Publications by authors named "Maria Faurholt-Jepsen"

74 Publications

Discriminating between patients with unipolar disorder, bipolar disorder, and healthy control individuals based on voice features collected from naturalistic smartphone calls.

Acta Psychiatr Scand 2021 Dec 19. Epub 2021 Dec 19.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark.

Background: It is of crucial importance to be able to discriminate unipolar disorder (UD) from bipolar disorder (BD), as treatments, as well as course of illness, differ between the two disorders.

Aims: To investigate whether voice features from naturalistic phone calls could discriminate between (1) UD, BD, and healthy control individuals (HC); (2) different states within UD.

Methods: Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 48 patients with UD, 121 patients with BD, and 38 HC were included. A total of 115,483 voice data entries were collected (UD [n = 16,454], BD [n = 78,733], and HC [n = 20,296]). Patients evaluated symptoms daily using a smartphone-based system, making it possible to define illness states within UD and BD. Data were analyzed using random forest algorithms.

Results: Compared with BD, UD was classified with a specificity of 0.84 (SD: 0.07)/AUC of 0.58 (SD: 0.07) and compared with HC with a sensitivity of 0.74 (SD: 0.10)/AUC = 0.74 (SD: 0.06). Compared with BD during euthymia, UD during euthymia was classified with a specificity of 0.79 (SD: 0.05)/AUC = 0.43 (SD: 0.16). Compared with BD during depression, UD during depression was classified with a specificity of 0.81 (SD: 0.09)/AUC = 0.48 (SD: 0.12). Within UD, compared with euthymia, depression was classified with a specificity of 0.70 (SD 0.31)/AUC = 0.65 (SD: 0.11). In all models, the user-dependent models outperformed the user-independent models.

Conclusions: The results from the present study are promising, but as reflected by the low AUCs, does not support that voice features collected during naturalistic phone calls at the current state of art can be implemented in clinical practice as a supplementary and assisting tool. Further studies are needed.
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http://dx.doi.org/10.1111/acps.13391DOI Listing
December 2021

Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states.

Int J Bipolar Disord 2021 Dec 1;9(1):38. Epub 2021 Dec 1.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.

Background: Voice features have been suggested as objective markers of bipolar disorder (BD).

Aims: To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD.

Methods: Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n  = 78.733), UR (n  = 8004), and HC (n  =  20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms.

Results: Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC  = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC  =  0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC  =  0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC  =  0.67 (SD 0.11).

Conclusions: Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.
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http://dx.doi.org/10.1186/s40345-021-00243-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632566PMC
December 2021

Effect of specialised versus generalised outpatient treatment for bipolar disorder: the CAG Bipolar trial - study protocol for a randomised controlled trial.

BMJ Open 2021 10 13;11(10):e048821. Epub 2021 Oct 13.

Psychiatric Center Copenhagen, Copenhagen, Denmark.

Introduction: Despite current available treatment patients with bipolar disorder often experience relapses and decreased overall functioning. Furthermore, patients with bipolar disorder are often not treated medically or psychologically according to guidelines and recommendations. A Clinical Academic Group is a new treatment initiative bringing together clinical services, research, education and training to offer care and treatment that is based on reliable evidence backed up by research. The present Clinical Academic Group for bipolar disorder (the CAG Bipolar) randomised controlled trial (RCT) aims for the first time to investigate whether specialised outpatient treatment in CAG Bipolar versus generalised community-based treatment improves patient outcomes and clinician's satisfaction with care in patients with bipolar disorder.

Methods And Analysis: The CAG Bipolar trial is a pragmatic randomised controlled parallel-group trial undertaken in the Capital Region of Denmark covering a catchment area of 1.85 million people. Patients with bipolar disorder are invited to participate as part of their outpatient treatment in the Mental Health Services. The included patients will be randomised to (1) specialised outpatient treatment in the CAG Bipolar (intervention group) or (2) generalised community-based outpatient treatment (control group). The trial started 13 January 2020 and has currently included more than 600 patients. The outcomes are (1) psychiatric hospitalisations and cumulated number and duration of psychiatric hospitalisations (primary), and (2) self-rated depressive symptoms, self-rated manic symptoms, quality of life, perceived stress, satisfaction with care, use of medication and the clinicians' satisfaction with their care (secondary). A total of 1000 patients with bipolar disorder will be included.

Ethics And Dissemination: The CAG Bipolar RCT is funded by the Capital Region of Denmark and ethical approval has been obtained from the Regional Ethical Committee in The Capital Region of Denmark (H-19067248). Results will be published in peer-reviewed academic journals, presented at scientific meetings and disseminated to patient organisations and media outlets.

Trial Registration Number: NCT04229875.
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http://dx.doi.org/10.1136/bmjopen-2021-048821DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515461PMC
October 2021

Automatically Generated Smartphone Data in Young Patients With Newly Diagnosed Bipolar Disorder and Healthy Controls.

Front Psychiatry 2021 25;12:559954. Epub 2021 Aug 25.

The Copenhagen Affective Disorder Research Center, Rigshospitalet, Copenhagen, Denmark.

Smartphones may facilitate continuous and fine-grained monitoring of behavioral activities 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. 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. (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 ( = 0.015) and the number of outgoing text messages ( = 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; = 0.043) and lower self-monitored mood and activity ('s < 0.001). (3) Smartphone-based self-monitored mood and activity were positively associated with step counts and the number of outgoing calls, respectively ('s < 0.001). 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.
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http://dx.doi.org/10.3389/fpsyt.2021.559954DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423997PMC
August 2021

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality.

World Psychiatry 2021 Oct;20(3):318-335

Division of Psychology and Mental Health, University of Manchester, Manchester, UK.

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies - such as smartphone apps, vir-tual reality, chatbots, and social media - have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions - including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders - as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.
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http://dx.doi.org/10.1002/wps.20883DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429349PMC
October 2021

Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder.

Front Psychiatry 2021 9;12:701360. Epub 2021 Jul 9.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen, Denmark.

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. 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., ) 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. 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 ( < 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 ( = 0.036) and increased number of incoming ( = 0.032), outgoing ( = 0.015) and missed calls ( = 0.007), and longer phone calls ( = 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. 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.
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http://dx.doi.org/10.3389/fpsyt.2021.701360DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336866PMC
July 2021

Prevalences of comorbid anxiety disorder and daily smartphone-based self-reported anxiety in patients with newly diagnosed bipolar disorder.

Evid Based Ment Health 2021 11 3;24(4):137-144. Epub 2021 Jun 3.

Psychiatric Center Copenhagen, Rigshospitalet, Region Hovedstadens Psykiatri, Kobenhavn, Hovedstaden, Denmark.

Background: Around 40% of patients with bipolar disorder (BD) additionally have anxiety disorder. The prevalence of anxiety in patients with newly diagnosed BD and their first-degree relatives (UR) has not been investigated.ObjectiveTo investigate (1) the prevalence of a comorbid anxiety diagnosis in patients with newly diagnosed BD and their UR, (2) sociodemographic and clinical differences between patients with and without a comorbid anxiety diagnosis and (3) the association between smartphone-based patient-reported anxiety and observer-based ratings of anxiety and functioning, respectively.

Methods: We recruited 372 patients with BD and 116 of their UR. Daily smartphone-based data were provided from 125 patients. SCAN was used to assess comorbid anxiety diagnoses.

Findings: In patients with BD, the prevalence of a comorbid anxiety disorder was 11.3% (N=42) and 10.3% and 5.9% in partial and full remission, respectively. In UR, the prevalence was 6.9%. Patients with a comorbid anxiety disorder had longer illness duration (p=0.016) and higher number of affective episodes (p=0.011). Smartphone-based patient-reported anxiety symptoms were associated with ratings of anxiety and impaired functioning (p<0.001).

Limitations: The SCAN interviews to diagnose comorbid anxiety disorder were carried out regardless of the participants' mood state.Clinical implicationsThe lower prevalence of anxiety in newly diagnosed BD than in later stages of BD indicates that anxiety increases with progression of BD. Comorbid anxiety seems associated with poorer clinical outcomes and functioning and smartphones are clinically useful for monitoring anxiety symptoms.

Trial Registration Number: ClinicalTrials.gov Registry (NCT02888262).
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http://dx.doi.org/10.1136/ebmental-2021-300259DOI Listing
November 2021

Apps for mental health care: The raise of digital psychiatry.

Eur Neuropsychopharmacol 2021 Jun 18;47:51-53. Epub 2021 May 18.

Psychiatric Center Copenhagen, Denmark.

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http://dx.doi.org/10.1016/j.euroneuro.2021.04.018DOI Listing
June 2021

The association between self-reported physical activity and objective measures of physical activity in participants with newly diagnosed bipolar disorder, unaffected relatives, and healthy individuals.

Nord J Psychiatry 2021 Apr 14;75(3):186-193. Epub 2020 Oct 14.

The Copenhagen Affective Disorder research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark.

Background: The association between the International Physical Activity Questionnaire Short Form (IPAQ-SF) and objective measures of physical activity has never been evaluated in participants with newly diagnosed bipolar disorder (BD). Our aim was to compare IPAQ-SF to objective measures in participants with newly diagnosed BD, their unaffected first-degree relatives (UR), and healthy control individuals (HC) in groups combined and stratified by group.

Materials And Methods: Physical activity measurements were collected on 20 participants with newly diagnosed BD, 20 of their UR, and 20 HC using individually calibrated combined acceleration and heart rate sensing (Actiheart) for seven days. IPAQ-SF was self-completed at baseline. Correlation between measurements from the two methods was examined with Spearman rank correlation coefficient and agreement levels examined with modified Bland-Altman plots.

Results: Physical activity energy expenditure (PAEE) from IPAQ-SF was weakly but significantly positively correlated with physical activity estimates measured using acceleration and heart rate in groups combined (Actiheart PAEE) (ρ= 0.301,  0.02). Correlations for each group were positive, but only in UR were it statistically significant (BD:  = 0.18, UR:  = 0.007, HC:  = 0.84). Self-reported PAEE and moderate-intensity were markedly underestimated [PAEE in all participants combined: 62.7 (Actiheart) vs. 24.3 kJ/day/kg (IPAQ-SF),  < 0.001], while vigorous-intensity was overestimated. Bland-Altman plots indicated proportional bias.

Conclusion: These results suggest that the use of the IPAQ-SF to monitor levels of physical activity in participants with newly diagnosed BD, in a psychiatric clinical setting, should be used with caution and consideration.
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http://dx.doi.org/10.1080/08039488.2020.1831063DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610645PMC
April 2021

Associations between the cortisol awakening response and patient-evaluated stress and mood instability in patients with bipolar disorder: an exploratory study.

Int J Bipolar Disord 2021 Mar 1;9(1). Epub 2021 Mar 1.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Blegdamsvej 9, Rigshospitalet, 2100, Copenhagen, Denmark.

Objective: The Cortisol Awakening Response (CAR) measured as the transient increase in cortisol levels following morning awakening appears to be a distinct feature of the HPA axis. Patients with bipolar disorder (BD) experience daily stress, mood instability (MI) and studies have shown disrupted HPA-axis dynamics.

Aims: to evaluate (1) patient-evaluated stress against the CAR, (2) associations between the CAR and mood symptoms, and (3) the effect of smartphone-based treatment on the CAR.

Methods: Patients with BD (n = 67) were randomized to the use of daily smartphone-based monitoring (the intervention group) or to the control group for six months. Clinically rated symptoms according to the Hamilton Depression Rating Scale 17-items (HDRS), the Young Mania Rating Scale (YMRS), patient-evaluated perceived stress using Cohen's Perceived Stress Scale (PSS) and salivary awakening cortisol samples used for measuring the CAR were collected at baseline, after three and six months. In the intervention group, smartphone-based data on stress and MI were rated daily during the entire study period.

Results: Smartphone-based patient-evaluated stress (B: 134.14, 95% CI: 1.35; 266.92, p = 0.048) and MI (B: 430.23, 95% CI: 52.41; 808.04, p = 0.026) mapped onto increased CAR. No statistically significant associations between the CAR and patient-evaluated PSS or the HDRS and the YMRS, respectively were found. There was no statistically significant effect of smartphone-based treatment on the CAR.

Conclusion: Our data, of preliminary character, found smartphone-based patient-evaluations of stress and mood instability as read outs that reflect CAR dynamics. Smartphone-supported clinical care did not in itself appear to disturb CAR dynamics.
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http://dx.doi.org/10.1186/s40345-020-00214-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7917033PMC
March 2021

Alterations in the Kynurenine Pathway of Tryptophan Metabolism Are Associated With Depression in People Living With HIV.

J Acquir Immune Defic Syndr 2021 06;87(2):e177-e181

Department of Infectious Diseases, University of Copenhagen, Copenhagen, Denmark.

Background: People living with HIV have increased risk of depression compared with uninfected controls. The determinants of this association are unclear. Alterations in kynurenine (Kyn) metabolism have been associated with depression in uninfected individuals, but whether they are involved in the development of depression in the context of HIV infection is unknown.

Methods: A total of 909 people living with HIV were recruited from the Copenhagen Comorbidity in HIV infection study. Information regarding demographics and depression was obtained from questionnaires. HIV-related variables and use of antidepressant medication were collected from patient records. Logistic regression models before and after adjustment for confounders were used to test our hypotheses.

Results: The prevalence of depression was 11%. Among traditional risk factors, only being unmarried was associated with greater odds of depression. Higher levels of quinolinic-to-kynurenic acid ratio (P = 0.018) and higher concentrations of quinolinic acid (P = 0.048) were found in individuals with depression than in those without. After adjusting for confounders, high levels of quinolinic-to-kynurenic acid ratio and high concentrations of quinolinic acid remained associated with depression [adjusted odds ratio 1.61 (1.01; 2.59) and adjusted odds ratio 1.68 (1.02; 2.77), respectively].

Conclusions: The results from this study suggest that alterations in the kynurenine pathway of tryptophan metabolism are associated with the presence of depression in the context of HIV infection.
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http://dx.doi.org/10.1097/QAI.0000000000002664DOI Listing
June 2021

The effect of smartphone-based monitoring and treatment on the rate and duration of psychiatric readmission in patients with unipolar depressive disorder: The RADMIS randomized controlled trial.

J Affect Disord 2021 03 30;282:354-363. Epub 2020 Dec 30.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Blegdamsvej 9, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.

Background: Patients with unipolar depressive disorder are frequently hospitalized, and the period following discharge is a high-risk-period. Smartphone-based treatments are receiving increasing attention among researchers, clinicians, and patients. We aimed to investigate whether a smartphone-based monitoring and treatment system reduces the rate and duration of readmissions, more than standard treatment, in patients with unipolar depressive disorder following hospitalization.

Methods: We conducted a pragmatic, investigator-blinded, randomized controlled trial. The intervention group received a smartphone-based monitoring and treatment system in addition to standard treatment. The system allowed patients to self-monitor symptoms and access psycho-educative information and cognitive modules. The patients were allocated a study-nurse who, based on the monitoring data, guided and supported them. The control group received standard treatment. The trial lasted six months, with outcome assessments at 0, 3, and 6 months.

Results: We included 120 patients with unipolar depressive disorder (ICD-10). Intention-to-treat analyses showed no statistically significant differences in time to readmission (Log-Rank p=0.9) or duration of readmissions (B=-16.41,95%CI:-47.32;25.5,p=0.3) (Primary outcomes). There were no differences in clinically rated depressive symptoms (p=0.6) or functioning (p=0.1) (secondary outcomes). The intervention group had higher levels of recovery (B=7,29, 95%CI:0.82;13,75,p=0.028) and a tendency towards higher quality of life (p=0.07), wellbeing (p=0,09) satisfaction with treatment (p=0.05) and behavioral activation (p=0.08) compared with the control group (tertiary outcomes).

Limitations: Patients and study-nurses were unblinded to allocation.

Conclusions: We found no effect of the intervention on primary or secondary outcomes. In tertiary outcomes, patients in the intervention group reported higher levels of recovery compared to the control group.
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http://dx.doi.org/10.1016/j.jad.2020.12.141DOI Listing
March 2021

Reducing the rate of psychiatric re-admissions in bipolar disorder using smartphones-The RADMIS trial.

Acta Psychiatr Scand 2021 05 26;143(5):453-465. Epub 2021 Jan 26.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark.

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.
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http://dx.doi.org/10.1111/acps.13274DOI Listing
May 2021

Electronic monitoring and mental health.

Nord J Psychiatry 2021 04 26;75(3):159. Epub 2020 Nov 26.

Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark.

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http://dx.doi.org/10.1080/08039488.2020.1852311DOI Listing
April 2021

Smartphone-based activity measurements in patients with newly diagnosed bipolar disorder, unaffected relatives and control individuals.

Int J Bipolar Disord 2020 Nov 2;8(1):32. Epub 2020 Nov 2.

The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.

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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http://dx.doi.org/10.1186/s40345-020-00195-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604277PMC
November 2020

Patient-evaluated cognitive function measured with smartphones and the association with objective cognitive function, perceived stress, quality of life and function capacity in patients with bipolar disorder.

Int J Bipolar Disord 2020 Oct 30;8(1):31. Epub 2020 Oct 30.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.

Background: Cognitive impairments in patients with bipolar disorder (BD) have been associated with reduced functioning.

Aims: To investigate the association between (1) patient-evaluated cognitive function measured daily using smartphones and stress, quality of life and functioning, respectively, and (2) patient-evaluated cognitive function and objectively measured cognitive function with neuropsychological tests.

Methods: Data from two randomized controlled trials were combined. Patients with BD (N = 117) and healthy controls (HC) (N = 40) evaluated their cognitive function daily for six to nine months using a smartphone. Patients completed the objective cognition screening tool, the Screen for Cognitive Impairment in Psychiatry and were rated with the Functional Assessment Short Test. Raters were blinded to smartphone data. Participants completed the Perceived Stress Scale and the WHO Quality of Life questionnaires. Data was collected at multiple time points per participant. p-values below 0.0023 were considered statistically significant.

Results: Patient-evaluated cognitive function was statistically significant associated with perceived stress, quality of life and functioning, respectively (all p-values < 0.0001). There was no association between patient-evaluated cognitive function and objectively measured cognitive function (B:0.0009, 95% CI 0.0017; 0.016, p = 0.015). Patients exhibited cognitive impairments in subjectively evaluated cognitive function in comparison with HC despite being in full or partly remission (B: - 0.36, 95% CI - 0.039; - 0.032, p < 0.0001).

Conclusion: The present association between patient-evaluated cognitive function on smartphones and perceived stress, quality of life and functional capacity suggests that smartphones can provide a valid tool to assess disability in remitted BD. Smartphone-based ratings of cognition could not provide insights into objective cognitive function.
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http://dx.doi.org/10.1186/s40345-020-00205-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596112PMC
October 2020

Smartphone-Based Self-Monitoring, Treatment, and Automatically Generated Data in Children, Adolescents, and Young Adults With Psychiatric Disorders: Systematic Review.

JMIR Ment Health 2020 Oct 29;7(10):e17453. Epub 2020 Oct 29.

The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Centre Copenhagen, Rigshospitalet, København Ø, Denmark.

Background: Psychiatric disorders often have an onset at an early age, and early identification and intervention help improve prognosis. A fine-grained, unobtrusive, and effective way to monitor symptoms and level of function could help distinguish severe psychiatric health problems from normal behavior and potentially lead to a more efficient use of clinical resources in the current health care system. The use of smartphones to monitor and treat children, adolescents, and young adults with psychiatric disorders has been widely investigated. However, no systematic review concerning smartphone-based monitoring and treatment in this population has been published.

Objective: This systematic review aims at describing the following 4 features of the eligible studies: (1) monitoring features such as self-assessment and automatically generated data, (2) treatment delivered by the app, (3) adherence to self-monitoring, and (4) results of the individual studies.

Methods: We conducted a systematic literature search of the PubMed, Embase, and PsycInfo databases. We searched for studies that (1) included a smartphone app to collect self-monitoring data, a smartphone app to collect automatically generated smartphone-based data, or a smartphone-based system for treatment; (2) had participants who were diagnosed with psychiatric disorders or received treatment for a psychiatric disorder, which was verified by an external clinician; (3) had participants who were younger than 25 years; and (4) were published in a peer-reviewed journal. This systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The risk of bias in each individual study was systematically assessed.

Results: A total of 2546 unique studies were identified through literature search; 15 of these fulfilled the criteria for inclusion. These studies covered 8 different diagnostic groups: psychosis, eating disorders, depression, autism, self-harm, anxiety, substance abuse, and suicidal behavior. Smartphone-based self-monitoring was used in all but 1 study, and 11 of them reported on the participants' adherence to self-monitoring. Most studies were feasibility/pilot studies, and all studies on feasibility reported positive attitudes toward the use of smartphones for self-monitoring. In 2 studies, automatically generated data were collected. Three studies were randomized controlled trials investigating the effectiveness of smartphone-based monitoring and treatment, with 2 of these showing a positive treatment effect. In 2 randomized controlled trials, the researchers were blinded for randomization, but the participants were not blinded in any of the studies. All studies were determined to be at high risk of bias in several areas.

Conclusions: Smartphones hold great potential as a modern, widely available technology platform to help diagnose, monitor, and treat psychiatric disorders in children and adolescents. However, a higher level of homogeneity and rigor among studies regarding their methodology and reporting of adherence would facilitate future reviews and meta-analyses.
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http://dx.doi.org/10.2196/17453DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661256PMC
October 2020

Daily mobility patterns in patients with bipolar disorder and healthy individuals.

J Affect Disord 2021 01 25;278:413-422. Epub 2020 Sep 25.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Blegdamsvej 9, DK- 2100 Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen; Monsenso Aps, Langelinie Alle 47, Copenhagen, Denmark.

Background: Alterations in energy and activity in bipolar disorder (BD) differ between affective states and compared with healthy control individuals (HC). Measurements of activity could discriminate between BD and HC and in the monitoring of affective states within BD. The aims were to investigate differences in 1) passively collected smartphone-based location data (location data) between BD and HC, and 2) location data in BD between affective states.

Methods: Daily, patients with BD and HC completed smartphone-based self-assessments of mood for up to nine months. Location data reflecting mobility patterns, routine and location entropy was collected daily. A total of 46 patients with BD and 31 HC providing daily data was included.

Results: A total of 4,859 observations of smartphone-based self-assessments of mood and mobility patterns were available from patients with BD and 1,747 observations from HC. Patients with BD had lower location entropy compared with HC (B= -0.14, 95% CI= -0.24; -0.034, p=0.009). Patients with BD during a depressive state were less mobile compared with a euthymic state. Patients with BD during an affective state had lower location entropy compared with a euthymic state (p<0.0001). The AUC of combined location data was rather high in classifying patients with BD compared with HC (AUC: 0.83).

Limitations: Individuals willing to use smartphones for daily self-monitoring may represent a more motivated group.

Conclusion: Alterations in location data reflecting mobility patterns may be a promising measure of illness and illness activity in patients with BD and may be used to monitor the effects of treatments.
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http://dx.doi.org/10.1016/j.jad.2020.09.087DOI Listing
January 2021

Daily self-reported and automatically generated smartphone-based sleep measurements in patients with newly diagnosed bipolar disorder, unaffected first-degree relatives and healthy control individuals.

Evid Based Ment Health 2020 11 24;23(4):146-153. Epub 2020 Aug 24.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark.

Objectives: (1) To investigate daily smartphone-based self-reported and automatically generated sleep measurements, respectively, against validated rating scales; (2) to investigate if daily smartphone-based self-reported sleep measurements reflected automatically generated sleep measurements and (3) to investigate the differences in smartphone-based sleep measurements between patients with bipolar disorder (BD), unaffected first-degree relatives (UR) and healthy control individuals (HC).

Methods: We included 203 patients with BD, 54 UR and 109 HC in this study. To investigate whether smartphone-based sleep calculated from self-reported bedtime, wake-up time and screen on/off time reflected validated rating scales, we used the Pittsburgh Sleep Quality Index (PSQI) and sleep items on the Hamilton Depression Rating Scale 17-item (HAMD-17) and the Young Mania Rating Scale (YMRS).

Findings: (1) Self-reported smartphone-based sleep was associated with the PSQI and sleep items of the HAMD and the YMRS. (2) Automatically generated smartphone-based sleep measurements were associated with daily self-reports of hours slept between 12:00 midnight and 06:00. (3) According to smartphone-based sleep, patients with BD slept less between 12:00 midnight and 06:00, with more interruption and daily variability compared with HC. However, differences in automatically generated smartphone-based sleep were not statistically significant.

Conclusion: Smartphone-based data may represent measurements of sleep patterns that discriminate between patients with BD and HC and potentially between UR and HC.

Clinical Implication: Detecting sleep disturbances and daily variability in sleep duration using smartphones may be helpful for both patients and clinicians for monitoring illness activity.

Trial Registration Number: clinicaltrials.gov (NCT02888262).
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http://dx.doi.org/10.1136/ebmental-2020-300148DOI Listing
November 2020

Mood, activity, and sleep measured via daily smartphone-based self-monitoring in young patients with newly diagnosed bipolar disorder, their unaffected relatives and healthy control individuals.

Eur Child Adolesc Psychiatry 2021 08 2;30(8):1209-1221. Epub 2020 Aug 2.

The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, 2100, Copenhagen, Denmark.

Diagnostic evaluations and early interventions of patients with bipolar disorder (BD) rely on clinical evaluations. Smartphones have been proposed to facilitate continuous and fine-grained self-monitoring of symptoms. The present study aimed to (1) validate daily smartphone-based self-monitored mood, activity, and sleep, against validated questionnaires and clinical ratings in young patients with newly diagnosed BD, unaffected relatives (UR), and healthy controls persons (HC); (2) investigate differences in daily smartphone-based self-monitored mood, activity, and sleep in young patients with newly diagnosed BD, UR, and HC; (3) investigate associations between self-monitored mood and self-monitored activity and sleep, respectively, in young patients with newly diagnosed BD. 105 young patients with newly diagnosed BD, 24 UR and 77 HC self-monitored 2 to 1077 days (median [IQR] = 65 [17.5-112.5]). There was a statistically significantly negative association between the mood item on Hamilton Depression Rating Scale (HAMD) and smartphone-based self-monitored mood (B = - 0.76, 95% CI - 0.91; - 0.63, p < 0.001) and between psychomotor item on HAMD and self-monitored activity (B = - 0.44, 95% CI - 0.63; - 0.25, p < 0.001). Smartphone-based self-monitored mood differed between young patients with newly diagnosed BD and HC (p < 0.001), and between UR and HC (p = 0.008) and was positively associated with smartphone-based self-reported activity (p < 0.001) and sleep duration (p < 0.001). The findings support the potential of smartphone-based self-monitoring of mood and activity as part of a biomarker for young patients with BD and UR. Smartphone-based self-monitored mood is better to discriminate between young patients with newly diagnosed BD and HC, and between UR and HC, compared with smartphone-based activity and sleep.Trial registration clinicaltrials.gov NCT0288826.
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http://dx.doi.org/10.1007/s00787-020-01611-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8310852PMC
August 2021

Daily estimates of clinical severity of symptoms in bipolar disorder from smartphone-based self-assessments.

Transl Psychiatry 2020 06 18;10(1):194. Epub 2020 Jun 18.

Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.

Currently, the golden standard for assessing the severity of depressive and manic symptoms in patients with bipolar disorder (BD) is clinical evaluations using validated rating scales such as the Hamilton Depression Rating Scale 17-items (HDRS) and the Young Mania Rating Scale (YMRS). Frequent automatic estimation of symptom severity could potentially help support monitoring of illness activity and allow for early treatment intervention between outpatient visits. The present study aimed (1) to assess the feasibility of producing daily estimates of clinical rating scores based on smartphone-based self-assessments of symptoms collected from a group of patients with BD; (2) to demonstrate how these estimates can be utilized to compute individual daily risk of relapse scores. Based on a total of 280 clinical ratings collected from 84 patients with BD along with daily smartphone-based self-assessments, we applied a hierarchical Bayesian modelling approach capable of providing individual estimates while learning characteristics of the patient population. The proposed method was compared to common baseline methods. The model concerning depression severity achieved a mean predicted R of 0.57 (SD = 0.10) and RMSE of 3.85 (SD = 0.47) on the HDRS, while the model concerning mania severity achieved a mean predicted R of 0.16 (SD = 0.25) and RMSE of 3.68 (SD = 0.54) on the YMRS. In both cases, smartphone-based self-reported mood was the most important predictor variable. The present study shows that daily smartphone-based self-assessments can be utilized to automatically estimate clinical ratings of severity of depression and mania in patients with BD and assist in identifying individuals with high risk of relapse.
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http://dx.doi.org/10.1038/s41398-020-00867-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303106PMC
June 2020

Mood instability in patients with newly diagnosed bipolar disorder, unaffected relatives, and healthy control individuals measured daily using smartphones.

J Affect Disord 2020 06 27;271:336-344. Epub 2020 Mar 27.

Faculty of Health and Medical Sciences, The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, University of Copenhagen, 6243, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark.

Objectives: To investigate whether mood instability (MI) qualify as a trait marker for bipolar disorder (BD) we investigated: 1) differences in smartphone-based self-reported MI between three groups: patients with newly diagnosed BD, unaffected first-degree relatives (UR), and healthy control individuals (HC); 2) the correlation between MI and functioning, stress, and duration of illness, respectively; and 3) the validity of smartphone-based self-evaluated mood ratings as compared to observer-based ratings of depressed and manic mood.

Methods: 203 patients with newly diagnosed BD, 54 UR and 109 HC were included as part of the longitudinal Bipolar Illness Onset study. Participants completed daily smartphone-based mood ratings for a period of up to two years and were clinically assessed with ratings of depression, mania and functioning.

Results: Mood instability scores were statistically significantly higher in patients with BD compared with HC (mean=1.18, 95%CI: 1.12;1.24 vs 1.05, 95%CI: 0.98;1.13, p = 0.007) and did not differ between patients with BD and UR (mean=1.17, 95%CI: 1.07;1.28, p = 0.91). For patients, increased MI scores correlated positively with impaired functioning (p<0.001), increased stress level (p<0.001) and increasing number of prior mood episodes (p<0.001). Smartphone-based mood ratings correlated with ratings of mood according to sub-item 1 on the Hamilton Depression Rating Scale 17-items and the Young Mania Rating Scale, respectively (p´s<0.001).

Limitation: The study had a smaller number of UR than planned.

Conclusion: Mood instability is increased in patients with newly diagnosed BD and unaffected relatives and associated with decreased functioning. The findings highlight MI as a potential trait marker for BD.
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http://dx.doi.org/10.1016/j.jad.2020.03.049DOI Listing
June 2020

Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach.

JMIR Mhealth Uhealth 2020 04 1;8(4):e15028. Epub 2020 Apr 1.

Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.

Background: Bipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood for one or more days.

Objective: This study aimed to examine the feasibility of forecasting daily subjective mood scores based on daily self-assessments collected from patients with bipolar disorder via a smartphone-based system in a randomized clinical trial.

Methods: We applied hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood for up to seven days based on 15,975 smartphone self-assessments from 84 patients with bipolar disorder participating in a randomized clinical trial. We reported the results of two time-series cross-validation 1-day forecast experiments corresponding to two different real-world scenarios and compared the outcomes with commonly used baseline methods. We then applied the best model to evaluate a 7-day forecast.

Results: The best performing model used a history of 4 days of self-assessment to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a 1-day forecast time-series cross-validation experiment and achieved the predicted metrics, R=0.51 and root mean squared error of 0.32, for mood scores on a scale of -3 to 3. When increasing the forecast horizon, forecast errors also increased and the forecast regressed toward the mean of data distribution.

Conclusions: Our proposed method can forecast mood for several days with low error compared with common baseline methods. The applicability of a mood forecast in the clinical treatment of bipolar disorder has also been discussed.
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http://dx.doi.org/10.2196/15028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367518PMC
April 2020

Differences in psychomotor activity and heart rate variability in patients with newly diagnosed bipolar disorder, unaffected relatives, and healthy individuals.

J Affect Disord 2020 04 23;266:30-36. Epub 2020 Jan 23.

The Copenhagen Affective Disorder research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, and Faculty of Health and Medical Sciences, University of Copenhagen, Denmark. Electronic address:

Background: Heart rate variability (HRV) and psychomotor activity have been found reduced in bipolar disorder (BD) but has never been investigated in newly diagnosed BD and unaffected relatives. The present study aimed to compare HRV and psychomotor activity between newly diagnosed patients with BD, their unaffected first-degree relatives (UR), and healthy control individuals (HC).

Methods: 20 newly diagnosed patients with BD, 20 of their UR, and 20 age- and sex-matched HC were included. Measurements of HRV for five minutes and heart rate and acceleration for seven days were conducted. Activity energy expenditure (AEE) was derived from the latter. Linear mixed effect regression models were conducted to compare the three groups.

Results: HRV did not differ in any measure between the three groups of participants. Similarly, AEE (kJ/day/kg) did not differ between the three groups in neither daily means (BD: 63.6, UR: 64.1, HC: 62.1) nor when divided into quarter-daily intervals.

Limitations: The relatively small size of the study may affect the validity of the results.

Conclusion: Patients with newly diagnosed BD and UR do not present with decreased HRV or AEE. These results contrast prior findings from BD patients with more advanced stages of the disorder, suggesting that these outcomes progress with illness duration.
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http://dx.doi.org/10.1016/j.jad.2020.01.110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116568PMC
April 2020

Hypomania/Mania by DSM-5 definition based on daily smartphone-based patient-reported assessments.

J Affect Disord 2020 03 7;264:272-278. Epub 2020 Jan 7.

Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Blegdamsvej 9, DK- 2100 Copenhagen, Denmark.

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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http://dx.doi.org/10.1016/j.jad.2020.01.014DOI Listing
March 2020

Validity and characteristics of patient-evaluated adherence to medication via smartphones in patients with bipolar disorder: exploratory reanalyses on pooled data from the MONARCA I and II trials.

Evid Based Ment Health 2020 Feb;23(1):2-7

Psychiatric Center Copenhagen, Rigshospitalet, Department O. Copenhagen, Copenhagen, Denmark.

Background: Non-adherence to medication is associated with increased risk of relapse in patients with bipolar disorder (BD).

Objectives: To (1) validate patient-evaluated adherence to medication measured via smartphones against validated adherence questionnaire; and (2) investigate characteristics for adherence to medication measured via smartphones.

Methods: Patients with BD (n=117) evaluated adherence to medication daily for 6-9 months via smartphones. The Medication Adherence Rating Scale (MARS) and the Rogers' Empowerment questionnaires were filled out. The 17-item Hamilton Depression Rating Scale, the Young Mania Rating Scale and the Functional Assessment Short Test were clinically rated. Data were collected multiple times per patient. The present study represents exploratory pooled reanalyses of data collected as part of two randomised controlled trials.

Findings: During the study 90.50% of the days were evaluated as 'medication taken', 6.91% as 'medication taken with changes' and 2.59% as 'medication not taken'. Adherence to medication measured via smartphones was valid compared with the MARS (B: -0.049, 95% CI -0.095 to -0.003, p=0.033). Younger age and longer illness duration were significant predictors for non-adherence to medication (model concerning age: B: 0.0039, 95% CI 0.00019 to 0.0076, p=0.040). Decreased affective symptoms measured with smartphone-based patient-reported mood and clinical ratings as well as decreased empowerment were associated with non-adherence.

Conclusions: Smartphone-based monitoring of adherence to medication was valid compared with validated adherence questionnaire. Younger age and longer illness duration were predictors for non-adherence. Increased empowerment was associated with adherence.

Clinical Implications: Using smartphones for empowerment of adherence using patient-reported measures may be helpful in everyday clinical settings.

Trial Registration Number: NCT01446406 and NCT02221336.
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http://dx.doi.org/10.1136/ebmental-2019-300106DOI Listing
February 2020

Using big data to advance mental health research.

Evid Based Ment Health 2020 02 20;23(1). Epub 2020 Jan 20.

Department of Psychiatry, School of Medicine, Stanford University, Stanford, California, USA.

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http://dx.doi.org/10.1136/ebmental-2020-300143DOI Listing
February 2020

Smartphones in mental health: a critical review of background issues, current status and future concerns.

Int J Bipolar Disord 2020 Jan 10;8(1). Epub 2020 Jan 10.

Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA.

There has been increasing interest in the use of smartphone applications (apps) and other consumer technology in mental health care for a number of years. However, the vision of data from apps seamlessly returned to, and integrated in, the electronic medical record (EMR) to assist both psychiatrists and patients has not been widely achieved, due in part to complex issues involved in the use of smartphone and other consumer technology in psychiatry. These issues include consumer technology usage, clinical utility, commercialization, and evolving consumer technology. Technological, legal and commercial issues, as well as medical issues, will determine the role of consumer technology in psychiatry. Recommendations for a more productive direction for the use of consumer technology in psychiatry are provided.
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http://dx.doi.org/10.1186/s40345-019-0164-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952480PMC
January 2020

Automatically generated smartphone data and subjective stress in healthy individuals - a pilot study.

Nord J Psychiatry 2020 May 27;74(4):293-300. Epub 2019 Dec 27.

The Copenhagen Affective Disorder research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen, Denmark.

Most people will also experience symptoms of stress at some point. Smartphone use has increased during the last decade and may be a new way of monitoring stress. Thus, it is of interest to investigate whether automatically generated smartphone data reflecting smartphone use is associated with subjective stress in healthy individuals. to investigate whether automatically generated smartphone data (e.g. the number of outgoing sms/day) was associated with (1) smartphone-based subjectively reported perceived stress, (2) perceived stress (Cohen's Perceived Stress Scale (PSS)) (3) functioning (Functioning Assessment Short Test (FAST)) and (4) non-clinical depressive symptoms (Hamilton Depression Rating Scale 17-items (HDRS)). A cohort of 40 healthy blood donors used an app for daily self-assessment of stress for 16 weeks. At baseline participants filled out the PSS and were clinically evaluated using the FAST and the HDRS. The PSS assessment was repeated at the end of the study. Associations were estimated with linear mixed effect regression and linear regression models. There were no statistically significant associations between automatically generated smartphone data and perceived stress, functioning or severity of depressive symptoms, respectively (e.g. the number of outgoing text messages/day and self-assessed stress ( 0.30, : -0.40; 0.99,  .40). Participants presented with low levels of stress during the study. Automatically generated smartphone data was not able to catch potential subjective stress among healthy individuals in the present study. Due to the small sample and low levels of stress the results should be interpreted with caution.
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http://dx.doi.org/10.1080/08039488.2019.1705904DOI Listing
May 2020

Consideration of confounding was suboptimal in the reporting of observational studies in psychiatry: a meta-epidemiological study.

J Clin Epidemiol 2020 03 3;119:75-84. Epub 2019 Dec 3.

Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University of Basel, Basel, Switzerland.

Objectives: When reporting observational studies, authors should explicitly discuss the potential for confounding and other biases, but it is unclear to what extent this is carried out within the psychiatric field.

Study Design And Setting: We reviewed a random sample of 120 articles in the five psychiatric specialty journals with the highest 5-year impact factor in 2015-2018. We evaluated how confounding and bias was considered in the reporting of the discussion and abstract and assessed the relationship with yearly citations.

Results: The term "confounding" was explicitly mentioned in the abstract or discussion in 66 articles (55.0%; 95% confidence interval (CI): 46.1-63.6) and the term "bias" in 68 articles (56.7%; 95% CI: 47.7-65.2). The authors of 25 articles (20.8%; 95% CI: 14.5-28.9) acknowledged unadjusted confounders. With one exception (0.8%, 95% CI: 0.0-4.6), authors never expressed any caution, limitation, or uncertainty in relation to confounding or other bias in their conclusions or in the abstract. Articles acknowledging nonadjusted confounders were not less frequently cited than articles that did not (median 7.9 vs. 5.6 citations per year, P = 0.03).

Conclusion: Confounding is overall inadequately addressed in the reporting and bias is often ignored in the interpretation of high-impact observational research in psychiatry.
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http://dx.doi.org/10.1016/j.jclinepi.2019.12.002DOI Listing
March 2020
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