Arrhythmia has a high incidence, and is a serious threat to human health. However, due to concealed symptoms and unpredictability of onset, it is difficult for traditional ECG equipment to capture the ECG data at the onset of the arrhythmic events, so it could be misdiagnosed and under-treated. Fortunately, the diagnosis rate of arrhythmia could be greatly enhanced by analyzing the uploaded real-time ECG data of individuals measured at home using the wearable single-lead ECG monitoring system under the guidance of community doctors, but there is a lack of relevant evidence from real-world studies.
To assess the diagnostic accuracies of three schemes for screening arrhythmia in the community using a wearable single-lead ECG monitoring system.
A real-world, community-based study design was used for comparing three schemes for screening arrhythmia using a wearable single-lead ECG monitoring system: scheme 1 was used for collecting 24-hour ECG data on any three nonconsecutive days in two weeks, scheme 2 was used for collecting 24-hour ECG data on any day and at least three 1-hour ECG data in two weeks, and scheme 3 was adopted for collecting 72-hour ECG data on any three consecutive days and at least one 1-hour ECG data on any one day in two weeks. Subjects were 435 community-living residents, who were recruited from Yinchuan from September 2020 to September 2021. They were divided into male group (177 cases) , female group (258 cases) ; young group (135 cases) , middle-aged group (200 cases) and elderly group (100 cases) by age; primary school group (77 cases) , middle school group (165 cases) and university group (193 cases) by educational level; arrhythmia group (233 cases) and non-arrhythmia group (202 cases) by the history of arrhythmia. Subjects measured the ECG either at the onset time of perceived arrhythmia or not using any one of the three screening schemes chosen voluntarily with the wearable single-lead ECG monitoring system, then uploaded the measurement results to the cloud platform. The number of participants using each of the three screening schemes was counted. The correlation of age, education level or history of arrhythmia with scheme selection was analyzed. And detection rates of the three screening schemes were compared.
The number of subjects who selected the three screening schemes was 321, 40 and 74, respectively. The average age of the subjects was significantly different (P=0.047) . There was no correlation between gender, education level, medical history and protocol selection (χ2=0.670, P=0.715; χ2=2.994, P=0.559; χ2=2.225, P=0.893) . There was a significant correlation between different age groups and protocol selection (χ2=9.939, P=0.041) . The arrhythmia detection rates of the three screening protocols were 85.67%, 82.50% and 85.14%, respectively, and the difference was not statistically significant (χ2=0.286, P=0.867) . There was no significant difference in the arrhythmia detection rate between the male group and the female group (χ2=0.966, P=0.707; χ2=0.917, P=0.678) . There was no significant difference in the detection rate of arrhythmia among young group, middle-aged group and elderly group (χ2=2.102, P=0.350; χ2=0.871, P=0.706; χ2=1.063, P=0.622) . There was no significant difference in the detection rate of arrhythmia among the three screening schemes in primary school group, middle school group and university group (χ2=2.421, P=0.271; χ2=1.115, P=0.633; χ2=2.181, P=0.353) . There was no significant difference in the arrhythmia detection rate between the three screening protocols in the history group and the no history group (χ2=1.442, P=0.507; χ2=0.548, P=0.818) . The frequency of 1-hour ECG data collection in protocol 2 was positively correlated with arrhythmia detection rate (rs=0.912, P=0.011) . The frequency of 1-hour ECG data collection in protocol 3 was positively correlated with arrhythmia detection rate (rs=0.852, P=0.026) . In protocol 2, the detection rate of arrhythmia in 24-hour ECG data was 72.5%, and that in 1-hour ECG data was 77.5%. There was a strong consistency between the two kinds of long-term ECG data (Kappa=0.601, P=0.001) . In protocol 3, the arrhythmia detection rate of 72-hour ECG data was 82.4%, and the arrhythmia detection rate of 1-hour ECG data was 63.5%. There was a medium consistency between the two kinds of long-term ECG data (Kappa=0.410, P<0.001) . In protocol 2, there was a strong consistency between the diagnosis results of 1-hour ECG data and the total protocol (Kappa=0.844, P<0.001) . There was a strong consistency between 24-hour ECG data diagnosis and total protocol diagnosis (Kappa=0.717, P<0.001) . In protocol 3, there was a moderate consistency between the 1-hour ECG data diagnosis and the total protocol diagnosis (Kappa=0.466, P<0.001) , and the consistency strength was general. The results of 72-hour diagnosis were strongly consistent with those of the total protocol (Kappa=0.901, P<0.001) .
There is no significant difference in the arrhythmia detection rate among the three arrhythmia screening schemes based on community mobile health care, which can be used regardless of whether there are symptoms or not. Subjects of different ages have different tendencies to choose the three screening schemes, and the frequency of 1-hour ECG data collection is positively correlated with the arrhythmia detection rate, which suggests that the community doctors should select the optimal compliance screening scheme according to patients' age, occupational characteristics, economic income and other factors, so as to truly enable the screening and management of arrhythmia in the community using mobile technologies.
There is currently insufficient effective treatment for hearing loss in the elderly since it is a hidden disease whose damage is irreversible. It is crucial to establish early warnings, screenings and interventions. As of now, there are few studies carried out on the assessment of hearing loss in the elderly based on risk factors at home and abroad. There are no standardized measurement tools or perfect scales.
Utilizing smart medicine and the WeChat platform, investigate high-risk factors of hearing loss in the elderly, develop screening software for hearing loss in the elderly, and explore the screening and management modes of hearing loss in the elderly.
Based on cross-sectional survey, five community health service centers in Pudong New Area of Shanghai were obtained. The tudy was performed from April to December 2019 to investigate the distribution of risk factors for hearing loss in the elderly in the community, and conditional logic was applied, and receiver operation charateristic curve (ROC curve) were used for risk stratification. The screening software for hearing loss in the elderly has been developed using JavaScript language during the period January and June 2020. Verification and evaluation of the screening software were performed between July 2020 to March 2021.
The study involved 401 elderly peoplein across-sectional design. Multivariate Logistic regression analysis revealed that aging〔OR=1.100, 95%CI (1.037, 1.166) 〕, noise history〔OR=3.886, 95%CI (1.077, 14.022) 〕, non-light diet〔OR=2.445, 95%CI (1.127, 5.305) 〕, hypertension〔OR=1.8393, 95%CI (1.015, 3.330) 〕, diabetes〔OR=4.310, 95%CI (1.817, 10.225) 〕and hyperuricemia〔OR=3.174, 95%CI (1.030, 9.779) 〕were independent risk factors (P<0.05) . A total of 18 factors (male, overweight/obesity, living alone, widowed/divorced, noise history, family history of deafness, non-light diet, no exercise habits, smoking, drinking, wearing headphones, hypertension, diabetes, hyperlipidemia, cardiovascular and cerebrovascular diseases, hyperuricemia, hypothyroidism, ototoxic drug use history) were included in the analysis as a result of the difference analysis and literature review. According to the ROC curve, combined scores of risk factors can predict hearing loss in the elderly with an area under the curve (AUC) of 0.777〔95%CI (0.721, 0.833) 〕, and the cut off value is 3.5. According to this study, a cumulative risk factor score of 4 defined the threshold for predicting hearing loss in the elderly. The elderly were then stratified into those with low risk of hearing loss (<4) and those with high risk of hearing loss (≥4) . The software for screening elderly hearing loss is developed on the WeChat platform. There are four parts in total: risk factors stratification assessment, screening version of the Hearing Impairment Scale (HHIE-s) for the elderly, general conclusions, and health education. From July to December 2020, a total of 78 elderly people were recruited to evaluatethe hearing loss screening software, with a completion rate of 55.1% (43/78) .A mode ratecor relation exists between cumulative risk factor scores and HHIE-s scale score (rs=0.360, P=0.018) . From January to March, 2021, a suitability evaluation questionnaire with 8 single item questions was administered to 106 general practitioners to determine the suitability of hearing loss screening software for the elderly. According to the 5-level Likert scale, the proportion of respondents who answered "completely agree" to each question is 85.8% (91/106) , 81.1% (86/106) , 71.7% (76/106) , 68.9% (73/106) , 68.0% (72/106) , 59.4% (63/106) , 15.1% (16/106) and 14.2% (15/106) respectively.
There are 18 risk factors for hearing loss in the elderly. The screening software for hearing loss in the elderly based on WeChat platform has a good effect in early warning of hearing loss in the elderly, which provides a medical basis for screening for hearing loss in the elderly. Hearing loss screening software can support real-time data transmission, optimize, and integrate the hierarchical assessment system of risk factors, HHIE-s and WeChat platform for self-health management. The screening software of hearing loss for the elderly offers general practitioners a new way to manage and control hearing loss provides a new way of hearing loss management and control for general practitioners at the grassroots.It is appropriate and enforceable.
