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The idea that increased levels of extraversion are beneficial to well-being is widespread. Drawing on the idea that behaving discordant to one's trait level is demanding and effortful to maintain, and that repeated taxations of one's self-regulatory resources are unpleasant, we examined the relationship between cumulative counterdispositional extraversion and positive feelings. In two experience-sampling (ESM) studies, participants repeatedly rated their level of state extraversion and positive feelings. Results revealed that cumulative positive deviations from one's trait extraversion level were positively associated with positive feelings, whereas cumulative negative deviations were negatively associated with positive feelings. This confirms the idea that, also when looking at cumulative instances of extraversion-related behaviors, higher levels of extraversion go hand in hand with higher levels of positive feelings. [ABSTRACT FROM AUTHOR]

Exposure to manufactured nanomaterials (MNs) is a growing concern for occupational health and safety. Reliable methods for assessing and predicting MN exposure are essential to mitigate associated risks. This study presents the development of the Nano Exposure Quantifier (NEQ), a mechanistic model designed to assess airborne MN exposure in the workplace. By utilizing a dataset of 128 MN measurements from existing exposure studies, the model demonstrates its effectiveness in estimating MN exposure levels for particles smaller than 10 µm. The NEQ provides estimates in terms of particle number concentration accompanied by a 95% confidence interval (CI), enabling a comprehensive assessment of MN exposure. The NEQ includes 2 quantitative models: a simplified tier 1 model and a more comprehensive tier 2 model. Both tier 1 and tier 2 models exhibit robust performance, with correlation coefficients (r) of 0.57 and 0.62, respectively. The models exhibit a moderate level of error, as indicated by residuals' standard deviation of 4.10 for tier 1 and 3.90 for tier 2. The tier 1 model demonstrates a slightly higher overestimation bias (1.15) compared to the tier 2 model (0.54). Overall, the NEQ offers a practical and reliable approach for estimating MN exposure in occupational settings. Future validation studies will investigate the impact of initial calibration efforts, heteroscedasticity, and further refine the model's accuracy., (© The Author(s) 2025. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Occupational exposure to particulate matter (PM) can result in multiple adverse health effects and should be minimized to protect workers' health. PM exposure at the workplace can be complex with many potential sources and fluctuations over time, making it difficult to control. Dynamic maps that visualize how PM is distributed throughout a workplace over time can help in gaining better insights into when and where exposure occurs. This study explored the use of spatiotemporal modeling followed by kriging for the development of dynamic PM concentration maps in an experimental setting and a workplace setting. Data was collected using personal low-cost PM sensors and an indoor location tracking system, mounted on a moving robot or worker. Maps were generated for an experimental study with one simulated robot worker and a workplace study with four workers. Cross-validation was performed to evaluate the performance and robustness of three types of spatiotemporal models (metric, separable, and summetric) and, as an additional external validation, model estimates were compared with measurements from sensors that were placed stationary in the laboratory or workplace. Spatiotemporal models and maps were generated for both the experimental and workplace studies, with average root mean squared error (RMSE) from 10-fold cross-validation ranging from 7-12 and 73-127 µg/m 3 , respectively. Workplace models were relatively more robust compared to the experimental study (relative SD ranging from 8-14% of the average RMSE vs. 27-56%, respectively), presumably due to the larger number of parallel measurements. Model estimates showed low to moderate fits compared to stationary sensor measurements (R 2 ranging from 0.1-0.5), indicating maps should be interpreted with caution and only used indicatively. Together, these findings show the feasibility of using spatiotemporal modeling for generating dynamic concentration maps based on personal data. The described method could be applied for exposure characterization within comparable study designs or can be expanded further, for example by developing real-time, location-based worker feedback systems, as efficient tools to visualize and communicate exposure risks.

Workplace exposure is an important source of ill health. The use of wearable sensors and sensing technologies may help improve and maintain worker health, safety, and wellbeing. Input from workers should inform the integration of these sensors into workplaces. We developed an online survey to understand the acceptability of wearable sensor technologies for occupational health and safety (OSH) management. The survey was disseminated to members of OSH-related organizations, mainly in the United Kingdom and the Netherlands. There were 158 respondents, with over half (n = 91, 58%) reporting current use of wearable sensors, including physical hazards (n = 57, 36%), air quality (n = 53, 34%), and location tracking (n = 36, 23%), although this prevalence likely also captures traditional monitoring equipment. There were no clear distinctions in wearable sensor use between the reported demographic and occupational characteristics, with the exception that hygienists were more likely than non-hygienists (e.g. safety professionals) to use wearable sensors (66% versus 34%). Overall, there was an interest in how sensors can help OSH professionals understand patterns of exposure and improve exposure management practices. Some wariness was expressed primarily around environmental and physical constraints, the quality of the data, and privacy concerns. This survey identified a need to better identify occupational situations that would benefit from wearable sensors and to evaluate existing devices that could be used for occupational hygiene. Further, this work underscores the importance of clearly defining "sensor" according to the occupational setting and context., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

An individual's likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due to their time-consuming expert curation process, job exposure matrices currently cover only a subset of possible workplace exposures and may not be regularly updated. Scientific literature articles describing exposure studies provide important supporting evidence for developing and updating job exposure matrices, since they report on exposures in a variety of occupational scenarios. However, the constant growth of scientific literature is increasing the challenges of efficiently identifying relevant articles and important content within them. Natural language processing methods emulate the human process of reading and understanding texts, but in a fraction of the time. Such methods can increase the efficiency of both finding relevant documents and pinpointing specific information within them, which could streamline the process of developing and updating job exposure matrices. Named entity recognition is a fundamental natural language processing method for language understanding, which automatically identifies mentions of domain-specific concepts (named entities) in documents, e.g., exposures, occupations and job tasks. State-of-the-art machine learning models typically use evidence from an annotated corpus, i.e., a set of documents in which named entities are manually marked up (annotated) by experts, to learn how to detect named entities automatically in new documents. We have developed a novel annotated corpus of scientific articles to support machine learning based named entity recognition relevant to occupational substance exposures. Through incremental refinements to the annotation process, we demonstrate that expert annotators can attain high levels of agreement, and that the corpus can be used to train high-performance named entity recognition models. The corpus thus constitutes an important foundation for the wider development of natural language processing tools to support the study of occupational exposures., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Thompson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

PSonyx is a newly isolated phage that infects Corynebacterium glutamicum . This siphovirus was isolated from a French pond in the south of Paris by students from Paris-Saclay University. Its 80,277-bp singleton genome carries 136 protein-coding genes and 5 tRNAs., Competing Interests: The authors declare no conflict of interest.

Although interest in within-person variability in grandiose narcissism is growing, measurement tools are lacking that allow studying fluctuations in this personality characteristic in a differentiated manner (i.e., distinguishing narcissistic admiration and rivalry). This study explores whether a measurement approach using the six-item version of the Narcissistic Grandiosity Scale (NGS Rosenthal et al. (2007)) and six additional newly formulated adjectives allows assessing state admiration and rivalry. Structural characteristics and convergent validity of this approach were examined in an experience sampling study in which 114 adults participated, providing state assessments twice a day (total number of observations = 1306). Multilevel bifactor analyses revealed three factors (i.e., one general and two specific factors) at both within- and between-person levels. Further, admiration and rivalry showed a pattern of within-person associations with fluctuations in self-esteem and Big Five states that were consistent with theoretical expectations. Finally, average state admiration and average state rivalry correlated substantively with trait measures of these respective constructs assessed one week prior to the experience sampling design., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Heyde et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The COVID-19 pandemic created an urgent need for eHealth as the relevance of infection control and social distancing continues. Evidence describing the acceptability of implementing eHealth into pediatric physical therapy services is limited., Purpose: To investigate the determinants of eHealth acceptance by Dutch pediatric physical therapists during the COVID-19 pandemic., Methods: A mixed-methods approach was used. It included a quantitative exploratory questionnaire of 154 pediatric physical therapists and qualitative in-depth interviews of 16 pediatric physical therapists., Results: The eHealth interventions were beneficial for collaboration between health care professionals and in addition to face-to-face therapy. eHealth interventions were, however, found to be unsuitable especially in the diagnostic phase. Barriers to more extensive application include costs, technical difficulties, and a perceived negative attitude of children., Conclusion: Pediatric physical therapists used eHealth interventions extensively in times of the COVID-19 pandemic. However, the acceptance of eHealth interventions is dependent on the pediatric physical therapist's perception of usefulness in private practice, rehabilitation setting, or clinical hospital., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Academy of Pediatric Physical Therapy of the American Physical Therapy Association.)

Objective: Some drugs need processing before they can be administered or dispensed. We measured airborne exposure of pharmacy staff to small particles when performing these tasks., Methods: Reconstitution of powdered drugs in vials; crushing, splitting, and counting of tablets; and opening of capsules, using different ventilation strategies, were investigated in five pharmacies after in a worst-case approach. Airborne particulate matter was determined for a range of particles sizes., Results: Mean particle concentrations ranged from not detectable to 1.03 μg/m 3 (<1 μm) and 589.7 μg/m 3 (<10 μm). Dust exhaust made tasks safer. Most hazardous was pouring out tablets from a bulk supply, and least hazardous was reconstitution of a powder for injection., Conclusions: Occupational exposure during routine handling of drugs can occur, but the risks vary greatly with the nature and duration of the tasks., Competing Interests: Conflict of Interest: None declared., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Occupational and Environmental Medicine.)

Low-cost particulate matter (PM) sensors provide new methods for monitoring occupational exposure to hazardous substances, such as flour dust. These devices have many possible benefits, but much remains unknown about their performance for different exposure monitoring strategies in the workplace. We explored the performance of PM sensors for four different monitoring strategies (time-weighted average and high time resolution, each quantitative and semi-quantitative) for assessing occupational exposure using low-cost PM sensors in a field study in the industrial bakery sector. Measurements were collected using four types of sensor (PATS+, Isensit, Airbeam2, and Munisense) and two reference devices (respirable gravimetric samplers and an established time-resolved device) at two large-scale bakeries, spread over 11 participants and 6 measurement days. Average PM2.5 concentrations of the low-cost sensors were compared with gravimetric respirable concentrations for 8-h shift periods and 1-min PM2.5 concentrations of the low-cost sensors were compared with time-resolved PM2.5 data from the reference device (quantitative monitoring strategy). Low-cost sensors were also ranked in terms of exposure for 8-h shifts and for 15-min periods with a shift (semi-quantitative monitoring strategy). Environmental factors and methodological variables, which can affect sensor performance, were investigated. Semi-quantitative monitoring strategies only showed more accurate results compared with quantitative strategies when these were based on shift-average exposures. The main factors that influenced sensor performance were the type of placement (positioning the devices stationary versus personal) and the company or workstation where measurements were collected. Together, these findings provide an overview of common strengths and drawbacks of low-cost sensors and different ways these can be applied in the workplace. This can be used as a starting point for further investigations and the development of guidance documents and data analysis methods., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

The Vasilikos Energy Center (VEC) is a large hydrocarbon industrial hub actively operating in Cyprus. There is strong public interest by the communities surrounding VEC to engage with all stakeholders towards the sustainable development of hydrocarbon in the region. The methodological framework of the exposome concept would allow for the holistic identification of all relevant environmental exposures by engaging the most relevant stakeholders in industrially contaminated sites. The main objectives of this study were to: (i) evaluate the stakeholders' perceptions of the environmental and public health risks and recommended actions associated with the VEC hydrocarbon activities, and (ii) assess the stakeholders' understanding and interest towards exposome-based technologies for use in oil and gas applications. Methods: Six major groups of stakeholders were identified: local authorities, small-medium industries (SMIs) (including multi-national companies), small-medium enterprises (SMEs), academia/professional associations, government, and the general public residing in the communities surrounding the VEC. During 2019-2021, a suite of stakeholder engagement initiatives was deployed, including semi-structured interviews (n = 32 ), a community survey for the general public ( n = 309), technical meetings, and workshops ( n = 4 ). Results from the semi-structured interviews, technical meetings and workshops were analyzed through thematic analysis and results from the community survey were analyzed using descriptive statistics. Results: Almost all stakeholders expressed the need for the implementation of a systematic health monitoring system for the VEC broader area and its surrounding residential communities, including frequent measurements of air pollutant emissions. Moreover, stricter policies by the government about licensing and monitoring of hydrocarbon activities and proper communication to the public and the mass media emerged as important needs. The exposome concept was not practiced by the SMEs, but SMIs showed willingness to use it in the future as part of their research and development activities. Conclusions: The sustainable development of hydrocarbon exploitation and processing prospects for Cyprus involves the VEC. Continuous and active collaboration and mutual feedback among all stakeholders involved with the VEC is essential, as this may allow future environmental and occupational health initiatives to be formalized.

Introduction: Oil and gas workers have been shown to be at increased risk of chronic diseases including cancer, asthma, chronic obstructive pulmonary disease, and hearing loss, among others. Technological advances may be used to assess the external (e.g. personal sensors, smartphone apps and online platforms, exposure models) and internal exposome (e.g. physiologically based kinetic modeling (PBK), biomonitoring, omics), offering numerous possibilities for chronic disease prevention strategies and risk management measures. The objective of this study was to review the literature on these technologies, by focusing on: (i) evaluating their applicability for exposome research in the oil and gas industry, and (ii) identifying key challenges that may hamper the successful application of such technologies in the oil and gas industry., Method: A scoping review was conducted by identifying peer-reviewed literature with searches in MEDLINE/PubMed and SciVerse Scopus. Two assessors trained on the search strategy screened retrieved articles on title and abstract. The inclusion criteria used for this review were: application of the aforementioned technologies at a workplace in the oil and gas industry or, application of these technologies for an exposure relevant to the oil and gas industry but in another occupational sector, English language and publication period 2005-end of 2019., Results: In total, 72 articles were included in this scoping review with most articles focused on omics and bioinformatics (N = 22), followed by biomonitoring and biomarkers (N = 20), external exposure modeling (N = 11), PBK modeling (N = 10), and personal sensors (N = 9). Several studies were identified in the oil and gas industry on the application of PBK models and biomarkers, mainly focusing on workers exposed to benzene. The application of personal sensors, new types of exposure models, and omics technology are still in their infancy with respect to the oil and gas industry. Nevertheless, applications of these technologies in other occupational sectors showed the potential for application in this sector., Discussion and Conclusion: New exposome technologies offer great promise for personal monitoring of workers in the oil and gas industry, but more applied research is needed in collaboration with the industry. Current challenges hindering a successful application of such technologies include (i) the technological readiness of sensors, (ii) the availability of data, (iii) the absence of standardized and validated methods, and (iv) the need for new study designs to study the development of disease during working life., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Air pollution, noise, and green space are important environmental exposures, having been linked to a variety of specific health outcomes. However, there are few studies addressing overall early life development. To assess their effects, associations between developmental milestones for a large population of 0-4-year old children in The Netherlands and environmental exposures were explored. Developmental milestones and background characteristics were provided by Preventive Child Health Care (PCHC) and supplemented with data from Statistics Netherlands. Milestones were summarized and standardized into an aggregate score measuring global development. Four age groups were selected. Environmental exposures were assigned to geocoded addresses using publicly available maps for PM 2.5 , PM 10 , PM coarse , NO 2 , EC, road traffic noise, and green space. Associations were investigated using single and multiple-exposure logistic regression models. 43,916 PCHC visits by 29,524 children were available. No consistent associations were found for air pollution and road traffic noise. Green space was positively associated in single and multiple-exposure models although it was not significant in all age groups (OR 1.01 (0.95; 1.08) (1 year) to 1.07 (1.01; 1.14) (2 years)). No consistent associations were found between air pollution, road traffic noise, and global child development. A positive association of green space was indicated.

This commentary explores the use of high-resolution data from new, miniature sensors to enrich models that predict exposures to chemical substances in the workplace. To optimally apply these sensors, one can expect an increased need for new models that will facilitate the interpretation and extrapolation of the acquired time-resolved data. We identified three key modelling approaches in the context of sensor data, namely (i) enrichment of existing time-integrated exposure models, (ii) (new) high-resolution (in time and space) empirical models, and (iii) new 'occupational dispersion' models. Each approach was evaluated in terms of their application in research, practice, and for policy purposes. It is expected that substance-specific sensor data will have the potential to transform workplace modelling by re-calibrating, refining, and validating existing (time-integrated) models. An increased shift towards 'sensor-driven' models is expected. It will allow for high-resolution modelling in time and space to identify peak exposures and will be beneficial for more individualized exposure assessment and real-time risk management. New 'occupational dispersion models' such as interpolation, computational fluid dynamic models, and assimilation techniques, together with sensor data, will be specifically useful. These techniques can be applied to develop site-specific concentration maps which calculate personal exposures and mitigate worker exposure through early warning systems, source finding and improved control design and control strategies. Critical development and investment needs for sensor data linked to (new) model development were identified such as (i) the generation of more sensor data with reliable sensor technologies (achieved by improved specificity, sensitivity, and accuracy of sensors), (ii) investing in statistical and new model developments, (iii) ensuring that we comply with privacy and security issues of concern, and (iv) acceptance by relevant target groups (such as employers and employees) and stimulation of these new technologies by policymakers and technology developers., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Background: Greenspace has been associated with health benefits in many contexts. An important pathway may be through outdoor physical activity. We use a novel approach to examine the link between greenspace microenvironments and outdoor physical activity levels in the HEALS study conducted in Edinburgh (UK), the Netherlands, and Athens and Thessaloniki (Greece)., Methods: Using physical activity tracker recordings, 118 HEALS participants with young children were classified with regard to daily minutes of moderate to vigorous physical activity (MVPA); 60 were classified with regard to the metabolic equivalent task (MET)-minutes for each of the 1014 active trips they made. Greenspace indicators were generated for Normalised Difference Vegetation Index (NDVI), tree cover density (TCD), and green land use (GLU). We employed linear mixed-effects models to analyse (1) daily MVPA in relation to greenspace within 300 m and 1000 m of residential addresses and (2) trip MET-minutes in relation to average greenspace within a 50 m buffer of walking/cycling routes. Models were adjusted for activity, walkability, bluespace, age, sex, car ownership, dog ownership, season, weekday/weekend day, and local meteorology., Results: There was no clear association between MVPA-minutes and any residential greenspace measure. For example, in fully adjusted models, a 10 percentage point increase in NDVI within 300 m of home was associated with a daily increase of 1.14 (95% CI - 0.41 to 2.70) minutes of MVPA. However, we did find evidence to indicate greenspace markers were positively linked to intensity and duration of activity: in fully adjusted models, 10 percentage point increases in trip NDVI, TCD, and GLU were associated with increases of 10.4 (95% CI: 4.43 to 16.4), 10.6 (95% CI: 4.96 to 16.3), and 3.36 (95% CI: 0.00 to 6.72) MET-minutes, respectively. The magnitude of associations with greenspace tended to be greater for cycling., Conclusions: More strenuous or longer walking and cycling trips occurred in environments with more greenspace, but levels of residential greenspace did not have a clear link with outdoor MVPA. To build on our research, we suggest future work examine larger, more diverse populations and investigate the influence of greenspace for trip purpose and route preference.

Will sensor-based exposure assessment be the future in workplace settings? Static instruments with embedded sensors are already applied to monitor levels of dangerous substances-in the context of acute health effects-at critical locations. However, with wearable, lightweight, miniaturized (low-cost) sensors developing quickly, much more is possible with sensors in relation to exposure assessment. Sensors can be applied in the work environment, on machines, or on employees and may include sensors that measure chemical exposures, but also sensors or other technologies that collect contextual information to support the exposure measurements. Like every technology it also has downsides. Sensors collect data on individuals that, depending on the purpose, need to be shared with others (e.g. health, safety and environment manager). One can imagine that people are afraid of misuse. To explore possible ethical and privacy issues that may come along with the introduction of sensors in the workplace, we organized a workshop with stakeholders (n = 32) to discuss three possible sensor-based scenarios in a structured way around five themes: purpose, efficacy, intrusiveness, proportionality, and fairness. The main conclusion of the discussions was that stakeholders currently see benefits in using sensors for applied targeted studies (short periods, clear reasons). In order to find acceptance for the implementation of sensors, all individuals affected by the sensors or its data need to be involved in the decisions on the purpose and application of sensors. Possible negative side effects need to be discussed and addressed. Continuous sensor-based monitoring of workers currently appears to be a bridge too far for the participants of this workshop., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

(1) Background: Small, lightweight, low-cost optical particulate matter (PM) monitors are becoming popular in the field of occupational exposure monitoring, because these devices allow for real-time static measurements to be collected at multiple locations throughout a work site as well as being used as wearables providing personal exposure estimates. Prior to deployment, devices should be evaluated to optimize and quantify measurement accuracy. However, this can turn out to be difficult, as no standardized methods are yet available and different deployments may require different evaluation procedures. To gain insight in the relevance of different variables that may affect the monitor readings, six PM monitors were selected based on current availability and evaluated in the laboratory; (2) Methods: Existing strategies that were judged appropriate for the evaluation of PM monitors were reviewed and seven evaluation variables were selected, namely the type of dust, within- and between-device variations, nature of the power supply, temperature, relative humidity, and exposure pattern (peak and constant). Each variable was tested and analyzed individually and, if found to affect the readings significantly, included in a final correction model specific to each monitor. Finally, the accuracy for each monitor after correction was calculated; (3) Results: The reference materials and exposure patterns were found to be main factors needing correction for most monitors. One PM monitor was found to be sufficiently accurate at concentrations up to 2000 µg/m 3 PM 2.5 , with other monitors appropriate at lower concentrations. The average accuracy increased by up to three-fold compared to when the correction model did not include evaluation variables; (4) Conclusions: Laboratory evaluation and readings correction can greatly increase the accuracy of PM monitors and set boundaries for appropriate use. However, this requires identifying the relevant evaluation variables, which are heavily reliant on how the monitors are used in the workplace. This, together with the lack of current consensus on standardized procedures, shows the need for harmonized PM monitor evaluation methods for occupational exposure monitoring.

Carbon nanotubes (CNTs) except MWCNT-7 have been classified as Group 3 ["Not classifiable as to its carcinogenicity to humans"] by the IARC. Despite considerable mechanistic evidence in vitro/in vivo, the classification highlights a general lack of data, especially among humans. In our previous study, we reported epigenetic changes in the MWCNT exposed workers. Here, we evaluated whether MWCNT can also cause alterations in aging related features including relative telomere length (TL) and/or mitochondrial copy number (mtDNAcn). Relative TL and mtDNAcn were measured on extracted DNA from peripheral blood from MWCNT exposed workers (N = 24) and non-exposed controls (N = 43) using a qPCR method. A higher mtDNAcn and longer TL were observed in MWCNT exposed workers when compared to controls. Independent of age, sex, smoking behavior, alcohol consumption and BMI, MWCNT-exposure was associated with an 18.30 % increase in blood TL (95 % CI: 7.15-30.62 %; p = 0.001) and 35.21 % increase in mtDNAcn (95 % CI: 19.12-53.46 %). Our results suggest that exposure to MWCNT can induce an increase in the mtDNAcn and TL; however, the mechanistic basis or consequence of such change requires further experimental studies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

This article describes a quasi-experiment that evaluates the relationship between a job crafting intervention and work engagement. More particularly, we focused on three different types of job crafting: crafting towards strengths, crafting towards interests, and crafting towards development. Building on the conservation of resources theory, we hypothesized that participating in a job crafting intervention will be positively associated with job crafting, which in turn will promote work engagement. Additionally, based on the activation theory, we hypothesized that employees with a relatively high workload will benefit more from a job crafting intervention compared with employees with a relatively low workload. In all, 99 employees from a Dutch health care organization participated in our study ( n = 45 in the treatment group; n = 54 in the control group). Results indicated that there was no association between the intervention and job crafting behaviors. However, the job crafting intervention was found to be positively related to interests crafting for workers with a relatively high workload, which in turn was associated with an increase in dedication and absorption. Additionally, we found that job crafting towards strengths was associated with all aspects of work engagement (vigor, dedication, and absorption), whereas job crafting towards interests was related to dedication and absorption, and crafting towards development was not associated with work engagement. We conclude that a job crafting intervention can be an effective tool for enhancing work engagement for employees with a high workload. (PsycINFO Database Record (c) 2020 APA, all rights reserved).