Effect of an Inflatable Air Mattress with Variable Rigidity on Sleep Quality

Several studies, wherein the structure or rigidity of a mattress was varied, have been conducted to improve sleep quality. These studies investigated the effect of variation in the surface characteristics of mattresses on sleep quality. The present study developed a mattress whose rigidity can be varied by controlling the amount of air in its air cells. To investigate the effect of the variable rigidity of the air mattress on sleep quality, participants (Male, Age: 23.9 ± 2.74, BMI: 23.3 ± 1.60) were instructed to sleep on the air mattress under different conditions, and their sleep quality was subjectively and objectively investigated. Subjectively, sleep quality is assessed based on the participants’ evaluations of the depth and length of their sleep. Objectively, sleep is estimated using the sleep stage information obtained by analysing the movements and brain waves of the participants during their sleep. A subjective assessment of the sleep quality demonstrates that the participants’ sleep was worse with the adjustment of the air mattress than that without; however, the objective sleep quality results demonstrates an improvement in the sleep quality when the rigidity of the air mattress is varied based on the participant’s preference. This paper proposes a design for mattresses that can result in more efficient sleep than that provided by traditional mattresses.

This paper introduces an air mattress whose rigidity can be varied corresponding to the preference of the users. Experiments were conducted to evaluate the effect of the proposed mattress on the quality and quantity of the sleep of its users. These indices are measured based on the users’ subjective sleep assessment and objective sleep scoring index, which are estimated via electroencephalogram (EEG) and accelerometer measurements.

To improve sleep quality, various mattresses have been designed in existing studies [ 25 , 26 , 27 ]. In a few studies, a thermal controlling device was applied to the developed mattress to improve the sleep quality of the users [ 28 , 29 , 30 , 31 ]; others studies have determined the relation between sleep quality and the rigidity of a mattress [ 27 , 32 , 33 ]. Furthermore, several studies have varied the materials used to design the mattress and have modified its structure [ 34 , 35 , 36 ]. Some studies investigated users’ preferences on mattresses, where Kim et al. proposed a methodology for finding the appropriate hardness based on various analyzes that measure the users’ experiences with mattresses and found their preferences to different types of mattress comfort were consistent with the outcomes using their methodology [ 37 ]. Park et al. investigated the users’ preferences to various mattress shapes corresponding to their postures lying on the mattresses and found that subjective sleeping comfort is related to bed adjustment [ 38 ]. In particular, Yoshida et al. reported that there is a relationship between the subjective preference and the stress distribution exerted on the body when a user lies on a mattress [ 39 ]. Based on this study, we hypothesized that a mattress customized according to the user’s preference would improve the quality of sleep.

Sleep quality is directly related to health and sleepiness [ 10 ]. The key feature of insomnia, which can lead to impairment of immune function [ 18 ], cardiac diseases [ 19 ], and neurodegenerative disorders [ 20 ], is sleep quality [ 21 ]. Sleep deficiency results in not only poor health, but also degradation of mental and social functioning [ 22 ]. Moreover, poor sleep quality has a negative effect on work productivity and safety [ 23 , 24 ]. Therefore, increasing sleep quality could considerably improve one’s health and quality of life, as well as their productivity at work.

Most people spend one-third of their lives sleeping [ 1 ]. Furthermore, sleep is essential to maintain good health as it helps revitalize and re-energize the body [ 2 , 3 , 4 ]. In addition, sleep is associated with weight management and longevity [ 5 , 6 , 7 ], and sleep quality is associated with self-report health, mood regulation, as well as feelings of anger, confusion, anxiety, and depression [ 8 , 9 , 10 , 11 , 12 ]. In industry, poor sleep quantity leads to high injury rates and loss of productivity [ 13 , 14 , 15 ]. Consequently, sufficient sleep as well as good sleep quality are critical to ensure good health and overall quality of life [ 16 ]. However, Robins et al. reported that solely 19.22% of the adults in the world exhibit good sleep quality [ 17 ].

2. Methodology

2.1. Study Design

Three conditions were set, as shown in , to vary the pressure of the air cells in the mattress. This experiment was conducted to compare sleep quality under three different mattress conditions. Under Condition A, three air cells were completely filled with air and their internal pressures were maintained at 25 kPa. Under Condition S, only the pressure of the air cell at the shoulder was varied corresponding to the user’s preference, and the other two air cells were completely filled. On average, the preferred pressure of the cell at the shoulder was 14.2 ± 4.4 kPa across the participants. Under the SH condition, only the air cell at the legs was completely filled, and the pressures of the other air cells were customized by the participants. The preferred pressures of the cells at the shoulder and hip were 15.8 ± 6.4 kPa and 15.2 ± 5.5 kPa, respectively.

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This experiment was approved by the Institutional Review Board of Seoul National University (IRB No. 1906-003-016).

2.2. Participants

To score the sleep quality of the participants, EEG and accelerometer data were recorded for 10 male adults who agreed to participate in the experiments. Their ages were 23.9 ± 2.74 and ranges from 21 to 28. Gender was controlled as male following a study that gender could affect sleep quality [40]. The manufactured mattress and air cells were more fit for the body size of the average Korean male, so males were recruited priory in this study. When recruiting participants, they were interviewed to determine whether they met the inclusion and exclusion criteria. The inclusion criterion was that the height is within the range of 170 to 175 cm to fit the size of the mattress. The exclusion criterion was that the participant may be at risk of leaving the mattress due to a bad sleeping habit. Participants with poor sleeping habits were excluded through screening interviews, as Tang et al. did [41]. The average of their BMI scores was 23.3 ± 1.60, and ranges from 20.76 to 25.95. Their Pittsburgh Sleep Quality Index (PSQI) score, which is a self-report questionnaire that assesses sleep quality over a month with a score from 0 to 21, was 5.6 ± 2.22 and the range of their PSQI score was 2 to 10 [42]. Among the participants, there were 5 poor sleepers with a PSQI score higher than 5. To ensure that the participants’ sleep quality was not affected by other factors, the participants were forbidden from consuming caffeine and alcohol for 48 h before the experiment [43,44]. In addition, participants who violate the above policy were excluded from the experiment on the same day through a screening interview before the experiment as Tang et al. did [45]. Second, participants finished their dinners before 7 p.m., had a shower between 9 and 10 p.m., and went to bed at 11 p.m. Lastly, each participant had a gap of 48 h between experiments to ensure that sleep under one mattress condition did not affect sleep under another mattress condition.

2.3. Materials

A mattress was developed whose height could be varied by controlling three same-sized air cells in the mattress, as shown in . The three air cells are located on the shoulder, hip, and legs of the participants. The size of the air cells and the mattress were designed to fit an average Korean male body [46]. The air cells are covered by latex foam, which is in turn covered by a layer of a bed sheet. The pressures of the air cells are varied using a control box hosed to the air cells, which decides the height of the mattress.

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2.4. Procedure

In this study, sleep scoring was not conducted through polysomnography (PSG), but was conducted through the estimation using a single channel EEG according to [47,48,49]. As shown in , four Ag/Ag-Cl electrodes were attached to positions FP1, FP2, A2, and A1 on the participants’ head. The electrodes at positions FP1 and FP2 record the EEG signals; the electrode at position A2 serves as a reference; and the electrode at position A1 is grounded. The two devices used to acquire EEG were BIOPAC MP36 (BIOPAC Systems Inc, Goleta, CA, USA), whose sampling rate was set to 500 Hz, and g.USBamp amplifier (g.tec medical engineering GmbH, Schiedlberg, Austria), whose sampling rate was 600 Hz. In addition, the participants wore wGT3X-BT (ActiGraph LLC, Pensacola, FL, USA) on their wrists such that their movement could be monitored; the sampling rate of the device was 100 Hz.

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To avoid the first night effect [50], all participants slept at the laboratory in the same environment with attaching all electrodes before the actual experiment. The experiment was conducted on three days, and one of three different mattress conditions was considered randomly for each day. shows the procedure of the experiment. On the first day, all the participants answered the PSQI questionnaire before going to bed. Before the electrodes were attached to their respective positions, their exfoliation was removed such that the maximum impedance was 5 kΩ [51]. As shown in , the participant had a 10 min adaption time lying on the mattress [52,53]. The participants went to bed at 11 p.m. and slept for 7 h in a room where the temperature and humidity were maintained at 24 ∘ C and 50% RH, respectively. After waking up at 6 am, the participants responded to a questionnaire designed to help subjectively evaluate their sleep quality. The questionnaire comprised two questions regarding ‘Sleep Length’ and ‘Sleep Depth’, which are the question number 5 and 8 in the “sleep diary” presented by Åkerstedt et al. [54]. For more detailed analysis than the original questionnaire, the selection range was widened from 5 to 7, i.e., a range of 1–7.

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2.5. Sleep Scoring Algorithm

Subjective sleep assessment was conducted through questionnaires. The length information of each sleep stage was obtained from a hypnogram, as an objective sleep evaluation, where the duration of REM and Deep stages (slow wave sleep, SWS) are the length information of the stages, conventionally used to measure the sleep quality [55]. REM stage maintains the necessary levels of central nervous system activities, promotes a recovery with providing periodic stimulation to the brain [56] and preserves emotional memory sources selectively [57]. SWS induces an endocrine environment that could strongly support the initiation of an adaptive immune response and cleans metabolites [58]. Additionally, RNR (REM to Non-REM ratio) and SSI (Stage Shift index) were calculated as sleep indices corresponding to the sleep quality. Mendonca et al. reported that the higher RNR and the lower SSI result in the improvement of sleep quality [55]. RNR is an index representing the ratio of the duration of the REM stage to that of the non-REM stage, as shown in Equation (1). SSI is an index obtained by dividing the number of times the sleep stage shift during whole sleep by the total sleep time (TST), as shown in Equation (2).

RNR(%)=ΣREM(min)ΣnonREM(min)

(1)

SSI=ΣSleepStageShiftTST(h)

(2)

To estimate the lengths of the sleep stages, an automatic sleep scoring algorithm is employed, which was introduced in a study by Onton et al. [49,59]. Onton et al. acquired EEG signals from the FP1-A2 and FP2-A2 channels. The signals of one channel were then classified, in terms of 30-s epoch units, into five sleep stages. This was realized by employing the hidden Markov model (HMM) algorithm along with Viterbi and expectation-maximization (EM) algorithms, in an unsupervised manner [60]. The hypnograms of the sleep stages were then estimated. In this study, N1 and N2 were merged to form a Light sleep stage and N3 was considered to be a Deep sleep stage, reducing the number of whole stages from five to four.

A single-channel EEG signal was segmented into 30-s epochs, which served as input to the HMM algorithm [61]; further, the frequency band powers of these epochs were calculated after being filtered using wavelet transform [49,62,63] (Wake: 35–50 Hz, REM: 20–30 Hz, Light: 10.15–15.75 Hz and Deep: 1–3 Hz). There are three parameters for the HMM algorithm, which are an initial probability (π), a transition matrix (Q), and an emission matrix (R). Since an initial sleep stage always starts with the participant being awake, referring to the paper by Lo et al. [64], the initial probability (π0) was set as Equation (3) and the initial transition matrix (Q0) of the HMM algorithm was initialized as Equation (4). In Equation (4), the element q(i,j) in the matrix represents the probability of transition from the stage i to the stage j on the next time step. The emission matrix (R) comprises the mean (μ) and standard deviation matrices (σ). The mean of the initial emission matrix (μ0) was set as Equation (5). Please note that the probability of one particular frequency band corresponding to a sleep stage (see the diagonal terms in Equation (5)) is higher than those of the others, which was initial expected based on a study by Onton et al. [49]. The standard deviation of the initial emission matrix (σ0) was set as Equation (6). The parameters (π,Q,R) were updated using the EM algorithm [65], after which the Viterbi algorithm provides estimations of the sleep stages based on the maximum posteriori [66].

π0=1000

(3)

Q0=q(W,W)q(W,R)q(W,L)q(W,D)q(R,W)q(R,R)q(R,L)q(R,D)q(L,W)q(L,R)q(L,L)q(L,D)q(D,W)q(D,R)q(D,L)q(D,D)=0.750.010.2400.050.880.0700.180.110.550.160.0200.140.84

(4)

μ0=0.70.10.10.10.10.70.10.10.10.10.70.10.10.10.10.7

(5)

σ0=1000010000100001

(6)

To validate the HMM algorithm as an automatic sleep scoring method, we obtained a public data set, Sleep-EDF Database Expanded (Sleep-EDFx), containing two EEG channels (Fpz-Cz and Pz-Oz), an electrooculogram (EOG), and an electromyogram (EMG) recorded at a sampling rate of 100 Hz [67,68]. According to the Rechtschaffen and Kales manuals, well-trained technicians manually scored the sleep stages, and thus, the Sleep-EDFx includes all the sleep stages, including wake, REM, S1, S2, S3, and S4. To calculate sleep quality for this data set, S1 and S2 are combined into the light sleep stage, S3 and S4 into Deep stage, reducing the six stages to four: wake, REM, Light, and Deep stages.

The accelerometer data were analyzed using the Sadeh algorithm [69], implemented in ActiLife6 (ActiGraph LLC, Pensacola, FL, USA) software, which estimates sleep and wake states. Sleep efficiency (SE) could be calculated using the formula, SE=Sleep/(Sleep+Wake), based on the estimation of the sleep and wake states. In addition, sleep onset latency (SOL) which is time taken to sleep, wake after sleep onset (WASO) which is total waking time after sleep, and total sleep time (TST) which is total sleep time during time in bed could also be obtained from the actigraphy information [70].

2.6. Analyses

To investigate how the proposed mattress affects the quality of sleep, a subjective sleep assessment and objective sleep evaluation were conducted. The subjective sleep assessment was conducted with a questionnaire written about the depth and length of sleep experienced by the participants. To analyze the condition of each mattress, comparative analysis among three different mattress conditions was conducted on the subjective sleep assessment under each mattress condition.

Objective sleep evaluation was conducted with various sleep parameters which obtained from actigraphy and EEG analysis. There are two methods for determining the sleep and wake states, which is a binary decision, using actigraphy analysis and EEG analysis as shown in a,b. We can get two indices each that can be obtained with wake information such as SE, TST, SOL, and WASO. In this study, comparative analysis is conducted to see how the above indices are affected by the mattress conditions. A hypnogram was estimated using the HMM algorithm, as shown in b, and sleep parameters such as the length of REM and Deep, RNR, and SSI were obtained from the estimated hypnogram. A comparative analysis was conducted to investigate these sleep parameters depending on the three mattress conditions.

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Since the number of participants in our study is 10, which is less than 30 samples, statistical analysis is performed for validation by nonparametric analysis rather than parametric analysis. Since the results of those sleep parameters in each mattress condition, indicate the conclusion of this study, a nonparametric wilcoxon signed-rank test was conducted between the base condition, A condition, and the condition with the highest score among S and SH condition [71].

There were five poor sleepers with a PSQI score higher than 5, so 10 subjects were divided into 5 poor sleepers and five good sleepers. A comparative analysis of each sleep index is conducted between the two groups to compare and analyze how the customized mattress affects the good sleeper and the poor sleeper.