Background: Complete diagnosis and therapy of seasonal allergic rhinoconjunctivitis require evidence that exposure to the sensitizing pollen triggers allergic symptoms. Electronic clinical diaries, by recording disease severity scores and pollen exposure, can demonstrate this association. However, patients who spontaneously download an e-diary app show very low adherence to their recording.
Objective: The objective of our study was to assess adherence of patients with seasonal allergic rhinitis to symptom recording via e-diary explicitly prescribed by an allergist within a blended care approach.
Methods: The @IT-2020 project is investigating the diagnostic synergy of mobile health and molecular allergology in patients with seasonal allergic rhinitis. In the pilot phase of the study, we recruited Italian children (Rome, Italy) and adults (Pordenone, Italy) with seasonal allergic rhinitis and instructed them to record their symptoms, medication intake, and general conditions daily through a mobile app (Allergy.Monitor) during the relevant pollen season.
Results: Overall, we recruited 101 Italian children (Rome) and 93 adults (Pordenone) with seasonal allergic rhinitis. Adherence to device use slowly declined during monitoring in 3 phases: phase A: first week, ≥1267/1358, 90%; phase B: second to sixth week, 4992/5884, 80% to 90%; and phase C: seventh week onward, 2063/2606, 70% to 80%. At the individual level, the adherence assessed in the second and third weeks of recording predicted with enough confidence (Rome: Spearman ρ=0.75; P<.001; Pordenone: ρ=0.81; P<.001) the overall patient adherence to recording and was inversely related to postponed reporting (ρ=–0.55; P<.001; in both centers). Recording adherence was significantly higher during the peak grass pollen season in Rome, but not in Pordenone.
Conclusions: Adherence to daily recording in an e-diary, prescribed and motivated by an allergist in a blended care setting, was very high. This observation supports the use of e-diaries in addition to face-to-face visits for diagnosis and treatment of seasonal allergic rhinitis and deserves further investigation in real-life contexts.

Adherence to Prescribed E-Diary Recording by Patients With Seasonal Allergic Rhinitis: Observational Study

The use of mobile communication devices in health care is spreading worldwide. A huge amount of health data collected by these devices (mobile health data) is nowadays available. Mobile health data may allow for real-time monitoring of patients and delivering ad-hoc treatment recommendations. This paper aims at showing how this may be done by exploiting the potentialities of fuzzy clustering techniques. In fact, such techniques can be fruitfully applied to mobile health data in order to identify clusters of patients for diagnostic classification and cluster-specific therapies. However, since mobile health data are full of noise, fuzzy clustering methods cannot be directly applied to mobile health data. Such data must be denoised prior to analyzing them. When longitudinal mobile health data are available, functional data analysis represents a powerful tool for filtering out the noise in the data. Fuzzy clustering methods for functional data can then be used to determine groups of patients. In this work we develop a fuzzy clustering method, based on the concept of medoid, for functional data and we apply it to longitudinal mHealth data on daily symptoms and consumptions of anti-symptomatic drugs collected by two sets of patients in Berlin (Germany) and Ascoli Piceno (Italy) suffering from allergic rhinoconjunctivitis. The studies showed that clusters of patients with similar changes in symptoms were identified opening the possibility of precision medicine.

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A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood.

In the last few years, we have witnessed an important development in the medical field of both Mobile Health, such as the use of mobile communication devices, and other telemedicine tools in general, in order to support the surveillance of diseases from the moment of the first diagnosis to the therapeutic follow-up. Long before COVID-19, some authors had analyzed various possible evidence-based scenarios and had indicated how the use of telemedicine could prove to be extremely useful in epidemic situations, especially for the management of chronic patients, such as immune-allergic ones, who are notoriously in greater need of regular follow-up; however, as expected, the advent of the COVID-19 pandemic has amplified the differences between various countries, from the point of view of the propensity to use technological solutions in the health sector. The hope is that one positive outcome of the ongoing pandemic is that it will lead to an acceleration, by all the stakeholders involved, of the process of modernization of health care.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/pai.13346

Telemedicine for allergic patients during COVID-19.

Objective This study explores how negative affect, perceived net equity, and uncertainty influence the public's privacy decision-making regarding the adoption of contact-tracing technology based on artificial intelligence (AI) during the COVID-19 pandemic. Methods Four hundred and eighteen adults in the US participated in the study via Amazon Mechanical Turk in August 2020. Statistical analyses were performed using the PROCESS macro. Indirect effects and their significance were estimated using bias-corrected bootstrap confidence intervals (CIs) with resampling set to n = 5000. Results Perceived net equity was positively associated with low levels of perceived uncertainty regarding a COVID-19 contact-tracing application and intention to adopt it. Low levels of perceived uncertainty were positively associated with intentions to adopt such an application, thereby suggesting that a perceived level of uncertainty mediates the association between perceived net equity and adoption intentions. Anxieties regarding AI technology and COVID-19 risks both moderate the associations among perceived net equity, perceived level of uncertainty, and intentions to adopt the contact-tracing technology. Conclusions Our findings highlight how the differing sources of emotion influence the associations among rational judgment, perceptions, and decision-making about new contact-tracing technology. Overall, the results suggest that both rational judgments and affective reactions to risks are important influencers of individuals’ perceptions and privacy-related decision-making regarding a new health technology during the pandemic.

The role of uncertainty and affect in decision-making on the adoption of AI-based contact-tracing technology during the COVID-19 pandemic

Background Researchers have found innovative ways of using mobile health (mHealth) technologies to prevent the spread of coronavirus disease 2019 (COVID-19). However, fewer studies have been done to determine their adoption and effectiveness. Objective This review summarises the published evidence on the effect of mHealth technologies on the adoption of COVID-19 preventive measures, prevention knowledge acquisition and risk perception as well as technology adoption features for COVID-19 prevention. Methods PubMed, IEEE and Google Scholar databases were searched for peer-reviewed literature from 1 January 2020 to 31 March 2022 for studies that evaluated the effect of mHealth technologies on COVID-19 preventive measures adoption, prevention knowledge acquisition and risk perception. Thirteen studies met the inclusion criteria and were included in this review. All the included studies were checked for quality using the mHealth evidence reporting and assessment (mERA) checklist. Results The review found out that the utilisation of mHealth interventions such as alert text messages, tracing apps and social media platforms was associated with adherence behaviour such as wearing masks, washing hands and using sanitisers, maintaining social distance and avoiding crowded places. The use of contact tracing was linked to low-risk perception as users considered themselves well informed about their status and less likely to pose transmission risks compared to non-users. Privacy and security issues, message personalisation and frequency, technical issues and trust concerns were identified as technology adoption features that influence the use of mHealth technologies for promoting COVID-19 prevention. Conclusion Utilisation of mHealth may be a feasible and effective way to prevent the spread of COVID-19. However, the small study samples and short study periods prevent generalisation of the findings and calls for larger, longitudinal studies that encompass diverse study settings.

Godfrey Rwambuka Mugyenyi

Papers

The role of mobile health technologies in promoting COVID-19 prevention: A narrative review of intervention effectiveness and adoption