The relationship of obesity to MSD is complex (57, 58). For instance, obesity and ineffective job-task training are considered independent risk factors for carpal tunnel syndrome (59, 60). Mechanical loading is considered an etiologic factor in MSD, particularly for concomitant osteoarthritis and obesity; other predisposing factors include joint injury, heredity, and aging (61, 62). In addition to biomechanical factors, studies of animal MSD models suggest multiple biochemical and molecular mechanisms that may interact with obesity to influence MSD development (61).

A systematic review of 21 longitudinal studies demonstrated that workplace physical factors are part of a cause–effect chain for upper-limb/neck MSD (63). These factors include manual material handling, vibration, trunk flexion/rotation, and working with hands above shoulder level. Linking obesity with complex outcomes such as MSD is challenging. For example, a Cochrane review has shown that upper-limb/neck MSD may not be a single entity (64), subgroups of which may have a different relationship to obesity and require different approaches to prevention and intervention.

Some cross-sectional (65, 66, 67) and longitudinal (68) studies have evaluated obesity and the development of MSD symptoms in working populations, but more studies are needed, particularly with more rigorous ascertainment of MSD triggering factors and symptoms. For example, the longitudinal study found associations between incident self-report shoulder pain, in the week prior to survey, and age, BMI, workplace physical factors, and psychosocial factors among workers at baseline and at three years (68). Extended work schedules (eg, shift work, long hours) were an independent risk factor for MSD among nurses (67), but work schedules are also considered an important occupational risk factor for obesity (see example 2.4). Taken together, this breadth of data examining different risk factors or outcomes in animal models as well as cross-sectional and longitudinal studies suggests the importance of considering the impact of obesity and occupational factors on MSD. One such conceptual model could focus on the impact of obesity and workplace physical factors on upper-limb/neck MSD (table 1, example 1.1).

The association of knee osteoarthritis with occupational kneeling and squatting has been shown to increase 7-fold between the lowest and highest categories of BMI (69, 70). The impact of heavy workload, obesity, and traumatic knee injuries on the development of osteoarthritis have been reported from a prospective study of a Finnish cohort with ≥20 years of follow-up (71). The combined impact of kneeling/squatting activities and obesity may also be important for the progression of existing knee osteoarthritis symptoms (72–74). Cross-sectional and longitudinal studies of obesity and occupational risk factors for osteoarthritis in working populations support the importance of a conceptual model of obesity, occupational kneeling/squatting, and osteoarthritis. Animal data suggest that the physiologic impact of obesity on osteoarthritis (61) may be of relevance (table 1, example 1.2).

Obesity increases the risk for asthma and is associated with greater asthma-related healthcare utilization and reduced quality of life (75). Obesity is associated with decreased sensitivity to glucocorticoids, a mainstay of asthma treatment (10, 75, 76). Occupational allergens such as chemicals, dusts, fumes, or other compounds can increase the risk of asthma onset or its exacerbation (77–83). An earlier peer review concluded that controlling obesity and occupational exposures are among the best opportunities for asthma prevention (84). The literature on the relationship of occupational allergens and obesity, respectively, to asthma is long-standing and robust. Thus, an important conceptual model is the impact of occupational exposures on the relationship between obesity and asthma (table 1, example 1.3).

Obesity increases the risk for hypertension through salt retention, insulin resistance, dyslipidemias, sympathetic nervous system activation, systemic inflammation, and risk of sleep apnea (8). Increased risk for sleep apnea stems from the impact of elevated blood pressure and constraints on the pulmonary system from increased BMI (85, 86). NAFLD has been associated with exposures to chemicals such as organic compounds or metals in cross-sectional analysis of population data (87, 88) and among workers with or without metabolic disease (89). The association of sleep apnea with NAFLD is gaining support (90) and the association of obesity with NAFLD is becoming a greater concern given the obesity epidemic (91). Although much of the data is cross-sectional or observational, the situation raises enough concern to suggest a conceptual model in which the combined presence of obesity and an occupational chemical exposure could impact the effects of sleep apnea on NAFLD (table 1, example 1.4). This example illustrates the potential for a disease process due to obesity, along with exposure to chemicals, to be relevant in the occupational setting. Additionally, it raises the issue of more complex associations in the context of occupational exposure to chemicals that may impact NAFLD status because of the role of obesity in both sleep apnea and NAFLD. This emphasizes the need to consider multiple disease processes based on occupational and personal risk factors for evaluating the impact of obesity and the work environment.

One of the most significant modifiable variables affecting obesity is physical activity (7). Recent analysis of 3539 adults from 1999–2004 National Health and Nutrition Examination Survey (NHANES) data found that having high versus low occupational activity decreased the odds risk ratio to 0.37 [95% confidence interval (95% CI) 0.24–0.57] for abdominal obesity (92). Cross-sectional analysis of 10 785 workers from the Australian National Health Survey found that mostly standing at work, versus mostly sitting, lowered the risk for overweight/obesity to 0.88 (95% CI 0.82–0.95); physical activity and leisure-time sitting did not impact this association (93). Although cross-sectional, this evidence suggests that occupational activity levels influence obesity. A meta-analysis of 15 prospective studies suggests that “light” physical activity, or even standing instead of sitting during leisure time, may decrease the risk of obesity-associated diseases, particularly diabetes, with reported associations predominantly independent of overall physical activity (94). Such evidence supports the idea that sedentary behavior and physical activity are separate behaviors. Current thinking suggests that degree of activity may be influenced by environment and genetics (49, 95–97). Studies have found that animals can be bred to be “obesity resistant” demonstrating more spontaneous physical activity (96), and certain genes, such as the brain-specific Bsx or “fidget” gene, may impact spontaneous physical activity (97), although these findings have not been confirmed among humans. A recent examination of the impact of sedentary behavior (hours of TV watching) on the BMI of individuals with a genetic predisposition for obesity (32 well-established BMI-associated genetic variants) found that prolonged sedentary activity exacerbated genetic effects, but increased physical activity independently weakened this genetic association (98). Extended periods of inactivity among both animals and humans cause physiologic and metabolic changes, suggesting that consideration of sedentary behaviors and physical activity may be needed for interventions in the occupational setting (94, 99, 100, 101). If an individual, with a genetic predisposition for low levels of physical activity, is employed in a sedentary profession, then these factors may independently engender obesity or may interact to produce greater weight gain. From the PRF perspective, decreases in exercise levels are considered a significant factor for obesity increasing with age (102). Metabolism, hormone function, body fat distribution patterns, and other biochemical and physiologic processes also change with age, often creating a setting fostering obesity (103). A conceptual model concerning the impact of occupational activity/sitting/sedentary work and age on obesity may be important for consideration in the occupational environment (table 2, example 2.1).

The association of obesity with occupational exposure to endocrine-disrupting chemicals is supported by evidence from animal models as well as observational and case–control studies. Fat-soluble xenobiotics, such as benzo(a)pyrene (104) and polybrominated diphenyl esters (105), deposit in adipose tissue and have been shown to affect lipolysis and thyroid function in rodents. A complex causal relationship between polychlorinated biphenyls and obesity has been theorized with varying impact of factors, such as degree of chlorination, and time course of exposure as well as gender, in analyses of US and Swedish cohorts (37). Obesogens, chemicals suspected to be causally related to obesity, also have been proposed to include phthalates, dithiocarbamates, aryl hydrocarbon receptor ligands, and organotins, despite normal diet and exercise activities (37, 106–110). A recent cross-sectional analysis of men and women participating in a prospective cohort study in the Netherlands provided evidence that occupational exposure to endocrine disruptors in either gender was associated with an increased time to pregnancy (109). Recently, a case–control study of 1005 breast cancer cases and 1146 community controls in Canada demonstrated increased exposure to carcinogens and endocrine disrupters in a range of occupations, including agriculture and automotive plastics manufacturing (110). An important conceptual model focuses on gender differences in the impact of various endocrine disruptors on obesity (table 2, example 2.2).

Job stress has been linked to BMI (19, 38, 111). A recent cross-sectional analysis across 13 European studies, with a total of 161 746 participants, found both weight gain and loss to be modestly associated with job strain (16). Job strain may be associated with type 2 diabetes through BMI and gender dependent mechanisms (112) and kidney dysfunction in a BMI dependent manner (113). Personal exercise behavior/activity impacts obesity and type 2 diabetes (15, 92, 93, 98). Although mostly cross-sectional, the association of job stress or strain with obesity supports an important conceptual model involving exercise behavior/activity, job stress or strain, and obesity, with several inter-relationships possible (table 2, example 2.3).

Shift workers have increased body weight, and working long hours has been positively associated with higher BMI (38, 111, 114). Shift work, sleep deprivation, and exposure to bright light at night have been associated with increased adiposity (115). Cross-sectional studies suggest that disrupting normal eating and sleeping patterns through night shift work or rapidly rotating day/night shifts are risks for obesity and metabolic syndrome (116). Cyclic, clock-like expression of “clock genes,” in central and peripheral tissue neurons, liver, and fat cells, with expression in rhythmic patterns synced to external environments, impacts food intake, energy expenditure, and sleep (117). In animal models of activity during normal sleep times or of fragmented sleep periods, disruption of this mechanism is associated with energy metabolism malfunction, development of obesity, and disrupted glucose utilization (118–121). Shift work and circadian disruption has also been associated with changes in gastrointestinal and metabolic processes such as fat absorption and metabolism, melatonin-related lipid metabolism, enteric autonomic function, and altered insulin signaling in human subcutaneous fat (121–125). Shift work also disrupts workers’ eating behaviors such as those related to food choice and availability as well as eating times and habits (67, 126). Recent studies have found, however, that disrupting only the fat cell clock gene Arntl caused mice to eat during their inactive phase, a type of “night eating syndrome”, and engendered obesity with no changes in activity or other behaviors. As the circadian clock was disrupted in only fat cells, these data suggest a strong communication network between fat stores and the brain (127, 128). Diet and metabolism impact obesity and CVD such as hypertension, with high calorie/high fat intake, metabolism, and high salt ingestion/retention playing differential and important roles (2, 7, 129). Animal data and observational studies in humans provide evidence on the effects of shift work/long work hours on obesity, and review the information on the role of diet and metabolism in obesity and CVD. This information strongly suggests the importance of a conceptual model that considers both physiologic and behavioral perspectives regarding the impact of shift work and diet/metabolism on obesity, with both factors significantly increasing the risk for CVD (table 2, example 2.4). This model also illustrates complexities that can arise when risk factors impact each other.

Analysis of the 1999–2004 NHANES data showed that chronic occupational noise exposure, compared with no exposure, resulted in 2–3 times greater prevalence of CVD, such as hypertension (130). Other findings strongly support the theory that noise exposure has an impact on hypertension, and some data suggest that hearing loss and hypertension severity is associated (131–133). As such, this cross-sectional data supports the need for further evaluation of noise and CVD. Given the relationship of diet, metabolism, obesity, and CVD (see example 2.4) a conceptual model that may be important to consider is occupational noise exposure, diet/metabolism, obesity, and CVD, providing the potential to examine more complex relationships (table 2, example 2.5).