Musculoskeletal Symptoms Among Farmers

Nathan Fethke, PhD  –  Research Team

Overview

Despite the large size of the agricultural workforce and the elevated prevalence of musculoskeletal pain compared to other industries, only a small number of studies have examined associations between agricultural activities and musculoskeletal pain. Inferences from these studies are limited by several methodological weaknesses. This study of musculoskeletal pain among farmers obtained information every six months (Spring 2012 through Spring 2015) using questionnaires completed by farmers in IL, IA, KS, MN, MO, NE, ND, SD, WI). The questionnaires allow us to track (over time) information about demographics, personal health (including musculoskeletal pain), occupational psychosocial stress, farm characteristics, and farm-related tasks and equipment. In addition, our field research staff made 99 trips to farm operations to obtain direct measures of exposure to physical risk factors for musculoskeletal pain, such as working postures, muscle activity levels, and vibration (during use of farm equipment and vehicles). We are combining the questionnaire information with the measured exposure information to better characterize associations between agricultural activities and musculoskeletal pain and provide a robust evidence-base from which to design future interventions.

 

Relevance

Musculoskeletal pain is a common work-related health outcome among farmers. This research study determines more precise associations between agricultural work and musculoskeletal pain by using direct measures of physical work hazards. Prospective observation of a large number of agricultural workers provides a strong and direct study design that provides a more complete understanding of associations between exposure to physical risk factors and musculoskeletal symptoms among agricultural workers. Moreover, we expect that this research will  provide an evidence base for the development of new intervention strategies to reduce the number and severity of musculoskeletal outcomes among this large population of workers.

 

Study Progress and Findings

Questionnaires

We enrolled 518 subjects at baseline to complete the study. The study participants are slightly older, have a higher income and greater number of acres of land than the representative population comprising the nine states of the Great Plains Region. Of these 518, 25 (4.8%) were not eligible to participate in the follow-up portion due to farming status (i.e. no longer farming, etc.).  We completed six rounds with response rates averaging about 68%.

StudyPartRepresentation

Geographical location of the 518 participants in the study.

 

Typical Commodities of Study Participants

Data from the baseline questionnaire shows the types of commodities study participants produce.

Physical Work Load Measures

We have also completed collection of on-farm measures of physical work exposure on 55 farmers. The farms are representative of a variety of production commodities including dairy, beef cattle, hogs, horses, poultry, sheep, goats, corn, soybeans, hay, alfalfa, poultry, tree harvesting, specialty items (heirloom seeds), specialty poultry, etc. During the farm visits, an inventory of farm equipment that is used is conducted.

SensorMyomonitor          Rion

Sensor that obtains posture data – worn like a watch on study participant (left) ; data logger that remotely records muscle activity (middle); sensor on vehicle seat that measures vibration (right)

The processing of the data obtained from the on-farm measures is proceeding. It is an extensive process to reduce the voluminous amount of data into a quantifiable form. An example of the vibration data is shown below.

Normalization

Obtaining “normalized” measures of muscle activity

Raw Vib Data

Raw vibration data

To date, we have obtained and processed measurements of Whole Body Vibration during operation of more than 112 agricultural vehicles and are looking at the effects of vehicle type (e.g., combine, tractor, or utility vehicle), vehicle age, season of measurement (spring, summer, fall, or winter) and the presence of vibration dampening seats.  There was large variation of vibration between and within categories of farm vehicle types.  However, preliminary results suggest that older vehicles produce greater WBV in comparison to newer models and that WBV is highest during the winter months. Daily WBV exposure guidelines may be reached in as little as 2 hours of operating a farm vehicle.  Combine seats also performed most favorably in minimizing exposure, reducing, on average, 50% of the vibration energy present at the vehicle floor, as compared to 23%-39% for other vehicle types.  Seasonal time-weighted average exposure estimates were computed by combining self-reported task duration with field measurements to identify associations to reported pain. The figure, below, illustrates the observed association between increasing levels of whole-body vibration and back pain, adjusted for several important co-factors, such as body mass index and occupational psychosocial stress.

skid loader

Seats in utility vehicles (such as skid steer loaders and fork trucks) tend to amplify WBV exposure on farms.

 

Outreach

All 55 individuals who participated in the exposure assessment were provided results of those measures relative to the range of all participants who were measured.  In addition, visual information on whole body vibration has been developed for dissemination to agricultural workers.

Publications

  1. Chen H, Schall M Jr., Fethke N. Effects of magnetic disturbance and motion speed on the accuracy of inertial measurement units. IEEE Transactions on Biomedical Engineering. (Submitted).
  2. Schall MC, Fethke NB, Chen H, Oyama S, Douphrate DI. (2016) Accuracy and repeatability of an inertial measurement unit system for field-based occupational studies.  Ergonomics 59(4):591-602.
  3. Fethke NB, Merlino L, Gerr F, Schall MC, Branch CA. (2015) Musculoskeletal pain among Midwest farmers and associations with agricultural activities. American Journal of Industrial Medicine. 58(3): 319‐330.
  4. Schall MC, Fethke NB, Chen H, Gerr F.  (2015) A comparison of instrumentation methods to estimate thoracolumbar motion in field-based occupational studies.  Applied Ergonomics. 48:224-231.