Inactivity Time Research Articles

Multiple behavioural risk factors and mental health complaints among adults in Estonia

Abstract Background Behavioural risk factors are inter-related and often coincide, resulting in potentially worse mental health outcomes. This study explores the association between multiple behavioural risk factors and mental health among adults in Estonia. Methods We used nationally representative survey data from 2020 (n = 6404) to study the co-occurrence of lifestyle risk factors (smoking, alcohol consumption, physical inactivity, unhealthy diet, drug use, and high screen time) in relation to mental health indicators. Latent class analysis (LCA) was used to identify lifestyle risk profiles, while regression analysis was used to examine associations. Results Mental health outcomes were more frequent among females and younger respondents, but 54.4% of respondents reported having at least one mental health problem. Using LCA, three lifestyle classes were identified: multiple risk factors (17.9% of respondents), healthy lifestyle (76.1%), and drug use lifestyle (5.9%). Compared to individuals in healthy lifestyle class, respondents in multiple risk factors had higher odds for experiencing depressiveness (OR 1.46; CI 1.24-1.72), stress (OR 1.26; CI 1.06-1.49), and suicidal thoughts (OR 1.50; CI 1.26-1.80), as well as self-reported diagnoses of depression and insomnia, and using medications, such as antidepressants, hypnotics and sedatives. Respondents in drug use lifestyle had 1.5-2.5 times higher odds for all mental health items compared to healthy lifestyle class with largest difference found for suicidal thoughts (OR 2.46; CI 1.92-3.16) and medication use (OR 2.58; CI 1.91-3.47). Conclusions The current data highlights the importance of preventive measures targeting multiple behavioural risk factors, given that these factors often coincide. Moreover, the study showed a high proportion of individuals with at least one mental health problem, highlighting the necessity to continue efforts in preventive and interventive methods. Key messages • The study highlights the significant association between multiple behavioural risk factors and mental health outcomes. • The study also emphasizes the importance of focused interventions targeting multiple behavioural risk factors to address the increased risk of mental health problems.

Unit extended exponential distribution with applications

In this paper, the unit extended exponential distribution is presented. The extended exponential distribution is a two-parameter lifespan distribution that has been demonstrated to be very adaptable. Our goal is to convert this flexibility between the unit interval and the extended exponential distribution. Several different types of probability density functions, including decreasing, left-skewed, right-skewed, and unimodal, have been shown. However, the hazard rate function may take on various forms, including J-shaped, U-shaped, and bathtub shapes. A few statistical properties, such as moments, mean, variance, skewness, kurtosis, expectation-weighted moments, incomplete moments, conditional moments, mean deviation, Lorenz and Bonferroni curves, mean residual life, mean inactivity time, and various entropy measures, round out the theoretical section. The maximum likelihood technique is utilized to estimate model parameters using unit data. This study provides simulation data to evaluate the proposed strategy. Two applications utilizing actual data sets emphasize the significance of the new model compared to existing unit models.

User Experience of a Large-Scale Smartphone-Based Observational Study in Multiple Sclerosis: Global, Open-Access, Digital-Only Study.

The Floodlight Open app is a digital health technology tool (DHTT) that comprises remote, smartphone sensor-based tests (daily activities) for assessing symptoms of multiple sclerosis (MS). User acquisition, engagement, and retention remain a barrier to successfully deploying such tools. This study aims to quantitatively and qualitatively investigate key user experience (UX) factors associated with the Floodlight Open app. Floodlight Open is a global, open-access, digital-only study designed to understand the drivers and barriers in deploying a DHTT in a naturalistic setting without supervision and onboarding by a clinician. Daily activities included tests assessing cognition (Information Processing Speed and Information Processing Speed Digit-Digit), hand-motor function (Pinching Test and Draw a Shape Test), and postural stability and gait (Static Balance Test, U-Turn Test, and Two-Minute Walk Test [2MWT]). All daily activities except the 2MWT were taken in a fixed sequence. Qualitative UX was studied through semistructured interviews in a substudy of US participants with MS. The quantitative UX analysis investigated the impact of new UX design features on user engagement and retention in US participants for 3 separate test series: all daily activities included in the fixed sequence (DA), all daily activities included in the fixed sequence except the Static Balance Test and U-Turn Test (DAx), and the 2MWT. The qualitative UX substudy (N=22) revealed the need for 2 new UX design features: a more seamless user journey during the activation process that eliminates the requirement of switching back and forth between the app and the email that the participants received upon registration, and configurable reminders and push notifications to help plan and remind the participants to complete their daily activities. Both UX design features were assessed in the quantitative UX analysis. Introducing the more seamless user journey (original user journey: n=608; more seamless user journey: n=481) improved the conversion rate of participants who enrolled in the study and proceeded to successfully activate the app from 53.9% (328/608) to 74.6% (359/481). Introducing reminders and push notifications (with reminders and notifications: n=350; without reminders and notifications: n=172) improved continuous usage time (proportion of participants with ≥3 consecutive days of usage: DA and DAx: ~30% vs ~12%; 2MWT: ~30% vs ~20%); test completion rates (maximum number of test series completed: DA: 279 vs 64; DAx: 283 vs 126; 2MWT: 302 vs 76); and user retention rates (at day 30: DA: 53/172, 30.8% vs 34/350, 9.7%; DAx: 53/172, 30.8% vs 60/350, 17.1%; 2MWT: 39/172, 22.6% vs 22/350, 6.2%). Inactivity times remained comparable. The remote assessment of MS with DHTTs is a relatively nascent but growing field of research. The continued assessment and improvement of UX design features can play a crucial role in the successful long-term adoption of new DHTTs.

Effect of Centella asiatica ethanol extract on zebrafish larvae (Danio rerio) insomnia model through inhibition of Orexin, ERK, Akt and p38

Background: Insomnia is difficulty initiating or maintaining sleep for at least three nights a week or more and lasting for at least 3 months. One of the molecules that play a role in the circadian rhythm of arousal system is hypocretin/orexin. Orexin activates the p38-MAPK signaling pathway and increases phosphorylated ERK1/2 levels. Centella asiatica (CA) has a role in the signal work of the MAPK/ERK, Akt, and p38 path in many various diseases. Methods: The research method used is true laboratory experimental. The research approach used was randomized control group post-test only. Zebrafish embryos aged 0-7 dpf were used in this study. The treatment group consisted of 5 groups: normal, insomnia, insomnia + 2.5 μg/mL CA, insomnia + 5 μg/mL CA, and insomnia + 10 μg/mL CA. The locomotor motion of zebrafish larvae was observed using Basler cameras on days five-, six- and seven-day post fertilization (dpf), then analyzed by using Western Blot method. Results: The results proved that exposure to CA extract was able to reduce the expression of orexin (91963 ± 9129) and p38 (117425 ± 6398) as an arousal trigger in the sleep-wake cycle, with the most optimal concentration of CA 5 μg/mL. Exposure to CA extract was also able to reduce the expression of ERK (94795 ± 30830) and Akt (60113.5 ± 27833.5) with an optimum concentration of CA 2.5 μg/mL. Conclusion: Exposure to CA extract was able to improve the sleep activity of zebrafish larvae insomnia model by extending the total inactivity time (cumulative duration) and shortening the duration of first sleep (latency to first) in light and dark phases through inhibition of orexin, ERK, p38, and Akt.

Physical activity and recreational screen time among Chinese children and adolescents: a national cross-sectional study.

The increasing prevalence of physical inactivity and prolonged Recreational Screen Time (RST) among children and adolescents is emerging as a significant public health concern. This study investigates the current status of Physical Activity (PA) and RST among Chinese children and adolescents from 2017 to 2019. It also examines variations in PA and RST across different school levels, genders, urban-rural areas, regions, and seasons. A national cross-sectional survey, conducted in China from 2017 to 2019, included 53,101 children and adolescents from grades 4 to 12 (aged 10 to 18 years old). Data on PA and RST were collected via self-administered questionnaires. The study employed descriptive statistics, calculated weighted prevalence rates, and conducted differential analysis across diverse demographic groups. Between 2017 and 2019 in China, merely 28.73% of children and adolescents adhered to World Health Organization's PA guidelines, while 76.09% met China's RST guidelines. Notably, females, higher-grade students, rural residents, and children and adolescents from southern regions exhibited significantly lower levels of PA compared to their male, lower-grade, urban, and northern counterparts. Concurrently, RST was significantly higher among males, lower-grade students, rural residents, and those from northern regions. Seasonal variations were also observed, with lower PA and higher RST in autumn and winter as compared to spring. The study reveals a concerning low level of PA among Chinese children and adolescents, with marked disparities in PA and RST across different groups. This underscores the need for targeted health promotion strategies to enhance PA and mitigate RST among various child and adolescent populations.

Modeling to COVID-19 and cancer data: Using the generalized Burr-Hatke model

This paper describes an extended version of the Burr–Hatke distribution via generalizing its survival function. This model is called as the generalized Burr-Hatke distribution. The suggested model's hazard rate function can be non-increasing (decreasing) form. Related statistical characteristics of the proposed model are identified. We provide closed-form expressions for the density function, hazard rate function, moments and their related metrics. In addition, some of the proposed model's reliability attributes, such as mean residual life and mean inactivity time, are determined both empirically and visually. The characterization of GBH model based on hazard rate, truncated and conditional moments is also provided. The maximum likelihood technique is employed for estimating the unknown model parameters. To explore the behavior of the estimates, Monte Carlo Simulation can be used. Finally, the new model's performance is verified using two sets of real data.

Device-assessed physical activity and sleep quality of post-COVID patients undergoing a rehabilitation program

BackgroundAn infection with SARS-CoV-2 can lead to persistent symptoms more than three months after the acute infection and has also an impact on patients’ physical activity behaviour and sleep quality. There is evidence, that inpatient post-COVID rehabilitation can improve physical capacity and mental health impairments, but less is known about the change in physical behaviour and sleep quality.MethodsThis longitudinal observational study used accelerometery to assess the level of physical activity and sleep quality before and after an inpatient rehabilitation program. The study sample consists of 100 post-COVID patients who acquired COVID-19 in the workplace. Group differences related to sex, age, COVID-19 severity, and pre-existing diseases were also analysed.ResultsLevel of physical activity and sleep quality didn’t increase after rehabilitation. Overall, there is a high extent of inactivity time and poor sleep quality at both measurement points. Regarding group differences, male patients showed a significantly higher inactivity time before rehabilitation, and younger patients (< 55 years) spend significant more time in vigorous physical activity than older patients. Post-COVID patients with pre-existing cardiovascular, respiratory, and metabolic disease show slightly less physical activity than post-COVID patients without these comorbidities. Female patients and younger patients showed better sleep quality in some sleep parameters at both measurement points. However, no differences could be detected related to COVID-19 severity.ConclusionsOngoing strategies should be implemented to address the high amount of inactivity time and the poor sleep quality in post-COVID patients.

ON THE MOMENTS OF THE 3-PARAMETER GOMPERTZ DISTRIBUTION

Gompertz distribution is a classical probability distribution extensively used in actuarial science, reliability, and survival analysis. Gompertz distribution also plays a role in various fields, such as biology, economics, and marketing analysis. Some extensions of this distribution have been studied and applied to various problems. In this article, we are concerned with some statistical properties of a 3-parameter Gompertz distribution. This extension of the Gompertz distribution introduced has been used in studying competing risk survival analysis. Our main results are the derivation of moments of this distribution and other statistical properties related to moments, such as moment generating function, mean residual life function, mean inactivity time and Lorenz curve. These results will serve as a complement to the theoretical aspect of the analysis of the distribution.

A multi-objective planning and scheduling model for elective and emergency cases in the operating room under uncertainty

Hospitals are paramount hubs for delivering healthcare services, with their Operating Rooms (ORs) as a pivotal and financially substantial component. Efficient surgery ward planning is crucial in healthcare institutions, aiming to improve medical service quality while reducing costs. This research delves into the intricacies of integrated OR planning and scheduling, focusing on elective and emergency patients in an uncertain environment. To address these challenges, a mixed integer programming (MIP) framework is developed to minimize inactivity and patient wait times while optimizing high-priority resource allocation. Both upstream and downstream units of the ward, the Pre-operative Holding Unit (PHU), Post Anesthesia Care Unit (PACU), and Intensive Care Unit (ICU) are included. The inherently uncertain aspects of surgery, including surgical duration, Length of Stay (LOS), and the influx of emergency patients, demand an intelligent optimization approach. Consequently, a robust optimization strategy is harnessed to effectively grapple with this pervasive uncertainty. A deterministic model is introduced and improved using an enhanced epsilon constraint method. The culmination of this analytical journey yields a collection of Pareto-optimal solutions. Empirical results, supported by managerial insights, highlight the superiority of the proposed method over the traditional weighting approach.

A decision tree algorithm to identify predictors of post-stroke complex regional pain syndrome

This prospective cohort study aimed to identify the risk factors for post-stroke complex regional pain syndrome (CRPS) using a decision tree algorithm while comprehensively assessing upper limb and lower limb disuse and physical inactivity. Upper limb disuse (Fugl-Meyer assessment of upper extremity [FMA-UE], Action Research Arm Test, Motor Activity Log), lower limb disuse (Fugl-Meyer Assessment of lower extremity [FMA-LE]), balance performance (Berg balance scale), and physical inactivity time (International Physical Activity Questionnaire-Short Form [IPAQ-SF]) of 195 stroke patients who visited the Kishiwada Rehabilitation Hospital were assessed at admission. The incidence of post-stroke CRPS was 15.4% in all stroke patients 3 months after admission. The IPAQ, FMA-UE, and FMA-LE were extracted as risk factors for post-stroke CRPS. According to the decision tree algorithm, the incidence of post-stroke CRPS was 1.5% in patients with a short physical inactivity time (IPAQ-SF < 635), while it increased to 84.6% in patients with a long inactivity time (IPAQ-SF ≥ 635) and severe disuse of upper and lower limbs (FMA-UE score < 19.5; FMA-LE score < 16.5). The incidence of post-stroke CRPS may increase with lower-limb disuse and physical inactivity, in addition to upper-limb disuse. Increasing physical activity and addressing lower- and upper-limb motor paralysis may reduce post-stroke CRPS.

Attenuation of cannabis withdrawal symptoms by Prosopis farcta extract, its luteolin and melatonin in mice: Involvement of brain-derived neurotrophic factor and dopamine.

The aim of this study was the identification of luteolin in Prosopis farcta extract (PFE) and melatonin to evaluate its effect on THC withdrawal syndrome in mice. Luteolin was identified by high-performance liquid chromatography (HPCL). Signs of toxicity of mice in PFE and luteolin were monitored for LD50 calculation. The behavioral symptoms of THC withdrawal (stereotypies, ambulation, and inactivity time) induced by the rimonabant challenge were illustrated in THC-dependent mice receiving PFE, luteolin, and melatonin. The expression of mature BDNF (mBDNF) was evaluated by Western blot analysis. The dopamine concentrations were measured using HPLC. PFE and luteolin LD50 were 650 and 220 mg/kg, respectively. PFE (300 mg/kg), all doses of luteolin, and melatonin increased significantly the mBDNF expression and decreased the dopamine concentration. The findings suggest that PFE, luteolin, and melatonin are mighty in reducing the signs of THC withdrawal. It seems these effects were due to a decrease in dopamine concentration level and an increase in mBDNF protein expression in mice brains.

Effect of Centella asiatica ethanol extract on zebrafish larvae ( Danio rerio) insomnia model through inhibition of Orexin, ERK, Akt and p38.

Background: Insomnia is difficulty initiating or maintaining sleep for at least three nights a week or more and lasting for at least 3 months. One of the molecules that play a role in the circadian rhythm of arousal system is hypocretin/orexin. Orexin activates the p38-MAPK signaling pathway and increases phosphorylated ERK1/2 levels. Centella asiatica (CA) has a role in the signal work of the MAPK/ERK, Akt, and p38 path in many various diseases. Methods: The research method used is true laboratory experimental. The research approach used was randomized control group post-test only. Zebrafish embryos aged 0-7 dpf were used in this study. The treatment group consisted of 5 groups: normal, insomnia, insomnia + 2.5 μg/mL CA, insomnia + 5 μg/mL CA, and insomnia + 10 μg/mL CA. The locomotor motion of zebrafish larvae was observed using Basler cameras on days five-, six- and seven-day post fertilization (dpf), then analyzed by using Western Blot method. Results: The results proved that exposure to CA extract was able to reduce the expression of orexin (91963 ± 9129) and p38 (117425 ± 6398) as an arousal trigger in the sleep-wake cycle, with the most optimal concentration of CA 5 μg/mL. Exposure to CA extract was also able to reduce the expression of ERK (94795 ± 30830) and Akt (60113.5 ± 27833.5) with an optimum concentration of CA 2.5 μg/mL. Conclusion: Exposure to CA extract was able to improve the sleep activity of zebrafish larvae insomnia model by extending the total inactivity time ( cumulative duration) and shortening the duration of first sleep ( latency to first) in light and dark phases through inhibition of orexin, ERK, p38, and Akt.

Dynamic multivariate quantile inactivity time and applications in investigation of a treatment effect

&lt;p&gt;To investigate potentially dependent lifetimes, it is necessary to extend the $ \alpha $-quantile inactivity time to bivariate and multivariate frameworks. To extend this measure to a dynamic multivariate framework, all possible trajectories at time $ t $ are considered. The behavior of the extended $ \alpha $-quantile of inactivity time was investigated in relation to the corresponding multivariate hazard rate function. The $ \alpha $-quantile of the inactivity order is defined and discussed for the multivariate case. The difference between the two bivariate $ \alpha $-quantile functions of inactivity, which is an important measure for studying the effect of treatment on lifespan, was also investigated. This measure was used to analyze the effect of laser treatment on the delay of blindness. Two bootstrap approaches were implemented to construct confidence bounds for the difference measure.&lt;/p&gt;

Estimation of monotone bivariate quantile inactivity time with medical applications

&lt;p&gt;In most lifetime models, the bivariate $ \alpha $-quantile inactivity time is a vector of increasing functions. A novel estimator of this vector was created and investigated under this assumption. It was expected that the application of this knowledge would improve the efficiency of the estimator. It was proven that the proposed estimator is consistent and converges weakly to a bivariate Gaussian process under a suitable transformation. A simulation study was conducted to compare the performance of the proposed estimator with that of the usual estimator. Finally, the application of the proposed estimator is illustrated by analyzing a dataset comprising the time to blindness in patients with diabetic retinopathy.&lt;/p&gt;

The causal relationship between physical activity, sedentary time and idiopathic pulmonary fibrosis risk: a Mendelian randomization study

BackgroundSeveral observational studies have found that physical inactivity and sedentary time are associated with idiopathic pulmonary fibrosis (IPF) risk. However, the causality between them still requires further investigation. Therefore, our study aimed to investigate the causal effect of physical activity (PA) and sedentary time on the risk of IPF via two-sample Mendelian randomization (MR) analysis.MethodsMultiple genome-wide association study (GWAS) data involving individuals of European ancestry were analyzed. The datasets encompassed published UK Biobank data (91,105–377,234 participants) and IPF data (2018 cases and 373,064 controls) from FinnGen Biobank. The inverse variance weighting (IVW) method was the primary approach for our analysis. Sensitivity analyses were implemented with Cochran’s Q test, MR-Egger regression, MR-PRESSO global test, and leave-one-out analysis.ResultsGenetically predicted self-reported PA was associated with lower IPF risk [OR = 0.27; 95% CI 0.09–0.82; P = 0.02]. No causal effects of accelerometry-based PA or sedentary time on the risk of IPF were observed.ConclusionsOur findings supported a protective relationship between self-reported PA and the risk for IPF. The results suggested that enhancing PA may be an effective preventive strategy for IPF.

The Type II Topp-Leone Inverse Power Lomax distribution with Simulation and Applications

In this work, we present a four-parameter lifetime model that can be used to model reliability issues, fatigue life studies, and survival data called the Type II Topp-Leone Inverse Power Lomax distribution. It has the Type II inverse Lomax, Inverse Power Lomax, and Inverse Lomax distributions as sub-models. Some of its statistical properties, including complete and incomplete moments, generating functions, characteristics functions, mean residual life, mean inactivity time, Renyi entropy, Tsallis entropy, order statistics, stress-strength reliability, and weighted probability moment, have formal formulas that we have developed. The model's parameters are estimated using the maximum likelihood estimation technique. The effectiveness of maximum likelihood estimators is evaluated in terms of absolute bias and simulation study standard error. Two lifetime data sets are used to demonstrate how the new model can be applied. Using the same comparative criteria, the proposed distribution offers a better fit than a few well-known distributions.

Preservation of mean inactivity time ordering for coherent systems

AbstractPreservation of stochastic orders through the system signature has captured the attention of researchers in recent years. Signature-based comparisons have been made for the usual stochastic order, hazard rate order, and likelihood ratio orders. However, for the mean residual life (MRL) order, it has recently been proved that the preservation result does not hold true in general, but rather holds for a particular class of distributions. In this paper, we study whether or not a similar preservation result holds for the mean inactivity time (MIT) order. We prove that the MIT order is not preserved from signatures to system lifetimes with independent and identically distributed (i.i.d.) components, but holds for special classes of distributions. The relationship between these classes and the order statistics is also highlighted. Furthermore, the distribution-free comparison of the performance of coherent systems with dependent and identically distributed (d.i.d.) components is studied under the MIT ordering, using diagonal-dependent copulas and distorted distributions.

Power Lambert uniform distribution: Statistical properties, actuarial measures, regression analysis, and applications

Here, we present a new bounded distribution known as the power Lambert uniform distribution, and we deduce some of its statistical properties such as quantile function, moments, incomplete moments, mean residual life and mean inactivity time, Lorenz, Bonferroni, and Zenga curves, and order statistics. We presented different shapes of the probability density function and the hazard function of the proposed model. Eleven traditional methods are used to estimate its parameters. The behavior of these estimators is investigated using simulation results. Some actuarial measures are derived mathematically for our proposed model. Some numerical computations for these actuarial measures are given for some choices of parameters and significance levels. A new quantile regression model is constructed based on the new unit distribution. The maximum likelihood estimation method is used to estimate the unknown parameters of the regression model. Furthermore, the usability of the new distribution and regression models is demonstrated with the COVID-19 and educational datasets, respectively.

Current Trends and Issues Involving Screen Time and Physical Activity Engagement Among School Students: A Thematic Review

Study purpose. Physical inactivity and excessive screen time engagement among school-aged children is a global issue which is known to be linked with sedentary lifestyle. Several studies were conducted about sedentary behavior among children, but still lacking is a review paper that analyzes the current trends and issues on physical inactivity and excessive screen time. This study aims to identify the current issues and trends on physical activity and screen time among school students discussed in the articles published from 2018-2022. Materials and methods. Quantitative and qualitative methods were used in this study. Quantitative approach was used to explain the trends and statistics in terms of literatures geographical dissemination and specified topic or themes developed within the period from 2018 to 2022. Qualitative approach was implemented to establish patterns and themes. This study generates themes and patterns from the articles published from 2018 to 2022. Thirty articles were included via database searched from SCOPUS, SportsDiscus and PubMed.gov. Thematic analysis was done using ATLAS.ti 22. Results. Five main themes were generated: 1) behavioral impact; 2) engagement level: age and gender specific; 3) association to socio-economic status; 4) impact on adiposity; and 5) school curriculum role. Conclusions. Excessive screen time and physical inactivity have a diverse effect on young populations health. Understanding the patterns and themes generated in this study will be of great help in terms of designing a program of activity that will counter measure the negative effect associated with sedentary lifestyle.

Adverse childhood experiences and clustering of high-risk behaviors among high school students: a cross-sectional study

ObjectivesThis study assessed the association between adverse childhood experiences (ACEs) and clustering of high-risk behaviors in a sample of high school students. Study designThis was a cross-sectional study. MethodsA sample of students who attended randomly selected classes in 99 high schools completed the 2019 Nevada Youth Risk Behavior Survey (N = 4959). The survey included six ACE measures: (1) physical abuse, (2) sexual abuse, (3) verbal abuse, (4) household physical abuse, (5) household mental illness, and (6) household substance abuse. Students were assigned a cumulative ACE score (range = 0–6). A count of high-risk behavior domains was created using multiple questions across the following domains: (1) violence behaviors, (2) suicidal indicators, (3) non-suicidal self-injury, (4) substance use, (5) high-risk sexual behaviors, (6) poor diet, (7) physical inactivity, and (8) high screen time (range = 0–8). The relationship between ACEs and the count of high-risk behavior domains was assessed using weighted negative binomial regression; incidence rate ratios (IRRs) were calculated adjusting for sociodemographic characteristics. ResultsMore than 40% of the sampled students reported high-risk behaviors across two or more domains. There was a strong, graded relationship between cumulative ACE score and the count of high-risk behavior domains. Compared with students who experienced zero ACEs, there was an increase in the count of high-risk behavior domains among students who experienced one ACE (adjusted IRR [aIRR] = 1.22, 95% confidence interval [CI] = 1.12–1.33), two ACEs (aIRR = 1.57, 95% CI = 1.42–1.73), three ACEs (aIRR = 1.73, 95% CI = 1.54–1.94), four ACEs (aIRR = 2.07, 95% CI = 1.84–2.33), five ACEs (aIRR = 2.69, 95% CI = 2.34–3.10), and six ACEs (aIRR = 2.91, 95% CI = 2.34–3.62). ConclusionTrauma-informed prevention efforts may be an efficient way to address multiple adolescent risk behaviors that cluster.