Home monitoring with connected mobile devices for asthma attack prediction with machine learning

ABSTRACT Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious.

Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms

are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any

objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma using three smart-monitoring devices

(smart-peak-flow-meter/smart-inhaler/smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air-quality reports, we collected a rich longitudinal dataset

to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly

available. Between June-2021 and June-2022, in the midst of UK’s COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data. SIMILAR CONTENT BEING VIEWED BY

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ENGAGEMENT WITH MHEALTH FOR CHRONIC DISEASE SELF-MANAGEMENT AND IMPACT ON MACHINE LEARNING PERFORMANCE Article Open access 12 March 2024 BACKGROUND Asthma is a long-term condition that

affecting around 5.4 million people in the UK and its impact on daily life can vary from day-to-day1. Since there is no known cure for asthma, self-management is a key part of patient care;

this involves detecting deterioration and taking appropriate action to maintain control and prevent the threatened attack2. Traditional self-management action plans use symptom scores,

sometimes supplemented by peak expiratory flow measurements, to determine a patient’s asthma condition3,4,5. Keeping track of relief inhaler usage can also help measure asthma control6.

However, patients may regard this level of monitoring as tedious as it involves high levels of active engagement on their part. This is especially true when they are feeling well, because

traditional monitoring is active and may yield repetitive readings. One method of alleviating some management burden is to reduce the manual labour associated with monitoring of asthma

condition7. Passive monitoring (i.e. collecting data with minimum active user engagement), such as wearing a smartwatch, can provide an avenue to reducing the burden of monitoring if it is

correctly used to give timely alerts8. Three recent studies that have investigated the use of mHealth for asthma management were the Asthma Mobile Health Study (AMHS)9,10, myAirCoach11, and

Biomedical REAl-Time Health Evaluation (BREATHE)12. The AMHS is the largest mHealth study for asthma conducted to date, containing large amounts of cross-sectional and longitudinal data.

However, apart from the possibility of linking location data with historical weather reports, the dataset did not include any additional objective data (e.g., data from wearables, peak flow

meters, or smart inhalers). To our knowledge, the dataset from myAirCoach and BREATHE are not publicly available and they have not yet been used to test any machine learning-based algorithms

for asthma attack prediction12,13,14. The AAMOS-00 observational study was carried out with the aim to provide a rich multi-dimensional dataset to develop better asthma attack prediction

algorithms15. It combined market-available devices and application programming interfaces (APIs) to investigate the feasibility of a single mHealth system which pulls together data from

sources available to asthma patients. Furthermore, the study included objective data sources (that can be collected passively) to complement current methods in asthma monitoring for

self-management. METHODS DATA SUMMARY A total of 22 patients participated in phase 2 of the study. Across 12 months, between 24th June 2021 and 2nd June 2022, 2,054 patient-days of data in

phase 2. All participants of phase 2 agreed to share their anonymised data16. The average retention was 123 days (67%) in phase 2. The participants in phase 2 were mostly female (77%),

average age of 40 years, mostly white (95%), and most (95%) had experienced an asthma attack in the past 12 months16 (see Table 1). A course of oral corticosteroids (OCS) for an asthma

attack in the past 12 months was an inclusion criterion for the study. This was asked twice, once before accessing the consent forms, and once at the start of the AAMOS-00 study (reported in

Table 1). A potential explanation for observing one patient who did not have a course of OCS in the past 12 months, was that the patient had a course of OCS nearly a full 12 months ago,

then had some delay with downloading Mobistudy and starting data collection. Thus, the number of patients who had a course of OCS for an asthma attack in the past 12 months was not 100%.

Participants were located across all four nations of the UK (England, Northern Ireland, Scotland, and Wales), with a majority (73%) from England16 (see Fig. 1). In phase 2, a total of 1,583

daily questionnaires16 and 324 weekly questionnaires16 were collected. There were 694 patient-days of smart inhaler16, 1,567 patient-days of smartwatch data16, 1,099 patient-days of peak

expiratory flow (PEF) recordings via the smart peak flow meter16, and 1,657 patient-days of sent locations16 (see Fig. 2). The total patient-days of relief puffs was relatively low since

patients who spent a day without using the relief inhaler did not add to this count. STUDY DESIGN The AAMOS-00 observational study conducted data collection over 14 months from April 2021 to

June 2022. The study had two phases. Phase 1 was one month of daily and weekly questionnaire monitoring, which was used to select participants who were likely to adhere to monitoring for a

long duration in phase 2. The seven-item daily questionnaire combined the AMHS9 daily questionnaire with the Royal College of Physicians “3 Questions” (RCP3)17 questions to measure daily

asthma control, triggers encountered, and medication usage (Supplementary Information). The AAMOS-00 study’s 11-item weekly questionnaire had incorporated aspects of the RCP317, Asthma

Control Questionnaire (ACQ)4, and AMHS9 weekly questionnaire to capture the asthma control and symptoms displayed in the past week, and any unscheduled care (Supplementary Information).

Phase 2 was six months of smart device monitoring, where participants received three smart monitoring devices (smartwatch, smart peak flow meter, and smart inhaler) to collect data daily in

addition to continuing daily questionnaires. Furthermore, daily location was used to link with weather, air quality, and pollen reports in the local area. The data collection was carried out

via the Mobistudy app, which integrated all data collection apart from the FindAir smart inhaler (see the “Mobile Monitoring Technology” section for more details about the implementation

and Fig. 3 for an overview of the system architecture). Study participants of phase 2 had a total of four daily tasks (daily questionnaire, send location, morning and evening peak flow

reading), two weekly tasks (smartwatch data upload and weekly questionnaire), and a passive monitoring task (smart relief inhaler usage). At the end of phase 2, we asked participants to

complete a questionnaire about the acceptability and usability of the system (Supplementary Information), to assess the current implementation and help future development. The three-part

questionnaire used three validated questionnaires: the System Usability Scale (SUS)18 assessed usability, the mHealth Technology Engagement Index (mTEI)19 assessed personal motivation to use

technology for self-management, and the User version of Mobile Application Rating Scale (uMARS)20 assessed app quality and perceived impact. We adapted some questions to better reflect the

AAMOS-00 study system. RECRUITMENT Participants across the UK were recruited via social media, invitation letters from Norfolk and Norwich University Hospitals (NNUH), and email invitations.

Participants were adults with asthma who had experienced an asthma attack in the past 12 months (definition: ATS/ERS Task Force 2009)21 and were prescribed with a pressurised metered dose

relief inhaler that was compatible with FindAir ONE. Social media recruitment consisted of disseminating invitations to the public via Twitter and Facebook via the Asthma + Lung UK and

Asthma UK Centre for Applied Research (AUKCAR) accounts, which total around 175,000 followers. The Norfolk and Norwich University Hospital helped identify potentially eligible patients for

the study and had sent them invitation letters. Email invitations were sent via the Asthma UKs Research and Policy Volunteers Bulletin, which is a channel to circulate research opportunities

conducted by the Asthma + Lung UK to volunteers. Patients who were interested in joining the study were directed to the recruitment website, where they found the participant information

sheets and the online consent form hosted on Online Surveys. Over the 10-month recruitment period from 15th February 2021 to 1st December 2021, 32 participants were recruited to phase 1 of

the study (see Fig. 4). After one-month of data collection with daily questionnaires, 23 participants who had completed at least half of the requested daily questionnaires (14 of 28 days)

were selected and invited to join the device monitoring portion of the study (phase 2). One participant declined the invitation. Twenty-two participants collected data for six months and one

participant pulled out of the study during phase 2, citing frustration with the technology. MOBILE MONITORING TECHNOLOGY MOBISTUDY Mobistudy22 is an open-source platform for facilitating

mobile-based studies, it is managed by Malmö University, Sweden. The platform has three key components: a mobile app for participants (available for Android and iOS), a representational

state transfer (REST) API server, and an online web portal for researchers to view the data in real time. The platform supports multiple studies and participants of the AAMOS-00 study were

given a study invite code to join the AAMOS-00 study within Mobistudy. The Mobistudy mobile app was central to data collection where each daily and weekly assessment (questionnaires, peak

flow measurement, smartwatch data upload, and sending location) appeared as an individual task of the home page on the participant’s app. SMART ASTHMA SMART PEAK FLOW METER The Smart Peak

Flow Meter by Smart Asthma (www.smartasthma.com) is an affordable (£34.99) mHealth peak flow meter that measures peak flow (PEF) with the help of the processing in the smartphone. There are

two modes of connection between the device and the smartphone, by direct audio jack connection or by a Bluetooth adapter. The device is a Class 2a medically certified device and has been

tested and validated against a pulmonary waveform generator, deemed to pose a low to medium risk to patients and thus complies with the UK’s and EU’s safety and performance

standards23,24,25. The device connected directly to the Mobistudy app, and the signal was translated into peak flow reading via the integrated library provided by Smart Asthma. XIAOMI

MIBAND3 SMARTWATCH The MiBand3 by Xiaomi (www.mi.com) is an affordable (£25.00) smartwatch that can be used to monitor activity and heart rate. The CE (Conformité Européenne) marked device

is lightweight and includes a touch screen where navigate to different screens such as total steps today, battery, exercise mode, and current heart rate. Four minute-by-minute signals were

collected with the MiBand3, heart rate reading, activity type, activity intensity, and total steps. The data upload from the smartwatch used a Bluetooth connection to Mobistudy and an

integrated library. FINDAIR ONE SMART INHALER The FindAir ONE by FindAir (www.findair.eu) is a smart inhaler (also known as a Bluetooth cap) for pressurized metered-dose inhalers (pMDI)

inhalers, to track when the inhaler is used, priced at €59.00 per year. The CE marked device has a battery life of 12 months after first use and is un-rechargeable. Once attached to the

inhaler, the smart inhaler automatically logs actuations when the inhaler is used. When the device is connected to the smartphone via Bluetooth, the stored data is transferred to the

smartphone and then FindAir’s server. We used a secure REST API connection to transfer data between the FindAir server to the study’s server (see Fig. 3). OPEN WEATHER MAPS AND AMBEE Based

on the location of the participant when completing the location task, the local weather, air quality, and pollen count were fetched using Open Weather Maps’26 and Ambee’s27 APIs (see Fig. 

3). The information included weather, temperature, humidity, cloud cover, wind, air quality index (AQI)28, and grass, tree, and weed pollen count. ONLINE SURVEYS The study consent forms and

the exit questionnaire about usability and acceptability were hosted on Online Surveys (https://www.onlinesurveys.ac.uk/), which is an online service where participants can visit a webpage

to complete the questionnaires and the data is then securely held by their servers. Afterwards, the responses were transferred to the study’s servers. DATA ANONYMISATION The directly

identifiable information fields29 were removed or replaced. These included name, dates (including date of birth and date of data entry), location, height, weight, medication used, and user

key. * The names of participants were removed. * The age in years was calculated at the end date of the study, which replaced the participant’s date of birth information. Furthermore, the

age was reduced in granularity via the use of age ranges. * Likewise, only the age range of the age of asthma diagnosis was made available. The ranges were early childhood (0 to 6 years

old), late childhood (7 to 11 years old), adolescence (12 to 18 years old), and late onset (19 + years old)30. * The daily locations of participants were not made available, but the local

environmental data (weather, air quality, and pollen count) collected during the study were made available. A single location of participants at the UK region level was not identifiable and

was made available. The information would be sufficient to link localised historic weather data. * Body mass index (BMI), calculated from height and weight, and theoretical maximum PEF,

calculated from height, age, and sex, were known risk factors of asthma attacks3,4 and important measures. The BMI range and theoretical maximum PEF rounded to the nearest 5 were made

available. * The list of medication used by patients were removed. * The participant user keys were replaced with a new random number between 100 and 999. * All dates of data entry in the

dataset were removed. The dates of data entry were transformed to the number of days after each participant started phase 2. Patient sex and race were made available, because there are known

sex and ethnic differences in asthma31,32,33. They were indirectly identifiable information and considered to have a low risk of deanonymisation. DATA PRE-PROCESSING The published dataset

was produced by combining raw JSON, CSV, and XSLX files from the aforementioned data sources. All the data pre-processing was conducted using R (v4.2.1)34 and the following packages:

fuzzyjoin35, gtools36, janitor37, jsonlite38, lubridate39, plyr40, PostcodesioR41, qdapTools42, rapportools43, readxl44, tidyverse45, zoo46. ETHICS Ethics approval was provided by the East

of England - Cambridge Central Research Ethics Committee. IRAS (Integrated Research Application System) project ID: 285505 with governance approval from ACCORD (Academic and Clinical Central

Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh. The anonymised data has been made available with the consent of each

participant. LIMITATIONS A major limitation of the AAMOS-00 study was the narrow inclusion criteria, which selected asthma patients who had an interest in monitoring and had experienced a

severe asthma attack in the past 12 months. Although the dataset contains limited patients, there are over 2000 unique patient days of longitudinal multi-dimensional data. Speaking with

patient and public involvement (PPI) members, we believed the average retention to daily monitoring in the general population would be substantially lower than what we observed in this

study. However, the average retention in phase 2 was 123 days, which included daily tasks, this was much longer than our initial estimate. Due to the technical implementation of using a

mobile phone app to accommodate the general population, the passive monitoring devices of location and smartwatch require daily and weekly active engagement with the app. This meant that

this study could not explore the potential of completely passive monitoring devices that require no user intervention. Furthermore, the technical issues encountered by some patients could

have affected the adherence to monitoring with the smart devices. The AAMOS-00 study was conducted over several periods of national lockdowns in the UK due to the COVID-19 pandemic47. During

this unique time period, the general public had spent more time indoors, had facemask wearing, kept social distancing, and had reduced road traffic48, which reduced the exposure to some

triggers such as virus, pollutants, and outdoor allergens. The effect of lockdowns was a substantial reduction in asthma attack rates49. Comparing this data with other dataset collected

during times without lockdowns could provide further insights into how a drastic societal change has affected asthma. DATA RECORDS Researchers are able to download the anonymised data from

phase 2 of the AAMOS-00 study16 via Edinburgh DataStore (a digital repository of research data produced at the University of Edinburgh) (https://datashare.ed.ac.uk/handle/10283/4761). The

dataset was designed for longitudinal analysis, linking the different signals by “user_key” and “date” would give the most holistic view of the patients. The date begins at 1 for each

patient, this represents the first day of data entry in phase 2 for each patient. Each increment is a calendar day. The dataset contains a separate file for each data signal. The list of

files provided are as follows: * Data Documentation – contains a information about the dataset and the study * Data Dictionary – contains the data definition for all variables * Daily

Questionnaire Data – includes the responses to the daily questionnaire (including symptoms, medication use, triggers encountered) * End of Study Questionnaire Data – includes the usability

and acceptability score collected at the end of the study * Environment Data – includes the daily weather, pollen, air pollution data * Patient Information – includes the patient information

(including age, sex, region) collected at the start of the study * Smart Peak Flow Meter Data – includes twice-daily measurements of peak flow measured via the smart peak flow meter * Smart

Inhaler Data – includes relief inhaler usage information (timestamp and medication name) collected via smart inhaler * Smartwatch Data 1 (data entries 1-1,000,000) – includes the

minute-by-minute data from the smartwatch (including activity type, intensity, steps taken, heart rate), set one * Smartwatch Data 2 (data entries 1,000,001–2,000,000) – set two of the

smartwatch data, continues from set one * Smartwatch Data 3 (data entries 2,000,001–2,101,829) – set three of the smartwatch data, continues from set two * Weekly Questionnaire Data –

includes the responses to the weekly questionnaire (including asthma control, symptoms, and any unscheduled care) Further details about all the variables in each data file can be found in

the Data Dictionary16 (Supplementary Table). TECHNICAL VALIDATION During the development of Mobistudy and the integration of external sensors, standard software engineering techniques for

quality assurance were put in place, such as extending testing and, when feasible, automated tests. Sensors integrated algorithms to extract physiological measurements from raw data, either

in their internal firmware (MiBand 3) or as software libraries (Smart Peak Flow meter), which were integrated into the app. All smartwatches underwent a software update, to ensure that the

latest firmware version (v2.4.0.32) was installed. Our research team, comprising clinicians, checked that all values from questionnaires and devices were clinically plausible. The smart peak

flow meters provided readings within our expectation. Only two peak flow measurements were outside theoretical range of values (based on age, sex, and height)50. For both readings, a valid

peak flow reading taken within minutes replaced the outlier value. From the smartwatch, the mean sleep duration per day was 7.6 hours (interquartile range of 3.1 hours), which was within the

expected range. Also from the smartwatch, the mean heart rate was 82 BPM, which was high but within physiological range (normal resting heart rate is between 50 and 90 BPM)51. The daily

asthma control questions displayed expected characteristics. As reported by Pinnock _et al_.52, the day-time symptoms alone is less of an indication of poor control than nocturnal symptoms

or activity limitation alone. The AAMOS-00 data supports the finding. In general, the day symptoms were almost always higher than night symptoms and activity limitation, whereas nocturnal

symptoms and activity limitation were relatively independent (see Fig. 5). As a feature, this means that day symptoms were superseded by the information of night symptoms and activity

limitation. Reframing this data as binary classification problem, we attained a high area under the receiver operating characteristic (ROC) curve (AUC) and area under the precision-recall

curve (AUPRC) to classify weeks where patients attended unscheduled asthma doctor appointments using daily data, suggesting the classifier performs well using this data. Data processing was

used to extract the weekly mean value of daily data in the dataset (from the daily questionnaire, environment, smartwatch, smart peak flow meter, and smart inhaler). This formed a dataset

with 15 observations in the positive class (attended unscheduled appointment during the week) and 159 observations in the negative class. Then using an 80%-20% training-test split, we

trained a random forest classifier and achieved good performance (AUC = 0.93 and AUPRC = 0.55) (see Fig. 6). USAGE NOTES The dataset is licensed under the Creative Commons Attribution 4.0

International Public License (CC BY 4.0). To download the dataset, visit the DataShare page (https://datashare.ed.ac.uk/handle/10283/4761). R scripts have been provided to assist the usage

of this dataset, including joining data tables, data wrangling, and an example binary classification problem. CODE AVAILABILITY The Mobistudy version 0.2.6 used in the AAMOS-00 study can be

found at https://github.com/Mobistudy. The software to translate the smart peak flow meter signal into peak flow was integrated with Mobistudy using a Cordova plugin, the code can be found

on GitHub: https://github.com/kevinchtsang/cordova-plugin-spf. The smartwatch was integrated into Mobistudy based on the open-source work by Volodymyr Shymanskyy

(https://github.com/vshymanskyy/miband-js), José Rebelo, and Gadgetbridge (www.gadgetbridge.org). R scripts to illustrate joining the data tables and forming a binary classification problem

can be found at https://github.com/kevinchtsang/AAMOS-00-Starter. REFERENCES * Asthma UK. _Asthma Facts and Statistics._ https://www.asthma.org.uk/about/media/facts-and-statistics/ (2021). *

Pinnock, H. _et al_. Systematic Meta-Review of Supported Self-Management for Asthma: A Healthcare Perspective. _BMC Med._ 15, 64 (2017). Article  PubMed  PubMed Central  Google Scholar  *

Scottish Intercollegiate Guidelines Network (SIGN)/ British Thoracic Society (BTS). _SIGN 158 British Guideline on the Management of Asthma_. (Edinburgh: SIGN, 2019). * Juniper, E. F.,

O’Byrne, P. M., Guyatt, G. H., Ferrie, P. J. & King, D. R. Development and Validation of a Questionnaire to Measure Asthma Control. _Eur. Respir. J._ 14, 902–907 (1999). Article  CAS 

PubMed  Google Scholar  * Tsang, K. C. H., Pinnock, H., Wilson, A. M. & Shah, S. A. Application of Machine Learning to Support Self-Management of Asthma with mHealth. in _2020 42nd

Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)_ 5673–5677 https://doi.org/10.1109/EMBC44109.2020.9175679 (IEEE, 2020). * Su, J. G. _et al_.

Feasibility of Deploying Inhaler Sensors to Identify the Impacts of Environmental Triggers and Built Environment Factors on Asthma Short-Acting Bronchodilator Use. _Environ. Health

Perspect._ 125, 254–261 (2017). Article  PubMed  Google Scholar  * May, C. R., Montori, V. M. & Mair, F. S. We Need Minimally Disruptive Medicine. _BMJ_ 339, 485–487 (2009). Article 

Google Scholar  * Tsang, K. C. H., Pinnock, H., Wilson, A. M. & Shah, S. A. Application Of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. _J. Asthma

Allergy_ 15, 855–873 (2022). Article  PubMed  PubMed Central  Google Scholar  * Chan, Y.-F. Y. _et al_. The Asthma Mobile Health Study, a Large-Scale Clinical Observational Study Using

ResearchKit. _Nat. Biotechnol._ 35, 354–362 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Chan, Y.-F. Y. _et al_. The Asthma Mobile Health Study, Smartphone Data Collected

Using ResearchKit. _Sci. Data_ 5, 180096 (2018). Article  PubMed  PubMed Central  Google Scholar  * Honkoop, P. J. _et al_. MyAirCoach: The Use of Home-Monitoring And mHealth Systems to

Predict Deterioration in Asthma Control and the Occurrence of Asthma Exacerbations; Study Protocol of an Observational Study. _BMJ Open_ 7, e013935 (2017). Article  PubMed  PubMed Central 

Google Scholar  * Bui, A. A. T. _et al_. Biomedical REAl-Time Health Evaluation (BREATHE): Toward an mHealth Informatics Platform. _JAMIA Open_ 3, 190–200 (2020). Article  PubMed  PubMed

Central  Google Scholar  * Khusial, R. J. _et al_. Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma. _J. Allergy Clin. Immunol. Pract._ 8, 1972–1979.e8 (2020). Article

  PubMed  Google Scholar  * myAirCoach. _D7.6 Data Management Plan (Second Year Update)._ https://www.myaircoach.eu/content/d76-data-management-plan-second-year-update (2016). * Tsang, K. C.

H., Pinnock, H., Wilson, A. M., Salvi, D. & Shah, S. A. Predicting Asthma Attacks Using Connected Mobile Devices and Machine Learning: the AAMOS-00 Observational Study Protocol. _BMJ

Open_ 12, e064166 (2022). Article  PubMed  PubMed Central  Google Scholar  * Tsang, K. C. H., Pinnock, H., Wilson, A. M., Salvi, D. & Shah, S. A. AAMOS-00 Study: Predicting Asthma

Attacks Using Connected Mobile Devices and Machine Learning. _Edinburgh DataShare._ https://doi.org/10.7488/ds/3775 (2022). * Pearson, M. & Bucknall, C. Measuring Clinical Outcome in

Asthma: A Patient-Focused Approach. _R. Coll. Physicians London_ (2000). * Brooke, J. SUS: A ‘Quick and Dirty’ Usability Scale. in _Usability Evaluation In Industry_ VOL. 189 207–212 (CRC

Press, 1996). * Dewar, A. R., Bull, T. P., Malvey, D. M. & Szalma, J. L. Developing a Measure of Engagement with Telehealth Systems: The mHealth Technology Engagement Index. _J. Telemed.

Telecare_ 23, 248–255 (2017). Article  PubMed  Google Scholar  * Stoyanov, S. R., Hides, L., Kavanagh, D. J. & Wilson, H. Development and Validation of the User Version of the Mobile

Application Rating Scale (uMARS). _JMIR mHealth uHealth_ 4, e72 (2016). Article  PubMed  PubMed Central  Google Scholar  * Reddel, H. K. _et al_. An Official American Thoracic

Society/European Respiratory Society Statement: Asthma Control and Exacerbations - Standardizing Endpoints for Clinical Asthma Trials and Clinical Practice. _Am. J. Respir. Crit. Care Med._

180, 59–99 (2009). Article  PubMed  Google Scholar  * Salvi, D. _et al_. Mobistudy: Mobile-Based, Platform-Independent, Multi-Dimensional Data Collection for Clinical Studies. in _11th

International Conference on the Internet of Things_ 219–222 https://doi.org/10.1145/3494322.3494363 (ACM, 2021). * VanZeller, C., Williams, A. & Pollock, I. Comparison of Bench Test

Results Measuring the Accuracy of Peak Flow Meters. _BMC Pulm. Med._ 19, 74 (2019). Article  PubMed  PubMed Central  Google Scholar  * Commision, E. CE marking. _European Commission_

https://ec.europa.eu/growth/single-market/ce-marking_en (2022). * Gov, U. K. Medical Devices: Conformity Assessment and the UKCA Mark. _GOV.UK_

https://www.gov.uk/guidance/medical-devices-conformity-assessment-and-the-ukca-mark (2020). * OpenWeatherMaps. _OpenWeatherMap._ https://openweathermap.org/ (2021). * Ambee. _Ambee._

https://www.getambee.com/ (2021). * Bartelds, H., Heich, H. & Léger, K. _CAQI Air Quality Index: Comparing Urban Air Quality across Borders-2012. Common Information to European Air_

www.citeair.eu (2012). * Hrynaszkiewicz, I., Norton, M. L., Vickers, A. J. & Altman, D. G. Preparing Raw Clinical Data for Publication: Guidance for Journal Editors, Authors, and Peer

Reviewers. _BMJ_ 340, c181–c181 (2010). Article  PubMed  PubMed Central  Google Scholar  * Trivedi, M. & Denton, E. Asthma In Children and Adults—What Are the Differences and What Can

They Tell Us About Asthma? _Front. Pediatr._ 7, 256 (2019). Article  PubMed  PubMed Central  Google Scholar  * Zein, J. G. & Erzurum, S. C. Asthma is Different in Women. _Curr. Allergy

Asthma Rep._ 15, 28 (2015). Article  PubMed  PubMed Central  Google Scholar  * Netuveli, G., Hurwitz, B. & Sheikh, A. Ethnic Variations in Incidence of Asthma Episodes in England &

Wales: National Study of 502,482 Patients in Primary Care. _Respir. Res._ 6, 120 (2005). Article  PubMed  PubMed Central  Google Scholar  * Busby, J. _et al_. Ethnic Differences in Severe

Asthma Clinical Care and Outcomes: An Analysis of United Kingdom Primary and Specialist Care. _J. Allergy Clin. Immunol. Pract._ 10, 495–505.e2 (2022). Article  PubMed  Google Scholar  *

Core R Team. R: A Language and Environment for Statistical Computing. _R Foundation for Statistical Computing_ VOL. 2 http://www.r-project.org (2022). * Robinson, D. _fuzzyjoin: Join Tables

Together on Inexact Matching_. https://cran.r-project.org/package=fuzzyjoin (2020). * Bolker, B., Warnes, G. R. & Lumley, T. _gtools: Various R Programming Tools._

https://cran.r-project.org/package=gtools (2022). * Firke, S. _janitor: Simple Tools for Examining and Cleaning Dirty Data._ https://cran.r-project.org/package=janitor (2021). * Ooms, J.

_The jsonlite Package: A Practical and Consistent Mapping Between JSON Data and R Objects_. Preprint at https://arxiv.org/abs/1403.2805 (2014). * Grolemund, G. & Wickham, H. Dates and

Times Made Easy with {lubridate}. _J. Stat. Softw._ 40, 1–25 (2011). Article  Google Scholar  * Wickham, H. The Split-Apply-Combine Strategy for Data Analysis. _J. Stat. Softw._ 40, 1–29

(2011). Article  Google Scholar  * Walczak, E. _PostcodesioR: API Wrapper Around ‘Postcodes.io’._ https://cran.r-project.org/package=PostcodesioR (2021). * Rinker, T. W. {qdapTools}: Tools

to Accompany the qdap Package. _University at Buffalo/SUNY_ http://github.com/trinker/qdapTools (2015). * Blagotić, A. & Daróczi, G. _Rapport: a report templating system._

https://cran.r-project.org/package=rapport (2022). * Bryan, H. W. and J. readxl: Read Excel Files. https://cran.r-project.org/package=readxl (2022). * Wickham, H. _et al_. Welcome to the

{tidyverse}. _J. Open Source Softw._ 4, 1686 (2019). Article  ADS  Google Scholar  * Zeileis, A. & Grothendieck, G. zoo: S3 Infrastructure for Regular and Irregular Time Series. _J.

Stat. Softw._ 14, 1–27 (2005). Article  Google Scholar  * Institute for Government analysis. Timeline of UK Government Coronavirus Lockdowns and Measures, March 2020 to December 2021.

https://www.instituteforgovernment.org.uk/sites/default/files/timeline-coronavirus-lockdown-december-2021.pdf (2020). * UK Government (Department for Transport). Impact of the Coronavirus

(COVID-19) Pandemic on Flow Weighting for Congestion Data.

https://www.gov.uk/government/publications/road-congestion-and-travel-times-statistics-guidance/impact-of-the-coronavirus-covid-19-pandemic-on-flow-weighting-for-congestion-data (2022). *

Shah, S. A., Quint, J. K. & Sheikh, A. Impact of COVID-19 Pandemic on Asthma Exacerbations: Retrospective Cohort Study of Over 500,000 Patients in a National English Primary Care

Database. _Lancet Reg. Heal. - Eur._ 19, 100428 (2022). Article  Google Scholar  * Nunn, A. J. & Gregg, I. New Regression Equations for Predicting Peak Expiratory Flow in Adults. _BMJ_

298, 1070 (1989). Article  Google Scholar  * Nanchen, D. Resting Heart Rate: What is Normal? _Heart_ 104, 1048–1049 (2018). Article  PubMed  Google Scholar  * Pinnock, H. _et al_. Clinical

Implications of the Royal College of Physicians Three Questions In Routine Asthma Care: A Real-Life Validation Study. _Prim. Care Respir. J._ 21, 288–94 (2012). Article  PubMed  PubMed

Central  Google Scholar  Download references ACKNOWLEDGEMENTS This work was supported by Asthma + Lung UK as part of the Asthma UK Centre for Applied Research grant number AUK-AC-2018-01.

Support from Malmö University was co-funded by the Knowledge Foundation KK-stiftelsen. We thank all the participants of the AAMOS-00 study, this research would not be possible without their

time and effort. We thank the AUKCAR PPI for their involvement in developing and analysing the study. We thank the Mobistudy team and Malmö University for their support with data collection.

We thank Smart Respiratory Products Ltd for providing the Smart Peak Flow Meter and associated software. We thank FindAir for providing the FindAir ONE devices and FindAir’s API. We thank

Ambee for providing the pollen data. We thank the Asthma + Lung UK and AUKCAR social media teams, Malcolm Marquette and the Norwich and Norfolk University Hospital for their assistance in

participant recruitment. We thank Dr Sarah Brown (Edinburgh Innovations, University of Edinburgh, UK) for organising the contracts required for the study. We thank Aryelly Rodriguez

(Edinburgh Clinical Trials Unit, University of Edinburgh, UK) for statistical advice in producing the anonymised AAMOS-00 dataset. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Asthma UK

Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK Kevin C. H. Tsang, Hilary Pinnock, Andrew M. Wilson & Syed Ahmar Shah * Medical Informatics, Usher

Institute, University of Edinburgh, Edinburgh, UK Kevin C. H. Tsang & Syed Ahmar Shah * Norwich Medical School, University of East Anglia, Norwich, UK Andrew M. Wilson * Norwich

University Hospital Foundation Trust, Colney Lane, Norwich, UK Andrew M. Wilson * Internet of Things and People Research Centre, Malmö University, Malmö, Sweden Dario Salvi Authors * Kevin

C. H. Tsang View author publications You can also search for this author inPubMed Google Scholar * Hilary Pinnock View author publications You can also search for this author inPubMed Google

Scholar * Andrew M. Wilson View author publications You can also search for this author inPubMed Google Scholar * Dario Salvi View author publications You can also search for this author

inPubMed Google Scholar * Syed Ahmar Shah View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS K.C.H.T., H.P., A.M.W. and S.A.S. designed the

study. S.A.S. is the study guarantor. K.C.H.T. and D.S. set up the data collection system. K.C.H.T. drafted the manuscript which was critically revised by H.P., A.M.W., D.S. and S.A.S. All

authors approved the final version of the manuscript. CORRESPONDING AUTHORS Correspondence to Kevin C. H. Tsang or Syed Ahmar Shah. ETHICS DECLARATIONS COMPETING INTERESTS The authors

declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Tsang, K.C.H., Pinnock, H., Wilson, A.M. _et al._ Home monitoring with connected

mobile devices for asthma attack prediction with machine learning. _Sci Data_ 10, 370 (2023). https://doi.org/10.1038/s41597-023-02241-9 Download citation * Received: 24 October 2022 *

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