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Very early exercise tailored by decisional algorithm helps relieve discomfort in ICU patients: an open-label pilot
Hélène Laurent1,2, Sylvie Aubreton3, Aurélie Vallat3
1Unit of Human Nutrition (UNH), University of Clermont Auvergne, National Institute for Research on Agriculture (INRAE), Clermont-Ferrand, France - hlaurent@chu-clermontferrand.fr.
Background:
Existing algorithms do not allow for setting up finely tuned progression or intensity for exercise training in intensive care units (ICUs).
Aim:
We aimed to assess the feasibility and tolerance of a very early exercise program tailored by using decisional algorithm that integrated both progression and intensity.
Design:
Open-label pilot study.
Setting:
ICU.
Population:
Thirty adults hospitalized in ICU.
Methods:
Once a day, patients performed manual range of motion, cycloergometry, and functional training exercises. The progression and intensity of training were standardized by using the constructed algorithm. The main outcome, discomfort on a 0-100 Visual Analog Scale, was assessed before and after each exercise session. Secondary outcomes were muscle strength, ICU length of stay and adverse events related to exercise.
Results:
Overall, 125 exercise sessions were performed. Discomfort during exercise sessions decreased significantly by the fifth session (P=0.049). Early exercise sessions were feasible and did not produce major adverse events.
Conclusions:
We confirmed the safety and feasibility of very early exercise programs in ICUs. Early exercise tailored by using a decisional algorithm helps relieve the discomfort of ICU patients.
Clinical Rehabilitation Impact:
In everyday practice, the use of decisional algorithms should be encouraged to initiate and standardize early exercise in ICUs.
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