- Research article
- Open Access
- Open Peer Review
Use of various obesity measurement and classification methods in occupational safety and health research: a systematic review of the literature
- Mahboobeh Ghesmaty Sangachin1,
- Lora A. Cavuoto1Email authorView ORCID ID profile and
- Youfa Wang2
- Received: 28 November 2017
- Accepted: 21 June 2018
- Published: 1 November 2018
Abstract
Background
This study systematically examined obesity research in occupational safety and health regarding the use of various obesity measurement and classification methods.
Methods
A systematic search of the PubMed database on English language publications from 2000 to 2015 using related keywords and search of citations resulted in selection of 126 studies. They were categorized into two groups based on their main research question: 1) general physical or mental work-related functioning; and 2) task or body part specific functioning.
Results
Regardless of the study group, body mass index (BMI) was the most frequently used measure. Over 63% of the studies relied solely on BMI to define obesity. In only 22% of the studies, body fat was directly measured by methods such as dual energy x-ray absorptiometry. Abdominal obesity was defined using waist circumference in recent years, and waist-hip ratio in earlier years. Inconsistent cut-offs have also been used across studies investigating similar topics.
Conclusions
Few authors acknowledged the limitations of using indirect obesity measures. This is in part due to the limited understanding of some occupational safety and health researchers regarding the complex issues surrounding obesity classification and also the mixed recommendations over the past 2–3 decades and across populations. Efforts need to be made to promote appropriate obesity measurement and reporting in this field.
Keywords
- Obesity
- Overweight
- Body mass index
- Occupational safety and health
- Ergonomics
Background
Obesity affects over 600 million adults worldwide and the number continues to grow [1]. Along with the rise in its worldwide prevalence [2, 3], the evidence for its adverse effects on individuals’ health has been accumulating. Obesity has been identified as a risk factor for cardiovascular disease [4], pulmonary embolism [5], large joint osteoarthritis (OA) [6], and certain types of cancer [7]. It has also been associated with a decrease in general physical function [8], as well as cognitive abilities [9]. The diversity in the adverse outcomes attributed to obesity, the complexity in the mechanisms leading to them, and the multi-factorial nature of this disease require the joint effort of different scientific disciplines to better understand the scope of the problem and to limit its detrimental effects.
With the prevalence of obesity among the workforce being equal to that of the general population [10], the occupational safety and health discipline has shown interest and effectively contributed to obesity research. The effects of obesity on work performance, physical capacity, and physical and cognitive function have been the research focus of many ergonomists, work analysts, and occupational health experts. As such, employees who are obese have been found to have higher rates of sick leave [11] and workplace injuries [12], along with increased employer-paid healthcare costs [13]. As these efforts expand to evaluate the relation between obesity and work [14], it is essential to explore how obesity status is measured in this field (e.g., body mass index (BMI) and body fat percentage (%BF)) as well as the basis for classifying individuals into distinct risk groups (e.g., types I and II obesity). In general, the issue of obesity measurement is two-fold: 1) selection of the appropriate measurement and 2) properly carrying out the measurement process to minimize measurement error.
Measurement selection
The World Health Organization (WHO) defines obesity as abnormal or excessive fat accumulation that may impair health [15], and this definition should serve as the basis for measurement selection. While underwater weighing and dual energy x-ray absorptiometry (DEXA) directly measure body fat, many indirect measures of adiposity have been used to measure obesity status. Anthropometric measures such as the weight-for-height index, BMI, waist circumference (WC), waist–hip ratio (WHR), and body fat percentage estimated by skinfold thickness (ST) are widely accepted indirect measures. Since the 1990s, BMI has been widely used to classify overweight and obesity, both in adults and children [16]. BMI has been suggested as an ideal measure of adiposity since it is easy to measure and is closely associated with obesity related health risks [17].
However, indirect measures such as BMI, fail to distinguish between fat, muscle or bone mass and are prone to misclassification, particularly among muscular subjects [18]. Mullie et al. [19] compared %BF, measured by bipolar bioelectrical impedance analysis (BIA), and BMI, for a cohort of 448 male military candidates and found a statistically significant difference between classifications of normal weight versus overweight. Almost 40% of the subjects classified as overweight with BMI > 25 kg/m2 had a %BF corresponding to normal weight. Similarly, Deurenberg et al. [20] observed a higher rate of misclassification with BMI compared to DEXA in 416 European individuals. This study showcased how individual results based on a single classification method should be interpreted with caution.
Reliance only on BMI can also lead to misclassification of those with excess body fat, but BMIs corresponding to normal weight. These “metabolically obese but normal weight” [21] individuals share many health risks with those categorized as obese both based on BMI and %BF [22]. The elevated visceral fat observed in this category is accompanied by increased levels of both liver and muscle fat [23]. In a workplace study, comparing new industry hires from 1990 to 1992 and from 2000 to 2002, there was no significant difference in BMI but a significant difference in %BF, measured by ST [24]. There were also significant differences in physical fitness as measured by timed sit-ups and squats, suggesting that employers would miss information regarding their employees’ fitness with reliance on BMI only. BMI is also not independently representative of body fatness. Significant dependencies on age and sex were observed in the relation between %BF and BMI in a study of 706 adult men and women [25]. BMI also overlooks the distribution of fat, which is an important factor in disease risk. For instance, android fat distribution (also referred to as abdominal, central, visceral, or upper body fat distribution) causes increased risk of diseases such as cardiovascular disease and type 2 diabetes [26], while gynoid fat distribution (i.e. larger hip and thigh circumferences) does not seem to have similar effects [27]. Indices such as WC and WHR are useful in characterizing the obesity morphology, particularly for studies where a difference in anthropometry among subjects is relevant to consider.
Misclassification and measurement error may be exacerbated in small sample sizes, which are common in exploratory laboratory-based occupational safety and health studies. Piers et al. [28] showed that despite the significant correlation between BMI and %BF (measured by underwater weighing method) of the 117 healthy samples, BMI only explained, on average, 50% of the variance in %BF. The reported poor sensitivity (47.7%) and positive predictive value (67.7%) of BMI makes it an unreliable measure of obesity in individuals. These findings not only suggest the inadequacy of BMI in classification of obesity status, particularly for individuals near the cutoff values, but also point out the importance of a rigorous obesity classification in studies with small sample sizes.
Measurement process and method
After selection of the suitable and hypothesis-relevant obesity measure, it is the researchers’ responsibility to ensure that the measurement guidelines are thoroughly followed to reduce measurement error. For instance, WC is widely accepted as a simple and reliable measure of obesity in general, and abdominal obesity, in particular. There exist guidelines to ensure WC is appropriately measured [29, 30]. However, Agarwal et al. [31] found significant differences in the measured WC across varying anatomical sites, phases of respiration, and time since last meal, when following either the WHO or the National Institute of Health (NIH) guidelines. Overlooking these details can lead to an increase in the measurement error and the steps taken to control them should be acknowledged in publications.
Similarly, the cut-off values used to classify subjects into distinct risk groups are also worth scrutiny. For instance, WHO identifies 25 and 30 kg/m2 as BMI cut points for overweight and obesity respectively. However, it has been shown that among certain populations (e.g., individuals of Asian descent) cardio-metabolic risk is increased at lower body mass indexes [32]. While some researchers advocate using international cut-offs [33], some find nationally and ethnically selected cut points, when available, more advantageous [34]. Overall, inconsistency in the cut-offs used across studies with similar topics is detrimental to the strength of the body of evidence.
This study aims to examine the obesity-related research in occupational health and safety regarding obesity measurement methods. The findings will show how researchers in the aforementioned fields are conducting obesity research and will inform future obesity research in the occupational safety and health domain.
Methods
Research strategy and study inclusion criteria
A systematic review of the PubMed database was undertaken with the following MeSH terms: (‘Work ‘or ‘Ergonomics’ or ‘Biomechanics\Biomechanical’ or ‘Occupational’ or ‘Motion’ or ‘Movement’) and (‘Obesity’ or ‘Skinfold Thickness’). In addition, a keyword search using Google Scholar and manual search of citations from relevant papers and literature reviews was done. The search was limited to journal articles dated between January 1, 2000 and December 31, 2014, published in English and studied human adults. An initial search was performed on 3 March 2015, and repeated on 24 September 2015 to update the search and results.
The study inclusion criteria were: (a) publication contributed to occupational health and safety rather than health promotion and (b) weight status was the independent variable or the major covariate included in the analysis and not the dependent variable. Review papers, simulation-based studies [35], and studies including normal weight subjects loaded with excess weight [36] were also excluded.
Flow Diagram of Study Selection. WHP indicates studies of Worksite Health Promotion that have addressed obesity. Search was also limited to studies that aimed to assess the effect of obesity on occupational or general physical functioning, rather than work-related risk factors for obesity. Studies using simulation software and loaded normal weight subjects were also excluded
Selected studies review process and data extraction
Selected articles were reviewed and the following information was extracted: publication year, country of origin (based on the first author), main research question, design, outcomes measures, subject population, primary method of obesity measurement and the corresponding cut-offs used, whether or not subjects’ weight status was self-reported, additional obesity classification methods applied, the statistical method used, sample size and number of subjects in overweight/obese (OW/OB) subgroup, whether or not the study included women in the sample, the main finding and if the results indicated significance of the obesity (and overweight if applicable) effect, and finally if the authors mentioned any potential limitations of the obesity measurement method they have used. For studies carried out in a controlled lab setting where normal weight and overweight/obese subjects were compared, the mean BMI (or any other primary obesity classification measurement) of the overweight/obese group was extracted.
Analysis
The 37 studies that explored effects of obesity on general job-related outcomes, ordered chronologically
Author, Year, Origin | Study Focus | Study Design | Subjects | Sample size (%OW/OB) | Outcome Variable(s) | Primary Obesity Measure / Other Measure(s) | Significant Obesity Effect | Significant Overweight Effect | Acknowledging Limitations of Obesity Measures | |
|---|---|---|---|---|---|---|---|---|---|---|
General physical or mental work-related functioning | Lee et al.,2001, Australia [48] | Asbestos exposure | Secondary data analysis | Former Australian mine workers | 693 (68%) | Pleural thickening | BMI | + | – | + |
Clark et al.,2002, USA [49] | Duty fitness | Cross-sectional | Active firefighters(white) | 218 (81%) | EKG, VO2 max, METS | BMI | + | + | + | |
Poston et al.,2002, USA [50] | Discharge from training | Prospective cohort | Airmen | 32,144 (19%) | Discharge status | *BMI | – | + | + | |
Arbabi et al.,2003, USA [51] | Crash injury patterns | Secondary data analysis | Hospital admits of car crash | 189 (57%) | Injury Scale and max AIS score, injury severity | BMI | + | – | + | |
Bungum et al.,2003, USA [52] | Healthcare costs, absenteeism | Cross-sectional | Permanent employees | 506 (74%) | Annual healthcare cost, absent days | BMI | + | NA | – | |
Moreau et al.,2004, Belgium [53] | Sick leave | Prospective cohort | Belgium workers | 20,463 (57%) | Sick leave | BMI / WC | + | + | – | |
Pronk et al.,2004, USA [54] | Work performance | Cross-sectional | Current active employees | 683 (43%) | # of work loss days, job performance, extra effort exerted, interpersonal relationships | BMI | + | – | – | |
Laitinen et al.,2005, Finland [55] | Working ability | Prospective cohort | Young adults | 11,637 (19%) | Perceived work ability | BMI / WHR | + | + | + | |
Ricci & Chee,2005, USA [56] | Lost productive time | Cross-sectional | Employed adults | 7472 (58%) | Self-reported lost productive time in past 2 weeks, lost labor costs | BMI | + | – | + | |
Arena et al.,2006, USA [57] | Short-term disability | Retrospective cohort | White collar employees | 1690 (37%) | Frequency + duration of short term disability | BMI | + | + | – | |
Cormier & Israel-Assayag,2006, Canada [58] | Inflammatory response | Retrospective + experimental | Pig farmers + general population | 14 (57%) | Inflammation biomarkers: C-reactive protein, interleukin 6,soluble adhesion molecules, | BMI / *Girth Size | NA | + | – | |
Nishitani & Sakakibara,2006, Japan [59] | Job stress | Cross-sectional | Japanese manufacturing workers | 208 (32%) | Job characteristics, eating behavior, | BMI | + | NA | – | |
Wang et al.,2006, USA [60] | Healthcare costs | Cross-sectional | Manufacturing company employee & spouses | 35,932 (74%) | Medical and pharmaceutical claims | BMI | + | + | – | |
Østbye et al.,2007, USA [61] | Compensation claims, costs, lost workdays | Retrospective cohort | Health care and university employees | 11,728 (56%) | Workers’ compensation claims, associated costs, and lost workdays | BMI | + | + | – | |
Charles et al.,2007, USA [62] | Hemato-logic parameters | Cross-sectional | Police officers | 104 (78%) | White blood cell and platelet counts | BMI / WC,WHR, hip circumference, abdominal height, waist to height ratio | + | + | – | |
Finkelstein et al.,2007, USA [63] | Injuries/ treatment costs | Cross-sectional | General population | 42,304 (62%) | Medically attended injury rates by mechanism and nature and related treatment costs | BMI | + | + | – | |
Jans et al.,2007, Netherlands [64] | Absenteeism | Prospective cohort | Employees in industrial, administrative, and service sectors | 1284 (40%) | Company-reported absenteeism | BMI | + | – | – | |
Gates et al.,2008, USA [65] | Presenteeism | Cross-sectional | Manufacturing company employees | 341 (78%) | Work Limitations Questionnaire | BMI | + | – | – | |
Soteriades et al.,2008, USA [66] | Job disability | Prospective cohort | Firefighters | 329 (88%) | Job disability | BMI | + | – | + | |
Claessen et al.,2009, Germany [67] | Work disability | Prospective cohort | Construction workers | 16,875 (63%) | # of cases | BMI | + | – | – | |
Vissers et al.,2009, Belgium [68] | Whole body vibration | Lab-based | Premenopausal women | 20 (100%) | Ventilation of oxygen, carbon dioxide, heart rate | *BMI / %BF: skinfold thickness | + | + | + | |
Bedno et al.,2010, USA [69] | Heat illness/healthcare utilization | Prospective cohort | Active duty US army members | 9667 (57%) | Heat illness incidence | weight for height / %BF, BMI | + | – | – | |
Robroek et al.,2010, Netherlands [70] | Productivity loss /sick leave | Cross-sectional | Workers | 10,624 (49%) | Sick leave, self-reported productivity loss | BMI | + | – | – | |
Vincent et al.,2010, USA [71] | Fear of movement | Cross-sectional | Patients with knee pain diagnoses | 278 (73%) | Fear of movement, knee function | *BMI | + (only morbid obesity) | – | – | |
Cowan et al.,2011, USA [72] | Training-related overuse injuries | Cross-sectional | Active duty US army members | 7323 (47%) | Musculoskeletal injuries incidence and healthcare utilization | Weight for height / %BF, BMI | + | NA | – | |
Poston et al.,2011, USA [73] | Absenteeism | Cross-sectional | Career firefighters | 478 (19%) | Injury, and injury-related absenteeism | BMI / %BF: BIA, WC | + | + | – | |
Haukka et al.,2012, Finland [74] | Multisite musculoskeletal pain | Prospective cohort | Kitchen workers | 385 (46%) | Multisite musculoskeletal pain (3 and above out of 7) | BMI | + | – | – | |
Caberlon et al.,2013,Brazil [75] | Musculoskeletal pain | Cross-sectional | Obesity treatment patients | 95 (100%) | Musculoskeletal symptoms | *BMI | + | NA | – | |
Gubata et al.,2013, USA [76] | Mental disorders | Prospective cohort | Active duty US army members | 11,369 (40%) | Onset of mental disorder | *Circumference taping / BMI, weight-for-height standard, %BF | – | – | – | |
Jahnke et al.,2013, USA [77] | (Musculoskeletal) injury | Prospective cohort | Firefighters | 301 (0%) | Incident injury, MS injury | BMI / %BF: BIA, WC | + | + | – | |
Kouvonen et al.,2013, UK [78] | Occupational injury | Prospective cohort | Finnish hospital workers | 69,515 (0%) | Occupational injury incident | BMI | + | + | – | |
Lin et al.,2013, USA [79] | Occupational injury | Prospective cohort | Civilian labor force | ~ 7000 (50%) | Injury at work | BMI | + | – | + | |
Roos et al.,2013, Finland [80] | Disability retirement | Prospective cohort | Middle aged employees | 6542 (50%) | Pensions register data & questionnaire | BMI | + | NA | – | |
Van der Starre et al.,2013, Netherlands [81] | Need for recovery | Cross-sectional | Office workers | 412 (42%) | Need for recovery after work | BMI | + | – | + | |
Viester et al.,2013, Netherlands [82] | Musculoskeletal symptoms/recovery | Cross-sectional + longitudinal | Dutch workforce | 44,793, 2nd phase: 7909, (43%) | Musculoskeletal symptoms | BMI | + | +/− | + | |
Gonzales et al.,2014, USA [83] | Cognitive functionality | Lab-based | General population | 73 (67%) | Blood oxygen level-dependent response | WC / BMI | + | NA | – | |
Smith et al.,2014, USA [84] | Mental disorders | Secondary data analysis | Military personnel | 15,195 (61%) | Mental health disorders | BMI | + | + | – |
The 89 studies that explored effects of obesity on task or body part specific functioning, are categorized into 7 groups based on their main focus and ordered chronologically within groups
Author, Year, Origin | Study Focus | Subjects | Sample size (%OW/OB) | Outcome Variable(s)/Method | Primary Obesity Measure / Other Measure(s) | OB BMI: mean(SD)/ range | Significant Obesity Effect | Significant Overweight Effect | Acknowledging Limitations of Obesity Measures | |
|---|---|---|---|---|---|---|---|---|---|---|
Gait Characteristics | DeVita et al., 2003, USA [85] | Lower extremity joint kinetics & energetics | General population | 39 (54%) | Motion analysis, force platform | BMI | 42.3(2.9) | + | NA | – |
Browning et al., 2006, USA [86] | Metabolic rates & energy cost | General population | 39 (49%) | Oxygen consumption, preferred walking speed | BMI / WHR, %BF: DEXA | M:33(2).1 F:33.8(3.3) | + | NA | – | |
Browning & Kram., 2007, USA [87] | Walking biomechanics(knee-joint loads) | Young adults | 20 (50%) | Ground reaction force, gait kinematics | BMI / *segment mass | M:34.1(3.7), F: 37(6) | + | NA | – | |
Lafortuna et al., 2008, Italy [88] | Energetics and cardiovascular responses of walking & cycling | Lean: hospital staff, OB: hospital admits (body mass reduction) | 21 (71%) | HR, Vo2 max, metabolic rate | BMI / %BF: BIA | 41.1(5) | + | NA | – | |
Lai et al., 2008, China [89] | Three-dimensional gait characteristics | General population | 28 (50%) | Motion analysis | BMI | 33.06(4.2) | + | NA | – | |
Browning et al., 2009, USA [90] | External mechanical work | Young adults | 20 (50%) | Ground reaction force | BMI | M:34.1(3.7), F:37(6) | – | NA | + | |
Malatesta et al., 2009, Switzerland [91] | Mechanical external work | General population | 49 (61%) | Center of mass displacement, mechanical external work, kinetic energy transduction | BMI | 39.6(0.6) | – | NA | – | |
Ko et al., 2010, USA [92] | Characteristics of gait | Older adults enrolled in aging research | 164 (66%) | Motion analysis, force platform | BMI | + | +/− | – | ||
Russell et al., 2010, USA [93] | Energy expenditure & biomechanical risk factors for knee OA | Young adults | 20 (50%) | O2 uptake, peak impact shock, peak external knee adduction moment knee adduction angular impulse | BMI | 33.09(4.22) | – | NA | – | |
Blaszczyk et al., 2011, Poland [94] | Basic spatiotemporal gait measures | General population + outpatient obesity treatment clinic | 136 (74%) | Stance & swing time, stride length | BMI | 37.2(5.2) | + | NA | + | |
Ehlen et al., 2011, US [95] | Energetics and biomechanics of gait | General population | 12 (100%) | Oxygen consumption, ground reaction forces, & three-dimensional lower-extremity kinematics | BMI / %BF: DEXA | 33.4(2.4) | NA | NA | – | |
Cimolin et al., 2011, Italy [96] | Gait pattern | Obese: admits to obesity multidisciplinary rehabilitation program | 28 (64%) | Gait Spatio-temporal parameters & kinematics | BMI / WC | OB + LBP: 42.4(5.5), OB - LBP: 39.3 | + | NA | – | |
Russell & Hamill., 2011, US [97] | Obesity × laterally wedged insole effect on gait kinetic and kinematic | Young females | 28 (50%) | Peak joint angles, external knee adduction moment & angular impulse | BMI / %BF: DEXA | 37.2(6.1) | + | NA | – | |
Wu et al., 2012, USA [98] | Gait adaptations & implication on risk of slip initiations | Young male students | 10 (50%) | Motion analysis, force plate | %BF from BIA / BMI | 33.7(2.8) | +/− | NA | – | |
Harding et al., 2012, Canada [99] | Knee OA × obesity effect on knee joint mechanics | General population + orthopedic clinic admits | 244 (72%) | Knee joint angles, joint moment | BMI / *thigh and calf circumference | 34.9(4) | + | + | + | |
Russell et al., 2013, USA [100] | Laterally wedged insoles × obesity effect on knee joint contact force | General population | 28 (50%) | Center of pressure on the tibial plateau | BMI / %BF: DEXA | 37.2(6.1) | NA | NA | – | |
Browning et al., 2013, USA [101] | Metabolic rate, stride kinematics & external mechanical work | young females | 37 (49%) | Oxygen uptake, ground reaction force, lower extremity kinematics | BMI / %BF: DEXA, *Trunk-to-leg fat mass ratio | 33.9(3.6) | – | NA | – | |
Ranavolo et al., 2013, Italy [102] | Walking coordination during walking | General population | 50 (50%) | Motion analysis | BMI / WC, %BF: Siri equation | Range(33.8–44) | + | NA | – | |
Vismara et al., 2014, Italy [103] | Changes in gait | General population | 32 (44%) | Motion analysis | BMI | 40.2(3.3) | + | NA | – | |
Haight et al., 2014, USA [104] | Compressive tibio-femoral forces | General population | 19 (47%) | Motion analysis (lower extremity biomechanics), EMG | BMI / %BF: DEXA | 35(3.8) | +/− | NA | – | |
Glave et al., 2014, USA [105] | Gait alterations | General population | 22 (50%) | Gait variables | BMI / %BF: DEXA | 31.42(7.3) | + | NA | + | |
Cau et al., 2014, Italy [106] | Gait strategy | Hospital patients for weight reduction programs & staff | 35 (57%) | Center of pressure parameters | BMI | 43(4.9) | + | NA | – | |
Lerner et al., 2014, USA [107] | Joint kinematics & individual muscle forces during gait | General population | 19 (47%) | Motion analysis, EMG data, ground reaction force | BMI / lean mass (kg): DEXA | 35(3.78) | + | NA | – | |
Disease Prevalence/Incidenc | Kouyoumdjian et al., 2000, Brazil [108] | Severity of Carpal tunnel syndrome | Carpel tunnel syndrome patients | 384 (13%) | Case - control study | BMI | + | NA | – | |
Young et al., 2001, USA [109] | Asthma risk | Military population and their families (17-69 yrs) | 38,924 (53%) | Case - control study | BMI | + | + | – | ||
J. D. Bland., 2005, UK [110] | Age × body mass index effect on carpel tunnel syndrome risk | Hospital admits | 4166 (14%) | Self-report CTS diagnosis | BMI | + | + | – | ||
Liuke et al., 2005, Finland [111] | Prevalence and progression of lumbar disc degeneration | Employed middle-aged men | 129 (50%) | Prospective cohort: MRI imaging | BMI | NA | + | – | ||
Dagan et al., 2006, Israel [112] | BMI as a screening method for detection of excessive daytime sleepiness | Professional drivers | 153 (100%) | Sleep characteristics | BMI | 36.78(7.32) | + | NA | – | |
Zhao et al., 2007, USA [113] | Osteoporosis | Chinese general population + US Caucasian general population | 6477 (0%) | Bone mass at the lumbar spine, total body bone mineral content | BMI / %BF: DEXA | + | + | + | ||
Sharifi-Mollayousefi et al., 2008, Iran [114] | BMI as independent risk determinants in the development and severity of Carpal tunnel syndrome | Patients with carpal tunnel syndrome (cases) and their relatives (controls) | 262 (50%) | Case-control study | BMI | + | NA | – | ||
Grotle et al., 2008, Norway [115] | OA incident in hip, knee, and hand | General population | 1675 (35%) | Prospective cohort: OA diagnosis | BMI | + | +/− | – | ||
Noorloos et al., 2008, Netherlands [116] | Obesity × whole body vibration effect on risk of LBP | Occupational vehicle drivers | 214 (69%) | Low back pain | BMI | – | – | – | ||
Toivanen et al., 2010, Finland [117] | Knee OA risk | Finnish adults aged 530 years | 823 (39%) | Prospective cohort: OA diagnosis | BMI | + | + | – | ||
Vismara et al., 2010, Italy [118] | LBP incidence | General population | 37 (70%) | Trunk angle during standing, forward flexion & lateral bending | BMI | LBP:41.9(5.3),Non:39.2(3.6) | + | NA | – | |
Wood et al., 2011, USA [119] | Pain experienced by persons with chronic back pain | Patients with lower back pain of over 3 months | 198 (62%) | Blood pressure, pain level | BMI | – | – | – | ||
Ackerman & Osborne., 2012, Australia [120] | Burden of hip & knee joint disease | General population | 1157 (55%) | OA diagnosis | BMI | + | + | – | ||
Jensen et al., 2012, Denmark [121] | LBP risk factor | Newly educated health care helpers | 1355 (41%) | Prospective cohort: Self-reported levels of LBP | BMI | 34.8(6.08) | – | – | – | |
Silvernail et al., 2013, USA [122] | Biomechanical risk factor for knee OA | Yong university and community members | 30 (67%) | Gait kinetic & kinematics | BMI / %BF: BIA | 34.4(3.9) | – | – | – | |
Seror & Seror., 2013, France [123] | Incidence of idiopathic median nerve lesion at the wrist | Patients with carpal tunnel syndrome | 676 (25%) | Electrophysiological evaluation outcomes | BMI | + | + | – | ||
Martin et al., 2013, USA [124] | Knee OA risk factor | British birth cohort participants | 2957 (0%) | Knee Osteoarthritis | BMI (z-score) | + | NA | – | ||
Romero-Vargas et al., 2013, Mexico [125] | Modifications on spino-pelvic parameters & type of lumbar lordosis | General population | 200 (80%) | Spino-pelvic values | BMI / WC | – | – | + | ||
Messier et al., 2014, USA [126] | Frontal plane knee alignment × obesity effect on knee joint loads in knee OA | Community dwelling older adults (age > 55 yrs) | 157 (100%) | knee osteoarthritis: X-ray at baseline | BMI | 33.4(3.7) | + | + | – | |
Urquhart et al., 2014, Australia [127] | Occupational activities × obesity effect on LBP | General population + weight loss clinic attendees | 145 (61%) | Low back pain intensity & disability | BMI | + | NA | – | ||
Evanoff et al., 2014, France [128] | Physical occupational exposures × obesity effect on post-retirement shoulder/knee pain | French national power utility employees | 9415 (52%) | Retrospective cohort: self-administered questionnaires | BMI | + | – | – | ||
Functional Capacity | Hulens et al., 2001, Belgium [129] | Submaximal & maximal exercise capacity | General population | 306 (74%) | Oxygen uptake, carbon dioxide production, respiratory quotient, breathing efficiency, mechanical efficiency & anaerobic threshold | BMI / %BF: BIA | 38.1(5.6) | + | NA | – |
Hulens et al., 2002, Belgium [130] | Peripheral muscle strength | Outpatient Endocrinology Clinic patients | 241 (100%) | Trunk strength, peak oxygen consumption | BMI / Fat free and fat mass: BIA | 37.5(5.4) | NA | NA | – | |
Maffiuletti et al., 2007,Switzerland [131] | Voluntary & stimulated fatigue of the quadriceps femoris muscle | Lean: hospital staff, obese: hospital admits for body mass reduction | 20 (50%) | Maximal voluntary isometric & isokinetic torque, torque loss | BMI / Fat free mass: BIA | 41.3(5.4) | + | NA | – | |
Segal et al., 2009, USA [132] | Forces on the medial compartment of the knee joint | General population | 59 (68%) | knee joint forces | BMI / WHR | Central: 35(4), lower body: 36.4 (5.4) | +/− | NA | + | |
Capodaglio et al., 2009, Italy [133] | Lower limb muscle function | General population | 40 (50%) | Isokinetic strength during knee flexion & extension | BMI | 38.1(3.1) | + | NA | + | |
Singh et al., 2009, USA [134] | Maximum acceptable weights of lift | General population | 60 (67%) | MAWL | BMI / WC,WHR,%BF estimated: ST | II: 37.13(1.58) III:47.84(9.85) | – | NA | – | |
Faria et al., 2009, Portugal [135] | Muscle–tendon unit stiffness | General population | 105 (77%) | Ankle muscle–tendon unit stiffness at 30% MVC | BMI | 32.1(1.3) | + | + | – | |
Park et al., 2010, USA [136] | Joint RoM | Young and university affiliated | 40 (50%) | RoM | BMI | 44(7.4) | +/− | NA | – | |
Blazek et al., 2013, USA [137] | Age × obesity effect on Knee adduction and flexion moments | General population | 96 (38%) | Ground reaction force magnitude, knee alignment, step width, toe-out angle, limb position | BMI | 35.3(3.9) | + | NA | – | |
Cavuoto & Nussbaum., 2013, USA [138] | Age × obesity effect on shoulder capacity | Young: students, old: retired or employed in non-physically demanding jobs | 32 (50%) | Endurance, discomfort, motor control, task performance | BMI / WC, WHR | Young: 34.1(2.8), Old: 36.4(3.3) | + | NA | + | |
Hamilton et al., 2013, USA [139] | BMI × workstation configuration effect on joint angles | General population | 30 (80%) | Joint angle, forward functional reach | BMI | I: 32(1.26) II:37(1.73) III:44(4.97) | – | – | – | |
Mignardot et al., 2013, France [140] | Motor control behavior | General population | 20 (60%) | Kinematic variables, Center of mass displacement characteristics | BMI | 36.6(3.3) | + | NA | – | |
Wearing et al., 2013, Australia [141] | Resistance exercise × obesity effect on immediate transverse strain of the Achilles tendon | University faculty | 20 (50%) | Sonographic examinations | BMI | 30(3.1) | + | + | + | |
Cavuoto & Nussbaum., 2013, USA [142] | Strength and functional performance | Local community | 36 (50%) | Endurance time, strength | BMI / WC,WHR | 33.6(3.1) | +/− | NA | + | |
Cavuoto & Nussbaum., 2014, USA [143] | Age × obesity effect on functional performance | General population | 32 (50%) | endurance, discomfort, motor control, task performance | BMI / WC,WHR | Young: 34.3(4), Old: 35.9(3.6) | + | NA | + | |
Mehta & Cavuoto., 2015, USA [144] | Obesity × age effects on handgrip endurance | General population | 45 (44%) | hand grip endurance | BMI | Young: 33.1(3.6),Old:36.1(8.1) | +/− | NA | – | |
Balance & Plantar Pressure | Hills et al., 2001, Australia [145] | Plantar pressure | General population | 70 (50%) | Pressure distribution | BMI | 38.75(5.97) | + | NA | + |
Gravante et al., 2003, Italy [146] | Centre of pressure location & plantar pressures | General population | 72 (53%) | Centre of pressure location, plantar ground contact surface areas & pressures | BMI / WHR | M:36(7.4), F:38(6.8) | +/− | NA | – | |
Birtane & Tuna., 2004, Turkey [147] | Plantar pressure distribution | General population | 50 (50%) | Pedobarographic evaluations | BMI | 32.2(2) | + | NA | – | |
Berrigan et al., 2006, Canada [148] | Balance control constraint during accurate and rapid arm movement | General population | 17 (53%) | Body kinematics, center of pressure, displacement, reaction time, movement time | BMI | 37(6.6) | + | NA | – | |
Teh et al., 2006, Singapore [149] | Pressure distribution under the feet | General population | 120 (42%) | Plantar pressure distribution | BMI | I: 34.3 II: 38.9(3.6) | +/− | NA | – | |
Singh et al., 2009, USA [150] | obesity × task duration effect on postural sway and functional reach | Students & sedentary office workers | 20 (50%) | Posture sway, functional reach | BMI / WHR | 45.96(7.85) | + | NA | – | |
Blaszczyk et al., 2009, Poland [151] | Postural control | Obesity treatment clinic patients | 133 (75%) | CP measures: voluntary displacement, path, range | BMI / %BF: BIA, WC,HC | 37.2(5.2) | – | NA | – | |
Park et al., 2009, USA [152] | Postural stress during static posture maintenance | General population | 40 (50%) | Rated perceived exertion | BMI / WHR,%BF estimated: ST | 46.26(4.99) | + | NA | – | |
Menegoni et al., 2009, Italy [153] | Static posture variability | Orthopedic Rehabilitation Unit patients and staff (control) | 54 (81% | Center of pressure velocity & displacements along the antero-posterior & medio-lateral axis | BMI | M:40.2(5), F: 41.1(4.1) | + | NA | + | |
Monteiro et al., 2010, Portugal [154] | Plantar pressure | Postmenopausal women | 239 (79%) | Foot-scan pressure plate | %BF from BIA / BMI | 29.6(3.2), 36.4(3.8) | +/− | NA | – | |
Miller et al., 2011, USA [155] | Balance recovery from small forward postural perturbations | Young adults (22 years old) | 20 (50%) | Peak COM displacement, peak COM velocity, peak ankle torque | BMI | 33.2(2.3) | – | NA | – | |
Matrangola & Madigan., 2011, USA [156] | Balance recovery using an ankle strategy | Young males | 20 (50% | Body angle, ground reaction force | BMI | 32.2(2.2) | +/− | NA | – | |
Peduzzi de Castro et al., 2014, Portugal [157] | Pressure relief insoles | General population | 31 (32%) | Ground reaction force, plantar pressure | BMI | 36.5(4.51) | + | NA | – | |
Task Functionality | Galli et al., 2000, Italy [158] | Motion strategies: sit-to-stand | General population + obese subjects suffering from chronic lower back pain | 40 (75%) | Movement kinetics & kinematics | BMI | 40(5.9) | + | NA | – |
Sibella et al., 2003, Italy [159] | Biomechanical model: sit-to-stand | Hospital recovers | 50 (80%) | Trunk flexion, feet movement, knee & hip joint torques | BMI | 37.9(4.9) | + | NA | – | |
Lafortuna et al., 2006, Italy [160] | Energy cost of submaximal cycling | Lean: hospital staff, obese: hospital admits for body mass reduction | 18 (50%) | Oxygen uptake, Vo2 max, anaerobic threshold, mechanical efficiency | BMI / %BF: BIA | 40(1.2) | + | NA | – | |
Gilleard & Smith., 2007, Australia [161] | Postural adaptations: trunk forward flexion motion in sitting and standing | General Population | 20 (50%) | Trunk flexion motion during forward flexion, trunk posture, hip joint moment | WC / BMI | 38.9(6.6) | +/− | NA | – | |
Xu et al., 2008, USA [162] | Lifting kinematics & kinetics | College students | 12 (50%) | Motion analysis | BMI | 33.28 (30.4–38.8) | – | NA | + | |
Taboga et al., 2012, Italy [163] | Mechanical work, energy cost of transport, and efficiency: running | Hospital admits-adults from metabolic disorders | 25 (40%) | Oxygen uptake, kinematics, center of mass location | BMI / %BF: BIA | 41.5(5.3) | + | NA | – | |
Hendrick et al., 2012, USA [164] | Neural processes of cognitive control: stop signal test | General population | 43 (30%) | Functional magnetic resonance imaging | BMI | 33.2(2.6) | + | NA | + | |
Singh et al., 2013, USA [165] | Contact forces & moments exerted by the abdomen on the thigh: seated reaching | Older adults | 10 (100%) | Motion analysis, force plate | BMI / WC | 39.04(5.02) | +/− | NA | + | |
Schmid et al., 2013, Switzerland [166] | Kinetic & kinematic variables: sit-to-stand test. | Going to attend a weight loss program at hospital | 36 (72%) | Vertical ground reaction forces, rising velocity (motion analysis) | BMI | I: 32.68(1.53), II: 39.42(2.71) | – | +/− | – | |
Pysiological Responses | Willenberg et al., 2010, Switzerland [167] | Venous flow parameters of the lower limbs | Students and medical staff | 45 (49%) | Venous hemodynamics: Diameter, flow volume, peak, mean, & minimum velocities | BMI / WHR, WC | 36.2(5.9) | + | NA | + |
Engelberger et al., 2014, Switzerland [168] | Diurnal leg volume increase | Obese subjects: weight management clinic patients, general population | 39 (62%) | Common femoral vein diameter, peak flow velocity, mean velocity & minimal velocity | BMI / WHR | 40.2(5.9) | + | NA | – | |
Yang et al., 2015, China [169] | Acute high-altitude exposure | Chinese railroad construction workers | 262 (46%) | Acute mountain sickness | BMI | 29.9(3.8) | – | NA | – | |
Miscellaneous | Menegoni et al., 2007, Italy [170] | Clinical protocol to characterize the trunk movements | Lean: hospital staff, obese: hospital admits for diet therapy and exercise classes | 20 (50%) | BMI | 38.7(3.5) | NA | NA | – | |
Forman et al., 2009, USA [171] | Restraint of automobile occupants | Post mortem human surrogates | 5 (40%) | Chest deformation, acceleration, tension in the restraint system, etc. | BMI | 40 | + | NA | – | |
Lerner et al., 2014, USA [172] | Obesity-specific kinematic marker set to account for subcutaneous adiposity | General population | 18 (50%) | Ground reaction force, walking kinematics, EMG | BMI | 35(3.78) | NA | NA | – | |
Thorp et al., 2014, Australia [173] | Standing workstations effect on fatigue, musculoskeletal discomfort & work productivity | Middle-aged sedentary employees | 23 (100%) | Self-reported fatigue, musculoskeletal discomfort, work productivity | BMI | 33.7(4.3) | + | + | – |
Results
Over the period of the review, there was an increasing interest in obesity research
Category 1: General physical or mental work-related functioning
The origin of the studies. As expected, North America had the highest number of publications in both GF and TBS categories
The obesity measurement methods used in studies. Regardless of the study category, BMI was the most frequently used method of obesity measurement
Category 2: Task or body part specific functioning
The majority of the reviewed studies (89) investigated a wide range of specific effects of obesity (see Table 2). North America and Europe contributed by 45 and 39% of such studies, respectively (see Fig. 3). Authors from Italy in particular contributed 16% of the publications, ranking higher than Asia and Australia, with five and six studies respectively. It should be noted that region of origin did not systematically affect the measurement approach used. Although the majority of the studies in this category were laboratory-based observational studies, BMI was still the most frequently used measure, with 59% of the studies relying solely on it to distinguish obese from non-obese. Study topics varied broadly, however, they were categorized in seven groups based on their main hypothesis and research focus (shown in Table 2). These groups, ordered based on number of studies, are discussed in more details as follows.
The obesity measurement methods used across the 7 sub-categories of studies that explored effects of obesity on task or body part specific functioning. With the exception of studies in gait categories, anthropometrics, in particular BMI, were still the most frequently used measures of obesity
The next largest group focused on the prevalence, incidence, burden, and changes in symptoms of diseases such as carpel tunnel syndrome, osteoarthritis, low back pain (LBP), asthma, and sleep disorders in association with obesity. This category included some large scale public health studies, hence there was more diversity in terms of study design. The median sample size was 384 (30–38,924). With the exclusion of two studies that did not report the proportion of OW/OB, on average 54 (24) % of the samples were obese or overweight. Six studies used patients and hospital admits as participants and six studies reported subjects belonging to a certain occupation. Eighteen studies relied solely on BMI, two added %BF and one added WC. Sixteen studies failed to report the obesity class of the obese group. In the four that did, all but one had mean BMI ≤ 35 kg/m2. Only two studies, which both had one additional obesity measure, mentioned the inadequacy of BMI.
Changes in functional capacity were the topic of 16 studies. Functional capacity encompasses all topics related to muscle strength, endurance, functional reach, range of motion (RoM), and motor control behavior. Participants in two studies were outpatient clinic or hospital patients (endocrinology and body mass reduction admits) and the rest were recruited from the general population. Eight studies used BMI as the primary and only obesity measure, while three studies also measured body fat. Four studies augmented BMI with other anthropometric measures and one study reported using four obesity measures including both direct and indirect. While no studies relied on self-reported height and weight data, three studies used cut-offs other than 25 kg/m2 and 30 kg/m2 to classify subjects into distinct groups. Only three studies had an overweight sample as well as obese. The median sample size was 40 (20–306). On average ~ 60% of the sample were OW/OB and the majority of reported mean BMI values were in the range of 35–40 kg/m2. One Australian study in particular, which used BMI and cut-off values of 23 kg/m2 and 27.5 kg/m2, had mean BMI of 30 kg/m2 for obese. All but three studies reported some significant obesity effect and two reported significant overweight effect. Authors of six studies, out of which five had used multiple anthropometric measures, included a mention of BMI’s limitation as a measure of obesity.
Issues related to balance, postural stability, and plantar pressure were discussed by 13 lab-based studies. Subjects were recruited from the general population in all but three studies, two of which had sampled from orthopedic rehabilitation and obesity treatment clinic patients. In one study, %BF was the primary obesity measurement used, but eight studies used BMI as the primary and only measure. Two other studies used both %BF and other anthropometric measures, and two studies used both BMI and WHR. It is noted that the two studies with the largest sample sizes used %BF measured by BIA. One included obesity clinic patients and the subjects in the other were part of a health promotion program for postmenopausal women. The median sample size among studies using only BMI was 40.5 (17–120) out of which on average 51(14) % were obese. No study in this sub-category included overweight subjects. Two studies, one testing Canadian and one testing Portuguese subjects reported using BMI cut-offs other than 25 and 30 kg/m2. With the exception of three studies, the average BMI reported for subjects was above > 35 kg/m2 and in four studies the mean BMI was > 40 kg/m2. In terms of significance of the obesity effect, two study reported no significant effect and four reported some but not all outcome measures to be significantly affected by obesity. Only two studies had a mention of inadequacy of BMI, and they both used BMI only.
The effects of obesity on functionality while performing specific occupationally-relevant tasks was investigated by nine lab-based studies. All but one study tested physical tasks such as the sit-to-stand movement, lifting, seated reach, cycling, and running. The remaining study focused on cognitive control. Three studies published by Italian authors tested hospital admits or recovering patients for body mass reduction or metabolic disorders. One study from Switzerland also recruited from individuals who were going to participate in a weight loss program at a hospital. Five studies relied on BMI only, while two added %BF measured by BIA and two added WC. The average sample size was 28.2 (SD = 14.5) and 60% of the included sample were obese. Only one study had an overweight group as well as obese. With the exception of two studies, the reported mean BMI for obese group was > 35 kg/m2. Five studies observed a significant effect of obesity on the performance of the specific tasks tested, while four reported no or partial effect. Three authors discussed how BMI is not the ideal obesity measure although only one used WC in addition.
Three studies discussed changes in physiological responses by obesity and the topics of four studies were not closely pertinent to the above mentioned subgroups. Details of these studies were reported in Table 2.
Discussion
Researchers worldwide have investigated the effect of obesity (sometimes including overweight) on a wide range of occupationally-relevant outcomes. Experts from diverse disciplines, including but not limited to, public health, medicine, health sciences and engineering, have contributed to our current understanding of the magnitude of an effect of obesity at work [37]. The diversity of scientific disciplines involved in obesity research has both advantages and disadvantages. It allows for more complex aspects of the obesity effect to be revealed by diverse methodologies. However, it increases the risk of misuse of methods due to unfamiliarity. In particular, the investigators’ understanding of obesity and the methods to measure it and classify individuals into distinct risk groups can affect the quality of the findings.
The present study focused primarily on examining the use of various obesity measurement methods and secondarily on sampling strategies. Two categories of publications were considered: those investigating the effect of obesity on occupational disease development or business outcomes and those studying how obesity alters task-level performance or functional capacity. As expected, studies in the first category had large sample sizes and were mostly public health studies, carried out by public health professionals. While the samples mostly consisted of participants from the general population or a certain occupation, the large sample sizes justified the use of BMI as the sole obesity measure in over 70% of these studies. It is noted that the vast majority of the publications in this category failed to report descriptive statistics regarding the obesity status of the obese group included in the sample. This could serve as a critical source of information for comparative analyses. The WHO expert consultation [38] suggests that wherever possible, researchers should use all BMI categories for reporting purposes, in order to facilitate international comparisons (i.e., 18.5, 20, 23, 25, 27.5, 30, 32.5 kg/m2, and in many populations, 35, 37.5, and 40 kg/m2).
Another issue with studies in the general category is in regard to abdominal obesity. It is often defined using waist circumference, especially in recent years, while waist-hip ratio was often used in earlier years. However, various cut points have been recommended over time, by different health organizations and across countries, and used across studies. Abdominal obesity is a major component of metabolic syndrome, a cluster of metabolic abnormalities that carry an increased risk of cardiovascular diseases and diabetes [39]. However, there is a subset of the obese population that are metabolically healthy and their inclusion in study samples can confound the results. Ortega et al., [40] studied a large cohort of 43,265 individuals and reported that when adjusting for fitness and other confounders, metabolically healthy but obese individuals had lower risk of all-cause mortality, non-fatal and fatal cardiovascular disease, and cancer mortality than their metabolically unhealthy obese counterparts. In their study, over 46% of the obese sample were metabolically healthy. From the reported exclusion criteria in the studies reported here, it cannot be decided whether obesity would have the same effects in the absence of other components of metabolic syndrome, particularly for outcomes such as healthcare cost, job disability, absenteeism, and presenteeism.
The studies in the second category focused on task-level performance or functional capacity. There are three main points of discussion identified for these studies: 1) selection of obesity measurement(s) (e.g. BMI, WC, %BF) and the corresponding cut-points to distinguish obese from non-obese, 2) the study participants, both in terms of sample size and the population targeted (e.g. young adults, certain occupation groups, hospital admits), and 3) measurement considerations (e.g. site of measurement for WC). While these factors are all individually important, their interaction may also present a challenge to studies. For instance, when using BMI in a study with a small sample size, recruiting only young adults may be more problematic [41, 42] than when a larger group of older adults are classified based on BMI.
In this category, BMI was still the most frequently used obesity classification measure. Overall, the selection of an obesity measure should depend on the hypothesized mechanism by which the obesity effect would manifest. While obesity presents by both changes in anthropometry and metabolic function, acknowledgement of the considered causal pathway is advantageous to study rigor. Also, while obesity morphology may not be as crucial to the outcomes in the studies of the previous category, it is highly relevant to the dependent variables investigated by the studies in this category. In particular, balance and gait parameters are likely to be affected by the distribution of weight in the body, therefore not only obesity status, but also fat distribution needs to be taken into account. BMI by itself fails to do so, however other anthropometric measures such as WC and WHR are able to distinguish central obesity from lower body and general obesity. Across the 36 studies in the two aforementioned sub-categories, only 10 studies used additional anthropometric measures.
Caution should be made in the use of BMI in studies with small samples that include young adults. Statistically significant age dependencies have been reported in the relation between %BF and BMI, such that older adults have higher %BF compared with younger adults with comparable BMIs [25]. WHO expert consultation acknowledges the issue by stating that most studies show the relation between BMI and %BF to be dependent on age and sex, and also different across ethnic groups. Experts affirmed that Asian populations have different associations between BMI and percentage of body fat than do Western populations [38], however, due to lack of comprehensive data from all Asians, they suggested retaining WHO BMI cut-off points as international classifications. Using ethnic-specific cut-offs may come at the expense of consistency among studies. As such, we observed two studies from Portugal in the balance sub-category that participants were recruited from the general population, one using a BMI cut-off of 25.5 kg/m2 and the other using 30 kg/m2to distinguish obese from non-obese. Arbitrary grouping of subjects, not backed up by ethnic or other expected underlying differences, as was the case in these two studies, should also be minimized. Overall, it is alarming that only 20% of the studies in this category acknowledged the aforementioned shortcomings of BMI as the obesity measure.
The majority of studies in this category (~ 80%) were observational studies. To isolate an obesity effect, and in contrast to the majority of studies in the first category, subjects were selected such that they were mostly otherwise healthy. The representativeness of this group and the extent to which the findings based from them can be generalized to the overall obese population is concerning. This exclusion of obese with comorbidities from the study samples in this category and their possible inclusion in samples of the first category may contribute to the higher proportion of publications in the first category to report a significant obesity effect in comparison to the second category.
Another issue with the sample representativeness is including only severe obesity (classes II (BMI 35–39.9 kg/m2) and III (BMI ≥ 40 kg/m2). While this practice may be statistically sound and increase the likelihood of capturing the obesity effect, it again limits the generalizability of the result. For instance, in the United States the prevalence of obesity is estimated to be over 35% but less than 15% of the obese population (~ 5% of the total population) are categorized as class II and less than 7% (~ 2.5%) as class III [43].
There are considerations for proper use of each measurement as well. WC for instance is shown to be significantly different across sites of measurement, postures, phases of respiration, and time since last meal [31]. By following the existing measurement guidelines [44] studies are less prone to error and consistency across subjects and studies is also warranted. Also, practices such as having a single trained staff doing all the measurements when possible, keeping the measurement conditions homogenous across all subjects and using multiple measurements are beneficial for internal validity and worthy of report in research manuscripts.
Overall, we assessed obesity research in the occupational health field and showcased the practices of obesity measurement since 2000. The present study has many strengths, but also some limitations. While obesity has become a global epidemic, this review was limited to PubMed database as well as Google Scholar journal articles available in English, primarily due to the authors’ time and language proficiency constraints. Also, studies related to health promotion at work were excluded [45, 46]. Health risk assessment is a common part of these programs in which obesity status is commonly assessed as a health risk, however the topic of these studies were beyond the scope of this review. Moreover, although the effect of certain work types, such as shift work on the onset of obesity among workers is of importance and has been widely studied [47], this review focused on the obesity effect on occupationally-relevant outcomes.
Conclusion
Obesity is a serious global public health threat. In order to build up a comprehensive profile of its effects, it is crucial to have easy-to-use yet reliable measures that allow for classification of individuals into distinct risk groups. A large body of research has been conducted in the occupational health field regarding obesity. Use of indirect measures such as BMI may be justifiable in large scale public health studies due to their ease of use and low cost. However, due to limitations of these measures, cautious use of them is suggested as the sole obesity measure in small-scale observational studies.
Abbreviations
- %BF:
-
Body Fat Percentage
- BIA:
-
Bioelectrical Impedance Analysis
- BMI:
-
Body Mass Index
- DEXA:
-
Energy X-ray Absorptiometry
- GF:
-
General Physical or Mental Work-Related Functioning
- LBP:
-
Low Back Pain
- NIH:
-
National Institute of Health
- OA:
-
Osteoarthritis
- OW/OB:
-
Overweight/Obese
- RoM:
-
Range of Motion
- ST:
-
Skinfold Thickness
- TBS:
-
Task or Body Part Specific Functioning
- WC:
-
Waist Circumference
- WHO:
-
World Health Organization
- WHR:
-
Waist–Hip Ratio
Declarations
Funding
Funding provided by the Grant or Cooperative Agreement Number, 1 R03 OH 010547–01, funded by the Centers for Disease Control and Prevention. YW’s related effort in this study was supported in part by research grants from the National Institute of Health (NIH, U54 HD070725; 1R01HD064685-01A1). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the funders.
Availability of data and materials
The datasets generated during the current study are available from the corresponding author on reasonable request.
Authors’ contributions
MGS participated in the design of the study and all the phases of the systematic review and drafted of the manuscript. LAC participated in the design of the study and provided oversight of the review and reviewed the draft of the manuscript. YW provided insight on the analysis of the results and reviewed the draft of the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
No IRB approval was sought for this study since it did not involve human subjects.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
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