Abstract Introduction Shiftworking firefighters often face chronic sleep disruptions, yet little is understood about how they manage these challenges. This study aims to examine how different sleep practice patterns relate to sleep disturbances among firefighters. Methods Career firefighters (N=290) working shift schedules from 20 fire agencies in Arizona were administered a survey as part of the Sleep Assistance for Firefighters (SAFFIRE) study. Demographics, occupational factors, sleep practices from the Sleep Practices and Attitudes Questionnaire, and sleep disturbance from PROMIS-Sleep Disturbance short-form (version 8b) were collected. An exploratory factor analysis was used to group similar sleep practices together. Next, a latent profile analysis classified firefighters into profiles based on shared sleep practice patterns. Finally, an ANCOVA compared mean sleep disturbance across profiles and firefighter rank while adjusting for years working nights and agency call volume. Results Results of the exploratory factor analysis grouped sleep practices into 1) staying in bed, 2) doing a productive activity (e.g., exercising, and doing chores), 3) doing a passive activity (e.g., listening to music, eating/drinking, and using a screened device), and 4) using alcohol and/or marijuana. The latent profile analysis then classified firefighters into Riser, Stay-in-Bed, and Substance-Use profiles. Mean sleep disturbance did not vary between profiles. However, firefighter rank (F = 23.31, p <.001, ηp2 = 0.08) and its interaction with sleep practice profiles (F = 3.57, p = 0.03, ηp2 = 0.03) were significantly associated with sleep disturbance. Conclusion Managers with the Riser and Stay-in-Bed profiles had significantly less disturbance compared to managers with the Substance-Use profile and non-managerial staff regardless of sleep practice profile. Future research needs to determine role-specific factors that contribute to differences in sleep disturbance between firefighter ranks. Support (if any) National Heart, Lung, and Blood Institute (R01HL162799)

Abstract Introduction Sleep coaching, based on Cognitive Behavioral Therapy for Insomnia (CBTi), may be an effective intervention for firefighters' sleep problems. However, little is known about how firefighters perceive such coaching. Given that self-reliance is linked to lower intentions to seek professional help, this study examined the relationships among self-reliance, stigma toward sleep coaching, and perceptions of its effectiveness. Methods Participants included firefighters (N =314) from 20 Arizona fire agencies, recruited through the Sleep Assistance for Firefighters (SAFFIRE) study. They completed a survey assessing self-reliance, stigma toward sleep coaching, and perceived effectiveness, adapted from the Determinants of Mental Health Treatment Seeking questionnaire. While controlling for age and years of service, multiple regression analyses were conducted to assess whether self-reliance predicted stigma and perceived effectiveness of sleep coaching. Results Results indicated that firefighters with greater self-reliance had higher stigma towards sleep coaching (B = 0.68, SE = 0.11, p < .001, R2 = 0.12). They were also less likely to view sleep coaching as an effective intervention (B = -0.75, SE = 0.08, p <.001, R2 = 0.23). Conclusion Our findings indicated that self-reliance is negatively associated with firefighters’ perceptions of sleep coaching. Addressing self-reliant tendencies by encouraging help-seeking behaviors may reduce stigma associated with sleep interventions and increase their adoption among firefighters. Support (if any) National Heart, Lung, and Blood Institute (R01HL162799)

Abstract Introduction Firefighters balance demanding work schedules and complex household responsibilities, yet little is known about the interpersonal and organizational factors that shape their sleep health. Perceived life chaos, characterized by unpredictable routines and difficulty planning daily activities, is a relatively new construct in sleep research and has not been examined in shift-working populations. This project aimed to disentangle the relative contributions of perceived life chaos, occupational factors, or household context to sleep disturbance among firefighters in 20 Arizona fire agencies. Methods Cross-sectional survey data were analyzed for 250 Arizona firefighters. Sleep disturbance was measured via the PROMIS Sleep Disturbance Short Form 8b. Perceived life chaos was assessed using a modified 4-item Chaos, Hubbub, and Order Scale. Occupational variables included rank, call volume, overtime hours, and years of night-shift exposure. Household variables included marital status, presence of a bed partner, household size, annual income, and secondary employment. Multivariable linear regression with backward selection was used to identify the most parsimonious set of predictors of sleep disturbance. Results Using backward selection, the final model predicting sleep disturbance retained four variables: perceived life chaos, occupational rank, marital status, and call volume. Higher perceived life chaos was significantly associated with greater disturbance (β= 0.43, SE = 0.12, p < 0.001). Fire managers reported less disturbance than non-managers (β= -2.61, SE = 0.77, p = 0.001). Compared with married firefighters, never-married firefighters reported lower disturbance (β = -2.33, SE = 1.02, p = 0.02). Higher call volume showed a trend toward increased disturbance (β= 0.64, SE = 0.35, p = 0.07) and was retained based on theoretical relevance. Other occupational and household variables, including overtime hours, years of night-shift exposure, and household composition, were excluded. Conclusion Perceived life chaos was the strongest predictor of sleep disturbance among firefighters, with additional contributions from occupational rank, call volume, and marital status. These findings highlight the value of examining interpersonal and organizational conditions that shape shift workers’ day-to-day routines when considering future approaches to support sleep health in the fire service. Support (if any) National Heart, Lung, and Blood Institute (R01HL162799)

Discrete conformal mappings based on circle packing, vertex scaling, and related structures has had significant activity since Thurston proposed circle packing as a way to approximate conformal maps in the 1980s. The first convergence result of Rodin-Sullivan (1987) proved that circle packing maps do indeed converge to conformal maps to the disk. Recent results have shown convergence of maps of other discrete conformal structures to conformal maps as well. We give a general theorem of convergence of discrete conformal mappings between surfaces that allows for a variety of discrete conformal structures and manifolds with or without boundary. The mappings are a composition of piecewise linear discrete conformal mappings and Riemannian barycentric coordinates, called barycentric discrete conformal maps. Estimates of the barycentric discrete conformal maps allow extraction of convergent subsequences and estimates for the pullback of the Riemannian metric, proving conformality. The theorem requires assumptions on fullness of simplices to prevent degenerate triangles and a local discrete conformal rigidity generalizing hexagonal rigidity of circle packings.

Insufficient sleep disproportionately imposes significant health risks to firefighters. However, access to evidence-based sleep interventions, such as cognitive behavioral therapy for insomnia (CBTi), remains limited in this population. To build a foundation for the implementation of worksite sleep health coaching in fire departments, this study assessed firefighters’ acceptability of nine CBTi-informed sleep interventions. The Patient-Reported Outcomes Measurement Information System–Sleep Disturbance–Short Form 8b was used to screen for firefighters working 24-hour+ shifts who presented sleep disorder symptoms (T score ≥ 55). Eligible participants included 232 firefighters from 20 Arizona fire agencies who completed a cross-sectional online survey, which incorporated the Theoretical Framework of Acceptability measure to assess their perceived sleep intervention acceptability. Over half of the firefighters agreed or strongly agreed that the CBTi-informed sleep interventions were likable (percentage: 53%-74%), acceptable (61%-88%), and anticipated to be effective (54%-66%), except for timed exposure to ambient light and darkness, which only 35% liked and 42% perceived as effective. The top three interventions with the highest percentage of firefighters rating them as likable, acceptable, and being anticipated as effective were sleep education (64%, 88%, 58%), sleep extension (69%, 76%, 63%), and recovery sleep enhancement (74%, 80%, 66%). Interestingly, a higher percentage of firefighters also perceived sleep education (34%) and sleep extension (35%) as very effortful compared to other interventions (average: 23%). Firefighters with greater sleep symptoms were less likely to like sleep extension (Odds Ratio = 0.92, 95% CI = 0.87-0.98, p =.017) and recovery sleep enhancement (OR = 0.93, 95% CI = 0.87-0.99, p =.025) whereas more likely to perceive sleep education as effort-intensive (OR = 1.08, 95% CI = 1.02-1.14, p =.018). A moderate to high percentage of firefighters demonstrated acceptability of the sleep interventions, with sleep education, sleep extension, and recovery sleep enhancement ranking the highest. Interestingly, sleep education and sleep extension were frequently viewed as acceptable despite being effort-intensive, highlighting the importance of assessing different dimensions of acceptability of sleep interventions. These findings suggest promise in translating these evidence-based interventions to the workplace to improve firefighter sleep health. Funded by the National Institutes of Health (R01HL162799).

ABSTRACT There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high‐dimensionality of the data, the high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be ( γ , σ)‐stable if the probability of the same classification is at least for points sampled in a Gaussian neighborhood of the point with a given standard deviation . We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

This paper proposes a generalized exact path kernel gEPK which naturally decomposes model predictions into localized input gradients or parameter gradients. Many cutting edge out-of-distribution (OOD) detection methods are in effect projections onto a reduced representation of the gEPK parameter gradient subspace. This decomposition is also shown to map the significant modes of variation that define how model predictions depend on training input gradients at arbitrary test points. These local features are independent of architecture and can be directly compared between models. Furthermore this method also allows measurement of signal manifold dimension and can inform theoretically principled methods for OOD detection on pre-trained models.

There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high-dimensionality of the data, high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be $(\gamma,\sigma)$-stable if the probability of the same classification is at least $\gamma$ for points sampled in a Gaussian neighborhood of the point with a given standard deviation $\sigma$. We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

This paper uses the technology of weighted and regular triangulations to study discrete versions of the Laplacian on piecewise Euclidean manifolds. Regular triangulations are studied in some detail, including flip algorithms. The Laplacian is then studied as an operator on functions of the vertices as a generalized weighted Laplacian on graphs.

Objectives: Skipping meals is linked to negative cardiometabolic health outcomes. Few studies have examined the effects of breakfast skipping after disruptive life events, like job loss. The present analyses examine whether sleep timing, duration, and continuity are associated with breakfast eating among 186 adults who recently (past 90 days) experienced involuntary unemployment from the Assessing Daily Activity Patterns Through Occupational Transitions (ADAPT) study. Methods: We conducted both cross-sectional and 18-month longitudinal analyses to assess the relationship between actigraphic sleep after job loss and breakfast eating. Results: Later sleep timing was associated with a lower percentage of days breakfast was eaten at baseline (B = -0.09, SE = 0.02, P < .001) and longitudinally over 18 months (estimate = -0.04; SE = 0.02; P < .05). No other sleep indices were associated with breakfast consumption cross-sectionally or prospectively. Conclusions: Unemployed adults with a delay in sleep timing are more likely to skip breakfast than adults with an advancement in sleep timing. Future studies are necessary to test chronobiological mechanisms by which sleep timing might impact breakfast eating. With the understanding that sleep timing is linked to breakfast eating, the advancement of sleep timing may provide a pathway for the promotion of breakfast eating, ultimately preventing cardiometabolic disease. (c) 2023 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.

We explore the equivalence between neural networks and kernel methods by deriving the first exact representation of any finite-size parametric classification model trained with gradient descent as a kernel machine. We compare our exact representation to the well-known Neural Tangent Kernel (NTK) and discuss approximation error relative to the NTK and other non-exact path kernel formulations. We experimentally demonstrate that the kernel can be computed for realistic networks up to machine precision. We use this exact kernel to show that our theoretical contribution can provide useful insights into the predictions made by neural networks, particularly the way in which they generalize.

Abstract Introduction This analysis used data from the Assessing Daily Activity Patterns Through Occupational Transitions (ADAPT) study to compare differences in employment status on activity variability, as an indicator of circadian fragmentation and stability. Circadian fragmentation refers to frequency of alterations between rest and activity relative to the daily rhythm and stability refers to day-to-day similarity. High fragmentation and low stability have been linked to a number of negative health outcomes including depression, obesity, and heightened mortality risk. Methods The sample consisted of 155 participants that included 702 total cases (n = 434 employed, n = 268 unemployed) assessed over 18 months. Participants were required to have involuntarily lost their jobs in the last 90 days. Employment status was determined for all participants at each visit based on demographic surveys. Daily activity patterns were assessed via actigraphy (Actiwatch-Spectrum) at 30 second epochs for 14 days. The nonparametric measures of intradaily variability (IV) and interdaily stability (IS) were used as measures of circadian fragmentation and stability. Several subsampling intervals were considered for IV, ranging from 5 minutes to 4 hours. Results Unemployment was associated with higher IV (for 1 hour subsampling intervals, t=5.23, p < .001) and lower IS than employment (t=2.38, p Conclusion In a sample of adults with variable employment status, employment was associated with less circadian fragmentation and more stability than unemployment. These findings suggest that circadian fragmentation and instability may be two mechanisms by which job loss increases negative health risk. Future planned studies examining prospective changes in circadian fragmentation/stability during employment transitions will help answer this question. Support (if any) NIH #1R01HL117995-01A1, NSF DMS 1937229, NSF CCF 1740858

This study prospectively examined change in waist circumference (WC) as a function of daily social rhythms and sleep in the aftermath of involuntary job loss. It was hypothesized that disrupted social rhythms and fragmented/short sleep after job loss would independently predict gains in WC over 18 months and that resiliency to WC gain would be conferred by the converse.

The finite volume Laplacian can be defined in all dimensions and is a natural way to approximate the operator on a simplicial mesh. In the most general setting, its definition with orthogonal duals may require that not all volumes are positive; an example is the case corresponding to two-dimensional finite elements on a non-Delaunay triangulation. Nonetheless, in many cases twoand three-dimensional Laplacians can be shown to be negative semidefinite with a kernel consisting of constants. This work generalizes work in two dimensions that gives a geometric description of the Laplacian determinant; in particular, it relates the Laplacian determinant on a simplex in any dimension to certain volume quantities derived from the simplex geometry.

Abstract Introduction ERS telecommunicators are the first of the first responders challenged with solving complex, time-sensitive problems while managing workplace presence. Very little is known about sleep, work, and lifestyle factors among workers in this industry. One study demonstrated that 85% of ERS telecommunicators are overweight, suggesting that job-related factors may place these workers at risk for sedentary lifestyles. To test this hypothesis, we examined whether 14 day total work duration moderated the daily relationship between prior-night total sleep time and next day energy expenditure. Methods Over the course of 14 days (M = 6.9 days on-shift; SD = 1.9 days), 47 ERS telecommunicators were instructed to (a) wear actigraphs on their waist to gather estimates of average energy expenditure (EE, kcal/hour), (b) wear actigraphs on their wrist to gather estimates of total sleep time (min), and (c) complete daily shift logs to gather information about work duration (hours). Mixed linear modeling was employed to examine whether prior night within-subject total sleep time (TST) predicted next day energy expenditure, as moderated by between-subject work hours (n = 525 cases). Results A significant cross-level Work Duration x TST interaction (Estimate = .007, SE = .002, p < .001, 95% CI [.003, .011]) indicated that less prior-night TST was associated with less next-day EE among telecommunicators who worked more hours over the last 14 days. Conversely, telecommunicators who worked fewer hours expended more energy per hour the next day when they slept less than usual. Simple effects indicated that for each extra 102 minutes sleep (+1 SD), telecommunicators expended 5 kcal/hr (90 kcal over 18 hours awake). These results remained stable when controlling for between-subject differences in sleep and within-subject changes in work duration, night-shift work, and other relevant covariates. Conclusion The effect of total sleep time on next-day EE is unique to each telecommunicator’s typical sleep levels and the total hours worked over the course of two weeks. These two risk factors operate on EE as a function of one another. Findings provide support for the implementation of policy-level intervention to minimize chronic overwork and individual-level intervention to support sleep prioritization. Support (If Any) UA Canyon Ranch Center for Health Promotion and Treatment

Abstract Introduction Few studies have examined circadian phase after job loss, an event that upends daily routine. It is common that a daily routine begins with the consumption of breakfast, and breakfast behavior may contribute to health status in adults. Therefore, we sought to examine whether a later midpoint of sleep was associated with breakfast skipping among adults whose schedules were no longer dictated by employment. Methods Data were obtained from the Assessing Daily Activity Patterns Through Occupational Transitions (ADAPT) study. The sample of 155 participants had involuntarily lost their jobs in the last 90 days. Both cross-sectional and 18-month longitudinal analyses assessed the relationship between sleep midpoint after job loss and current and later breakfast skipping. Assessment periods were 14 days. Sleep was measured via actigraphy, and breakfast skipping was measured via daily diary (1 = had breakfast; 0 = did not have breakfast). The midpoint of sleep was calculated as the circular center based on actigraphy sleep onset and offset times. Results The midpoint of sleep at baseline was negatively associated with breakfast consumption at baseline (B = -.09, SE = .02, p = .000). Also, a later midpoint was associated with breakfast skipping over the next 18 months (estimate = -.08; SE = .02; p = .000). Prospective findings remained significant when adjusting for gender, ethnicity, age, perceived stress, body mass index (BMI), education, and reemployment over time. Education (estimate = 14.26, SE = 6.23, p < .05) and BMI (estimate = -.51, SE = .25, p < .05) were the only significant covariates. No other sleep indices predicted breakfast behavior cross-sectionally or prospectively. Conclusion Consistent with research in adolescents, unemployed adults with a later circadian phase are more likely to skip breakfast more often. Breakfast skipping was also associated with higher BMI. Taken together, these findings provide support for the future testing of sleep/wake scheduling interventions to modify breakfast skipping and potentially mitigate weight gain after job loss. Support (if any) #1R01HL117995-01A1

EDITORIAL article Front. Appl. Math. Stat., 07 April 2021 | https://doi.org/10.3389/fams.2021.674785

Abstract Objective Unemployment is an established risk factor for obesity. However, few studies have examined obesity‐related health behavior after involuntary job loss specifically. Job loss confers a disruption in daily time structure that could lead to negative metabolic and psychological outcomes through chronobiological mechanisms. This study examines whether individuals with unstable social rhythms after involuntary job loss present with higher abdominal adiposity than individuals with more consistent social rhythms and whether this relationship varies as a function of depressive symptoms. Methods Cross‐sectional baseline data (n = 191) from the ongoing Assessing Daily Activity Patterns in occupational Transitions (ADAPT) study were analyzed using linear regression techniques. Participants completed the Social Rhythm Metric‐17 (SRM) daily over 2 weeks. They also completed the Beck Depression Inventory II (BDI‐II) and participated in standardized waist circumference measurements (cm). Results A significant interaction emerged between SRM and BDI‐II demonstrating that less consistent social rhythms were associated with larger waist circumference at lower levels of depressive symptoms. Additional exploratory analyses demonstrated a positive association between the number of daily activities performed alone and waist circumference when controlling for symptoms of depression. Conclusion These findings are the first to demonstrate a relationship between social rhythm stability and abdominal adiposity in adults who have recently, involuntarily lost their jobs. Results highlight the moderating role of depressive symptoms on daily routine in studies of metabolic health. Future prospective analysis is necessary to examine causal pathways.

Abstract Introduction Involuntary job loss is an acute stressor that disrupts daily time structure and activity and exacerbates economic hardship and psychological distress. Studies show that unemployment is associated with negative obesity-related health outcomes, such as metabolic syndrome. However, very little is known about daily routine, depression, and obesity in individuals who have recently experienced involuntary job loss. We hypothesized that individuals with less consistent daily routines, or unstable social rhythms, after job-loss would have more abdominal adiposity than individuals with more consistent social rhythms. We also hypothesized that this relationship would vary as a function of depressive symptoms. Methods Cross-sectional baseline data (n = 186) from the ongoing ADAPT study (Assessing Daily Activity Patterns through occupational Transitions) were analyzed using linear regression techniques. Participants were predominantly female (62%) with a mean age of 41.12 years (SD = 10.16 years); 31% were Hispanic or Latino. Over two weeks, participants completed the daily Social Rhythm Metric-17 (SRM), Beck Depression Inventory II (BDI), and waist circumference (adiposity) measurements (cm). Results A significant BDI x SRM interaction was detected in the prediction of waist circumference, B = .36, SE = .18, p < .05, 95% CI [.002, .709], R2 = .07). The SRM was inversely associated with waist circumference, B = -5.57, SE = 2.25, p < .05, 95% CI [-9.98, -1.13], only at lower levels of BDI (-1 SD below the mean). Results from the Johnson-Neyman technique identified that the conditional effect of SRM on waist circumference was statistically significant at a BDI raw score of 8.33 (0-13 points is minimal depression) with ~45% of cases within this region. Conclusion A less consistent daily routine was associated with a larger waist circumference among individuals with minimal depressive symptoms. These findings are the first to demonstrate a relationship between social rhythm stability and abdominal adiposity in adults at high risk for central obesity. Results highlight the moderating role of depression in obesity prevention. Future prospective analysis is necessary to examine causal pathways. Support #1R01HL117995-01A1