NEW DELHI: People with poor objective sleep quality exhibit unfavourable physical health indicators, particularly elevated blood pressure, a study has found. Objective sleep quality consists not only of the total sleep duration, but also the amount of the different sleep stages, the duration of wake period, and the frequency of awakenings.
Researchers from the University of Tsukuba in Japan conducted a comprehensive study involving 100 adults aged 30-59 years by employing electroencephalogram (EEG) measurements to assess sleep quality for five nights at the participants’ homes.
Electroencephalography is a method to record an electrogram of the spontaneous electrical activity of the brain.
Additionally, detailed health examinations were conducted at a health care facility in Tokyo.
Ten sleep parameters derived from the EEG data collected during the five-night home study were used to categorise participants into three groups-namely, the good sleep group (comprising 39 participants), the intermediate group (comprising 46 participants), and the poor sleep group (comprising 15 participants).
The study, published in the journal Scientific Reports, utilised a form of unsupervised machine learning within the domain of artificial intelligence (AI) to evaluate 50 physical health parameters across these groups.
The researchers observed statistically significant differences in systolic and diastolic blood pressure, Îł-GTP (a marker of liver function), and serum creatinine (a marker of kidney function).
Of these, the differences in systolic blood pressure — which measures the pressure in arteries when the heart beats — were particularly pronounced, consistently higher among participants of the poor sleep group, the researchers said.
The study unveiled a weak correlation between objective sleep quality, as measured by EEG during sleep, and subjective sleep quality.
Notably, only objective sleep quality exhibited an association with systolic blood pressure, the researchers said.
Additionally, the research identified specific combinations that displayed relatively strong correlations between the 10 EEG-derived sleep metrics and the 50 physical health parameters.
The study highlights the utility of home-based EEG for the objective assessment of sleep quality, offering valuable applications in clinical practice and research endeavours.
Researchers from the University of Tsukuba in Japan conducted a comprehensive study involving 100 adults aged 30-59 years by employing electroencephalogram (EEG) measurements to assess sleep quality for five nights at the participants’ homes.
Electroencephalography is a method to record an electrogram of the spontaneous electrical activity of the brain.
Additionally, detailed health examinations were conducted at a health care facility in Tokyo.
Ten sleep parameters derived from the EEG data collected during the five-night home study were used to categorise participants into three groups-namely, the good sleep group (comprising 39 participants), the intermediate group (comprising 46 participants), and the poor sleep group (comprising 15 participants).
The study, published in the journal Scientific Reports, utilised a form of unsupervised machine learning within the domain of artificial intelligence (AI) to evaluate 50 physical health parameters across these groups.
The researchers observed statistically significant differences in systolic and diastolic blood pressure, Îł-GTP (a marker of liver function), and serum creatinine (a marker of kidney function).
Of these, the differences in systolic blood pressure — which measures the pressure in arteries when the heart beats — were particularly pronounced, consistently higher among participants of the poor sleep group, the researchers said.
The study unveiled a weak correlation between objective sleep quality, as measured by EEG during sleep, and subjective sleep quality.
Notably, only objective sleep quality exhibited an association with systolic blood pressure, the researchers said.
Additionally, the research identified specific combinations that displayed relatively strong correlations between the 10 EEG-derived sleep metrics and the 50 physical health parameters.
The study highlights the utility of home-based EEG for the objective assessment of sleep quality, offering valuable applications in clinical practice and research endeavours.
