Cardiometabolic risk factors and mental health status among truck drivers: a systematic review.

OBJECTIVE: This study aimed to systematically review and summarise the literature on cardiometabolic risk factors, lifestyle health behaviours and mental health status of truck drivers globally to ascertain the scale of these health concerns.
DESIGN: Systematic review reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DATA SOURCES: PubMed, Scopus, PsycINFO and Web of Science were searched in January 2019 and updated in January 2020, from the date of inception to 16 January 2020. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Papers were included if they (1) reported independent data on truck drivers, (2) included quantitative data on outcomes related to cardiometabolic markers of health, mental health and/or health behaviours, (3) were written in English and (4) were published in a peer-reviewed journal. Grey literature was ineligible for this review. DATA EXTRACTION AND SYNTHESIS: One reviewer independently extracted data and assessed methodological quality using a checklist based on the National Heart, Lung and Blood Institute Quality Assessment tool. 20% were independently assessed for eligibility and quality by a second reviewer. Due to heterogeneity of the outcomes, results were narratively presented.
RESULTS: 3601 titles and abstracts were screened. Seventy-three studies met the inclusion criteria. Truck driving is associated with enforced sedentarism, long and irregular working hours, lack of healthy foods, social isolation and chronic time pressures. Strong evidence was observed for truck drivers to generally exhibit poor cardiometabolic risk profiles including overweight and obesity, hypertension, hypercholesterolaemia, high blood glucose, poor mental health and cigarette smoking.
CONCLUSIONS: Improving truck driver health is vital for the longevity of the trucking industry, and for the safety of all road users. The workplace plays a vital role in truck driver health; policies, regulations and procedures are required to address this health crisis. PROSPERO REGISTRATION NUMBER: CRD42019124499. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

This review encompasses a comprehensive range of cardiometabolic markers of health and lifestyle factors that provides a rich description of the overarching health of truck drivers globally.

This is the first systematic review to address the gap in the evidence base by examining the cardiometabolic health of truck drivers globally, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Meta-analysis was not possible due to heterogeneity in methods and descriptive presentation of results.

This study assessed the risk of bias of each included paper including the informativeness and methodological quality.

Findings could be impacted by a ‘healthy worker effect’, in that truck drivers in most countries must undergo and pass a medical assessment to maintain their licence, drivers who are at-risk may either choose not to enrol in studies or fail the medical examination and would therefore be excluded from the study samples, which could potentially bias the overall health profile of participants included within the reviewed studies.

Introduction

Truck driving encompasses an abundance of integral, interlinking health risk factors that create barriers to the adoption of healthy lifestyle behaviours. These include long hours of enforced sedentarism, reducing opportunities for physical activity, limited availability of healthy food, irregular shift patterns and sleep deprivation.1 The isolated nature of the work results in a lack of peer social modelling and poor mental health.2 This is exacerbated by intense job demands and low levels of perceived job control, resulting in chronic time pressures compounded by tight delivery schedules and traffic conditions.3 Truck driving is a male-dominated industry comprising an ageing workforce.4 These factors are likely associated with the high prevalence of obesity, chronic diseases, mental ill health and reduced life expectancies seen in truck drivers in comparison to other occupational groups.5–8 The health and well-being of professional truck drivers is of public concern given their health impacts the safety of all road users.6 For example, truck drivers with obesity, cardiovascular disease, depression, anxiety or with ≥3 comorbidities have a significantly increased risk of preventable crashes.9–13

There is a developing body of evidence relating to the physical and mental health of truck drivers which consist of small-scale independent studies. When efficiently aggregated together, these studies will establish whether the findings are consistent and hence provide greater strength with the ability to establish a better understanding of the international prevalence. This prompts the need for a systematic review and appraisal of data to facilitate health promotion interventions for this underserved occupational group. The objective of this paper was to systematically review the literature regarding the cardiometabolic and mental health profile of truck drivers globally.

Methods

The review protocol was reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA, see online supplemental material 1 and 2).14

Search strategy

A literature search for relevant papers was conducted in four electronic bibliographic databases: PubMed, Scopus, PsycINFO and Web of Science. Several synonyms of ‘truck driver’ were used such as ‘HGV’ and ‘lorry driver’, a truck driver in this paper is defined as somebody who earns a living transporting goods over land by operating a heavy goods vehicle, this means that the criteria would accept lorry drivers but reject bus, coach, van, taxi and train drivers. These were combined with a comprehensive list of forty key terms for risk factors, related health outcomes and lifestyle factors/behaviours to locate potentially relevant studies. Keywords were created with guidance from a research librarian to ensure a thorough scope was reached. An example of the search strategy used for PubMed is provided in online supplemental material 3. In addition, manual searches of reference lists of primary studies, reviews and identified articles were conducted. The search strategy was carried out in January 2019 and updated in January 2020.

Patient and public involvement

No patient involved.

Inclusion criteria

To be included in the review, the studies were required to (1) report independent data on truck drivers, (eg, if data were combined with bus drivers then they were excluded), (2) include quantitative data on outcomes related to cardiometabolic markers of health, mental health and/or health behaviours, (3) be written in English and (4) be published in a peer-reviewed journal from the date of inception up to the 16 January 2020. All study designs were considered for inclusion.

Identification of relevant studies

The titles and abstracts of identified articles were simultaneously screened for eligibility by one reviewer (AG), 20% of which were randomly selected and verified by a second reviewer to ensure consistency (Y-LC), which resulted in a 96% agreement rate. If abstracts were not available or provided insufficient information, the entire article was retrieved and screened against the inclusion criteria. Any discrepancies in identification of relevant studies were discussed. If a decision could not be reached then a third reviewer was consulted (NPe).

Data extraction

Data were extracted from all included papers on standardised Excel spreadsheets by one reviewer (AG). Extracted data included: study design, country, sample size, sociodemographic information, measurement methods, main findings, funding source, markers of health (anthropometric measurements, blood pressure, blood profile, mental health) and lifestyle health behaviours (diet, physical activity, addictive behaviours).

Methodological quality assessment

Included studies were evaluated on their methodological quality using a checklist based on the National Heart, Lung and Blood Institute Quality Assessment tool for cross-sectional and cohort studies,15 though questions were tailored for this review. The questions referred to informativeness (two items) and study methodology (four items). It was scored as positive (+), negative (−) or not reported/applicable (?). The primary author (AG) scored the quality of each article, the second author (Y-LC) independently scored 20%. The results of the scoring were compared, and differences were discussed and rectified with the assistance of a third author (NPe). Papers were deemed good if they met ≥5 criteria, fair if they met 4 and poor if they met ≤3 criteria.

Results

Search and selection

The literature search yielded 3601 potentially relevant papers, of which 95 were included, representing 73 studies (figure 1). Online supplemental material 4 shows the studies which consist of multiple papers. Sample sizes ranged from 12 to 95 567. Study designs included cross-sectional (53 studies, 68 papers), repeated cross-sectional (3 studies, 5 papers), case–control (5 studies/papers), retrospective cohort (4 studies/papers), prospective cohort (6 studies/papers) and interventions (5 studies, 7 papers) table 1. Due to heterogeneity between study methodologies, a qualitative synthesis of the data was deemed most appropriate to draw conclusions about the cardiometabolic and mental health of truck drivers.16 See online supplemental material 5 for the full summary of included papers.

Figure 1

PRISMA flow diagram of literature search and selection of studies in review. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Table 1

Characteristics of the papers (N=95) included in the review, split by sample size, study design, data collection methods, country of study and methodological quality assessment

No of studies (N)	Markers of health						Health behaviours
	BMI	Circumferences (Waist/hip)	Blood Pressure (measured and reported hypertension)	Blood profile (cholesterol)	Blood profile(glucose, diabetes)	Mental health(stress, fatigue, depression, anxiety)	Diet	Physical activity	Addictive behaviours (alcohol, smoking, illicit drugs)
	n=53 NRP=71	n=16 NRP=23	n=37 NRP=51	n=17 NRP=25	n=30 NRP=43	n=29 NRP=34	n=17 NRP=18	n=25 NRP=30	n=47 NRP=58
Sample size
1–100 n=20 NRP=23	n=14 NRP=17 (C17 18) (I19–21) (J9) (L22)23–32	n=6 NRP=9 (C17 18) (I19–21)26–28 30	n=10 NRP=12 (C17 18) (I19 21)23 25–28 33 34 93	n=3 NRP=4 (I19 21)26 28	n=7 NRP=9 (I19–21)25–28 33 34	n=9 NRP=9 (I21) (L22)	n=7 NRP=8 (C17 18) (I20)26 28 30 32 35	n=7 NRP=8 (C17) (I19 20) (L22)26 28 30 36	n=9 NRP=11 (C18) (I19–21)23 27 32–36
101–500 n=30 NRP=42	n=23 NRP=31 (A37–39 94 95) (D40) (E41 42) (F43 96) (G2 44) (H45 99) (J46)(L47)48–59 97 100 103	n=5 NRP=9 (A37 38 60 94) (D40) (E41 42) (L47) 97	n=13 NRP=21 (A37–39 60–62) (D40 63) (E41 42) (F43 96) (J46) (L47)3 49 53 54 64 97 103	n=7 NRP=13 (A37–39 60–62) (D40) (E41 42) (L47)49 53 54	n=12 NRP=19 (A37–39 60–62) (D40) (E41 42) (F43 96) (L47)3 48 49 53 54 64 97	n=13 NRP=16 (A62 94 95) (D63) (F43 96) (G2) (H45) (L47)3 58 64–68	n=8 NRP=8 (E42) (F43) (L47)48 50 53 56 59	n=14 NRP=16 (A38 39 62) (D40) (E42) (G44) (H99) (L47)48 49 53 55 56 58 59 97 103	n=20 NRP=26 (A38 39 62) (D40 63) (E41 42) (F43 96) (G2) (H45 99)(J46) (L47)3 48 49 53 54 56 58 65 67 68 97 103
501–1000 n=11 NRP=12	n=6 NRP=7 (K11 69)70–74	n=2 NRP=3 (K11 69)73	n=5 NRP=6 (K11 69)70 72 73 75	n=2 NRP=3 (K11 69)73	n=5 NRP=6 (K11 69)70 72 73 75	n=5 NRP=513 71 73 75 76 112	n=1 NRP=173	n=2 NRP=3 (K11 69)73	n=7 NRP=8 (K11 69)70 72–75 77
>1000 n=14 NRP=18	n=11 NRP=15 (B5 12 78 79) (M80 81)82–89 98	n=3 NRP=383 84 87	n=10 NRP=12 (B)5 12 78 79 (M)	n=5 NRP=583 87–89 98	n=6 NRP=9 (B5 12 78 79)83 87–89 98	n=3 NRP=4 (B12 79)90	n=1 NRP=186	n=3 NRP=385 86 89	n=12 NRP=14 (B5 12)(M80 81)82 83 85–92
Study design
Cross-sectional n=52 NRP=68	n=36 NRP=47 (A37–39 94 95) (D40) (F43 96) (G2 44) (H45 99) (K11 69) (I19–21)(J9) (L22 47) (M80 81)25 27 29 30 32 48 49 52 53 55–59 70 71 73 74 83–86 89 97 103	n=13 NRP=19 (A37 38 60 94) (D40) (I19–21) (K11 69) (L47)27 30 34 72 83 84 97 103	n=22 NRP=31 (A37–39 60–62) (D40 63) (F43 96) (I19 21) (K11 69) (L47) (M80)3 25 27 33 49 53 64 70 73 75 83 86 89 93 97	n=10 NRP=17 (A37–39 60–62) (D40) (I19 21) (K11 69) (L47)49 53 73 83 89	n=21 NRP=30 (A37–39 60–62) (D40) (F43 96) (I19–21) (K11 69) (L47)3 25 27 33 34 48 49 53 64 70 73 75 83 89 97	n=23 NRP=27 (A62 94 95) (D63) (F43 96) (G2) (H45) (I21) (L)	n=12 NRP=12 (F43) (I20)30 32 35 47 48 53 56 59 73 86	n=21 NRP=26 (A38 39 62) (D40) (G44) (H99) (I19 20) (K11 69) (L22 47)30 36 48 49 53 55 56 58 59 73 85 86 89 97 103	n=36 NRP=45 (A38 39 62)(D40 63) (F43 96) (G2) (H45 99)(I19–21) (K11 69) (L47) (M80 81)3 27 32–36 48 49 53 56 58 65 68 70 73–75 77 83 85 86 89–91 97 103
Repeated cross-sectional n=3 NRP=5	n=2 NRP=4 (B5 78 79)87	n=1 NRP=187	n=2 NRP=4 (B5 78 79)87	n=1 NRP=187	n=2 NRP=4 (B5 78 79)87	n=1 NRP=1 (B79)	n=0 NRP=0	n=0 NRP=0	n=3 NRP=3 (B5)87 92
Case–control n=5 NRP=5	n=4 NRP=4 (J46)23 24 54	n=0 NRP=0	n=3 NRP=3 (J46)23 54	n=1 NRP=154	n=1 NRP=154	n=2 NRP=224 110	n=0 NRP=0	n=0 NRP=0	n=3 NRP=3 (J46)23 54
Retrospective cohort n=4 NRP=4	n=3 NRP=3 (B12)82 98	n=0 NRP=0	n=3 NRP=3 (B12)82 98	n=1 NRP=198	n=2 NRP=2 (B12) 98	n=1 NRP=1 (B12)	n=0 NRP=0	n=0 NRP=0	n=2 NRP=2 (B12)82
Prospective cohort n=6 NRP=6	n=4 NRP=450 51 72 100	n=0 NRP=0	n=1 NRP=172	n=0 NRP=0	n=1 NRP=172	n=2 NRP=213 112	n=1 NRP=150	n=0 NRP=0	n=1 NRP=172
Intervention n=5 NRP=7	n=5 NRP=7 (C17 18) (E41 42)26 28 31	n=4 NRP=6 (C17 18) (E41 42)26 28	n=4 NRP=6 (C17 18) (E41 42)26 28	n=3 NRP=4 (E41 42)26 28	n=3 NRP=4 (E41 42)26 28	n=1 NRP=126	n=4 NRP=5 (C17 18) (E42)26 28	n=4 NRP=4 (C17) (E42)26 28	n=2 NRP=3 (C18)(E41 42)
Data collection methods
Self-Report Questionnaire n=58 NRP=73	n=16 NRP=20 (F43 96) (G2 44) (H45 99) (M80 81)24 32 48 50 51 56 57 59 74 82 86	n=0 NRP=0	n=9 NRP=11 (A61 62) (F43 96) (M80)3 64 75 82 88 89	n=4 NRP=4 (A62)53 89 98	n=11 NRP=12 (A62) (F43 96)3 25 34 48 64 75 88 89 98	n=23 NRP=27 (A62 94 95) (D63) (F43 96) (G2) (H45) (L22 47)	n=16 NRP=17 (C17 18) (E42) (F43) (L47)26 28 30 32 35 48 50 53 56 59 73 86	n=23 NRP=26 (A38 62) (D40) (E42) (G44) (H99) (I19 20) (K11 69)26 28 30 36 48 49 53 55 56 58 59 73 85 86 89 97 103	n=40 NRP=49 (A38 62) (C18) (D40 63) (E41 42) (F)43 96 (G2) (H45 99) (I19–21) (K11 69) (J46) (L47) (M80 81)3 32 34–36 48 49 53 54 56 58 65 68 72–75 77 82 83 85 86 88–91 97 103
Interview n=8 NRP=8	n=1 NRP=170	n=0 NRP=0	n=2 NRP=233 70	n=0 NRP=0	n=2 NRP=233 70	n=4 NRP=413 27 33 110	n=0 NRP=0	n=1 NRP=1 (A39)	n=6 NRP=6 (A39)23 27 33 70 87
Objectively measured n=37 NRP=50	n=30 NRP=40 (A37–39 94 95) (C17 18) D40 E41 42 (I19–21) (K11 69) (L22 47)25–28 30 49 53–55 58 72 73 83 84 87–89 97 98 100 103	n=16 NRP=24 (A37 38 60 94) (C17 18) (D40) (E41 42) (I19–21) (K11 69) (L47)26–28 30 73 83 84 87 97	n=24 NRP=32 (A37–39 60) (C17 18) (D40 63) (I19 21) (K11 69) (J46) (L47)23 25–28 34 49 53 54 73 83 86 87 93 97 98 103	n=14 NRP=21 (A37–39 60 61) (D40) (E41 42) (I19 21) (K11 69)26 28 47 49 54 73 83 87 88	n=18 NRP=26 (A37–39 60 61) (D40) (E41 42) (I19–21) (K11 69) (L47)26–28 49 53 54 72 73 83 87 88 97	n=1 NRP=1 (I21)	n=1 NRP=1 (I20)	n=2 NRP=3 (C17) (L22 47)	n=1 NRP=192
Secondary data n=1 NRP=4	n=1 NRP=4 (B5 12 78 79)	n=0 NRP=0	n=1 NRP=4 (B)5 12 78 79	n=0 NRP=0	n=1 NRP=4 (B5 12 78 79)	n=1 NRP=2 (B12 79)	n=0 NRP=0	n=0 NRP=0	n=1 NRP=2 (B5 12)
Not reported n=5 NRP=6	n=4 NRP=5 (J9 46)23 29 71	n=0 NRP=0	n=1 NRP=172	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0
Country
Australia n=8 NRP=9	n=6 NRP=7 (C17 18)24 50 51 59	n=1 NRP=2 (C17 18)	n=2 NRP=3 (C17 18)70	n=0 NRP=0	n=1 NRP=170	n=3 NRP=324 35 90	n=4 NRP=5 (C17 18)35 50 59	n=2 NRP=2 (C17)59	n=3 NRP=3 (C18)35 70
Brazil n=11 NRP=15	n=9 NRP=12 (D40) (H45 99) (I19–21)32 53 74 87 88 97	n=4 NRP=6 (D40) (I19–21)87 97	n=7 NRP=9 (D40 63) (I19 21)3 53 87 88 97	n=5 NRP=6 (D)40 (I19 21)53 87 88	n=7 NRP=9 (D40) (I19–21)3 53 87 88 97	n=4 NRP=4 (D63) (H45) (I21)3	n=3 NRP=3 (I20)32 53	n=5 NRP=6 (D40) (H99) (I19 20)53 97	n=11 NRP=15 (D40 63) (H45 99) (I19–21)3 32 53 74 87 88 92 97
Bulgaria n=1 NRP=1	n=0 NRP=0	n=0 NRP=0	n=1 NRP=1 33	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0
Canada n=1 NRP=1	n=1 NRP=148	n=0 NRP=0	n=0 NRP=0	n=0 NRP=048	n=1 NRP=1	n=0 NRP=048	n=1 NRP=148	n=1 NRP=148	n=1 NRP=1
China n=2 NRP=2	n=1 NRP=123	n=0 NRP=0	n=1 NRP=123	n=0 NRP=0	n=0 NRP=0	n=1 NRP=166	n=0 NRP=0	n=0 NRP=0	n=1 NRP=123
Ethiopia n=1 NRP=1	n=1 NRP=158	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=1 NRP=158	n=0 NRP=0	n=1 NRP=158	n=1 NRP=158
Germany n=1 NRP=1	n=1 NRP=152	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0
Hong Kong n=2 NRP=2	n=1 NRP=156	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=1 NRP=165	n=1 NRP=156	n=1 NRP=156	n=2 NRP=256 65
India n=3 NRP=3	n=2 NRP=227 103	n=1 NRP=127	n=3 NRP=327 34 110	n=0 NRP=0	n=2 NRP=227 34	n=1 NRP=127	n=0 NRP=0	n=1 NRP=1103	n=3 NRP=327 34 103
Iran n=3 NRP=3	n=1 NRP=183	n=1 NRP=183	n=1 NRP=183	n=1 NRP=183	n=1 NRP=183	n=2 NRP=213 110	n=0 NRP=0	n=0 NRP=0	n=1 NRP=183
Italy n=3 NRP=4	n=3 NRP=4 (F43 96)71 72	n=0 NRP=0	n=2 NRP=3 (F43 96)72	n=0 NRP=0	n=2 NRP=3 (F43 96)72	n=2 NRP=3 (F43 96)71	n=1 NRP=1 (F43)	n=0 NRP=0	n=2 NRP=3 (F43 96)72
Japan n=4 NRP=5	n=4 NRP=5 (M80 81)54 82 85	n=0 NRP=0	n=3 NRP=3 (M80)54 82	n=1 NRP=154	n=1 NRP=154	n=0 NRP=0	n=0 NRP=0	n=1 NRP=185	n=4 NRP=5 (M80 81)54 82 85
Kenya n=1 NRP=1	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=1 NRP=168	n=0 NRP=0	n=0 NRP=0	n=1 NRP=168
Mexico n=1 NRP=1	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=1 NRP=177
Netherlands n=2 NRP=2	n=1 NRP=129	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0	n=2 NRP=229 112	n=0 NRP=0	n=0 NRP=0	n=0 NRP=0
South Africa n=1 NRP=1	n=1 NRP=173	n=1 NRP=173	n=1 NRP=173	n=1 NRP=173	n=1 NRP=173	n=1 NRP=173	n=1 NRP=173	n=1 NRP=173	n=1 NRP=173
UK n=1 NRP=2	n=1 NRP=2 (L22 47)	n=1 NRP=1 (L47)	n=1 NRP=1 (L47)	n=1 NRP=1 (L47)	n=1 NRP=1 (L47)	n=1 NRP=2 (L22 47)	n=1 NRP=1 (L47)	n=1 NRP=2 (L22 47)	n=1 NRP=1 (L47)
USA n=27 NRP=41	n=19 NRP=30 (A37–39 94 95) (B5 12 78 79) (E41 42) (G2 44) (J9 46) (K11 69)25 26 28 30 31 49 55 57 84 86 89 98 100	n=7 NRP=12 (A37 38 60 94) (E41 42) (K11 69)28 30 84	n=15 NRP=25 (A37–39 60–62) (B5 12 78 79) (E41 42) (K11 69) (J46)25 26 28 33 49 64 75 86 89 98	n=8 NRP=15 (A)37–39 60–62 (E41 42) (K11 69)26 28 49 89 98	n=13 NRP=23 (A37–39 60–62) (B37–39 60–62) (E41 42) (K11 69)25 26 28 33 49 64 75 89 98	n=9 NRP=12 (A62 94 95) (B12 79) (G2)26 33 64 67 75 76	n=5 NRP=5 (E42)26 28 30 86	n=12 NRP=15 (A38 39 62) (E42) (G44) (K11 69)26 28 30 36 49 55 86 89	n=13 NRP=18 (A38 39 62) (B5 12) (E41 42) (K11 69) (G2) (J46)33 36 49 75 86 89 91
Methodological Quality Assessment
Poor n=29 NRP=32	n=19 NRP=22 (G2 44) (J9 46) (K11 69)23–25 29 31 50–52 57 59 71 72 74 83 88 100	n=2 NRP=3 (K11 69)83	n=12 NRP=13 (D63) (J46) (K11 69)3 23 25 33 64 72 83 88 93	n=3 NRP=4 (K11 69)83 88	n=8 NRP=9 (K11 69)3 25 33 64 72 83 88	n=10 NRP=10 (D63) (G2)3 24 29 33 35 71 76 90	n=3 NRP=335 50 59	n=4 NRP=5 (G44) (K11 69)36 59	n=15 NRP=16 (D63) (G2) (J46)(K11 69)3 23 33 35 36 64 72 74 83 88 90
Fair n=33 NRP=40	n=25 NRP=32 (B)5 12 78 79 (C17) (D40) (F43 96) (H45 99) (I19 21) (M80 81)26 28 32 48 49 53–56 70 73 82 84–86 89 97	n=8 NRP=9 (C17) (D40) (I19 21)26 28 73 84 97	n=20 NRP=25 (B)5 12 78 79 (C17) (D40) (F43 96) (I19 21) (M80)26 28 34 49 53 54 70 73 75 82 86 89 97 98	n=10 NRP=11 (D40) (I19 21)26 28 49 53 54 73 89 98	n=17 NRP=22 (B5 12 78 79) (D40) (F)43 96 (I19 21)26 28 34 48 49 53 54 70 73 75 89 97 98	n=11 NRP=13 (B12 79) (F43 96) (H45) (I21)13 65 66 68 73 75 110	n=10 NRP=10 (C17) (F43)26 28 32 48 53 56 73 86	n=15 NRP=15 (C17) (D)40 (H99) (I19)26 28 32 48 53 56 73 85 86 89	n=24 NRP=29 (B5 12) (D40) (H45 99)(F43 96) (I19 21) (M80 81)26 32 34 48 49 53 54 56 68 70 73 82 85 86 89 91 97
Good n=14 NRP=23	n=10 NRP=16 (A37–39 94 95) (C18) (E41 42) (I20) (L22 47)27 30 58 87 92 103	n=8 NRP=12 (A37 38 60 94) (C18) (E41 42) (I20) (L22 47)27 30 87	n=7 NRP=13 (A37–39 60–62) (C18) (E41 42) (L47)27 87 103	n=4 NRP=10 (A37–39 60–62) (E41 42) (L47)87	n=6 NRP=12 (A37–39 60–62) (E41 42) (I20) (L47)27 87	n=5 NRP=8 (A62 94 95) (L22 47)27 58 112	n=5 NRP=5 (C18) (E)42 (I20) (L47)30	n=7 NRP=10 (A38 39 62) (E42) (I20) (L22 47)30 58 103	n=12 NRP=15 (A38 39 62) (C18)(E)41 42 (I20) (L47)27 58 67 77 87 92 103

Letters A-M represent studies comprising of multiple papers.

BMI, body mass index; NRP, number of representing papers.

Quality assessment

A summary of the quality assessment of the included papers is shown in table 2. Individual paper quality assessment has been provided in online supplemental material 6.

Table 2

Criteria list for assessment of methodological quality of papers

Questions	Criteria measured	% Papers meeting criteria (n=95)
Q1: Was the research question or objective in this paper clearly stated?	Informativeness	95
Q2: Was the study population clearly specified and defined?	Informativeness	93
Q3: Was the participation rate of eligible persons at least 50%?	Methodology	32
Q4: Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants?	Methodology	69
Q5: Was a sample size justification, power description or variance and effect estimates provided?	Methodology	17
Q6: Were the markers of health/health behaviours/outcomes clearly defined, valid, reliable and implemented consistently across all study participants?	Methodology	78
Quality of papers (no of Questions meeting criteria)
Good (≥5 items)		25
Fair (4 items)		42
Poor (≤3 items)		33

Demographics

Sixty-five studies included information on sex distribution2 3 5 9 11–13 17–92 of which, only 15% included <90% males.11 23 26 30 32 36 41 55 57 64 69 Twenty-eight studies were conducted in North America, 12 in South America, 14 in Asia, 8 in Europe, 8 in Australia and 3 in Africa (table 1). Of the studies which reported average age of participants, the means ranged from 22.866 to 56.3 years.91 Seventy-five per cent of studies reported mean ages over 40 years.2 5 11–13 17 18 22 25 26 28 30 31 33 35–39 41–44 47–57 59–61 64 65 69–72 74 75 78 79 81–83 85 87–89 91–98 Twenty-four studies reported sample ethnicity,2 11 25 26 31 33 36–41 44 47 49 50 55 57 60 61 63 64 66 67 73 75 84 89 94 97 98 with all but three including a majority of white participants.33 66 73 Thirty-five studies described participants’ education level,2 13 19 20 25 27 33 34 36 40 41 43–45 47–50 57 58 63–69 71 73 75 77 83 86 89–91 94 96 97 99 the percentage of those not completing high/secondary school ranged from 11.5%69 to 96.7%,27 though the overall trend was towards most participants having a low level of education, with the majority not completing secondary/high school education. Thirty-six studies reported classifications of truck drivers. Twenty-seven of these were long haul (ie, typically drives >250 miles per drop),9 22 25 27 28 30 33 34 36–40 49–51 53 55 60–65 68 70 73 75 83 85 89 93–95 97 100 two short haul (ie, typically drives <150 miles per drop)48 54 and six reported a combination of both long and short haul.17 32 59 69 72 101 Two studies compared day shift and irregular shift truck drivers.19–21 32

Markers of health

Anthropometrics

Fifty-three studies reported body mass index (BMI) (table 1), 63.8% objectively measured height and weight, while 36.2% relied on self-reported data. Mean BMI ranged from 22.027 to 39.7 kg/m2.28 Based on the WHO guidance for BMI thresholds for White European and Asian populations,102 only two studies reported an average healthy BMI27 103 within their samples. Twenty-seven studies reported a sample mean BMI within the overweight category;9 19–24 29 32 40 43 45–48 52–54 58 71–74 80–82 85–89 96 97 99 23 studies were within the obese category.2 5 11 12 17 18 25 26 28 30 31 37–39 41 42 44 49–51 55–57 59 69 70 78 79 83 84 94 95 98 100 One study reported obesity was twice as prevalent in a sample of US truck drivers than the US adult population.89

Sixteen studies included waist circumferences,11 17–21 26–28 30 37 38 40–42 47 60 69 73 83 84 87 94 97 with 12 of these reporting average waist circumferences which indicate increased risk of developing adverse health conditions (>102 cm Europids/>90 cm South Asians).11 17 18 26 28 30 37 38 41 42 47 60 69 83 84 87 94 97 104

Blood pressure

Nineteen studies included blood pressure measurements,5 11 18 21 23 26 28 37–39 41 42 47 53 54 60 69 73 79 80 82 83 93 97 98 with average blood pressures ranging from 116/8023 to 142/87 mmHg.18 Twenty-nine studies included prevalence of self-reported or objectively measured hypertension,3 5 11 12 17 25 27 33 34 39–43 46 49 53 61–64 69 70 72 75 78–80 82 86–89 96–98 103 with thresholds ranging from >140/>90 to>165/>95 mmHg, though many studies did not report the hypertension thresholds applied. Hypertension prevalence ranged from 3%34 to 63%.17 Two studies found truck drivers with obesity had a higher prevalence of hypertension.46 88 Seventy-seven per cent of studies5 11 17 19 27 39–42 49 53 62 63 69 75 79 82 86–88 97 98 103 indicated that the prevalence of hypertension in truck drivers was higher than the global prevalence (26.4%).105

Blood profile

Total cholesterol

Twelve studies objectively measured total cholesterol levels.11 19 21 26 28 37–42 47 49 54 61 69 73 88 Most measurements were in mg/dL, mean readings ranged from 168.2 mg/dL37 to 202.8 mg/dL,54 though the latter reading was not fasted. Six studies measured the prevalence of high total cholesterol levels.19 38–40 49 69 88 Thresholds varied from >19019 to >240 mg/dL,88 though the remainder38–40 69 106 reported high total cholesterol based on the Adult Treatment Panel (ATP) III classification of >200 mg/dL.107 Of those using the >200 mg/dL threshold, studies reported prevalence of borderline-high total cholesterol as 11.4%,38 39 33.0%,40 38.8%69 and 45.8%.106

High-Density Lipoprotein (HDL)

Eleven studies reported HDL,11 19 21 26 28 37–42 47 54 60 61 69 73 83 most measurements were in mg/dL, mean readings ranged from 29.0 mg/dL28 to 52.5 mg/dL.54 Forty three per cent of studies (n=7 studies) reported mean HDL level as low (<40 mg/dL),11 28 37–39 60 61 69 based on the ATP III Classification of HDL cholesterol.107

Low-Density Lipoprotein (LDL)

Ten studies measured LDL levels,19 21 26 28 37 40–42 47 61 69 73 83 most measurements were in mg/dL, mean readings ranged from 103.5 mg/dL28 to 150.4 mg/dL.83 According to ATP III, all mean LDL values reported in mg/dL were above optimal. Cavagioni et al found 25.2% of truck drivers had an LDL of >130 mg/dL,40 which is borderline-high based on the ATP III classification of LDL cholesterol.107 This is supported by Lemke et al who found that 52.8% of truck drivers had an LDL level of >115 mg/dL.61

Triglycerides

Nine studies reported triglyceride levels,19 21 26 37 40–42 47 60 61 73 83 mean readings ranged from 158.5 mg/dL40 to 203.1 mg/dL.21 All reported mean triglyceride levels were >150 mg/dL, which based on the ATP III classification of triglycerides is borderline-high.107

Glycaemic control

Seventeen studies measured fasting blood glucose levels,11 19 21 26–28 37–42 47 49 53 54 60 69 73 83 87 88 97 most were reported in mg/dL, mean readings ranged from 86.3 mg/dL37 to 125.0 mg/dL.53 Thirty three per cent of reported mean glucose levels were >110 mg/dL,11 28 53 69 which based on the ATP III classification of fasting glucose, is a risk factor for the metabolic syndrome.107 Three studies measured Hemoglobin A1c (HbA1c),11 49 54 69 with means ranging from 5.0%69 to 6.1%,49 two studies of which, reported means within the pre-diabetic range of HbA1c level (>5.7%) according to the American Diabetes Association.108 While three reported plasma glucose levels41 47 73 which ranged from 5.2 to 5.7 mmol/L.

Diabetes

Twenty-four studies reported prevalence of type 2 diabetes.3 5 11 12 25 27 33 34 37 41–43 48 53 61 62 64 69 70 72 73 75 78 79 83 87–89 96–98 Self-reported previously diagnosed type 2 diabetes3 5 12 25 33 34 43 48 61 62 64 69 70 72 75 78 79 89 96 98 ranged from 1.5%34 to 17%,25 while prevalence in those with objectively measured abnormal glucose levels11 19 27 37 42 53 73 83 87 88 ranged from 3.4%27 to 18.3%.37

Metabolic syndrome

Four studies reported metabolic syndrome prevalence.37–39 41 60 83 87 Apostolopoulos et al found a significantly higher proportion of US truck drivers had metabolic syndrome than a National Health and Nutrition Examination Survey comparative group (73.7% vs 33.8%, respectively),37 similar findings have been reported elsewhere.41 Ebrahimi et al also found a high prevalence of the metabolic syndrome in Iranian truck drivers of 26.1% based on the ATP definition107 and 32.6% based on the International Diabetes Federation definition of metabolic syndrome.83 109

Mental health

Depression

Nineteen studies reported depression and depressive symptoms prevalence.2 3 13 22 24 33 35 43 47 62–68 73 75 76 90 96 110 There were limited consistent measurements of depression; self-reported questionnaires were the measurement tool used in 17 studies,2 3 13 22 24 35 47 62–68 73 75 76 90 110 one study conducted qualitative interviews,33 and one study reported depression medication use as 0.6%.43 96 Self-reported symptoms of depression ranged from 6%90 to 26.9%,2 40.8% of a sample of Kenyan truck drivers reported feeling depressed.68 In a large sample of US occupational groups, Fan et al found truck drivers had a twofold higher prevalence of depression than the average US worker.76 Alavi et al found that depression could increase the likelihood of a road traffic collision in truck drivers (OR 2.4).13

Anxiety

Eight studies reported anxiety prevalence.2 13 22 24 33 43 47 62 67 90 96 Those that compared truck drivers anxiety levels to national averages, found truck drivers had significantly higher values of anxiety.2 24 47 67 Anxiety was associated with a fourfold increase in likelihood of crashes.13 Though it appeared self-reported medication use for anxiety was low compared with that of the general population.43 96 111

Stress

Ten studies reported on psychological stress.7 21 27 62 67 76 90 94 95 99 110 112 Ulhoa et al found cortisol levels 30 minutes after wakening were higher in irregular shift workers compared with day-shift workers.21 Orris et al used a Symptom Checklist 90 R questionnaire and found US truck drivers were in the 91st percentile for psychological distress compared with the US working population.67 Similar results were also seen in China.66 In a separate study, Ulhoa et al used a Self-Report Questionnaire and found that 6.1% of the sample had minor psychiatric disorders, with the most common reported stressors being traffic congestion, strict vehicle tracking control, and extended working hours. Thirty-three per cent reported high job demand and 54.9% reported low job control, which were associated with minor psychiatric disorders.45 Croon et al conducted a 2-year prospective cohort study which found that job movement to any job outside of the trucking industry resulted in a larger strain reduction compared with job movement within the trucking industry.112

Isolation

Four studies reported on isolation.2 33 45 110 Ulhôa et al found 60.8% of truck driver reported low social support in the workplace.45 Hatami et al showed significantly reduced prevalence of depression in truck drivers who have a codriver than solo truck drivers.110 Shattell et al reported 27.9% of truck drivers mentioned being lonely2 and 27.1% mentioned they did not have good friendships, with 15.3% saying they had no friends.33

Health behaviours

Diet

Seventeen studies reported truck drivers’ diet17 18 20 26 28 30 32 35 42 43 47 48 50 53 56 59 73 86; of which nine studies measured daily fruit and vegetable intake.17 18 26 30 42 47 50 56 59 73 This ranged from 1.72 servings/day30 to 5 servings/day.47 It is suggested that at least five portions (>400 g) of varied fruits and vegetables should be consumed each day to prevent all-cause mortality, cancers and cardiovascular disease.113 Wong et al found that only 1.1% of truck drivers from Hong Kong ate ≥5 portions of fruits/vegetables per day.56 Reis et al reported that 58.3% of Brazilian long haul truck drivers ate an ‘unhealthy diet’,53 similar results were also found in Canada (48%),48 though neither study reported how this was defined. Additionally, Olson et al found that 36.6% of calories in the diet came from fat,26 exceeding the higher threshold of 15–30% based on WHO guidelines.114 Korelitz et al reported 13.6% of truck drivers ate >3 snacks per day, while the majority of participants (51%) ate two meals per day.86 Wong et al reported that 24.9% of truck drivers from Hong Kong ate at restaurants>10 times a week, this was significantly higher than their professional driver counterparts such as taxi and bus drivers.56 This is pertinent as the average calorie content in meals from restaurants regularly exceeds that of home cooked meals.115

Physical activity

Twenty-six studies observed physical activity levels in truck drivers,11 17 19 20 26 28 30 36 38–40 42 44 47 49 53 55 56 58 59 69 73 85 86 89 97 99 103 however, only two studies objectively measured physical activity using an accelerometer (GENEActiv)17 18 and inclinometer (activPAL3).22 47 Though measurements of physical activity were heterogenous, overall the vast majority of truck drivers did not reach WHO physical activity guidelines.116 Sangaleti et al found 72.8% of Brazilian truck drivers were physically inactive,97 this is supported by Marqueze et al who found 66.7% of truck drivers completed <10 min of physical activity a week.19 Similar results were found in USA and Ethiopia, with 69.4% and 63.8% of truck drivers reporting no regular exercise, respectively.44 58 The highest self-reported regular exercise was 57.7% of truck drivers within a different US sample11 69 and 42.9% of truck drivers in Hong Kong,56 however, Korelitz et al found only 8% of US truck drivers reported regular physical activity, while 49.6% reported never doing physical activity.86 Layne et al found minimal differences between male and female truck drivers levels of physical activity.36

Sedentary behaviour

Seven studies reported sedentary behaviour.11 17 22 38–40 47 62 97 99 The percentage of waking time spent sedentary in truck drivers varied from 39.4%38 39 to 78.1%.53 One study objectively measured sitting time in a sample of truck drivers using inclinometers (activPAL3) and observed truck drivers spent 13 hours/day sitting on work-days, and 8 hours/day sitting on non-workdays.47 Shorter accelerometer-determined sedentary durations of 9.1 hours/workday were found in an Australian sample.17 A sample of truck drivers in the USA also self-reported sitting for an average of 4.3 hours/day outside of work.11

Addictive behaviour

Smoking

Thirty-eight studies reported on smoking status.3 11 18–21 23 27 34 36 38–42 46–49 53 54 56 63 65 69 70 72 77 80 82 83 86–89 91 96 97 103 Prevalence of current smokers ranged from 10%73 to 72.9%,83 however, categorising ‘current smokers’ varied between studies and was not defined in some. Jain et al found long haul truck drivers were more likely to smoke than delivery drivers, dock workers, mechanics or clerks.91 Mansur et al found that the smoking prevalence in truck drivers has decreased from 2006 to 2011 by 1.4%,87 though Sieber et al showed that compared with the US adult working population, US long haul truck drivers were more than twice as likely to smoke (51% vs 19%).89 Moreno et al found that although overall prevalence of smoking was high, those who were obese were less likely to smoke.88 77% of studies that reported smoking status, reported higher smoking prevalence rates than the global average of 20%.117

Alcohol consumption

Thirty-five studies measured alcohol consumption.2 3 5 11 18 20 21 23 27 32–35 40 43 47 53 54 56 58 62 63 65 68–70 72 73 75 80–82 85–90 97 99 Alcohol consumption was diversely measured. da Silva-Júnior et al reported 48.3% of a sample of Brazilian truck drivers admitted to drinking alcohol during working hours, and 88.6% were aware of similar behaviour in colleagues.3 Rosso et al also found 24.2% of a sample of Italian truck drivers reported drinking during working hours or breaks.43 Kartikeyan et al observed that over half of the Indian truck drivers assessed were classified as alcoholics (ie, consuming >75 g alcohol/day),34 this is supported by Sharma et al, where 57.6% of their Indian truck driving sample were classified as alcoholics.27 Rice et al reported that 14.2% of Australian truck drivers admit to drinking excessively.35 Hilton et al found that using the Alcohol Use Disorders Identification Test questionnaire, 26.8% of their sample of Australian truck drivers were considered alcohol dependent.90 Mansur found that alcohol consumption in Brazilian truck drivers has decreased from 36.6% in 2006 to 23% in 2011, however, illicit drug use has nearly doubled from 4.1% in 2006 to 8.1% in 2011.87

Illicit substances

Seventeen studies measured illicit substance use.2 3 12 33 35 40 45 49 63 68 70 72–75 87 92 97 Overall self-reported drug use was low. Ulhôa et al reported that 0% of Brazilian participants asked used psychotropic drugs,45 which is similar to findings from a US study.12 However, Shattell et al found a relatively high prevalence of reported use of cannabis (3.4%), opioids (2.5%) and sedatives (1.8%) in the past month in a sample of US truck drivers.2 Riva et al found a higher drug use in a sample of Italian truck drivers where 15.6% reported using recreational drugs, and of those, 12% reported they were addicted.72 7.8% of a sample of Brazilian truck drivers tested positive for psychoactive substances.92 Shattell et al interviewed 60 specially selected long haul truck drivers based on their involvement in illegal/illicit behaviours, they found 88.1% admitted to drug use, and of those, 81.6% used crack cocaine.33 Self-reported stimulant use was high in several studies.3 40 63 97

Discussion

The aim of this review was to systematically summarise the literature regarding cardiometabolic risk factors, lifestyle health behaviours and mental health status of truck drivers globally. Findings suggest truck drivers are an at-risk occupational group, exhibiting unhealthy lifestyle behaviours, such as high levels of physical inactivity and sedentary behaviour, poor dietary choices, high consumption of alcohol and high smoking prevalence. This is reinforced by the reported markers of health such as high obesity levels, adverse blood profiles and mental ill health.

Another considerable risk factor for developing cardiometabolic health conditions is age, with truck drivers included in this review typically being middle aged. This is representative of that seen in the real-world setting, where truck drivers are classified as an ageing workforce.118 Nevertheless, one repeated cross-sectional study in the USA found that the average age of truck drivers has dropped between 2005 and 2012,79 which is an optimistic trajectory for the future of the road transportation and logistics industry.

It is apparent that truck driving is not conducive to health. Predictive factors for obesity were found to be time behind the wheel,96 travelling more than 40 000 miles per year96 and working >11 hours/day.95 Interestingly, truck drivers with obesity were significantly more likely to rate the exercise environment as bad, and therefore, had more perceived barriers to exercise.99 A repeated cross-sectional study of USA truck drivers found that BMI has increased significantly from 2005 to 2012.79 Similarly, the presence of dyslipidaemia had significantly increased from 2006 to 2011.87 The increase in incidence of obesity and cardiometabolic biomarkers is alarming for the future of truck driving, especially considering drivers with obesity have significantly higher crash risks than healthy weight drivers.10 44 Obesity also has an economic impact to the employer, as truck drivers with overweight and obesity exhibit higher annual healthcare costs than healthy weight truck drivers.98 This is particularly pertinent for companies that cover employee health insurance costs.

Implications for future research

Future research is required to evaluate the health of truck drivers in lesser studied regions to gain a more global understanding of the health and implications of truck driving worldwide. Health and well-being programmes that focus on health education should be implemented and prioritised within the logistics industry. In truck drivers, improved education levels have been associated with increased physical activity,99 reduced BMI43 and improved mental health status.3 Where they exist, health and well-being programmes within this industry have been recognised as having the potential of providing beneficial impact on employee health, morale and employee–employer relations.119 This would also directly benefit employers with increased employee retention, reduced healthcare costs, improved safety and job desirability, combatting the global driver shortage.118

Despite their potential, limited health promotion programmes currently exist within the logistics industry and most have been minimally effective.6 Though primary prevention is vital, interventions should aim to reach a multitude of risk factors by applying multiple components, targeting health literacy and improving workplace opportunities to make healthy lifestyle choices, in order to gain maximum effectiveness. Research should then determine which components are most effective for improving driver health,26 and which facilitators and barriers may have affected participant adherence.120 All stakeholders including drivers, employers, unions, clinicians, truck stops, regulatory agencies and policy-makers should be engaged to achieve these ambitions.

Strengths and limitations

A key strength is the comprehensive review itself, encompassing a large range of cardiometabolic risk factors, health-related behaviours and mental health, thus providing a complete scope of the health status of truck drivers. Strengths also include the thorough search strategy; however, it is possible that some relevant papers were missed due to the English-only search criteria. A further strength is the stringent independent scoring of each paper’s methodological quality. One reviewer screened all titles, abstracts, and full texts for inclusion followed by a methodological quality assessment of each included paper. Subsequently, 20% were randomly selected for independent verification at each stage by a second reviewer, which is identified as a limitation of this paper based on the PRISMA guidelines.

Similarly, 33% of included papers were scored ‘poor’ in the quality assessment, and 24% included small sample sizes (n=<100) which may impact the confidence within the results.

Due to the heterogeneity of measuring and reporting findings from each study, a descriptive synthesis of results appeared most appropriate, though this may have led to inconsistent statistical findings between papers. As a recommendation for future research, authors are encouraged to report and use consistent thresholds for defining the prevalence of certain risk factors to enable a meta-analysis. By presenting all study characteristics and results in online supplemental file 4, transparency has been added. A limitation is the unequal geographical distribution of papers (table 1), ethnicity was predominantly White in all but two studies,33 66 suggesting the review may not provide a complete representation of the global cardiometabolic and mental health status of truck drivers.

Truck driving is a very heterogeneous job, there are many varieties of truck drivers, predominantly long haul or short haul. Typically, short haul truck drivers are more physically active as they are often loading and unloading their trucks, the distances they travel are smaller and hence their sedentary time is often broken up. They also tend to return home after most shifts and drive less miles overall, which results in an overall healthier way of life. Only 36 included studies reported on the type of truck driver, which warrants consideration.

Another important factor is the law in many countries requires truck drivers to be of adequate health in order to legally operate commercial vehicles on public roads, and as a result, truck drivers are required to undergo regular medical examinations. Truck drivers may be disqualified for a multitude of health conditions and subsequently leave the industry. Consequently, a substantial healthy worker effect is created, as those who are unfit to work will not be included in studies.121 This may lead to the prevalence of diseases being low, while the prevalence of risk factors for chronic disease are substantial.48 This reflects the findings from this systematic review, where for example, only 41% of papers reported a mean higher prevalence of type 2 diabetes than the global average (9.3% in 2019).122

Notably, addictive behaviour such as alcohol and illicit drug consumptions are likely to be underreported due to fears that it may lead to disciplinary action.123 Participation bias may also impact these findings, for example, drivers at risk of certain health conditions may choose to not enrol in such studies. This is exacerbated by the certain levels of mistrust frequently seen among truck drivers, often resulting in their reluctance to release personal information, making them a hard-to-reach population.124

Similar reviews examining the health of truck drivers have found comparable findings. Abu Dabrh et al found that the average BMI of 32 combined studies of truck drivers was 30.5 kg/m2 and the prevalence of hypertension was 23% (n=1000).125 A systematic review which examined the health of truck drivers in North America also found the average BMI for all nine included studies was >30 kg/m2.126 This is further supported by Prince et al who compared cardiometabolic health variability between occupational groups, and found driving-based workers had a higher BMI and blood pressure than other occupational groups.127 Similarly, a literature review of truck drivers found hypertension in seven studies ranged from 24% to 47.9%, the majority were above the global average.105 128

Conclusion

Based on the results of this review, there is strong evidence to indicate that truck drivers have a detrimental cardiometabolic and mental health profile in comparison to general populations. Obesity levels and other cardiometabolic risk factors in truck drivers are alarming considering it impacts the safety of all road users. Improving truck driver health is vital for the longevity of the trucking industry. It is imperative to understand that the systemic working conditions that truck drivers endure create a fundamental barrier to the adoption of a healthy lifestyle. These unique job demands include the long irregular hours of enforced sedentarism, which suppress the opportunity for physical activity, alongside the lack of healthy food choices at truck stops, which creates an obesogenic working environment. The workplace plays a vital role in truck driver health; policies, regulations and procedures are required to change in order to address this health crisis.