Reliability and Validity of Self-Screening Tool for Hearing Loss in Older Adults

Can Videoconferencing Be a Viable Method to Measure Speech Perception?

PURPOSE

Telehealth has proven effective for service delivery to remote and rural locations and was helpful during lockdowns when patients were unable to see clinicians in person. To assure the reliability of clinical services, the aim of the present study was to evaluate a telehealth protocol to measure speech perception skills through virtual meeting applications such as Zoom.

METHOD

A total of 20 participants with normal hearing and cognition participated in the study. Participants' speech perception performance was measured in two sessions: one over a Zoom call and one in person in the laboratory. Speech perception was measured using Quick Speech-in-Noise Test, Bamford-Kowal-Bench Speech-in-Noise Test, and Words-in-Noise Test.

RESULTS

Statistical analysis revealed no significant differences between speech perception scores obtained over the Zoom and in-person methodologies for any of the tests. Additionally, our study found no significant difference in the scores obtained between wired and wireless headphones during Zoom calls.

CONCLUSION

These results suggest that Zoom can be used as a reliable method to measure speech perception in young individuals with normal hearing using these three tests in situations where conventional methods cannot be utilized.

A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population

BACKGROUND

Hearing impairment (HI) has become a major public health issue in China. Currently, due to the limitations of primary health care, the gold standard for HI diagnosis (pure-tone hearing test) is not suitable for large-scale use in community settings. Therefore, the purpose of this study was to develop a cost-effective HI screening model for the general population using machine learning (ML) methods and data gathered from community-based scenarios, aiming to help improve the hearing-related health outcomes of community residents.

METHODS

This study recruited 3371 community residents from 7 health centres in Zhejiang, China. Sixty-eight indicators derived from questionnaire surveys and routine haematological tests were delivered and used for modelling. Seven commonly used ML models (the naive Bayes (NB), K-nearest neighbours (KNN), support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), boosting, and least absolute shrinkage and selection operator (LASSO regression)) were adopted and compared to develop the final high-frequency hearing impairment (HFHI) screening model for community residents. The model was constructed with a nomogram to obtain the risk score of the probability of individuals suffering from HFHI. According to the risk score, the population was divided into three risk stratifications (low, medium and high) and the risk factor characteristics of each dimension under different risk stratifications were identified.

RESULTS

Among all the algorithms used, the LASSO-based model achieved the best performance on the validation set by attaining an area under the curve (AUC) of 0.868 (95% confidence interval (CI): 0.847-0.889) and reaching precision, specificity and F-score values all greater than 80%. Five demographic indicators, 7 disease-related features, 5 behavioural factors, 2 environmental exposures, 2 hearing cognitive factors, and 13 blood test indicators were identified in the final screening model. A total of 91.42% (1235/1129) of the subjects in the high-risk group were confirmed to have HI by audiometry, which was 3.99 times greater than that in the low-risk group (22.91%, 301/1314). The high-risk population was mainly characterized as older, low-income and low-educated males, especially those with multiple chronic conditions, noise exposure, poor lifestyle, abnormal blood indices (e.g., red cell distribution width (RDW) and platelet distribution width (PDW)) and liver function indicators (e.g., triglyceride (TG), indirect bilirubin (IBIL), aspartate aminotransferase (AST) and low-density lipoprotein (LDL)). An HFHI nomogram was further generated to improve the operability of the screening model for community applications.

CONCLUSIONS

The HFHI risk screening model developed based on ML algorithms can more accurately identify residents with HFHI by categorizing them into the high-risk groups, which can further help to identify modifiable and immutable risk factors for residents at high risk of HI and promote their personalized HI prevention or intervention.

Construction and Evaluation of a High-Frequency Hearing Loss Screening Tool for Community Residents

Early screening and detection of individuals at high risk of high-frequency hearing loss and identification of risk factors are critical to reduce the prevalence at community level. However, unlike those for individuals facing occupational auditory hazards, a limited number of hearing loss screening models have been developed for community residents. Therefore, this study used lasso regression with 10-fold cross-validation for feature selection and model construction on 38 questionnaire-based variables of 4010 subjects and applied the model to training and testing cohorts to obtain a risk score. The model achieved an area under the curve (AUC) of 0.844 in the model validation stage and individuals’ risk scores were subsequently stratified into low-, medium-, and high-risk categories. A total of 92.79% (1094/1179) of subjects in the high-risk category were confirmed to have hearing loss by audiometry test, which was 3.7 times higher than that in the low-risk group (25.18%, 457/1815). Half of the key indicators were related to modifiable contexts, and they were identified as significantly associated with the incident hearing loss. These results demonstrated that the developed model would be feasible to identify residents at high risk of hearing loss via regular community-level health examinations and detecting individualized risk factors, and eventually provide precision interventions.

The impact of COVID-19 on provision of UK audiology services & on attitudes towards delivery of telehealth services

OBJECTIVE

To (i) identify the impact of COVID-19 on provision of UK audiology services across sectors (ii) compare teleaudiology service provision between private and public sectors before and after the introduction of restrictions and (iii) identify barriers to teleaudiology delivery amongst UK hearing care professionals in both sectors.

DESIGN

A mixed-methods cross-sectional survey study design. Responses to the structured questionnaire were analysed using descriptive and non-parametric statistics.

STUDY SAMPLE

UK based hearing care professionals (HCP) (n = 323) completed the survey (218 public sector; 89 private sector).

RESULTS

Changes in working patterns varied greatly between different sectors, with 61% of national employed and 26% of independent HCPs being furloughed, compared with 1% in the public sector. Use of telehealth was under-utilised across all sectors and groups in UK hearing healthcare, despite 92% of public and 75% of private HCPs reporting feeling comfortable conducting remote consultations.

CONCLUSION

This study highlights a variation in teleaudiology adoption and key barriers across sector in the UK. A collaborative approach between hearing device manufacturers, research centres, HCPs and professional bodies is required for the creation of targeted guidance and training materials according to sector, to support clinicians in effective teleaudiology provision.

Development of the Hearing Rehabilitation for Older Adults (HeRO) Healthcare Mobile Application and Its Likely Utility for Elderly Users

The present study aimed to develop a healthcare application for the elderly who suspect or know they have a hearing loss, namely, the Hearing Rehabilitation for Older Adults (HeRO), which is available in a mobile device, and then to confirm its probability of acceptance among elderly users. Under a web server system, HeRO which had four types of tailored training for the aged auditory system (i.e., syllable, sentence, discourse, working memory) and a self-reported questionnaire to screen amount of the hearing loss was completed for the elderly. To verify whether the HeRO contents and functions were user-friendly to the elderly users, the technology acceptance model (TAM) was used. Forty-four older adults were asked to use the developed application for 10 days and then respond to a TAM questionnaire with 25 items. The Cronbach’s α coefficient of each subcategory was very high. The construct validity of all subcategories showed high eigenvalues using principal component analysis. Furthermore, our regression model statistically supported a persuasive intention to use the healthcare application because the elderly readily accept it and find it easy to manipulate. We expect the current technology to be applied to the general public as well as the elderly who want to explore digital health.