Modeling of the Rating of Perceived Exertion Based on Heart Rate Using Machine Learning Methods

Abstract Rating of perceived exertion (RPE) can serve as a more convenient and economical alternative to heart rate (HR) for exercise intensity control. This study aims to explore the influence of factors, such as indicators of demographic, anthropometric, body composition, cardiovascular function and basic exercise ability on the relationship between HR and RPE, and to develop the model predicting RPE from HR. 48 healthy participants were recruited to perform an incrementally 6-stage pedaling test. HR and RPE were collected during each stage. The influencing factors were identified with the forward selection method to train Gaussian Process regression (GPR), support vector machine (SVM) and linear regression models. Metrics of R2, adjusted R2 and RMSE were calculated to evaluate the performance of the models. The GPR model outperformed the SVM and linear regression models, and achieved an R2 of 0.95, adjusted R2 of 0.89 and RMSE of 0.52. Indicators of age, resting heart rate (RHR), Central arterial pressure (CAP), body fat rate (BFR) and body mass index (BMI) were identified as factors that best predicted the relationship between RPE and HR. It is possible to use GPR model to estimate RPE from HR accurately, after adjusting for age, RHR, CAP, BFR and BMI

Effectiveness of personalized lifestyle intervention for community-dwelling, middle-aged and older patients with hypertension: evidence from a health promotion program in Chinese community

The objective of this study was to preliminary evaluate the effectiveness of multicomponent, personalized lifestyle intervention for middle-aged and older patients with hypertension in a limited- resource Chinese community. A single-arm, pre-post intervention design was used. 169 community- dwelling patients were enrolled and trained community health workers conducted intervention activities in a local community health center. The intervention consisted of key strategies for promoting dietary habits, physical activity and health-related behaviors, where participants received individualized lifestyle guidance. Of enrolled participants, 122 completed the study. The 6-month intervention was efficient in improving dietary habits and health-related behavior. No significant changes was found in physical activity. Clinically significant were found in SBP by 8.3mmHg, DBP by 4.1mmHg (p<0.001) and improvements in secondary outcomes. Strategies for personalized intervention and motivational interviewing techniques provided great reference to the practice of public health care, with essential lessons learned from exercise promotion

Named Entity Recognition in Chinese Medical Literature Using Pretraining Models

The medical literature contains valuable knowledge, such as the clinical symptoms, diagnosis, and treatments of a particular disease. Named Entity Recognition (NER) is the initial step in extracting this knowledge from unstructured text and presenting it as a Knowledge Graph (KG). However, the previous approaches of NER have often suffered from small-scale human-labelled training data. Furthermore, extracting knowledge from Chinese medical literature is a more complex task because there is no segmentation between Chinese characters. Recently, the pretraining models, which obtain representations with the prior semantic knowledge on large-scale unlabelled corpora, have achieved state-of-the-art results for a wide variety of Natural Language Processing (NLP) tasks. However, the capabilities of pretraining models have not been fully exploited, and applications of other pretraining models except BERT in specific domains, such as NER in Chinese medical literature, are also of interest. In this paper, we enhance the performance of NER in Chinese medical literature using pretraining models. First, we propose a method of data augmentation by replacing the words in the training set with synonyms through the Mask Language Model (MLM), which is a pretraining task. Then, we consider NER as the downstream task of the pretraining model and transfer the prior semantic knowledge obtained during pretraining to it. Finally, we conduct experiments to compare the performances of six pretraining models (BERT, BERT-WWM, BERT-WWM-EXT, ERNIE, ERNIE-tiny, and RoBERTa) in recognizing named entities from Chinese medical literature. The effects of feature extraction and fine-tuning, as well as different downstream model structures, are also explored. Experimental results demonstrate that the method of data augmentation we proposed can obtain meaningful improvements in the performance of recognition. Besides, RoBERTa-CRF achieves the highest F1-score compared with the previous methods and other pretraining models

Ankle reflex and neurological symptom score: a primary level screening method for diabetic peripheral neuropathy

Herein, we aimed to develop an easily available and efficient screening method for diabetic peripheral neuropathy (DPN) suitable for primary care settings, emphasizing simplicity, speed, and accuracy. Nerve conduction studies were conducted on 214 patients with diabetes, encompassing the outcomes of five distinct assessments: diabetic neuropathy symptom (DNS), vibration perception threshold (VPT), and nerve screening. The diagnostic accuracy of the VPT and nerve screening was evaluated by comparing them with that of the nerve conduction study. To assess diagnostic efficacy, various combinations were examined, including DNS combined with VPT, pain, temperature, touch, and ankle reflex. The diagnostic performance of DNS was superior to that of the five neurological screening items and VPT, with sensitivity, specificity, and accuracy of 0.68, 0.81, and 0.73, respectively. Among the two combined methods, “DNS + ankle reflex” was identified as having the highest diagnostic value, with an area under the curve, a sensitivity, a specificity, and an accuracy of 0.81, 0.89, 0.70, and 0.80, respectively. Furthermore, a combination of “DNS + ankle reflex + touch + pain + VPT” achieved the best performance among the five combinations, with an area under the curve, sensitivity, specificity, and accuracy of 0.85, 0.93, 0.68, and 0.81, respectively. The combination of DNS, ankle reflex, touch, pain, and VPT methods showed the highest diagnostic value for DPN. However, considering factors including accuracy, time, and economic cost, we recommend using a simpler combination of DNS and ankle reflex for large-scale screening of patients with DPN

Evaluating a WeChat-Based Health Behavioral Digital Intervention for Patients With Hypertension: Protocol for a Randomized Controlled Trial

BackgroundHypertension is the most prevalent chronic condition and a significant risk factor for cardiovascular and kidney diseases. The efficacy of health behavioral interventions in blood pressure (BP) control has been demonstrated by a large and expanding body of literature, with “adherence” playing a crucial role. WeChat is the most common social communication mobile app in China, and it has been shown to be an acceptable delivery platform for delivering health interventions. The WeChat-based health behavioral digital intervention program (WHBDIP) showed high feasibility and efficacy. However, the results regarding BP improvement between the WHBDIP and control groups were inconsistent. ObjectiveThe objective of this study is to develop a WHBDIP and assess its efficacy in controlling BP and improving adherence among patients with hypertension. MethodsA 2-arm, parallel-group, and randomized trial design was used. Patients older than 60 years and with hypertension were randomly assigned to either the control group or the experimental group, which received a 12-week intervention. The program, primarily developed based on the Behavior Change Wheel (BCW) theory, offers health education on exercise, diet, BP monitoring, and medicine adherence (MA). It also includes other behavior interventions guided by an intervention manual, incorporating behavior change techniques (BCTs). The primary outcomes encompass BP and adherence indicators, while the secondary outcomes encompass cardiovascular function indicators, body composition indicators, learning performance, satisfaction, and acceptability. The exercise and blood pressure monitoring adherence (BPMA) indicators for the WHBDIP group were assessed weekly via WeChat during the initial 3 months, while other outcome data for both groups will be collected at the baseline assessment phase, 3 months after the intervention, and 1 year after the program. ResultsThe trial will assess the efficacy of WHBDIP for patients with hypertension (N=68). The WHBDIP seeks to enhance participants' knowledge of healthy behaviors and assist patients in developing positive health behaviors to improve their health outcomes. Patient recruitment for individuals with hypertension commenced on September 5, 2022, and concluded on September 19, 2022. The 3-month intervention and phased data collection were finalized in January 2023. Data analysis will commence in August 2023, and the final 1-year health outcome results will be collected in September 2023. ConclusionsA successful WHBDIP will establish the management mode as a feasible approach for hypertension management in the community. Additionally, it will pave the way for the development of related mobile health programs. Trial RegistrationChinese Clinical Trial Registry ChiCTR2200062643; https://tinyurl.com/mwyv67wk International Registered Report Identifier (IRRID)PRR1-10.2196/4688