List of Online-first articles on the SJWEH website http://www.sjweh.fi/list_onlinefirst_rss.php en-US SJWEH 1 lodo@ttl.fi (Lisa O\'Donoghue-Lindy) risto@toivonen.biz (Risto Toivonen) http://www.sjweh.fi/show_abstract.php?abstract_id=4340 http://www.sjweh.fi/show_abstract.php?abstract_id=4340 Thu, 15 Oct 2026 19:06:24 +0200 Editorial Artificial intelligence at work AI is an umbrella term for computing systems that perform tasks that have traditionally required human intelligence including recognizing patterns, making predictions, or generating decisions (9). Most modern AI systems rely on algorithms that learn patterns from large training datasets rather than following explicit, hand-coded rules. Machine learning, a core subfield, encompasses methods for this data-driven learning, with deep learning, a further subfield of machine learning that uses multi-layered neural networks, handling more complex patterns. Other subfields apply these techniques to specific data types, such as natural language processing (text) and computer vision (images and video). Examples of AI adoption to perform specific job tasks exist in virtually all industries including those which have traditionally been open to adopting new technologies (eg, healthcare and manufacturing) and those less prone to technological change (eg, arts and culture) (10, 11). While in the early stages, AI is also being adapted to OHS activities within workplaces, such as in tools that monitor safety compliance in high-risk settings or promote healthy behaviors among workers (8). These examples have yet to show a mass disruption to the availability of jobs but can result in a re-organization of work between humans and intelligent machines that is fundamentally changing the way workers perform their jobs as well as their employment and work conditions (12). While research is scant, in table 1, we have synthesized available literature to describe how AI systems may impact different dimensions of the work environment such as work arrangements, and physical and psychosocial conditions. OHS AI research challenges through the lens of the Partnership on AI and Quality of Work (PAIQ) PAIQ is a seven-year project based at the Institute for Work & Health in Toronto, Canada that brings academic researchers together with multi-sectoral stakeholders to unpack how AI adoption impacts work and employment conditions and worker health and well-being (13). Drawing from our experience in PAIQ, we present three principal challenges that OHS researchers may face when engaging in research on AI in the workplace. Challenge 1: Rapid speed of AI advancement. The applications and abilities of AI have and continue to change very rapidly with many frontier models surpassing human-level performance (14). Attributed to scalable architecture, improved computing power and infrastructure, and significant financial investment, AI has increased in its processing speed, ability to learn from diverse datasets, and capacity to perform tasks with increasing autonomy. AI advancements will contribute to an emerging number of applications within the workplace and thus ongoing changes to different dimensions of the work environment. Many occupational hazards (eg, chemical, ergonomic, physical, and psychosocial) can have a fixed impact on work environments. In contrast, AI’s ability to learn and adapt may result in the technology constantly shifting its impact on the health and safety profile of an organization. These shifts may be exacerbated by AI advancement that can result in the technology reasoning and acting with a decreasing amount of human input. Keeping up with the pace of AI technological change is challenging, especially within the constraints of traditional time-intensive academic research protocols. There is a risk that the production of rigorous evidence will be obsolete by the time the findings are published. To match the speed of AI advancement with the ability of OHS scholars to study its impacts, dynamic data collection approaches are needed, such as yearly scans of evidence sources to identify emerging threats and opportunities of AI in the workplace. As an extension of this strategy, PAIQ’s use of a living systematic review methodology allows for the tracking of peer-reviewed research on new forms of AI and their impact on work environments and different worker health outcomes (15). Challenge 2: AI invisibility and difficulties with primary data collection. AI is commonly embedded in existing digital technology within a workplace such as automated vehicles, mobile phones, robots, or the Internet-of-Things devices, and its impact on working experiences is not always visible to workers encountering the technology (2). In the same vein, employers choosing to adopt AI systems may have less clarity on how the technology is functioning and affecting workers. Despite its potential invisibility, AI may be actively dictating work task performance and/or pace of work, monitoring workers’ performance, and making decisions. Workers and employers participating in OHS research may struggle to identify how AI is affecting different dimensions of work and employment conditions, creating challenges for primary data collection (eg, surveys or qualitative interviews). Therefore, OHS research can benefit from diverse disciplinary lenses—that is, from both computer and data sciences as well as the social sciences and health sciences—to better understand how AI design elements as well as workplace characteristics (eg, social structures, institutions, and power relations) intersect to contribute to AI’s impact on work environments and worker health. Challenge 3: AI upskilling for OHS researchers and practitioners. Computer and data science are not typically part of the OHS researcher’s sphere of knowledge. OHS scholars may be unprepared to examine how algorithm design, data selection and training, and deployment decisions may play a specific role in changing work conditions and worker health. Lacking deeper AI knowledge creates challenges to proposing and pursuing research that can lead to the identification of AI hazards and understanding the mechanisms of harm. We also run the risk of developing applied solutions that do not address the salient challenges AI may pose to worker health within workplaces. OHS researchers who gain greater familiarity with how different AI subtypes and systems behave, fail, and interact with humans on the job may be better equipped to study how the design, training, and application of AI can change different dimensions of the work environment and identify why and how the technology may positively or negatively impact worker health. The need for gaining expertise on AI may be particularly pronounced with the growing deployment of frontier systems that have the potential to more drastically change job performance and dimensions of the work environment. Concluding remarks The adoption of AI at work creates new challenges and opportunities for the health, safety, and well-being of workers (16). The OHS sector offers a valuable perspective to the emerging discourse on AI safety and pro-worker AI by drawing greater attention to the intersection between the work environment and worker health (17). Importantly, OHS expertise has the potential to offer practical solutions that can be used to safeguard workers from novel AI-related hazards that may contribute to injury or illness. For OHS researchers to fully engage in the research and provide expertise to the AI research landscape, there is a need to transform the ways in which research is conducted to deploy dynamic research approaches, account for the invisibility of AI, and build AI literacy. While taking these steps requires an investment, they will enable OHS researchers to play a more prominent role in making an AI future healthier and safer for workers. Acknowledgments The study was funded by a Social Sciences and Humanities Research Council of Canada (SSHRC) Partnership Grant (#895-2025-1000) supporting PAIQ (www.iwh.on.ca/projects/partnership-on-ai-and-quality-of-work). We would like to acknowledge contributions from our multidisciplinary research team and multi-sector partners who are part of PAIQ. Authors are employees of the Institute for Work & Health (IWH), which is supported through funding from the Ontario Ministry of Labour, Immigration, Training and Skills Development (MLITSD). The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the MLITSD; endorsement is not intended nor should it be inferred. References 1. Mayer H, Yee L, Chui M, Roberts R. Superagency in the workplace: empowering people to unlock AI’s full potential [Internet]. 2025 [cited 2026 August 13]. Available from: https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work. 2. Jetha A, Bierbrier J, Smith P. The AI shift: reconceptualizing health, safety, and equity in the future of work. In: Vanroelen C, Seubert L, Bodin T, editors. Occupational Health in a New World of Work. Cham: Springer Nature Switzerland; 2026. p. 1–22. 3. Government of Canada. Pan-Canadian artificial intelligence strategy [Internet]. Ottawa, ON 2025 [cited 2026 August 13]. Available from: https://ised-isde.canada.ca/site/ised/en/pan-canadian-artificial-intelligence-strategy. 4. Acemoglu D, Autor D, Johnson S. Building pro-worker artificial intelligence. The Hamilton Project; 2026 [cited 2026 August 13]. Available from: https://www.brookings.edu/wp-content/uploads/2026/02/20260223_THP_ProWorkerAI_Paper.pdf. 5. European Commission. AI Act. European Commission [Internet]. 2025 [cited 2026 August 13]. Available from: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai. 6. Håkansta C, Asp A, Palm K. Effects of work-related digital technology on occupational health in the public sector: A scoping review. Work 2025 Jun;81(2):2477–90. https://doi.org/10.1177/10519815251320274. 7. Bowdler M, Lahti H, Jelenko M, Buresti G, Valtonen T. Algorithmic management and psychosocial risks at work: an emerging occupational safety and health challenge. Scand J Work Environ Health 2026 Jan;52(1):1–5. https://doi.org/10.5271/sjweh.4270. 8. Jetha A, Bakhtari H, Irvin E, Biswas A, Smith MJ, Mustard C et al. Do occupational health and safety tools that utilize artificial intelligence have a measurable impact on worker injury or illness? Findings from a systematic review. Syst Rev 2025 Jul;14(1):146. https://doi.org/10.1186/s13643-025-02869-1. 9. Stryker C, Kavlakoglu E. What is artificial intelligence (AI) [Internet]. IBM Think; 2024 [cited 2026 August 13]. Available from: https://www.ibm.com/think/topics/artificial-intelligence. 10. Beyai J. AI generated art implications [Internet]. Cultural Policy Hub, OCAD University; 2023 [cited 2026 August 13]. Available from: https://culturalpolicyhub.ocadu.ca/news/ai-generated-art-implications. 11. Deloitte. The AI Dossier: 125+ AI use cases [Internet]. 2026 [cited 2026 August 13]. Available from: https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/ai-use-cases.html. 12. Massenkoff M, McCrory P. Labor market impacts of AI: a new measure and early evidence [Internet]. Anthropic; 2026 [cited 2026 August 13]. Available from: https://www.anthropic.com/research/labor-market-impacts. 13. Institute for Work and Health. Partnership on AI and the quality of work (PAIQ) [Internet]. Institute for Work & Health; [cited 2026 August 13]. Available from: https://www.iwh.on.ca/projects/partnership-ai-quality-of-work. 14. Sajadieh S, Fattorini L, Perrault R, Gil Y, Parli V, Santarlasci L et al. 2 Technical performance [Internet]. The AI Index 2026 Annual Report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA; 2026 [cited 2026 August 13]. Available from: https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_2_technical.pdf. 15. Jetha A, Crouch M, Vold K, Peters SE, Vietas J, Sriharan A et al. Artificial intelligence in the workplace: a living systematic review protocol on worker safety, health, and well-being implications. Syst Rev 2025 Dec;14(1):255. https://doi.org/10.1186/s13643-025-03000-0. 16. Jetha A, Bakhtari H, Rosella LC, Gignac MA, Biswas A, Shahidi FV et al. Artificial intelligence and the work-health interface: A research agenda for a technologically transforming world of work. Am J Ind Med 2023 Oct;66(10):815–30. https://doi.org/10.1002/ajim.23517. 17. Probst T, Shoss M, Bankins S, Behrend T, Dragano N, Jetha A et al. Aligning artificial intelligence with worker health, well-being, and safety: an occupational health psychology agenda. Occup Health Sci 2026;10(2):15. https://doi.org/10.1007/s41542-026-00262-5. by Jetha A, Bowring J. doi:10.5271/sjweh.4339]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4339 http://www.sjweh.fi/show_abstract.php?abstract_id=4339 Thu, 15 Oct 2026 10:38:42 +0200 Discussion paper by Fadel M, Undem K, Goldberg M, Leclerc A, Evanoff B, Descatha A. doi:10.5271/sjweh.4332]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4332 http://www.sjweh.fi/show_abstract.php?abstract_id=4332 Tue, 13 Oct 2026 10:44:59 +0200 Original article nd and 4^th night shifts and between night shifts taken together versus days off. Results There were no statistically significant differences between diurnal rhythms on the 2^nd and 4^th consecutive night shifts. We observed differences in levels (mesor and amplitude) of cortisol and melatonin on night shifts compared to days off with the highest levels during work days. The timing (acrophase) of testosterone, but not cortisol and melatonin, differed between night shifts and days off. Thus, observed differences between the various hormonal diurnal rhythms to night work indicate internal desynchronization. Conclusion Studies of how diurnal rhythms of multiple biological systems are affected by night work and the link to health effects are warranted to further elucidate the mechanisms linking night work to adverse health effects. by Garde AH, Nabe-Nielsen K, Hansen ÅM, Kosmadopoulos A, Aagaard A, Lammers-van der Holst H, Reeh RH, Larsen AD. doi:10.5271/sjweh.4337]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4337 http://www.sjweh.fi/show_abstract.php?abstract_id=4337 Mon, 12 Oct 2026 10:47:15 +0200 Original article by Kobayashi F, Narisada A. doi:10.5271/sjweh.4338]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4338 http://www.sjweh.fi/show_abstract.php?abstract_id=4338 Sun, 11 Oct 2026 12:01:10 +0200 Original article by Sato K, Kato H, Inoue K, Kondo N, Kawachi I. doi:10.5271/sjweh.4336]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4336 http://www.sjweh.fi/show_abstract.php?abstract_id=4336 Fri, 09 Oct 2026 18:45:39 +0200 Original article eff-FS-TWA), adjusted for clothing factors, was calculated to provide an estimate of heat stress exposure over a full work shift. Multiple linear regression determined the effects of heat stress exposure on the average number of sugarcane stalks harvested per hour adjusting for available confounders. Results Recorded WBGT_eff-FS-TWA values averaged 31.3°C [standard deviation (SD) 2.8, range 22.9–35.4°C]. The mean number of stalks harvested per hour was 387, SD 185, with higher rates observed when harvesting burnt sugarcane and working solo. For every 1°C rise in WBGT_eff-FS-TWA above the lowest observed baseline of 22.9°C, the average number of sugarcane stalks harvested decreased by 9 stalks per hour (β -8.59, 95% confidence interval -16.19– -0.99). Conclusion Increased heat stress exposure decreases overall productivity. As global temperatures rise, heat stress increases, significantly impacting agricultural output alongside worker health. by Tantipanjaporn T, Povey A, Shiels HA, Gittins M, van Tongeren M. doi:10.5271/sjweh.4335]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4335 http://www.sjweh.fi/show_abstract.php?abstract_id=4335 Fri, 02 Oct 2026 14:04:16 +0200 Original article by Leskinen T, Tuominen M, Suorsa K, Pasanen J, Pasanen S, Iisalo V, Ruohomäki V, Pahkala K, Heinonen OJ, Stenholm S. doi:10.5271/sjweh.4331]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4331 http://www.sjweh.fi/show_abstract.php?abstract_id=4331 Thu, 01 Oct 2026 00:28:03 +0200 Original article th Revision (ICD-10) sub-code distributions. Segmented regression with two intervention points used Newey–West heteroscedasticity- and autocorrelation-consistent (HAC) standard errors. Results The proportion of recognized OD among women rose from 0.9% (2007) to 25.5% (2024) but with marked asymmetry: respiratory OD remained 96% male while nervous-system OD shifted from 24% female (2018) to 75% female (2024), driven by ICD-10 G62.2 toxic polyneuropathy (26 of 40 female cases in 2024). Female musculoskeletal OD rose from 20% to 34% with dominant upper-extremity repetitive-motion sub-codes; no female silicosis was recorded 2018–2024. Annual OI rose 9.1-fold (80 602 to 733 646); recognized OD declined (1208 to 888). The 2012 OHS Law produced an OI level change of +584 per 100 000 (P<0.001) but no statistically significant OD level change (P=0.32); the OI/OD ratio rose from 67 to 826. Conclusions The convergence of women’s share of OD toward 25% masks marked diagnostic asymmetries: women are visible primarily through feminized service-sector and chemical-exposure categories and scarcely in pneumoconioses. Reform priorities include diagnosis-stage notification, active screening of female workers in mixed-exposure environments, and gender-sensitive occupational history-taking. by Ekimci Deniz F, Töreyin ZN, Akgün M. doi:10.5271/sjweh.4330]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4330 http://www.sjweh.fi/show_abstract.php?abstract_id=4330 Thu, 24 Sep 2026 14:03:24 +0200 Original article by Ulstein J, Undem K, Sundstrup E, Nielsen MB, Hasting RL. doi:10.5271/sjweh.4329]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4329 http://www.sjweh.fi/show_abstract.php?abstract_id=4329 Wed, 23 Sep 2026 16:29:18 +0200 Original article 90% night shifts (OR 1.26, 95% CI 1.01–1.58), rotating shifts (OR 1.42, 95% CI 1.22–1.65) and with longer night work history (OR_{≥16 years} 1.28, 95% CI 1.08–1.52). These variables and longer weekly working hours [OR _{6.10h increase} 1.13, 95% CI 1.04–1.22) were associated with short sleep. Conclusions Data-driven analyses of work schedules may provide a more comprehensive evaluation of the impact of real-world shift work patterns on sleep. by Santonja I, Dros C, Haider S, Winzer E, Wakolbinger M, Scionti K, Schoissengeier V, Wrzesińska MA, Binder-Olibrowska KW, Rakoczy J, Aufschnaiter AL, Höfler C, Wallner M, Cuparencu C, Wagner K-H, Boshuizen HC, Vingerhoeds MH, Lammers-van der Holst H, Papantoniou K. doi:10.5271/sjweh.4334]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4334 http://www.sjweh.fi/show_abstract.php?abstract_id=4334 Tue, 22 Sep 2026 14:45:24 +0200 Amendments and corrections by Magnusson Hanson LL, Madsen IEH, Blomqvist S, Holmgren R, Sørensen K, Rugulies R. doi:10.5271/sjweh.4321]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4321 http://www.sjweh.fi/show_abstract.php?abstract_id=4321 Mon, 14 Sep 2026 17:42:18 +0200 Review by McKenzie AR, Croak B, Brokjøb LG, Stevelink SAM, Madan I, Brinchmann B, Nilsen W, Walker-Bone K, Aars NA, Mykletun A. doi:10.5271/sjweh.4328]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4328 http://www.sjweh.fi/show_abstract.php?abstract_id=4328 Mon, 14 Sep 2026 14:20:52 +0200 Discussion paper by Albin M, Turner MC, Svensson S, Njoroge M, Viegas S, Mathieu C, Pearce N, Burdorf A, Straif K, Broberg K, Kogevinas M. doi:10.5271/sjweh.4327]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4327 http://www.sjweh.fi/show_abstract.php?abstract_id=4327 Tue, 08 Sep 2026 23:08:18 +0200 Original article 21 times (HR 1.11, 95% CI 1.03–1.20) compared with those with none. Lastly, among non-night workers, decreased hazards were observed for quick returns from afternoon shifts (<11 hours) compared with those with no quick returns. Conclusion Exclusive night work is associated with increased risks of multiple PAD compared to day work, implying an increase in risk of mental disorders among nurses and nurse assistants. by Addo S, Nilsson T, Gustavsson P, Bodin T, Bigert C, Lashari A, Härmä M, Brulin E. doi:10.5271/sjweh.4325]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4325 http://www.sjweh.fi/show_abstract.php?abstract_id=4325 Mon, 07 Sep 2026 14:04:48 +0200 Original article 23 years. We estimated Cox models within each cohort; the main model was adjusted for sex, age, education, and occupational class. Additional analyses examined JS categories (low JS, active, passive, high JS), subgroups by sex and occupational classes, and person-time < and ≥65 years. Results were pooled using random-effects meta-analysis. Relevant published studies were identified for synthesis with our results. Results Over 444 767 person-years, 842 CVD deaths occurred. High JS was associated with CVD mortality [hazard ratio (HR) 1.14, 95% confidence interval (CI) 1.01–1.27], with no evidence of heterogeneity. Results were consistent across JS categories, by sex (men: HR 1.15, 95% CI 1.07–1.23; women: HR 1.11, 95% CI 0.88–1.39), and across person-time <65 years (HR 1.24, 95% CI 0.91–1.67) and ≥65 years (HR 1.10, 95% CI 0.80–1.50). The association was stronger among manual workers (HR 1.25, 95% CI 0.84–1.86). Job demands appeared to contribute more than job control. Four published studies with comparable follow-up but heterogeneous study designs reported estimates consistent with our findings. Conclusions High JS is a long-term predictor of CVD mortality, consistently across sexes and during both working age and after retirement. Workplace redesign to reduce job demands, particularly among manual workers, is warranted within a comprehensive prevention strategy. by Giusti EM, Li X, Veronesi G, Linkohr B, Peters A, Waldenberger M, Borchini R, Cesana G, Li J, Rückert-Eheberg I-M, Ferrario MM. doi:10.5271/sjweh.4322]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4322 http://www.sjweh.fi/show_abstract.php?abstract_id=4322 Wed, 19 Aug 2026 20:47:28 +0200 Original article 52 hours per week. Dental caries was identified from clinical oral examinations performed by licensed dentists. Associations were estimated using generalized estimating equations adjusted for age, gender, and educational level. Subgroup and sensitivity analyses were also performed. Results Long working hours were significantly associated with dental caries (odds ratio 1.024, 95% confidence interval 1.007–1.041). In the subgroup analyses, compared with their respective counterparts, the association between long working hours and dental caries was significantly stronger among women, individuals with a college-level education or higher, and those with proactive oral health behaviors such as dental visits and toothbrushing before sleep. Conclusions Long working hours were independently associated with the risk of dental caries; women and highly educated workers exhibited a stronger association with dental caries. These findings suggest the potential value of integrating oral health strategies into occupational health policies, particularly among more vulnerable subgroups. by Park MY, Ahn J. doi:10.5271/sjweh.4319]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4319 http://www.sjweh.fi/show_abstract.php?abstract_id=4319 Tue, 18 Aug 2026 16:22:32 +0200 Original article adj) 1.18, 95% confidence interval (CI) 1.14–1.22] to PE (RR_adj 1.39, 95% CI 1.31–1.47). Among the dimensions of PE, low income was the strongest contributor (RR 1.44, 95% CI 1.39–1.50), while unstable employment had a modest effect (RR 1.16, 95% CI 1.12–1.20). Conclusion PE is associated with elevated risk of COPD in the Swedish workforce. These findings emphasize the role of employment conditions as a social determinant of respiratory health. by Lundh F, Gunn V, Kreshpaj B, Kvart S, Bodin T, Albin M, Selander J, Ekström S, Wiebert P, Matilla-Santander N. doi:10.5271/sjweh.4318]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4318 http://www.sjweh.fi/show_abstract.php?abstract_id=4318 Tue, 04 Aug 2026 22:33:33 +0200 Original article 3 months) after a SARS-CoV-2 infection. Poisson regression analysis was performed to evaluate the independent and joint effects of night shift work and sleep disturbances including chronic insomnia with risk of long-COVID. Analyses were stratified by sex, body mass index and chronotype. Results During the two-year follow-up, 284 of the 1899 newly infected participants reported long-COVID symptoms. Compared to day workers, night shift workers had 88% higher risk of developing long-COVID [95% confidence interval (CI) 1.29–2.72], after adjusting for potential confounders. The risk further increased among night workers with obesity. Chronic insomnia in the general population was associated with 42% increased risk of long-COVID (95% CI 1.12–1.78). Night workers with chronic insomnia had a 2.7 fold higher long-COVID risk (95% CI 1.60–4.51) compared to day workers without chronic insomnia. Associations persisted in various sensitivity analyses. Conclusions Our findings from a large prospective cohort indicate that night shift work and poor sleep are independently and jointly associated with a higher risk of long-COVID. by Papantoniou K, Espinosa A, Karachaliou M, Lassale C, Castaño-Vinyals G, Harding BN, De Matteis S, Straif K, Alonso-Nogués E, Dobaño C, Moncunill G, Blay N, Iraola-Guzmán S, de Cid R, Garcia-Aymerich J, Kogevinas M. doi:10.5271/sjweh.4317]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4317 http://www.sjweh.fi/show_abstract.php?abstract_id=4317 Thu, 02 Jul 2026 16:47:52 +0200 Original article by Kim K, Kang J, Lim DM, Jang S, Lee Y, Lee YH, Yoon HJ, Yang S, Kim SH, Lee D, Jang WI, Sung H, Kim M-J, Son E, Kim H-S, Lee D, Park HR, Seo S, Kim YH. doi:10.5271/sjweh.4294]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4294 http://www.sjweh.fi/show_abstract.php?abstract_id=4294 Thu, 30 Apr 2026 01:52:12 +0200 Review 2=43.91%], gastrointestinal temperature (MD -0.26°C; -0.52– -0.001; I²=98.13%), skin temperature (MD -1.14°C; -1.85– -0.42; I²=96.56%), and heart rate (MD -7.69 bpm; -11.22– -4.15; I²=73.19%). Egger’s tests suggested possible small-study effects for skin temperature and heart rate but not for core temperatures. Subgroup analyses indicated more consistent benefits with water/ice and PCM garments. Overall certainty ranged from low to very low due to risk of bias, inconsistency, and imprecision. Conclusions Wearable cooling attenuates physiological heat strain and improves thermal comfort, particularly via water/ice and PCM systems. Given high heterogeneity and low certainty, adequately powered, standardized, head-to-head trials are needed. by Sheth A, Viramgami A, Thakor M, Modi B, Bagepally B, Kar A, Ram E, Dave A, Balachandar R]]>