Improvements in Mindfulness Facets Predict Burnout and Sleep Disturbance Outcomes in Law Enforcement Officers

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Morgan Penberthy, Olivia Boyd, Michael Christopher This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7926560/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mindfulness-Based Resilience Training (MBRT) was designed to improve LEO mental health and performance, and to enhance resilience in the context of acute and chronic stressors inherent to policing. Prior research has demonstrated preliminary efficacy of MBRT in improving a variety of outcomes, including burnout and sleep disturbance. The primary goal of this study was to conduct a secondary analysis to determine which mindfulness facets accounted for the most variance in reducing burnout and sleep disturbances following participation in MBRT among LEOs ( N = 98). The findings indicate that increases in nonjudgment and nonreactivity were significant predictors of reductions in sleep disturbances, while acting with awareness was not. We also found that increases in nonjudging and acting with awareness were significant predictors of reductions in burnout, while nonreactivity was not. Our findings suggest that integrating mindfulness practices that emphasize nonjudgment, nonreactivity, and acting with awareness may help mitigate burnout and sleep disturbances. Future research should investigate how mindfulness training can be integrated with LEO occupational culture and leadership practices, with an emphasis on strategies that facilitate the effective dissemination and uptake of this intervention. ClinicalTrials.gov Identifiers: NCT02521454; NCT03784846 Dates of registration: August 13, 2015; December 24, 2018 Law enforcement burnout sleep disturbance mindfulness occupational health Introduction Law enforcement officers (LEOs) encounter numerous operational stressors, including violence (e.g., domestic violence, child abuse, homicide), danger (e.g., life-threatening assaults), and mental and physical trauma, resulting in higher levels of stress relative to most other professions (Bishopp & Boots, 2014 ; El Sayed et al, 2019 ). These are compounded by organizational stressors such as shift changes, lack of departmental support, and insufficient resources (Poirier et al., 2023 ; Stormer, 2021 ). Consequently, prolonged exposure to unique stressors for LEOs increases risk for adverse health outcomes. These health risks include higher rates of burnout, sleep disturbance, cardiovascular disease, high blood pressure, post-traumatic stress disorder (PTSD), depression, suicidality, and anxiety, compared to the general population (Iqbal et al., 2024 ; Stegerhoek et al., 2024 ). Burnout, a long-lasting psychological strain characterized by exhaustion and reduced interest in work, is a severe consequence of ongoing occupational stress (Jacobs, 2024 ). It is estimated that approximately 20% of LEOs across the U.S. experience severe levels of burnout (McCarty et al., 2019 ). Chronic stress contributes to burnout, and it is associated with poor physical health outcomes, including cardiovascular diseases, musculoskeletal pain, and respiratory diseases (Chen et al., 2022 ; John et al., 2024 ). Recent meta-analyses indicate burnout is linked to additional negative behavioral outcomes, such as increased turnover (Li & Yao, 2022 ) and alcohol misuse (Ryan et al., 2023 ). Specific to the context of policing, LEOs experiencing burnout are more likely to distance themselves from the public while on duty (Padyab et al., 2016 ), be overly aggressive (Kurtz et al., 2015), use excessive force (Jetelina et al., 2020 ), and show poorer decision-making (Morgado et al., 2015 ). As a result, U.S. cities spend millions of dollars each year on police misconduct cases (Thomson-Devaux & Sharma, 2021). This increases the risk of heightened tensions between LEOs, community stakeholders, and the public, deteriorating trust and cooperation (Gau & Brunson, 2015 ). Sleep disturbances, which are disruptions in the quality, timing, or duration of sleep that impair daytime functioning and overall well-being, are linked to burnout (Peterson et al., 2019 ; Nordin et al., 2023 ). Results from a large meta-analysis (Garbarino et al., 2019) indicate that 51% of LEOs experience sleep disturbances as measured by the Pittsburgh Sleep Quality Index (Buysse et al., 1989 ), and Rajaratnam et al. ( 2011 ) found that 40.4% of LEOs screen positive for at least one sleep disorder. Similar to burnout, sleep disturbances have been shown to result in negative outcomes across professions, such as increased risk of errors (Alfonsi et al., 2021 ), workplace accidents (Glick et al., 2023 ), and musculoskeletal disorders (Khoshakhlagh et al., 2023 ). Sleep disturbances among LEOs have been linked to PTSD (Chopko et al., 2021 ), impaired cognitive functioning (Sørengaard et al., 2021 ), elevated systolic blood pressure (Tseng et al., 2024 ), and unintentional injuries and fatalities (Vila, 2006 ; Rajaratnam et al., 2011 ). Despite high prevalence and severe consequences, sleep disturbances continue to be underdiagnosed (Grandner & Chakravorty, 2017 ) and undertreated (Bragg et al., 2019 ), particularly among LEOs (Gullion et al., 2025 ). Despite elevated rates of burnout and sleep disturbances – and the resultant negative consequences for public safety – effective LEO trainings and interventions are lacking. Results from several meta-analyses suggest standard psychological interventions are not effective in reducing LEO and other first responder stress and related outcomes, such as burnout and sleep disturbances (Patterson et al., 2014 ; Alshahrani et al., 2022 ). Mindfulness-based interventions (MBIs), however, may be a promising alternative intervention that can be adapted to the fast-paced, high-impact, and unpredictable settings in which LEOs are trained to respond. MBIs have shown promise in reducing the negative effects of burnout and sleep disturbances among members of other high-stress professions, such as physicians (Fendel et al., 2021 ) and nurses (Al-Hammouri & Rababah, 2024 ). A key, practical component of these interventions is training individuals to enhance their focus using accessible tools such as breathing techniques and grounding practices that support present-moment awareness. Sharp focus and present-moment awareness are vital for LEOs handling firearms, operating in high-risk situations, and making split-second decisions that can be the difference between fatal and non-fatal outcomes. Nonjudgment, nonreactivity, and acting with awareness are three specific facets of mindfulness that may be particularly beneficial to the unique needs of LEOs. Nonjudgment is the ability to observe thoughts and emotions without self-criticism (Baer et al., 2006 ). Kinnunen et al. ( 2020 ) found benefits of enhanced nonjudgment following an MBI, including sustained reductions in burnout observed at 10-month follow-up. Nonreactivity is the ability to allow thoughts and emotions to arise without becoming cognitively and emotionally engaged with them (Baer et al., 2006 ). In a sample of LEOs, nonreactivity moderated the relationship between operational stressors and perceived stress, suggesting it may be effective in mitigating the impact of stress-related outcomes such as burnout and sleep disturbances (Kaplan et al., 2018 ). Acting with awareness is focusing on the present moment rather than operating on autopilot (Baer et al., 2006 ). Hülsheger et al. ( 2013 ) found that higher levels of acting with awareness were associated with reduced occupational cognitive rumination, a cycle of overthinking about past events or potential future difficulties, which has been found to be a perpetuating factor of sleep disturbances. Burnout and sleep disturbances are two prevalent and interrelated negative health outcomes for LEOs and represent important targets for therapeutic intervention. Across several small intervention trials, Christopher et al. ( 2016 , 2018 , 2024 ) examined the effectiveness of a Mindfulness-Based Resilience Training (MBRT), an MBI designed to address the specific needs and culture of LEOs. They found significant reductions in burnout and sleep disturbances, along with improvements in several other health outcomes. Given the particularly pernicious effects of burnout and sleep disturbances on LEO health, well-being, and job performance, the aim of the present study was to examine which specific facets of mindfulness accounted for the most significant improvement in these outcomes. More specifically, using data pooled from several small MBRT trials, we examined whether improvements in nonjudgment, nonreactivity, and acting with awareness facets predicted reduced burnout and sleep disturbances at post-training. Method Participants and Procedure LEOs were recruited from urban police departments in the Pacific Northwest, the Southwest, and the Upper Midwest, comprising a total of 15 law enforcement agencies. To be eligible, participants needed to be sworn LEOs. Data were combined from three separate randomized clinical trials assessing the impact of MBRT on health and performance outcomes among LEOs. Study 1 included n = 59 participants at baseline (see Christopher et al. ( 2016 ) for study details), study 2 included n = 61 participants at baseline (see Christopher et al. ( 2018 ) for study details), and study 3 included n = 109 participants at baseline (see Christopher et al. ( 2024 ) for study details). Study 1 was a single-arm pilot trial in which all 59 participants were enrolled in MBRT. In study 2, after baseline assessment, participants were randomized to an 8-week MBRT ( n = 31) or no-intervention control (NIC, n = 30) condition. In study 3, after baseline assessment, participants were randomized to MBRT ( n = 33), an active control condition ( n = 24), or NIC ( n = 16). Participants in the current study were comprised of the pooled subsample of participants who were enrolled in, and completed MBRT, in studies 1 ( n = 43), 2 ( n = 24), and 3 ( n = 31) for a total n = 98. At baseline, mean participant age was 41.98 years ( SD = 8.10) and mean years as an LEO was 14.43 ( SD = 7.40). Thirty-one percent ( n = 30) of participants identified as female and 69% as male ( n = 68). In terms of race, 85.6% ( n = 84) of participants identified as White American, 2.4% ( n = 2) as Black/African American, 4.8% ( n = 5) as Asian American, 3.2% ( n = 3) as Native American, and 4% ( n = 5) identified as other, and in terms of ethnicity 16.7% ( n = 17) identified as Hispanic or Latino and 83.3% ( n = 81) identified as Not Hispanic or Latino. Measures Self-report measures The Patient Reported Outcome Measurement Information System (PROMIS®; Yu et al., 2012 0 short form version was used to assess sleep disturbance (6-items). PROMIS® sleep disturbances measure ranges from 32–76, with higher scores indicating a higher rate of the measured outcome. Scores were converted to standardized T scores ( M = 50; SD = 10), centered on the U.S. population mean. The PROMIS® sleep disturbances demonstrated acceptable internal consistency and correlations with expected legacy measures (Yu et al, 2012 ). In the present sample, internal consistency was good to excellent (pre-MBRT α = 0.83; post-MBRT α = 0.90). The Oldenburg Burnout Inventory (OLBI; Demerouti et al, 2003 ; Halbesleben & Demerouti, 2005 ) is a 16-item measure of burnout that assesses exhaustion and disengagement from work. The OLBI ranges from 1–4, with higher scores indicating greater burnout. The OLBI has acceptable internal consistency, factorial validity, and expected correlations with other constructs (Demerouti & Bakker, 2010). In the present sample, the internal consistency was excellent (pre-MBRT α = 0.92; post-MBRT α = 0.92). The Five Facet Mindfulness Questionnaire-Short Form (FFMQ-24; Bohlmeijer et al., 2011 ), a 24-item version of the FFMQ (Baer et al., 2006 ), assesses the dispositional tendency to be mindful in daily life. Items are rated on a 5-point Likert-type scale (1 = never or very rarely true to 5 = very often or always true ). The observing and describing facets of the scale have demonstrated weaker psychometric properties and issues with novice and non-meditating samples (de Bruin et al., 2012 ; Lilja et al., 2011 ). Thus, the current study used three of the five facets—acting with awareness, nonjudging of experience, and nonreactivity to inner experience to create a total mindfulness score. Each facet has five items, resulting in a 15-item scale ranging from 15 to 75, with higher scores indicating greater mindfulness. In the present sample, the internal consistency was good to excellent (acting with awareness pre-MBRT α = 0.86; post-MBRT α = 0.94, nonjudging of experiences pre-MBRT α = 0.92; post-MBRT α = 0.88, and nonreactivity to inner experience pre-MBRT α = 0.88; post-MBRT α = 0.88). Intervention MBRT is a culturally tailored group-based program integrating training in mindfulness practices to facilitate stress resilience with principles of cognitive behavioral therapy and psychoeducation. The curriculum structure is modeled after the mindfulness-based relapse prevention clinical protocol (Bowen et al., 2021 ). Content and language have been altered to be more relevant to LEOs, with an emphasis on working with reactivity to stressors inherent to police work, including critical incidents, job dissatisfaction, public scrutiny, and other job-related challenges. MBRT was delivered in 8 weekly group sessions. The first session was an extended 6-hour intensive introduction to mindfulness training, and week 7 was a 4-hour intensive practice session. Other sessions lasted one hour. Sessions include didactic content, experiential exercises such as the body scan, sitting and walking meditation, mindful movement, and compassion practice, as well as small- and large-group discussions. Resilience-enhancing practices, including those focused on acceptance, stress reactivity/recovery, and self-compassion, emphasize didactic and experiential exercises that facilitate recovery from stressors. Data analytic approach To create measures of change across the training, we regressed each variable at the end of the training on the same variable at baseline and saved the standardized residuals (e.g., we regressed responses on the FFMQ at post-MBRT on FFMQ responses at baseline), creating a residualized change score variable for each measure. To determine whether increases in each of the three facets of mindfulness (i.e., acting with awareness, nonjudging of inner experience, and nonreactivity) were predictive of sleep disturbance and burnout, a hierarchical linear regression analyses was used. In the regression, acting with awareness, nonjudging, and nonreactivity residualized change scores were entered as predictors of post-MBRT sleep disturbance (or burnout) scores in step two, after controlling for pre-MBRT sleep disturbance (or burnout) in step one. Data were missing completely at random, and we therefore used a complete case analysis approach (Ross et al., 2020 ). Results All variables approximated a normal distribution, with no problematic univariate or multivariate outliers. As shown in Table 1 , the means for all variables moved in the expected direction from pre- to post-MBRT, and all changes were statistically significant. Table 1 Pre- and Post-Mindfulness-Based Resilience Training Study Outcomes (N = 98) Pre M (SD) Post M (SD) t Cohen’s d Sleep Disturbances 52.89 (7.25) 49.94 (7.48) 4.26** 6.99 Burnout 2.42 (0.43) 2.25 (0.43) 4.26** .41 Nonjudging 3.40 (0.80) 3.74 (0.71) -5.02** .69 Nonreactivity 3.23 (0.72) 3.60 (0.66) -4.90** .75 Acting With Awareness 3.23 (0.83) 3.58 (0.66) -4.45** .78 Note. All p -values are two-tailed. ** p < .01 In the sleep disturbances regression model (Table 2 ), pre-MBRT sleep disturbance accounted for a significant amount of variance in the prediction of post-MBRT sleep disturbance (ΔR 2 = .36, ΔF = 55.84, p < .001) in step one. In the second step, adding the residualized change scores for the three facets of mindfulness also accounted for a significant amount of variance (ΔR 2 = .12, ΔF = 7.34, p < .001). Improved nonjudging (β = − .22, p = .005, sr 2 = .08) and nonreactivity (β = − .16, p = .05, sr 2 = .04) were significant predictors of reductions in post-MBRT sleep disturbance, but acting with awareness (β = − .08, p = .31, sr 2 = .01) was not. In the burnout regression model (Table 2 ), pre-MBRT burnout accounted for a significant amount of variance in the prediction of post-MBRT burnout (ΔR 2 = .34, ΔF = 50.01, p < .001) in step one. In the second step, adding the residualized change scores for the three facets of mindfulness also accounted for a significant amount of variance (ΔR 2 = .19, ΔF = 13.04, p < .001). Improved nonjudging (β = − .22, p = .003, sr 2 = .08) and acting with awareness (β = − .23, p = .003, sr 2 = .09) were significant predictors of reductions in post-MBRT burnout, but nonreactivity (β = − .15, p = .06, sr 2 = .04) was not. Table 2 Hierarchical Multiple Regression Predicting Post-MBRT Sleep Disturbances and Burnout (N = 98) Unstandardized Coefficients Outcome & Step Predictor B SE β t R² R² change F change Sleep Disturbances1 . 36 .36 55.84** Pre-MBRT Sleep Disturbances .59 .08 .60 7.47** 2 .48 .11 7.34** Nonjudging -1.60 .56 − .22 -2.85* Nonreactivity -1.14 .58 − .16 -1.96* Acting with Awareness − .59 .57 − .08 -1.03 Burnout 1 .34 .34 50.04** Pre-MBRT Burnout .58 .08 .58 7.07** 2 .53 .19 13.04** Nonjudging -1.54 .51 − .22 -3.00* Nonreactivity -1.01 .54 − .15 -1.88 Acting with Awareness -1.60 .52 − .23 -3.08* * p < .05, ** p < .01 Discussion The present study was a secondary analysis of LEOs who participated in one of several MBRT trials. The particular focus of this study was to determine which mindfulness facets accounted for the most variance in the reduction of burnout and sleep disturbances among LEOs to better understand the role specific mindfulness facets play in improving officer health. Increases in nonjudging and nonreactivity significantly predicted reductions in sleep disturbances. These findings converge with existing literature suggesting that increased nonjudging leads to reductions in sleep disturbances among healthy adults and psychiatric inpatients (Ioverno et al., 2022 ; Smith et al., 2020). Nonjudging may reduce internalized stress and negative self-evaluation that interfere with sleep, thus mitigating sleep disturbances (Garland et al., 2014). Similarly, nonreactivity is related to reductions in sleep disturbance among young adults and individuals with sleep anxiety (Gao et al., 2022 ; Jaurequi et al., 2022). Nonreactivity may allow LEOs to disengage from arousing thoughts, such as those related to their high-stress occupation, which interfere with sleep and promote fewer sleep disturbances (Hoeve et al., 2021 ). Analyses also revealed that increases in nonjudging and acting with awareness significantly predicted reductions in burnout among LEOs, confirming study hypotheses and converging with existing literature. This converges with previous research suggesting that acting with awareness predicted reduced burnout among nurses (Zhao et al., 2019 ) and that increased nonjudging predicted decreased burnout in general employees (Kinnunen et al., 2020 ). Among LEOs, acting with awareness predicted improvements in stress (Hoeve et al., 2021 ). Contrary to hypotheses, improvements in acting with awareness did not predict reduced sleep disturbances, and nonreactivity did not predict reduced burnout post-MBRT. These findings contradict existing literature suggesting that nonreactivity predicts reductions in stress among other high-stress populations, such as healthcare workers (Benzo et al., 2018 ). Nonreactivity alone, but not acting with awareness, may help LEOs tolerate occupational stressors without addressing cognitive appraisals that contribute to burnout. Nonjudging was the only mindfulness facet that predicted both reduced sleep disturbances and burnout among LEOs. Nonjudging practices may be useful for LEOs and those in other high-stress professions because they may provide officers opportunities to engage neutrally with their inner experiences, disrupting rumination and self-criticism that amplify stress reactivity. By noticing thoughts and sensations without judgment, LEOs may reduce physiological arousal and improve threat discrimination under pressure, which together may lower burnout risk and improve sleep quality. Overall, these findings highlight the importance of mindfulness training – particularly the cultivation of nonjudgment, nonreactivity, and acting with awareness – in bolstering the physical and mental well-being of LEOs. Improvements in outcomes such as sleep disturbances and burnout among this sample may not only promote healthier behavior and engagement among LEOs but also within the communities they serve. Such findings emphasize the important role of mindfulness training in mitigating the far-reaching effects of burnout and sleep disturbances within these populations, including reduced aggression and use of excessive force toward civilians (Ribeiro, 2020). Several limitations should be acknowledged when interpreting the results of this study. First, this was a largely homogeneous sample (primarily white males), which precludes generalizability of findings. Future studies with larger and more diverse LEO samples are needed. Second, reliance on self-report measures may have introduced social desirability bias, particularly among LEOs, who may be less inclined to underreport mental health concerns. Third, although the study identified which mindfulness facets were most strongly associated with reductions in sleep disturbances and burnout, thereby informing their potential relevance to health and well-being in this population, causal interpretations cannot be made due to the correlational nature of the analyses. Additionally, unmeasured variables may have contributed to the observed relationships, and alternative explanations cannot be ruled out. Despite these limitations, the impact of nonjudgment, nonreactivity, and acting with awareness on LEO outcomes underscores the importance of integrating these facets into future training initiatives and policy development. Formal MBIs, as well as streamlined app-based mindfulness tools for LEOs, should prioritize enhancing nonreactivity to reduce sleep disturbances, support officers in acting with awareness to mitigate burnout, and cultivate nonjudgmental work environments that normalize self-care and incorporate mindfulness into training to reduce negative stress-related outcomes. Future researchers should consider investigating how mindfulness training can be integrated into LEO occupational culture and leadership approaches using various strategies to enhance dissemination and uptake. Such approaches may help identify barriers to implementing mindfulness practices within law enforcement agencies, thus providing a more robust understanding of how to effectively integrate contemplative practices, including those that emphasize nonjudgment, nonreactivity, and acting with awareness, in this high-stress occupational setting. Declarations Funding Research reported in this publication was supported by the National Center for Complementary & Integrative Health of the National Institutes of Health under Award Numbers R21AT008854 and R01AT009841. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Conflicts of interest/Competing interests [masked for review] received funding from the National Institutes of Health during the conduct of the study. All other co-authors have no funding to disclose. My co-authors and I do not have any interests that might be interpreted as influencing this research Ethics approval The Pacific University IRB approved all procedures (IRB# 089-15 and 090-18). Additionally, all relevant American Psychological Association ethical standards and the code of ethics of the World Medical Association (Declaration of Helsinki) were followed in the conduct of the study. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent for publication All authors provided consent to submit this manuscript for publication. Availability of data and materials The datasets in the current study are available from the corresponding author on reasonable request. References Alfonsi, V., Scarpelli, S., Gorgoni, M., Pazzaglia, M., Giannini, A. M., & De Gennaro, L. (2021). Sleep-related problems in night shift nurses: towards an individualized interventional practice. Frontiers in Human Neuroscience , 15 , 644570. https://doi.org/10.3389/fnhum.2021.644570 Al‐Hammouri, M. M., & Rababah, J. A. (2024). A brief mindfulness‐based intervention, sleep quality, sleep duration, and fatigue among nurses: A randomized controlled trial. Journal of Clinical Psychology , 80 (7), 1504-1514. https://doi.org/10.1002/jclp.23677 Alshahrani, K. M., Johnson, J., Prudenzi, A., & O’Connor, D. B. (2022). The effectiveness of psychological interventions for reducing PTSD and psychological distress in first responders: A systematic review and meta-analysis. PLOS ONE, 17 (8), e0272732. https://doi.org/10.1371/journal.pone.0272732 Baer, R. A., Smith, G. T., Hopkins, J., Krietemeyer, J., & Toney, L. (2006). Using self-report assessment methods to explore facets of mindfulness. Assessment, 13 (1), 27-45. https://doi.org/10.1177/1073191105283504 Benzo, R. P., Anderson, P. M., Bronars, C., & Clark, M. (2018). Mindfulness for Healthcare Providers: The Role of Non-Reactivity in Reducing Stress. EXPLORE, 14(6), 453-456. https://doi.org/https://doi.org/10.1016/j.explore.2018.03.008 Bishopp, S. A., & Boots, D. P. (2014). General strain theory, exposure to violence, and suicide ideation among police officers: A gendered approach. Journal of Criminal Justice, 42 (6), 538-548. https://doi.org/10.1016/j.jcrimjus.2014.09.007 Bohlmeijer, E., Ten Klooster, P. M., Fledderus, M., Veehof, M., & Baer, R. (2011). Psychometric properties of the Five Facet Mindfulness Questionnaire in depressed adults and development of a short form. Assessment, 18 (3), 308-320. https://doi.org/10.1177/1073191111408231 Bowen, S., Chawla, N., Grow, J., & Marlatt, G. A. (2021). Mindfulness-based relapse prevention for addictive behaviors . Guilford Publications. Bragg, S., Benich, J., Christian, N., Visserman, J., & Freedy, J. (2019). Updates in insomnia diagnosis and treatment. The International Journal of Psychiatry in Medicine, 54 (4–5), 275–289. https://doi.org/10.1177/0091217419860716 Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., & Kupfer, D.J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28 , 193–213. Chen, Y. H., Yeh, C. J., Pan, L. F., & Jong, G. P. (2022). Relationships between alcohol use, musculoskeletal pain, and work-related burnout. Medicina , 58 (8), 1022. https://doi.org/10.3390/medicina58081022 Christopher, M. S., Goerling, R. J., Rogers, B. S., Hunsinger, M., Baron, G., Bergman, A. L., & Zava, D. T. (2016). A pilot study evaluating the effectiveness of a mindfulness-based intervention on cortisol awakening response and health outcomes among law enforcement officers. Journal of police and criminal psychology, 31 , 15-28. https://doi.org/10.1007/s11896-015-9161-x Christopher, M. S., Hunsinger, M., Goerling, L. R. J., Bowen, S., Rogers, B. S., Gross, C. R., Dapolonia, E., & Pruessner, J. C. (2018). Mindfulness-based resilience training to reduce health risk, stress reactivity, and aggression among law enforcement officers: A feasibility and preliminary efficacy trial. Psychiatry research, 264 , 104-115. https://doi.org/10.1016/j.psychres.2018.03.059 Christopher, M., Bowen, S., Witkiewitz, K., Grupe, D., Goerling, R., Hunsinger, M., Oken, B., Korecki, T., & Rosenbaum, N. (2024). A multisite feasibility randomized clinical trial of mindfulness-based resilience training for aggression, stress, and health in law enforcement officers. BMC complementary medicine and therapies, 24 (1), 142. https://doi.org/10.1186/s12906-024-04452-y Chopko, B. A., Palmieri, P. A., & Adams, R. E. (2021). Trauma-related sleep problems and associated health outcomes in police officers: A path analysis. Journal of interpersonal violence, 36 (5-6), NP2725-NP2748. https://doi.org/10.1177/0886260518767912 de Bruin, E. I., Topper, M., Muskens, J. G. A. M., Bögels, S. M., & Kamphuis, J. H. (2012). Psychometric properties of the Five Facets Mindfulness Questionnaire (FFMQ) in a meditating and a non-meditating sample. Assessment, 19 (2), 187-197. https://doi.org/10.1177/1073191112446654 Demerouti, E., Bakker, A. B., Vardakou, I., & Kantas, A. (2003). The convergent validity of two burnout instruments: A multitrait-multimethod analysis. European Journal of Psychological Assessment, 19 (1), 12. https://doi.org/10.1027/1015-5759.19.1.12 Demerouti, E., Mostert, K., & Bakker, A. B. (2010). Burnout and work engagement: a thorough investigation of the independency of both constructs. Journal of occupational health psychology, 15 (3), 209. https://doi.org/10.1037/a0019408 El Sayed, S. A., Sanford, S. M., & Kerley, K. R. (2019). Understanding workplace stress among federal law enforcement officers. American journal of criminal justice, 44 , 409-429. https://doi.org/10.1007/s12103-019-09474-8 Fendel, J. C., Bürkle, J. J., & Göritz, A. S. (2021). Mindfulness-based interventions to reduce burnout and stress in physicians: a systematic review and meta-analysis. Academic Medicine , 96 (5), 751-764. DOI: 10.1097/ACM.0000000000003936 Gau, J. M., & Brunson, R. K. (2015). Procedural injustice, lost legitimacy, and self-help: Young males’ adaptations to perceived unfairness in urban policing tactics. Journal of contemporary criminal justice, 31 (2), 132-150. https://doi.org/10.1177/1043986214568841 Glick, D. R., Abariga, S. A., Thomas, I., Shipper, A. G., Gunia, B. C., Grandner, M. A., ... & Wickwire, E. M. (2023). Economic impact of insufficient and disturbed sleep in the workplace. Pharmacoeconomics , 41 (7), 771-785. https://doi.org/10.1007/s40273-023-01249-8 Gao, M., Roy, A., Deluty, A., Sharkey, K. M., Hoge, E. A., Liu, T., & Brewer, J. A. (2022). Targeting Anxiety to Improve Sleep Disturbance: A Randomized Clinical Trial of App-Based Mindfulness Training. Biopsychosocial Science and Medicine , 84 (5), 632-642. https://doi.org/10.1097/psy.0000000000001083 Grandner, M. A., & Chakravorty, S. (2017). Insomnia in primary care: Misreported, mishandled, and just plain missed. Journal of Clinical Sleep Medicine, 13 (8), 937–939. https://doi.org/10.5664/jcsm.6688 Gullion, C. L., Spadola, C. E., Groton, D. B., Fallik, S. W., & Guastaferro, W. P. (2025). “Sleep affects everything”: understanding challenges and interventions for police sleep health. Policing: An International Journal . https://doi.org/10.1108/PIJPSM-12-2024-0209 Halbesleben, J. R., & Demerouti, E. (2005). The construct validity of an alternative measure of burnout: Investigating the English translation of the Oldenburg Burnout Inventory. Work & stress, 19 (3), 208-220. https://doi.org/10.1080/02678370500340728 Hoeve, M., de Bruin, E. I., van Rooij, F., & Bögels, S. M. (2021). Effects of a mindfulness-based intervention for police officers. Mindfulness , 12 (7), 1672–1684. https://doi.org/10.1007/s12671-021-01631-7 Hülsheger, U. R., Alberts, H. J., Feinholdt, A., & Lang, J. W. (2013). Benefits of mindfulness at work: the role of mindfulness in emotion regulation, emotional exhaustion, and job satisfaction. Journal of applied psychology, 98 (2), 310. https://doi.org/10.1037/a0031313 Iqbal, S., Howse, J., Banu, S., Lateef, A., Khan, S., Barlas, Z., Moon, S., Moukaddam, N., & Shah, A.A.. (2024). The effects of stress on health. Psychiatric Annals, 54 (10), e272-e276. https://doi.org/10.3928/00485713-20241004-02 Ioverno, S., Pallini, S., Farina, F., Lizzadro, F., & Barcaccia, B. (2022). The less you judge, the better you sleep: The benefits of mindfulness and forgiveness for insomnia and sleep problems. Bulletin of the Menninger Clinic , 86 (4), 324-338. https://doi.org/10.1521/bumc.2022.86.4.324 Jacobs, C. (2024). Occupational stress and burnout. Jetelina, K., Molsberry, R., Gonzalez, J., Beauchamp, A., & Hall, T. (2020). Prevalence of Mental Illness and Mental Health Care Use Among Police Officers. JAMA Network Open, 3 (10), e2019658-e2019658. doi:10.1001/jamanetworkopen.2020.19658 John, A., Bouillon-Minois, J. B., Bagheri, R., Pélissier, C., Charbotel, B., Llorca, P. M., Zak, M., Ugbolue, U. C., Baker, J. S., & Dutheil, F. (2024). The influence of burnout on cardiovascular disease: a systematic review and meta-analysis. Frontiers in Psychiatry, 15 , 1326745. https://doi.org/10.3389/fpsyt.2024.1326745 Kaplan, J. B., Christopher, M. S., & Bowen, S. (2018). Dispositional mindfulness moderates the relationship between occupational stressors and perceived stress among law enforcement personnel. Journal of Police and Criminal Psychology , 33 , 227-232. https://doi.org/10.1007/s11896-017-9246-9 Khoshakhlagh, A. H., Al Sulaie, S., Yazdanirad, S., Orr, R. M., Dehdarirad, H., & Milajerdi, A. (2023). Global prevalence and associated factors of sleep disorders and poor sleep quality among firefighters: A systematic review and meta-analysis. Heliyon , 9 (2). DOI: 10.1016/j.heliyon.2023.e13250 Kinnunen, S. M., Puolakanaho, A., Tolvanen, A., Mäkikangas, A., & Lappalainen, R. (2020). Improvements in mindfulness facets mediate the alleviation of burnout dimensions. Mindfulness, 11 , 2779-2792. https://doi.org/10.1007/s12671-020-01490-8 Li, R., & Yao, M. (2022). What promotes teachers’ turnover intention? Evidence from a meta-analysis. Educational Research Review, 37 , 100477. https://doi.org/10.1016/j.edurev.2022.100477 Lilja, J. L., Frodi-Lundgren, A., Hanse, J. J., Josefsson, T., Lundh, L.-G., Sköld, C., Hansen, E., & Broberg, A. G. (2011). Five Facets Mindfulness Questionnaire—reliability and factor structure: A Swedish version. Cognitive Behaviour Therapy, 40 (4), 291-303. https://doi.org/10.1080/16506073.2011.580367 Márquez, M. A., Galiana, L., Oliver, A., & Sansó, N. (2021). The impact of a mindfulness‐based intervention on the quality of life of Spanish national police officers. Health & Social Care in the Community , 29 (5), 1491–1501. https://doi.org/10.1111/hsc.13209 McCarty, W. P., Aldirawi, H., Dewald, S., & Palacios, M. (2019). Burnout in Blue: An Analysis of the Extent and Primary Predictors of Burnout Among Law Enforcement Officers in the United States. Police Quarterly , 22 (3), 278-304. https://doi.org/10.1177/1098611119828038 Morgado, P., Sousa, N., & Cerqueira, J. J. (2015). The impact of stress in decision making in the context of uncertainty. Journal of Neuroscience Research, 93 (6), 839-847. https://doi.org/10.1002/jnr.23521 Nordin, G., Sundqvist, R., Nordin, S., & Gruber, M. (2023). Somatic symptoms in sleep disturbance. Psychology, Health & Medicine, 28 (4), 884-894. https://doi.org/10.1080/13548506.2021.1985149 Padyab, M., Backteman-Erlanson, S., & Brulin, C. (2016). Burnout, coping, stress of conscience and psychosocial work environment among patrolling police officers. Journal of police and Criminal Psychology, 31 , 229-237. https://doi.org/10.1007/s11896-015-9189-y Patterson, G. T., Chung, I. W., & Swan, P. W. (2014). Stress management interventions for police officers and recruits: a meta-analysis. Journal of experimental criminology, 10 , 487-513. https://doi.org/10.1007/s11292-014-9214-7 Peterson, S. A., Wolkow, A. P., Lockley, S. W., O'Brien, C. S., Qadri, S., Sullivan, J. P., Czeisler, C. A., Rajaratnam, S. M. W., & Barger, L. K. (2019). Associations between shift work characteristics, shift work schedules, sleep and burnout in North American police officers: a cross-sectional study. BMJ open, 9 (11), e030302. doi: 10.1136/bmjopen-2019-030302 Poirier, S., Allard-Gaudreau, N., Gendron, P., Houle, J., & Trudeau, F. (2023). Health, safety, and wellness concerns among law enforcement officers: an inductive approach. Workplace health & safety , 71 (1), 34-42. https://doi.org/10.1177/21650799221134422 Rajaratnam, S. M., Barger, L. K., Lockley, S. W., Shea, S. A., Wang, W., Landrigan, C. P., O'Brien, C. S., Qadri, S., Sullivan, J. P., Cade, B. E., Epstein, L. J., White, D. P., Czeisler, C. A., & Harvard Work Hours, Health and Safety Group. (2011). Sleep disorders, health, and safety in police officers. Jama, 306 (23), 2567-2578. doi:10.1001/jama.2011.1851 Ross, R. K., Breskin, A., & Westreich, D. (2020). When is a complete-case approach to missing data valid? The importance of effect-measure modification. American journal of epidemiology, 189 (12), 1583-1589. https://doi.org/10.1093/aje/kwaa124 Ryan, E., Hore, K., Power, J., & Jackson, T. (2023). The relationship between physician burnout and depression, anxiety, suicidality and substance abuse: A mixed methods systematic review. Frontiers in public health , 11 , 1133484. https://doi.org/10.3389/fpubh.2023.1133484 Sørengaard, T. A., Olsen, A., Langvik, E., & Saksvik-Lehouillier, I. (2021). Associations between sleep and work-related cognitive and emotional functioning in police employees. Safety and health at work , 12 (3), 359-364. https://doi.org/10.1016/j.shaw.2021.03.002 Stegerhoek, P., Kooijman, K., Ziesemer, K., IJzerman, H., Kuijer, P. P. F., & Verhagen, E. (2024). Risk factors for adverse health in military and law enforcement personnel; an umbrella review. BMC Public Health, 24 (1), 3151. https://doi.org/10.1186/s12889-024-20553-2 Stormer, M. R. (2021). Occupational stress and job satisfaction in law enforcement: Contributing factors and the roles leaders play (Doctoral dissertation, The Chicago School of Professional Psychology). Thomson-Devaux, A., Bronner, L., & Sharma, D. (2021). Police misconduct costs cities millions every year. but that’s where the accountability ends. The Marshall Project. https://www.themarshallproject.org/2021/02/22/police-misconduct-costs-cities-millions-every-year-but-that-s-where-the-accountability-ends. Tseng, Y. J., Leicht, A. S., Pagaduan, J. C., Chien, L. C., Wang, Y. L., Kao, C. S., ... & Chen, Y. S. (2024). Effects of shift work on sleep quality and cardiovascular function in Taiwanese police officers. Chronobiology International , 41 (4), 530-538. https://doi.org/10.1080/07420528.2024.2324023 Vila, B. (2006). Impact of long work hours on police officers and the communities they serve. American journal of industrial medicine, 49 (11), 972-980. https://doi.org/10.1002/ajim.20333 Yu, L., Buysse, D. J., Germain, A., Moul, D. E., Stover, A., Dodds, N. E., ... & Pilkonis, P. A. (2012). Development of short forms from the PROMIS™ sleep disturbance and sleep-related impairment item banks. Behavioral sleep medicine, 10 (1), 6-24. https://doi.org/10.1080/15402002.2012.636266 Zhao, J., Li, X., Xiao, H., Cui, N., Sun, L., & Xu, Y. (2019). Mindfulness and burnout among bedside registered nurses: A cross-sectional study. Nursing & Health Sciences , 21 (1), 126-131. https://doi.org/https://doi.org/10.1111/nhs.12582 Additional Declarations Competing interest reported. Dr. Christopher received funding from the National Institutes of Health during the conduct of the study. Ms. Pham, Ms. Penberthy, Ms. Boyd have no funding to disclose. My co-authors and I do not have any interests that might be interpreted as influencing this research. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7926560","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595231249,"identity":"5bb49d28-87cc-4adf-a178-8d8c486ef334","order_by":0,"name":"Kaylia Pham","email":"","orcid":"","institution":"Pacific University","correspondingAuthor":false,"prefix":"","firstName":"Kaylia","middleName":"","lastName":"Pham","suffix":""},{"id":595231250,"identity":"e1bdd0df-db6d-42ba-a52d-2973b1cb6cf1","order_by":1,"name":"J. Morgan Penberthy","email":"","orcid":"","institution":"Pacific University","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"Morgan","lastName":"Penberthy","suffix":""},{"id":595231251,"identity":"00c7c5a2-bab0-4e75-887e-db29e1a3ff6f","order_by":2,"name":"Olivia Boyd","email":"","orcid":"","institution":"Pacific University","correspondingAuthor":false,"prefix":"","firstName":"Olivia","middleName":"","lastName":"Boyd","suffix":""},{"id":595231252,"identity":"5b3c2e8d-b023-43ad-b062-adc9334efd40","order_by":3,"name":"Michael Christopher","email":"data:image/png;base64,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","orcid":"","institution":"Boise State University","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"","lastName":"Christopher","suffix":""}],"badges":[],"createdAt":"2025-10-22 20:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7926560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7926560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505067,"identity":"29706b62-57f9-4243-af08-412a36a9fe89","added_by":"auto","created_at":"2026-02-26 13:22:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":674776,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7926560/v1/58882c8c-5061-45a2-9334-f7c751c3b55a.pdf"}],"financialInterests":"Competing interest reported. Dr. Christopher received funding from the National Institutes of Health during the conduct of the study. Ms. Pham, Ms. Penberthy, Ms. Boyd have no funding to disclose. My co-authors and I do not have any interests that might be interpreted as influencing this research.","formattedTitle":"Improvements in Mindfulness Facets Predict Burnout and Sleep Disturbance Outcomes in Law Enforcement Officers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLaw enforcement officers (LEOs) encounter numerous operational stressors, including violence (e.g., domestic violence, child abuse, homicide), danger (e.g., life-threatening assaults), and mental and physical trauma, resulting in higher levels of stress relative to most other professions (Bishopp \u0026amp; Boots, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; El Sayed et al, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These are compounded by organizational stressors such as shift changes, lack of departmental support, and insufficient resources (Poirier et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Stormer, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, prolonged exposure to unique stressors for LEOs increases risk for adverse health outcomes. These health risks include higher rates of burnout, sleep disturbance, cardiovascular disease, high blood pressure, post-traumatic stress disorder (PTSD), depression, suicidality, and anxiety, compared to the general population (Iqbal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Stegerhoek et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBurnout, a long-lasting psychological strain characterized by exhaustion and reduced interest in work, is a severe consequence of ongoing occupational stress (Jacobs, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is estimated that approximately 20% of LEOs across the U.S. experience severe levels of burnout (McCarty et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Chronic stress contributes to burnout, and it is associated with poor physical health outcomes, including cardiovascular diseases, musculoskeletal pain, and respiratory diseases (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; John et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent meta-analyses indicate burnout is linked to additional negative behavioral outcomes, such as increased turnover (Li \u0026amp; Yao, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and alcohol misuse (Ryan et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Specific to the context of policing, LEOs experiencing burnout are more likely to distance themselves from the public while on duty (Padyab et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), be overly aggressive (Kurtz et al., 2015), use excessive force (Jetelina et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and show poorer decision-making (Morgado et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As a result, U.S. cities spend millions of dollars each year on police misconduct cases (Thomson-Devaux \u0026amp; Sharma, 2021). This increases the risk of heightened tensions between LEOs, community stakeholders, and the public, deteriorating trust and cooperation (Gau \u0026amp; Brunson, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSleep disturbances, which are disruptions in the quality, timing, or duration of sleep that impair daytime functioning and overall well-being, are linked to burnout (Peterson et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nordin et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Results from a large meta-analysis (Garbarino et al., 2019) indicate that 51% of LEOs experience sleep disturbances as measured by the Pittsburgh Sleep Quality Index (Buysse et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), and Rajaratnam et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found that 40.4% of LEOs screen positive for at least one sleep disorder. Similar to burnout, sleep disturbances have been shown to result in negative outcomes across professions, such as increased risk of errors (Alfonsi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), workplace accidents (Glick et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and musculoskeletal disorders (Khoshakhlagh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sleep disturbances among LEOs have been linked to PTSD (Chopko et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), impaired cognitive functioning (S\u0026oslash;rengaard et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), elevated systolic blood pressure (Tseng et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and unintentional injuries and fatalities (Vila, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rajaratnam et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Despite high prevalence and severe consequences, sleep disturbances continue to be underdiagnosed (Grandner \u0026amp; Chakravorty, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and undertreated (Bragg et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), particularly among LEOs (Gullion et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite elevated rates of burnout and sleep disturbances \u0026ndash; and the resultant negative consequences for public safety \u0026ndash; effective LEO trainings and interventions are lacking. Results from several meta-analyses suggest standard psychological interventions are not effective in reducing LEO and other first responder stress and related outcomes, such as burnout and sleep disturbances (Patterson et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Alshahrani et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mindfulness-based interventions (MBIs), however, may be a promising alternative intervention that can be adapted to the fast-paced, high-impact, and unpredictable settings in which LEOs are trained to respond. MBIs have shown promise in reducing the negative effects of burnout and sleep disturbances among members of other high-stress professions, such as physicians (Fendel et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and nurses (Al-Hammouri \u0026amp; Rababah, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A key, practical component of these interventions is training individuals to enhance their focus using accessible tools such as breathing techniques and grounding practices that support present-moment awareness. Sharp focus and present-moment awareness are vital for LEOs handling firearms, operating in high-risk situations, and making split-second decisions that can be the difference between fatal and non-fatal outcomes.\u003c/p\u003e \u003cp\u003eNonjudgment, nonreactivity, and acting with awareness are three specific facets of mindfulness that may be particularly beneficial to the unique needs of LEOs. Nonjudgment is the ability to observe thoughts and emotions without self-criticism (Baer et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Kinnunen et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found benefits of enhanced nonjudgment following an MBI, including sustained reductions in burnout observed at 10-month follow-up. Nonreactivity is the ability to allow thoughts and emotions to arise without becoming cognitively and emotionally engaged with them (Baer et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In a sample of LEOs, nonreactivity moderated the relationship between operational stressors and perceived stress, suggesting it may be effective in mitigating the impact of stress-related outcomes such as burnout and sleep disturbances (Kaplan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Acting with awareness is focusing on the present moment rather than operating on autopilot (Baer et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). H\u0026uuml;lsheger et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found that higher levels of acting with awareness were associated with reduced occupational cognitive rumination, a cycle of overthinking about past events or potential future difficulties, which has been found to be a perpetuating factor of sleep disturbances.\u003c/p\u003e \u003cp\u003eBurnout and sleep disturbances are two prevalent and interrelated negative health outcomes for LEOs and represent important targets for therapeutic intervention. Across several small intervention trials, Christopher et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) examined the effectiveness of a Mindfulness-Based Resilience Training (MBRT), an MBI designed to address the specific needs and culture of LEOs. They found significant reductions in burnout and sleep disturbances, along with improvements in several other health outcomes. Given the particularly pernicious effects of burnout and sleep disturbances on LEO health, well-being, and job performance, the aim of the present study was to examine which specific facets of mindfulness accounted for the most significant improvement in these outcomes. More specifically, using data pooled from several small MBRT trials, we examined whether improvements in nonjudgment, nonreactivity, and acting with awareness facets predicted reduced burnout and sleep disturbances at post-training.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Procedure\u003c/h2\u003e \u003cp\u003eLEOs were recruited from urban police departments in the Pacific Northwest, the Southwest, and the Upper Midwest, comprising a total of 15 law enforcement agencies. To be eligible, participants needed to be sworn LEOs. Data were combined from three separate randomized clinical trials assessing the impact of MBRT on health and performance outcomes among LEOs. Study 1 included \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;59 participants at baseline (see Christopher et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for study details), study 2 included \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61 participants at baseline (see Christopher et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for study details), and study 3 included \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;109 participants at baseline (see Christopher et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for study details). Study 1 was a single-arm pilot trial in which all 59 participants were enrolled in MBRT. In study 2, after baseline assessment, participants were randomized to an 8-week MBRT (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31) or no-intervention control (NIC, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30) condition. In study 3, after baseline assessment, participants were randomized to MBRT (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33), an active control condition (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24), or NIC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16). Participants in the current study were comprised of the pooled subsample of participants who were enrolled in, and completed MBRT, in studies 1 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;43), 2 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24), and 3 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31) for a total \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;98.\u003c/p\u003e \u003cp\u003eAt baseline, mean participant age was 41.98 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.10) and mean years as an LEO was 14.43 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.40). Thirty-one percent (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30) of participants identified as female and 69% as male (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;68). In terms of race, 85.6% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;84) of participants identified as White American, 2.4% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) as Black/African American, 4.8% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5) as Asian American, 3.2% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) as Native American, and 4% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5) identified as other, and in terms of ethnicity 16.7% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17) identified as Hispanic or Latino and 83.3% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;81) identified as Not Hispanic or Latino.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSelf-report measures\u003c/h2\u003e \u003cp\u003eThe Patient Reported Outcome Measurement Information System (PROMIS\u0026reg;; Yu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e0 short form version was used to assess sleep disturbance (6-items). PROMIS\u0026reg; sleep disturbances measure ranges from 32\u0026ndash;76, with higher scores indicating a higher rate of the measured outcome. Scores were converted to standardized T scores (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10), centered on the U.S. population mean. The PROMIS\u0026reg; sleep disturbances demonstrated acceptable internal consistency and correlations with expected legacy measures (Yu et al, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the present sample, internal consistency was good to excellent (pre-MBRT α\u0026thinsp;=\u0026thinsp;0.83; post-MBRT α\u0026thinsp;=\u0026thinsp;0.90).\u003c/p\u003e \u003cp\u003eThe Oldenburg Burnout Inventory (OLBI; Demerouti et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Halbesleben \u0026amp; Demerouti, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) is a 16-item measure of burnout that assesses exhaustion and disengagement from work. The OLBI ranges from 1\u0026ndash;4, with higher scores indicating greater burnout. The OLBI has acceptable internal consistency, factorial validity, and expected correlations with other constructs (Demerouti \u0026amp; Bakker, 2010). In the present sample, the internal consistency was excellent (pre-MBRT α\u0026thinsp;=\u0026thinsp;0.92; post-MBRT α\u0026thinsp;=\u0026thinsp;0.92).\u003c/p\u003e \u003cp\u003eThe Five Facet Mindfulness Questionnaire-Short Form (FFMQ-24; Bohlmeijer et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), a 24-item version of the FFMQ (Baer et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), assesses the dispositional tendency to be mindful in daily life. Items are rated on a 5-point Likert-type scale (1\u0026thinsp;=\u0026thinsp;\u003cem\u003enever or very rarely true\u003c/em\u003e to 5\u0026thinsp;=\u0026thinsp;\u003cem\u003every often or always true\u003c/em\u003e). The observing and describing facets of the scale have demonstrated weaker psychometric properties and issues with novice and non-meditating samples (de Bruin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lilja et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, the current study used three of the five facets\u0026mdash;acting with awareness, nonjudging of experience, and nonreactivity to inner experience to create a total mindfulness score. Each facet has five items, resulting in a 15-item scale ranging from 15 to 75, with higher scores indicating greater mindfulness. In the present sample, the internal consistency was good to excellent (acting with awareness pre-MBRT α\u0026thinsp;=\u0026thinsp;0.86; post-MBRT α\u0026thinsp;=\u0026thinsp;0.94, nonjudging of experiences pre-MBRT α\u0026thinsp;=\u0026thinsp;0.92; post-MBRT α\u0026thinsp;=\u0026thinsp;0.88, and nonreactivity to inner experience pre-MBRT α\u0026thinsp;=\u0026thinsp;0.88; post-MBRT α\u0026thinsp;=\u0026thinsp;0.88).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIntervention\u003c/h3\u003e\n\u003cp\u003eMBRT is a culturally tailored group-based program integrating training in mindfulness practices to facilitate stress resilience with principles of cognitive behavioral therapy and psychoeducation. The curriculum structure is modeled after the mindfulness-based relapse prevention clinical protocol (Bowen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Content and language have been altered to be more relevant to LEOs, with an emphasis on working with reactivity to stressors inherent to police work, including critical incidents, job dissatisfaction, public scrutiny, and other job-related challenges. MBRT was delivered in 8 weekly group sessions. The first session was an extended 6-hour intensive introduction to mindfulness training, and week 7 was a 4-hour intensive practice session. Other sessions lasted one hour. Sessions include didactic content, experiential exercises such as the body scan, sitting and walking meditation, mindful movement, and compassion practice, as well as small- and large-group discussions. Resilience-enhancing practices, including those focused on acceptance, stress reactivity/recovery, and self-compassion, emphasize didactic and experiential exercises that facilitate recovery from stressors.\u003c/p\u003e\n\u003ch3\u003eData analytic approach\u003c/h3\u003e\n\u003cp\u003eTo create measures of change across the training, we regressed each variable at the end of the training on the same variable at baseline and saved the standardized residuals (e.g., we regressed responses on the FFMQ at post-MBRT on FFMQ responses at baseline), creating a residualized change score variable for each measure. To determine whether increases in each of the three facets of mindfulness (i.e., acting with awareness, nonjudging of inner experience, and nonreactivity) were predictive of sleep disturbance and burnout, a hierarchical linear regression analyses was used. In the regression, acting with awareness, nonjudging, and nonreactivity residualized change scores were entered as predictors of post-MBRT sleep disturbance (or burnout) scores in step two, after controlling for pre-MBRT sleep disturbance (or burnout) in step one. Data were missing completely at random, and we therefore used a complete case analysis approach (Ross et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAll variables approximated a normal distribution, with no problematic univariate or multivariate outliers. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the means for all variables moved in the expected direction from pre- to post-MBRT, and all changes were statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePre- and Post-Mindfulness-Based Resilience Training Study Outcomes (N\u0026thinsp;=\u0026thinsp;98)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePre M (SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePost M (SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSleep Disturbances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.89 (7.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e49.94 (7.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.42 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.25 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNonjudging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.74 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.02**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNonreactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.23 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.60 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.90**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eActing With Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.23 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.58 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.45**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. All \u003cem\u003ep\u003c/em\u003e-values are two-tailed. **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the sleep disturbances regression model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), pre-MBRT sleep disturbance accounted for a significant amount of variance in the prediction of post-MBRT sleep disturbance (ΔR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.36, ΔF\u0026thinsp;=\u0026thinsp;55.84, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001) in step one. In the second step, adding the residualized change scores for the three facets of mindfulness also accounted for a significant amount of variance (ΔR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.12, ΔF\u0026thinsp;=\u0026thinsp;7.34, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001). Improved nonjudging (β = \u0026minus;\u0026thinsp;.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.005, \u003cem\u003esr\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.08) and nonreactivity (β = \u0026minus;\u0026thinsp;.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05, \u003cem\u003esr\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.04) were significant predictors of reductions in post-MBRT sleep disturbance, but acting with awareness (β = \u0026minus;\u0026thinsp;.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.31, \u003cem\u003esr\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.01) was not.\u003c/p\u003e \u003cp\u003eIn the burnout regression model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), pre-MBRT burnout accounted for a significant amount of variance in the prediction of post-MBRT burnout (ΔR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.34, ΔF\u0026thinsp;=\u0026thinsp;50.01, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001) in step one. In the second step, adding the residualized change scores for the three facets of mindfulness also accounted for a significant amount of variance (ΔR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.19, ΔF\u0026thinsp;=\u0026thinsp;13.04, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001). Improved nonjudging (β = \u0026minus;\u0026thinsp;.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003, \u003cem\u003esr\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.08) and acting with awareness (β = \u0026minus;\u0026thinsp;.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003, \u003cem\u003esr\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.09) were significant predictors of reductions in post-MBRT burnout, but nonreactivity (β = \u0026minus;\u0026thinsp;.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.06, \u003cem\u003esr\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.04) was not.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHierarchical Multiple Regression Predicting Post-MBRT Sleep Disturbances and Burnout (N\u0026thinsp;=\u0026thinsp;98)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eUnstandardized Coefficients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome \u0026amp; Step\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u0026sup2; change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep Disturbances1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.\u003c/p\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.84**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-MBRT Sleep Disturbances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.34**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonjudging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.85*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonreactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.96*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActing with Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.04**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-MBRT Burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.07**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.04**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonjudging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonreactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActing with Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.08*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study was a secondary analysis of LEOs who participated in one of several MBRT trials. The particular focus of this study was to determine which mindfulness facets accounted for the most variance in the reduction of burnout and sleep disturbances among LEOs to better understand the role specific mindfulness facets play in improving officer health. Increases in nonjudging and nonreactivity significantly predicted reductions in sleep disturbances. These findings converge with existing literature suggesting that increased nonjudging leads to reductions in sleep disturbances among healthy adults and psychiatric inpatients (Ioverno et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Smith et al., 2020). Nonjudging may reduce internalized stress and negative self-evaluation that interfere with sleep, thus mitigating sleep disturbances (Garland et al., 2014). Similarly, nonreactivity is related to reductions in sleep disturbance among young adults and individuals with sleep anxiety (Gao et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jaurequi et al., 2022). Nonreactivity may allow LEOs to disengage from arousing thoughts, such as those related to their high-stress occupation, which interfere with sleep and promote fewer sleep disturbances (Hoeve et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalyses also revealed that increases in nonjudging and acting with awareness significantly predicted reductions in burnout among LEOs, confirming study hypotheses and converging with existing literature. This converges with previous research suggesting that acting with awareness predicted reduced burnout among nurses (Zhao et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and that increased nonjudging predicted decreased burnout in general employees (Kinnunen et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among LEOs, acting with awareness predicted improvements in stress (Hoeve et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eContrary to hypotheses, improvements in acting with awareness did not predict reduced sleep disturbances, and nonreactivity did not predict reduced burnout post-MBRT. These findings contradict existing literature suggesting that nonreactivity predicts reductions in stress among other high-stress populations, such as healthcare workers (Benzo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nonreactivity alone, but not acting with awareness, may help LEOs tolerate occupational stressors without addressing cognitive appraisals that contribute to burnout.\u003c/p\u003e \u003cp\u003eNonjudging was the only mindfulness facet that predicted both reduced sleep disturbances and burnout among LEOs. Nonjudging practices may be useful for LEOs and those in other high-stress professions because they may provide officers opportunities to engage neutrally with their inner experiences, disrupting rumination and self-criticism that amplify stress reactivity. By noticing thoughts and sensations without judgment, LEOs may reduce physiological arousal and improve threat discrimination under pressure, which together may lower burnout risk and improve sleep quality. Overall, these findings highlight the importance of mindfulness training \u0026ndash; particularly the cultivation of nonjudgment, nonreactivity, and acting with awareness \u0026ndash; in bolstering the physical and mental well-being of LEOs. Improvements in outcomes such as sleep disturbances and burnout among this sample may not only promote healthier behavior and engagement among LEOs but also within the communities they serve. Such findings emphasize the important role of mindfulness training in mitigating the far-reaching effects of burnout and sleep disturbances within these populations, including reduced aggression and use of excessive force toward civilians (Ribeiro, 2020).\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged when interpreting the results of this study. First, this was a largely homogeneous sample (primarily white males), which precludes generalizability of findings. Future studies with larger and more diverse LEO samples are needed. Second, reliance on self-report measures may have introduced social desirability bias, particularly among LEOs, who may be less inclined to underreport mental health concerns. Third, although the study identified which mindfulness facets were most strongly associated with reductions in sleep disturbances and burnout, thereby informing their potential relevance to health and well-being in this population, causal interpretations cannot be made due to the correlational nature of the analyses. Additionally, unmeasured variables may have contributed to the observed relationships, and alternative explanations cannot be ruled out.\u003c/p\u003e \u003cp\u003eDespite these limitations, the impact of nonjudgment, nonreactivity, and acting with awareness on LEO outcomes underscores the importance of integrating these facets into future training initiatives and policy development. Formal MBIs, as well as streamlined app-based mindfulness tools for LEOs, should prioritize enhancing nonreactivity to reduce sleep disturbances, support officers in acting with awareness to mitigate burnout, and cultivate nonjudgmental work environments that normalize self-care and incorporate mindfulness into training to reduce negative stress-related outcomes. Future researchers should consider investigating how mindfulness training can be integrated into LEO occupational culture and leadership approaches using various strategies to enhance dissemination and uptake. Such approaches may help identify barriers to implementing mindfulness practices within law enforcement agencies, thus providing a more robust understanding of how to effectively integrate contemplative practices, including those that emphasize nonjudgment, nonreactivity, and acting with awareness, in this high-stress occupational setting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch reported in this publication was supported by the National Center for Complementary \u0026amp; Integrative Health of the National Institutes of Health under Award Numbers R21AT008854 and R01AT009841. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[masked for review] received funding from the National Institutes of Health during the conduct of the study.\u0026nbsp;All other co-authors have no funding to disclose. My co-authors and I do not have any interests that might be interpreted as influencing this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Pacific University IRB approved all procedures (IRB# 089-15 and 090-18). Additionally,\u0026nbsp;all relevant American Psychological Association ethical standards and the code of ethics of the World Medical\u0026nbsp;Association (Declaration of Helsinki) were followed in the conduct of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors provided consent to submit this manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets in the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlfonsi, V., Scarpelli, S., Gorgoni, M., Pazzaglia, M., Giannini, A. M., \u0026amp; De Gennaro, L. (2021). Sleep-related problems in night shift nurses: towards an individualized interventional practice. \u003cem\u003eFrontiers in Human Neuroscience\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 644570. https://doi.org/10.3389/fnhum.2021.644570\u003c/li\u003e\n\u003cli\u003eAl‐Hammouri, M. M., \u0026amp; Rababah, J. A. (2024). A brief mindfulness‐based intervention, sleep quality, sleep duration, and fatigue among nurses: A randomized controlled trial. \u003cem\u003eJournal of Clinical Psychology\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(7), 1504-1514. https://doi.org/10.1002/jclp.23677\u003c/li\u003e\n\u003cli\u003eAlshahrani, K. M., Johnson, J., Prudenzi, A., \u0026amp; O\u0026rsquo;Connor, D. B. (2022). The effectiveness of psychological interventions for reducing PTSD and psychological distress in first responders: A systematic review and meta-analysis. \u003cem\u003ePLOS ONE, 17\u003c/em\u003e(8), e0272732. https://doi.org/10.1371/journal.pone.0272732\u003c/li\u003e\n\u003cli\u003eBaer, R. A., Smith, G. T., Hopkins, J., Krietemeyer, J., \u0026amp; Toney, L. (2006). Using self-report assessment methods to explore facets of mindfulness. \u003cem\u003eAssessment, 13\u003c/em\u003e(1), 27-45. https://doi.org/10.1177/1073191105283504\u003c/li\u003e\n\u003cli\u003eBenzo, R. P., Anderson, P. M., Bronars, C., \u0026amp; Clark, M. (2018). Mindfulness for Healthcare Providers: The Role of Non-Reactivity in Reducing Stress. EXPLORE, 14(6), 453-456. https://doi.org/https://doi.org/10.1016/j.explore.2018.03.008 \u003c/li\u003e\n\u003cli\u003eBishopp, S. A., \u0026amp; Boots, D. P. (2014). General strain theory, exposure to violence, and suicide ideation among police officers: A gendered approach. \u003cem\u003eJournal of Criminal Justice, 42\u003c/em\u003e(6), 538-548. https://doi.org/10.1016/j.jcrimjus.2014.09.007 \u003c/li\u003e\n\u003cli\u003eBohlmeijer, E., Ten Klooster, P. M., Fledderus, M., Veehof, M., \u0026amp; Baer, R. (2011). Psychometric properties of the Five Facet Mindfulness Questionnaire in depressed adults and development of a short form. \u003cem\u003eAssessment, 18\u003c/em\u003e(3), 308-320. https://doi.org/10.1177/1073191111408231 \u003c/li\u003e\n\u003cli\u003eBowen, S., Chawla, N., Grow, J., \u0026amp; Marlatt, G. A. (2021). \u003cem\u003eMindfulness-based relapse prevention for addictive behaviors\u003c/em\u003e. Guilford Publications.\u003c/li\u003e\n\u003cli\u003eBragg, S., Benich, J., Christian, N., Visserman, J., \u0026amp; Freedy, J. (2019). Updates in insomnia diagnosis and treatment. \u003cem\u003eThe International Journal of Psychiatry in Medicine, 54\u003c/em\u003e(4\u0026ndash;5), 275\u0026ndash;289. https://doi.org/10.1177/0091217419860716\u003c/li\u003e\n\u003cli\u003eBuysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., \u0026amp; Kupfer, D.J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. \u003cem\u003ePsychiatry Research, 28\u003c/em\u003e, 193\u0026ndash;213.\u003c/li\u003e\n\u003cli\u003eChen, Y. H., Yeh, C. J., Pan, L. F., \u0026amp; Jong, G. P. (2022). Relationships between alcohol use, musculoskeletal pain, and work-related burnout. \u003cem\u003eMedicina\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(8), 1022. https://doi.org/10.3390/medicina58081022\u003c/li\u003e\n\u003cli\u003eChristopher, M. S., Goerling, R. J., Rogers, B. S., Hunsinger, M., Baron, G., Bergman, A. L., \u0026amp; Zava, D. T. (2016). A pilot study evaluating the effectiveness of a mindfulness-based intervention on cortisol awakening response and health outcomes among law enforcement officers. \u003cem\u003eJournal of police and criminal psychology, 31\u003c/em\u003e, 15-28. https://doi.org/10.1007/s11896-015-9161-x\u003c/li\u003e\n\u003cli\u003eChristopher, M. S., Hunsinger, M., Goerling, L. R. J., Bowen, S., Rogers, B. S., Gross, C. R., Dapolonia, E., \u0026amp; Pruessner, J. C. (2018). Mindfulness-based resilience training to reduce health risk, stress reactivity, and aggression among law enforcement officers: A feasibility and preliminary efficacy trial.\u003cem\u003e Psychiatry research, 264\u003c/em\u003e, 104-115. https://doi.org/10.1016/j.psychres.2018.03.059\u003c/li\u003e\n\u003cli\u003eChristopher, M., Bowen, S., Witkiewitz, K., Grupe, D., Goerling, R., Hunsinger, M., Oken, B., Korecki, T., \u0026amp; Rosenbaum, N. (2024). A multisite feasibility randomized clinical trial of mindfulness-based resilience training for aggression, stress, and health in law enforcement officers. \u003cem\u003eBMC complementary medicine and therapies, 24\u003c/em\u003e(1), 142. https://doi.org/10.1186/s12906-024-04452-y\u003c/li\u003e\n\u003cli\u003eChopko, B. A., Palmieri, P. A., \u0026amp; Adams, R. E. (2021). Trauma-related sleep problems and associated health outcomes in police officers: A path analysis. \u003cem\u003eJournal of interpersonal violence, 36\u003c/em\u003e(5-6), NP2725-NP2748. https://doi.org/10.1177/0886260518767912\u003c/li\u003e\n\u003cli\u003ede Bruin, E. I., Topper, M., Muskens, J. G. A. M., B\u0026ouml;gels, S. M., \u0026amp; Kamphuis, J. H. (2012). Psychometric properties of the Five Facets Mindfulness Questionnaire (FFMQ) in a meditating and a non-meditating sample. \u003cem\u003eAssessment, 19\u003c/em\u003e(2), 187-197. https://doi.org/10.1177/1073191112446654\u003c/li\u003e\n\u003cli\u003eDemerouti, E., Bakker, A. B., Vardakou, I., \u0026amp; Kantas, A. (2003). The convergent validity of two burnout instruments: A multitrait-multimethod analysis. \u003cem\u003eEuropean Journal of Psychological Assessment, 19\u003c/em\u003e(1), 12. https://doi.org/10.1027/1015-5759.19.1.12\u003c/li\u003e\n\u003cli\u003eDemerouti, E., Mostert, K., \u0026amp; Bakker, A. B. (2010). Burnout and work engagement: a thorough investigation of the independency of both constructs. \u003cem\u003eJournal of occupational health psychology, 15\u003c/em\u003e(3), 209. https://doi.org/10.1037/a0019408\u003c/li\u003e\n\u003cli\u003eEl Sayed, S. A., Sanford, S. M., \u0026amp; Kerley, K. R. (2019). Understanding workplace stress among federal law enforcement officers. \u003cem\u003eAmerican journal of criminal justice, 44\u003c/em\u003e, 409-429. https://doi.org/10.1007/s12103-019-09474-8\u003c/li\u003e\n\u003cli\u003eFendel, J. C., B\u0026uuml;rkle, J. J., \u0026amp; G\u0026ouml;ritz, A. S. (2021). Mindfulness-based interventions to reduce burnout and stress in physicians: a systematic review and meta-analysis. \u003cem\u003eAcademic Medicine\u003c/em\u003e, \u003cem\u003e96\u003c/em\u003e(5), 751-764. \u003cem\u003eDOI: \u003c/em\u003e10.1097/ACM.0000000000003936\u003c/li\u003e\n\u003cli\u003eGau, J. M., \u0026amp; Brunson, R. K. (2015). Procedural injustice, lost legitimacy, and self-help: Young males\u0026rsquo; adaptations to perceived unfairness in urban policing tactics. \u003cem\u003eJournal of contemporary criminal justice, 31\u003c/em\u003e(2), 132-150. https://doi.org/10.1177/1043986214568841 \u003c/li\u003e\n\u003cli\u003eGlick, D. R., Abariga, S. A., Thomas, I., Shipper, A. G., Gunia, B. C., Grandner, M. A., ... \u0026amp; Wickwire, E. M. (2023). Economic impact of insufficient and disturbed sleep in the workplace. \u003cem\u003ePharmacoeconomics\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(7), 771-785. https://doi.org/10.1007/s40273-023-01249-8\u003c/li\u003e\n\u003cli\u003eGao, M., Roy, A., Deluty, A., Sharkey, K. M., Hoge, E. A., Liu, T., \u0026amp; Brewer, J. A. (2022). Targeting Anxiety to Improve Sleep Disturbance: A Randomized Clinical Trial of App-Based Mindfulness Training. \u003cem\u003eBiopsychosocial Science and Medicine\u003c/em\u003e,\u003cem\u003e 84\u003c/em\u003e(5), 632-642. https://doi.org/10.1097/psy.0000000000001083 \u003c/li\u003e\n\u003cli\u003eGrandner, M. A., \u0026amp; Chakravorty, S. (2017). Insomnia in primary care: Misreported, mishandled, and just plain missed. \u003cem\u003eJournal of Clinical Sleep Medicine, 13\u003c/em\u003e(8), 937\u0026ndash;939. https://doi.org/10.5664/jcsm.6688\u003c/li\u003e\n\u003cli\u003eGullion, C. L., Spadola, C. E., Groton, D. B., Fallik, S. W., \u0026amp; Guastaferro, W. P. (2025). \u0026ldquo;Sleep affects everything\u0026rdquo;: understanding challenges and interventions for police sleep health. \u003cem\u003ePolicing: An International Journal\u003c/em\u003e. https://doi.org/10.1108/PIJPSM-12-2024-0209\u003c/li\u003e\n\u003cli\u003eHalbesleben, J. R., \u0026amp; Demerouti, E. (2005). The construct validity of an alternative measure of burnout: Investigating the English translation of the Oldenburg Burnout Inventory. \u003cem\u003eWork \u0026amp; stress, 19\u003c/em\u003e(3), 208-220. https://doi.org/10.1080/02678370500340728\u003c/li\u003e\n\u003cli\u003eHoeve, M., de Bruin, E. I., van Rooij, F., \u0026amp; B\u0026ouml;gels, S. M. (2021). Effects of a mindfulness-based intervention for police officers. \u003cem\u003eMindfulness\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(7), 1672\u0026ndash;1684. https://doi.org/10.1007/s12671-021-01631-7\u003c/li\u003e\n\u003cli\u003eH\u0026uuml;lsheger, U. R., Alberts, H. J., Feinholdt, A., \u0026amp; Lang, J. W. (2013). Benefits of mindfulness at work: the role of mindfulness in emotion regulation, emotional exhaustion, and job satisfaction. \u003cem\u003eJournal of applied psychology, 98\u003c/em\u003e(2), 310. https://doi.org/10.1037/a0031313\u003c/li\u003e\n\u003cli\u003eIqbal, S., Howse, J., Banu, S., Lateef, A., Khan, S., Barlas, Z., Moon, S., Moukaddam, N., \u0026amp; Shah, A.A.. (2024). The effects of stress on health. \u003cem\u003ePsychiatric Annals, 54\u003c/em\u003e(10), e272-e276. https://doi.org/10.3928/00485713-20241004-02\u003c/li\u003e\n\u003cli\u003eIoverno, S., Pallini, S., Farina, F., Lizzadro, F., \u0026amp; Barcaccia, B. (2022). The less you judge, the better you sleep: The benefits of mindfulness and forgiveness for insomnia and sleep problems. \u003cem\u003eBulletin of the Menninger Clinic\u003c/em\u003e,\u003cem\u003e 86\u003c/em\u003e(4), 324-338. https://doi.org/10.1521/bumc.2022.86.4.324 \u003c/li\u003e\n\u003cli\u003eJacobs, C. (2024). Occupational stress and burnout.\u003c/li\u003e\n\u003cli\u003eJetelina, K., Molsberry, R., Gonzalez, J., Beauchamp, A., \u0026amp;amp; Hall, T. (2020). Prevalence of Mental Illness and Mental Health Care Use Among Police Officers. \u003cem\u003eJAMA Network Open, 3\u003c/em\u003e(10), e2019658-e2019658. doi:10.1001/jamanetworkopen.2020.19658\u003c/li\u003e\n\u003cli\u003eJohn, A., Bouillon-Minois, J. B., Bagheri, R., P\u0026eacute;lissier, C., Charbotel, B., Llorca, P. M., Zak, M., Ugbolue, U. C., Baker, J. S., \u0026amp; Dutheil, F. (2024). The influence of burnout on cardiovascular disease: a systematic review and meta-analysis. \u003cem\u003eFrontiers in Psychiatry, 15\u003c/em\u003e, 1326745. https://doi.org/10.3389/fpsyt.2024.1326745\u003c/li\u003e\n\u003cli\u003eKaplan, J. B., Christopher, M. S., \u0026amp; Bowen, S. (2018). Dispositional mindfulness moderates the relationship between occupational stressors and perceived stress among law enforcement personnel. \u003cem\u003eJournal of Police and Criminal Psychology\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, 227-232. https://doi.org/10.1007/s11896-017-9246-9\u003c/li\u003e\n\u003cli\u003eKhoshakhlagh, A. H., Al Sulaie, S., Yazdanirad, S., Orr, R. M., Dehdarirad, H., \u0026amp; Milajerdi, A. (2023). Global prevalence and associated factors of sleep disorders and poor sleep quality among firefighters: A systematic review and meta-analysis. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2). DOI: 10.1016/j.heliyon.2023.e13250\u003c/li\u003e\n\u003cli\u003eKinnunen, S. M., Puolakanaho, A., Tolvanen, A., M\u0026auml;kikangas, A., \u0026amp; Lappalainen, R. (2020). Improvements in mindfulness facets mediate the alleviation of burnout dimensions. \u003cem\u003eMindfulness, 11\u003c/em\u003e, 2779-2792. https://doi.org/10.1007/s12671-020-01490-8\u003c/li\u003e\n\u003cli\u003eLi, R., \u0026amp; Yao, M. (2022). What promotes teachers\u0026rsquo; turnover intention? Evidence from a meta-analysis. \u003cem\u003eEducational Research Review, 37\u003c/em\u003e, 100477. https://doi.org/10.1016/j.edurev.2022.100477\u003c/li\u003e\n\u003cli\u003eLilja, J. L., Frodi-Lundgren, A., Hanse, J. J., Josefsson, T., Lundh, L.-G., Sk\u0026ouml;ld, C., Hansen, E., \u0026amp; Broberg, A. G. (2011). Five Facets Mindfulness Questionnaire\u0026mdash;reliability and factor structure: A Swedish version. \u003cem\u003eCognitive Behaviour Therapy, 40\u003c/em\u003e(4), 291-303. https://doi.org/10.1080/16506073.2011.580367\u003c/li\u003e\n\u003cli\u003eM\u0026aacute;rquez, M. A., Galiana, L., Oliver, A., \u0026amp; Sans\u0026oacute;, N. (2021). The impact of a mindfulness‐based intervention on the quality of life of Spanish national police officers. \u003cem\u003eHealth \u0026amp; Social Care in the Community\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(5), 1491\u0026ndash;1501. https://doi.org/10.1111/hsc.13209\u003c/li\u003e\n\u003cli\u003eMcCarty, W. P., Aldirawi, H., Dewald, S., \u0026amp; Palacios, M. (2019). Burnout in Blue: An Analysis of the Extent and Primary Predictors of Burnout Among Law Enforcement Officers in the United States. \u003cem\u003ePolice Quarterly\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3), 278-304. https://doi.org/10.1177/1098611119828038\u003c/li\u003e\n\u003cli\u003eMorgado, P., Sousa, N., \u0026amp;amp; Cerqueira, J. J. (2015). The impact of stress in decision making in the context of uncertainty. \u003cem\u003eJournal of Neuroscience Research, 93\u003c/em\u003e(6), 839-847. https://doi.org/10.1002/jnr.23521\u003c/li\u003e\n\u003cli\u003eNordin, G., Sundqvist, R., Nordin, S., \u0026amp; Gruber, M. (2023). Somatic symptoms in sleep disturbance. \u003cem\u003ePsychology, Health \u0026amp; Medicine, 28\u003c/em\u003e(4), 884-894. https://doi.org/10.1080/13548506.2021.1985149\u003c/li\u003e\n\u003cli\u003ePadyab, M., Backteman-Erlanson, S., \u0026amp; Brulin, C. (2016). Burnout, coping, stress of conscience and psychosocial work environment among patrolling police officers. \u003cem\u003eJournal of police and Criminal Psychology, 31\u003c/em\u003e, 229-237. https://doi.org/10.1007/s11896-015-9189-y\u003c/li\u003e\n\u003cli\u003ePatterson, G. T., Chung, I. W., \u0026amp; Swan, P. W. (2014). Stress management interventions for police officers and recruits: a meta-analysis. \u003cem\u003eJournal of experimental criminology, 10\u003c/em\u003e, 487-513. https://doi.org/10.1007/s11292-014-9214-7\u003c/li\u003e\n\u003cli\u003ePeterson, S. A., Wolkow, A. P., Lockley, S. W., O\u0026apos;Brien, C. S., Qadri, S., Sullivan, J. P., Czeisler, C. A., Rajaratnam, S. M. W., \u0026amp; Barger, L. K. (2019). Associations between shift work characteristics, shift work schedules, sleep and burnout in North American police officers: a cross-sectional study. \u003cem\u003eBMJ open, 9\u003c/em\u003e(11), e030302. doi: 10.1136/bmjopen-2019-030302\u003c/li\u003e\n\u003cli\u003ePoirier, S., Allard-Gaudreau, N., Gendron, P., Houle, J., \u0026amp; Trudeau, F. (2023). Health, safety, and wellness concerns among law enforcement officers: an inductive approach. \u003cem\u003eWorkplace health \u0026amp; safety\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e(1), 34-42. https://doi.org/10.1177/21650799221134422\u003c/li\u003e\n\u003cli\u003eRajaratnam, S. M., Barger, L. K., Lockley, S. W., Shea, S. A., Wang, W., Landrigan, C. P., O\u0026apos;Brien, C. S., Qadri, S., Sullivan, J. P., Cade, B. E., Epstein, L. J., White, D. P., Czeisler, C. A., \u0026amp; Harvard Work Hours, Health and Safety Group. (2011). Sleep disorders, health, and safety in police officers. \u003cem\u003eJama, 306\u003c/em\u003e(23), 2567-2578. doi:10.1001/jama.2011.1851\u003c/li\u003e\n\u003cli\u003eRoss, R. K., Breskin, A., \u0026amp; Westreich, D. (2020). When is a complete-case approach to missing data valid? The importance of effect-measure modification. \u003cem\u003eAmerican journal of epidemiology, 189\u003c/em\u003e(12), 1583-1589. https://doi.org/10.1093/aje/kwaa124 \u003c/li\u003e\n\u003cli\u003eRyan, E., Hore, K., Power, J., \u0026amp; Jackson, T. (2023). The relationship between physician burnout and depression, anxiety, suicidality and substance abuse: A mixed methods systematic review. \u003cem\u003eFrontiers in public health\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 1133484. https://doi.org/10.3389/fpubh.2023.1133484\u003c/li\u003e\n\u003cli\u003eS\u0026oslash;rengaard, T. A., Olsen, A., Langvik, E., \u0026amp; Saksvik-Lehouillier, I. (2021). Associations between sleep and work-related cognitive and emotional functioning in police employees. \u003cem\u003eSafety and health at work\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), 359-364. https://doi.org/10.1016/j.shaw.2021.03.002\u003c/li\u003e\n\u003cli\u003eStegerhoek, P., Kooijman, K., Ziesemer, K., IJzerman, H., Kuijer, P. P. F., \u0026amp; Verhagen, E. (2024). Risk factors for adverse health in military and law enforcement personnel; an umbrella review. \u003cem\u003eBMC Public Health, 24\u003c/em\u003e(1), 3151. https://doi.org/10.1186/s12889-024-20553-2\u003c/li\u003e\n\u003cli\u003eStormer, M. R. (2021). \u003cem\u003eOccupational stress and job satisfaction in law enforcement: Contributing factors and the roles leaders play\u003c/em\u003e (Doctoral dissertation, The Chicago School of Professional Psychology).\u003c/li\u003e\n\u003cli\u003eThomson-Devaux, A., Bronner, L., \u0026amp; Sharma, D. (2021). Police misconduct costs cities millions every year. but that\u0026rsquo;s where the accountability ends. \u003cem\u003eThe Marshall Project.\u003c/em\u003e https://www.themarshallproject.org/2021/02/22/police-misconduct-costs-cities-millions-every-year-but-that-s-where-the-accountability-ends.\u003c/li\u003e\n\u003cli\u003eTseng, Y. J., Leicht, A. S., Pagaduan, J. C., Chien, L. C., Wang, Y. L., Kao, C. S., ... \u0026amp; Chen, Y. S. (2024). Effects of shift work on sleep quality and cardiovascular function in Taiwanese police officers. \u003cem\u003eChronobiology International\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(4), 530-538. https://doi.org/10.1080/07420528.2024.2324023\u003c/li\u003e\n\u003cli\u003eVila, B. (2006). Impact of long work hours on police officers and the communities they serve. \u003cem\u003eAmerican journal of industrial medicine, 49\u003c/em\u003e(11), 972-980. https://doi.org/10.1002/ajim.20333\u003c/li\u003e\n\u003cli\u003eYu, L., Buysse, D. J., Germain, A., Moul, D. E., Stover, A., Dodds, N. E., ... \u0026amp; Pilkonis, P. A. (2012). Development of short forms from the PROMIS\u0026trade; sleep disturbance and sleep-related impairment item banks. \u003cem\u003eBehavioral sleep medicine, 10\u003c/em\u003e(1), 6-24. https://doi.org/10.1080/15402002.2012.636266\u003c/li\u003e\n\u003cli\u003eZhao, J., Li, X., Xiao, H., Cui, N., Sun, L., \u0026amp; Xu, Y. (2019). Mindfulness and burnout among bedside registered nurses: A cross-sectional study. \u003cem\u003eNursing \u0026amp; Health Sciences\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(1), 126-131. https://doi.org/https://doi.org/10.1111/nhs.12582 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Law enforcement, burnout, sleep disturbance, mindfulness, occupational health","lastPublishedDoi":"10.21203/rs.3.rs-7926560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7926560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMindfulness-Based Resilience Training (MBRT) was designed to improve LEO mental health and performance, and to enhance resilience in the context of acute and chronic stressors inherent to policing. Prior research has demonstrated preliminary efficacy of MBRT in improving a variety of outcomes, including burnout and sleep disturbance. The primary goal of this study was to conduct a secondary analysis to determine which mindfulness facets accounted for the most variance in reducing burnout and sleep disturbances following participation in MBRT among LEOs (\u003cem\u003eN\u003c/em\u003e = 98). The findings indicate that increases in nonjudgment and nonreactivity were significant predictors of reductions in sleep disturbances, while acting with awareness was not. We also found that increases in nonjudging and acting with awareness were significant predictors of reductions in burnout, while nonreactivity was not. Our findings suggest that integrating mindfulness practices that emphasize nonjudgment, nonreactivity, and acting with awareness may help mitigate burnout and sleep disturbances. Future research should investigate how mindfulness training can be integrated with LEO occupational culture and leadership practices, with an emphasis on strategies that facilitate the effective dissemination and uptake of this intervention.\u003c/p\u003e\n\u003cp\u003eClinicalTrials.gov Identifiers: NCT02521454; NCT03784846\u003c/p\u003e\n\u003cp\u003eDates of registration: August 13, 2015; December 24, 2018\u003c/p\u003e","manuscriptTitle":"Improvements in Mindfulness Facets Predict Burnout and Sleep Disturbance Outcomes in Law Enforcement Officers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 13:31:56","doi":"10.21203/rs.3.rs-7926560/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"991cd9e7-6ed2-4b28-8143-bdf61f96f9cb","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T01:39:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 13:31:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7926560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7926560","identity":"rs-7926560","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}