INTRODUCTION:Satisfaction with treatments may affect medication adherence and use patterns, including the use of co-medication. We aimed to compare different medications for allergic rhinitis (AR) on (i) patients' satisfaction and (ii) co-medication use frequency. METHODS:We assessed data from the mHealth app MASK-air. We evaluated days on which users with self-reported AR had used-alone or in co-medication-intranasal corticosteroids (INCS), intranasal antihistamines (INAH), fixed combinations of INAH+INCS, or oral antihistamines (OAH). We built multivariable regression models to compare these different AR medication classes (as well as individual medications) on their (i) treatment satisfaction levels (measured using a specific daily visual analogue scale ['VAS satisfaction']) and (ii) odds of being used in co-medication. RESULTS:We assessed 28,177 days reported by 1691 MASK-air users. For all medication classes, co-medication usage was associated with lower treatment satisfaction. When used in monotherapy, OAH were associated with lower VAS satisfaction than INCS (-1.7 points; 95% CI = -2.7; -0.7) or INAH+INCS (-2.1 points; 95% CI = -3.5; -0.7). INCS displayed higher odds of being used in co-medication than OAH (OR = 1.3; 95% CI = 1.0; 1.6) or INAH+INCS (OR = 1.3; 95% CI = 0.8; 1.8). When comparing individual intranasal medications, fluticasone furoate and fluticasone propionate tended to be more frequently used in co-medication. Among individual OAH, desloratadine and rupatadine were associated with higher satisfaction, while fexofenadine was more frequently used in co-medication. CONCLUSION:Using patient-reported data, we evaluated different medication classes and treatments in terms of satisfaction and co-medication frequency. These results provide key insights into the acceptability of AR treatments and will contribute to future treatment guidelines.

BACKGROUND AND RESEARCH QUESTION:We aimed to assess whether levels of digital biomarkers can reflect monthly patterns of asthma control. STUDY DESIGN AND METHODS:We performed a longitudinal study on patients with asthma and comorbid rhinitis who filled ≥26 days of data in a month in the MASK-air® app and who reported at least 1 day of treatment with an inhaled corticosteroid with or without a long-acting β2-agonist (ICS ± LABA). We applied k-means cluster analysis to define clusters of months according to daily asthma control and medication use. Clusters were compared using digital biomarkers (visual analogue scale [VAS] on asthma symptoms and electronic daily asthma control score [e-DASTHMA]). We compared patients who did not switch with patients who switched their ICS ± LABA. RESULTS:We assessed 243 patients and 1358 months. We identified three clusters of poor asthma control despite high ICS ± LABA adherence, one cluster of poor asthma control and poor ICS ± LABA adherence, one cluster of good asthma control and high ICS ± LABA adherence and one cluster of good asthma control despite poor ICS ± LABA adherence. These clusters displayed relevant differences in VAS asthma and e-DASTHMA levels. Similar clusters were found in 'non-switchers' versus 'switchers'. CONCLUSION:Levels of digital biomarkers reflect asthma control patterns and might be used to monitor patients with asthma.

Background ChatGPT was not intended for use in health care, but it has potential benefits that depend on end-user understanding and acceptability, which is where health care students become crucial. There is still a limited amount of research in this area. Objective The primary aim of our study was to assess the frequency of ChatGPT use, the perceived level of knowledge, the perceived risks associated with its use, and the ethical issues, as well as attitudes toward the use of ChatGPT in the context of education in the field of health. In addition, we aimed to examine whether there were differences across groups based on demographic variables. The second part of the study aimed to assess the association between the frequency of use, the level of perceived knowledge, the level of risk perception, and the level of perception of ethics as predictive factors for participants’ attitudes toward the use of ChatGPT. Methods A cross-sectional survey was conducted from May to June 2023 encompassing students of medicine, nursing, dentistry, nutrition, and laboratory science across the Americas. The study used descriptive analysis, chi-square tests, and ANOVA to assess statistical significance across different categories. The study used several ordinal logistic regression models to analyze the impact of predictive factors (frequency of use, perception of knowledge, perception of risk, and ethics perception scores) on attitude as the dependent variable. The models were adjusted for gender, institution type, major, and country. Stata was used to conduct all the analyses. Results Of 2661 health care students, 42.99% (n=1144) were unaware of ChatGPT. The median score of knowledge was “minimal” (median 2.00, IQR 1.00-3.00). Most respondents (median 2.61, IQR 2.11-3.11) regarded ChatGPT as neither ethical nor unethical. Most participants (median 3.89, IQR 3.44-4.34) “somewhat agreed” that ChatGPT (1) benefits health care settings, (2) provides trustworthy data, (3) is a helpful tool for clinical and educational medical information access, and (4) makes the work easier. In total, 70% (7/10) of people used it for homework. As the perceived knowledge of ChatGPT increased, there was a stronger tendency with regard to having a favorable attitude toward ChatGPT. Higher ethical consideration perception ratings increased the likelihood of considering ChatGPT as a source of trustworthy health care information (odds ratio [OR] 1.620, 95% CI 1.498-1.752), beneficial in medical issues (OR 1.495, 95% CI 1.452-1.539), and useful for medical literature (OR 1.494, 95% CI 1.426-1.564; P<.001 for all results). Conclusions Over 40% of American health care students (1144/2661, 42.99%) were unaware of ChatGPT despite its extensive use in the health field. Our data revealed the positive attitudes toward ChatGPT and the desire to learn more about it. Medical educators must explore how chatbots may be included in undergraduate health care education programs.

Background: ChatGPT was not intended for use in health care, but it has potential benefits that depend on end-user understanding and acceptability, which is where health care students become crucial. There is still a limited amount of research in this area. Objective: The primary aim of our study was to assess the frequency of ChatGPT use, the perceived level of knowledge, the perceived risks associated with its use, and the ethical issues, as well as attitudes toward the use of ChatGPT in the context of education in the field of health. In addition, we aimed to examine whether there were differences across groups based on demographic variables. The second part of the study aimed to assess the association between the frequency of use, the level of perceived knowledge, the level of risk perception, and the level of perception of ethics as predictive factors for participants' attitudes toward the use of ChatGPT. Methods: A cross-sectional survey was conducted from May to June 2023 encompassing students of medicine, nursing, dentistry, nutrition, and laboratory science across the Americas. The study used descriptive analysis, chi-square tests, and ANOVA to assess statistical significance across different categories. The study used several ordinal logistic regression models to analyze the impact of predictive factors (frequency of use, perception of knowledge, perception of risk, and ethics perception scores) on attitude as the dependent variable. The models were adjusted for gender, institution type, major, and country. Stata was used to conduct all the analyses. Results: Of 2661 health care students, 42.99% (n=1144) were unaware of ChatGPT. The median score of knowledge was "minimal" (median 2.00, IQR 1.00-3.00). Most respondents (median 2.61, IQR 2.11-3.11) regarded ChatGPT as neither ethical nor unethical. Most participants (median 3.89, IQR 3.44-4.34) "somewhat agreed" that ChatGPT (1) benefits health care settings, (2) provides trustworthy data, (3) is a helpful tool for clinical and educational medical information access, and (4) makes the work easier. In total, 70% (7/10) of people used it for homework. As the perceived knowledge of ChatGPT increased, there was a stronger tendency with regard to having a favorable attitude toward ChatGPT. Higher ethical consideration perception ratings increased the likelihood of considering ChatGPT as a source of trustworthy health care information (odds ratio [OR] 1.620, 95% CI 1.498-1.752), beneficial in medical issues (OR 1.495, 95% CI 1.452-1.539), and useful for medical literature (OR 1.494, 95% CI 1.426-1.564; P<.001 for all results). Conclusions: Over 40% of American health care students (1144/2661, 42.99%) were unaware of ChatGPT despite its extensive use in the health field. Our data revealed the positive attitudes toward ChatGPT and the desire to learn more about it. Medical educators must explore how chatbots may be included in undergraduate health care education programs.

mHealth, such as apps running on consumer smart devices is becoming increasingly popular and has the potential to profoundly affect healthcare and health outcomes. Allergic Rhinitis and its impact on asthma (ARIA) has evolved from a guideline using the best evidence-based approach to care pathways suited to real-life using mobile technology in allergic rhinitis (AR) and asthma multimorbidity MASK-rhinitis (Mobile Airways Sentinel networK), the Phase 3 ARIA initiative, is based on the freely available app. The Allergy Diary is used by people who were informed by physicians, searched the internet, Apple App Store, Google Play. Turkish Validation has been available and currently in use.

BACKGROUND:Allergic rhinitis (AR) management has changed in recent years following the switch from the concept of disease severity to the concept of disease control, publication of the AR clinical decision support system (CDSS) and development of mobile health (m-health) tools for patients (eg Allergy Diary). The Allergy Diary Companion app for healthcare providers is currently being developed and will be launched in 2018. It incorporates the AR CDSS to provide evidence-based treatment recommendations, linking all key stakeholders in AR management.OBJECTIVE:To produce an electronic version of the AR CDSS (e-CDSS) for incorporation into the Allergy Diary Companion, to describe the app interfaces used to collect information necessary to inform the e-CDSS and to summarize some key features of the Allergy Diary Companion.METHODS:The steps involved in producing the e-CDSS and incorporating it into the Allergy Diary Companion were (a) generation of treatment management scenarios; (b) expert consensus on treatment recommendations; (c) generation of electronic decisional algorithms to describe all AR CDSS scenarios; (d) digitization of these algorithms to form the e-CDSS; and (e) embedding the e-CDSS into the app to permit easy user e-CDSS interfacing.RESULTS:Key experts in the AR field agreed on the AR CDSS approach to AR management and on specific treatment recommendations provided by Allergy Diary Companion. Based on this consensus, decision processes were developed and programmed into the Allergy Diary Companion using Titanium Appcelerator (JavaScript) for IOS tablets. To our knowledge, this is the first time the development of any m-health tool has been described in this transparent and detailed way, providing confidence, not only in the app, but also in the provided management recommendations.CONCLUSION:The Allergy Diary Companion for providers provides guideline and expert-endorsed AR management recommendations. [MASK paper No 32].