Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service

Bastl, Katharina; Berger, Uwe; Kmenta, Maximilian

doi:10.2196/jmir.7426

Cited by 35 publications

(34 citation statements)

References 27 publications

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“…Pollen apps are common in North America and Europe, and are available for parts of Australia. Although many provide forecasts, few are based on accurately measured aeroallergens or validated forecasting methods [47]. In Australia, availability of this information is largely dependent upon sparsely located, universitybased research teams [48].…”

Section: Discussionmentioning

confidence: 99%

Using smartphone technology to reduce health impacts from atmospheric environmental hazards

Johnston

Wheeler

Williamson

et al. 2018

Environ. Res. Lett.

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Background: Global environmental change is exacerbating human vulnerability to adverse atmospheric conditions including air pollution, aeroallergens such as pollen, and extreme weather events. Public information and advisories are a central component of responses to mitigate the human impacts of environmental hazards. Digital technologies are emerging as a means of providing personalised, timely and accessible warnings.Method: We describe AirRater, an integrated online platform that combines symptom surveillance, environmental monitoring, and notifications of changing environmental conditions via a free smartphone app. It was developed and launched in Tasmania, Australia (population 510 000), with the aim of reducing health impacts and improving quality of life in people with conditions such as asthma and allergic rhinitis. We present environmental data, user uptake and results from three online evaluation surveys conducted during the first 22 months of operation, from October 2015 through August 2017.Results: There were 3,443 downloads of the app from all regions of Tasmania. Of the 1,959 individuals who registered, 79% reported having either asthma or allergic rhinitis. Downloads increased during adverse environmental conditions and following publicity. Symptom reports per active user were highest during spring (72%), lowest in autumn (37%) and spiked during periods of reduced air quality. In response to online surveys, most users reported that the app was useful and had improved their understanding of how environmental conditions affect their health, and in some cases had prompted action such as the timely use of medication.Conclusion: Active engagement and consistent positive feedback from users demonstrates the potential for considerable individual, clinical and wider public health benefits from integrated and personalised monitoring systems such as AirRater. The perceived health benefits require objective verification, and such systems need to address several challenges in providing timely, reliable and valid environmental data.

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Section: Discussionmentioning

confidence: 99%

Using smartphone technology to reduce health impacts from atmospheric environmental hazards

Johnston

Wheeler

Williamson

et al. 2018

Environ. Res. Lett.

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“…Jean Bousquet, MD (1-3), Ignacio J Ansotegui, MD (4), Josep M Anto, PhD (5)(6)(7)(8), Sylvie Arnavielhe, PhD (9), Claus Bachert, MD (10), Xavier Basagaña, PhD (5,7,8), Annabelle Bédard, PhD (5,7,8), Anna Bedbrook, BSc (2), Matteo Bonini, MD (11), Sinthia Bosnic-Anticevich, PhD (12), Fulvio Braido, MD (13), Vicky Cardona, MD (14), Wienczyslawa Czarlewski, MD (15), Alvaro A Cruz, MD (16), Pascal Demoly, MD (17)(18), Govert De Vries, MSc (19), Stephanie Dramburg, MD (47), Eve Mathieu-Dupas, PhD (9), Marina Erhola, PhD (20), Wytske J Fokkens, MD (21), Joao A Fonseca, MD (22), Tari Haahtela, MD (23), Peter W Hellings, MD (24), Maddalena Illario, MD (25), Juan Carlos Ivancevich, MD (26), Vesa Jormanainen, MD (20), Ludger Klimek, MD (27), Piotr Kuna, MD (28), Violeta Kvedariene, MD (29), Daniel Laune, PhD (9), Désirée Larenas-Linnemann, MD (30), Olga Lourenço, PhD (31), Gabrielle L Onorato, MSc (2), Paolo M Matricardi, MD (32), Erik Melén, MD (33), Joaquim Mullol, MD (34), Nikos G Papa...…”

Section: Mobile Technology In Allergic Rhinitis: Evolution In Managemmentioning

confidence: 99%

“…A study that analyzed 9 mobile apps delivering pollen information and pollen forecasts found that the quality of pollen forecasts needs to be improved. It recommended quality control for pollen forecasts to avoid potential harm to patients (29). AR patients could consider the need to avoid the more polluted routes when walking, biking, or exercising.…”

Section: -Mhealth Apps For Allergic Rhinitis a Apps Informing On Rimentioning

confidence: 99%

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Mobile Technology in Allergic Rhinitis: Evolution in Management or Revolution in Health and Care?

Bousquet

Ansotegui

Antó

et al. 2019

The Journal of Allergy and Clinical Immunology: In Practice

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…In addition, social media data have been used to improve our understanding of Ebola [12] and Zika virus infections [13][14][15]. While disease surveillance efforts tend to focus on acute infectious diseases, studies have also been conducted on chronic diseases such as cancer [16], hypertension [16], asthma [16][17][18], diabetes [19], and seasonal allergic rhinitis (SAR) [20][21][22]. Systematic reviews have also been conducted on disease surveillance based on social media data [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis (Preprint)

Wakamiya¹,

Matsune²,

Okubo³

et al. 2018

Preprint

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Background: Health-related social media data are increasingly used in disease surveillance studies, which have demonstrated moderately high correlations between the number of social media posts and the number of patients. However, there is a need to understand the causal relationship between the behavior of social media users and the actual number of patients to increase the credibility of disease surveillance based on social media data. Objective: To clarify the causal relationships among pollen count, the posting behavior of social media users, and the number of SAR patients in the real world. Methods: This analysis was conducted using datasets of pollen count, tweet numbers, and SAR patient numbers from Kanagawa Prefecture, Japan. We examined daily pollen counts for Japanese cedar (the major cause of SAR in Japan) and hinoki cypress (which commonly complicates SAR) from February 1 to May 31, 2017. The daily numbers of tweets that included the keyword "kafunshō" (or SAR) were calculated between January 1 and May 31, 2017. Daily SAR patient numbers from January 1 to May 31, 2017 were obtained from three healthcare institutes that participated in the study. The Granger causality test was used to examine the causal relationships among pollen count, tweet numbers, and the number of SAR patients during the study period from February to May 2017. To determine if time-variant factors affect these causal relationships, we also analyzed the main SAR phase (February to April) when Japanese cedar trees actively produce and release pollen. Results: Increases in pollen count were found to cause increases in the number of tweets during the overall study period (P = .04), but not the main SAR phase (P = . 05). In contrast, increases in pollen count were found to increase patient numbers in both the study period (P = .04) and the main SAR phase (P = .01). In addition, increases in the number of tweets also caused increases in patient numbers during the main SAR phase (P = .02), but not the overall study period (P = .89). Patient numbers did not affect the number of tweets in both the overall study period (P = . 24) and the main SAR phase (P = .47). Conclusions: Understanding the causal relationships between these factors is an important step to increase the credibility of surveillance systems that use social media data. Further in-depth studies are needed to identify the determinants of social media posts described in this exploratory analysis.

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Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service

Cited by 35 publications

References 27 publications

Using smartphone technology to reduce health impacts from atmospheric environmental hazards

Using smartphone technology to reduce health impacts from atmospheric environmental hazards

Mobile Technology in Allergic Rhinitis: Evolution in Management or Revolution in Health and Care?

Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis (Preprint)

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