Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data

Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices. Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies. Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman's rank correlation coefficient (R) of 0.56 and -0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics. Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.

Ensemble averaging for categorical variables: Validation study of imputing lost data in 24-h recorded postures of inpatients

Acceleration sensors are widely used in consumer wearable devices and smartphones. Postures estimated from recorded accelerations are commonly used as features indicating the activities of patients in medical studies. However, recording for over 24 h is more likely to result in data losses than recording for a few hours, especially when consumer-grade wearable devices are used. Here, to impute postures over a period of 24 h, we propose an imputation method that uses ensemble averaging. This method outputs a time series of postures over 24 h with less lost data by calculating the ratios of postures taken at the same time of day during several measurement-session days. Whereas conventional imputation methods are based on approaches with groups of subjects having multiple variables, the proposed method imputes the lost data variables individually and does not require other variables except posture. We validated the method on 306 measurement data from 99 stroke inpatients in a hospital rehabilitation ward. First, to classify postures from acceleration data measured by a wearable sensor placed on the patient's trunk, we preliminary estimated possible thresholds for classifying postures as 'reclining' and 'sitting or standing' by investigating the valleys in the histogram of occurrences of trunk angles during a long-term recording. Next, the imputations of the proposed method were validated. The proposed method significantly reduced the missing data rate from 5.76% to 0.21%, outperforming a conventional method.

Validity of trunk acceleration measurement with a chest-worn monitor for assessment of physical activity intensity

Background

Recent advancements in wearable technology have enabled easy measurement of daily activities, potentially applicable in rehabilitation practice for various purposes such as maintaining and increasing patients’ activity levels. In this study, we aimed to examine the validity of trunk acceleration measurement using a chest monitor embedded in a smart clothing system (‘hitoe’ system), an emerging wearable system, in assessing the physical activity in an experimental setting with healthy subjects (Study 1) and in a clinical setting with post-stroke patients (Study 2).

Methods

Study 1 involved the participation of 14 healthy individuals. The trunk acceleration, heart rate (HR), and oxygen consumption were simultaneously measured during treadmill testing with a Bruce protocol. Trunk acceleration and HR were measured using the "hitoe" system, a smart clothing system with embedded chest sensors. Expiratory gas analysis was performed to measure oxygen consumption. Three parameters, moving average (MA), moving standard deviation (MSD), and moving root mean square (RMS), were calculated from the norm of the trunk acceleration. The relationships between these accelerometer-based parameters and oxygen consumption-based physical activity intensity measured with the percent VO2 reserve (%VO₂R) were examined. In Study 2, 48 h of simultaneous measurement of trunk acceleration and heart rate-based physical activity intensity in terms of percent heart rate reserve (%HRR) was conducted with the "hitoe" system in 136 post-stroke patients.

Results

The values of MA, MSD, RMS, and %VO₂R were significantly different between levels 1, 2, 3, and 4 in the Bruce protocol (P < 0.01). The average coefficients of determination for individual regression for %VO₂R versus MA, %VO₂R versus MSD, and %VO₂R versus RMS were 0.89 ± 0.05, 0.96 ± 0.03, and 0.91 ± 0.05, respectively. Among the parameters examined, MSD showed the best correlation with %VO₂R, indicating high validity of the parameter for assessing physical activity intensity. The 48-h measurement of MSD and %HRR in post-stroke patients showed significant within-individual correlation (P < 0.05) in 131 out of 136 patients (correlation coefficient: 0.60 ± 0.16).

Conclusions

The results support the validity of the MSD calculated from the trunk acceleration measured with a smart clothing system in assessing the physical activity intensity.

Trial registration: UMIN000034967. Registered 21 November 2018 (retrospectively registered).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13102-022-00492-4.

Validity of simplified, calibration-less exercise intensity measurement using resting heart rate during sleep: a method-comparison study with respiratory gas analysis

Background

The recent development of wearable devices has enabled easy and continuous measurement of heart rate (HR). Exercise intensity can be calculated from HR with indices such as percent HR reserve (%HRR); however, this requires an accurate measurement of resting HR, which can be time-consuming. The use of HR during sleep may be a substitute that considers the calibration-less measurement of %HRR. This study examined the validity of %HRR on resting HR during sleep in comparison to percent oxygen consumption reserve (%VO₂R) as a gold standard. Additionally, a 24/7%HRR measurement using this method is demonstrated.

Methods

Twelve healthy adults aged 29 ± 5 years underwent treadmill testing using the Bruce protocol and a 6-min walk test (6MWT). The %VO₂R during each test was calculated according to a standard protocol. The %HRR during each exercise test was calculated either from resting HR in a sitting position (%HRR_sitting), when lying awake (%HRR_lying), or during sleep (%HRR_sleeping). Differences between %VO₂R and %HRR values were examined using Bland-Altman plots. A 180-day, 24/7%HRR measurement with three healthy adults was also conducted. The %HRR values during working days and holidays were compared.

Results

In the treadmill testing, the mean difference between %VO₂R and %HRR_sleeping was 1.7% (95% confidence interval [CI], − 0.2 to 3.6%). The %HRR_sitting and %HRR_lying values were 10.8% (95% CI, 8.8 to 12.7%) and 7.7% (95% CI, 5.4 to 9.9%), respectively. In the 6MWT, mean differences between %VO₂R and %HRR_sitting, %HRR_lying and %HRR_sleeping were 12.7% (95% CI, 10.0 to 15.5%), 7.0% (95% CI, 4.0 to 10.0%) and − 2.9% (95% CI, − 5.0% to − 0.7%), respectively. The 180-day, 24/7%HRR measurement presented significant differences in %HRR patterns between working days and holidays in all three participants.

Conclusions

The results suggest %HRR_sleeping is valid in comparison to %VO₂R. The results may encourage a calibration-less, 24/7 measurement model of exercise intensity using wearable devices.

Trial registration

UMIN000034967.

Registered 21 November 2018 (retrospectively registered).

Effect of die material hue and value on polymerization of indirect resin inlays

This study investigated the effects of hue and value of mold materials on the polymerization of resin composite inlays fabricated by an indirect process. Three colors of molds were used, and 10 specimens each of three resin composite materials were light polymerized on each of the die materials for each test. The specimens were postirradiation heat polymerized. Compressive strength, flexural strength, Knoop hardness, and bond strength to enamel and dentin were measured. Although differences in properties between specimens polymerized on different die materials were not always significant, the values obtained from the white mold were highest in all tests followed by those obtained on the gray and then the black molds. Correlations (r2) between lightness or value and compressive and flexural strength, hardness, and bond to enamel and dentin values were greater than 0.90. Die color and value appear to be important factors influencing the properties of indirect resin composite inlays even though they are heat polymerized after light activation.

Tissue-specific and periodic changes in the nuclease sensitivity of the fibroin gene chromatin in the silkworm Bombyx mori

Nuclei of substantial purity were isolated from the middle or posterior silk glands of the silkworm Bombyx mori larvae. Both the fibroin H- and L-chain gene sequences in the isolated nuclei from the posterior silk glands of the fifth instar larvae, where the genes are transcribed actively, are extremely sensitive to the digestion with DNaseI; on the other hand, these sequences in the middle silk gland nuclei from the same larvae, where the genes are not expressed, are markedly resistant to the digestion. The H-chain gene sequences in the posterior silk gland nuclei from the fifth instar larvae are also highly susceptible to the digestion with micrococcal nuclease, HinfI, and HhaI. The digestion products with micrococcal nuclease show a continuous size distribution. The H-chain gene sequences in the middle silk gland nuclei or the posterior silk gland nuclei from the fourth molting stage are cleaved partially into nucleosome dimer to oligomer sizes upon digestion with higher concentrations of micrococcal nuclease, suggesting that the inactive forms of the H-chain gene chromatin are constructed by folding of the chromatin fiber containing a regular array of nucleosomes. Hypersensitive sites to micrococcal nuclease are present near both ends of the second exon, a major body of the fibroin H-chain gene, in both the active and inactive forms of the chromatin. The DNaseI or micrococcal nuclease sensitivity of the H-chain gene chromatin in the posterior silk gland nuclei shows periodical changes corresponding to the intermolt-molt-intermolt cycle.