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Ho FYY, Poon CY, Wong VWH, Chan KW, Law KW, Yeung WF, Chung KF. Actigraphic monitoring of sleep and circadian rest-activity rhythm in individuals with major depressive disorder or depressive symptoms: A meta-analysis. J Affect Disord 2024; 361:224-244. [PMID: 38851435 DOI: 10.1016/j.jad.2024.05.155] [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: 08/08/2023] [Revised: 05/10/2024] [Accepted: 05/28/2024] [Indexed: 06/10/2024]
Abstract
BACKGROUND Disrupted sleep and rest-activity pattern are common clinical features in depressed individuals. This meta-analysis compared sleep and circadian rest-activity rhythms in people with major depressive disorder (MDD) or depressive symptoms and healthy controls. METHODS Eligible studies were identified in five databases up to December 2023. The search yielded 53 studies with a total of 11,115 participants, including 4000 depressed participants and 7115 healthy controls. RESULTS Pooled meta-analyses demonstrated that depressed individuals have significantly longer sleep latency (SMD = 0.23, 95 % CI: 0.12 to 0.33) and wake time after sleep onset (SMD = 0.37, 95 % CI: 0.22 to 0.52), lower sleep efficiency (SMD = -0.41, 95 % CI: -0.56 to -0.25), more nocturnal awakenings (SMD = 0.58, 95 % CI: 0.29 to 0.88), lower MESOR (SMD = -0.54, 95 % CI: -0.81 to -0.28), amplitude (SMD = -0.33, 95 % CI: -0.57 to -0.09), and interdaily stability (SMD = -0.17, 95 % CI: -0.28 to -0.05), less daytime (SMD = -0.79, 95 % CI: -1.08 to -0.49) and total activities (SMD = -0.89, 95 % CI: -1.28 to -0.50) when compared with healthy controls. LIMITATIONS Most of the included studies reported separate sleep and activity parameters instead of 24-hour rest-activity rhythms. The variabilities among actigraphy devices and the types of participants recruited also impede precise comparisons. CONCLUSIONS The findings emerging from this study offered a better understanding of sleep and rest-activity rhythm in individuals with MDD or depressive symptoms. Future studies could advocate for deriving objective, distinctive 24-hour rest-activity profiles contributing to the risk of depression. PROSPERO REGISTRATION NUMBER CRD42021259780.
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Affiliation(s)
- Fiona Yan-Yee Ho
- Department of Psychology, The Chinese University of Hong Kong, Hong Kong.
| | - Chun-Yin Poon
- Department of Psychology, The Chinese University of Hong Kong, Hong Kong
| | | | - Ka-Wai Chan
- Department of Psychology, The Chinese University of Hong Kong, Hong Kong
| | - Ka-Wai Law
- Department of Psychology, The Chinese University of Hong Kong, Hong Kong
| | - Wing-Fai Yeung
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong
| | - Ka-Fai Chung
- Department of Psychiatry, The University of Hong Kong, Hong Kong
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Anmella G, Corponi F, Li BM, Mas A, Garriga M, Sanabra M, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Giménez-Palomo A, Agasi I, Bastidas A, Cavero M, Bioque M, García-Rizo C, Madero S, Arbelo N, Murru A, Amoretti S, Martínez-Aran A, Ruiz V, Rivas Y, Fico G, De Prisco M, Oliva V, Solanes A, Radua J, Samalin L, Young AH, Vergari A, Vieta E, Hidalgo-Mazzei D. Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study. BJPsych Open 2024; 10:e137. [PMID: 39086306 DOI: 10.1192/bjo.2024.716] [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] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions. AIMS The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder. METHOD We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients. RESULTS Recruitment is ongoing. CONCLUSIONS This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.
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Affiliation(s)
- Gerard Anmella
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | | | - Bryan M Li
- School of Informatics, University of Edinburgh, UK
| | - Ariadna Mas
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Marina Garriga
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Miriam Sanabra
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Isabella Pacchiarotti
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Marc Valentí
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Iria Grande
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Antoni Benabarre
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Anna Giménez-Palomo
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Isabel Agasi
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain
| | - Anna Bastidas
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Myriam Cavero
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Miquel Bioque
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; Institute of Neurosciences (UBNeuro), University of Barcelona, Spain; and Barcelona Clinic Schizophrenia Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Clemente García-Rizo
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; Institute of Neurosciences (UBNeuro), University of Barcelona, Spain; and Barcelona Clinic Schizophrenia Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Santiago Madero
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; Institute of Neurosciences (UBNeuro), University of Barcelona, Spain; and Barcelona Clinic Schizophrenia Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Néstor Arbelo
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; Institute of Neurosciences (UBNeuro), University of Barcelona, Spain; and Barcelona Clinic Schizophrenia Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Andrea Murru
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Silvia Amoretti
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Anabel Martínez-Aran
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Victoria Ruiz
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain
| | - Yudit Rivas
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain
| | - Giovanna Fico
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Michele De Prisco
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Vincenzo Oliva
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Aleix Solanes
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; Institute of Neurosciences (UBNeuro), University of Barcelona, Spain; and Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; Institute of Neurosciences (UBNeuro), University of Barcelona, Spain; Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Early Psychosis: Interventions & Clinical Detection (EPIC) Laboratory, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Sweden
| | - Ludovic Samalin
- Institut Pascal (UMR 6602), Department of Psychiatry, CHU Clermont-Ferrand, University of Clermont Auvergne, CNRS, Clermont Auvergne INP, France; and Association Française de Psychiatrie Biologique et Neuropsychopharmacologie (AFPBN), Saint Germain en Laye, France
| | - Allan H Young
- Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | | | - Eduard Vieta
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
| | - Diego Hidalgo-Mazzei
- Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Spain; Department of Psychiatry and Psychology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Spain; and Institute of Neurosciences (UBNeuro), University of Barcelona, Spain
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Búzás A, Makai A, Groma GI, Dancsházy Z, Szendi I, Kish LB, Santa-Maria AR, Dér A. Hierarchical organization of human physical activity. Sci Rep 2024; 14:5981. [PMID: 38472275 DOI: 10.1038/s41598-024-56185-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/04/2024] [Indexed: 03/14/2024] Open
Abstract
Human physical activity (HPA), a fundamental physiological signal characteristic of bodily motion is of rapidly growing interest in multidisciplinary research. Here we report the existence of hitherto unidentified hierarchical levels in the temporal organization of HPA on the ultradian scale: on the minute's scale, passive periods are followed by activity bursts of similar intensity ('quanta') that are organized into superstructures on the hours- and on the daily scale. The time course of HPA can be considered a stochastic, quasi-binary process, where quanta, assigned to task-oriented actions are organized into work packages on higher levels of hierarchy. In order to grasp the essence of this complex dynamic behaviour, we established a stochastic mathematical model which could reproduce the main statistical features of real activity time series. The results are expected to provide important data for developing novel behavioural models and advancing the diagnostics of neurological or psychiatric diseases.
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Affiliation(s)
- András Búzás
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary
| | - András Makai
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary
| | - Géza I Groma
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary
| | - Zsolt Dancsházy
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary
| | - István Szendi
- Department of Psychiatry, Kiskunhalas Semmelweis Hospital, 1 Dr. Monszpart László Street, Kiskunhalas, 6400, Hungary
| | - Laszlo B Kish
- Department of Electrical and Computer Engineering, Texas A&M University, TAMUS 3128, College Station, TX, 77843-3128, USA
| | - Ana Raquel Santa-Maria
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
| | - András Dér
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary.
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Minaeva O, Schat E, Ceulemans E, Kunkels YK, Smit AC, Wichers M, Booij SH, Riese H. Individual-specific change points in circadian rest-activity rhythm and sleep in individuals tapering their antidepressant medication: an actigraphy study. Sci Rep 2024; 14:855. [PMID: 38195786 PMCID: PMC10776866 DOI: 10.1038/s41598-023-50960-1] [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: 09/29/2022] [Accepted: 12/26/2023] [Indexed: 01/11/2024] Open
Abstract
Group-level studies showed associations between depressive symptoms and circadian rhythm elements, though whether these associations replicate at the within-person level remains unclear. We investigated whether changes in circadian rhythm elements (namely, rest-activity rhythm, physical activity, and sleep) occur close to depressive symptom transitions and whether there are differences in the amount and direction of circadian rhythm changes in individuals with and without transitions. We used 4 months of actigraphy data from 34 remitted individuals tapering antidepressants (20 with and 14 without depressive symptom transitions) to assess circadian rhythm variables. Within-person kernel change point analyses were used to detect change points (CPs) and their timing in circadian rhythm variables. In 69% of individuals experiencing transitions, CPs were detected near the time of the transition. No-transition participants had an average of 0.64 CPs per individual, which could not be attributed to other known events, compared to those with transitions, who averaged 1 CP per individual. The direction of change varied between individuals, although some variables showed clear patterns in one direction. Results supported the hypothesis that CPs in circadian rhythm occurred more frequently close to transitions in depression. However, a larger sample is needed to understand which circadian rhythm variables change for whom, and more single-subject research to untangle the meaning of the large individual differences.
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Affiliation(s)
- Olga Minaeva
- Department of Psychiatry (CC72), Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands.
| | - Evelien Schat
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Eva Ceulemans
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Yoram K Kunkels
- Department of Psychiatry (CC72), Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Arnout C Smit
- Department of Psychiatry (CC72), Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands
- Clinical Psychology, Faculty of Behavioral and Movement Sciences, VU Amsterdam, Amsterdam, The Netherlands
| | - Marieke Wichers
- Department of Psychiatry (CC72), Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Sanne H Booij
- Department of Psychiatry (CC72), Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands
- Lentis, Center for Integrative Psychiatry, Groningen, The Netherlands
| | - Harriëtte Riese
- Department of Psychiatry (CC72), Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands
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Zhang Y, Deng X, Wang X, Luo H, Lei X, Luo Q. Can daily actigraphic profiles distinguish between different mood states in inpatients with bipolar disorder? An observational study. Front Psychiatry 2023; 14:1145964. [PMID: 37363166 PMCID: PMC10287980 DOI: 10.3389/fpsyt.2023.1145964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/18/2023] [Indexed: 06/28/2023] Open
Abstract
Background Criterion A changes for bipolar disorder (BD) in the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition yield new difficulties in diagnosis. Actigraphy has been used to capture the activity features of patients with BD. However, it remains unclear whether long-term actigraphic data could distinguish between different mood states in hospitalized patients with BD. Methods In this observational study, 30 hospitalized patients with BD were included. Wrist-worn actigraphs were used to monitor motor activity. The patients were divided into bipolar disorder-depression (BD-D), bipolar disorder-mania (BD-M), and bipolar disorder-mixed state (BD-MS) groups. Motor activity differences were estimated using non-parametric analyses between and within the three groups. Results The mean 24 h activity level differed between the groups. In the between-group analysis, the intra-individual fluctuation and minute-to-minute variability in the morning and the mean activity level and minute-to-minute variability in the evening significantly differed between the BD-M and BD-MS groups. In the within-group analysis, the BD-M group showed a disrupted rhythm and reduced activity complexity at night. Both the BD-D and BD-MS groups demonstrated significant differences between several parameters obtained in the morning and evening. Conclusion The mean activity levels during the relatively long monitoring period and the intra-day variation within the groups could reflect the differences in motor activity. Sustained activity monitoring may clarify the emotional states and provide information for clinical diagnosis.
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Affiliation(s)
- Yinlin Zhang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xinyi Deng
- Sleep and Neuroimaging Center, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xueqian Wang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huirong Luo
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xu Lei
- Sleep and Neuroimaging Center, Faculty of Psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Qinghua Luo
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, Jutla A, Kotov R, Paulus MP, Rubio JM, Sanacora G, Veenstra-VanderWeele J, Krystal JH. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 2023; 22:236-262. [PMID: 37159365 PMCID: PMC10168176 DOI: 10.1002/wps.21078] [Citation(s) in RCA: 51] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 05/11/2023] Open
Abstract
The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.
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Affiliation(s)
- Anissa Abi-Dargham
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Scott J Moeller
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Farzana Ali
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Amandeep Jutla
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Roman Kotov
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | | | - Jose M Rubio
- Zucker School of Medicine at Hofstra-Northwell, Hempstead, NY, USA
- Feinstein Institute for Medical Research - Northwell, Manhasset, NY, USA
- Zucker Hillside Hospital - Northwell Health, Glen Oaks, NY, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jeremy Veenstra-VanderWeele
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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Ali FZ, Parsey RV, Lin S, Schwartz J, DeLorenzo C. Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response. NPJ Digit Med 2023; 6:81. [PMID: 37120493 PMCID: PMC10148831 DOI: 10.1038/s41746-023-00827-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/11/2023] [Indexed: 05/01/2023] Open
Abstract
Major depressive disorder (MDD) is associated with circadian rhythm disruption. Yet, no circadian rhythm biomarkers have been clinically validated for assessing antidepressant response. In this study, 40 participants with MDD provided actigraphy data using wearable devices for one week after initiating antidepressant treatment in a randomized, double-blind, placebo-controlled trial. Their depression severity was calculated pretreatment, after one week and eight weeks of treatment. This study assesses the relationship between parametric and nonparametric measures of circadian rhythm and change in depression. Results show significant association between a lower circadian quotient (reflecting less robust rhythmicity) and improvement in depression from baseline following first week of treatment (estimate = 0.11, F = 7.01, P = 0.01). There is insufficient evidence of an association between circadian rhythm measures acquired during the first week of treatment and outcomes after eight weeks of treatment. Despite this lack of association with future treatment outcome, this scalable, cost-effective biomarker may be useful for timely mental health care through remote monitoring of real-time changes in current depression.
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Affiliation(s)
- Farzana Z Ali
- Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA.
| | - Ramin V Parsey
- Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Psychology, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Radiology, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Shan Lin
- Department of Electrical and Computer Engineering, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Joseph Schwartz
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Christine DeLorenzo
- Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Psychiatry, Columbia University, 1051 Riverside Drive, New York, NY, 10032, USA
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Anmella G, Corponi F, Li BM, Mas A, Sanabra M, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Giménez-Palomo A, Garriga M, Agasi I, Bastidas A, Cavero M, Fernández-Plaza T, Arbelo N, Bioque M, García-Rizo C, Verdolini N, Madero S, Murru A, Amoretti S, Martínez-Aran A, Ruiz V, Fico G, De Prisco M, Oliva V, Solanes A, Radua J, Samalin L, Young AH, Vieta E, Vergari A, Hidalgo-Mazzei D. Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study. JMIR Mhealth Uhealth 2023; 11:e45405. [PMID: 36939345 DOI: 10.2196/45405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/20/2023] [Accepted: 03/07/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity alongside physiological alterations that wearables can capture. OBJECTIVE We explored whether physiological wearable data could predict: (aim 1) the severity of an acute affective episode at the intra-individual level, (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to the prior predictions, generalization across patients, and associations between affective symptoms and physiological data. METHODS We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded with a research-grade wearable (Empatica E4) across three consecutive timepoints (acute, response, and remission of episode). Euthymic patients and healthy controls (HC) were recorded during a single session (∼48 hours). Manic and depressive symptoms were assessed with standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), temperature (TEMP), blood volume pulse (BVP), heart rate (HR), and electrodermal activity (EDA). For data pre-processing, invalid physiological data were removed using a rule-based filter, channels were time-aligned at 1 second time units and then segmented window lengths of 32 seconds, since those parameters showed the best performances. We developed deep learning predictive models, assessed channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel fully automated method for analysis of physiological data from a research-grade wearable device, including a rule-based filter for invalid data and a viable supervised learning pipeline for time-series analyses. RESULTS 35 sessions (1,512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 HC (age 39.7±12.6; 31.6% female) were analyzed. (aim 1) The severity of mood episodes was predicted with moderate (62%-85%) accuracies. (aim 2) The polarity of episodes was predicted with moderate (70%) accuracy. The most relevant features for the former tasks were ACC, EDA, and HR. Kendall W showed fair agreement (0.383) in feature importance across classification tasks. Generalization of the former models were of overall low accuracy, with better results for the intra-individual models. "Increased motor activity" was associated with ACC (NMI>0.55), "aggressive behavior" with EDA (NMI=1.0), "insomnia" with ACC (NMI∼0.6), "motor inhibition" with ACC (NMI∼0.75), and "psychic anxiety" with EDA (NMI=0.52). CONCLUSIONS Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression respectively. These findings represent a promising pathway towards personalized psychiatry, in which physiological wearable data could allow early identification and intervention of mood episodes. CLINICALTRIAL
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Affiliation(s)
- Gerard Anmella
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Filippo Corponi
- School of informatics, University of Edinburgh, UK., Edinburgh, GB
| | - Bryan M Li
- School of informatics, University of Edinburgh, UK., Edinburgh, GB
| | - Ariadna Mas
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Miriam Sanabra
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Isabella Pacchiarotti
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Marc Valentí
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Iria Grande
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Antoni Benabarre
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Anna Giménez-Palomo
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Marina Garriga
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Isabel Agasi
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Anna Bastidas
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Myriam Cavero
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | | | - Néstor Arbelo
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Miquel Bioque
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Clemente García-Rizo
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Norma Verdolini
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Santiago Madero
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Andrea Murru
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Silvia Amoretti
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Anabel Martínez-Aran
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Victoria Ruiz
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Giovanna Fico
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Michele De Prisco
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Vincenzo Oliva
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Aleix Solanes
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Barcelona, ES
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Barcelona, ES
| | - Ludovic Samalin
- Department of Psychiatry, CHU Clermont-Ferrand, University of Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal (UMR 6602), Clermont-Ferrand, France., Clermont-Ferrand, FR
| | - Allan H Young
- Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom., London, GB
| | - Eduard Vieta
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
| | - Antonio Vergari
- School of informatics, University of Edinburgh, UK., Edinburgh, GB
| | - Diego Hidalgo-Mazzei
- Hospital Clínic de Barcelona, Villarroel St., 170, 08036 Barcelona, Spain., Barcelona, ES
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Hong S, Kim B, Lee SY. A public health crisis in the university: Impact of crisis response strategies on universities' transparency and post-crisis relationships during COVID 19 pandemic. PUBLIC RELATIONS REVIEW 2023; 49:102287. [PMID: 36712229 PMCID: PMC9874050 DOI: 10.1016/j.pubrev.2023.102287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 11/30/2022] [Accepted: 01/22/2023] [Indexed: 06/18/2023]
Abstract
During the COVID 19 pandemic, one of the most critical tasks of the university was to effectively communicate with students, faculty, and staff members. This study aims to explore perceived universities' crisis response messages during the pandemic and examine the effectiveness of each response strategy on public relations outcomes. A survey with 346 university students in the U.S., results showed how defensive and accommodative response strategies differently affected PR outcomes. Accommodative strategies generated higher OPR and greater perceived transparency efforts among students, while several defensive strategies affected students' negative evaluations on post-crisis OPR and perceived transparency of their universities. Such results revealed valuable insights that make significant contributions to theory and practices in university crisis communication and management, especially when dealing with public health crises that are seen as external locus of control.
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Affiliation(s)
- Seoyeon Hong
- Ric Edelman College of Commination and Creative Arts, Rowan University, United States
| | - Bokyung Kim
- Ric Edelman College of Commination and Creative Arts, Rowan University, United States
| | - So Young Lee
- Ric Edelman College of Commination and Creative Arts, Rowan University, United States
- Department of Journalism, Public Relations and Advertising, Soongsil University, South Korea
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10
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Sleep changes during a spontaneous manic episode: PSG assessment in a clinical context. Psychiatry Res 2023; 323:115136. [PMID: 36893568 DOI: 10.1016/j.psychres.2023.115136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 03/11/2023]
Abstract
Sleep plays a key role in the pathogenesis and clinical presentation of mood disorders. However, only a few studies have investigated sleep architecture during the manic episodes of Bipolar Disorder (BD) and changes in sleep parameters that follow clinical variations. Twenty-one patients (8 males, 13 females) affected by BD, manic phase, underwent polysomnographic recordings (PSG) at the beginning of the admission in our ward (T0) and after three weeks of hospital treatment (T1). All participants were clinically evaluated using Young Mania Rating Scale (YMRS), Pittsburgh Sleep Quality Index (PSQI) and Morningness-Eveningness Questionnaire (MEQ). During the admission, we observed an increase in both quantity (Total Sleep Time - TST) and quality (Sleep Efficiency - SE) of sleep. In addition, clinical improvement, evaluated with YMRS and PSQI scales, was accompanied by a significant increase in the percentage of REM sleep. According to our findings, the improvement of manic symptoms is accompanied by an increase in "REM pressure" (increase in REM% and REM density, reduction of REM latency). Overall, changes in sleep architecture appear to be markers sensitive to clinical variations during manic phases of Bipolar Disorder.
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11
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Esaki Y, Obayashi K, Saeki K, Fujita K, Iwata N, Kitajima T. Habitual light exposure and circadian activity rhythms in bipolar disorder: A cross-sectional analysis of the APPLE cohort. J Affect Disord 2023; 323:762-769. [PMID: 36538951 DOI: 10.1016/j.jad.2022.12.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/04/2022] [Accepted: 12/10/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Circadian activity rhythm disruption is a core feature in bipolar disorder. We investigated whether light exposure in daily life is associated with circadian activity rhythms in patients with bipolar disorder. METHODS In a cross-sectional study, we enrolled 194 outpatients with bipolar disorder who were participants of the Association between Pathology of Bipolar Disorder and Light Exposure in Daily Life (APPLE) cohort study. The participants' physical activity and daytime illuminance were measured using an actigraph over 7 consecutive days. Nighttime illuminance in the bedroom was measured using a portable photometer. Circadian activity rhythm parameters were calculated using cosinor analysis and a nonparametric circadian rhythm analysis. RESULTS The median daytime illuminance and nighttime illuminance were 224.5 lx (interquartile range, 154.5-307.5 lx) and 2.3 lx (0.3-9.4 lx), respectively. Multivariable linear regression analysis, adjusted for potential confounding factors, showed that higher daytime illuminance was significantly associated with higher amplitude and most active continuous 10-hour period, advanced acrophase, higher interdaily stability, and lower intradaily variability. Higher nighttime illuminance was significantly associated with lower relative amplitude, delayed onset of the least active continuous 5-hour period, and higher intradaily variability. LIMITATIONS As this was a cross-sectional study, the results do not necessarily imply that light exposure alters circadian activity rhythms. CONCLUSIONS Daytime light exposure was associated with a positive effect and nighttime light exposure with a negative effect on circadian activity rhythms in bipolar disorder.
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Affiliation(s)
- Yuichi Esaki
- Department of Psychiatry, Okehazama Hospital, Aichi, Japan; Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan.
| | - Kenji Obayashi
- Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan
| | - Keigo Saeki
- Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan
| | - Kiyoshi Fujita
- Department of Psychiatry, Okehazama Hospital, Aichi, Japan; The Neuroscience Research Center, Aichi, Japan
| | - Nakao Iwata
- Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan
| | - Tsuyoshi Kitajima
- Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan
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12
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Associations Between Wearable-Specific Indicators of Physical Activity Behaviour and Insulin Sensitivity and Glycated Haemoglobin in the General Population: Results from the ORISCAV-LUX 2 Study. SPORTS MEDICINE - OPEN 2022; 8:146. [PMID: 36507935 PMCID: PMC9743939 DOI: 10.1186/s40798-022-00541-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 11/23/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Parameters derived from an acceleration signal, such as the time accumulated in sedentary behaviour or moderate to vigorous physical activity (MVPA), may not be sufficient to describe physical activity (PA) which is a complex behaviour. Incorporating more advanced wearable-specific indicators of PA behaviour (WIPAB) may be useful when characterising PA profiles and investigating associations with health. We investigated the associations of novel objective measures of PA behaviour with glycated haemoglobin (HbA1c) and insulin sensitivity (Quicki index). METHODS This observational study included 1026 adults (55% women) aged 18-79y who were recruited from the general population in Luxembourg. Participants provided ≥ 4 valid days of triaxial accelerometry data which was used to derive WIPAB variables related to the activity intensity, accumulation pattern and the temporal correlation and regularity of the acceleration time series. RESULTS Adjusted general linear models showed that more time spent in MVPA and a higher average acceleration were both associated with a higher insulin sensitivity. More time accumulated in sedentary behaviour was associated with lower insulin sensitivity. With regard to WIPAB variables, parameters that were indicative of higher PA intensity, including a shallower intensity gradient and higher average accelerations registered during the most active 8 h and 15 min of the day, were associated with higher insulin sensitivity. Results for the power law exponent alpha, and the proportion of daily time accumulated in sedentary bouts > 60 min, indicated that activity which was characterised by long sedentary bouts was associated with lower insulin sensitivity. A greater proportion of time spent in MVPA bouts > 10 min was associated with higher insulin sensitivity. A higher scaling exponent alpha at small time scales (< 90 min), which shows greater correlation in the acceleration time series over short durations, was associated with higher insulin sensitivity. When measured over the entirety of the time series, metrics that reflected a more complex, irregular and unpredictable activity profile, such as the sample entropy, were associated with lower HbA1c levels and higher insulin sensitivity. CONCLUSION Our investigation of novel WIPAB variables shows that parameters related to activity intensity, accumulation pattern, temporal correlation and regularity are associated with insulin sensitivity in an adult general population.
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Ali FZ, Wengler K, He X, Nguyen MH, Parsey RV, DeLorenzo C. Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression. NEUROSCIENCE INFORMATICS 2022; 2:100110. [PMID: 36699194 PMCID: PMC9873411 DOI: 10.1016/j.neuri.2022.100110] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Introduction Pretreatment positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) and magnetic resonance spectroscopy (MRS) may identify biomarkers for predicting remission (absence of depression). Yet, no such image-based biomarkers have achieved clinical validity. The purpose of this study was to identify biomarkers of remission using machine learning (ML) with pretreatment FDG-PET/MRS neuroimaging, to reduce patient suffering and economic burden from ineffective trials. Methods This study used simultaneous PET/MRS neuroimaging from a double-blind, placebo-controlled, randomized antidepressant trial on 60 participants with major depressive disorder (MDD) before initiating treatment. After eight weeks of treatment, those with ≤ 7 on 17-item Hamilton Depression Rating Scale were designated a priori as remitters (free of depression, 37%). Metabolic rate of glucose uptake (metabolism) from 22 brain regions were acquired from PET. Concentrations (mM) of glutamine and glutamate and gamma-aminobutyric acid (GABA) in anterior cingulate cortex were quantified from MRS. The data were randomly split into 67% train and cross-validation (n = 40), and 33% test (n = 20) sets. The imaging features, along with age, sex, handedness, and treatment assignment (selective serotonin reuptake inhibitor or SSRI vs. placebo) were entered into the eXtreme Gradient Boosting (XGBoost) classifier for training. Results In test data, the model showed 62% sensitivity, 92% specificity, and 77% weighted accuracy. Pretreatment metabolism of left hippocampus from PET was the most predictive of remission. Conclusions The pretreatment neuroimaging takes around 60 minutes but has potential to prevent weeks of failed treatment trials. This study effectively addresses common issues for neuroimaging analysis, such as small sample size, high dimensionality, and class imbalance.
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Affiliation(s)
- Farzana Z. Ali
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Kenneth Wengler
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Xiang He
- Department of Radiology, Stony Brook Medicine, Stony Brook, NY, USA
- Department of Radiology, Northshore University Hospital, Manhasset, NY, USA
| | - Minh Hoai Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Ramin V. Parsey
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
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14
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Claudio A, Andrea F. Circadian neuromarkers of mood disorders. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2022. [DOI: 10.1016/j.jadr.2022.100384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Zhang J, Merikangas KR, Li H, Shou H. TWO-SAMPLE TESTS FOR MULTIVARIATE REPEATED MEASUREMENTS OF HISTOGRAM OBJECTS WITH APPLICATIONS TO WEARABLE DEVICE DATA. Ann Appl Stat 2022; 16:2396-2416. [PMID: 38037595 PMCID: PMC10688324 DOI: 10.1214/21-aoas1596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Repeated observations have become increasingly common in biomedical research and longitudinal studies. For instance, wearable sensor devices are deployed to continuously track physiological and biological signals from each individual over multiple days. It remains of great interest to appropriately evaluate how the daily distribution of biosignals might differ across disease groups and demographics. Hence, these data could be formulated as multivariate complex object data, such as probability densities, histograms, and observations on a tree. Traditional statistical methods would often fail to apply, as they are sampled from an arbitrary non-Euclidean metric space. In this paper we propose novel, nonparametric, graph-based two-sample tests for object data with the same structure of repeated measures. We treat the repeatedly measured object data as multivariate object data, which requires the same number of repeated observations per individual but eliminates any assumptions on the errors of the repeated observations. A set of test statistics are proposed to capture various possible alternatives. We derive their asymptotic null distributions under the permutation null. These tests exhibit substantial power improvements over the existing methods while controlling the type I errors under finite samples as shown through simulation studies. The proposed tests are demonstrated to provide additional insights on the location, inter- and intra-individual variability of the daily physical activity distributions in a sample of studies for mood disorders.
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Affiliation(s)
- Jingru Zhang
- Division of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine
| | - Kathleen R. Merikangas
- Genetic Epidemiology Research Branch, National Institute of Mental Health, National Institutes of Health
| | - Hongzhe Li
- Division of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine
| | - Haochang Shou
- Division of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine
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Gianfredi V, Ferrara P, Pennisi F, Casu G, Amerio A, Odone A, Nucci D, Dinu M. Association between Daily Pattern of Physical Activity and Depression: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19116505. [PMID: 35682090 PMCID: PMC9180107 DOI: 10.3390/ijerph19116505] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 01/27/2023]
Abstract
Recent research suggested that daily pattern of physical activity (PA) may have an important association with depression, but findings are limited and contradictory. Our aim was to conduct a systematic review of the literature to summarize the literature evidence on the association between timing of PA and depression. A comprehensive search of PubMed/Medline and Scopus databases has been performed, and a total of five manuscripts have been thoroughly reviewed. The performed descriptive analysis shows lower levels of PA among individuals with depression or depressive symptoms, although evidence on the 24 h pattern of PA and depression is limited. An interesting finding is the association between lower PA during the morning, higher PA late in the evening (night), and depression or depressive symptoms. However, definitive conclusions could not be drawn due to the observational nature of the studies, their limited number, the high heterogeneity in the sample populations, and the studies’ differing outcome definitions and exposure assessments. Future studies considering not only the level of PA but also its daily variability might be important to further explore this novel area of research.
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Affiliation(s)
- Vincenza Gianfredi
- Department of Biomedical Sciences for Health, University of Milan, Via Pascal 36, 20133 Milan, Italy;
- CAPHRI Care and Public Health Research Institute, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Pietro Ferrara
- Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy; (P.F.); (A.O.)
- Center for Public Health Research, University of Milan-Bicocca, 20900 Monza, Italy
| | - Flavia Pennisi
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, 20132 Milan, Italy; (F.P.); (G.C.)
| | - Giulia Casu
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, 20132 Milan, Italy; (F.P.); (G.C.)
| | - Andrea Amerio
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Section of Psychiatry, University of Genoa, 16146 Genoa, Italy;
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
- Department of Psychiatry, Tufts University, Boston, MA 02155, USA
| | - Anna Odone
- Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy; (P.F.); (A.O.)
| | - Daniele Nucci
- Nutritional Support Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
- Correspondence:
| | - Monica Dinu
- Department of Experimental and Clinical Medicine, University of Florence, 50121 Florence, Italy;
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17
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Wüthrich F, Nabb CB, Mittal VA, Shankman SA, Walther S. Actigraphically measured psychomotor slowing in depression: systematic review and meta-analysis. Psychol Med 2022; 52:1208-1221. [PMID: 35550677 PMCID: PMC9875557 DOI: 10.1017/s0033291722000903] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Psychomotor slowing is a key feature of depressive disorders. Despite its great clinical importance, the pathophysiology and prevalence across different diagnoses and mood states are still poorly understood. Actigraphy allows unbiased, objective, and naturalistic assessment of physical activity as a marker of psychomotor slowing. Yet, the true effect-sizes remain unclear as recent, large systematic reviews are missing. We conducted a novel meta-analysis on actigraphically measured slowing in depression with strict inclusion and exclusion criteria for diagnosis ascertainment and sample duplications. Medline/PubMed and Web-of-Science were searched with terms combining mood-keywords and actigraphy-keywords until September 2021. Original research measuring actigraphy for ⩾24 h in at least two groups of depressed, remitted, or healthy participants and applying operationalized diagnosis was included. Studies in somatically ill patients, N < 10 participants/group, and studies using consumer-devices were excluded. Activity-levels between groups were compared using random-effects models with standardized-mean-differences and several moderators were examined. In total, 34 studies (n = 1804 patients) were included. Patients had lower activity than controls [standardized mean difference (s.m.d.) = -0.78, 95% confidence interval (CI) -0.99 to -0.57]. Compared to controls, patients with unipolar and bipolar disorder had lower activity than controls whether in depressed (unipolar: s.m.d. = -0.82, 95% CI -1.07 to -0.56; bipolar: s.m.d. = -0.94, 95% CI -1.41 to -0.46), or remitted/euthymic mood (unipolar: s.m.d. = -0.28, 95% CI -0.56 to 0.0; bipolar: s.m.d. = -0.92, 95% CI -1.36 to -0.47). None of the examined moderators had any significant effect. To date, this is the largest meta-analysis on actigraphically measured slowing in mood disorders. They are associated with lower activity, even in the remitted/euthymic mood-state. Studying objective motor behavior via actigraphy holds promise for informing screening and staging of affective disorders.
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Affiliation(s)
- Florian Wüthrich
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Carver B Nabb
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA
| | - Vijay A Mittal
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston/Chicago, IL, USA
- Institute for Policy Research, Northwestern University, Evanston, IL, USA
- Medical Social Sciences, Northwestern University, Chicago, IL, USA
| | - Stewart A Shankman
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
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18
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Tseng YC, Lin ECL, Wu CH, Huang HL, Chen PS. Associations among smartphone app-based measurements of mood, sleep and activity in bipolar disorder. Psychiatry Res 2022; 310:114425. [PMID: 35152069 DOI: 10.1016/j.psychres.2022.114425] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 10/19/2022]
Abstract
The recent popularization of smart technology presents new opportunities for continual, digital-monitoring of patient status. In this project, we used a smartphone app to track the mood, sleep, and activity levels of 159 outpatients with bipolar disorder (BD). The participants were asked to report their daily wake/sleep time and emotional status in the app, while daily activity data were automatically collected via GPS. We performed repeated-measures correlation analysis to examine possible correlations between the readouts. Mood, sleep and activity levels all showed intra-variable correlations with readings on the next day, in the next week, and in the next month. Furthermore, mood and sleep at the reference time were positively correlated with activity in subsequent weeks or months, and activity was positively correlated with mood and sleep in the same time ranges. Thus, our results were in line with previous studies, showing that mood, sleep, and activity levels are interdependent in patients with BD. With the association between mood on future activity level was most significant, and the correlations between each readout and the others were dependent on time frame. Our findings suggest our smartphone app has potential to provide an informative and reliable means for real-time tracking of BD status.
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Affiliation(s)
- Yu-Ching Tseng
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Esther Ching-Lan Lin
- Department of Nursing, College of Medicine, National Cheng Kung University and Hospital, Tainan City, Taiwan
| | - Chung Hsien Wu
- Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan City, Taiwan
| | - Huei-Lin Huang
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Po See Chen
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
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19
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El-Mallakh RS, Gao Y, Roberts M, Hamlyn J. Sleep deprivation is associated with increased circulating levels of endogenous ouabain: Potential role in bipolar disorder. Psychiatry Res 2022; 309:114399. [PMID: 35078006 DOI: 10.1016/j.psychres.2022.114399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 11/30/2022]
Abstract
Endogenously produced cardiac glycosides, like endogenous ouabain (EO), are putative hormones that have been implicated in the pathophysiology of bipolar disorder. Individuals with bipolar disorder appear to be unable to sufficiently upregulate production of EO in situations of increased need. This study was performed to determine the effect of sleep deprivation on the circulating levels of EO. Plasma EO concentrations were measured by ouabain-radioimmunoassay in heterozygote Na,K-ATPase a2 knockout (KO) mice, which have been used as an animal model of mania, and wildtype siblings at baseline and after sleep fragmentation utilizing the moving bar method. a2 KO animals had elevated endogenous ouabain concentrations compared to wild type controls (0.82 ± SD 0.22 nM vs 0.26 ± 0.02, P = 0.03). Sleep fragmentation increased ouabain concentrations in wild type mice (0.53 ± 0.08 nM sleep fragmentation vs 0.26 ± 0.02 nM baseline, P = 0.04), but not in a2 KO mice (0.60 ± 0.07 nM sleep fragmentation vs 0.82 ± 0.22 nM baseline, P > 0.05). These studies demonstrate that sleep disturbance can increase EO in control mice but animals that exhibit some manic behaviors are unable to increase EO production.
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Affiliation(s)
- Rif S El-Mallakh
- Mood Disorders Research Program, Department of Psychiatry and Behavioral Sciences, University of Louisville School of Medicine, 401 East Chestnut Street, Suite 610, Louisville, KY 40202, USA.
| | - Yonglin Gao
- Mood Disorders Research Program, Department of Psychiatry and Behavioral Sciences, University of Louisville School of Medicine, 401 East Chestnut Street, Suite 610, Louisville, KY 40202, USA
| | - Michael Roberts
- Mood Disorders Research Program, Department of Psychiatry and Behavioral Sciences, University of Louisville School of Medicine, 401 East Chestnut Street, Suite 610, Louisville, KY 40202, USA
| | - John Hamlyn
- Department of Physiology, School of Medicine, University of Maryland Baltimore, 685 West Baltimore Street, Baltimore, MS 21201, USA
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20
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Abstract
BACKGROUND Bipolar disorder (BD) is linked to circadian rhythm disruptions resulting in aberrant motor activity patterns. We aimed to explore whether motor activity alone, as assessed by longitudinal actigraphy, can be used to classify accurately BD patients and healthy controls (HCs) into their respective groups. METHODS Ninety-day actigraphy records from 25 interepisode BD patients (ie, Montgomery-Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) < 15) and 25 sex- and age-matched HCs were used in order to identify latent actigraphic biomarkers capable of discriminating between BD patients and HCs. Mean values and time variations of a set of standard actigraphy features were analyzed and further validated using the random forest classifier. RESULTS Using all actigraphy features, this method correctly assigned 88% (sensitivity = 85%, specificity = 91%) of BD patients and HCs to their respective group. The classification success may be confounded by differences in employment between BD patients and HCs. When motor activity features resistant to the employment status were used (the strongest feature being time variation of intradaily variability, Cohen's d = 1.33), 79% of the subjects (sensitivity = 76%, specificity = 81%) were correctly classified. CONCLUSION A machine-learning actigraphy-based model was capable of distinguishing between interepisode BD patients and HCs solely on the basis of motor activity. The classification remained valid even when features influenced by employment status were omitted. The findings suggest that temporal variability of actigraphic parameters may provide discriminative power for differentiating between BD patients and HCs while being less affected by employment status.
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21
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Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder. PLoS One 2022; 17:e0262232. [PMID: 35061801 PMCID: PMC8782466 DOI: 10.1371/journal.pone.0262232] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 12/20/2021] [Indexed: 02/07/2023] Open
Abstract
Changes in motor activity are core symptoms of mood episodes in bipolar disorder. The manic state is characterized by increased variance, augmented complexity and irregular circadian rhythmicity when compared to healthy controls. No previous studies have compared mania to euthymia intra-individually in motor activity. The aim of this study was to characterize differences in motor activity when comparing manic patients to their euthymic selves. Motor activity was collected from 16 bipolar inpatients in mania and remission. 24-h recordings and 2-h time series in the morning and evening were analyzed for mean activity, variability and complexity. Lastly, the recordings were analyzed with the similarity graph algorithm and graph theory concepts such as edges, bridges, connected components and cliques. The similarity graph measures fluctuations in activity reasonably comparable to both variability and complexity measures. However, direct comparisons are difficult as most graph measures reveal variability in constricted time windows. Compared to sample entropy, the similarity graph is less sensitive to outliers. The little-understood estimate Bridges is possibly revealing underlying dynamics in the time series. When compared to euthymia, over the duration of approximately one circadian cycle, the manic state presented reduced variability, displayed by decreased standard deviation (p = 0.013) and augmented complexity shown by increased sample entropy (p = 0.025). During mania there were also fewer edges (p = 0.039) and more bridges (p = 0.026). Similar significant changes in variability and complexity were observed in the 2-h morning and evening sequences, mainly in the estimates of the similarity graph algorithm. Finally, augmented complexity was present in morning samples during mania, displayed by increased sample entropy (p = 0.015). In conclusion, the motor activity of mania is characterized by altered complexity and variability when compared within-subject to euthymia.
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22
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Panchal P, de Queiroz Campos G, Goldman DA, Auerbach RP, Merikangas KR, Swartz HA, Sankar A, Blumberg HP. Toward a Digital Future in Bipolar Disorder Assessment: A Systematic Review of Disruptions in the Rest-Activity Cycle as Measured by Actigraphy. Front Psychiatry 2022; 13:780726. [PMID: 35677875 PMCID: PMC9167949 DOI: 10.3389/fpsyt.2022.780726] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Disruptions in rest and activity patterns are core features of bipolar disorder (BD). However, previous methods have been limited in fully characterizing the patterns. There is still a need to capture dysfunction in daily activity as well as rest patterns in order to more holistically understand the nature of 24-h rhythms in BD. Recent developments in the standardization, processing, and analyses of wearable digital actigraphy devices are advancing longitudinal investigation of rest-activity patterns in real time. The current systematic review aimed to summarize the literature on actigraphy measures of rest-activity patterns in BD to inform the future use of this technology. METHODS A comprehensive systematic review using PRISMA guidelines was conducted through PubMed, MEDLINE, PsycINFO, and EMBASE databases, for papers published up to February 2021. Relevant articles utilizing actigraphy measures were extracted and summarized. These papers contributed to three research areas addressed, pertaining to the nature of rest-activity patterns in BD, and the effects of therapeutic interventions on these patterns. RESULTS Seventy articles were included. BD was associated with longer sleep onset latency and duration, particularly during depressive episodes and with predictive value for worsening of future manic symptoms. Lower overall daily activity was also associated with BD, especially during depressive episodes, while more variable activity patterns within a day were seen in mania. A small number of studies linked these disruptions with differential patterns of brain functioning and cognitive impairments, as well as more adverse outcomes including increased suicide risk. The stabilizing effect of therapeutic options, including pharmacotherapies and chronotherapies, on activity patterns was supported. CONCLUSION The use of actigraphy provides valuable information about rest-activity patterns in BD. Although results suggest that variability in rhythms over time may be a specific feature of BD, definitive conclusions are limited by the small number of studies assessing longitudinal changes over days. Thus, there is an urgent need to extend this work to examine patterns of rhythmicity and regularity in BD. Actigraphy research holds great promise to identify a much-needed specific phenotypic marker for BD that will aid in the development of improved detection, treatment, and prevention options.
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Affiliation(s)
- Priyanka Panchal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.,Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | | | - Danielle A Goldman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.,Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
| | - Randy P Auerbach
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Kathleen R Merikangas
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, MD, United States
| | - Holly A Swartz
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Anjali Sankar
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.,Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Hilary P Blumberg
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.,Department of Radiology and Biomedical Imaging, and the Child Study Center, Yale School of Medicine, New Haven, CT, United States
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23
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Backes A, Gupta T, Schmitz S, Fagherazzi G, van Hees V, Malisoux L. Advanced analytical methods to assess physical activity behavior using accelerometer time series: A scoping review. Scand J Med Sci Sports 2021; 32:18-44. [PMID: 34695249 PMCID: PMC9298329 DOI: 10.1111/sms.14085] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 10/12/2021] [Accepted: 10/18/2021] [Indexed: 11/29/2022]
Abstract
Physical activity (PA) is a complex human behavior, which implies that multiple dimensions need to be taken into account in order to reveal a complete picture of the PA behavior profile of an individual. This scoping review aimed to map advanced analytical methods and their summary variables, hereinafter referred to as wearable‐specific indicators of PA behavior (WIPAB), used to assess PA behavior. The strengths and limitations of those indicators as well as potential associations with certain health‐related factors were also investigated. Three databases (MEDLINE, Embase, and Web of Science) were screened for articles published in English between January 2010 and April 2020. Articles, which assessed the PA behavior, gathered objective measures of PA using tri‐axial accelerometers, and investigated WIPAB, were selected. All studies reporting WIPAB in the context of PA monitoring were synthesized and presented in four summary tables: study characteristics, details of the WIPAB, strengths, and limitations, and measures of association between those indicators and health‐related factors. In total, 7247 records were identified, of which 24 articles were included after assessing titles, abstracts, and full texts. Thirteen WIPAB were identified, which can be classified into three different categories specifically focusing on (1) the activity intensity distribution, (2) activity accumulation, and (3) the temporal correlation and regularity of the acceleration signal. Only five of the thirteen WIPAB identified in this review have been used in the literature so far to investigate the relationship between PA behavior and health, while they may provide useful additional information to the conventional PA variables.
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Affiliation(s)
- Anne Backes
- Physical Activity, Sport and Health Research Group, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Tripti Gupta
- Physical Activity, Sport and Health Research Group, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Susanne Schmitz
- Competence Center for Methodology and Statistics, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Guy Fagherazzi
- Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Vincent van Hees
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Accelting, Almere, The Netherlands
| | - Laurent Malisoux
- Physical Activity, Sport and Health Research Group, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
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24
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Faltraco F, Palm D, Coogan A, Simon F, Tucha O, Thome J. Molecular Link between Circadian Rhythmicity and Mood Disorders. Curr Med Chem 2021; 29:5692-5709. [PMID: 34620057 DOI: 10.2174/0929867328666211007113725] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 08/17/2021] [Accepted: 08/26/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND The internal clock is driven by circadian genes [e.g., Clock, Bmal1, Per1-3, Cry1-2], hormones [e.g., melatonin, cortisol], as well as zeitgeber ['synchronisers']. Chronic disturbances in the circadian rhythm in patients diagnosed with mood disorders have been recognised for more than 50 years. OBJECTIVES The aim of this review is to summarise the current knowledge and literature regarding circadian rhythms in the context of mood disorders, focussing on the role of circadian genes, hormones, and neurotransmitters. METHOD The review presents the current knowledge and literature regarding circadian rhythms in mood disorders using the Pubmed database. Articles with a focus on circadian rhythms and mood disorders [n=123], particularly from 1973 to 2020, were included. RESULTS The article suggests a molecular link between disruptions in the circadian rhythm and mood disorders. Circadian disturbances, caused by the dysregulation of circadian genes, hormones, and neurotransmitters, often result in a clinical picture resembling depression. CONCLUSION Circadian rhythms are intrinsically linked to affective disorders, such as unipolar depression and bipolar disorder.
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Affiliation(s)
- Frank Faltraco
- Department of Psychiatry and Psychotherapy, University Medical Center Rostock, Rostock. Germany
| | - Denise Palm
- Department of Psychiatry and Psychotherapy, University Medical Center Rostock, Rostock. Germany
| | - Andrew Coogan
- Department of Psychology, Maynooth University, National University of Ireland, Maynooth. Ireland
| | - Frederick Simon
- Department of Psychiatry and Psychotherapy, University Medical Center Rostock, Rostock. Germany
| | - Oliver Tucha
- Department of Psychiatry and Psychotherapy, University Medical Center Rostock, Rostock. Germany
| | - Johannes Thome
- Department of Psychiatry and Psychotherapy, University Medical Center Rostock, Rostock. Germany
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25
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Akıncı E, İnce B. Evaluation of the Process of Acute Treatment for Depression in Terms of Monitoring Activity and Sleep Efficiency with Actigraphy. PSYCHIAT CLIN PSYCH 2021; 31:213-218. [PMID: 38765236 PMCID: PMC11079718 DOI: 10.5152/pcp.2021.21335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/30/2021] [Indexed: 05/21/2024] Open
Abstract
Background This study aimed to evaluate and follow-up the process of acute treatment for depression in terms of activity and sleep efficiency using actigraphy, and thus increase the opportunities for objective measurement in the monitoring of treatment. Methods A total of 20 patients with depression, and 22 and age- and gender-matched healthy volunteers were included in the study. All subjects were evaluated using a sociodemographic data form, the Hamilton Depression Rating Scale (HDRS), and actigraphy for measurement of motor activity and sleep efficiency. Results The activity levels and sleep efficiency of the controls were significantly higher than the pre-and post-treatment activity levels and sleep efficiency of the patients. After the treatment process, both motor activity and sleep efficiency were found to be significantly increased in the patients. A highly significant negative correlation was found between the HDRS scores and average activity counts for active intervals (r = -0.779, P < .001), and between the HDRS scores and sleep efficiency (r = -0.616, P < .001). On the other hand, a significant negative effect was found between depression and average activity counts for active intervals (RR:0.880; 95% CI:0.782-0.991). Conclusions Actigraphy is a useful technique for quantifying physical activities and sleep efficiency in depressed patients. Furthermore, it may provide objective follow-up data in assessing the effects of treatment for depression.
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Affiliation(s)
- Erhan Akıncı
- Department of Psychiatry, Canakkale Onsekiz Mart University School of Medicine, Canakkale, Turkey
| | - Bahri İnce
- Department of Psychiatry, Bakirkoy Training and Research Hospital for Psychiatry, Neurology and Neurosurgery, İstanbul, Turkey
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26
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Complexity of Body Movements during Sleep in Children with Autism Spectrum Disorder. ENTROPY 2021; 23:e23040418. [PMID: 33807381 PMCID: PMC8066562 DOI: 10.3390/e23040418] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 03/30/2021] [Accepted: 03/30/2021] [Indexed: 12/15/2022]
Abstract
Recently, measuring the complexity of body movements during sleep has been proven as an objective biomarker of various psychiatric disorders. Although sleep problems are common in children with autism spectrum disorder (ASD) and might exacerbate ASD symptoms, their objectivity as a biomarker remains to be established. Therefore, details of body movement complexity during sleep as estimated by actigraphy were investigated in typically developing (TD) children and in children with ASD. Several complexity analyses were applied to raw and thresholded data of actigraphy from 17 TD children and 17 children with ASD. Determinism, irregularity and unpredictability, and long-range temporal correlation were examined respectively using the false nearest neighbor (FNN) algorithm, information-theoretic analyses, and detrended fluctuation analysis (DFA). Although the FNN algorithm did not reveal determinism in body movements, surrogate analyses identified the influence of nonlinear processes on the irregularity and long-range temporal correlation of body movements. Additionally, the irregularity and unpredictability of body movements measured by expanded sample entropy were significantly lower in ASD than in TD children up to two hours after sleep onset and at approximately six hours after sleep onset. This difference was found especially for the high-irregularity period. Through this study, we characterized details of the complexity of body movements during sleep and demonstrated the group difference of body movement complexity across TD children and children with ASD. Complexity analyses of body movements during sleep have provided valuable insights into sleep profiles. Body movement complexity might be useful as a biomarker for ASD.
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27
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Circadian depression: A mood disorder phenotype. Neurosci Biobehav Rev 2021; 126:79-101. [PMID: 33689801 DOI: 10.1016/j.neubiorev.2021.02.045] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 02/18/2021] [Accepted: 02/28/2021] [Indexed: 12/15/2022]
Abstract
Major mood syndromes are among the most common and disabling mental disorders. However, a lack of clear delineation of their underlying pathophysiological mechanisms is a major barrier to prevention and optimised treatments. Dysfunction of the 24-h circadian system is a candidate mechanism that has genetic, behavioural, and neurobiological links to mood syndromes. Here, we outline evidence for a new clinical phenotype, which we have called 'circadian depression'. We propose that key clinical characteristics of circadian depression include disrupted 24-h sleep-wake cycles, reduced motor activity, low subjective energy, and weight gain. The illness course includes early age-of-onset, phenomena suggestive of bipolarity (defined by bidirectional associations between objective motor and subjective energy/mood states), poor response to conventional antidepressant medications, and concurrent cardiometabolic and inflammatory disturbances. Identifying this phenotype could be clinically valuable, as circadian-targeted strategies show promise for reducing depressive symptoms and stabilising illness course. Further investigation of underlying circadian disturbances in mood syndromes is needed to evaluate the clinical utility of this phenotype and guide the optimal use of circadian-targeted interventions.
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28
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Fasmer OB, Fasmer EE, Mjeldheim K, Førland W, Syrstad VEG, Jakobsen P, Berle JØ, Henriksen TEG, Sepasdar Z, Hauge ER, Oedegaard KJ. Diurnal variation of motor activity in adult ADHD patients analyzed with methods from graph theory. PLoS One 2020; 15:e0241991. [PMID: 33166350 PMCID: PMC7652335 DOI: 10.1371/journal.pone.0241991] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 10/25/2020] [Indexed: 02/07/2023] Open
Abstract
Attention-deficit /hyperactivity disorder (ADHD) is a common neurodevelopmental syndrome characterized by age-inappropriate levels of motor activity, impulsivity and attention. The aim of the present study was to study diurnal variation of motor activity in adult ADHD patients, compared to healthy controls and clinical controls with mood and anxiety disorders. Wrist-worn actigraphs were used to record motor activity in a sample of 81 patients and 30 healthy controls. Time series from registrations in the morning and evening were analyzed using measures of variability, complexity and a newly developed method, the similarity algorithm, based on transforming time series into graphs. In healthy controls the evening registrations showed higher variability and lower complexity compared to morning registrations, however this was evident only in the female controls. In the two patient groups the same measures were not significantly different, with one exception, the graph measure bridges. This was the measure that most clearly separated morning and evening registrations and was significantly different both in healthy controls and in patients with a diagnosis of ADHD. These findings suggest that actigraph registrations, combined with mathematical methods based on graph theory, may be used to elucidate the mechanisms responsible for the diurnal regulation of motor activity.
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Affiliation(s)
- Ole Bernt Fasmer
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- * E-mail:
| | | | | | | | - Vigdis Elin Giæver Syrstad
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Petter Jakobsen
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Jan Øystein Berle
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Tone E. G. Henriksen
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Division of Mental Health Care, Valen Hospital, Fonna Local Health Authority, Valen, Norway
| | - Zahra Sepasdar
- School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Erik R. Hauge
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Ketil J. Oedegaard
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
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Stanislaus S, Vinberg M, Melbye S, Frost M, Busk J, Bardram JE, Kessing LV, Faurholt-Jepsen M. Smartphone-based activity measurements in patients with newly diagnosed bipolar disorder, unaffected relatives and control individuals. Int J Bipolar Disord 2020; 8:32. [PMID: 33135120 PMCID: PMC7604277 DOI: 10.1186/s40345-020-00195-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 07/23/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND In DSM-5 activity is a core criterion for diagnosing hypomania and mania. However, there are no guidelines for quantifying changes in activity. The objectives of the study were (1) to investigate daily smartphone-based self-reported and automatically-generated activity, respectively, against validated measurements of activity; (2) to validate daily smartphone-based self-reported activity and automatically-generated activity against each other; (3) to investigate differences in daily self-reported and automatically-generated smartphone-based activity between patients with bipolar disorder (BD), unaffected relatives (UR) and healthy control individuals (HC). METHODS A total of 203 patients with BD, 54 UR, and 109 HC were included. On a smartphone-based app, the participants daily reported their activity level on a scale from -3 to + 3. Additionally, participants owning an android smartphone provided automatically-generated data, including step counts, screen on/off logs, and call- and text-logs. Smartphone-based activity was validated against an activity questionnaire the International Physical Activity Questionnaire (IPAQ) and activity items on observer-based rating scales of depression using the Hamilton Depression Rating scale (HAMD), mania using Young Mania Rating scale (YMRS) and functioning using the Functional Assessment Short Test (FAST). In these analyses, we calculated averages of smartphone-based activity measurements reported in the period corresponding to the days assessed by the questionnaires and rating scales. RESULTS (1) Smartphone-based self-reported activity was a valid measure according to scores on the IPAQ and activity items on the HAMD and YMRS, and was associated with FAST scores, whereas the majority of automatically-generated smartphone-based activity measurements were not. (2) Daily smartphone-based self-reported and automatically-generated activity correlated with each other with nearly all measurements. (3) Patients with BD had decreased daily self-reported activity compared with HC. Patients with BD had decreased physical (number of steps) and social activity (more missed calls) but a longer call duration compared with HC. UR also had decreased physical activity compared with HC but did not differ on daily self-reported activity or social activity. CONCLUSION Daily self-reported activity measured via smartphone represents overall activity and correlates with measurements of automatically generated smartphone-based activity. Detecting activity levels using smartphones may be clinically helpful in diagnosis and illness monitoring in patients with bipolar disorder. Trial registration clinicaltrials.gov NCT02888262.
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Affiliation(s)
- Sharleny Stanislaus
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Maj Vinberg
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Sigurd Melbye
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Mads Frost
- Monsenso ApS, Langelinie Allé 47, Copenhagen, Denmark
| | - Jonas Busk
- Copenhagen Center for Health Technology (CACHET), Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Jakob E Bardram
- Copenhagen Center for Health Technology (CACHET), Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Lars Vedel Kessing
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Maria Faurholt-Jepsen
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
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Cuesta-Frau D, Schneider J, Bakštein E, Vostatek P, Spaniel F, Novák D. Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study. ENTROPY 2020; 22:e22111243. [PMID: 33287011 PMCID: PMC7711446 DOI: 10.3390/e22111243] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 10/27/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022]
Abstract
Bipolar Disorder (BD) is an illness with high prevalence and a huge social and economic impact. It is recurrent, with a long-term evolution in most cases. Early treatment and continuous monitoring have proven to be very effective in mitigating the causes and consequences of BD. However, no tools are currently available for a massive and semi-automatic BD patient monitoring and control. Taking advantage of recent technological developments in the field of wearables, this paper studies the feasibility of a BD episodes classification analysis while using entropy measures, an approach successfully applied in a myriad of other physiological frameworks. This is a very difficult task, since actigraphy records are highly non-stationary and corrupted with artifacts (no activity). The method devised uses a preprocessing stage to extract epochs of activity, and then applies a quantification measure, Slope Entropy, recently proposed, which outperforms the most common entropy measures used in biomedical time series. The results confirm the feasibility of the approach proposed, since the three states that are involved in BD, depression, mania, and remission, can be significantly distinguished.
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Affiliation(s)
- David Cuesta-Frau
- Technological Institute of Informatics, Alcoi Campus, Universitat Politècnica de València, 46022 Valencia, Spain
- Correspondence: ; Tel.: +34-966-528-505
| | - Jakub Schneider
- Department of Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic; (J.S.); (E.B.); (D.N.)
- National Institute of Mental Health, 250 67 Klecany, Czech Republic;
| | - Eduard Bakštein
- Department of Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic; (J.S.); (E.B.); (D.N.)
- National Institute of Mental Health, 250 67 Klecany, Czech Republic;
| | | | - Filip Spaniel
- National Institute of Mental Health, 250 67 Klecany, Czech Republic;
| | - Daniel Novák
- Department of Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic; (J.S.); (E.B.); (D.N.)
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Murray G, Gottlieb J, Hidalgo MP, Etain B, Ritter P, Skene DJ, Garbazza C, Bullock B, Merikangas K, Zipunnikov V, Shou H, Gonzalez R, Scott J, Geoffroy PA, Frey BN. Measuring circadian function in bipolar disorders: Empirical and conceptual review of physiological, actigraphic, and self-report approaches. Bipolar Disord 2020; 22:693-710. [PMID: 32564457 DOI: 10.1111/bdi.12963] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Interest in biological clock pathways in bipolar disorders (BD) continues to grow, but there has yet to be an audit of circadian measurement tools for use in BD research and practice. PROCEDURE The International Society for Bipolar Disorders Chronobiology Task Force conducted a critical integrative review of circadian methods that have real-world applicability. Consensus discussion led to the selection of three domains to review-melatonin assessment, actigraphy, and self-report. RESULTS Measurement approaches used to quantify circadian function in BD are described in sufficient detail for researchers and clinicians to make pragmatic decisions about their use. A novel integration of the measurement literature is offered in the form of a provisional taxonomy distinguishing between circadian measures (the instruments and methods used to quantify circadian function, such as dim light melatonin onset) and circadian constructs (the biobehavioral processes to be measured, such as circadian phase). CONCLUSIONS Circadian variables are an important target of measurement in clinical practice and biomarker research. To improve reproducibility and clinical application of circadian constructs, an informed systematic approach to measurement is required. We trust that this review will decrease ambiguity in the literature and support theory-based consideration of measurement options.
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Affiliation(s)
- Greg Murray
- Centre for Mental Health, Swinburne University of Technology, Victoria, Australia
| | - John Gottlieb
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Chicago Psychiatry Associates, Chicago, IL, USA
| | - Maria Paz Hidalgo
- Laboratorio de Cronobiologia e Sono, Hospital de Porto Alegre, Porto Alegre, Brazil.,Graduate Program in Psychiatry and Behavioral Sciences, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Bruno Etain
- Département de Psychiatrie et de Médecine Addictologique and INSERM UMRS 1144, Université de Paris, AP-HP, Groupe Hospitalo-universitaire AP-HP Nord, Paris, France
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Debra J Skene
- Chronobiology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Corrado Garbazza
- Centre for Chronobiology, University of Basel, Basel, Switzerland.,Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland
| | - Ben Bullock
- Centre for Mental Health, Swinburne University of Technology, Victoria, Australia
| | - Kathleen Merikangas
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, USA
| | - Vadim Zipunnikov
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert Gonzalez
- Department of Psychiatry and Behavioral Health, Penn State Health Milton S. Hershey Medical Center, Hershey, PA
| | - Jan Scott
- Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - Pierre A Geoffroy
- Département de psychiatrie et d'addictologie, AP-HP, Hopital Bichat - Claude Bernard, Paris, France.,Université de Paris, NeuroDiderot, France
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.,Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, ON, Canada
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Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge. Sci Rep 2020; 10:17286. [PMID: 33057207 PMCID: PMC7560898 DOI: 10.1038/s41598-020-74425-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 09/30/2020] [Indexed: 01/10/2023] Open
Abstract
Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphy. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Participants were recruited from the acute psychiatric inpatient ward at Hospital Rey Juan Carlos (Madrid, Spain). They wore miniature wrist wireless inertial sensors (actigraphs) throughout the admission. We modeled activity levels against the normalized length of admission-'Progress Towards Discharge' (PTD)-using a Hierarchical Generalized Linear Regression Model. The estimated date of hospital discharge based on early measures of motor activity and the actual hospital discharge date were compared by a Hierarchical Gaussian Process model. Twenty-three depressed patients (14 females, age: 50.17 ± 12.72 years) were recruited. Activity levels increased during the admission (mean slope of the linear function: 0.12 ± 0.13). For n = 18 inpatients (78.26%) hospitalised for at least 7 days, the mean error of Prediction of Hospital Discharge Date at day 7 was 0.231 ± 22.98 days (95% CI 14.222-14.684). These n = 18 patients were predicted to need, on average, 7 more days in hospital (for a total length of stay of 14 days) (PTD = 0.53). Motor activity increased during the admission in this sample of depressed patients and early patterns of actigraphically recorded activity allowed for accurate prediction of hospital discharge date.
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33
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Gregório ML, Wazen GLL, Kemp AH, Milan-Mattos JC, Porta A, Catai AM, de Godoy MF. Non-linear analysis of the heart rate variability in characterization of manic and euthymic phases of bipolar disorder. J Affect Disord 2020; 275:136-144. [PMID: 32658816 DOI: 10.1016/j.jad.2020.07.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 06/18/2020] [Accepted: 07/05/2020] [Indexed: 01/25/2023]
Abstract
BACKGROUND - Bipolar Disorder (BD) has been associated with autonomic nervous system (ANS) dysregulation, with a consequent increase in mortality. Recent work highlights the non-linear analysis of ANS function. Our objective was to compare ANS modulation using recurrence plots (RP) and symbolic analysis (SA) in manic and euthymic phases of BD to controls. METHODS - Eighteen male patients (33.1 ± 12.0 years) were assessed during mania and at discharge in the euthymic phase compared and to a healthy group matched by age (33.9 ± 10.8 years). Electrocardiographic series (1000 RR intervals, at rest, in supine position) were captured using Polar Advantage RS800CX equipment and Heart Rate Variability (HRV) was analysed using RP and SA. Statistical analysis was performed using ANOVA with Tukey's post-test. The threshold for statistical significance was set at P < 0.05 and Cohen's d effect size was also quantified considering d > 0.8 as an important effect. The study was registered into the Clinical Trials Registration (ClinicalTrials.gov: NCT01272518). RESULTS Manic group presented significantly higher linearity before treatment (P<0.05) compared to controls considering RP variables. Cohen's d values had a large effect size ranging from 0.888 to 1.227. In the manic phase, SA showed predominance of the sympathetic component (OV%) with reduction of the parasympathetic component (2LV% and 2UV%) with reversion post treatment including higher Shannon Entropy (SE) indicating higher complexity. LIMITATIONS - short follow-up (1 month) and small number of patients. CONCLUSIONS - Non-linear analyzes may be used as supplementary tools for understanding autonomic function in BD during mania and after drug treatment.
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Affiliation(s)
- Michele Lima Gregório
- Transdisciplinary Nucleus for the Study of Chaos and Complexity, NUTECC, São José do Rio Preto Medical School, FAMERP, Avenida Brigadeiro Faria Lima, 54-16 CEP, 15090-000 São José do Rio Preto, SP, Brazil.
| | - Guilherme Luiz Lopes Wazen
- Department of Psychiatry, São José do Rio Preto Medical School, FAMERP, Avenida Brigadeiro Faria Lima, 54-16 CEP, 15090-000 São José do Rio Preto, SP, Brazil
| | - Andrew Haddon Kemp
- Department of Psychology, College of Human and Health Sciences, Swansea University, Singleton Park, Wales SA2 8PP, United Kingdom
| | - Juliana Cristina Milan-Mattos
- Cardiovascular Physical Therapy Laboratory, Department of Physical Therapy, Federal University of São Carlos, São Paulo, Brazil
| | - Alberto Porta
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy; Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, Milan, Italy.
| | - Aparecida Maria Catai
- Cardiovascular Physical Therapy Laboratory, Department of Physical Therapy, Federal University of São Carlos, São Paulo, Brazil.
| | - Moacir Fernandes de Godoy
- Transdisciplinary Nucleus for the Study of Chaos and Complexity, NUTECC, São José do Rio Preto Medical School, FAMERP, Avenida Brigadeiro Faria Lima, 54-16 CEP, 15090-000 São José do Rio Preto, SP, Brazil; Department of Cardiology and Cardiovascular Surgery, São José do Rio Preto Medical School, FAMERP, Avenida Brigadeiro Faria Lima, 5416 CEP, 15090-000 São José do Rio Preto, SP, Brazil
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34
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Li P, Lim ASP, Gao L, Hu C, Yu L, Bennett DA, Buchman AS, Hu K. More random motor activity fluctuations predict incident frailty, disability, and mortality. Sci Transl Med 2020; 11:11/516/eaax1977. [PMID: 31666398 DOI: 10.1126/scitranslmed.aax1977] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 09/20/2019] [Indexed: 12/11/2022]
Abstract
Mobile healthcare increasingly relies on analytical tools that can extract meaningful information from ambulatory physiological recordings. We tested whether a nonlinear tool of fractal physiology could predict long-term health consequences in a large, elderly cohort. Fractal physiology is an emerging field that aims to study how fractal temporal structures in physiological fluctuations generated by complex physiological networks can provide important information about system adaptability. We assessed fractal temporal correlations in the spontaneous fluctuations of ambulatory motor activity of 1275 older participants at baseline, with a follow-up period of up to 13 years. We found that people with reduced temporal correlations (more random activity fluctuations) at baseline had increased risk of frailty, disability, and all-cause death during follow-up. Specifically, for 1-SD decrease in the temporal activity correlations of this studied cohort, the risk of frailty increased by 31%, the risk of disability increased by 15 to 25%, and the risk of death increased by 26%. These incidences occurred on average 4.7 years (frailty), 3 to 4.2 years (disability), and 5.8 years (death) after baseline. These observations were independent of age, sex, education, chronic health conditions, depressive symptoms, cognition, motor function, and total daily activity. The temporal structures in daily motor activity fluctuations may contain unique prognostic information regarding wellness and health in the elderly population.
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Affiliation(s)
- Peng Li
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA. .,Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Andrew S P Lim
- Division of Neurology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada
| | - Lei Gao
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA.,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Chelsea Hu
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Kun Hu
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA. .,Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
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35
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Jakobsen P, Garcia-Ceja E, Riegler M, Stabell LA, Nordgreen T, Torresen J, Fasmer OB, Oedegaard KJ. Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls. PLoS One 2020; 15:e0231995. [PMID: 32833958 PMCID: PMC7446864 DOI: 10.1371/journal.pone.0231995] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 08/09/2020] [Indexed: 11/18/2022] Open
Abstract
Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.
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Affiliation(s)
- Petter Jakobsen
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- * E-mail:
| | | | - Michael Riegler
- Simula Metropolitan Center for Digitalisation, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Lena Antonsen Stabell
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Tine Nordgreen
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway
| | - Jim Torresen
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Ole Bernt Fasmer
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Ketil Joachim Oedegaard
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
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36
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Mansur RB, Lee Y, McIntyre RS, Brietzke E. What is bipolar disorder? A disease model of dysregulated energy expenditure. Neurosci Biobehav Rev 2020; 113:529-545. [PMID: 32305381 DOI: 10.1016/j.neubiorev.2020.04.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/30/2020] [Accepted: 04/05/2020] [Indexed: 12/24/2022]
Abstract
Advances in the understanding and management of bipolar disorder (BD) have been slow to emerge. Despite notable recent developments in neurosciences, our conceptualization of the nature of this mental disorder has not meaningfully progressed. One of the key reasons for this scenario is the continuing lack of a comprehensive disease model. Within the increasing complexity of modern research methods, there is a clear need for an overarching theoretical framework, in which findings are assimilated and predictions are generated. In this review and hypothesis article, we propose such a framework, one in which dysregulated energy expenditure is a primary, sufficient cause for BD. Our proposed model is centered on the disruption of the molecular and cellular network regulating energy production and expenditure, as well its potential secondary adaptations and compensatory mechanisms. We also focus on the putative longitudinal progression of this pathological process, considering its most likely periods for onset, such as critical periods that challenges energy homeostasis (e.g. neurodevelopment, social isolation), and the resulting short and long-term phenotypical manifestations.
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Affiliation(s)
- Rodrigo B Mansur
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
| | - Yena Lee
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Elisa Brietzke
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Kingston General Hospital, Providence Care Hospital, Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
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37
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Esaki Y, Obayashi K, Saeki K, Fujita K, Iwata N, Kitajima T. Association between light exposure at night and manic symptoms in bipolar disorder: cross-sectional analysis of the APPLE cohort. Chronobiol Int 2020; 37:887-896. [DOI: 10.1080/07420528.2020.1746799] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Yuichi Esaki
- Department of Psychiatry, Okehazama Hospital, Aichi, Japan
- Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan
| | - Kenji Obayashi
- Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan
| | - Keigo Saeki
- Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan
| | - Kiyoshi Fujita
- Department of Psychiatry, Okehazama Hospital, Aichi, Japan
- Department of Psychiatry, The Neuroscience Research Center, Aichi, Japan
| | - Nakao Iwata
- Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan
| | - Tsuyoshi Kitajima
- Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan
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McGowan N, Goodwin G, Bilderbeck A, Saunders K. Actigraphic patterns, impulsivity and mood instability in bipolar disorder, borderline personality disorder and healthy controls. Acta Psychiatr Scand 2020; 141:374-384. [PMID: 31916240 PMCID: PMC7216871 DOI: 10.1111/acps.13148] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 01/02/2020] [Accepted: 01/05/2020] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To differentiate the relation between the structure and timing of rest-activity patterns and symptoms of impulsivity and mood instability in bipolar disorder (BD), borderline personality disorder (BPD) and healthy controls (HC). METHODS Eighty-seven participants (31 BD, 21 BPD and 35 HC) underwent actigraph monitoring for 28 days as part of the Automated Monitoring of Symptom Severity (AMoSS) study. Impulsivity was assessed at study entry using the BIS-11. Mood instability was subsequently longitudinally monitored using the digital Mood Zoom questionnaire. RESULTS BPD participants show several robust and significant correlations between non-parametric circadian rest-activity variables and worsened symptoms. Impulsivity was associated with low interdaily stability (r = -0.663) and weak amplitude (r = -0.616). Mood instability was associated with low interdaily stability (r = -0.773), greater rhythm fragmentation (r = 0.662), weak amplitude (r = -0.694) and later onset of daily activity (r = 0.553). These associations were not present for BD or HCs. Classification analysis using actigraphic measures determined that later L5 onset reliably distinguished BPD from BD and HC but did not sufficiently discriminate between BD and HC. CONCLUSIONS Rest-activity pattern disturbance indicative of perturbed sleep and circadian function is an important predictor of symptom severity in BPD. This appears to validate the greater subjective complaints of BPD individuals that are sometimes regarded as exaggerated by clinicians. We suggest that treatment strategies directed towards improving sleep and circadian entrainment may in the future be investigated in BPD.
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Affiliation(s)
- N.M. McGowan
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - G.M. Goodwin
- Department of PsychiatryUniversity of OxfordOxfordUK,Oxford Health NHS Foundation TrustWarneford HospitalOxfordUK
| | | | - K.E.A. Saunders
- Department of PsychiatryUniversity of OxfordOxfordUK,Oxford Health NHS Foundation TrustWarneford HospitalOxfordUK,NIHR Oxford Health Biomedical Research CentreOxfordUK
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Faurholt-Jepsen M, Christensen EM, Frost M, Bardram JE, Vinberg M, Kessing LV. Hypomania/Mania by DSM-5 definition based on daily smartphone-based patient-reported assessments. J Affect Disord 2020; 264:272-278. [PMID: 32056761 DOI: 10.1016/j.jad.2020.01.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/20/2019] [Accepted: 01/03/2020] [Indexed: 12/16/2022]
Abstract
INTRODUCTION The DSM-5 has introduced elevated/irritable mood and increased activity/ energy as equal and necessary criterion A symptoms for a diagnosis of (hypo)mania. The impact of these changes is poorly elucidated. The aim of the study was to investigate differences in the prevalence of elevated/irritable mood with and without co-occurring increased activity, and the associations between these, in patients with an ICD-10 and DSM-IV diagnosis of BD, using real life daily smartphone-based patient-reported measures of mood, irritability and activity. METHODS Data from two RCTs investigating the effect of smartphone-based treatment in patients with BD were combined. Patients with BD (N = 117) evaluated mood, irritability and activity level daily for six to nine months via a smartphone-based system. Analyses in this study are exploratory post hoc analyses based on previously published data. RESULTS During the follow-up period, patients reported elevated mood 8.0% of the time, irritability 28.4% of the time and increased activity 20.6% of the time. Co-occurring elevated/irritable mood and activity were prevalent 0.12% of the time for four consecutive days (duration criteria for a hypomanic episode) compared to 24% of the time with elevated/irritable mood without co-occurring increased activity. In linear mixed effect models accommodating for inter-individual and intra-individual variation, there was a statistically significant positive association between mood and activity (B: 0.14, 95% CI: 0.046; 0.24, p = 0.004). There was no association between irritability and activity (p = 0.23). CONCLUSION Based on real life daily assessments, the prevalence of (hypo)manic episodes is substantial reduced as a result of the introduction of DSM-5 and with potentially clinical consequences.
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Affiliation(s)
- Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Blegdamsvej 9, DK- 2100 Copenhagen, Denmark.
| | - Ellen Margrethe Christensen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Blegdamsvej 9, DK- 2100 Copenhagen, Denmark
| | - Mads Frost
- Monsenso Aps, Langelinie Alle 47, Copenhagen, Denmark
| | - Jakob Eyvind Bardram
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Maj Vinberg
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Blegdamsvej 9, DK- 2100 Copenhagen, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Blegdamsvej 9, DK- 2100 Copenhagen, Denmark
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40
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Gonzalez R, Gonzalez SD, McCarthy MJ. Using Chronobiological Phenotypes to Address Heterogeneity in Bipolar Disorder. MOLECULAR NEUROPSYCHIATRY 2020; 5:72-84. [PMID: 32399471 DOI: 10.1159/000506636] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 02/18/2020] [Indexed: 12/12/2022]
Abstract
Bipolar disorder (BD) is a neuropsychiatric mood disorder characterized by recurrent episodes of mania and depression in addition to disruptions in sleep, energy, appetite, and cognitive functions-rhythmic behaviors that typically change on daily cycles. BD symptoms can also be provoked by seasonal changes, sleep, and/or circadian disruption, indicating that chronobiological factors linked to the circadian clock may be a common feature in the disorder. Research indicates that BD exists on a clinical spectrum, with distinct subtypes often intersecting with other psychiatric disorders. This heterogeneity has been a major challenge to BD research and contributes to problems in diagnostic stability and treatment outcomes. To address this heterogeneity, we propose that chronobiologically related biomarkers could be useful in classifying BD into objectively measurable phenotypes to establish better diagnoses, inform treatments, and perhaps lead to better clinical outcomes. Presently, we review the biological basis of circadian time keeping in humans, discuss the links of BD to the circadian clock, and pre-sent recent studies that evaluated chronobiological measures as a basis for establishing BD phenotypes. We conclude that chronobiology may inform future research using other novel techniques such as genomics, cell biology, and advanced behavioral analyses to establish new and more biologically based BD phenotypes.
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Affiliation(s)
- Robert Gonzalez
- Department of Psychiatry and Behavioral Health, Penn State Health, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - Suzanne D Gonzalez
- Department of Psychiatry and Behavioral Health, Penn State Health, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA.,Department of Pharmacology, Penn State Health, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - Michael J McCarthy
- VA San Diego Healthcare System, San Diego, California, USA.,Department of Psychiatry and Center for Chronobiology, University of California, San Diego, La Jolla, California, USA
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Actigraphy assessment of motor activity and sleep in patients with alcohol withdrawal syndrome and the effects of intranasal oxytocin. PLoS One 2020; 15:e0228700. [PMID: 32053696 PMCID: PMC7018062 DOI: 10.1371/journal.pone.0228700] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 01/21/2020] [Indexed: 12/26/2022] Open
Abstract
Background and aims The alcohol withdrawal syndrome increases autonomic activation and stress in patients during detoxification, leading to alterations in motor activity and sleep irregularities. Intranasal oxytocin has been proposed as a possible treatment of acute alcohol withdrawal. The aim of the present study was to explore whether actigraphy could be used as a tool to register symptoms during alcohol detoxification, whether oxytocin affected actigraphy variables related to motor activity and sleep compared to placebo during detoxification, and whether actigraphy-recorded motor function during detoxification was different from that in healthy controls. Methods This study was a part of a randomized, double blind, placebo-controlled trial in which 40 patients with alcohol use disorder admitted for acute detoxification were included. Of these, 20 received insufflations with intranasal oxytocin and 20 received placebo. Outcomes were actigraphy-recorded motor activity during 5-hour sequences following the insufflations and a full 24-hour period, as well as actigraphy-recorded sleep. Results were related to clinical variables of alcohol intake and withdrawal, including self-reported sleep. Finally, the actigraphy results were compared to those in a group of 34 healthy individuals. Results There were no significant differences between the oxytocin group and the placebo group for any of actigraphy variables registered. Neither were there any correlations between actigraphy-recorded motor function and clinical symptoms of alcohol withdrawal, but there was a significant association between self-reported and actigraphy-recorded sleep. Compared to healthy controls, motor activity during alcohol withdrawal was lower in the evenings and showed increased variability. Conclusion Intranasal oxytocin did not affect actigraphy-recorded motor activity nor sleep in patients with acute alcohol withdrawal. There were no findings indicating that actigraphy can be used to evaluate the degree of withdrawal symptoms during detoxification. However, patients undergoing acute alcohol withdrawal had a motor activity pattern different from than in healthy controls.
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42
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Curtis BJ, Williams PG, Anderson JS. Objective cognitive functioning in self-reported habitual short sleepers not reporting daytime dysfunction: examination of impulsivity via delay discounting. Sleep 2019; 41:5025755. [PMID: 29931335 DOI: 10.1093/sleep/zsy115] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Indexed: 11/12/2022] Open
Abstract
Study Objectives (1) Examine performance on an objective measure of reward-related cognitive impulsivity (delay discounting) among self-reported habitual short sleepers and medium (i.e. recommended 7-9 hours) length sleepers either reporting or not reporting daytime dysfunction; (2) Inform the debate regarding what type and duration of short sleep (e.g. 21 to 24 hours of total sleep deprivation, self-reported habitual short sleep duration) meaningfully influences cognitive impulsivity; (3) Compare the predictive utility of sleep duration and perceived dysfunction to other factors previously shown to influence cognitive impulsivity via delay discounting performance (age, income, education, and fluid intelligence). Methods We analyzed data from 1190 adults from the Human Connectome Project database. Participants were grouped on whether they reported habitual short (≤6 hours) vs. medium length (7-9 hours) sleep duration and whether they perceived daytime dysfunction using the Pittsburgh Sleep Quality Index. Results All short sleepers exhibited increased delay discounting compared to all medium length sleepers, regardless of perceived dysfunction. Of the variables examined, self-reported sleep duration was the strongest predictor of delay discounting behavior between groups and across all 1190 participants. Conclusions Individuals who report habitual short sleep are likely to exhibit increased reward-related cognitive impulsivity regardless of perceived sleep-related daytime impairment. Therefore, there is a reason to suspect that these individuals exhibit more daytime dysfunction, in the form of reward-related cognitive impulsivity, than they may assume. Current findings suggest that assessment of sleep duration over the prior month has meaningful predictive utility for human reward-related impulsivity.
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Affiliation(s)
- Brian J Curtis
- Department of Psychology, University of Utah, Salt Lake City, UT
| | - Paula G Williams
- Department of Psychology, University of Utah, Salt Lake City, UT
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Schwab KE, To AQ, Chang J, Ronish B, Needham DM, Martin JL, Kamdar BB. Actigraphy to Measure Physical Activity in the Intensive Care Unit: A Systematic Review. J Intensive Care Med 2019; 35:1323-1331. [PMID: 31331220 DOI: 10.1177/0885066619863654] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE In the intensive care unit (ICU), prolonged inactivity is common, increasing patients' risk for adverse outcomes, including ICU-acquired weakness. Hence, interventions to minimize inactivity are gaining popularity, highlighting actigraphy, a measure of activity involving a wristwatch-like accelerometer, as a method to inform these efforts. Therefore, we performed a systematic review of studies that used actigraphy to measure patient activity in the ICU setting. DATA SOURCES We searched PubMed, EMBASE, CINAHL, Cochrane Library, and ProQuest from inception until December 2016. STUDY SELECTION Two reviewers independently screened studies for inclusion. A study was eligible for inclusion if it was published in a peer-reviewed journal and used actigraphy to measure activity in ≥5 ICU patients. DATA EXTRACTION Two reviewers independently performed data abstraction and risk of bias assessment. Abstracted actigraphy-based activity data included total activity time and activity counts. RESULTS Of 16 studies (607 ICU patients) identified, 14 (88%) were observational, 2 (12%) were randomized control trials, and 5 (31%) were published after 2009. Mean patient activity levels per 15 to 60 second epoch ranged from 25 to 37 daytime and 2 to 19 nighttime movements. Actigraphy was evaluated in the context of ICU and post-ICU outcomes in 11 (69%) and 5 (31%) studies, respectively, and demonstrated potential associations between actigraphy-based activity levels and delirium, sedation, pain, anxiety, time to extubation, and length of stay. CONCLUSION Actigraphy has demonstrated that patients are profoundly inactive in the ICU with actigraphy-based activity levels potentially associated with important measures, such as delirium, sedation, and length of stay. Larger and more rigorous studies are needed to further evaluate these associations and the overall utility of actigraphy in the ICU setting.
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Affiliation(s)
- Kristin E Schwab
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, 8783University of California, Los Angeles, CA, USA
| | - An Q To
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, 8783University of California, Los Angeles, CA, USA
| | - Jennifer Chang
- Department of Medicine, David Geffen School of Medicine at UCLA, 8783University of California, Los Angeles, CA, USA
| | - Bonnie Ronish
- Division of Pulmonary and Critical Care Medicine, 7060University of Utah, Salt Lake City, UT, USA
| | - Dale M Needham
- Division of Pulmonary and Critical Care Medicine, 1466Johns Hopkins University, Baltimore, MD, USA.,Department of Physical Medicine and Rehabilitation, 1466Johns Hopkins University, Baltimore, MD, USA
| | - Jennifer L Martin
- Department of Medicine, David Geffen School of Medicine at UCLA, 8783University of California, Los Angeles, CA, USA.,Geriatric Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Biren B Kamdar
- Division of Pulmonary, Critical Care and Sleep Medicine, UC San Diego 8784(UCSD) School of Medicine, University of California, San Diego, CA, USA
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Tazawa Y, Wada M, Mitsukura Y, Takamiya A, Kitazawa M, Yoshimura M, Mimura M, Kishimoto T. Actigraphy for evaluation of mood disorders: A systematic review and meta-analysis. J Affect Disord 2019; 253:257-269. [PMID: 31060012 DOI: 10.1016/j.jad.2019.04.087] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 04/01/2019] [Accepted: 04/21/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Actigraphy has enabled consecutive observation of individual health conditions such as sleep or daily activity. This study aimed to examine the usefulness of actigraphy in evaluating depressive and/or bipolar disorder symptoms. METHOD A systematic review and meta-analysis was conducted. We selected studies that used actigraphy to compare either patients vs. healthy controls, or pre- vs. post-treatment data from the same patient group. Common actigraphy measurements, namely daily activity and sleep-related data, were extracted and synthesized. RESULTS Thirty-eight studies (n = 3,758) were included in the analysis. Compared with healthy controls, depressive patients were less active (standardized mean difference; SMD=1.27, 95%CI=[0.97, 1.57], P<0.001) and had longer wake after sleep onset (SMD= - 0.729, 95%CI=[- 1.20, - 0.25], p = 0.003). Total sleep time (SMD= - 0.33, 95%CI=[- 0.55, - 0.11], P = 0.004), sleep latency (SMD= - 0.22, 95%CI=[- 0.42, - 0.02], P = 0.032), and wake after sleep onset (SMD= - 0.22, 95%CI=[- 0.39, - 0.04], P = 0.015) were longer in euthymic/remitted patients compared to healthy controls. In pre- and post-treatment comparisons, sleep latency (SMD=- 0.85, 95%CI=[- 1.53, - 0.17], P = 0.015), wake after sleep onset (SMD= - 0.65, 95%CI=[- 1.20, - 0.10], P = 0.022), and sleep efficiency (SMD=0.77, 95%CI=[0.29, 1.24], P = 0.002) showed significant improvement. LIMITATION The sample sizes for each outcome were small. The type of actigraphy devices and patients' illness severity differed across studies. It is possible that hospitalizations and medication influenced the outcomes. CONCLUSION We found significant differences between healthy controls and mood disorders patients for some actigraphy-measured modalities. Specific measurement patterns characterizing each mood disorder/status were also found. Additional actigraphy data linked to severity and/or treatment could enhance the clinical utility of actigraphy.
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Affiliation(s)
- Yuuki Tazawa
- Keio University School of Medicine, Department of Neuropsychiatry, Tokyo, Japan
| | - Masataka Wada
- Keio University School of Medicine, Department of Neuropsychiatry, Tokyo, Japan
| | - Yasue Mitsukura
- Keio University, Faculty of Science and Technology, Kanagawa, Japan
| | - Akihiro Takamiya
- Keio University School of Medicine, Department of Neuropsychiatry, Tokyo, Japan
| | - Momoko Kitazawa
- Keio University School of Medicine, Department of Neuropsychiatry, Tokyo, Japan
| | - Michitaka Yoshimura
- Keio University School of Medicine, Department of Neuropsychiatry, Tokyo, Japan
| | - Masaru Mimura
- Keio University School of Medicine, Department of Neuropsychiatry, Tokyo, Japan
| | - Taishiro Kishimoto
- Keio University School of Medicine, Department of Neuropsychiatry, Tokyo, Japan.
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Krane-Gartiser K, Scott J, Nevoret C, Benard V, Benizri C, Brochard H, Geoffroy PA, Katsahian S, Maruani J, Yeim S, Leboyer M, Bellivier F, Etain B. Which actigraphic variables optimally characterize the sleep-wake cycle of individuals with bipolar disorders? Acta Psychiatr Scand 2019; 139:269-279. [PMID: 30689212 DOI: 10.1111/acps.13003] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2019] [Indexed: 01/07/2023]
Abstract
OBJECTIVE To examine which combination of objectively measured actigraphy parameters best characterizes the sleep-wake cycle of euthymic individuals with bipolar disorder (BD) compared with healthy controls (HC). METHODS Sixty-one BD cases and 61 matched HC undertook 21 consecutive days of actigraphy. Groups were compared using discriminant function analyses (DFA) that explored dimensions derived from mean values of sleep parameters (Model 1); variability of sleep parameters (2); daytime activity (3); and combined sleep and activity parameters (4). Exploratory within-group analyses examined characteristics associated with misclassification. RESULTS After controlling for depressive symptoms, the combined model (4) correctly classified 75% cases, while the sleep models (1 and 2) correctly classified 87% controls. The area under the curve favored the combined model (0.86). Age was significantly associated with misclassification among HC, while a diagnosis of BD-II was associated with an increased risk of misclassifications of cases. CONCLUSION Including sleep variability and activity parameters alongside measures of sleep quantity improves the characterization of cases of euthymic BD and helps distinguish them from HC. If replicated, the findings indicate that traditional approaches to actigraphy (examining mean values for the standard set of sleep parameters) may represent a suboptimal approach to understanding sleep-wake cycles in BD.
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Affiliation(s)
- K Krane-Gartiser
- Department of Mental Health, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Psychiatry, St. Olav's University Hospital, Trondheim, Norway.,INSERM U1144, Paris, France
| | - J Scott
- Department of Mental Health, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Academic Psychiatry, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.,Sorbonne Paris Cité, Université Paris Diderot, Paris, France.,Centre for Affective Disorders, Institute of Psychiatry, London, UK
| | - C Nevoret
- INSERM, UMR_S 1138, Université Paris Descartes, Sorbonne Universités, UPMC Université Paris 06, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France.,Assistance Publique - Hôpitaux de Paris, Hôpital Européen Georges-Pompidou, Unité d'Épidémiologie et de Recherche Clinique, Paris, France.,INSERM, Centre d'Investigation Clinique 1418, Module Épidémiologie Clinique, Paris, France
| | | | - C Benizri
- Equipe Psychiatrie Translationnelle, INSERM U955, Créteil, France
| | - H Brochard
- Equipe Psychiatrie Translationnelle, INSERM U955, Créteil, France.,Pôle sectoriel, Centre Hospitalier Fondation Vallée, Gentilly, France
| | - P A Geoffroy
- INSERM U1144, Paris, France.,Sorbonne Paris Cité, Université Paris Diderot, Paris, France.,Département de Psychiatrie et de Médecine Addictologique, AP-HP, GH Saint-Louis - Lariboisière - F. Widal, Paris, France.,Fondation FondaMental, Créteil, France
| | - S Katsahian
- INSERM, UMR_S 1138, Université Paris Descartes, Sorbonne Universités, UPMC Université Paris 06, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France.,Assistance Publique - Hôpitaux de Paris, Hôpital Européen Georges-Pompidou, Unité d'Épidémiologie et de Recherche Clinique, Paris, France.,INSERM, Centre d'Investigation Clinique 1418, Module Épidémiologie Clinique, Paris, France
| | - J Maruani
- Département de Psychiatrie et de Médecine Addictologique, AP-HP, GH Saint-Louis - Lariboisière - F. Widal, Paris, France
| | - S Yeim
- Sorbonne Paris Cité, Université Paris Diderot, Paris, France.,Département de Psychiatrie et de Médecine Addictologique, AP-HP, GH Saint-Louis - Lariboisière - F. Widal, Paris, France
| | - M Leboyer
- Equipe Psychiatrie Translationnelle, INSERM U955, Créteil, France.,Fondation FondaMental, Créteil, France.,AP-HP, Hôpitaux Universitaires Henri Mondor, DHU Pepsy, Pôle de Psychiatrie et d'Addictologie, Créteil, France.,Université Paris Est Créteil, Creteil, France
| | - F Bellivier
- INSERM U1144, Paris, France.,Sorbonne Paris Cité, Université Paris Diderot, Paris, France.,Département de Psychiatrie et de Médecine Addictologique, AP-HP, GH Saint-Louis - Lariboisière - F. Widal, Paris, France.,Fondation FondaMental, Créteil, France
| | - B Etain
- INSERM U1144, Paris, France.,Sorbonne Paris Cité, Université Paris Diderot, Paris, France.,Centre for Affective Disorders, Institute of Psychiatry, London, UK.,Département de Psychiatrie et de Médecine Addictologique, AP-HP, GH Saint-Louis - Lariboisière - F. Widal, Paris, France.,Fondation FondaMental, Créteil, France
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Leroux A, Di J, Smirnova E, Mcguffey EJ, Cao Q, Bayatmokhtari E, Tabacu L, Zipunnikov V, Urbanek JK, Crainiceanu C. Organizing and analyzing the activity data in NHANES. STATISTICS IN BIOSCIENCES 2019; 11:262-287. [PMID: 32047572 DOI: 10.1007/s12561-018-09229-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
The NHANES study contains objectively measured physical activity data collected using hip-worn accelerometers from multiple cohorts. However, using the accelerometry data has proven daunting because: 1) currently, there are no agreed upon standard protocols for data storage and analysis; 2) data exhibit heterogeneous patterns of missingness due to varying degrees of adherence to wear-time protocols; 3) sampling weights need to be carefully adjusted and accounted for in individual analyses; 4) there is a lack of reproducible software that transforms the data from its published format into analytic form; and 5) the high dimensional nature of accelerometry data complicates analyses. Here, we provide a framework for processing, storing, and analyzing the NHANES accelerometry data for the 2003-2004 and 2005-2006 surveys. We also provide an NHANES data package in R, to help disseminate high quality, processed activity data combined with mortality and demographic information. Thus, we provide the tools to transition from "available data online" to "easily accessible and usable data", which substantially reduces the large upfront costs of initiating studies of association between physical activity and human health outcomes using NHANES. We apply these tools in an analysis showing that accelerometry features have the potential to predict 5-year all cause mortality better than known risk factors such as age, cigarette smoking, and various comorbidities.
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Affiliation(s)
- Andrew Leroux
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Junrui Di
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Ekaterina Smirnova
- Department of Biostatistics, Virginia Commonwealth University, Richmond, USA.,Department of Mathematical Sciences, University of Montana, Missoula, USA
| | | | - Quy Cao
- Department of Mathematical Sciences, University of Montana, Missoula, USA
| | | | - Lucia Tabacu
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, USA
| | - Vadim Zipunnikov
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Jacek K Urbanek
- Division of Geriatric Medicine and Gerontology, Department of Medicine, Center on Aging and Health, School of Medicine, Johns Hopkins University, Baltimore, USA
| | - Ciprian Crainiceanu
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
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Merikangas KR, Swendsen J, Hickie IB, Cui L, Shou H, Merikangas AK, Zhang J, Lamers F, Crainiceanu C, Volkow ND, Zipunnikov V. Real-time Mobile Monitoring of the Dynamic Associations Among Motor Activity, Energy, Mood, and Sleep in Adults With Bipolar Disorder. JAMA Psychiatry 2019; 76:190-198. [PMID: 30540352 PMCID: PMC6439734 DOI: 10.1001/jamapsychiatry.2018.3546] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
IMPORTANCE Biologic systems involved in the regulation of motor activity are intricately linked with other homeostatic systems such as sleep, feeding behavior, energy, and mood. Mobile monitoring technology (eg, actigraphy and ecological momentary assessment devices) allows the assessment of these multiple systems in real time. However, most clinical studies of mental disorders that use mobile devices have not focused on the dynamic associations between these systems. OBJECTIVES To examine the directional associations among motor activity, energy, mood, and sleep using mobile monitoring in a community-identified sample, and to evaluate whether these within-day associations differ between people with a history of bipolar or other mood disorders and controls without mood disorders. DESIGN, SETTING, AND PARTICIPANTS This study used a nested case-control design of 242 adults, a subsample of a community-based sample of adults. Probands were recruited by mail from the greater Washington, DC, metropolitan area from January 2005 to June 2013. Enrichment of the sample for mood disorders was provided by volunteers or referrals from the National Institutes of Health Clinical Center or by participants in the National Institute of Mental Health Mood and Anxiety Disorders Program. The inclusion criteria were the ability to speak English, availability to participate, and consent to contact at least 2 living first-degree relatives. Data analysis was performed from June 2013 through July 2018. MAIN OUTCOMES AND MEASURES Motor activity and sleep duration data were obtained from minute-to-minute activity counts from an actigraphy device worn on the nondominant wrist for 2 weeks. Mood and energy levels were assessed by subjective analogue ratings on the ecological momentary assessment (using a personal digital assistant) by participants 4 times per day for 2 weeks. RESULTS Of the total 242 participants, 92 (38.1%) were men and 150 (61.9%) were women, with a mean (SD) age of 48 (16.9) years. Among the participants, 54 (22.3%) had bipolar disorder (25 with bipolar I; 29 with bipolar II), 91 (37.6%) had major depressive disorder, and 97 (40.1%) were controls with no history of mood disorders. A unidirectional association was found between motor activity and subjective mood level (β = -0.018, P = .04). Bidirectional associations were observed between motor activity (β = 0.176; P = .03) and subjective energy level (β = 0.027; P = .03) as well as between motor activity (β = -0.027; P = .04) and sleep duration (β = -0.154; P = .04). Greater cross-domain reactivity was observed in bipolar disorder across all outcomes, including motor activity, sleep, mood, and energy. CONCLUSIONS AND RELEVANCE These findings suggest that interventions focused on motor activity and energy may have greater efficacy than current approaches that target depressed mood; both active and passive tracking of multiple regulatory systems are important in designing therapeutic targets.
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Affiliation(s)
- Kathleen Ries Merikangas
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
| | - Joel Swendsen
- University of Bordeaux, National Center for Scientific Research, Bordeaux, France,EPHE PSL Research University, Paris, France
| | - Ian B. Hickie
- Brain & Mind Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Lihong Cui
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
| | - Haochang Shou
- Department of Biostatistics, University of Pennsylvania, Philadelphia
| | - Alison K. Merikangas
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jihui Zhang
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, People's Republic of China
| | - Femke Lamers
- Department of Psychiatry and EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, the Netherlands
| | - Ciprian Crainiceanu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Nora D. Volkow
- National Institute of Drug Abuse, Bethesda, Maryland,Laboratory of Neuroimaging, National Institute of Alcohol Abuse and Alcoholism, Bethesda, Maryland
| | - Vadim Zipunnikov
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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48
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Tanaka T, Kokubo K, Iwasa K, Sawa K, Yamada N, Komori M. Intraday Activity Levels May Better Reflect the Differences Between Major Depressive Disorder and Bipolar Disorder Than Average Daily Activity Levels. Front Psychol 2018; 9:2314. [PMID: 30581399 PMCID: PMC6292921 DOI: 10.3389/fpsyg.2018.02314] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Accepted: 11/05/2018] [Indexed: 11/13/2022] Open
Abstract
It is important to establish an objective index to differentiate mood disorders (i.e., bipolar disorder; BD and major depressive disorder; MDD). The present study focused on the pattern of changes of physical activity in the amount of activity intraday, and examined the relationship between activity patterns and mood disorders. One hundred and eighteen inpatients with MDD or BD in a depressive state provided the activity data by using wearable activity trackers for 3 weeks. In order to illuminate the characteristic patterns of intraday activities, Principal Component Analysis (PCA) was adopted to extract the main components of intraday activity changes. We found that some of the PCs reflected the differences between the types of mood disorder. BD participants showed high activity pattern in the morning and low activity pattern in evenings. However, MDD showed the opposite. Our results suggest that activity tracking focused on daytime activity patterns may provide objective auxiliary diagnostic information.
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Affiliation(s)
- Tsunehiko Tanaka
- Educational Psychology Course, Faculty of Education, Niigata University, Niigata, Japan.,Department of Psychiatry, Shiga University of Medical Science, Ōtsu, Japan
| | - Kumiko Kokubo
- Graduate School of Engineering, Osaka Electro-Communication University, Neyagawa, Japan
| | - Kazunori Iwasa
- Department of Educational Psychology, Shujitsu University, Okayama, Japan
| | - Kosuke Sawa
- Faculty of Human Sciences, Department of Psychology, Senshu University, Kawasaki, Japan
| | - Naoto Yamada
- Department of Psychiatry, Shiga University of Medical Science, Ōtsu, Japan.,Kamibayashi Memorial Hospital, Ichinomiya, Japan
| | - Masashi Komori
- Faculty of Information and Communication Engineering, Osaka Electro-Communication University, Neyagawa, Japan
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49
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Motor activity patterns in acute schizophrenia and other psychotic disorders can be differentiated from bipolar mania and unipolar depression. Psychiatry Res 2018; 270:418-425. [PMID: 30312969 DOI: 10.1016/j.psychres.2018.10.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 10/01/2018] [Accepted: 10/02/2018] [Indexed: 12/22/2022]
Abstract
The purpose of this study was to compare 24-h motor activity patterns between and within three groups of acutely admitted inpatients with schizophrenia and psychotic disorders (n = 28), bipolar mania (n = 18) and motor-retarded unipolar depression (n = 25) and one group of non-hospitalized healthy individuals (n = 28). Motor activity was measured by wrist actigraphy, and analytical approaches using linear and non-linear variability and irregularity measures were undertaken. In between-group comparisons, the schizophrenia group showed more irregular activity patterns than depression cases and healthy individuals. The schizophrenia and mania cases were clinically similar with respect to high prevalence of psychotic symptoms. Although they could not be separated by a formal statistical test, the schizophrenia cases showed more normal amplitudes in morning to evening mean activity and activity variability. Schizophrenia constituted an independent entity in terms of motor activation that could be distinguished from the other diagnostic groups of psychotic and non-psychotic affective disorders. Despite limitations such as small subgroups, short recordings and confounding effects of medication/hospitalization, these results suggest that detailed temporal analysis of motor activity patterns can identify similarities and differences between prevalent functional psychiatric disorders. For this purpose, irregularity measures seem particularly useful to characterize psychotic symptoms and should be explored in larger samples with longer-term recordings, while searching for underlying mechanisms of motor activity disturbances.
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50
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Rohani DA, Faurholt-Jepsen M, Kessing LV, Bardram JE. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR Mhealth Uhealth 2018; 6:e165. [PMID: 30104184 PMCID: PMC6111148 DOI: 10.2196/mhealth.9691] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 05/13/2018] [Accepted: 06/18/2018] [Indexed: 12/14/2022] Open
Abstract
Background Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published. Objective The objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies. Methods We conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017. Results A total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity). Conclusions Several studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.
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Affiliation(s)
- Darius A Rohani
- Embedded Systems Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Center for Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Centre, Psychiatric Centre Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Centre, Psychiatric Centre Copenhagen, Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jakob E Bardram
- Embedded Systems Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Center for Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
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