Is increased "stay away from bed" time associated with improved clinical rehabilitation outcomes in Japanese rehabilitation hospitals? A prospective observational study and clinical practice

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.

Repeated passive mobilization to stimulate vascular function in individuals of advanced age who are chronically bedridden. A randomized controlled trial

BACKGROUND

Vascular dysfunction and associated disorders are major side effects of chronic bed rest, yet passive mobilization as a potential treatment has only been theorized so far. This study investigated the effects of passive mobilization treatment on vascular function in older, chronically bedridden people.

METHODS

The study sample was 45 chronically bedridden people of advanced age (mean age 87 years; 56% female; mean bed rest 4 years) randomly assigned to a treatment (n=23) or a control group (CTRL, n=22). The treatment group received passive mobilization twice daily (30 min, 5 times/week) for 4 weeks. A kinesiologist performed passive mobilization by passive knee flexion/extension at 1 Hz in one leg (treated leg, T-leg vs ctrl-leg). The CTRL group received routine treatment. The primary outcome was changes in peak blood flow (∆Peak) as measured with the single passive leg movement test (sPLM) at the common femoral artery.

RESULTS

∆Peak was increased in both legs in the Treatment group (+90.9 ml/min, p<0.001, in T-leg and +25.7 ml/min, p=0.039 in ctrl-leg). No difference in peak blood flow after routine treatment was found in the CTRL group.

CONCLUSION

Improvement in vascular function after 4 weeks of passive mobilization was recorded in the treatment group. Passive mobilization may be advantageously included in standard clinical practice as an effective strategy to treat vascular dysfunction in persons with severely limited mobility.

Undernutrition, Sarcopenia, and Frailty in Fragility Hip Fracture: Advanced Strategies for Improving Clinical Outcomes

Geriatric patients with hip fractures often experience overlap in problems related to nutrition, including undernutrition, sarcopenia, and frailty. Such problems are powerful predictors of adverse responses, although few healthcare professionals are aware of them and therefore do not implement effective interventions. This review aimed to summarize the impact of undernutrition, sarcopenia, and frailty on clinical outcomes in elderly individuals with hip fractures and identify successful strategies that integrate nutrition and rehabilitation. We searched PubMed (MEDLINE) and Cochrane Central Register of Controlled Trials (CENTRAL) for relevant literature published over the last 10 years and found that advanced interventions targeting the aforementioned conditions helped to significantly improve postoperative outcomes among these patients. Going forward, protocols from advanced interventions for detecting, diagnosing, and treating nutrition problems in geriatric patients with hip fractures should become standard practice in healthcare settings.