Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech

ABSTRACT Speech patterns are modulated by the emotional and neurophysiological state of the speaker. There exists a growing body of work that computationally examines this modulation in patients suffering from depression, autism, and post-traumatic stress disorder. However, the majority of the work in this area focuses on the analysis of structured speech collected in controlled environments. Here we expand on the existing literature by examining bipolar disorder (BP). BP is characterized by mood transitions, varying from a healthy euthymic state to states characterized by mania or depression. The speech patterns associated with these mood states provide a unique opportunity to study the modulations characteristic of mood variation. We describe methodology to collect unstructured speech continuously and unobtrusively via the recording of day-to-day cellular phone conversations. Our pilot investigation suggests that manic and depressive mood states can be recognized from this speech data, providing new insight into the feasibility of unobtrusive, unstructured, and continuous speech-based wellness monitoring for individuals with BP

Smartphone-based monitoring of mood disorders : a review

Peer reviewe

Smartphone-based objective monitoring in bipolar disorder:status and considerations

Abstract In 2001, the WHO stated that: “The use of mobile and wireless technologies to support the achievement of health objectives (mHealth) has the potential to transform the face of health service delivery across the globe”. Within mental health, interventions and monitoring systems for depression, anxiety, substance abuse, eating disorder, schizophrenia and bipolar disorder have been developed and used. The present paper presents the status and findings from studies using automatically generated objective smartphone data in the monitoring of bipolar disorder, and addresses considerations on the current literature and methodological as well as clinical aspects to consider in the future studies

The Bipolar Illness Onset study: research protocol for the BIO cohort study

Introduction Bipolar disorder is an often disabling mental illness with a lifetime prevalence of 1%–2%, a high risk of recurrence of manic and depressive episodes, a lifelong elevated risk of suicide and a substantial heritability. The course of illness is frequently characterised by progressive shortening of interepisode intervals with each recurrence and increasing cognitive dysfunction in a subset of individuals with this condition. Clinically, diagnostic boundaries between bipolar disorder and other psychiatric disorders such as unipolar depression are unclear although pharmacological and psychological treatment strategies differ substantially. Patients with bipolar disorder are often misdiagnosed and the mean delay between onset and diagnosis is 5–10 years. Although the risk of relapse of depression and mania is high it is for most patients impossible to predict and consequently prevent upcoming episodes in an individual tailored way. The identification of objective biomarkers can both inform bipolar disorder diagnosis and provide biological targets for the development of new and personalised treatments. Accurate diagnosis of bipolar disorder in its early stages could help prevent the long-term detrimental effects of the illness. The present Bipolar Illness Onset study aims to identify (1) a composite blood-based biomarker, (2) a composite electronic smartphone-based biomarker and (3) a neurocognitive and neuroimaging-based signature for bipolar disorder. Methods and analysis The study will include 300 patients with newly diagnosed/first-episode bipolar disorder, 200 of their healthy siblings or offspring and 100 healthy individuals without a family history of affective disorder. All participants will be followed longitudinally with repeated blood samples and other biological tissues, self-monitored and automatically generated smartphone data, neuropsychological tests and a subset of the cohort with neuroimaging during a 5 to 10-year study period. Ethics and dissemination The study has been approved by the Local Ethical Committee (H-7-2014-007) and the data agency, Capital Region of Copenhagen (RHP-2015-023), and the findings will be widely disseminated at international conferences and meetings including conferences for the International Society for Bipolar Disorders and the World Federation of Societies for Biological Psychiatry and in scientific peer-reviewed papers

Quantifying Coherence In a Transdiagnostic Sample: A Methodological Investigation of Computationally-Derived Coherence Using Ambulatory Assessment

Schizophrenia is a clinical diagnosis assigned to individuals that experience positive (e.g., hallucinations and delusions), negative (e.g., blunted affect), and disorganized (e.g., incoherent speech) symptoms. One particularly disabling symptom is incoherence, which is defined as the meaning-based relationship between ideas. This symptom can drastically affect an individual’s quality of life by affecting areas such as social and occupational functioning. Currently, the mechanism behind this symptom is unknown and requires further study. One way to examine incoherence is to understand its level of expression in other clinical populations. With the advent of computationally-derived natural language processing (NLP), coherence can be quantified with more fine-grained detail at potentially lower levels of expression. Latent Semantic Analysis (LSA) is one promising methodology to examine coherence, but many unanswered technical questions about its application, specifically in clinical populations, still remain. Previous research has shown LSA can be used on speech from individuals with schizophrenia, who display the most extreme form of incoherence. To test LSA’s utility in other clinical populations and to specify parameters for its use, the current study used LSA on a “transdiagnostic” adult sample with varying forms of psychopathology. The current study aimed to extend previous findings in a different clinical sample and examine how coherence changes over time as a function of treatment. Results suggest that more traditional measures of coherence (i.e., clinician-ratings) were moderately correlated with LSA-measured coherence (r = 0.51). The optimal window size to differentiate high from low clinician-rated recalls was the entire recall, rather than eight words, as was previously found. Evidence for LSA-measured coherence’s dynamic nature was found as its reliability fell in the moderate range (a =0.72). This was close to clinician-rated coherence, with its reliability falling in the good range (a =0.79). Lastly, evidence supporting the incremental validity of LSA-measured coherence was not found as it was unable to provide unique variance in a model predicting clinical outcomes. Implications for these findings include additional evidence that newer computerized methodologies are related to traditional clinical measures and may provide insight into the dynamic nature of coherence