Obstructive sleep apnea (OSA) is a common sleep disorder and polysomnography (PSG) is the gold standard for its diagnosis and treatment monitoring. There are nowadays several activity trackers measuring sleep quality through the detection of sleep stages. To allow an easier monitoring of the treatment efficacy at home, this work explores the possibility of using one of those commercial smart-bands. To this aim, we studied the signals provided by PSG and a Fitbit smart-band on 26 consecutive patients, admitted to the hospital after the diagnosis of OSA, and submitted to ventilation or positional treatment. They underwent monitoring for three nights (basal, titration, and control). We developed both a visualization software allowing doctors to visually compare the two hypnograms, and a set of statistics for assessing the concordance of the two methods. Results indicate that Fitbit can detect normal sleep patterns, while it is less able to detect the abnormal ones.

Obstructive Sleep Apnea Home-Monitoring Using a Commercial Wearable Device

Mokhtaran M.;Sacchi L.;Tibollo V.;Ramella V.;Quaglini S.;
2022-01-01

Abstract

Obstructive sleep apnea (OSA) is a common sleep disorder and polysomnography (PSG) is the gold standard for its diagnosis and treatment monitoring. There are nowadays several activity trackers measuring sleep quality through the detection of sleep stages. To allow an easier monitoring of the treatment efficacy at home, this work explores the possibility of using one of those commercial smart-bands. To this aim, we studied the signals provided by PSG and a Fitbit smart-band on 26 consecutive patients, admitted to the hospital after the diagnosis of OSA, and submitted to ventilation or positional treatment. They underwent monitoring for three nights (basal, titration, and control). We developed both a visualization software allowing doctors to visually compare the two hypnograms, and a set of statistics for assessing the concordance of the two methods. Results indicate that Fitbit can detect normal sleep patterns, while it is less able to detect the abnormal ones.
2022
9781643682648
9781643682655
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1477665
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