Download Tag: journal articles

The importance of not looking the other way: Prehire on- and off-the-job misbehavior predicts subsequent police misconduct.

Dilchert, S., Mercado, B. K., & Ones, D. S.
Journal of Applied Psychology, 111
(2026)

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.

Body temperature correlates with depressive symptom severity: Analysis and extension of prior findings in observational cohorts with more than 19,000 participants

Soltani, S., Hartogensis, W., Dilchert, S., Flores, K., Lowry, C. A., Raison, C., Hecht, F. M., Smarr, B. L., Mason, A. E.
Frontiers in Psychiatry, 17
(2026)

Objective: Prior studies have reported that higher body temperature correlates with greater depressive symptoms, but did not adjust for potential confounders.
Methods: We used linear regression models and linear mixed models to assess associations between self-reported depressive symptoms and body temperature (once-daily self-reported temperature from a thermometer and minute-level distal temperature from a commercially available wearable device, worn on the finger). Adjusted models covaried for biological sex, age, daylight length, ambient temperature, and number of medical conditions. Distal body temperature metrics included awake and asleep temperatures, the asleep–awake temperature difference, and the diurnal temperature amplitude. Distal body temperature models also covaried for daylight length and ambient temperature over time. Finally, we used adjusted logistic regression models to assess associations between all body temperature metrics and four categories of depressive symptom severity.
Results: In an adjusted linear regression model, the positive association between depressive symptoms and self-reported body temperature remained significant (b = 0.940, 95% CI [0.686, 1.193], p = 1.7×10–12). Adjusted associations with awake distal body temperature (b = 0.300, 95% CI [0.229, 0.371], p = 1.2×10–15), diurnal distal body temperature amplitude (b = -0.204, 95% CI [-0.248, -0.159], p=6.1×10– 18), and asleep-awake distal body temperature difference (b = -0.308, 95% CI [-0.375, -0.240], p = 6.1×10–18) were stronger than in unadjusted models. Standardized effect estimates from the adjusted linear regression models indicated that the magnitude of these associations was robust but small in magnitude, with standardized coefficients ranging from -0.067 to 0.061 and partial R² values ranging from 0.0006 to 0.0041. Adjusted linear mixed models using repeated measurements showed similar significant associations between distal body temperature metrics and depressive symptom severity. Adjusted logistic regression models showed that increases in self-reported body temperature and awake distal body temperature conferred significantly higher odds of a more severe depressive symptom category; increases in diurnal distal body temperature amplitude and asleep-awake temperature difference conferred significantly lower odds. Conclusion: Higher body temperature was associated with increased depressive symptoms, and this association was robust to adjustment for potential confounders. These findings support body temperature as a physiological correlate of depressive symptom severity and motivate further investigation of thermoregulatory measures as part of multimodal approaches for monitoring depressive symptoms.

Wearable-derived skin temperature dynamics during sleep reveal cardiovascular perfusion deficits through mechanistic modeling

Burks, J. H., Hartogensis, W., Dilchert, S., Mason, A. E., & Smarr, B. L.
njp Digital Medicine, 9, 464
(2026)

Classical statistics are commonly used to find differences between distributions of average skin temperature across populations. However, skin temperature is affected by many endogenous (within body) and exogenous (outside body) factors, and these factors induce causal changes in longitudinal skin temperature that can obfuscate the interpretation of average population differences. Moreover, interpretations are increasingly difficult to make when using temperature signals sampled longitudinally in uncontrolled settings. A potential way to better handle the inherent complexity of skin temperature dynamics in uncontrolled settings is to explicitly account for the effects of causal factors on the short- and long-term trajectories of temperature. In this work, we find that a physics-informed model of skin temperature and activity during sleep accounts for significantly more variance than an equally parsimonious linear model. Furthermore, this model enables separation of cohorts with cardiovascular conditions that are known to affect skin thermoregulation, an important improvement over classic statistical modeling.

Age-related patterns in distal skin temperature during naps

Soltani, S., Hartogensis, W., Kasl, P., Dasgupta, S., Dilchert, S., Hecht, F. M., Smarr, B. L., & Mason, A. E.
Sleep, 49(6), zsag077
(2026)

Study Objectives: 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.
Methods: 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.
Results: Older age groups napped more frequently and consistently throughout the week. Individuals aged 30–49 years showed the greatest increase in nap frequency fromweekdays toweekends, whereas younger (18–19 years) and older (≥60 years) age groups showed the smallest increases. Sleep timing anddistal 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 onweekends relative toweekdays. 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).
Conclusion: 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.

“静默‘离”职”的前因后果：于无声中 重塑职场规则 [The causes and consequences of silent “resignations”: Quietly reshaping workplace rules]

Dilchert, S., Stanek, K. C., & Ones, D. S. (Adapted by 司马岳玲)
管理视野 [Management Insights], 44(1), 22-25
2026

“静默‘离’职”是近年来备受关注的职场现象，在社交媒体和公共讨论中频繁出现。本研究将
“静默‘离’职”概念化为一种多维度的心理退缩状态，认为它超越了单纯的任务脱离。研究发现，
这一状态与员工的心理健康和工作表现显著相关，且在年轻员工和新入职者中更为普遍。本研究
为理解和应对这一新兴职场现象提供了明确的概念定位与实证工具。

Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis

Viswanath, V. K., Navaneethan, S., Burks, J. H., Soltani, S., Kasl, P., Hartogensis, W., Dilchert, S., Hecht, F. M., Mason, A. E., Wang, E. J., & Smarr, B. L.
Communications Medicine, 6(1), 223
(2026)

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.
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).
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.
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.

Expanding our understanding of quiet quitting: Antecedents, correlates, and consequences

Dilchert, S., Stanek, K. C., & Ones, D. S.
Human Resource Management, 65(3), 679-704.
(2026)

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.

Multiscale Average Absolute Difference (MSAAD): A computationally efficient and nonparametric adaptation of line length for noisy, uncontrolled wearables time series

Burks, J. H., Hartogensis, W., Dilchert, S., Mason, A. E., & Smarr, B. L.
Algorithms, 18(9)
(2005)

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.

Survival of the greenest: Environmental sustainability and longevity of organizations

Haner, D. M., Wang, Y., Ones, D. S., Dilchert, S., Yazar, Y., & Kaura, K. Frontiers in Organizational Psychology, 3, 1521537.
(2025)

Much research has been devoted to how environmental sustainability of organizations is related to organizational reputation and financial performance, but little is known about whether and how organizational environmental sustainability relates to longevity of organizations. We quantitatively examined the relation between organizational longevity and environmental sustainability of organizations, hypothesizing a positive relationship. Using two large samples of organizations—one from the U.S., and another from multiple regions (Europe, the Middle East and North Africa, and Asia, analyzed separately)—results indicate a significant, replicable positive relation between organizational longevity and environmental sustainability performance. Statistically controlling for organizational wealth and size of workforces did not appreciably diminish relations. Additionally, older organizations demonstrated better resource use and management, operational eco-eciency, climate strategy, and environmental reporting. However, differences in innovation were less pronounced, though still favored older organizations. We discuss the implications for human resources and evolutionary theories of organizations, suggesting it is not the largest companies that endure, nor the wealthiest, but those most committed to environmental sustainability.

Testing the impact of intensive, longitudinal sampling on assessments of statistical power and effect size within a heterogeneous human population: Natural experiment using change in heart rate on weekends as a surrogate intervention

Soltani, S., Viswanath, V. K., Kasl, P., Hartogensis, W., Dilchert, S., Hecht, F. M., Mason, A. E., & Smarr, B. L.
Journal of Medical Internet Research, 27
2025

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.