Effects of an Upper-Body Training Program Involving Resistance Exercise and High-Intensity Arm Cranking on Peak Handcycling Performance and Wheelchair Propulsion Efficiency in Able-Bodied Men.

Chaikhot, D, Reed, K, Petroongrad, W, Athanasiou, F, van Kooten, D, and Hettinga, FJ. Effects of an upper-body training program involving resistance exercise and high-intensity arm cranking on peak handcycling performance and wheelchair propulsion efficiency in able-bodied men. J Strength Cond Res 34(8): 2267-2275, 2020-The aim of this study was to determine the training effects of an upper-body training program involving resistance exercise and high-intensity arm cranking on peak handcycling performance, propulsion efficiency, and biomechanical characteristics of wheelchair propulsion in able-bodied men. The training group (n = 10) received a 4-week upper-body resistance training (RT), 70% of 1 repetition maximum, 3 sets of 10 repetitions, 8 exercise stations, 2 times per week, combined with high-intensity interval training (HIIT) 2 times per week. High-intensity interval training consisted of arm-crank exercise, 7 intervals of 2 minutes at 80-90% of peak heart rate (HRpeak) with 2-minute active rest at 50-60% of HRpeak. The control group (n = 10) received no training. Both groups performed a preincremental and postincremental handcycling test until volitional exhaustion to evaluate fitness and a 4-minute submaximal wheelchair propulsion test at comfortable speed (CS), 125 and 145% of CS, to evaluate gross mechanical efficiency (GE), fraction of effective force (FEF), percentage of peak oxygen consumption (% V[Combining Dot Above]O2peak), and propulsion characteristics. Repeated-measures analysis of variance was performed (p < 0.05). Training resulted in a 28.2 ± 16.5% increase in peak power output, 13.3 ± 7.5% increase in V[Combining Dot Above]O2peak, 5.6 ± 0.9% increase in HRpeak, and 3.8 ± 1.5% decrease in HRrest. No training effects on FEF, GE, % V[Combining Dot Above]O2peak, and push characteristics were identified. In conclusion, the combined RT and arm-cranking HIIT improved fitness. However, it seems that this training did not result in improvements in propulsion efficiency and push characteristics. Additional wheelchair skill training may be needed to fully benefit from this advantage in daily life propulsion.

Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review.

Objective: Natural language processing (NLP) can enhance research on activities of daily living (ADL) by extracting structured information from unstructured electronic health records (EHRs) notes. This review aims to give insight into the state-of-the-art, usability, and performance of NLP systems to extract information on ADL from EHRs. Materials and Methods: A systematic review was conducted based on searches in Pubmed, Embase, Cinahl, Web of Science, and Scopus. Studies published between 2017 and 2022 were selected based on predefined eligibility criteria. Results: The review identified 22 studies. Most studies (65%) used NLP for classifying unstructured EHR data on 1 or 2 ADL. Deep learning, combined with a ruled-based method or machine learning, was the approach most commonly used. NLP systems varied widely in terms of the pre-processing and algorithms. Common performance evaluation methods were cross-validation and train/test datasets, with F1, precision, and sensitivity as the most frequently reported evaluation metrics. Most studies reported relativity high overall scores on the evaluation metrics. Discussion: NLP systems are valuable for the extraction of unstructured EHR data on ADL. However, comparing the performance of NLP systems is difficult due to the diversity of the studies and challenges related to the dataset, including restricted access to EHR data, inadequate documentation, lack of granularity, and small datasets. Conclusion: This systematic review indicates that NLP is promising for deriving information on ADL from unstructured EHR notes. However, what the best-performing NLP system is, depends on characteristics of the dataset, research question, and type of ADL.

Are there associations with age and sex in walking stability in healthy older adults?

The variability of the centre of pressure (COP) during walking can provide information in relation to stability when walking. The aim of this study was to investigate if age and sex were associated with COP variability, COP excursions, and COP velocities during walking. One-hundred and fourteen older adults (age 65.1±5.5 yrs.) participated in the study. A Kistler force platform (1000Hz) recorded the ground reaction forces and COPs during walking at a self-selected walking speed. The stance phase was divided, using the vertical GRF, into four sub-phases: loading response (LR), mid-stance (MSt), terminal stance (TSt), and pre-swing (PSw). The standard deviations of the COP displacement (variability), the COP velocity, and COP excursion in the medial-lateral and anterior-posterior directions, as well as the resultant magnitude were assessed. When controlling for walking speed, a greater age was associated with a higher variability and excursion of the COP during LR only suggesting that stability is maintained during the majority of the stance phase. During LR lower COP velocity was significantly associated for females for anterior-posterior and total COP, which may be a strategy to facilitate stability before, and moving into, MSt and TSt.