Niclas Palmius

SleepAp: An Automated Obstructive Sleep Apnoea Screening Application for Smartphones

Obstructive Sleep Apnoea (OSA) is a sleep disorder with long term consequences. It is often diagnosed with an overnight sleep study or polysomnogram. Monitoring can be costly with long wait times for diagnosis. In this paper we describe a novel OSA screening framework and prototype phone application (app). A database of 856 patients that underwent at-home polysomnography was collected. Features were derived from audio, actigraphy, photoplethysmography (PPG) and demographics, and used as the inputs of a support vector machine (SVM) classifier. The SVM was trained on 735 patients (368 non-OSA and 567 OSA) and tested on 121 patients (44-77 split). Classification on the test set had an accuracy of up to 92.3%. The signal processing and machine learning algorithms were ported to Java and integrated into the phone app. The app records the audio, actigraphy and PPG signals, implements the clinically validated STOP-BANG questionnaire, derives features from the signals, and finally classifies the patient as needing treatment or not using the trained SVM. The resulting software could provide a new, easy-to-use, low-cost and widely available modality for OSA screening.

Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder.

Background Traditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose. Methods We introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customised smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorders (BPD) and 51 Healthy control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools. Results MZ correlated well (|R|>0.5,p<0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (|R|>0.3,p<0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires. Limitations All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings. Conclusions MZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.

Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder

Variable mood is an important feature of psychiatric disorders. However, its measurement and relationship to objective measureas of physiology and behaviour have rarely been studied. Smart-phones facilitate continuous personalized prospective monitoring of subjective experience and behavioural and physiological signals can be measured through wearable devices. Such passive data streams allow novel estimates of diurnal variability. Phase and amplitude of diurnal rhythms were quantified using new techniques that fitted sinusoids to heart rate (HR) and acceleration signals. We investigated mood and diurnal variation for four days in 20 outpatients with bipolar disorder (BD), 14 with borderline personality disorder (BPD) and 20 healthy controls (HC) using a smart-phone app, portable electrocardiogram (ECG), and actigraphy. Variability in negative affect, positive affect, and irritability was elevated in patient groups compared with HC. The study demonstrated convincing associations between variability in subjective mood and objective variability in diurnal physiology. For BPD there was a pattern of positive correlations between mood variability and variation in activity, sleep and HR. The findings suggest BPD is linked more than currently believed with a disorder of diurnal rhythm; in both BPD and BD reducing the variability of sleep phase may be a way to reduce variability of subjective mood.

Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment

Background We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. Objective The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. Methods We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. Results We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. Conclusions This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice.

Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder.

It has long been proposed that diurnal rhythms are disturbed in bipolar disorder (BD). Such changes are obvious in episodes of mania or depression. However, detailed study of patients between episodes has been rare and comparison with other psychiatric disorders rarer still. Our hypothesis was that evidence for desynchronization of diurnal rhythms would be evident in BD and that we could test the specificity of any effect by studying borderline personality disorder (BPD). Individuals with BD (n = 36), BPD (n = 22) and healthy volunteers (HC, n = 25) wore a portable heart rate and actigraphy device and used a smart-phone to record self-assessed mood scores 10 times per day for 1 week. Average diurnal patterns of heart rate (HR), activity and sleep were compared within and across groups. Desynchronization in the phase of diurnal rhythms of HR compared with activity were found in BPD (+3 h) and BD (+1 h), but not in HC. A clear diurnal pattern for positive mood was found in all subject groups. The coherence between negative and irritable mood and HR showed a four-cycle per day component in BD and BPD, which was not present in HC. The findings highlight marked de-synchronisation of measured diurnal function in both BD but particularly BPD and suggest an increased association with negative and irritable mood at ultradian frequencies. These findings enhance our understanding of the underlying physiological changes associated with BPD and BD, and suggest objective markers for monitoring and potential treatment targets. Improved mood stabilisation is a translational objective for management of both patient groups.

Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study

Background Objective behavioral markers of mental illness, often recorded through smartphones or wearable devices, have the potential to transform how mental health services are delivered and to help users monitor their own health. Linking objective markers to illness is commonly performed using population-level models, which assume that everyone is the same. The reality is that there are large levels of natural interindividual variability, both in terms of response to illness and in usual behavioral patterns, as well as intraindividual variability that these models do not consider. Objective The objective of this study was to demonstrate the utility of splitting the population into subsets of individuals that exhibit similar relationships between their objective markers and their mental states. Using these subsets, “group-personalized” models can be built for individuals based on other individuals to whom they are most similar. Methods We collected geolocation data from 59 participants who were part of the Automated Monitoring of Symptom Severity study at the University of Oxford. This was an observational data collection study. Participants were diagnosed with bipolar disorder (n=20); borderline personality disorder (n=17); or were healthy controls (n=22). Geolocation data were collected using a custom Android app installed on participants’ smartphones, and participants weekly reported their symptoms of depression using the 16-item quick inventory of depressive symptomatology questionnaire. Population-level models were built to estimate levels of depression using features derived from the geolocation data recorded from participants, and it was hypothesized that results could be improved by splitting individuals into subgroups with similar relationships between their behavioral features and depressive symptoms. We developed a new model using a Dirichlet process prior for splitting individuals into groups, with a Bayesian Lasso model in each group to link behavioral features with mental illness. The result is a model for each individual that incorporates information from other similar individuals to augment the limited training data available. Results The new group-personalized regression model showed a significant improvement over population-level models in predicting mental health severity (P<.001). Analysis of subgroups showed that different groups were characterized by different features derived from raw geolocation data. Conclusions This study demonstrates the importance of handling interindividual variability when developing models of mental illness. Population-level models do not capture nuances in how different individuals respond to illness, and the group-personalized model demonstrates a potential way to overcome these limitations when estimating mental state from objective behavioral features.