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  • 1
    Online Resource
    Online Resource
    Can Face- and Smartphone-Touching Behaviors Be Altered with Personal Hygiene Reminders during the COVID-19 Pandemic Period? An Observational Study
    MDPI AG ; 2021
    In:  International Journal of Environmental Research and Public Health Vol. 18, No. 19 ( 2021-09-24), p. 10038-
    Details
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 18, No. 19 ( 2021-09-24), p. 10038-
    Abstract: As part of the new measures to prevent the spread of the 2019 coronavirus disease (COVID-19), medical students were advised to wear a mask in class and avoid touching their faces. Few studies have analyzed the influence of health education on the frequency of face- and smartphone-touching behaviors during the COVID-19 pandemic. This research compared the frequency of in-class face- and smartphone-touching behaviors of medical students before and after the delivery of personal hygiene education during the COVID-19 pandemic. A behavioral observational study was conducted involving medical students at Taipei Medical University. Eighty medical students were recruited during a lecture on otorhinolaryngology. All medical students were required to wear a mask. Their face- and smartphone-touching behavior was observed by viewing the 4 k resolution video tape recorded in class. The recording lasted for 2 h, comprising 1 h prior to the health educational reminder and 1 h afterwards. The frequencies of hand-to-face contact and hand-to-smartphone contact were analyzed before and after the delivery of health education emphasizing personal hygiene. Comprehensive health education and reminders effectively reduce the rate of face- and smartphone-touching behaviors.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    URL: Article
    DOI: 10.3390/ijerph181910038
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2175195-X
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  • 2
    Online Resource
    Online Resource
    The Development and Evaluation of a Smartphone-Aided Diagnosis Application to Measure Tympanic Membrane Perforations
    SAGE Publications ; 2022
    In:  Ear, Nose & Throat Journal
    Details
    In: Ear, Nose & Throat Journal, SAGE Publications
    Abstract: Chronic otitis media is a long-term infection of the middle ear. It is characterized by persistent discharge from the middle ear through a perforated tympanic membrane. It is one of the most common causes of preventable hearing loss, especially in developing countries. Precise estimation of the size of tympanic membrane perforation is essential for successful clinical management. In this study, we developed a smartphone-based application to calculate the ratio of the area of tympanic membrane perforation to the area of the tympanic membrane. Twelve standardized patients and 60 medical students were involved to assess the area of tympanic membrane perforation, in particular, the percentage of perforation size. Methods In total, 60 student doctors (including year 5 and year 6 medical students, intern and post-graduate year training of doctors) were recruited during their rotation at the Otolaryngology department of Taipei Medical University Shuang-Ho Hospital. Twelve standardized patients with chronic otitis media were recruited through a single otology practice. Oto-endoscopic examination was performed for all patients by using a commercially-available digital oto-endoscope, and clinical images of the tympanic membrane perforation were obtained. To demonstrate the variability of perforation size estimation by different student doctors, we calculated the percentage of perforation using the smartphone-based application for 12 tympanic membranes objectively and compared the results with those visually estimated by the 60 student doctors subjectively. Results The variance in the visual estimation by the 60 student doctors was large. By contrast, variances in smartphone-based application calculations were smaller, indicating consistency in the results obtained from different users. The smartphone-based application accurately estimated the presence of perforation for tympanic membranes with high consistency. The differences in visual estimations can be considerably great and the variances can be large among different individuals. Conclusions The smartphone-based application is a dependable tool for precisely evaluating the size of tympanic membrane perforation.
    Type of Medium: Online Resource
    ISSN: 0145-5613 , 1942-7522
    URL: Article
    DOI: 10.1177/01455613221123361
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2067528-8
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  • 3
    Online Resource
    Online Resource
    Validation of a Personalized Hearing Screening Mobile Health Application for Persons with Moderate Hearing Impairment
    MDPI AG ; 2021
    In:  Journal of Personalized Medicine Vol. 11, No. 10 ( 2021-10-16), p. 1035-
    Details
    In: Journal of Personalized Medicine, MDPI AG, Vol. 11, No. 10 ( 2021-10-16), p. 1035-
    Abstract: Hearing impairment is a frequent human sensory impairment. It was estimated that over 50% of those aged 〉 75 years experience hearing impairment in the United States. Several hearing impairment–related factors are detectable through screening; thus, further deterioration can be avoided. Early identification of hearing impairment is the key to effective management. However, hearing screening resources are scarce or inaccessible, underlining the importance of developing user-friendly mobile health care systems for universal hearing screening. Mobile health (mHealth) applications (apps) act as platforms for personalized hearing screening to evaluate an individual’s risk of developing hearing impairment. We aimed to evaluate and compare the accuracy of smartphone-based air conduction and bone conduction audiometry self-tests with that of standard air conduction and bone conduction pure-tone audiometry tests. Moreover, we evaluated the use of smartphone-based air conduction and bone conduction audiometry self-tests in conductive hearing loss diagnosis. We recruited 103 patients (206 ears) from an otology clinic. All patients were aged ≥20 years. Patients who were diagnosed with active otorrhea was excluded. Moderate hearing impairment was defined as hearing loss with mean hearing thresholds 〉 40 dB. All patients underwent four hearing tests performed by a board-certified audiologist: a smartphone-based air conduction audiometry self-test, smartphone-based bone conduction audiometry self-test, standard air-conduction pure-tone audiometry, and standard bone conduction pure-tone audiometry. We compared and analyzed the results of the smartphone-based air conduction and bone conduction audiometry self-tests with those of the standard air conduction and bone conduction pure-tone audiometry tests. The sensitivity of the smartphone-based air conduction audiometry self-test was 0.80 (95% confidence interval CI = 0.71–0.88) and its specificity was 0.84 (95% CI = 0.76–0.90), respectively. The sensitivity of the smartphone-based bone conduction audiometry self-test was 0.64 (95% CI = 0.53–0.75) and its specificity was 0.71 (95% CI = 0.62–0.78). Among all the ears, 24 were diagnosed with conductive hearing loss. The smartphone-based audiometry self-tests correctly diagnosed conductive hearing loss in 17 of those ears. The personalized smartphone-based audiometry self-tests correctly diagnosed hearing loss with high sensitivity and high specificity, and they can be a reliable screening test to rule out moderate hearing impairment among the population. It provided patients with moderate hearing impairment with personalized strategies for symptomatic control and facilitated individual case management for medical practitioners.
    Type of Medium: Online Resource
    ISSN: 2075-4426
    URL: Article
    DOI: 10.3390/jpm11101035
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662248-8
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  • 4
    Online Resource
    Online Resource
    Aberrant Driving Behavior Prediction for Urban Bus Drivers in Taiwan Using Heart Rate Variability and Various Machine Learning Approaches: A Pilot Study
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2677, No. 3 ( 2023-03), p. 1304-1320
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2677, No. 3 ( 2023-03), p. 1304-1320
    Abstract: Objective: Aberrant driving behavior (ADB) decreases road safety and is particularly relevant for urban bus drivers, who are required to drive daily shifts of considerable duration. Although numerous frameworks based on human physiological features have been applied to predict ADB, the research remains at an early stage. This study used heart rate variability (HRV) parameters to establish ADB occurrence prediction models with various machine learning approaches. Methods: Twelve Taiwanese urban bus drivers were recruited for four consecutive days of naturalistic driving data collection (from their routine routes) between March and April 2020; driving behaviors and physiological signals were obtained from provided devices. Weather and traffic congestion information was determined from public data, while sleep quality and professional driving experience were self-reported. To develop the ADB prediction model, several machine learning models—logistic regression, random forest, naive Bayes, support vector machine, and gated recurrent unit (GRU)—were trained and 10-fold cross-validated by using the testing data. Results: Most drivers with ADB reported deficient sleep quality (≤80%), with significantly higher mean scores on the Karolinska Sleepiness Scale and driver behavior questionnaire subcategory of lapses and errors than drivers without ADB. Next, HRV indices significantly differed between the measurement of a pre-ADB event and a baseline. The accuracy of the GRU models ranged from 78.84% ± 1.49% to 89.57% ± 1.31%. Conclusion: Drivers with ADB tend to have inadequate sleep quality, which may increase their fatigue levels and impair driving performance. The established time-series models can be considered for ADB occurrence prediction among urban bus drivers.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981221123802
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 2403378-9
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  • 5
    Online Resource
    Online Resource
    Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers
    Informa UK Limited ; 2022
    In:  International Journal of Occupational Safety and Ergonomics
    Details
    In: International Journal of Occupational Safety and Ergonomics, Informa UK Limited
    Type of Medium: Online Resource
    ISSN: 1080-3548 , 2376-9130
    URL: Article
    DOI: 10.1080/10803548.2022.2135281
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2073423-2
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  • 6
    Online Resource
    Online Resource
    Group A streptococcal pharyngitis
    BMJ ; 2021
    In:  BMJ Case Reports Vol. 14, No. 9 ( 2021-09), p. e244871-
    Details
    In: BMJ Case Reports, BMJ, Vol. 14, No. 9 ( 2021-09), p. e244871-
    Type of Medium: Online Resource
    ISSN: 1757-790X
    URL: Article
    DOI: 10.1136/bcr-2021-244871
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 2467301-8
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  • 7
    Online Resource
    Online Resource
    Anosmia caused by brain tumor
    Elsevier BV ; 2023
    In:  The American Journal of the Medical Sciences Vol. 365, No. 1 ( 2023-01), p. e12-
    Details
    In: The American Journal of the Medical Sciences, Elsevier BV, Vol. 365, No. 1 ( 2023-01), p. e12-
    Type of Medium: Online Resource
    ISSN: 0002-9629
    URL: Article
    DOI: 10.1016/j.amjms.2022.08.014
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2083424-X
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  • 8
    Online Resource
    Online Resource
    Screening for Hearing Impairment in Older Adults by Smartphone-Based Audiometry, Self-Perception, HHIE Screening Questionnaire, and Free-Field Voice Test: Comparative Evaluation of the Screening Accuracy With Standard Pure-Tone Audiometry
    JMIR Publications Inc. ; 2020
    In:  JMIR mHealth and uHealth Vol. 8, No. 10 ( 2020-10-27), p. e17213-
    Details
    In: JMIR mHealth and uHealth, JMIR Publications Inc., Vol. 8, No. 10 ( 2020-10-27), p. e17213-
    Abstract: Hearing impairment is the most frequent sensory deficit in humans, affecting more than 360 million people worldwide. In fact, hearing impairment is not merely a health problem, but it also has a great impact on the educational performance, economic income, and quality of life. Hearing impairment is therefore an important social concern. Objective We aimed to evaluate and compare the accuracy of self-perception, Hearing Handicap Inventory for the Elderly-Screening (HHIE-S) questionnaire, free-field voice test, and smartphone-based audiometry as tests for screening moderate hearing impairment in older adults in China. Methods In this study, 41 patients were recruited through a single otology practice. All patients were older than 65 years. Patients with otorrhea and cognitive impairment were excluded. Moderate hearing impairment was defined as mean hearing thresholds at 500, 1000, 2000, and 4000 Hz 〉 40 dB hearing loss (pure-tone average 〉 40 dB hearing loss). All patients completed 5 hearing tests, namely, the self-perception test, HHIE-S questionnaire test, free-field voice test, smartphone-based audiometry test, and standard pure-tone audiometry by the same audiologist. We compared the results of these tests to the standard audiogram in the better-hearing ear. Results The sensitivity and the specificity of the self-perception test were 0.58 (95% CI 0.29-0.84) and 0.34 (95% CI 0.19-0.54), respectively. The sensitivity and the specificity of the HHIE-S questionnaire test were 0.67 (95% CI 0.35-0.89) and 0.31 (95% CI 0.316-0.51), respectively. The sensitivity and the specificity of the free-field voice test were 0.83 (95% CI 0.51-0.97) and 0.41 (95% CI 0.24-0.61), respectively. The sensitivity and the specificity of the smartphone-based audiometry test were 0.92 (95% CI 0.60-0.99) and 0.76 (95% CI 0.56-0.89), respectively. Smartphone-based audiometry correctly diagnosed the presence of hearing loss with high sensitivity and high specificity. Conclusions Smartphone-based audiometry may be a dependable screening test to rule out moderate hearing impairment in the older population.
    Type of Medium: Online Resource
    ISSN: 2291-5222
    URL: Article
    DOI: 10.2196/17213
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2020
    detail.hit.zdb_id: 2719220-9
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  • 9
    Online Resource
    Online Resource
    Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability
    SAGE Publications ; 2023
    In:  Human Factors: The Journal of the Human Factors and Ergonomics Society
    Details
    In: Human Factors: The Journal of the Human Factors and Ergonomics Society, SAGE Publications
    Abstract: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. Background Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. Method This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. Results Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. Conclusion HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. Application The established models can be used in realistic driving scenarios.
    Type of Medium: Online Resource
    ISSN: 0018-7208 , 1547-8181
    URL: Article
    DOI: 10.1177/00187208231183874
    RVK:
    CL 1000
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 2066426-6
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  • 10
    Online Resource
    Online Resource
    Rhinitis medicamentosa
    BMJ ; 2021
    In:  BMJ Case Reports Vol. 14, No. 11 ( 2021-11), p. e247051-
    Details
    In: BMJ Case Reports, BMJ, Vol. 14, No. 11 ( 2021-11), p. e247051-
    Type of Medium: Online Resource
    ISSN: 1757-790X
    URL: Article
    DOI: 10.1136/bcr-2021-247051
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 2467301-8
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