The impact of different types of shift work on blood pressure and hypertension: a systematic review and meta-analysis

International Journal of Environmental Research and Public Health Systematic Review The Impact of Different Types of Shift Work on Blood Pressure and Hypertension: A Systematic Review and Meta-Analysis Sara Gamboa Madeira 1,2,* , Carina Fernandes 3,4, Teresa Paiva 5,6, Carlos Santos Moreira 7 and Daniel Caldeira 8,9,10   Citation: Gamboa Madeira, S.; Fernandes, C.; Paiva, T.; Santos Moreira, C.; Caldeira, D. The Impact of Different Types of Shift Work on Blood Pressure and Hypertension: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2021,18, 6738. https:// doi.org/10.3390/ijerph18136738 Academic Editors: Tae-Won Jang, Hyoung-Ryoul Kim, Mo-Yeol Kang and Hye-Eun Lee Received: 24 May 2021 Accepted: 17 June 2021 Published: 23 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Instituto de Saúde Ambiental (ISAMB), Faculdade de Medicina, Universidade de Lisboa, 1649-026 Lisbon, Portugal 2Family Health Unit Mactamã, Administração Regional de Saúde de Lisboa e Vale do Tejo, 2745-862 Lisbon, Portugal 3Escola Nacional de Saúde Pública, Universidade Nova de Lisboa, 1600-560 Lisbon, Portugal; [email protected] 4Neurology Department, Hospital das Forças Armadas, 1649-020 Lisbon, Portugal 5Sleep Medicine Center (CENC), 1070-068 Lisbon, Portugal; [email protected] 6Comprehensive Health Research Center (CHRC), Nova Medical School, Universidade Nova de Lisboa, 1169-056 Lisbon, Portugal 7Medicine Clinic I, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal; [email protected] 8Cardiology Department, Hospital de Santa Maria/Santa Maria University Hospital—Centro Hospitalar Universitário Lisboa Norte (CHULN), 1649-028 Lisbon, Portugal; [email protected] 9Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal 10 Centro Cardiovascular da Universidade de Lisboa (CCUL), CAML, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal *Correspondence: [email protected] Abstract: Shift work (SW) encompasses 20% of the European workforce. Moreover, high blood pressure (BP) remains a leading cause of death globally. This review aimed to synthesize the magnitude of the potential impact of SW on systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension (HTN). MEDLINE, EMBASE and CENTRAL databases were searched for epidemiological studies evaluating BP and/or HTN diagnosis among shift workers, compared with day workers. Random-effects meta-analyses were performed and the results were expressed as pooled mean differences or odds ratios and 95% confidence intervals (95% CI). The Newcastle–Ottawa Scale was used to assess the risk of bias. Forty-five studies were included, involving 117,252 workers. We found a significant increase in both SBD and DBP among permanent night workers (2.52 mmHg, 95% CI 0.75–4.29 and 1.76 mmHg, 95% CI 0.41–3.12, respectively). For rotational shift workers, both with and without night work, we found a significant increase but only for SBP (0.65 mmHg, 95% CI 0.07–1.22 and 1.28 mmHg, 95% CI 0.18–2.39, respectively). No differences were found for HTN. Our findings suggest that SW is associated with an increase of BP, mainly for permanent night workers and for SBP. This is of special interest given the large number of susceptible workers exposed over time. Keywords: cardiovascular disease; blood pressure; occupational health; work schedule; permanent shift; rotating shift; night shift; systematic review 1. Introduction Hypertension (HTN) is a major preventable cause of cardiovascular diseases (CVDs) and all-cause mortality in the European continent, with an overall prevalence of 30–45% [ 1 ]. There is a relationship between blood pressure (BP) and CVD events [ 2 ], and BP decrease in hypertensive patients has shown to improve the prognosis [ 3 ]. Guidelines on CVD prevention stress the importance of a holistic approach, including non-traditional risk Int. J. Environ. Res. Public Health 2021,18, 6738. https://doi.org/10.3390/ijerph18136738 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021,18, 6738 2 of 19 factors such as socioeconomic status and occupational factors [ 4 ]. Shift work (SW) plays an important role in the “24/7” modern societies, involving about 20% of the European and the American workforces [ 5 ]. However, this work arrangement frequently disrupts sleepwake cycle and circadian rhythms, which may affect cardiovascular function including BP. Since shift work is a growing societal trend and high BP a leading risk factor for cardiovascular diseases, it is crucial to clarify the potential impact of shift work, especially when robust data is lacking. The single previous systematic review in this topic focused only on the HTN risk and used heterogeneous definitions for HTN diagnosis and simplistic SW categorization [ 6 ]. Therefore, we aimed to determine not only the HTN risk but also the magnitude of BP change among shift workers in comparison with day workers. 2. Materials and Methods This systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines [ 7 ] and its protocol was registered (Available online: https://osf.io/m47qc (Accessed on 24 May 2021)). 2.1. Literature Search and Selection A literature search was performed by personnel experienced in designing strategies for systematic reviews in health sciences databases. The search was performed in MEDLINE, EMBASE and The Cochrane Library electronic database (CENTRAL), on 18 February 2019. There were no limits regarding year of publication, language, study design or geographic origin. Animal studies were excluded. The search strategy is detailed on the supplementary material (Table S1). Two reviewers (SGM and CF) independently evaluated the title and abstract of the retrieved papers to determine if these met the inclusion criteria, using a pre-piloted form. Studies fulfilling the inclusion criteria and those uncertain were analyzed in full-text independently by the two reviewers. At this stage we only considered articles published in English and the reasons for exclusion were recorded. Abstracts and conference papers were excluded. Disagreements were solved through consensus or using a third party (DC). 2.2. Inclusion Criteria We included studies that reported data about BP values and/or diagnosis of HTN in both shift workers and a control group of day workers. We were lenient and broad regarding the definition of shift work, therefore we considered any shift provided if represented a nonstandard schedule, excluding long work hours (e.g., weekend work). If studies reported BP values, we sought the systolic and/or diastolic BP mean values and standard deviation (or other measurement of variability), in both groups. Data from linear regression models on BP values (mmHg), reporting a β coefficient and 95% CI, were also considered. HTN diagnosis was recorded when it was established using the cut-off values of the current European Guidelines (i.e., systolic BP ≥ 140 mmHg and/or diastolic BP ≥90 mmHg in office) [ 1 ]. HTN diagnosis was also considered when the subject was under anti-hypertensive medication. Studies in which this diagnosis relied on subjects’ self-report or those having other HTN definition thresholds were excluded. Data from binary logistic regression models, reporting estimation of risk (e.g., odds ratio) were included. Studies enrolling exclusively special populations (e.g., pregnant women or clinical populations) and laboratory protocols were excluded since our focus was on “reallife” settings. Additionally, when different papers included, either totally or partially, the same subjects, we selected the study which more accurately and comprehensively answered our research question. For more details see the supplementary material (Table S2). 2.3. Data Extraction Data was independently extracted from the included studies by two reviewers (SGM and CF) into a standardized form. Disagreements were solved through consensus. The following data were extracted: study design and follow-up (for longitudinal studies), Int. J. Environ. Res. Public Health 2021,18, 6738 3 of 19 occupational setting, sample size, mean age, sex, shift work schedule definition and source of information and method of BP assessment. For outcomes, systolic and diastolic BP mean and standard deviation or standard error, HTN diagnosis, effect size measurements with 95% confidence intervals and confounding variables. Adjusted risk estimates were preferred. When more than one regression model was presented, the one that best fitted our research question was included. 2.4. Methodologic Quality Assessment The methodologic quality assessment was also performed independently by two reviewers (SGM and CF). Included studies were graded according to the adequate version of the Newcastle–Ottawa Quality Assessment Scale (NOS) [ 8 , 9 ]. This tool evaluates three dimensions (selection, comparability and outcome), distributed across eight items. A maximum of one point for each item within the “Selection” and “Outcome” categories and maximum of two points for “Comparability” can be given. Higher scores represent a higher methodologic quality; less than 5 points was considered as low quality/high risk of bias [ 9 ]. For outcome assessment in cohort studies, the adequate follow-up was defined as 5 years, based on the dose-response relationship between shift work and cardiovascular outcomes suggested in previous studies [10]. 2.5. Data Analysis For analysis purpose, we defined categories of SW considering 4 types: permanent night shifts (PN), rotational shifts including nights (R + N), rotational shifts without nights (RN) and an additional category for the remainder (NS; “Not Specified”). Studies that included several types of SW (e.g., permanent night workers and rotational shifts including nights) were considered independent entries and included in independent meta-analyses. Pooled mean difference and 95% CI were estimated for continuous outcomes (systolic BP and diastolic BP) to quantify the difference in means between each SW type and controls. Pooled odds ratio (OR) and 95% CI were determined for the dichotomous variable (HTN diagnosis), through random-effects models. The statistical analyses were performed using RevMan 5.4 software (The Nordic Cochrane Centre, The Cochrane Collaboration). Heterogeneity of the pooled effect size estimates was assessed through the I 2 statistic to quantify the proportion of the total variation across studies that resulted from heterogeneity rather than chance. Publication bias was assessed through visual inspection of funnel plot asymmetry (see supplementary material-Figure S1) and, also, by Egger test. Whenever more than ten studies were involved in the meta-analysis of continuous outcomes variables (i.e., SBP and DBP) [ 11 ] a meta-regression analysis was performed in order to assess if specific factors (covariates) influence the magnitude of the estimate of effect estimate across studies [ 11 , 12 ]. Similarly to what has been conducted in previous studies on this topic [ 13 ], we include covariates related to participants characteristics such as sex (proportion of males) and age (mean values) but also important cardiovascular risk factors such as smoking (proportion of smokers) and body mass index (BMI; average values). We performed univariate and multivariate meta-regression analysis. 3. Results 3.1. Search Results Of the 1336 articles retrieved from the electronic database search, 117 underwent fulltext assessment. At full-text appraisal, 72 studies were excluded (Figure 1). At this stage, retrieval of conference abstracts, lacking a full-text article, lead to their exclusion (labelled as “abstract only”). When the same population was used in different studies, only one of the studies was selected (the exclusion was labelled as “duplicate”; more detailed information is provided in the supplementary material-Table S2). Forty-five independent studies met the inclusion criteria. Of these, 41 were included in the meta-analysis for systolic BP, 39 for diastolic BP and 14 for HTN diagnosis (Figure 1). A total of 117,252 workers were implicated, 46,345 of which shift workers (SWs) and 70,907 daytime workers (DWs). Int. J. Environ. Res. Public Health 2021,18, 6738 4 of 19 Int. J. Environ. Res. Public Health 2021, 18, x 4 of 18 the studies was selected (the exclusion was labelled as “duplicate”; more detailed information is provided in the supplementary material-Table S2). Forty-five independent studies met the inclusion criteria. Of these, 41 were included in the meta-analysis for systolic BP, 39 for diastolic BP and 14 for HTN diagnosis (Figure 1). A total of 117,252 workers were implicated, 46,345 of which shift workers (SWs) and 70,907 daytime workers (DWs). Figure 1. PRISMA flow diagram of literature search, screening and eligibility of the included studies in the meta-analysis. 3.2. Study Characteristics Main characteristics of the 45 included studies [14–58] are presented in Table 1. Most studies had a cross-sectional design or provided only cross-sectional information. Three studies provided longitudinal data, two being retrospective cohorts [17,36] and one a prospective cohort [49]. The follow-up periods ranged from 10 to 31 years. Most studies were settled in Asia (n = 21), mostly in Japan, followed by Europe (n = 13), America (n = 9) and, lastly, Africa (n = 2). Industry was the most frequent occupational setting (n = 25), followed by transportation (n = 4) and nursing staff (n = 4). Nevertheless, the specific job performed by the participants was not always explicit, both for SWs and DWs. In six studies, the authors highlighted that the SWs were mainly blue-collar workers (e.g., machine operators) while DWs were mainly white-collar (e.g., administrative). Sample sizes ranged from 47 to 26,463 participants. Most studies included only male workers (n= 26), while 9 studies addressed only females and 10 studies incorporated both sexes. Overall, the participants’ mean age was 39.61 years, specifically, 39.64 for SWs and 39.58 for DWs. Figure 1. PRISMA flow diagram of literature search, screening and eligibility of the included studies in the meta-analysis. 3.2. Study Characteristics Main characteristics of the 45 included studies [14–58] are presented in Table 1. Most studies had a cross-sectional design or provided only cross-sectional information. Three studies provided longitudinal data, two being retrospective cohorts [ 17 , 36 ] and one a prospective cohort [ 49 ]. The follow-up periods ranged from 10 to 31 years. Most studies were settled in Asia (n= 21), mostly in Japan, followed by Europe (n= 13), America ( n= 9 ) and, lastly, Africa (n= 2). Industry was the most frequent occupational setting (n= 25), followed by transportation (n= 4) and nursing staff (n= 4). Nevertheless, the specific job performed by the participants was not always explicit, both for SWs and DWs. In six studies, the authors highlighted that the SWs were mainly blue-collar workers (e.g., machine operators) while DWs were mainly white-collar (e.g., administrative). Sample sizes ranged from 47 to 26,463 participants. Most studies included only male workers (n= 26), while 9 studies addressed only females and 10 studies incorporated both sexes. Overall, the participants’ mean age was 39.61 years, specifically, 39.64 for SWs and 39.58 for DWs. Int. J. Environ. Res. Public Health 2021,18, 6738 5 of 19 Table 1. Main characteristics of the 45 included studies. Author Year Design Country Population Sex Shift Work Sample Size (SWs/DWs) Mean Age i (SWs/DWs) Outcome Outcome Adjustments NOS Asare-Anane 2015 [14] CS Ghana cocoa industry F&M NS 113/87 42.0/40.3 SBP DBP No 4 Attarchi 2012 [15] CS Iran tire manufacturing factory M NS 88/76 38.5/40.2 * SBP * DBP * age, BMI, smoking, salt, exercise, family HTN, job duration 8 Balieiro 2014 [16] CS Brazil bus drivers M PN 81/69 44.0/46.7 SBP DBP No 4 Biggi 2008 [17] CH (76-07) Italy street cleaning and waste collection M PN 331/157 47.0/42.3 * HTN ** SBP ** DBP * age, job company and branch, study period ** plus smoking and alcohol 8 Bursey 1990 [18] CS UK nuclear fuel factory M R + N 57/57 50/50 SBP DBP No 5 Chan 1993 [19] CS Singapore electronics industry FR+N PN R + N 55/75 PN B73/63 PN C58/59 R + N 28/30 PN B,C NotR SBP DBP HTN No 4 Chen 2010 [20] CS Taiwan semiconductor manufacturing F PN 561/656 32.7/34.9 SBP DBP No 4 De Bacquer 2009 [21]CS Belgium nine companies and public administration M R + N 309/1220 44.7/43.1 SBP DBP No 6 Gaudemaris 2011 [22] CS France nursing staff F PN NS PN 149 NS 1802/1863 NotR SBP DBP No 6 Di Lorenzo 2003 [23]CS Italy chemical industry M R + N 185/134 48.7/48.9 SBP DBP No 6 Int. J. Environ. Res. Public Health 2021,18, 6738 6 of 19 Table 1. Cont. Author Year Design Country Population Sex Shift Work Sample Size (SWs/DWs) Mean Age i (SWs/DWs) Outcome Outcome Adjustments NOS Ely 1986 [24] CS US police officers M R+N PN R+N41 PN 80/156 R + N 37.4 PN 38.1/40.0 SBP DBP No 6 Ohlander 2015 [25] CS Germany car manufacturing F&M R+N R-N PN R + N 198 R-N 9572 PN 3568/12,005 R + N 40.0 R-N 38.3 PN 41.4/37.8 SBP DBP * HTN * age, sex, BMI, lipids, smoking, alcohol, exercise, sleep disorders, job status, noise, heat, social disruption 8 Fesharaki 2014 [26] CS Iran steel and polyacryl companies MR+N R-N R + N 4050 R-N 597/3966 R + N 41.62 R-N 43.31/41.33 * SBP * DBP * age, BMI, education, work experience, marital status 8 Guo 2013 [27] CS China motor corporation F&M R + N 9118/17,345 62.4/64.22 SBP DBP No 6 Ghiasvand 2006 [28]CS Iran railroad company M NS 158/266 46.4/38.69 SBP DBP *HTN * age, BMI, eating habits 6 Ishizuka 1993 [29] CS Japan machine plant M R + N 38/21 31.6/36.9 SBP DBP No 5 Jermendy 2012 [30] CS Hungary multiple occupations F&M R + N M 54/67 F 180/180 M 42.2/42.5 F 44.5/42.9 SBP DBP No 4 Kantermann 2013 [31] CS Belgium steel factory M R + N 32/15 39.5/45.0 SBP DBP No 4 Kawabe 2014 [32] CS Japan 12 large companies F&M R+N R-N PN R +N 243 R-N 1017 PN 73/3094 R + N 40.1 R-N 37.9 PN 50.8/42.6 SBP DBP No 5 Int. J. Environ. Res. Public Health 2021,18, 6738 7 of 19 Table 1. Cont. Author Year Design Country Population Sex Shift Work Sample Size (SWs/DWs) Mean Age i (SWs/DWs) Outcome Outcome Adjustments NOS Kawada 2014 [33] CS Japan car manufacturing MR+N R-N R+N99 R-N 686/868 R + N 44.5 R-N 44.3/44.4 SBP DBP No 5 Kawakami 1998 [34]CS Japan electrical company M R + N H161/123 A280/355 P186/178 L546/1053 NotR * SBP * DBP * age, obesity, exercise, alcohol, education 8 Knutsson 1988 [35] CS Sweden paper and cellulose plants M R +N 361/240 43.2/44.8 SBP DBP No 4 Kubo 2013 [36] CH (12.7 y) Japan industry manufacturing M R + N 964/9209 22.3/23.8 SBP DBP * HTN * age, smoking, alcohol, exercise, BP and BMI at baseline and follow-up 8 Lang 1988 [37] CS Senegal hotel, canning, cotton printing, tobacco, oil, companies F&M NS 396/900 M 39.3 ±9.7 F 35.4 ±8.8 * SBP * DBP * age 5 Lercher 1993 [38] CS Austria rural community F&M PN 22/147 [25–62]* SBP * DBP * age, sex, education, smoking, BMI, other occupational risk factors 8 Lin 2015 [39] CS Taiwan electronics company F&M RN M 447/375 F 118/137 M 31.5/33.8 F 32.5/31.7 SBP DBP No 4 Marqueze 2013 [40] CS Brazil truck drivers M PN 31/26 39.8 ±6.6 HTN No 5 Nazri 2008 [41] CS Malaysia semiconductors factory M R + N 76/72 31.60/32.32 * HTN * age, BMI, smoking, exercise, education, marital status, job, working hours and duration 7 Int. J. Environ. Res. Public Health 2021,18, 6738 8 of 19 Table 1. Cont. Author Year Design Country Population Sex Shift Work Sample Size (SWs/DWs) Mean Age i (SWs/DWs) Outcome Outcome Adjustments NOS Mohebbi 2012 [42] CS Iran long distance drivers M PN 3039/3039 [20–60]SBP DBP No 4 Morikawa 2007 [43] CS Japan zipper and sash factory M R + N 434/712 33.5/36.4 SBP DBP No+ 5 Moy 2010 [44] CS Malaysia medical university F R + N 112/268 49.8/49.2 SBP DBP No 6 Murata 1999 [45] CS Japan copper-smelting plant M R + N 158/75 36/36 SBP DBP No 5 Nagaya 2002 [46] CS Japan manual production, security, transportation M R + N 826/2824 45.6/47.1 SBP DBP * HTN * age, BMI, job, alcohol, smoking, exercise 7 Pimenta 2012 [47] CS Brazil public university F&M PN 81/130 [30–62] HTN No 4 Puttonen 2009 [48] CS Finland population-based F&M NS M 157/555 F 208/623 [24–39]SBP DBP No 5 Sakata 2003 [49] CH (91-01) Japan steel company M R + N 2316/3022 NotR SBP DBP * HTN * age, BMI, alcohol, smoking, exercise, TC, creatinine, UA GTP, HbA1c 9 Santhanam 2014 [50]CS USA NHANES F NS 681/2481 32.9/32.4 SBP HTN No 4 Sfreddo 2010 [51] CS Brazil nursing staff F PN 182/311 36.4/33.1 SBP DBP HTN No 7 Int. J. Environ. Res. Public Health 2021,18, 6738 9 of 19 Table 1. Cont. Author Year Design Country Population Sex Shift Work Sample Size (SWs/DWs) Mean Age i (SWs/DWs) Outcome Outcome Adjustments NOS Sookoian 2007 [52] CS Argentina 1 factory F R + N 474/877 36/34 SBP DBP No 5 Suessenbacher 2011 [53] CS Austria glass factory M R + N 48/47 48/47 HTN No 5 Tanigawa 2006 [54] CS Japan 3 nuclear power plants M R + N 253/206 40.4/41.5 SBP DBP No 6 Virkkunen 2007 [55] CS Finland paper and pulp or oil industries M R + N 27/285 [40–55]SBP HTN No 5 Yamasaki 1998 [56] CS USA nursing staff F NS 35/58 40.7 [30–59] SBPAMBP DBPAMBP No 6 Ohira 2000 [57] CS Japan nuclear power plant M R + N 27/26 30.5/31.8 * SBPAMBP DBPAMBP * age, BMI, alcohol, exercise, anger score 6 Kario 2002 [58] CS USA nursing staff F PN 33/54 40/41 SBPAMBP DBPAMBP No 5 CS: cross-sectional study or cross-sectional data; CH: cohort study (dates of baseline and last follow-up or mean years of follow-up); F: female; M: male; SWs: shift workers; DWs: day workers; R + N: rotational shifts including nights; R-N: rotational shifts without nights; PN: permanent night shifts; NS: not specified; NotR: not reported; SBP: systolic blood pressure; DBP: diastolic blood pressure; AMBP: data collected with ambulatory blood pressure monitor; NOS: Newcastle–Ottawa Quality Score; BMI: body mass index; UK: United Kingdom; USA: United States of America; NHANES: National Health and Nutrition Examination Survey; TC: total cholesterol; GTP: gamma glutamyl transferase; HbA1c: glycated hemoglobin; UA: uric acid; B : Factory B; C : Factory C; H : high strain; A : active strain; P : passive strain; L : low strain; * and ** (asterisks): indicate outcomes that were adjusted and the respective confounding variables adjusted; i when mean age regarding SWs and DWs is not provided, information about the total sample is displayed both as mean ±standard deviation or range (min–max). Int. J. Environ. Res. Public Health 2021,18, 6738 16 of 19 aspect was the division in specific types of SW, according to night-time work. This aimed to counteract the notoriously heterogenous nature of the SW definition and operationalization, allowing for more homogenous exposed groups concerning the circadian system and more precise results. Moreover, this strategy allowed for the same study providing data for more than one meta-analysis. On the other hand, when we segregated the results into SW types, some groups resulted in too few studies. High levels of heterogeneity among pooled results were found. This may be due to the wide heterogeneity in the work settings and tasks performed in the included studies. In fact, although we have tried to mitigate the SW variability, even our SW types may encompass different working times, schemes, speed and direction of rotation. Additionally, the duration and intensity of the SW exposure (e.g., average number of shifts) may be implicated, since most studies did not provide any information about these features. Another possible limitation is a geographic bias, with almost half of the studies developed in Asia. 5. Conclusions There is sufficient evidence for a potential link between permanent night shift work and an increase in blood pressure values. Regarding rotational shift work, both including nights or not, the evidence is only for an increment in systolic BP. As for hypertension, no increased risk was found. Although the effect on BP values was rather small, this can be of special interest in borderline situations or in susceptible populations with concurrent cardiovascular risk factors. Occupational health services may play an important role in limiting shift work health consequences by promoting healthy behaviors, while closely monitoring the more vulnerable workers. Considerations about circadian human physiology could support the design of least detrimental work schedules and select more adequate workers for certain shifts, according to their own individual chronotype. To accurately define the impact of shift work on blood pressure, interventional and longitudinal studies with appropriate follow-up are needed, which should include comprehensive shift work descriptions, continuous BP monitoring and, also, adjustment for relevant lifestyle, occupational and sleep parameters. Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/ijerph18136738/s1. Figure S1: Funnel plots and p-value (for Egger test) for each outcome; Table S1: Search strategy; Table S2: Key studies excluded at full-text stage, with reasons; Table S3: Newcastle–Ottawa Quality Assessment Score (NOS). Table S4: Results from univariate meta-regression analysis. Table S5. Results from multivariate meta-regression analysis. Author Contributions: S.G.M. created the concept of the study, searched the articles and took the lead in writing the manuscript. S.G.M. and C.F. wrote the study protocol, performed article screening, data extraction, analysis and risk of bias assessment. D.C. contributed to the study design, solved disagreements, performed the statistical analyses and coordinated database search and extraction. S.G.M., C.F., T.P., C.S.M. and D.C. were involved in the results interpretation, discussion and critically revised the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Ph.D. research Grant PDE/BDE/127787/2016 from Fundação para a Ciência e Tecnologia (FCT) /Fundo Social Europeu. Institutional Review Board Statement: Ethical review and approval were waived for this research since data extracted and analyzed was from already published studies. 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