Assessment of salivary cortisol dynamics in an infantry training exercise: a pilot study.

INTRODUCTION: Measuring cortisol during military training offers insights into physiological responses to stress. We attempted precisely timed, cortisol awakening response (CAR) and pre-sleep cortisol (PSC), and diurnal slope (peak morning minus evening cortisol), during a British Army exercise. We aimed to understand cortisol dynamics and evaluate the feasibility of CAR and PSC in this environment. METHOD: Setting: high-intensity, 10-day infantry exercise. Participants: regular infantry soldiers exercising (EX, n=25) or headquarters-based (HQ, n=6). Participants undertook PSC and WAKE and WAKE+30 min samples after 1-2 days, 5-6 days and 9-10 days. Wrist-worn GENEActiv accelerometers were used to assess sleep duration in EX only. Samples taken ±15 min from prespecified time points were deemed adherent. Validated questionnaires were used to measure resilience and perceived stress. Cortisol and cortisone were measured simultaneously by liquid chromatography tandem mass spectrometry. RESULTS: From adherent participants' samples, CAR was positive and tended to decrease as the exercise progressed. From all available data, HQ demonstrated greater diurnal slope than EX (F=7.68, p=0.02), reflecting higher morning cortisol (F=4.72, p=0.038) and lower PSC (p=0.04). No differences were seen in cortisol:cortisone ratio. 26.1% of CAR samples were adherent, with moderately strong associations between adherence and stress (r=0.41, p=0.009) but no association between adherence and day of exercise (χ2=0.27, p=0.8), sleep duration (r=-0.112, p=0.43) or resilience (r=-0.79, p=0.75). Test-retest reliability ratings for CAR were Cronbach's α of 0.48, -11.7 and 0.34 for the beginning, middle and end of the exercise, respectively. CONCLUSIONS: We observed a reduction in morning cortisol and decreased diurnal slope during a high-intensity military exercise, compared with the HQ comparator cohort in whom diurnal slope was preserved. A carefully timed CAR was not feasible in this setting.

Predicting time to asystole following withdrawal of life-sustaining treatment: a systematic review.

The planned withdrawal of life-sustaining treatment is a common practice in the intensive care unit for patients where ongoing organ support is recognised to be futile. Predicting the time to asystole following withdrawal of life-sustaining treatment is crucial for setting expectations, resource utilisation and identifying patients suitable for organ donation after circulatory death. This systematic review evaluates the literature for variables associated with, and predictive models for, time to asystole in patients managed on intensive care units. We conducted a comprehensive structured search of the MEDLINE and Embase databases. Studies evaluating patients managed on adult intensive care units undergoing withdrawal of life-sustaining treatment with recorded time to asystole were included. Data extraction and PROBAST quality assessment were performed and a narrative summary of the literature was provided. Twenty-three studies (7387 patients) met the inclusion criteria. Variables associated with imminent asystole (<60 min) included: deteriorating oxygenation; absence of corneal reflexes; absence of a cough reflex; blood pressure; use of vasopressors; and use of comfort medications. We identified a total of 20 unique predictive models using a wide range of variables and techniques. Many of these models also underwent secondary validation in further studies or were adapted to develop new models. This review identifies variables associated with time to asystole following withdrawal of life-sustaining treatment and summarises existing predictive models. Although several predictive models have been developed, their generalisability and performance varied. Further research and validation are needed to improve the accuracy and widespread adoption of predictive models for patients managed in intensive care units who may be eligible to donate organs following their diagnosis of death by circulatory criteria.

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.

Detecting Bipolar Depression From Geographic Location Data.

OBJECTIVE: This paper aims to identify periods of depression using geolocation movements recorded from mobile phones in a prospective community study of individuals with bipolar disorder (BD). METHODS: Anonymized geographic location recordings from 22 BD participants and 14 healthy controls (HC) were collected over 3 months. Participants reported their depressive symptomatology using a weekly questionnaire (QIDS-SR16). Recorded location data were preprocessed by detecting and removing imprecise data points and features were extracted to assess the level and regularity of geographic movements of the participant. A subset of features were selected using a wrapper feature selection method and presented to 1) a linear regression model and a quadratic generalized linear model with a logistic link function for questionnaire score estimation; and 2) a quadratic discriminant analysis classifier for depression detection in BD participants based on their questionnaire responses. R esults: HC participants did not report depressive symptoms and their features showed similar distributions to nondepressed BD participants. Questionnaire score estimation using geolocation-derived features from BD participants demonstrated an optimal mean absolute error rate of 3.73, while depression detection demonstrated an optimal (median ± IQR) [Formula: see text] score of 0.857 ± 0.022 using five features (classification accuracy: 0.849 ± 0.016; sensitivity: 0.839 ± 0.014; specificity: 0.872 ± 0.047). CONCLUSION: These results demonstrate a strong link between geographic movements and depression in bipolar disorder. S ignificance: To our knowledge, this is the first community study of passively recorded objective markers of depression in bipolar disorder of this scale. The techniques could help individuals monitor their depression and enable healthcare providers to detect those in need of care or treatment.

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.