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

Stanislaus, Sharleny; Faurholt‐Jepsen, Maria; Vinberg, Maj; Coello, Klara; Kjærstad, Hanne Lie; Melbye, Sigurd; Sletved, Kimie Stefanie Ormstrup; Christensen, Ellen Margrethe; Frost, Mads; Bardram, Jakob E.; Kessing, Lars Vedel

doi:10.1016/j.jad.2020.03.049

Cited by 29 publications

(35 citation statements)

References 43 publications

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“…A full-text review of the 118 remaining articles was conducted. A total of 13 studies were included in the review (Depp et al 2015 ; Wenze et al 2014 , 2016 ; Faurholt-Jepsen et al 2014 , 2015 , 2020 ; Til et al 2020 ; Hidalgo-Mazzei et al 2016 , 2018 ; Beiwinkel et al 2016 ; Tsanas et al 2016 ; Stanislaus et al 2020 ; Schwartz et al 2016 ). The most common reasons for exclusion in the full text article stage were that no adherence characteristics to the studied app were found in the article (n = 39) or that a non-eligible intervention was reported (n = 26).…”

Section: Resultsmentioning

confidence: 99%

“…They were conducted in the United States (Depp et al 2015 ; Wenze et al 2014 , 2016 ; Til et al 2020 ; Schwartz et al 2016 ), Denmark (Faurholt-Jepsen et al 2014 , 2015 , 2020 ; Stanislaus et al 2020 ), England (Tsanas et al 2016 ), Spain (Hidalgo-Mazzei et al 2016 ), Germany (Beiwinkel et al 2016 ) and in spanish language countries (Hidalgo-Mazzei et al 2018 ). Among them, three were randomised controlled trials (Depp et al 2015 ; Faurholt-Jepsen et al 2020 ; Til et al 2020 ) and 10 were non randomized studies (Wenze et al 2014 , 2016 ; Faurholt-Jepsen et al 2015 , 2014 ; Hidalgo-Mazzei et al 2016 , 2018 ; Tsanas et al 2016 ; Stanislaus et al 2020 ; Schwartz et al 2016 ). Among the non randomized studies, four were feasibility studies (Wenze et al 2014 , 2016 ; Hidalgo-Mazzei et al 2016 , 2018 ) and two were pilot studies (Faurholt-Jepsen et al 2014 ; Beiwinkel et al 2016 ).…”

Section: Resultsmentioning

confidence: 99%

“…Among the non randomized studies, four were feasibility studies (Wenze et al 2014 , 2016 ; Hidalgo-Mazzei et al 2016 , 2018 ) and two were pilot studies (Faurholt-Jepsen et al 2014 ; Beiwinkel et al 2016 ). Study lengths ranged from 14 to 800 days, with a mean of 213.1 days (SD: 215.3),just over half of the studies (7/13) lasted 6 months or more (Depp et al 2015 ; Faurholt-Jepsen et al 2015 , 2020 , 2015 ; Hidalgo-Mazzei et al 2018 ; Beiwinkel et al 2016 ; Stanislaus et al 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…The quality assessment of the studies included in this systematic review outlines a wide heterogeneity in studies design, populations and outcomes see Additional File 1 , 2 , 3 , Five studies were rated with poor quality because of the small number of participants enrolled (Wenze et al 2014 , 2016 ; Faurholt-Jepsen et al 2014 ; Beiwinkel et al 2016 ; Schwartz et al 2016 ). Six studies were rated with fair quality (Faurholt-Jepsen et al 2015 ; Til et al 2020 ; Hidalgo-Mazzei et al 2016 , 2018 ; Tsanas et al 2016 ; Stanislaus et al 2020 ) and 2 were rated with good quality (Depp et al 2015 ; Faurholt-Jepsen et al 2020 ).…”

Section: Resultsmentioning

confidence: 99%

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Patients’ adherence to smartphone apps in the management of bipolar disorder: a systematic review

Patoz

Hidalgo‐Mazzei

Pereira

et al. 2021

Int J Bipolar Disord

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Background Despite an increasing number of available mental health apps in the bipolar disorder field, these tools remain scarcely implemented in everyday practice and are quickly discontinued by patients after downloading. The aim of this study is to explore adherence characteristics of bipolar disorder patients to dedicated smartphone interventions in research studies. Methods A systematic review following PRISMA guidelines was conducted. Three databases (EMBASE, PsychInfo and MEDLINE) were searched using the following keywords: "bipolar disorder" or "mood disorder" or “bipolar” combined with “digital” or “mobile” or “phone” or “smartphone” or “mHealth” or “ehealth” or "mobile health" or “app” or “mobile-health”. Results Thirteen articles remained in the review after exclusion criteria were applied. Of the 118 eligible studies, 39 did not provide adherence characteristics. Among the selected papers, study length, sample size and definition of measures of adherence were strongly heterogeneous. Activity rates ranged from 58 to 91.6%. Conclusion The adherence of bipolar patients to apps is understudied. Standardised measures of adherence should be defined and systematically evaluated in future studies dedicated to these tools.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Patients’ adherence to smartphone apps in the management of bipolar disorder: a systematic review

Patoz

Hidalgo‐Mazzei

Pereira

et al. 2021

Int J Bipolar Disord

View full text Add to dashboard Cite

show abstract

“…The present trial used a smartphone-based system developed and tested by our group in multiple studies, [25][26][27][60][61][62] which has been shown usable and useful by patients with bipolar disorder with a high self-assessment adherence (73-97%). 63 Further, we used a single-blind design, and thus, the outcome measures collected by the researchers were blinded to allocation.…”

Section: Advantagesmentioning

confidence: 99%

Reducing the rate of psychiatric re‐admissions in bipolar disorder using smartphones—The RADMIS trial

Faurholt‐Jepsen

Tønning

Fros³

et al. 2021

Acta Psychiatr Scand

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

et al. 2021

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As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies -such as smartphone apps, virtual 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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Mood instability in patients with newly diagnosed bipolar disorder, unaffected relatives, and healthy control individuals measured daily using smartphones

Cited by 29 publications

References 43 publications

Patients’ adherence to smartphone apps in the management of bipolar disorder: a systematic review

Patients’ adherence to smartphone apps in the management of bipolar disorder: a systematic review

Reducing the rate of psychiatric re‐admissions in bipolar disorder using smartphones—The RADMIS trial

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

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