Dienst van SURF
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On duty, police officers are exposed to a variety of acute, threatening stress situations and organizational demands. In line with the allostatic load model, the resulting acute and chronic stress might have tremendous consequences for police officers’ work performance and psychological and physical health. To date, limited research has been conducted into the underlying biological, dynamic mechanisms of stress in police service. Therefore, this ecological momentary assessment study examined the associations of stress, mood and biological stress markers of a 28-year-old male police officer in a N-of-1 study over three weeks (90 data points). Four times a day (directly after waking up, 30 minutes later, 6 hours later, before going to bed), he answered questions about the perceived stress and mood using a smartphone application. With each data entry, he collected saliva samples for the later assessment of salivary cortisol (sCort) and alpha-amylase (sAA). In addition, data was collected after six police incidents during duty. sCort and sAA were not related to perceived stress in daily life and did not increase in police incidents. Regarding mood measures, deterioration of calmness, but not valence and energy was associated with perceived stress. The results suggest continued police service to constitute a major chronic stressor resulting in an inability to mount a proper response to further acute stress. As an indicator of allostatic load, psychological and biological hyporesponsivity in moments of stress may have negative consequences for police officers’ health and behavior in critical situations that require optimal performance. Prospectively, this research design may also become relevant when evaluating the efficacy of individualized stress management interventions in police training.
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.
MULTIFILE
Occupational stress can cause health problems, productivity loss or absenteeism. Resilience interventions that help employees positively adapt to adversity can help prevent the negative consequences of occupational stress. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee's context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. This paper presents the conceptual framework and methods behind the WearMe project, which aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented as the basis for the WearMe project. The operationalization of the concepts and the daily measurement cycle are described, including the use of wearable sensor technology (e.g., sleep tracking and heart rate variability measurements) and Ecological Momentary Assessment (mobile app). Analyses target the development of within-subject (n=1) and between-subjects models and include repeated measures correlation, multilevel modelling, time series analysis and Bayesian network statistics. Future work will focus on further developing these models and eventually explore the effectiveness of the envisioned personalized resilience system.