Objectives:To test the hypothesis that daily acute intermittent hypoxia (dAIH) and dAIH combined with overground walking improve walking speed and endurance in persons with chronic incomplete spinal cord injury (iSCI).Methods:Nineteen subjects completed the randomized, double-blind, placebo-controlled, crossover study. Participants received 15, 90-second hypoxic exposures (dAIH, fraction of inspired oxygen [Fio2] = 0.09) or daily normoxia (dSHAM, Fio2 = 0.21) at 60-second normoxic intervals on 5 consecutive days; dAIH was given alone or combined with 30 minutes of overground walking 1 hour later. Walking speed and endurance were quantified using 10-Meter and 6-Minute Walk Tests. The trial is registered at ClinicalTrials.gov (NCT01272349).Results:dAIH improved walking speed and endurance. Ten-Meter Walk time improved with dAIH vs dSHAM after 1 day (mean difference [MD] 3.8 seconds, 95% confidence interval [CI] 1.1–6.5 seconds, p = 0.006) and 2 weeks (MD 3.8 seconds, 95% CI 0.9–6.7 seconds, p = 0.010). Six-Minute Walk distance increased with combined dAIH + walking vs dSHAM + walking after 5 days (MD 94.4 m, 95% CI 17.5–171.3 m, p = 0.017) and 1-week follow-up (MD 97.0 m, 95% CI 20.1–173.9 m, p = 0.014). dAIH + walking increased walking distance more than dAIH after 1 day (MD 67.7 m, 95% CI 1.3–134.1 m, p = 0.046), 5 days (MD 107.0 m, 95% CI 40.6–173.4 m, p = 0.002), and 1-week follow-up (MD 136.0 m, 95% CI 65.3–206.6 m, p < 0.001).Conclusions:dAIH ± walking improved walking speed and distance in persons with chronic iSCI. The impact of dAIH is enhanced by combination with walking, demonstrating that combinatorial therapies may promote greater functional benefits in persons with iSCI.Classification of evidence:This study provides Class I evidence that transient hypoxia (through measured breathing treatments), along with overground walking training, improves walking speed and endurance after iSCI.
Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.
To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient. These capabilities are usually assessed by a clinician and reported by the Medicare K-level designation of mobility. However, it is not clear how the K-level designation objectively relates to the use of prostheses outside of a clinical environment. Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels. We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week. This relationship suggests that accelerometry-based technologies such as mobile phones can be used to evaluate real world activity for mobility assessment. Quantifying everyday activity promises to improve assessment of real world prosthesis use, leading to a better matching of prostheses to individuals and enabling better evaluations of future prosthetic devices.
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