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Hospital Bed Research to Improve Patient and Nurse Safety and Performance

In their article "Quantification of Patient Migration in Bed: Catalyst to Improve Hospital Bed Design to Reduce Shear and Friction Forces and Nurses’ Injuries," Susan E. Kotowski, Kermit G. Davis, both from the University of Cincinnati, Neal Wiggermann, and Rachel Williamson, both from Hill Rom, Inc. set out to quantify patient movement in hospital beds relative to typical bed designs and articulations. Their goal was to develop and test experimental methodologies and metrics that will assist hospital bed designers in developing beds that minimize patient movement/migration, therefore also reducing exposure to shear and friction forces that result in pressure ulcers and skin tears as well as reducing the need for caregiver manual patient handling repositioning tasks.

A link to the full research articles are included in the Reference section, below, and the article was available at no charge at the time of this writing.

Kotowski et al provide the following problem scoping background:

  • pressure ulcers and skin tears continue to "plague hospitals" and may become more common as patient populations become heavier and older;
  • insurers and Medicare (USA Government subsidized health care for senior citizens) are beginning to deny coverage for events related to hospital stays;
  • in the USA alone, at least 2.5 million pressure ulcers occur annually, costing $2.2 to $3.6 billion;
  • at least 200 factors are associated with pressure ulcers and skin tears, but two major factors are friction and shear forces from patient movement in bed;
  • bed articulations, such as raising the head of the bed during meals, can create shear forces in the skin and underlying structures and friction forces between the skin and mattress;
  • elderly patients are more susceptible to skin tears;
  • when a patient slides away from the-head-of-bed, a nurse or nurses will often manually reposition the patient;
  • research estimates that at least 90% of nurses perform repositioning at least once per shift, and manual repositioning frequency has been estimated, depending on the study, from 10 to 19 times per week, to as much as 10 times per shift;
  • 95% of nurses adopt a two-nurse repositioning technique;
  • between 27% and 48% of all nurse back injuries are associated with patient repositioning tasks, making it the leading cause of such injuries and one of the top three riskiest nursing tasks;
  • some researchers' biomechanical analysis of repositioning tasks estimate spinal compression loading in a range from 4,200 N to 13,230 N (944 lb. to 2,974 lb.), whereas assisting in and out of bed tasks were calculated to be from 4,000 N to 8,900 N (899 lb. to 2,001 lb.);
  • other researchers suggest the spinal loading during repositioning are lower, estimating a compression force range of 2,610 N to 3,180 N (587 lb. to 2,001 lb.) and shear force range of 350 N to 385 N (79 lb. to 87 lb.);
  • in contrast, researchers suggest maximum compression loading tolerances of 3,400 N (764 lb.) and shear of 1,000 N (225 lb.);

A better understanding of how patients migrate in articulating beds, Kotowski et al postulate, will assist bed designers with new designs that minimize patient migration and the associated patient risks as well as minimizing the need for nurse repositioning tasks.


Complete details of experimental methods and results are available in the articles cited below, but key methods include:

  • the study was conducted in laboratory conditions in order to control specific variables and obtain detailed measurements;
  • 6 males and six females participated in the study as representative these selection criteria:

    • tall–average weight (>76%tile height, <25 kg/cm2 BMI),
    • tall–overweight (>76%tile height, BMI between 25 kg/cm2 and 30 kg/cm2),
    • average height–average weight (between 26%tile and 75%tile height, <25 kg/cm2 BMI),
    • average height–overweight (between 26%tile and 75%tile height, BMI between 25 kg/cm2 and 30 kg/cm2),
    • short–average weight (<25%tile height, <25 kg/cm2 BMI), and
    • short–overweight (<25%tile height, BMI between 25 kg/cm2 and 30 kg/cm2);
  • a 7-camera motion capture system was used to collect 3-dimensional body landmark data;
  • the 'patients' were instructed to 'act comatose' throughout each experimental trial;
  • the independent variables were:

    • bed type,
    • mattress pressure, and
    • knee elevation;
  • all bed frames were comprised of a head, seat, thigh, and foot section, and various bed articulations were tested;
  • all beds had powered air pressure mattresses, and normal (relative to body weight) pressure and maximum (determined by manufacturer) inflation pressures were tested;
  • the dependent variables were:

    • net displacement,
    • cumulative movement, and
    • torso compression.

Results and Discussion

The complete results are discussed in detail in the article cited and linked to below. Since the researchers do not describe the 3 beds tested in detail, differences between beds are not reviewed herein. Instead, general trends and the usefulness of the metrics applied in this research are the focus herein.

According to Kotowski et al:

Net displacement captures the final migration of the body for the shoulder, [hip], and ankle;

Cumulative movement provides a measure of the entire movement of the body regions throughout the articulation;

Torso compression provides an indication of the “scrunching” pressure on the body as a measure of the distance between the shoulder and [hip].

Furthermore, they state:

These metrics provide potential surrogate measurements for friction and shear forces between the patient and the bed or for how frequently a caregiver will be required to reposition a patient up in the bed.

The net displacement metric recorded a consistent trend, with all three beds showing:

  • migration toward the foot-of-bed for shoulders, hips and ankles; and
  • the 'patient' moved roughly 2 cm toward the-foot-of-bed in an 0-45-0 articulation and 3 cm to 4 cm down in a 30-chair-30 articulation;

The researchers also investigated the potential effect of mattresses design, all of which contained air bladders, and mattress movement, finding that 10% to 20% of the net displacement data and 15% to 50% of the cumulative movement, particularly for the hip, were related to mattress movement.

The cumulative movement metric demonstrated significant differences for bed type, maximum inflation, and knee elevation, and also demonstrated differences between the three bed designs. 

A lower torso compression metric was seen as creating a more comfortable feeling with less 'balling up' of the 'patient', which the researchers suggest could also indicate lower shear and friction forces, allowing freer patient movement.

Kotowski et al discuss other factors that could affect their methodology and data. For example, the movements of specific bony anatomical points were tracked in this study, but soft tissues displacements, especially with obese patients, are likely to move differently than the bony prominences. They also note that their experimental trials were isolated single articulations, and that patients in an actual hospital setting are likely to go through multiple bed articulations each day, as well as different articulation configurations than those tested in this study.

At the end of their discussion, Kotowski et al recognize that there are many factors that affect patient migration, and also noted that while their study did not have enough participants to reach broad conclusions, patient gender also appeared to affect migration patterns. The researchers conclude with an important statement that echoes throughout ergonomics and design:

These findings indicate that hospital designers will need to consider that a “one-size-fits all” approach may not be effective.



Susan E. Kotowski, Kermit G. Davis, Neal Wiggermann, and Rachel Williamson, (2013). Quantification of Patient Migration in Bed: Catalyst to Improve Hospital Bed Design to Reduce Shear and Friction Forces and Nurses’ Injuries. Human Factors, published online before print January 18, 2013, doi: 10.1177/0018720812474300. The complete article is available at (free at the time of this writing, but future third party publisher fees may apply):

This article originally appeared in The Ergonomics Report™ on 2013-02-06.