Considerations for the Future: Family Planning and Infertility During Psychiatry Training.

During medical school, residency, or fellowship, many trainees struggle to balance their careers with starting a family. Some may feel the optimal time for parenthood is after completion of training, but the effect of increasing age on fertility is a real consideration for female physicians.1 Several studies have explored the impact of pregnancy and parental leave during surgical residency, yet little has been published on these topics during psychiatry training. This is surprising as psychiatry residents often address the challenges of integrating work and parenthood with their patients, yet it has not traditionally been within the culture of medicine to openly discuss this with colleagues. It is critical to address pregnancy and parenthood routinely during training and in the literature to reiterate the importance of work-life integration. In this article, we discuss current practices for psychiatry residents and advocate for the development of a standardized policy across psychiatry training programs that considers multiple aspects of childbearing including maternal mental health, family leave, and infertility.

Reading the mind in the eyes in PTSD: Limited Moderation by the presence of a service dog.

Persons with posttraumatic stress disorder (PTSD) frequently experience relationship failures in family and occupational domains resulting in loss of social supports. Prior research has implicated impairments in social cognition. The Reading the Mind in the Eyes Test (RMET) measures a key component of social cognition, the ability to infer the internal states of other persons based on features of the eyes region of the face; however, studies administering this popular test to persons with PTSD have yielded mixed results. This study assessed RMET performance in 47 male U.S. military Veterans with chronic, severe PTSD. Employing a within-subjects design that avoided selection biases, it aimed specifically to determine whether components of RMET performance, including accuracy, response latency, and stimulus dwell time, were improved by the company of a service dog, an intervention that has improved social function in other populations. RMET accuracies and response latencies in this PTSD sample were in the normal range. The presence of a familiar service dog did not improve RMET accuracy, reduce response latencies, or increase dwell times. Dog presence increased the speed of visual scanning perhaps consistent with reduced social fear.

Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study.

BACKGROUND: Digital phenotyping and machine learning are currently being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance mental health globally. OBJECTIVE: This study aimed to apply machine learning in an acute mental health setting for suicide risk prediction. This study uses a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data, which have typically been collected from health care records. METHODS: We created a smartphone app called Strength Within Me, which was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of inpatients with acute mental health (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine the best fit. RESULTS: K-nearest neighbors (KNN; k=2) with uniform weighting and the Euclidean distance metric emerged as the most promising algorithm, with 68% mean accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross-validation) and an average area under the curve of 0.65. We applied a combined 5×2 F test to test the model performance of KNN against the baseline classifier that guesses training majority, random forest, support vector machine and logistic regression, and achieved F statistics of 10.7 (P=.009) and 17.6 (P=.003) for training majority and random forest, respectively, rejecting the null of performance being the same. Therefore, we have taken the first steps in prototyping a system that could continuously and accurately assess the risk of suicide via mobile devices. CONCLUSIONS: Predicting for suicidality is an underaddressed area of research to which this paper makes a useful contribution. This is part of the first generation of studies to suggest that it is feasible to utilize smartphone-generated user input and passive sensor data to generate a risk algorithm among inpatients at suicide risk. The model reveals fair concordance between phone-derived and research-generated clinical data, and with iterative development, it has the potential for accurate discriminant risk prediction. However, although full automation and independence of clinical judgment or input would be a worthy development for those individuals who are less likely to access specialist mental health services, and for providing a timely response in a crisis situation, the ethical and legal implications of such advances in the field of psychiatry need to be acknowledged.

Emotion regulation mediates the relationship between ACES and physical and mental health.

OBJECTIVE: Adverse Childhood Experiences (ACEs) have consistently been associated with a range of negative psychological and physical outcomes in adulthood. Despite the strength of this association, no studies to date have investigated psychological processes that might underlie this relationship. The current study evaluated emotion regulation as a potential mediator between ACEs and three outcomes: PTSD symptoms, depression and poor physical health, all of which are frequently co-occurring among women with ACEs. METHOD: Mediational analyses were conducted with baseline data from a sample of 290 women enrolled in a clinical trial for PTSD. Emotion regulation was assessed with the Difficulties in Emotional Regulation Scale (DERS), PTSD with the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), depression with the Brief Symptom Inventory Depression subscale (BSI-D) and physical health with a shortened version of Medical Outcomes Study Short Form (SF-8). RESULTS: Emotion regulation significantly mediated the relationship between ACEs and all three outcomes. The estimates of the standardized indirect effects of ACEs on the health outcomes as mediated through DERS scores were as follows: PTSD ß = 0.1, p < .001; depression ß = 0.16, p < .001; physical health ß = 0.07, p = .002. CONCLUSION: Interventions that focus on improving emotion regulation skills might provide an efficient "transdiagnostic" treatment strategy for both psychological and physical health problems. The study successfully tested a mediational model that identified a common pathway influencing both mental and physical health symptoms. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

What Do We Mean by Physician Wellness? A Systematic Review of Its Definition and Measurement.

OBJECTIVE: Physician wellness (well-being) is recognized for its intrinsic importance and impact on patient care, but it is a construct that lacks conceptual clarity. The authors conducted a systematic review to characterize the conceptualization of physician wellness in the literature by synthesizing definitions and measures used to operationalize the construct. METHODS: A total of 3057 references identified from PubMed, Web of Science, and a manual reference check were reviewed for studies that quantitatively assessed the "wellness" or "well-being" of physicians. Definitions of physician wellness were thematically synthesized. Measures of physician wellness were classified based on their dimensional, contextual, and valence attributes, and changes in the operationalization of physician wellness were assessed over time (1989-2015). RESULTS: Only 14% of included papers (11/78) explicitly defined physician wellness. At least one measure of mental, social, physical, and integrated well-being was present in 89, 50, 49, and 37% of papers, respectively. The number of papers operationalizing physician wellness using integrated, general-life well-being measures (e.g., meaning in life) increased [X 2 = 5.08, p = 0.02] over time. Changes in measurement across mental, physical, and social domains remained stable over time. CONCLUSIONS: Conceptualizations of physician wellness varied widely, with greatest emphasis on negative moods/emotions (e.g., burnout). Clarity and consensus regarding the conceptual definition of physician wellness is needed to advance the development of valid and reliable physician wellness measures, improve the consistency by which the construct is operationalized, and increase comparability of findings across studies. To guide future physician wellness assessments and interventions, the authors propose a holistic definition.

Calibrating actigraphy to improve sleep efficiency estimates.

Actigraphy (ACT) can enhance treatment for insomnia by providing objective estimates of sleep efficiency; however, only two studies have assessed the accuracy of actigraphy-based estimates of sleep efficiency (ACT-SE) in sleep-disordered samples studied at home. Both found poor correspondence with polysomnography-based estimates (PSG-SE). The current study tested that concordance in a third sample and piloted a method for improving ACT-SE. Participants in one of four diagnostic categories (panic disorder, post-traumatic stress disorder, comorbid post-traumatic stress and panic disorder and controls without sleep complaints) underwent in-home recording of sleep using concurrent ambulatory PSG and actigraphy. Precisely synchronized PSG and ACT recordings were obtained from 41 participants. Sleep efficiency was scored independently using conventional methods, and ACT-SE/PSG-SE concordance examined. Next, ACT data recorded initially at 0.5 Hz were resampled to 30-s epochs and rescaled on a per-participant basis to yield optimized concordance between PSG- and ACT-based sleep efficiency estimates. Using standard scoring of ACT, the correlation between ACT-SE and PSG-SE across participants was statistically significant (r = 0.35, P < 0.025), although ACT-SE failed to replicate a main effect of diagnosis. Individualized calibration of ACT against a night of PSG yielded a significantly higher correlation between ACT-SE and PSG-SE (r = 0.65, P < 0.001; z = 1.692, P = 0.0452, one-tailed) and a significant main effect of diagnosis that was highly correspondent with the effect on PSG-SE. ACT-based estimates of sleep efficiency in sleep-disordered patients tested at home can be improved significantly by calibration against a single night of concurrent PSG.