1
|
Ortiz A, Maslej MM, Husain MI, Daskalakis ZJ, Mulsant BH. Apps and gaps in bipolar disorder: A systematic review on electronic monitoring for episode prediction. J Affect Disord 2021; 295:1190-1200. [PMID: 34706433 DOI: 10.1016/j.jad.2021.08.140] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/18/2021] [Accepted: 08/27/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Long-term clinical monitoring in bipolar disorder (BD) is an important therapeutic tool. The availability of smartphones and wearables has sparked the development of automated applications to remotely monitor patients. This systematic review focus on the current state of electronic (e-) monitoring for episode prediction in BD. METHODS We systematically reviewed the literature on e-monitoring for episode prediction in adult BD patients. The systematic review was done according to the guidelines for reporting of systematic reviews and meta-analyses (PRISMA) and was registered in PROSPERO on April 29, 2020 (CRD42020155795). We conducted a search of Web of Science, MEDLINE, EMBASE, and PsycINFO (all 2000-2020) databases. We identified and extracted data from 17 published reports on 15 relevant studies. RESULTS Studies were heterogeneous and most had substantial methodological and technical limitations. Models varied widely in their performance. Published metrics were too heterogeneous to lend themselves to a meta-analysis. Four studies reported sensitivity (range: 0.21 - 0.95); and two reported specificity for prediction of mood episodes (range: 0.36 - 0.99). Two studies reported accuracy (range: 0.64 - 0.88) and four reported area under the curve (AUC; range: 0.52-0.95). Overall, models were better in predicting manic or hypomanic episodes, but their performance depended on feature type. LIMITATIONS Our conclusions are tempered by the lack of appropriate information impeding our ability to synthesize the available evidence. CONCLUSIONS Given the clinical variability in BD, predicting mood episodes remains a challenging task. Emerging e-monitoring technology for episode prediction in BD requires more development before it can be adopted clinically.
Collapse
Affiliation(s)
- Abigail Ortiz
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Marta M Maslej
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - M Ishrat Husain
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of California San Diego, United States
| | - Benoit H Mulsant
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health, Toronto, ON, Canada
| |
Collapse
|
2
|
Kessing LV, González-Pinto A, Fagiolini A, Bechdolf A, Reif A, Yildiz A, Etain B, Henry C, Severus E, Reininghaus EZ, Morken G, Goodwin GM, Scott J, Geddes JR, Rietschel M, Landén M, Manchia M, Bauer M, Martinez-Cengotitabengoa M, Andreassen OA, Ritter P, Kupka R, Licht RW, Nielsen RE, Schulze TG, Hajek T, Lagerberg TV, Bergink V, Vieta E. DSM-5 and ICD-11 criteria for bipolar disorder: Implications for the prevalence of bipolar disorder and validity of the diagnosis - A narrative review from the ECNP bipolar disorders network. Eur Neuropsychopharmacol 2021; 47:54-61. [PMID: 33541809 DOI: 10.1016/j.euroneuro.2021.01.097] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 01/18/2021] [Indexed: 12/16/2022]
Abstract
This narrative review summarizes and discusses the implications of the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 and the upcoming International Classification of Diseases (ICD)-11 classification systems on the prevalence of bipolar disorder and on the validity of the DSM-5 diagnosis of bipolar disorder according to the Robin and Guze criteria of diagnostic validity. Here we review and discuss current data on the prevalence of bipolar disorder diagnosed according to DSM-5 versus DSM-IV, and data on characteristics of bipolar disorder in the two diagnostic systems in relation to extended Robin and Guze criteria: 1) clinical presentation, 2) associations with para-clinical data such as brain imaging and blood-based biomarkers, 3) delimitation from other disorders, 4) associations with family history / genetics, 5) prognosis and long-term follow-up, and 6) treatment effects. The review highlights that few studies have investigated consequences for the prevalence of the diagnosis of bipolar disorder and for the validity of the diagnosis. Findings from these studies suggest a substantial decrease in the point prevalence of a diagnosis of bipolar with DSM-5 compared with DSM-IV, ranging from 30-50%, but a smaller decrease in the prevalence during lifetime, corresponding to a 6% reduction. It is concluded that it is likely that the use of DSM-5 and ICD-11 will result in diagnostic delay and delayed early intervention in bipolar disorder. Finally, we recommend areas for future research.
Collapse
Affiliation(s)
- Lars Vedel Kessing
- Copenhagen Affective Disorder research Centre (CADIC), Psychiatric Centre Copenhagen, Department O, University Hospital of Copenhagen, Rigshospitalet, and University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark.
| | - Ana González-Pinto
- Department of Psychiatry, BIOARABA, Hospital Universitario de Alava, UPV/EHU. CIBERSAM, Vitoria, Spain
| | - Andrea Fagiolini
- Department of Mental Health and Sensory Organs, University of Siena School of Medicine, Siena, Italy
| | - Andreas Bechdolf
- Department of Psychiatry, Psychotherapy and Psychosomatics, Vivantes Hospital am Urban and Vivantes Hospital im Friedrichshain/Charite Medicine Berlin and University of Cologne, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Ayşegül Yildiz
- Department of Psychiatry, Dokuz Eylül University, İzmir, Turkey
| | - Bruno Etain
- Université de Paris and INSERM UMRS 1144, Paris, France
| | - Chantal Henry
- Department of Psychiatry, Service Hospitalo-Universitaire, GHU Paris Psychiatrie & Neuroscience, Paris, France
| | - Emanuel Severus
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Gunnar Morken
- Department of Psychiatry, St Olav University Hospital & Department of Mental Health, Norwegian University of Science and Technology - NTNU, Trondheim, Norway
| | - Guy M Goodwin
- Department of Psychiatry, University of Oxford and Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Jan Scott
- Institute of Neuroscience, Newcastle University, Newcastle, United Kingdom
| | - John R Geddes
- Department of Psychiatry, University of Oxford and Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Mikael Landén
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italia; Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Monica Martinez-Cengotitabengoa
- Osakidetza, Basque Health Service. Bioaraba, Health Research Institute, University of the Basque Country, UPV/EHU, Spain; Psychology Clinic of East Anglia. 68 Bishopgate, NR1 4AA, Norwich, United Kingdom
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Philipp Ritter
- Department of Psychiatry, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
| | - Ralph Kupka
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rasmus W Licht
- Aalborg University Hospital, Psychiatry, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - René Ernst Nielsen
- Aalborg University Hospital, Psychiatry, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Germany
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada; National Institute of Mental Health, Klecany, Czech Republic
| | - Trine Vik Lagerberg
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Veerle Bergink
- Department of Psychiatry and Department of Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine and Mount Sinai, New York, USA; Department of Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| |
Collapse
|
3
|
Faurholt-Jepsen M, Lindbjerg Tønning M, Fros M, Martiny K, Tuxen N, Rosenberg N, Busk J, Winther O, Thaysen-Petersen D, Aamund KA, Tolderlund L, Bardram JE, Kessing LV. Reducing the rate of psychiatric re-admissions in bipolar disorder using smartphones-The RADMIS trial. Acta Psychiatr Scand 2021; 143:453-465. [PMID: 33354769 DOI: 10.1111/acps.13274] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 12/16/2020] [Accepted: 12/20/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES The MONARCA I and II trials were negative but suggested that smartphone-based monitoring may increase quality of life and reduce perceived stress in bipolar disorder (BD). The present trial was the first to investigate the effect of smartphone-based monitoring on the rate and duration of readmissions in BD. METHODS This was a randomized controlled single-blind parallel-group trial. Patients with BD (ICD-10) discharged from hospitalization in the Mental Health Services, Capital Region of Denmark were randomized 1:1 to daily smartphone-based monitoring including a feedback loop (+ standard treatment) or to standard treatment for 6 months. Primary outcomes: the rate and duration of psychiatric readmissions. RESULTS We included 98 patients with BD. In ITT analyses, there was no statistically significant difference in rates (hazard rate: 1.05, 95% CI: 0.54; 1.91, p = 0.88) or duration of readmission between the two groups (B: 3.67, 95% CI: -4.77; 12.11, p = 0.39). There was no difference in scores on the Hamilton Depression Rating Scale (B = -0.11, 95% CI: -2.50; 2.29, p = 0.93). The intervention group had higher scores on the Young Mania Rating Scale (B: 1.89, 95% CI: 0.0078; 3.78, p = 0.050). The intervention group reported lower levels of perceived stress (B: -7.18, 95% CI: -13.50; -0.86, p = 0.026) and lower levels of rumination (B: -6.09, 95% CI: -11.19; -1.00, p = 0.019). CONCLUSIONS Smartphone-based monitoring did not reduce rate and duration of readmissions. There was no difference in levels of depressive symptoms. The intervention group had higher levels of manic symptoms, but lower perceived stress and rumination compared with the control group.
Collapse
Affiliation(s)
- Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Morten Lindbjerg Tønning
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | | | - Klaus Martiny
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Nanna Tuxen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Nicole Rosenberg
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Jonas Busk
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Ole Winther
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.,Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.,Centre for Genomic Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | | | | | - Jakob Eyvind Bardram
- Monsenso Aps, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| |
Collapse
|
4
|
Melbye S, Stanislaus S, Vinberg M, Frost M, Bardram JE, Kessing LV, Faurholt-Jepsen M. Automatically Generated Smartphone Data in Young Patients With Newly Diagnosed Bipolar Disorder and Healthy Controls. Front Psychiatry 2021; 12:559954. [PMID: 34512403 PMCID: PMC8423997 DOI: 10.3389/fpsyt.2021.559954] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/27/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Smartphones may facilitate continuous and fine-grained monitoring of behavioral activities via automatically generated data and could prove to be especially valuable in monitoring illness activity in young patients with bipolar disorder (BD), who often present with rapid changes in mood and related symptoms. The present pilot study in young patients with newly diagnosed BD and healthy controls (HC) aimed to (1) validate automatically generated smartphone data reflecting physical and social activity and phone usage against validated clinical rating scales and questionnaires; (2) investigate differences in automatically generated smartphone data between young patients with newly diagnosed BD and HC; and (3) investigate associations between automatically generated smartphone data and smartphone-based self-monitored mood and activity in young patients with newly diagnosed BD. Methods: A total of 40 young patients with newly diagnosed BD and 21 HC aged 15-25 years provided daily automatically generated smartphone data for 3-779 days [median (IQR) = 140 (11.5-268.5)], in addition to daily smartphone-based self-monitoring of activity and mood. All participants were assessed with clinical rating scales. Results: (1) The number of outgoing phone calls was positively associated with scores on the Young Mania Rating Scale and subitems concerning activity and speech. The number of missed calls (p = 0.015) and the number of outgoing text messages (p = 0.017) were positively associated with the level of psychomotor agitation according to the Hamilton Depression Rating scale subitem 9. (2) Young patients with newly diagnosed BD had a higher number of incoming calls compared with HC (BD: mean = 1.419, 95% CI: 1.162, 1.677; HC: mean = 0.972, 95% CI: 0.637, 1.308; p = 0.043) and lower self-monitored mood and activity (p's < 0.001). (3) Smartphone-based self-monitored mood and activity were positively associated with step counts and the number of outgoing calls, respectively (p's < 0.001). Conclusion: Automatically generated data on physical and social activity and phone usage seem to reflect symptoms. These data differ between young patients with newly diagnosed BD and HC and reflect changes in illness activity in young patients with BD. Automatically generated smartphone-based data could be a useful clinical tool in diagnosing and monitoring illness activity in young patients with BD.
Collapse
Affiliation(s)
- Sigurd Melbye
- The Copenhagen Affective Disorder Research Center, Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sharleny Stanislaus
- The Copenhagen Affective Disorder Research Center, Rigshospitalet, Copenhagen, Denmark
| | - Maj Vinberg
- The Copenhagen Affective Disorder Research Center, Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Research Unit, Psychiatric Center North Zealand, Hillerød, Denmark
| | | | - Jakob Eyvind Bardram
- Monsenso ApS, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lars Vedel Kessing
- The Copenhagen Affective Disorder Research Center, Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maria Faurholt-Jepsen
- The Copenhagen Affective Disorder Research Center, Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
5
|
Tønning ML, Faurholt-Jepsen M, Frost M, Bardram JE, Kessing LV. Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder. Front Psychiatry 2021; 12:701360. [PMID: 34366933 PMCID: PMC8336866 DOI: 10.3389/fpsyt.2021.701360] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/15/2021] [Indexed: 12/27/2022] Open
Abstract
Background: Smartphones comprise a promising tool for symptom monitoring in patients with unipolar depressive disorder (UD) collected as either patient-reportings or possibly as automatically generated smartphone data. However, only limited research has been conducted in clinical populations. We investigated the association between smartphone-collected monitoring data and validated psychiatric ratings and questionnaires in a well-characterized clinical sample of patients diagnosed with UD. Methods: Smartphone data, clinical ratings, and questionnaires from patients with UD were collected 6 months following discharge from psychiatric hospitalization as part of a randomized controlled study. Smartphone data were collected daily, and clinical ratings (i.e., Hamilton Depression Rating Scale 17-item) were conducted three times during the study. We investigated associations between (1) smartphone-based patient-reported mood and activity and clinical ratings and questionnaires; (2) automatically generated smartphone data resembling physical activity, social activity, and phone usage and clinical ratings; and (3) automatically generated smartphone data and same-day smartphone-based patient-reported mood and activity. Results: A total of 74 patients provided 11,368 days of smartphone data, 196 ratings, and 147 questionnaires. We found that: (1) patient-reported mood and activity were associated with clinical ratings and questionnaires (p < 0.001), so that higher symptom scores were associated with lower patient-reported mood and activity, (2) Out of 30 investigated associations on automatically generated data and clinical ratings of depression, only four showed statistical significance. Further, lower psychosocial functioning was associated with fewer daily steps (p = 0.036) and increased number of incoming (p = 0.032), outgoing (p = 0.015) and missed calls (p = 0.007), and longer phone calls (p = 0.012); (3) Out of 20 investigated associations between automatically generated data and daily patient-reported mood and activity, 12 showed statistical significance. For example, lower patient-reported activity was associated with fewer daily steps, shorter distance traveled, increased incoming and missed calls, and increased screen-time. Conclusion: Smartphone-based self-monitoring is feasible and associated with clinical ratings in UD. Some automatically generated data on behavior may reflect clinical features and psychosocial functioning, but these should be more clearly identified in future studies, potentially combining patient-reported and smartphone-generated data.
Collapse
Affiliation(s)
- Morten Lindbjerg Tønning
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Jakob Eyvind Bardram
- Monsenso A/S, Copenhagen, Denmark.,Copenhagen Center for Health Technology, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|