Participants

Participants were recruited using e-mail advertising sent to postgraduate students from the School of Engineering, University of Warwick. Exclusion criteria included history of sleep disorders, neurological or physical disabilities and pharmacological treatment potentially affecting sleep patterns and postural control (e.g. anti-depressants, hypnotics and stimulants).

Baseline characteristics, such as age, height, weight, general health status and use of medications, were collected during a baseline assessment and briefing session. Participants were also asked to complete the Pittsburgh Sleep Quality Index (PSQI) instrument18. The PSQI questionnaire provides a global score computed from nineteen self-rated questions related to sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleeping medication and daytime dysfunction. The PSQI global score was used to compare baseline sleep quality over the past month between groups.

All subjects provided informed consent prior to participation in the study. This research was performed in accordance with relevant regulations, thus it was approved by the Biomedical and Scientific Research Ethics Committee of the University of Warwick (REGO-2014-1039).

Equipment

Participants’ monitoring during sleep was performed using the Zephyr BioHarnessTM 3.0 (Medtronic, Inc., Annapolis, MD, USA), a patch-type device that measures tri-axial trunk acceleration and one-lead electrocardiogram (ECG) signals at a sampling frequency of 100 Hz and 1 kHz, respectively, and a resolution of 12 bits per sample. The device uses proprietary algorithms to compute user’s activity level and posture based on the acceleration signals. Activity level is reported in gravitational force units (i.e., g-force or simply g, where 1 g = 9.806 m/s2) within a range of 0 to 16 g and is computed as \(Activity=\sqrt{{x}^{2}+{y}^{2}+{z}^{2}}\), where x, y and z (the vertical, medial-lateral and anterior-posterior axes, respectively) are the averages of the three-axial acceleration magnitudes over the previous 1-second window. Posture is reported in degrees as the angle of deviation from the vertical axis. Activity level and Posture time-series are reported with a frequency of 1 sample per second. Moreover, this device performs R peak detection on the ECG waveform and reports R-R intervals in milliseconds. Raw three-axial accelerations, ECG signals, R-R interval time-series, and a summary file containing the activity and posture time-series are stored in the internal memory of the device during usage and can be downloaded for further processing. The validity and reliability of the Zephyr BioHarnessTM have been found to be strong to very strong for heart rate, acceleration and posture monitoring at low to moderate physical activity levels19,20.

Balance testing was performed using the Tekscan® F-Scan® system (Tekscan, Inc., South Boston, MA, USA), a plantar pressure measurement and analysis system. This system is based on a pair of ultra-thin (0.15 mm) instrumented insoles with a spatial resolution of 3.9 pressure-sensing elements per cm2. Bi-plantar pressure data were collected at a rate of 200 frames per second. Based on pressure data, the F-Scan Research 7 software computes the foot COP location for each frame. COP displacement is stored as time-series of numerical data in the anterior-posterior (AP) and medial-lateral (ML) axes in relation to the subject’s orientation. Figure 1 shows a typical bi-plantar pressure distribution map during quiet standing and the resulting centre of pressure displacement trajectory. As per the manufacturer’s recommended procedures, the F-Scan® system was calibrated for each participant following the point calibration routine, the suggested method for standing balance trials. This calibration procedure requires each sensor to be individually calibrated by having the subject standing on a single foot at a time for a few seconds (~5 seconds). In fact, Hsiao et al. emphasised on the importance of calibrating the system in actual experimental conditions prior to use21. Following the proper calibration procedure, the accuracy of the F-Scan® has been found to be satisfactory (i.e., with a measurement error less than 6%) when the sensors are subjected to static loads (e.g. during quiet standing) and the pressure applied during the protocol is comparable with that used during calibration21. These considerations are worth mentioning, as some studies have questioned the validity and reliability of the Tekscan® F-Scan® system, when utilised with dynamic loads (i.e. walking22) or when the sensors were calibrated using two pressure values and tested over a wider range23.

Figure 1 Plantar pressure map and centre of pressure trajectory. Left: Representative bi-plantar pressure map during quiet standing. The black and white circle represents the foot centre of pressure computed from pressure distribution data. Right: Representative centre of pressure trajectory (left foot) for a 20-second window. Full size image

Study protocol

A schematic of the study protocol is shown in Fig. 2. After baseline assessment, participants underwent sleep and balance assessment for two consecutive days. For sleep assessment, they were asked to wear the BioHarnessTM during sleep; i.e., to apply it at the time of usual bedtime and to take it off after the final awakening. Additionally, subjects were required to complete the Consensus Sleep Diary24 every morning immediately after getting out of bed during their participation in the study. Participants were invited to stick to their regular sleep schedule and habits (i.e. no intervention was applied).

Figure 2 Schematic diagram of the study protocol. Sleep monitoring was performed using a wearable device that records trunk acceleration and electrocardiogram signals. Balance testing was performed using a plantar pressure measurement and analysis system based on a pair of instrumented insoles. Full size image

Balance was assessed in two morning sessions starting at the same time of the day (9:00 or 10:00 a.m.) for any given participant. Previous studies have suggested that COP measures of postural control change throughout the day, allegedly following a circadian pattern7,8,9. By starting both sessions at the same time of the day, the influence of time of day on postural control measures was discarded as a potential confounder. At each session, participants were asked to complete four quiet standing trials with eyes open. Namely, they were instructed to stand quietly on the foot pressure sensors with arms hanging naturally at their sides and eyes staring at a fixed point on the wall in front of them. The sensors were attached to the floor side-by-side in a comfortable position for each participant (about shoulder width). The duration of each trial was 30 seconds and a brief resting interval (~15 seconds) was allowed between trials. Participants wore socks but no shoes during the whole duration of the session.

Data processing

Data collected via the sleep diary, the BioHarness and the Tekscan system were processed as follows in order to compute a set of sleep and balance measures (see Table 1 for a summary of those measures with their definitions).

Table 1 Summary of sleep and centre of pressure displacement measures Full size table

Sleep Diary Measures

Five sleep measures were extracted from the sleep diary: (1) sleep onset latency (SOL); (2) wake after sleep onset (WASO), a measure of sleep fragmentation; (3) total sleep time (TST) or sleep duration; (4) sleep efficiency (SE), and; (5) subjective sleep quality (SSQ).

Sleep Activity Level Measures

Activity level signals were processed to compute six measures of activity during sleep. Firstly, raw signals were trimmed using posture data to discard intervals outside the sleep period (i.e. before getting into and after getting out of bed). Subsequently, the signals were segmented into continuous, non-overlapping 1-minute epochs and activity counts were computed for each epoch using the zero-crossing mode; i.e., the activity level was compared with the reference activity level, and each threshold crossing generated an activity count25. The threshold was set to 0.1 g for high sensitivity. Finally, the following activity measures were computed from the activity counts: mean and standard deviation of activity counts per epoch (ACT_MEAN and ACT_SD, respectively), activity index (AI), fragmentation index (FI), and maximum and average duration of the inactive intervals (MAX_REST and AVG_REST, respectively). These measures were computed using in-house written scripts in Matlab R2016b (The Mathworks, Inc., Natick, MA, USA).

Heart Rate Variability Measures

Heart rate variability (HRV) measures were computed from R-R series in order to characterise autonomic cardiac modulation during sleep. A higher parasympathetic tone has been observed during non-rapid eye movement sleep (NREM), particularly during deep sleep; in contrast, a higher sympathetic tone has been observed during wake intervals, rapid eye movement sleep (REM) and sleep arousals26. Therefore, the HRV analysis provided with an indication of the presence of wake intervals and arousals, as well as of shorter deep sleep periods.

Firstly, the R-R series were trimmed using posture data to discard heart beats outside the sleep period. Subsequently, the software HRVanalysis27 was used to compute four HRV measures: two frequency-domain measures (LF and HF power) and two nonlinear measures (Approximate entropy and Sample entropy). The algorithms for anomalous R-R peaks exclusion and correction implemented in the HRVanalysis software were applied. Additionally, three frequency-domain measures (LF normalised, HF normalised and LF/HF ratio) were computed using in-house written scripts in Matlab R2016b. The meaning of these HRV measures has been widely described in detail elsewhere28,29. In the context of sleep assessment, those features are associated with specific sleep stages and relevant phenomena (e.g. arousals)26. In the frequency-domain, HF power describes the parasympathetic activity, whereas LF power describes both parasympathetic and sympathetic activity. Thus, the relationship between both branches is normally explored with the normalised frequency values and the LF/HF ratio. Finally, entropy measures represent an index of complexity in the cardiac signal. An increase in complexity (i.e., an increase in the entropy measure) is associated with parasympathetic modulation and its decrease is interpreted as the result of an increased sympathetic tone.

Balance Measures

COP time-series were trimmed to discard the initial and last 5 seconds of each trial in order to account for the “adaptation phase” of the participant to the quiet standing task and for the effects of fatigue or lack of attention associated to a sustained task, respectively5. Subsequently, the COP time-series were passed through a fourth-order zero-phase Butterworth low-pass digital filter with a cut-off frequency of 5 Hz in order to remove acquisition noise. Afterwards, they were detrended (i.e., the mean was subtracted). Hence, the analysis of the COP displacement was carried out relative to its mean position and not to the origin of the sensor’s coordinate system. Finally, three COP displacement measures were computed as described in detail by Duarte et al.2: Area, Amplitude and Standard deviation. These measures were computed for left and right feet independently. Additionally, the measures for left and right feet were averaged. Amplitude and Standard deviation were computed in the AP axis only, as previous studies have shown that it is mainly on this axis that balance alterations are observed10,12,14,15. Scripts for COP data processing were also written in Matlab R2016b.

The datasets generated during the current study are available from the corresponding author on reasonable request.

Statistical analysis

Participants were stratified according to the sleep quality scores they reported in the sleep diary (SSQ) in Control group (i.e., participants who reported no variation in sleep quality over two consecutive nights) and Case group (i.e., participants who reported a variation in sleep quality over two consecutive nights; e.g. good sleep quality in one night and poor sleep quality in the other). The validity of self-reported sleep quality was tested by running pairwise comparisons for all other sleep measures within each group. By definition, no differences over consecutive nights were expected for the Control group, while significant differences were expected for the Case group. Two-sided Wilcoxon paired tests with a significance level set at 0.05 were used for these comparisons, given that most sleep measures exhibited a non-normal distribution (Shapiro-Wilk test with a p-value < 0.05).

Subsequently, a repeated measures ANOVA-type rank test for factorial designs was performed in order to test the main effects and the interaction effects of Group and Session on balance measures30. This test was developed for experimental designs where subjects are stratified in several groups, as well as observed at different time points (i.e., mixed designs). Importantly, these tests have been found to be robust with respect to outliers and small sample sizes. The computational implementation of this test provided by the authors through the R package nparLD version 2.1 was used31. The main effects and interaction effects of Group and Session were tested for all balance measures. A p-value < 0.05 was accepted as indicative of statistical significance. This analysis was performed in R version 3.4.1 (R Foundation for Statistical Computing, Vienna, Austria).

Finally, differences in balance measures between sessions were investigated for each group (i.e. post hoc comparisons): for the Control group, pairwise comparisons were always made between Session 2 and Session 1, given that by definition for this group sleep quality was equally rated in both sleep opportunities; for the Case group, pairwise comparisons were done between the session with the poorest sleep quality and the session with the best sleep quality, regardless of the order in which they were chronologically presented. Two-tailed Wilcoxon paired tests were performed given the non-normal distribution of most balance measures (Shapiro-Wilk test with a p-value < 0.05). A p-value < 0.05 was accepted as indicative of statistical significance. These tests were conducted in Matlab R2016b.