The impact of different step rate threshold methods on physical activity intensity in older adults

BackgroundOlder adults benefit most from engaging in higher-intensity physical activity, which is often determined using step rate thresholds. Fixed step rate thresholds that correspond to moderate (MPA) and vigorous-intensity physical activity (VPA) have been developed for heuristic activity promotion. The activPAL monitor uses step rate thresholds to determine activity intensity. Stepping thresholds may also vary based on body mass index (BMI) or aerobic fitness level in older adults. Despite the various thresholds used in the literature, it is unclear whether they produce similar outcomes.Research QuestionHow does time spent in physical activity intensities compare between different step rate thresholds in older adults?MethodsThirty-eight participants (24♀; 67 ± 4 years; BMI: 26.6 ± 4.4 kg/m²) wore an activPAL monitor 24-hr/day for up to 7-d (total: 205-d). Aerobic fitness (V̇O₂max: 23 ± 8 ml/kg/min) was determined via indirect calorimetry during a maximal, graded cycling test. Time spent in each intensity category (light-physical-activity [LPA], MPA, VPA) was determined using the fixed (MPA/VPA) 100/130, 110/130, and activPAL step rate thresholds (74/212), as well as BMI-adjusted absolute (108.5 ± 2.5/134.0 ± 4.8) and BMI-adjusted relative (40%/60% V̇O₂max; 111.4 ± 14.7/132.0 ± 19.0) cut-offs. Times spent in each intensity category were compared between methods.ResultsThe activPAL and 100/130 thresholds yielded less LPA and more MPA than all other methods. The activPAL had no time spent in VPA at all. The BMI-adjusted absolute and relative thresholds produced statistically equivalent time in LPA and MPA (via equivalence testing), but not VPA. No two methods yielded similar time spent in LPA, MPA, or VPA.SignificanceThe choice of step rate threshold has a major impact on physical activity intensity outcomes in older adults. Inherently, strategies that adjust for older adults’ body size and/or aerobic fitness level provide a more individualized data processing strategy than fixed thresholds that assume the same threshold for all older adults     

2010—2016年重庆市中毒病例流行病学特征及影响因素分析

对2010年1月—2016年7月重庆市中毒救治指定医疗机构收治的2 497例中毒患者进行回顾性分析。结果显示，中毒患者中男性1 230例（49.3%）、女性1 267例（50.7%）；分布最多的3个年龄段依次是41~50岁（18.5%）、≤10岁（16.4%）和21~30岁（13.0%）；死亡16例，病死率0.6%。中毒类型构成排前三位的是细菌（33.8%）、化学品（32.2%）、植物（14.8%）。中毒预后多因素Logistic回归分析结果显示重度中毒患者结局死亡的可能性是轻度患者的1 102倍。提示重庆市应重点关注农村地区41~50岁人群的细菌、化学品、药物中毒，同时应该加强儿童中毒防控工作。     

Predefined vs data-guided training prescription based on autonomic nervous system variation: A systematic review

Monitoring variations in the functioning of the autonomic nervous system may help personalize training of runners and provide more pronounced physiological adaptations and performance improvements. We systematically reviewed the scientific literature comparing physiological adaptations and/or improvements in performance following training based on responses of the autonomic nervous system (ie, changes in heart rate variability) and predefined training. PubMed, SPORTDiscus, and Web of Science were searched systematically in July 2019. Keywords related to endurance, running, autonomic nervous system, and training. Studies were included if they (a) involved interventions consisting predominantly of running training; (b) lasted at least 3 weeks; (c) reported pre- and post-intervention assessment of running performance and/or physiological parameters; (d) included an experimental group performing training adjusted continuously on the basis of alterations in HRV and a control group; and (e) involved healthy runners. Five studies involving six interventions and 166 participants fulfilled our inclusion criteria. Four HRV-based interventions reduced the amount of moderate- and/or high-intensity training significantly. In five interventions, improvements in performance parameters (3000 m, 5000 m, Loadmax, Tlim) were more pronounced following HRV-based training. Peak oxygen uptake () and submaximal running parameters (eg, LT1, LT2) improved following both HRV-based and predefined training, with no clear difference in the extent of improvement in . Submaximal running parameters tended to improve more following HRV-based training. Research findings to date have been limited and inconsistent. Both HRV-based and predefined training improve running performance and certain submaximal physiological adaptations, with effects of the former training tending to be greater.     

Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

Tester and testing procedure influence clinically determined gait speed

BackgroundThere is a clinical need to be able to reliably detect meaningful changes (0.1 to 0.2 m/s) in usual gait speed (UGS) considering reduced gait speed is associated with morbidity and mortality.Research questionWhat is the impact of tester on UGS assessment, and the influence of test repetition (trial 1 vs. 2), timing method (manual stopwatch vs. automated timing), and starting condition (stationary vs. dynamic start) on the ability to detect changes in UGS and fast gait speed (FGS)?MethodsUGS and FGS was assessed in 725 participants on a 8-m course with infrared timing gates positioned at 0, 2, 4 and 6 m. Testing was performed by one of 13 testers trained by a single researcher. Time to walk 4-m from a stationary start (i.e. from 0-m to 4-m) was measured manually using a stopwatch and automatically via the timing gates at 0-m and 4-m. Time taken to walk 4-m with a dynamic start was measured during the same trial by recording the time to walk between the timing gates at 2-m and 6-m (i.e. after 2-m acceleration).ResultsTesters differed for UGS measured using manual vs. automated timing (p = 0.02), with five and two testers recording slower and faster UGS using manual timing, respectively. 95% limits of agreement for trial 1 vs. 2, manual vs. automated timing, and dynamic vs. stationary start ranged from ±0.15 m/s to ±0.20 m/s, coinciding with the range for a clinically meaningful change. Limits of agreement for FGS were larger ranging from ±0.26 m/s to ±0.35 m/s.SignificanceRepeat testing of UGS should performed by the same tester or using an automated timing method to control for tester effects. Test protocol should remain constant both between and within participants as protocol deviations may result in detection of an artificial clinically meaningful change.