Portable technologies for digital phenotyping of bipolar disorder: A systematic review

Saccaro, Luigi Francesco; Amatori, Giulia; Cappelli, Andrea; Mazziotti, Raffaele; Dell’Osso, Liliana; Rutigliano, Grazia

doi:10.1016/j.jad.2021.08.052

Cited by 22 publications

(18 citation statements)

References 86 publications

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“…To some extent, smartphone-based DP offers a potentially objective characterization of such mental disorders (Carmi et al, 2022;Mendes et al, 2022). Thus, DP may generate new objective markers for expressions of psychiatric disease (Onnela and Rauch, 2016;Saccaro et al, 2021). For example, El-Shaarawi (2021) demonstrated how anxiety impacts the activity levels of individuals, allowing a better understanding of their life experiences by analyzing GPS and accelerometer data extracted from smartphone sensors.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

Hocherman

Mizrachi

Ben‐Gal

2022

Preprint

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Digital Phenotyping (DP) entails exploring digital expressions of human personality using behavioral cues drawn from smartphones’ digital footprints. Most personality-oriented DP studies focus narrowly on the Big5 model. This research aims to broaden this approach, using fifty-four personality constructs rooted in fifteen leading personality theories beyond the Big5. Our sample consisted of 104 respondents from whom smartphone data was collected over 7–10 days. We implemented both deductive (hypothesis-testing) and inductive (machine learning) modelling methods. Results show that fifteen of the sixteen broad personality constructs were successfully predicted from smartphone data (forty-eight sub-personality items of the fifty-nine types and personality traits). The best overall predictive model was Gradient Boosted Trees with communication-related features having the highest predictive weight. DP has the potential to transform the field of personality research and may be applied in areas such as HR analytics, personality-based targeted marketing, individualized homeland security, financial risk assessments, personalized medicine, and more.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

Hocherman

Mizrachi

Ben‐Gal

2022

Preprint

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“…The pooled mean age was 39.56 years. The duration of RCTs ranged from 10 weeks 45 to 9 months 55 with a median of 25 weeks 12–26 …”

Section: Resultsmentioning

confidence: 99%

“…The duration of RCTs ranged from 10 weeks 45 to 9 months 55 with a median of 25 weeks. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] The three observational studies included a total of 278 participants with BD (60% female)-54 with BD-I (specified in two studies 48,50 ). The duration of the studies ranged between 3 48 and 6 months.…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

“…translates into altered emotion, speech, and behavior. 18 For instance, BD patients usually show overactivity, euphoria, racing thoughts, and increased self-esteem-during manic episodes-in contrast to low energy, depressed mood, inability to concentrate, and feelings of worthlessness-during depressive episodes. 19 The foregoing biphasic alterations in motor, social and speech activity can accurately reflect the mood state and their digital quantification using smartphones has been precisely correlated to different mood states.…”

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confidence: 99%

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Smartphone‐based interventions in bipolar disorder: Systematic review and meta‐analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force

et al. 2022

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Bipolar disorders (BD) are chronic and usually recurrent major mood disorders with onset typically during adolescence and early adulthood, a lifetime prevalence estimated at 2.4%, and a course of disease that entails fluctuations between depressive and manic episodes. 1 Higher recurrences of mood episodes in BD have been related to reduced response and poor adherence to treatments, progressive neuroanatomic brain changes, and cognitive dysfunction; which, in turn, is associated with poor clinical course and functional impairment. 2,3 The recurrent, and sometimes progressive and severe nature of BD, along with high rates of morbidity and mortality, 4 translates into an estimated 8 to 12 years shortened life expectancy, 5 reduced quality of life, and substantial burden of disease, making BD one of the main causes of disability among young people. 6,7 This recurrent illness course may be stabilized and overall prognosis improved on appropriately selected prophylactic and maintenance pharmacological treatments, and adjunctive psychosocial interventions, which have proven effective in preventing mood recurrences in BD, 8 especially in the early course of the disease. 9,10 However, people with BD often lack insight about their symptoms and the need of treatment, especially in manic phases, 11,12 and access to evidence-based psychosocial interventions for BD remains limited.In addition, common psychiatric clinical monitoring through routine medical visits mainly consists of periodic cross-sectional symptoms assessments that rely on self-reports, posing several limitations due to recall and confirmation bias and misinterpretations. 13 Hence, more effective strategies for the clinical management of BD are imperative.Advancements in digital technologies might hold potential solutions to the above challenges. Nowadays, smartphones and wearables can capture behavioral, cognitive, and mood information in an objective, continuous, passive, unobtrusive way, 14 which is known as digital phenotyping. 15 In the last 5 years, the use of smartphones and wearables in psychiatric research has become widespread and yielded promising results. 16,17 BD represents the ideal diagnostic framework for digital phenotyping, as its biphasic nature overtly

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“…Despite the vast burden of BD 1 , and notwithstanding significant efforts to identify effective interventions employing recent technologies 6 , we are still lacking comprehensive pathophysiological insight, as well as neurobiological diagnostic or therapeutic markers. As a result, a high proportion of people struggling with BD do not benefit from adapted and currently available therapies 1 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder

Saccaro¹,

Gaviria²,

Ville³

et al. 2022

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Objectives: Bipolar disorder (BD) is a severe, chronic, affective disorder characterized by recurrent oscillations between mood states, psychomotor and cognitive symptoms, which can linger in euthymic states as residual symptoms. Hippocampal alterations may play a key role in the neural processing of BD symptoms. However, its dynamic functional connectivity (dFC) remains unclear. Therefore, the present study explores hippocampal dFC in relation to BD symptoms.Methods: We assessed hippocampus-based dFC co-activation patterns (CAPs) on resting-state fMRI data of 25 euthymic BD patients and 25 age- and sex-matched healthy controls (HC).Results: Bilateral hippocampal dFC with somatomotor networks (SMN) was reduced in BD, compared to HC, while dFC between the left hippocampus and midcingulo-insular salience system (SN) was higher in BD. Interactions analysis between CAPs and clinical scores revealed that dFC between the bilateral hippocampus and the default-like network (DMN) correlated with depression scores in BD. Furthermore, pathological hyperconnectivity between the default mode network (DMN) and SMN and the frontoparietal network (FPN) was modulated by the same depression scores in BD.Conclusions: Overall, we observed alterations of large-scale functional brain networks associated with decreased flexibility in cognitive control, salience detection, and emotion processing in BD. Additionally, the present study provides new insights on the neural architecture underlying a self-centered perspective on the environment in BD patients. dFC markers may improve detection, treatment, and follow-up of BD patients and of disabling residual depressive symptoms in particular.

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Portable technologies for digital phenotyping of bipolar disorder: A systematic review

Cited by 22 publications

References 86 publications

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

Smartphone‐based interventions in bipolar disorder: Systematic review and meta‐analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force

Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder

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