Efficient one-day sampling of mechanical job exposure data—a study based on upper trapezius activity in cleaners and office workers

Svend Erik Mathiassen, Alex Burdorf, Allard J. Van Der Beek, Gert Åke Hansson

Research output: Contribution to journalArticleAcademicpeer-review

65 Citations (Scopus)


This ergonomics exposure assessment study compared the efficiency of eight 1-day-only strategies, that is, the relationship between the number of data collected per subject and the precision of the resulting mean exposure estimate. Whole-day electromyographic recordings from the right upper trapezius muscle in 24 cleaners and 23 office workers were processed to give minute-by-minute values of gap time and jerk time—parameters representing the level and frequency dimensions of muscle activation, respectively. On-site observations provided data on time spent in each of eight exhaustive task categories in the job, seven of which were associated with activities during work, and the last comprising breaks. On average, sampling at fixed intervals without regard to tasks doubled efficiency as compared with random sampling, which in turn was several times as efficient as consecutive sampling. Stratified sampling according to the two broad categories, work and breaks, increased efficiency for random and fixed-interval sampling, but the gain was distinct only among cleaners (about 20%). The commonly used strategy in ergonomic studies of sampling consecutively for short periods within tasks was highly inefficient. Further classification of work into the seven subcategories resulted in marginal additional increases in efficiency; on average less than 2%. A decision algorithm is given for determining appropriate sampling strategies in different types of jobs.

Original languageEnglish
Pages (from-to)196-211
Number of pages16
JournalAmerican Industrial Hygiene Association Journal
Issue number2
Publication statusPublished - 1 Mar 2003


  • Data collection
  • Efficiency
  • Ergonomics
  • Mechanical exposure
  • Precision
  • Task analysis

Cite this