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Very early exercise tailored by using a decisional algorithm helps relieve discomfort in adults in an intensive care unit: an open-label pilot study.

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
30 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
In total, 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.