Predictive validity of a selection instrument measuring resilience: The INSPIRE resilience scale.

Military personnel needs to be resilient to be able to remain effective, motivated, and in good mental and physical health. Military organizations select on resilience to determine whether candidates are psychologically fit for their job. The INSPIRE Resilience Scale (IRS) is such a selection instrument that aims to assess the psychological resilience potential of candidates in high-risk professions. A longitudinal predictive validity study was conducted in five European Defense organizations and in the Dutch National Police. The IRS was submitted in selection (N = 11,404), and criterion data about performance and health were collected in the second half of the first training year (N = 726). The results based on correlational and regression analyses showed that the IRS scores significantly predicted the criterion measures. Emotional stability, part of the IRS, turned out to be the best predictor. Results also showed that candidates who dropped out of training had significantly lower means on the IRS in selection than candidates who were still in training in the second half of the first training year. Overall, the IRS proved to be a valid instrument to assess resilience potential in candidates for high-risk professions. Selecting on resilience may therefore contribute to training success and reduction of health problems.

Does Wearable-Measured Heart Rate Variability During Sleep Predict Perceived Morning Mental and Physical Fitness?

The emergence of wearable sensor technology may provide opportunities for automated measurement of psychophysiological markers of mental and physical fitness, which can be used for personalized feedback. This study explores to what extent within-subject changes in resting heart rate variability (HRV) during sleep predict the perceived mental and physical fitness of military personnel on the subsequent morning. Participants wore a Garmin wrist-worn wearable and filled in a short morning questionnaire on their perceived mental and physical fitness during a period of up to 46 days. A custom-built smartphone app was used to directly retrieve heart rate and accelerometer data from the wearable, on which open-source algorithms for sleep detection and artefact filtering were applied. A sample of 571 complete observations in 63 participants were analyzed using linear mixed models. Resting HRV during sleep was a small predictor of perceived physical fitness (marginal R2 = .031), but not of mental fitness. The items on perceived mental and physical fitness were strongly correlated (r = .77). Based on the current findings, resting HRV during sleep appears to be more related to the physical component of perceived fitness than its mental component. Recommendations for future studies include improvements in the measurement of sleep and resting HRV, as well as further investigation of the potential impact of resting HRV as a buffer on stress-related outcomes.

Wearable-Measured Sleep and Resting Heart Rate Variability as an Outcome of and Predictor for Subjective Stress Measures: A Multiple N-of-1 Observational Study.

The effects of stress may be alleviated when its impact or a decreased stress-resilience are detected early. This study explores whether wearable-measured sleep and resting HRV in police officers can be predicted by stress-related Ecological Momentary Assessment (EMA) measures in preceding days and predict stress-related EMA outcomes in subsequent days. Eight police officers used an Oura ring to collect daily Total Sleep Time (TST) and resting Heart Rate Variability (HRV) and an EMA app for measuring demands, stress, mental exhaustion, and vigor during 15-55 weeks. Vector Autoregression (VAR) models were created and complemented by Granger causation tests and Impulse Response Function visualizations. Demands negatively predicted TST and HRV in one participant. TST negatively predicted demands, stress, and mental exhaustion in two, three, and five participants, respectively, and positively predicted vigor in five participants. HRV negatively predicted demands in two participants, and stress and mental exhaustion in one participant. Changes in HRV lasted longer than those in TST. Bidirectional associations of TST and resting HRV with stress-related outcomes were observed at a weak-to-moderate strength, but not consistently across participants. TST and resting HRV are more consistent predictors of stress-resilience in upcoming days than indicators of stress-related measures in prior days.

A Loss Cycle of Burnout Symptoms and Reduced Coping Self-Efficacy: A Latent Change Score Modelling Approach.

Police officers are frequently faced with chronic and acute stressors, such as excessive workload, organizational stressors and emotionally charged reports. This study aims to examine the relationship between a form of chronic strain (ie, burnout symptoms) and a resource (ie, coping self-efficacy) in a sample of Dutch police officers. Specifically, we aim to investigate the existence of a loss cycle of resources. We use Latent Change Score modeling to investigate the potential depletion or loss cycle of coping self-efficacy as a result of burnout symptoms in a sample of 95 police officers who completed a survey on three consecutive timepoints. The lag between the measurements was approximately one year. We found that, during both one-year intervals, within-person increases in burnout symptoms were related to within-person decreases in coping self-efficacy. Also, the results emphasize the buffering role of coping self-efficacy for burnout symptoms, as within-person decreases in coping self-efficacy during the first year were associated with within-person increases in burnout symptoms during the following year. Together, the results imply that a loss cycle of coping self-efficacy and burnout symptoms may occur. For this we used Latent Change Score modeling, which is a relatively new approach which provides researchers with the opportunity to analyse multi-wave longitudinal data while focusing on within-person changes over time. Practically, police organizations are advised to monitor personnel wellbeing and resources, to maintain and promote sustainable employability of police officers and to be able to timely provide individuals with interventions. Limitations discussed are the use of self-report measures and large intervals between the measurements. Finally, future directions of research are discussed that would circumvent the reported limitations, such as multiple wave with shorter lags and incorporating confounding factors that could affect coping self-efficacy.

Detection of arousal and valence from facial expressions and physiological responses evoked by different types of stressors.

Automatically detecting mental state such as stress from video images of the face could support evaluating stress responses in applicants for high risk jobs or contribute to timely stress detection in challenging operational settings (e.g., aircrew, command center operators). Challenges in automatically estimating mental state include the generalization of models across contexts and across participants. We here aim to create robust models by training them using data from different contexts and including physiological features. Fifty-one participants were exposed to different types of stressors (cognitive, social evaluative and startle) and baseline variants of the stressors. Video, electrocardiogram (ECG), electrodermal activity (EDA) and self-reports (arousal and valence) were recorded. Logistic regression models aimed to classify between high and low arousal and valence across participants, where "high" and "low" were defined relative to the center of the rating scale. Accuracy scores of different models were evaluated: models trained and tested within a specific context (either a baseline or stressor variant of a task), intermediate context (baseline and stressor variant of a task), or general context (all conditions together). Furthermore, for these different model variants, only the video data was included, only the physiological data, or both video and physiological data. We found that all (video, physiological and video-physio) models could successfully distinguish between high- and low-rated arousal and valence, though performance tended to be better for (1) arousal than valence, (2) specific context than intermediate and general contexts, (3) video-physio data than video or physiological data alone. Automatic feature selection resulted in inclusion of 3-20 features, where the models based on video-physio data usually included features from video, ECG and EDA. Still, performance of video-only models approached the performance of video-physio models. Arousal and valence ratings by three experienced human observers scores based on part of the video data did not match with self-reports. In sum, we showed that it is possible to automatically monitor arousal and valence even in relatively general contexts and better than humans can (in the given circumstances), and that non-contact video images of faces capture an important part of the information, which has practical advantages.

Trends in Daily Heart Rate Variability Fluctuations Are Associated with Longitudinal Changes in Stress and Somatisation in Police Officers.

The emergence of wearable sensors that allow for unobtrusive monitoring of physiological and behavioural patterns introduces new opportunities to study the impact of stress in a real-world context. This study explores to what extent within-subject trends in daily Heart Rate Variability (HRV) and daily HRV fluctuations are associated with longitudinal changes in stress, depression, anxiety, and somatisation. Nine Dutch police officers collected daily nocturnal HRV data using an Oura ring during 15-55 weeks. Participants filled in the Four-Dimensional Symptoms Questionnaire every 5 weeks. A sample of 47 five-week observations was collected and analysed using multiple regression. After controlling for trends in total sleep time, moderate-to-vigorous physical activity and alcohol use, an increasing trend in the seven-day rolling standard deviation of the HRV (HRVsd) was associated with increases in stress and somatisation over 5 weeks. Furthermore, an increasing HRV trend buffered against the association between HRVsd trend and somatisation change, undoing this association when it was combined with increasing HRV. Depression and anxiety could not be related to trends in HRV or HRVsd, which was related to observed floor effects. These results show that monitoring trends in daily HRV via wearables holds promise for automated stress monitoring and providing personalised feedback.

Chronic stressors and burnout in Dutch police officers: Two studies into the complex role of coping self-efficacy.

Introduction: Burnout complaints are high for Dutch police officers. According to Hobfoll's Conservation of Resources theory, resources such as coping self-efficacy can play an important role in the burnout process. The aim of this study was to investigate the buffering effect of coping self-efficacy on burnout, as well as a possible depletion effect of burnout on coping self-efficacy. Methods: As such, this research consists of two studies namely, a two-wave study (N = 166) and three-wave study (N = 95) on Dutch police officers. They expand on previous research regarding coping resources and police burnout. Results: Both studies show that the chronic stressor, work scheduling, was positively associated with burnout. Also, coping self-efficacy weakened the effect of work scheduling (Study 1) and workload (Study 2) on burnout. Moreover, there was a direct negative relationship between burnout and coping self-efficacy. Discussion: The results indicate that burnout can lead to lower coping resources, initiating a potential cycle of resource loss and burnout. Further investigation into this depletion effect is required to provide police officers and organisations with tools to prevent burnout.

Team Coping: Cross-Level Influence of Team Member Coping Activities on Individual Burnout.

Coping with stress has been primarily investigated as an individual-level phenomenon. In work settings, however, an individual's exposure to demands is often shared with co-workers, and the process of dealing with these demands takes place in the interaction with them. Coping, therefore, may be conceptualized as a multilevel construct. This paper introduces the team coping concept and shows that including coping as a higher-level team property may help explain individual-level outcomes. Specifically, we investigated the effects of exposure to danger during deployment on burnout symptoms in military service members and examined to what extent this relationship was moderated by individual-level and team-level functional coping. We hypothesized that the relationship between individuals' exposure to danger and burnout is contingent on both. In line with our predictions, we found that service members who were highly exposed to danger, and did not engage in much functional coping, suffered most from burnout symptoms, but only when their teammates did not engage in much functional coping either. When their teammates did engage in much functional coping, the effect of exposure to danger on burnout was buffered. Hence, team members' coping efforts functioned as a resilience resource for these service members.

Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured With a Wearable and Smartphone App: Within-Subject Design Using Continuous Monitoring.

BACKGROUND: The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual's resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. OBJECTIVE: This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. METHODS: In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. RESULTS: Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). CONCLUSIONS: To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes.

PLATT: a flexible platform for experimental research on team performance in complex environments.

The present article introduces PLATT, a recently developed task environment for controlled experimental research on team performance in complex environments. PLATT was developed to meet the research demands posed by the complexity that present-day teams face. It consists of a flexible, modular software architecture and research-specific scenarios. The scenarios can target various types of tasks (e.g., planning, problem solving, and decision making) in different operational contexts. Different software configurations can be used to investigate questions pertaining to team structure, team virtuality, and multiteam systems. We describe the software architecture, one of the scenarios, and the broad range of automated and embedded measurement possibilities that PLATT offers. To illustrate PLATT's possibilities, in the present article, we describe a number of experiments that have used PLATT for a variety of research questions. We conclude that PLATT meets the formulated research demands and provides researchers with a flexible platform to investigate the complex issues that present-day teams face.

Perceived demands during modern military operations.

Using a cross-sectional design, this study explored operational demands during the International Security Assistance Force for Afghanistan (2009-2010) across distinct military units. A total of 1,413 Dutch soldiers, nested within four types of units (i.e., combat, combat support, service support, and command support units) filled out a 23-item self-survey in which they were asked to evaluate the extent to which they experienced operational characteristics as demanding. Exploratory factor analysis identified six underlying dimensions of demands. Multivariate analysis of variance revealed that distinct units are characterized by their own unique constellation of perceived demands, even after controlling for previous deployment experience. Most notable findings were found when comparing combat units to other types of units. These insights can be used to better prepare different types of military units for deployment, and support them in the specific demands they face during deployment.