BackgroundThe occurrence of freezing of gait (FOG) is often observed in moderate to last-stage Parkinson’s disease (PD), leading to a high risk of falls. The emergence of the wearable device has offered the possibility of FOG detection and falls of patients with PD allowing high validation in a low-cost way.ObjectiveThis systematic review seeks to provide a comprehensive overview of existing literature to establish the forefront of sensors type, placement and algorithm to detect FOG and falls among patients with PD.MethodsTwo electronic databases were screened by title and abstract to summarize the state of art on FOG and fall detection with any wearable technology among patients with PD. To be eligible for inclusion, papers were required to be full-text articles published in English, and the last search was completed on September 26, 2022. Studies were excluded if they; (i) only examined cueing function for FOG, (ii) only used non-wearable devices to detect or predict FOG or falls, and (iii) did not provide sufficient details about the study design and results. A total of 1,748 articles were retrieved from two databases. However, only 75 articles were deemed to meet the inclusion criteria according to the title, abstract and full-text reviewed. Variable was extracted from chosen research, including authorship, details of the experimental object, type of sensor, device location, activities, year of publication, evaluation in real-time, the algorithm and detection performance.ResultsA total of 72 on FOG detection and 3 on fall detection were selected for data extraction. There were wide varieties of the studied population (from 1 to 131), type of sensor, placement and algorithm. The thigh and ankle were the most popular device location, and the combination of accelerometer and gyroscope was the most frequently used inertial measurement unit (IMU). Furthermore, 41.3% of the studies used the dataset as a resource to examine the validity of their algorithm. The results also showed that increasingly complex machine-learning algorithms had become the trend in FOG and fall detection.ConclusionThese data support the application of the wearable device to access FOG and falls among patients with PD and controls. Machine learning algorithms and multiple types of sensors have become the recent trend in this field. Future work should consider an adequate sample size, and the experiment should be performed in a free-living environment. Moreover, a consensus on provoking FOG/fall, methods of assessing validity and algorithm are necessary.Systematic Review Registration: PROSPERO, identifier CRD42022370911.

The quantification of home advantage is the key to understanding the home advantage. Most studies on the quantification of home advantage focus on home winning percentage, while little has been done on quantifying home advantage using technical and tactical performance. This study analyzed 480 matches from four seasons of the UEFA Champion League from 2016 to 2020, and used quantitative differences (QD) to evaluate teams’ overall performance. Both total shots and dribbles had a significant association with home winning percentage (HWP) when home total shot (HTS) was higher than away total shots (ATS), and the difference was in (9,10], and home dribbles (HD) was higher than away dribbles (AD) and in (2,4]. Besides, a significant association existed between aerial duel won and HWP when home aerial success (HADS) was lower than away aerial success (AADS), and the difference was in (0,2] and (3,4]. Besides, total shots and overall QD had a significant association with the same historical outcomes when HTS>ATS and was in (4,5] and (9,10], and summary of home performance (SHP) was higher than summary of away performance (SAP) and in (0.4,0.5], (0.6,0.7] and (0.9,1.1]. In conclusion, total shots, dribbles and aerial duel won could qualify the home advantage and elevate HWP. Meanwhile, a team has a high possibility of obtaining the same results when the difference in total shots and overall QD reaches certain intervals, which can be used to predict game outcomes in the future.

BACKGROUND The prognosis evaluation of liver failure should run through the whole diagnosis. The quantitative difference (QD) may be beneficial in the prognosis evaluation of acute-on-chronic liver failure (ACLF). OBJECTIVE This study aims to verify whether the QD algorithm has the same function or is even better than the Model for End-Stage Liver Disease (MELD). METHODS Conventional treatment (n=12) or double plasma molecular absorption system (DPMAS) with conventional treatment (n=15). The prognosis evaluation was performed by the MELD and QD scoring system. RESULTS A very signification reduction was observed in alanine aminotransferase, aspartate aminotransferase and conjugated bilirubin, both in P-value (P<0.01) and QD (>1.69), and a significant decrease in hemoglobin, red blood cell count and total bilirubin were observed in DPMAS group (P<0.05), but not in QD (≤1.69). Meanwhile, there was a significant association between MELD and QD scores. It significantly differed between groups divided by patients’ status after treatment. Besides, the QD algorithm can also show patients' improvement, such as fatigue and jaundice. CONCLUSIONS Compared with the conventional treatment group, DPMAS can reduce alanine aminotransferase, aspartate aminotransferase and unconjugated bilirubin. As a dynamic algorithm, the QD scoring system can evaluate the therapeutic effect of patients with ACLF, which proves that it has the same function as MELD, but considers more indicators and patient variability.