Stephan Dilchert

This research addresses an often-overlooked opportunity for police reform: the predictive value of prehire misbehavior data for reducing posthire police misconduct. While most reform efforts focus on officers' actions after they are hired, our findings highlight the impact of rigorous screening before hire. We examined how specific prior employment and nonwork misbehaviors related to general occupational instability, trouble in previous law enforcement roles, prior temper problems and violence, and irresponsible behaviors predicted future misconduct among 6,075 police officers tracked over 5 years. Notably, some prehire behaviors significantly elevated risks of citizen complaints and misconduct-related lawsuits, with hazard ratios up to 14.59. Contrary to common assumptions, candidates with prior law enforcement experience showed increased liability, including excessive use of force, suggesting that this background does not inherently reduce misconduct risk. After identifying the strongest predictors of police misconduct, we also examined their relation to termination for cause and assessed how agency decision makers respond differently to prehire versus posthire misbehaviors. By integrating findings on the predictive value of specific prehire misbehaviors, we offer targeted, evidence-based guidance and actionable recommendations for police agencies and policymakers. This work provides a scientifically grounded foundation for effective and consistent police screening decisions, offering a framework for establishing long-overdue national police hiring standards. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Naps are common worldwide; despite their prevalence, physiological changes during naps are less well-characterized than during nighttime sleep. We aimed to characterize napping patterns and their associated physiological changes across age groups. We used longitudinal wearable device data from 20,027 individuals from the TemPredict Study to assess (1) napping frequency and timing and (2) distal skin temperature changes around naps. Older age groups napped more frequently and consistently throughout the week. Individuals aged 30–49 years showed the greatest increase in nap frequency from weekdays to weekends, whereas younger (18–19 years) and older (>60 years) age groups showed the smallest increases. Sleep timing and distal skin temperature rhythms appeared tightly coupled, even during the daytime. Both sleep timing and distal skin temperature rhythms appeared phase-delayed among younger age groups relative to older age groups. Distal skin temperature was higher during naps on weekends relative to weekdays. Older individuals (>65 years) had lower distal skin temperatures during naps relative to younger individuals (≤30 years) (weekends: p<.0001; δ=-0.20; weekdays: p<.0001; δ=-0.19). Longer naps were generally associated with greater distal skin temperature before napping, especially among younger individuals (≤30 years). We observed age-related differences in napping patterns and distal skin temperature around and during naps. Future research should examine whether such distal skin temperature changes around naps relate to sleep pressure and age-related disease risk.

Background: Diabetes Mellitus is a common, chronic metabolic disorder affecting the cardiovascular system, autonomic nervous system, and sleep quality. Diabetes affects diverse physiological data including heart rate variability, distal body temperature, and sleep duration. We hypothesized that biologically informed features from wearable device data, combined with appropriate application of longitudinal data, can capture physiological covariates of diabetes and support the noninvasive detection of diabetes. Methods: We obtained 4 months and 7 days of wearables data (Oura Ring) from 389 individuals self-reporting diabetes and 10,820 people self-reporting no diabetes diagnosis from the TemPredict database. We selected 36 features of sleep, circadian disruption, and distal body temperature from literature and evaluated whether time windows of these features could be classified to be from individuals self-reporting diabetes (N = 236) or self-reporting no diabetes diagnosis (N = 282). Results: Here we show longer time windows of input perform better, with the best algorithm (21-nights) achieving 0.88 Area under ROC (AUROC) and 0.80 Area under Precision Recall (AUPRC) (0.30 improvement over random). Feature analyses reveal the importance of further derived distal body temperature features (increase AUROC by 0.0724), especially to differentiate other chronic conditions from diabetes. The model achieves 0.80 AUROC and 0.28 improvement over random in AUPRC in an imbalanced cohort drawn from 6,658 individuals, emulating a general population. Conclusions: These results indicate the value of biologically informed features and longitudinal data for identifying people with diabetes and further, suggest that these methods could make such separations possible for other chronic conditions that affect sleep and inflammation.

Quiet quitting remains an undertheorized and inconsistently defined construct in the scholarly literature. There is little understanding of or agreement about its construct domain, and no unifying conceptual framework to distinguish it from related constructs. Popular accounts often equate quiet quitting with employee work disengagement. Drawing on a synthesis of academic and practitioner literatures, we conceptualized quiet quitting as a form of psychological withdrawal that extends beyond in‐role task disengagement to include disengagement from colleagues and the organization more broadly. Its key components include a weakening of social connections at work, diminishing job engagement, a generalized disinclination toward work, and the desire to set boundaries between work and personal life. To reflect this multifaceted conceptualization, we developed the Multidimensional Quiet Quitting (MQQ) scale, comprising four interrelated dimensions: (1) lack of job‐specific engagement, (2) lack of generalized work engagement, (3) lack of social connections at work, and (4) boundary setting. Using a content validation sample of working adults ( N = 114), we evaluated the scale's content validity. We then examined its latent structure and internal consistency using experience sampling data from 124 professionals over 10 weeks, followed by confirmatory factor analysis in an independent replication sample ( N = 290). Finally, we mapped the nomological network of quiet quitting by examining associations with theoretically relevant antecedents and outcomes. Results indicated that organizational newcomers and younger employees were slightly more prone to quiet quitting, while job autonomy, work responsibility, marriage, and childcare responsibilities served as protective factors. Quiet quitters also tended to be less extraverted, conscientious, emotionally stable, and agreeable. Quiet quitting and disengagement were each associated with concurrent and subsequent mental health and work outcomes. Quiet quitting components incrementally predicted stress, job satisfaction, burnout, and work effectiveness beyond traditional work disengagement, an overlapping construct. By specifying and measuring quiet quitting as a multidimensional construct, this study provides much‐needed construct clarity and demonstrates empirical effects associated with the phenomenon. Implications for research and practice are discussed.

With the rise in physiological data sampled from wearable devices, efficient methods must be developed to encode temporal information for the comparison of time series arising from uncontrolled monitoring. We present a fast, nonparametric method called Multiscale Average Absolute Difference (MSAAD) to extract multiscale temporal features from wearable device data for purposes ranging from statistical analysis to machine learning inference. MSAAD outperforms comparable algorithms like multiscale sample entropy (MSSE) and multiscale Katz Fractal Dimension (MS-KFD) in terms of calculation stability on short realizations and faster runtime. MSAAD outperforms MSSE and MS-KFD by being able to separate diabetic and non-diabetic cohorts with moderate and large effect sizes in both sexes. Furthermore, it is capable of capturing “critical slowing down” in the temperature dynamics of aging populations, a phenomenon that has been previously observed in controlled settings. We propose that MSAAD is a scalable, interpretable time series feature that is capable of identifying meaningful differences in physiological time series data without making assumptions regarding underlying process models. MSAAD could improve the ability to derive insight from time series data mining for health applications.

Background: The recent emergence of wearable devices has made feasible the passive gathering of intensive, longitudinal data from large groups of individuals. This form of data is effective at capturing physiological changes between participants (interindividual variability) and changes within participants over time (intraindividual variability). The emergence of longitudinal datasets provides an opportunity to quantify the contribution of such longitudinal data to the control of these sources of variability for applications such as responder analysis, where traditional, sparser sampling methods may hinder the categorization of individuals into these phenotypes. Objective: This study aimed to quantify the gains made in statistical power and effect size among statistical comparisons when controlling for interindividual variability and intraindividual variability compared with controlling for neither. Methods: Here, we test the gains in statistical power from controlling for interindividual and intraindividual variability of resting heart rate, collected in 2020 for over 40,000 individuals as part of the TemPredict study on COVID-19 detection. We compared heart rate on weekends with that on weekdays because weekends predictably change the behavior of most individuals, though not all, and in different ways. Weekends also repeat consistently, making their effects on heart rate feasible to assess with confidence over large populations. We therefore used weekends as a model system to test the impact of different statistical controls on detecting a recurring event with a clear ground truth. We randomly and iteratively sampled heart rate from weekday and weekend nights, controlling for interindividual variability, intraindividual variability, both, or neither. Results: Between-participant variability appeared to be a greater source of structured variability than within-participant fluctuations. Accounting for interindividual variability through within-individual sampling required 40× fewer pairs of samples to achieve statistical significance with 4× to 5× greater effect size at significance. Within-individual sampling revealed differential effects of weekends on heart rate, which were obscured by aggregated sampling methods. Conclusions: This work highlights the leverage provided by longitudinal, within-individual sampling to increase statistical power among populations with heterogeneous effects.

Background: A substantially lower proportion of female individuals participate in sufficient daily activity compared to male individuals despite the known health benefits of exercise. Investment in female sports and exercise medicine research may help close this gap; however, female individuals are underrepresented in this research. Hesitancy to include female participants is partly due to assumptions that biological rhythms driven by menstrual cycles and occurring on the timescale of approximately 28 days increase intraindividual biological variability and weaken statistical power. An analysis of continuous skin temperature data measured using a commercial wearable device found that temperature cycles indicative of menstrual cycles did not substantially increase variability in female individuals’ skin temperature. In this study, we explore physical activity (PA) data as a variable more related to behavior, whereas temperature is more reflective of physiological changes. Objective: We aimed to determine whether intraindividual variability of PA is affected by biological sex, and if so, whether having menstrual cycles (as indicated by temperature rhythms) contributes to increased female intraindividual PA variability. We then sought to compare the effect of sex and menstrual cycles on PA variability to the effect of PA rhythms on the timescales of days and weeks and to the effect of nonrhythmic temporal structure in PA on the timescale of decades of life (age). Methods: We used minute-level metabolic equivalent of task data collected using a wearable device across a 206-day study period for each of 596 individuals as an index of PA to assess the magnitudes of variability in PA accounted for by biological sex and temporal structure on different timescales. Intraindividual variability in PA was represented by the consecutive disparity index. Results: Female individuals (regardless of whether they had menstrual cycles) demonstrated lower intraindividual variability in PA than male individuals (Kruskal-Wallis H=29.51; P<.001). Furthermore, individuals with menstrual cycles did not have greater intraindividual variability than those without menstrual cycles (Kruskal-Wallis H=0.54; P=.46). PA rhythms differed at the weekly timescale: individuals with increased or decreased PA on weekends had larger intraindividual variability (Kruskal-Wallis H=10.13; P=.001). In addition, intraindividual variability differed by decade of life, with older age groups tending to have less variability in PA (Kruskal-Wallis H=40.55; P<.001; Bonferroni-corrected significance threshold for 15 comparisons: P=.003). A generalized additive model predicting the consecutive disparity index of 24-hour metabolic equivalent of task sums (intraindividual variability of PA) showed that sex, age, and weekly rhythm accounted for only 11% of the population variability in intraindividual PA variability. Conclusions: The exclusion of people from PA research based on their biological sex, age, the presence of menstrual cycles, or the presence of weekly rhythms in PA is not supported by our analysis.

We critically evaluate Dupré and Wille's (2024) proposal for using assessments for organizational personality development through the lens of empirical evidence on adult personality change. We present an overview of research on personality stability and malleability throughout adulthood examining rank‐order stability, mean‐level changes, and the impact of life events and interventions. Empirical evidence reveals that while personality exhibits some plasticity in young adulthood, significant changes become increasingly rare beyond age 30. For older employees, personality remains highly stable, making age an important consideration in workforce development. Life experiences and intentional interventions have been shown to prompt modest personality changes, with emotional stability being the most malleable trait. We quantify these changes, noting shifts of up to two‐thirds of a standard deviation in emotional stability through targeted interventions, with more limited effects on other Big Five traits. We also provide insights for organizational assessment practices, including the need for tailored personality (re‐)assessment intervals and age‐based norm composition for better utilization of personality information. With Cybernetic Trait Complexes Theory, we introduce a framework for aligning personality traits with situational cues in work environments. This approach emphasizes trait activation rather than personality modification, allowing organizations to harness employees' strengths by strategically designing environments that naturally encourage beneficial trait expression. This shifts the focus from personality change to strategic activation of beneficial traits through environmental design. We describe how organizations can leverage employees' existing personality trait complexes while fostering incremental behavioral adaptations, offering a pragmatic alternative to traditional employee development approaches. By aligning individuals with environments that activate their traits, organizations can enhance both personal and organizational outcomes, contributing to broader societal benefits as well.

BACKGROUND A substantially lower proportion of female individuals participate in sufficient daily activity compared to male individuals despite the known health benefits of exercise. Investment in female sports and exercise medicine research may help close this gap; however, female individuals are underrepresented in this research. Hesitancy to include female participants is partly due to assumptions that biological rhythms driven by menstrual cycles and occurring on the timescale of approximately 28 days increase intraindividual biological variability and weaken statistical power. An analysis of continuous skin temperature data measured using a commercial wearable device found that temperature cycles indicative of menstrual cycles did not substantially increase variability in female individuals’ skin temperature. In this study, we explore physical activity (PA) data as a variable more related to behavior, whereas temperature is more reflective of physiological changes. OBJECTIVE We aimed to determine whether intraindividual variability of PA is affected by biological sex, and if so, whether having menstrual cycles (as indicated by temperature rhythms) contributes to increased female intraindividual PA variability. We then sought to compare the effect of sex and menstrual cycles on PA variability to the effect of PA rhythms on the timescales of days and weeks and to the effect of nonrhythmic temporal structure in PA on the timescale of decades of life (age). METHODS We used minute-level metabolic equivalent of task data collected using a wearable device across a 206-day study period for each of 596 individuals as an index of PA to assess the magnitudes of variability in PA accounted for by biological sex and temporal structure on different timescales. Intraindividual variability in PA was represented by the consecutive disparity index. RESULTS Female individuals (regardless of whether they had menstrual cycles) demonstrated lower intraindividual variability in PA than male individuals (Kruskal-Wallis H=29.51; P<.001). Furthermore, individuals with menstrual cycles did not have greater intraindividual variability than those without menstrual cycles (Kruskal-Wallis H=0.54; P=.46). PA rhythms differed at the weekly timescale: individuals with increased or decreased PA on weekends had larger intraindividual variability (Kruskal-Wallis H=10.13; P=.001). In addition, intraindividual variability differed by decade of life, with older age groups tending to have less variability in PA (Kruskal-Wallis H=40.55; P<.001; Bonferroni-corrected significance threshold for 15 comparisons: P=.003). A generalized additive model predicting the consecutive disparity index of 24-hour metabolic equivalent of task sums (intraindividual variability of PA) showed that sex, age, and weekly rhythm accounted for only 11% of the population variability in intraindividual PA variability. CONCLUSIONS The exclusion of people from PA research based on their biological sex, age, the presence of menstrual cycles, or the presence of weekly rhythms in PA is not supported by our analysis.

Sleep monitoring has become widespread with the rise of affordable wearable devices. However, converting sleep data into actionable change remains challenging as diverse factors can cause combinations of sleep parameters to differ both between people and within people over time. Researchers have attempted to combine sleep parameters to improve detecting similarities between nights of sleep. The cluster of similar combinations of sleep parameters from a night of sleep defines that night’s sleep phenotype. To date, quantitative models of sleep phenotype made from data collected from large populations have used cross-sectional data, which preclude longitudinal analyses that could better quantify differences within individuals over time. In analyses reported here, we used five million nights of wearable sleep data to test (a) whether an individual’s sleep phenotype changes over time and (b) whether these changes elucidate new information about acute periods of illness (e.g., flu, fever, COVID-19). We found evidence for 13 sleep phenotypes associated with sleep quality and that individuals transition between these phenotypes over time. Patterns of transitions significantly differ (i) between individuals (with vs. without a chronic health condition; chi-square test; p-value < 1e−100) and (ii) within individuals over time (before vs. during an acute condition; Chi-Square test; p-value < 1e−100). Finally, we found that the patterns of transitions carried more information about chronic and acute health conditions than did phenotype membership alone (longitudinal analyses yielded 2–10× as much information as cross-sectional analyses). These results support the use of temporal dynamics in the future development of longitudinal sleep analyses.