Impact of metamorphoses from desk to E-commute - an acumen of professionals: A cross-sectional study.

Introduction: The deadly pandemic COVID-19 has brought the world to a standstill. Due to worldwide lockdown, economy was severely compromised. Everyone was in fear of the consequences that would be faced. Many unprecedented changes happened because of this pandemic. People confined themselves at home to maintain social distancing and mitigation of risk factors. This posed many challenges to the individuals or work force with, and many times the work was compromised. Objectives: To explore the current and changes in work pattern and to assess the overall quality of life of professionals working from home. Materials and Methods: A cross-sectional study was conducted by circulating a structured questionnaire through online platform. A snowball sampling method was adopted. Totally, 520 respondents participated in this study. Results: Out of 520 participants, males accounted for 57.9% and 41.3% were females. Majority (45.8% males and 40.9% females) were engaged in the software or IT companies. A higher proportion of males were engaged in administration (6%), architecture (10%) and transportation (5.3%) sector, whereas more females were engaged in finance (10.7%) and law (3.3%) sector. 73.1% males worked for more than 12 hours a day in contrast to only 26.9% of females. As the working hours are reducing, the overall quality of life of the participants was increasing significantly with P = 0.008. For those with moderately increased work-life balance, near about half of the participants had overall quality of life between 50 and 75% (P < 0.001). Conclusions: In order to increase efficiency at work place, definite laws should be in place to protect the mental as well as overall well-being of professionals working from home. Elaborative research in this field is required in order to generalize the results.

Urban delineation through a prism of intraday commute patterns.

Introduction: Urban mobility patterns are crucial for effective urban and transportation planning. This study investigates the dynamics of urban mobility in Brno, Czech Republic, utilizing the rich dataset provided by passive mobile phone data. Understanding these patterns is essential for optimizing infrastructure and planning strategies. Methods: We developed a methodological framework that incorporates bidirectional commute flows and integrates both urban and suburban commute networks. This comprehensive approach allows for a detailed representation of Brno's mobility landscape. By employing clustering techniques, we aimed to identify distinct mobility patterns within the city. Results: Our analysis revealed consistent structural features within Brno's mobility patterns. We identified three distinct clusters: a central business district, residential communities, and an intermediate hybrid cluster. These clusters highlight the diversity of mobility demands across different parts of the city. Discussion: The study demonstrates the significant potential of passive mobile phone data in enhancing our understanding of urban mobility patterns. The insights gained from intraday mobility data are invaluable for transportation planning decisions, allowing for the optimization of infrastructure utilization. The identification of distinct mobility patterns underscores the practical utility of our methodological advancements in informing more effective and efficient transportation planning strategies.

Walking to work: The role of walkability around the workplace in a Dutch adult commuting population.

Current evidence on neighborhood walkability and active commuting focuses on residential rather than workplace environment. This cross-sectional study investigated whether higher workplace walkability (WW) was associated with commute walking, both independently and together with residential walkability, using data from 6769 respondents of the 2017 Dutch national travel survey. In a fully adjusted logistic regression model, 10% increase in WW was associated with 32% higher odds of commute walking (Odds ratio (OR): 1.31, 95% Confidence Interval (CI: 1.27-1.36). The estimates were stronger in rural dwellers than urban residents, (ORrural 1.49, 95%CI: 1.34-1.64 vs ORhighly.urban 1.19, 95%CI: 1.13-1.26). In participants with both high residential walkability and WW, we observed 215% higher odds (OR 3.15, 95% CI: 2.48-3.99) of commute walking compared to those with low walkability in both. Our study indicated the importance and complementary nature of walkable residence and workplace in contribution to physical activity of working individuals through active commuting.

Development and evaluation of a scale to measure nurses' unsafe driving behaviour while commuting.

Driving is the most prevalent form of commuting for most workers but is also perhaps the most hazardous mode of travel with unsafe driving contributing significantly to road traffic accidents. Despite nurses having been reported as being at higher risk of commuter-related accidents over the last three decades, little is known about unsafe driving behaviours among nurses while commuting, which is unique from other driving routines. Additionally, the lack of appropriate tools to measure such behaviours is apparent. This study aims i) to identify unsafe driving behaviours among nurses while commuting and ii) to develop a scale to assess nurses' unsafe commuting driving behaviours. The study employed a multiphase and multimethod approach to develop the scale, which was subject to stringent validation and evaluation. Themes were specified via the Nominal Group Technique (NGT). Six themes were identified namely: i) violations and reckless driving, ii) negative emotions, iii) drowsy driving iv) mind wandering, v) error and vi) carelessness. Content and face validity were sought through expert review. A total of 442 nurses' data were collected across multisite hospitals for evaluation. Exploratory factor analysis (EFA) resulted in recovered structure and was confirmed through Confirmatory Factor Analysis (CFA) with structural equation analyses being conducted to test predictive validity. All constructs met adequate validity and reliability. Nurses' unsafe driving behaviours while commuting were identified with a novel scale to assess them being both developed and validated. The resulting MyUDWC scale is a suitable tool for measuring nurses' unsafe driving behaviours while commuting.

Estimating morning and evening commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology.

Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.

Novel method to plan and design services. Using software to optimise the head and neck cancer patient's commute to hospital.

Travelling for hospital appointments represents a significant burden to patients. We have developed a computer programme that accurately evaluates patient commutes between their home and treatment hospital in public and private transport. This has been applied to a cohort of Head and Neck Cancer (HNC) patients to plan the locations of satellite hospitals and assess their impact on patients' commutes. Patients diagnosed with HNC were identified from our hospital's database between December 2019 and January 2022. Using Python, commuting distances from patients' postcodes to our tertiary referral hospital were calculated. These commutes incorporated routes along defined roads, traffic data, and were calculated using public and private transport. Patient commutes from their postcodes to four satellite hospitals were also calculated. We identified their closest hospital and compared that journey to the patients' journey to our tertiary centre. We included 709 patients in our analysis. Patients would have a significantly shorter journey distance and time in both public and private transport if satellite hospitals were used for appointments alongside our tertiary centre. Average travel times would reduce by 10 minutes in private and 25 minutes in public transport. Furthermore, 70% of patients required ≥2 forms of public transport to get to our hospital. This would drop to 44.1% of patients if satellite hospitals were included in our service. Our programme would allow the most accessible sites to be identified for establishing outreach clinics at appropriate satellite hospitals, therefore improving patient access to healthcare.

A review of personal exposure studies in selected Asian countries' public transport microenvironments: lessons learned and future directions.

This comprehensive paper conducts an in-depth review of personal exposure and air pollutant levels within the microenvironments of Asian city transportation. Our methodology involved a systematic analysis of an extensive body of literature from diverse sources, encompassing a substantial quantity of studies conducted across multiple Asian cities. The investigation scrutinizes exposure to various pollutants, including particulate matters (PM10, PM2.5, and PM1), carbon dioxide (CO2), formaldehyde (CH2O), and total volatile organic compounds (TVOC), during transportation modes such as car travel, bus commuting, walking, and train rides. Notably, our review reveals a predominant focus on PM2.5, followed by PM10, PM1, CO2, and TVOC, with limited attention given to CH2O exposure. Across the spectrum of Asian cities and transportation modes, exposure concentrations exhibited considerable variability, a phenomenon attributed to a multitude of factors. Primary sources of exposure encompass motor vehicle emissions, traffic dynamics, road dust, and open bus doors. Furthermore, our findings illuminate the influence of external environments, particularly in proximity to train stations, on pollutant levels inside trains. Crucial factors affecting exposure encompass ventilation conditions, travel-specific variables, seat locations, vehicle types, and meteorological influences. The culmination of this rigorous review underscores the need for standardized measurements, enhanced ventilation systems, air filtration mechanisms, the adoption of clean energy sources, and comprehensive public education initiatives aimed at reducing pollutant exposure within city transportation microenvironments. Importantly, our study contributes to the growing body of knowledge surrounding this subject, offering valuable insights for policymakers and researchers dedicated to advancing air quality standards and safeguarding public health.

Graph structure reforming framework enhanced by commute time distance for graph classification.

As a graph data mining task, graph classification has high academic value and wide practical application. Among them, the graph neural network-based method is one of the mainstream methods. Most graph neural networks (GNNs) follow the message passing paradigm and can be called Message Passing Neural Networks (MPNNs), achieving good results in structural data-related tasks. However, it has also been reported that these methods suffer from over-squashing and limited expressive power. In recent years, many works have proposed different solutions to these problems separately, but none has yet considered these shortcomings in a comprehensive way. After considering these several aspects comprehensively, we identify two specific defects: information loss caused by local information aggregation, and an inability to capture higher-order structures. To solve these issues, we propose a plug-and-play framework based on Commute Time Distance (CTD), in which information is propagated in commute time distance neighborhoods. By considering both local and global graph connections, the commute time distance between two nodes is evaluated with reference to the path length and the number of paths in the whole graph. Moreover, the proposed framework CTD-MPNNs (Commute Time Distance-based Message Passing Neural Networks) can capture higher-order structural information by utilizing commute paths to enhance the expressive power of GNNs. Thus, our proposed framework can propagate and aggregate messages from defined important neighbors and model more powerful GNNs. We conduct extensive experiments using various real-world graph classification benchmarks. The experimental performance demonstrates the effectiveness of our framework. Codes are released on https://github.com/Haldate-Yu/CTD-MPNNs.

The longitudinal association between neighbourhood quality and cardiovascular risk factors among youth receiving obesity treatment.

BACKGROUND: Neighbourhood factors are associated with cardiovascular health in adults, but these relationships are under-explored in youth. OBJECTIVES: To characterize the associations between neighbourhood factors and child and adolescent health among youth with obesity. METHODS: Data were drawn from patient health records at a pediatric weight management clinic (n = 2838) and the Child Opportunity Index (COI). Exposures were area-level neighbourhood factors (commute duration, walkability, greenspace and industrial pollutants). Outcomes included BMI relative to the 95th percentile (BMIp95) and blood pressure (continuous variables). Longitudinal models examined associations between COI indicators and outcomes. RESULTS: Shorter commute duration (ß = -4.31, 95% CI: -5.92, -2.71) and greater walkability (ß = -4.40, 95% CI: -5.98, -2.82) were negatively associated with BMIp95. Increased greenspace availability was positively associated with BMIp95 (ß = 1.93, 95% CI: 0.19, 3.67). None of the COI indicators were associated with cardiovascular outcomes in the full sample. Analyses stratified by sex and race/ethnicity showed similar patterns for BMIp95. For commute duration, there was a negative association with blood pressure for female, non-Hispanic White and other race/ethnicity youth. CONCLUSIONS: Neighbourhood factors should be considered as contextual factors when treating youth with obesity. Additional research is needed to understand the relationship between neighbourhood factors and cardiovascular outcomes.

Transportation-related Environmental Mixtures and Diabetes Prevalence and Control in Urban/Metropolitan Counties in the United States.

Diabetes rates in the United States are staggering and climbing. Importantly, traditional risk factors fail to completely account for the magnitude of the diabetes epidemic. Environmental exposures, including urban and metropolitan transportation quality, are implicated as contributors to disease. Using data from the county-level Environmental Quality Index (EQI) developed for the United States, we analyzed associations between transportation and air quality environmental metrics with overall diabetes prevalence and control within urban/metropolitan counties in the United States from 2006 to 2012. Additionally, we examined effect modification by race/ethnicity through stratification based on the county-level proportion of minority residents. Last, we applied mixture methods to evaluate the effect of simultaneous poor transportation factors and worse air quality on the same outcomes. We found that increased county-level particulate matter air pollution and nitrogen dioxide along with reduced public transportation usage and lower walkability were all associated with increased diabetes prevalence. The minority proportion of the population influences some of these relationships as some of the effects of air pollution and the transportation-related environment are worse among counties with more minority residents. Furthermore, the transportation and air quality mixtures were found to be associated with increased diabetes prevalence and reduced diabetes control. These data further support the burgeoning evidence that poor environments amplify diabetes risk. Future cohort studies should explore the utility of environmental policies and urban planning as tools for improving metabolic health.

Correlation of commute time with the risk of subjective mental health problems: 6th Korean Working Conditions Survey (KWCS).

Background: Studies conducted so far on the link between commute time and mental health among Koreans remain insufficient. In this study, we attempted to identify the relationship between commute time and subjective mental health using the 6th Korean Working Conditions Survey (KWCS). Methods: Self-reported commute time was divided into four groups: ≤ 30 (group 1), 30-60 (group 2), 60-120 (group 3), and > 120 minutes (group 4). Subjective depression was defined as a score of 50 points or less on the WHO-5 well-being index. Subjective anxiety and fatigue were defined as answering 'yes' to the questionnaire on whether they had experienced it over the past year. The analysis of variance, t-test, and χ2 test was used to analyze the differences among the characteristics of the study participants according to commute time, depression, anxiety, and fatigue. Odds ratios (ORs) and 95% confidence intervals (CIs) for depression, anxiety, and fatigue according to commute time were calculated using multivariate logistic regression models adjusted for sex, age, monthly income, occupation, company size, weekly working hours, and shift work status. Results: Long commute times showed increased ORs and graded increasing trends for depression, anxiety, and fatigue. The ORs for depression increased significantly in group 2 (1.06 [1.01-1.11]), group 3 (1.23 [1.13-1.33]), and group 4 (1.31 [1.09-1.57]) compared to group 1 (reference). The ORs for anxiety increased significantly in group 2 (1.17 [1.06-1.29]), group 3 (1.43 [1.23-1.65]) and group 4 (1.89 [1.42-2.53]). The ORs for fatigue increased significantly in group 2 (1.09 [1.04-1.15]), group 3 (1.32 [1.21-1.43]), and group 4 (1.51 [1.25-1.82]). Conclusions: This study highlights that the risk of depression, anxiety, and fatigue increases with commute time.

Modeling the COVID-19 Travel Choices in Colombia and India: A Hybrid Multiple Discrete-Continuous Nested Extreme Value Approach.

The COVID-19 pandemic has affected many daily activities, primarily as a result of the perceived contagion risk and government restrictions to mitigate the spread of the virus. To this end, drastic changes in the trip choices for commuting to work have been reported and studied, mostly through descriptive analysis. On the other hand, modeling-based research that can simultaneously understand both changes in mode choice and its frequency at an individual level has not been much used in existing studies. As such, this study aims to understand the changes in mode-choice preference and the frequency of trips, comparing pre-COVID with during-COVID scenarios, in two different countries of the Global South: Colombia and India. A hybrid multiple discrete-continuous nested extreme value model was implemented using the data obtained from online surveys in Colombia and India during the early COVID-19 period of March and April 2020. This study found that, in both countries, utility related to active modes (more used) and public transportation (less used) changed during the pandemic. In addition, this study highlights potential risks in likely unsustainable futures where there may be increased use of private vehicles such as cars and motorcycles, in both countries. It was also identified that perceptions toward government responses had a significant impact on the choices in Colombia, though this was not the case in India. These results may help decision makers focus on public policies to encourage sustainable transportation by avoiding the detrimental long-term behavioral changes resulting from the COVID-19 pandemic.

Impacts of commute mode on body mass index: A longitudinal analysis before and during the COVID-19 pandemic.

Introduction: COVID-19 has impacted millions of commuters by decreasing their mobility and transport patterns. While these changes in travel have been studied, less is known about how commute changes may have impacted individuals' body mass index (BMI). The present longitudinal study explores the relationship between commute mode and BMI of employed individuals in Montréal, Canada. Methods: This study uses panel data drawn from two waves of the Montréal Mobility Survey (MMS) conducted before and during the COVID-19 pandemic (n = 458). BMI was modeled separately for women and men as a function of commuting mode, WalkScore©, sociodemographic, and behavioral covariates using a multilevel regression modeling approach. Results: For women, BMI significantly increased during the COVID-19 pandemic, but telecommuting frequency, and more specifically telecommuting as a replacement of driving, led to a statistically significant decrease in BMI. For men, higher levels of residential local accessibility decreased BMI, while telecommuting did not have a statistically significant effect on BMI. Conclusions: This study's findings confirm previously observed gendered differences in the relations between the built environment, transport behaviors, and BMI, while offering new insights regarding the impacts of the changes in commute patterns linked to the COVID-19 pandemic. Since some of the COVID-19 impacts on commute are expected to be lasting, findings from this research can be of use by health and transport practitioners as they work towards generating policies that improve population health.

Does work-related and commuting physical activity predict changes in physical activity and sedentary behavior during the transition to retirement? GPS and accelerometer study.

We examined how GPS and accelerometer measured work-related and commuting physical activity contribute to changes in physical activity and sedentary behavior during the retirement transition in the Finnish Retirement and Aging study (n = 118). Lower work-related activity was associated with a decrease in sedentary time and an increase in light physical activity during retirement. Conversely, higher work-related activity was associated with an increase in sedentary time and a decrease in light physical activity, except among those active workers who also were active commuters. Thus, both work-related and commuting physical activity predict changes in physical activity and sedentary behavior when retiring.

Social determinants of mental health: the roles of traumatic events, financial strain, housing instability, food insecurity, and commute time.

OBJECTIVE: To identify social determinants of mental health embedded within college students' living and learning conditions. PARTICIPANTS: Participants included 215 mostly undergraduate (95%) business students at a diverse, urban west coast public university (48% women; mean age 24). METHODS: Participants completed an online self-report survey measuring affective state, global mental health, anxious and depressive symptoms, as well as social determinants of mental health. Data were analyzed using multiple regression controlling for self-esteem, gender, and race/ethnicity. RESULTS: Results suggest that higher family income is positively related to mental health, whereas more adverse events (e.g., assault, robbery, serious illness or injury), food insecurity, and commute time are negatively related to mental health. Moderation results indicate a moderate buffering effect of belonging on global mental health among students who experience zero adverse events. CONCLUSIONS: Social determinants can shed light on student's precarious living and learning conditions and resultant effects on students' mental health.

Sleep Debt Mediates the Relationship between Work-Related Social Factors, Presenteeism, and Well-Being in Japanese Workers.

Sleep debt is associated with presenteeism and mental health; however, the association of sleep debt with presenteeism and well-being in the context of work-related social factors (commuting time, workdays, and working hours) has not been fully elucidated. This study aimed to examine whether work-related social factors are associated with presenteeism and well-being via sleep debt. The participant group comprised 872 full-time and 526 part-time workers (mean age: 44.65 ± 12.37 and 48.47 ± 12.93 years, respectively). For both the full-time and part-time workers, increased sleep debt was significantly associated with presenteeism (ß = -0.171; ß = -0.160) and low well-being (ß = -0.135; ß = -0.153). Notably, commuting time was significantly associated with increased sleep debt in full-time workers (ß = 0.09). In contrast, the number of workdays was significantly associated with increased sleep debt in part-time workers (ß = -0.102). Working hours were not significantly associated with sleep debt for both full- and part-time workers. These results reveal that sleep debt might lead to various risks among workers, elucidating the work-related social factors related to sleep debt. They also highlight the importance of considering work-related social factors when addressing sleep debt.

Mediating Effect of Work-Family Conflict on the Relationship Between Long Commuting Time and Workers' Anxiety and Insomnia.

Background: Our study aimed to investigate the mediating role of work-family conflict (WFC) on the relationship between long commutes and workers' anxiety and insomnia. Methods: Our study measured the two dimensions of WFC, time-related, and strain-related, which were considered multiple mediators. The mediating effect of WFC on anxiety and insomnia was investigated by decomposing the total effect into a direct effect (long commuting time → anxiety or insomnia) and an indirect effect (long commuting time → WFC → anxiety or insomnia). The combined indirect effect (joint indirect effect) of strain-related WFC and time-related WFC was estimated. The effects were presented as odds ratios and 95% confidence intervals (CIs). Results: The direct effect of 120 min or longer of commuting time was 1.39 (95% CI: 1.17-1.65) times increase in the odds of anxiety and 1.64 (95% CI: 1.41-1.90) times increase in the odds of insomnia than those whose commuting time was less than 60 min. In the case of indirect effects, those whose commuting time was 120 min or longer had 1.13 times higher odds of anxiety (95% CI: 1.07-1.18) and 1.12 times higher odds of insomnia (95% CI: 1.07-1.17) via WFC. The joint indirect effects accounted for 26.4% and 18.5% of the total effect on anxiety and insomnia, respectively. The longer the commuting time, the stronger both direct and indirect effects. Conclusions: Our findings highlight the mediating effect of WFC on the relationship between long commuting times and workers' anxiety and insomnia.

Associations between commute mode use and self-rated health and work ability among Finnish public sector employees.

AIM: To determine the extent to which level of active commute mode use is associated with self-rated health and work ability. METHODS: The data were sourced from the Finnish Public Sector Study survey in 2020 (n = 38,223). The associations between active commuting - assessed with the frequency of using active commute modes - and self-rated health and work ability were examined with negative binomial regression analyses. Passive commuting and low-to-moderate levels of active commuting were compared with active commuting, and the models were adjusted for sociodemographic factors, working time mode, and lifestyle risk factors. We also assessed separate associations between walking and cycling as a mode of commuting by additionally considering the commuting distance and the outcomes. RESULTS: After adjustment, when using active commuters as a reference, passive commuters had a 1.23-fold (95% confidence intervals (CI) 1.19 to 1.29) risk of suboptimal self-rated health and a 1.18-fold (95% CI 1.13 to 1.22) risk of suboptimal work ability. More frequent and/or longer distance by foot and especially by bicycle, was positively associated with health and work ability. Never commuting by bicycle was associated with a 1.65-fold (95% CI 1.55 to 1.74) risk of suboptimal health and a 1.27-fold (95% CI 1.21 to 1.34) risk of suboptimal work ability when using high-dose bicycle commuting as a reference. CONCLUSIONS: Passive commuting was associated with suboptimal self-rated health and suboptimal work ability. Our results suggest that using active commute modes, particularly cycling, may be beneficial for employee health and work ability.

Rotation work in the resources sector: a systematic review of the impact on workers' families.

OBJECTIVE: Rotation work involves travelling to work in remote areas for a block of time and alternate with spending another block of time at home; such work arrangements have become common in the resources sector. The intermittent absence of workers from the home may adversely affect the health of the workers' families. This study synthesises research on mental and physical health outcomes in partners and children of rotation workers in the resources sector. DESIGN: A systematic review was conducted. Studies were retrieved from PubMed, Medline, EMBASE, CINAHL, PsycINFO, and Scopus. Nineteen studies were included and findings were summarised narratively. RESULTS: The impact of rotation work on the mental health and well-being of partners and children of rotation workers remains unclear. However, on days where workers are away, partners may experience greater loneliness and poorer sleep quality. CONCLUSION: Partners may benefit from support, particularly when they have younger children and/or their spouses first begin rotation work. Research is limited, particularly regarding the impact on health-related behaviours and physical health outcomes. REGISTRATION: This review was registered on PROSPERO (ID: CRD42020167649).

Do 20-minute neighbourhoods moderate associations between work and commute hours with food consumption?

OBJECTIVE: To examine associations between work and commute hours with food consumption and test whether neighbourhood type (20-minute neighbourhood (20MN)/non-20MN) moderate associations. DESIGN: Cross-sectional analysis of the Places and Locations for Activity and Nutrition study (ProjectPLAN). Exposures were work hours (not working (0 h), working up to full-time (1-38 h/week), working overtime (> 38 h/week)), and among those employed, combined weekly work and commute hours (continuous). Outcomes were usual consumption of fruit, vegetables, takeaway food, snacks and soft drinks, and number of discretionary food types (takeaway, snacks and soft drinks) consumed weekly. Generalised linear models were fitted to examine associations between each exposure and outcome. The moderating role of neighbourhood type was examined through interaction terms between each exposure and neighbourhood type (20MN/non-20MN). SETTING: Melbourne and Adelaide, Australia, 2018-2019. PARTICIPANTS: Adults ≥ 18 years old (n 769). RESULTS: Although all confidence intervals contained the null, overall, patterns suggested non-workers and overtime workers have less healthy food behaviours than up-to-full-time workers. Among those employed, analysis of continuous work and commute hours data suggested longer work and commute hours were positively associated with takeaway consumption (OR = 1·014, 95 % CI 0·999, 1·030, P-value = 0·066). Patterns of better behaviours were observed across most outcomes for those in 20MN than non-20MN. However, differences in associations between work and commute hours with food consumption across neighbourhood type were negligible. CONCLUSIONS: Longer work and commute hours may induce poorer food behaviours. There was weak evidence to suggest 20MN moderate associations between work and commute hours with food consumption, although behaviours appeared healthier for those in 20MN.