM share features in common, but key differences support the proposition that UM may be a distinct and more clinically homogenous disorder. UM was characterised by a higher proportion of males, early chronotype, increased activity and shorter sleep duration.
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Affiliation(s)
- Natasha Sangha
- Mental Health and Wellbeing, Institute of Health and WellbeingUniversity of GlasgowGlasgowUK
| | - Laura Lyall
- Mental Health and Wellbeing, Institute of Health and WellbeingUniversity of GlasgowGlasgowUK
| | - Cathy Wyse
- Department of BiologyMaynooth UniversityKildareIreland
| | - Breda Cullen
- Mental Health and Wellbeing, Institute of Health and WellbeingUniversity of GlasgowGlasgowUK
| | - Heather C. Whalley
- Centre for Clinical Brain Sciences, Division of PsychiatryUniversity of EdinburghEdinburghUK
| | - Daniel J. Smith
- Centre for Clinical Brain Sciences, Division of PsychiatryUniversity of EdinburghEdinburghUK
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Anmella G, Faurholt‐Jepsen M, Hidalgo‐Mazzei D, Radua J, Passos IC, Kapczinski F, Minuzzi L, Alda M, Meier S, Hajek T, Ballester P, Birmaher B, Hafeman D, Goldstein T, Brietzke E, Duffy A, Haarman B, López‐Jaramillo C, Yatham LN, Lam RW, Isometsa E, Mansur R, McIntyre RS, Mwangi B, Vieta E, Kessing LV. Smartphone-based interventions in bipolar disorder: Systematic review and meta-analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force. Bipolar Disord 2022; 24:580-614. [PMID: 35839276 PMCID: PMC9804696 DOI: 10.1111/bdi.13243] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established. OBJECTIVES To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators. METHODS We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668). RESULTS The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators. CONCLUSION We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.
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Affiliation(s)
- Gerard Anmella
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of NeuroscienceHospital Clinic, University of Barcelona, IDIBAPS, CIBERSAMBarcelonaCataloniaSpain
| | - Maria Faurholt‐Jepsen
- Copenhagen Affective Disorder research Center (CADIC)Psychiatric Center CopenhagenCopenhagenDenmark
| | - Diego Hidalgo‐Mazzei
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of NeuroscienceHospital Clinic, University of Barcelona, IDIBAPS, CIBERSAMBarcelonaCataloniaSpain
| | - Joaquim Radua
- Imaging of Mood‐ and Anxiety‐Related Disorders (IMARD) groupIDIBAPS, CIBERSAMBarcelonaSpain,Early Psychosis: Interventions and Clinical‐detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & NeuroscienceKing's College LondonLondonUK,Centre for Psychiatric Research and Education, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
| | - Ives C. Passos
- Laboratory of Molecular Psychiatry and Bipolar Disorder Program, Programa de Pós‐Graduação em Psiquiatria e Ciências do Comportamento, Centro de Pesquisa Experimental do Hospital de Clínicas de Porto AlegreUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
| | - Flavio Kapczinski
- Department of Psychiatry and Behavioural NeurosciencesMcMaster UniversityHamiltonONCanada
| | - Luciano Minuzzi
- Department of Psychiatry and Behavioural NeurosciencesMcMaster UniversityHamiltonONCanada
| | - Martin Alda
- Department of PsychiatryDalhousie UniversityHalifaxNSCanada
| | - Sandra Meier
- Department of PsychiatryDalhousie UniversityHalifaxNSCanada
| | - Tomas Hajek
- Department of PsychiatryDalhousie UniversityHalifaxNSCanada,National Institute of Mental HealthKlecanyCzech Republic
| | - Pedro Ballester
- Neuroscience Graduate ProgramMcMaster UniversityHamiltonCanada
| | - Boris Birmaher
- Department of Psychiatry, Western Psychiatric Institute and ClinicUniversity of Pittsburgh School of MedicinePittsburghPAUSA
| | - Danella Hafeman
- Department of Psychiatry, Western Psychiatric Institute and ClinicUniversity of Pittsburgh School of MedicinePittsburghPAUSA
| | - Tina Goldstein
- Department of Psychiatry, Western Psychiatric Institute and ClinicUniversity of Pittsburgh School of MedicinePittsburghPAUSA
| | - Elisa Brietzke
- Department of PsychiatryQueen's UniversityKingstonONCanada
| | - Anne Duffy
- Department of PsychiatryQueen's UniversityKingstonONCanada
| | - Benno Haarman
- Department of PsychiatryUniversity Medical Center Groningen, University of GroningenGroningenThe Netherlands
| | - Carlos López‐Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, Faculty of MedicineUniversity of AntioquiaMedellínColombia,Mood Disorders ProgramHospital Universitario San Vicente FundaciónMedellínColombia
| | - Lakshmi N. Yatham
- Department of PsychiatryUniversity of British ColumbiaVancouverBCCanada
| | - Raymond W. Lam
- Department of PsychiatryUniversity of British ColumbiaVancouverBCCanada
| | - Erkki Isometsa
- Department of PsychiatryUniversity of Helsinki and Helsinki University Central HospitalHelsinkiFinland
| | - Rodrigo Mansur
- Mood Disorders Psychopharmacology Unit (MDPU)University Health Network, University of TorontoTorontoONCanada
| | | | - Benson Mwangi
- Department of Psychiatry and Behavioral Sciences, UT Center of Excellence on Mood Disorders, McGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTXUSA
| | - Eduard Vieta
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of NeuroscienceHospital Clinic, University of Barcelona, IDIBAPS, CIBERSAMBarcelonaCataloniaSpain
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder research Center (CADIC)Psychiatric Center CopenhagenCopenhagenDenmark,Department of Clinical MedicineUniversity of CopenhagenCopenhagenDenmark
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Ortiz A, Hintze A, Burnett R, Gonzalez-Torres C, Unger S, Yang D, Miao J, Alda M, Mulsant BH. Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study. BMC Psychiatry 2022; 22:288. [PMID: 35459150 PMCID: PMC9026652 DOI: 10.1186/s12888-022-03923-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Predictive models for mental disorders or behaviors (e.g., suicide) have been successfully developed at the level of populations, yet current demographic and clinical variables are neither sensitive nor specific enough for making individual clinical predictions. Forecasting episodes of illness is particularly relevant in bipolar disorder (BD), a mood disorder with high recurrence, disability, and suicide rates. Thus, to understand the dynamic changes involved in episode generation in BD, we propose to extract and interpret individual illness trajectories and patterns suggestive of relapse using passive sensing, nonlinear techniques, and deep anomaly detection. Here we describe the study we have designed to test this hypothesis and the rationale for its design. METHOD This is a protocol for a contactless cohort study in 200 adult BD patients. Participants will be followed for up to 2 years during which they will be monitored continuously using passive sensing, a wearable that collects multimodal physiological (heart rate variability) and objective (sleep, activity) data. Participants will complete (i) a comprehensive baseline assessment; (ii) weekly assessments; (iii) daily assessments using electronic rating scales. Data will be analyzed using nonlinear techniques and deep anomaly detection to forecast episodes of illness. DISCUSSION This proposed contactless, large cohort study aims to obtain and combine high-dimensional, multimodal physiological, objective, and subjective data. Our work, by conceptualizing mood as a dynamic property of biological systems, will demonstrate the feasibility of incorporating individual variability in a model informing clinical trajectories and predicting relapse in BD.
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Affiliation(s)
- Abigail Ortiz
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
- Centre for Addiction and Mental Health (CAMH), 100 Stokes St., Rm 4229, Toronto, ON, Canada.
| | - Arend Hintze
- Department of Computer Science, Dalarna University, Dalarna, Sweden
| | - Rachael Burnett
- Centre for Addiction and Mental Health (CAMH), 100 Stokes St., Rm 4229, Toronto, ON, Canada
| | - Christina Gonzalez-Torres
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health (CAMH), 100 Stokes St., Rm 4229, Toronto, ON, Canada
| | - Samantha Unger
- Centre for Addiction and Mental Health (CAMH), 100 Stokes St., Rm 4229, Toronto, ON, Canada
| | - Dandan Yang
- Centre for Addiction and Mental Health (CAMH), 100 Stokes St., Rm 4229, Toronto, ON, Canada
- Department of Pharmacology, University of Toronto, Toronto, Ontario, Canada
| | - Jingshan Miao
- Centre for Addiction and Mental Health (CAMH), 100 Stokes St., Rm 4229, Toronto, ON, Canada
- Department of Pharmacology, University of Toronto, Toronto, Ontario, Canada
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
- National Institute of Mental Health, Klecany, Czech Republic
| | - Benoit H Mulsant
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health (CAMH), 100 Stokes St., Rm 4229, Toronto, ON, Canada
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10
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Ortiz A, Bradler K, Mowete M, MacLean S, Garnham J, Slaney C, Mulsant BH, Alda M. The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes. Int J Bipolar Disord 2021; 9:30. [PMID: 34596784 PMCID: PMC8486895 DOI: 10.1186/s40345-021-00235-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/17/2021] [Indexed: 03/06/2023] Open
Abstract
BACKGROUND Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. Nonlinear techniques are employed to understand and model the behavior of complex systems. Our aim was to assess the underlying nonlinear properties that account for mood and energy fluctuations in patients with BD; and to compare whether these processes were different in healthy controls (HC) and unaffected first-degree relatives (FDR). We used three different nonlinear techniques: Lyapunov exponent, detrended fluctuation analysis and fractal dimension to assess the underlying behavior of mood and energy fluctuations in all groups; and subsequently to assess whether these arise from different processes in each of these groups. RESULTS There was a positive, short-term autocorrelation for both mood and energy series in all three groups. In the mood series, the largest Lyapunov exponent was found in HC (1.84), compared to BD (1.63) and FDR (1.71) groups [F (2, 87) = 8.42, p < 0.005]. A post-hoc Tukey test showed that Lyapunov exponent in HC was significantly higher than both the BD (p = 0.003) and FDR groups (p = 0.03). Similarly, in the energy series, the largest Lyapunov exponent was found in HC (1.85), compared to BD (1.76) and FDR (1.67) [F (2, 87) = 11.02; p < 0.005]. There were no significant differences between groups for the detrended fluctuation analysis or fractal dimension. CONCLUSIONS The underlying nature of mood variability is in keeping with that of a chaotic system, which means that fluctuations are generated by deterministic nonlinear process(es) in HC, BD, and FDR. The value of this complex modeling lies in analyzing the nature of the processes involved in mood regulation. It also suggests that the window for episode prediction in BD will be inevitably short.
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Affiliation(s)
- Abigail Ortiz
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Centre for Addiction & Mental Health, CAMH 100 Stokes St., Rm 4229, Toronto, ON, M6J 1H4, Canada.
| | | | - Maxine Mowete
- Department of Electrical Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Stephane MacLean
- Institute for Mental Health Research, The Royal Ottawa Hospital, Ottawa, ON, Canada
| | | | | | - Benoit H Mulsant
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Centre for Addiction & Mental Health, CAMH 100 Stokes St., Rm 4229, Toronto, ON, M6J 1H4, Canada
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- National Institute of Mental Health, Klecany, Czech Republic
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