Study design

This is a prospective observational cohort study in 300 individuals with asthma, each followed-up for 6 months to generate a longitudinal dataset of factors associated with asthma attacks.

Study population

Individuals will be eligible for inclusion if they meet the following criteria:

1. Aged 12 years or older at the time of enrolment.

2. Have a physician diagnosis of asthma.

3. History of a moderate or severe asthma attack in the last 12 months as defined by American Thoracic Society(ATS)/European Respiratory Society (ERS) Statement on Asthma Control and Exacerbations20 (see study outcomes section).

4. Currently managing asthma with preventative and/or relief medication delivered via either a pressurised metered dose inhaler (pMDI), Turbuhaler, Ellipta or other device compatible with the study digital inhaler sensors, prescribed by a NZ health practitioner.

5. Residing in Auckland or Waikato, or able to travel to either of these places for the study visits.

6. Own or be willing to use a smartphone with Bluetooth capability that is compatible with the study devices and can host the study apps;

7. Available and able to use the technologies for a period of 6 months.

8. Able to provide written informed consent and to follow study procedures or protocols.

Individuals on treatment with other concomitant asthma medication will be eligible for inclusion. Participants can also change their medication during the study, including starting or stopping new treatments. These changes will be noted by the study investigators and accounted for during data analysis. The lower limit of the inclusion age range was chosen as digital literacy is likely to be too low for children below 12 years of age to use the study devices independently.

Exclusion criteria will be:

1. A diagnosis of lung disease other than asthma for example, COPD, bronchiectasis, cystic fibrosis, bronchopulmonary dysplasia.

2. Smoking history (self-reported) of>10 pack years.

3. Planning to be overseas for more than 28 consecutive days in the next 6 months, unless the participant agrees to extend their period of follow-up to account for time overseas.

Participants will be recruited from two study sites—Auckland and in Waikato. In Auckland, the study sites will be Auckland City Hospital (Te Whatu Ora Te Toka Tumai Auckland) and Papakura Marae Health, located in Central and South Auckland, respectively. In Waikato, the study site will be Waikato Hospital (Te Whatu Ora Waikato). Community recruitment from both regions will be augmented with referrals from Asthma NZ nurses, personal and research networks and social media advertising.

Sample size

The target sample size was selected based on the feasibility of recruiting eligible patients across the funded study timeframe. We will aim to recruit 300 participants who will provide a total of 54 000 daily measurements on average. We will purposively sample for at least 15% Māori and 10% Pacific peoples. Based on the primary outcome of asthma attacks, we expect an estimated 48 individuals to have one or more attacks, based on a mean 20%±5% hypothesised population attack rate based on data from prior work21 22 at 95% CI, with an estimated 20% dropout rate.23 This is a conservative estimate with our recent work finding that asthma attack rates have increased significantly in the last 10 years with the most recent data showing a 25% attack rate with an average of 1.7 attacks per patient per year.24 Based on the events per variable method, the ratio of the number of individuals with the outcome event to the number of candidate predictors should ideally be 10 individuals per a candidate risk factor for the outcome event.25 26 With our sample size, there is a risk of overfitting as there may not be sufficient events for the number of variables we wish to include in the model. However, the rate of attacks in the population may be higher than our conservative estimate as we are recruiting a population with a history of previous attacks.

Screening and eligibility assessment

Potential participants who meet the inclusion criteria will be identified by the healthcare teams at each respective site, based on prospective appointments and from existing patient lists, and referred to the research team. For Auckland hospital (Te Toka Tumai), participants will be identified by the research team from an electronic report generated by the Business Intelligence Unit. The report captures patients 12 years or older who present with the International Classification of Diseases V.10-AM asthma-related diagnostic codes J45-J46; R05-R06; and J458. Posters informing participants of the study will be posted in ambulatory waiting areas and in the emergency departments.

Recruitment

The research team will assess potential participants to confirm eligibility for inclusion and then invite eligible participants by email for enrolment. Enrolment will not take place until a minimum period of 2 weeks has elapsed since their last hospital admission or presentation to primary care to allow stabilisation of the patient, unless the attendance was for a routine review. All study procedures will be explained to eligible participants by a member of the study team and in-person written informed consent will be obtained. Participants will consent for themselves, depending on their capacity to give valid consent. For children under the age of 16 years, researchers will also get consent from the parents or guardians.

Each participant will be assigned a unique random three-digit participant identifier as they enter the study to ensure data are deidentified. A unique email and password will be created for participants to log into the study-related apps (smartwatch, smart inhaler, cough monitor, food diary and study app), so that no personal emails will need to be used. The participant identifier will be used in all study-related documentation from case record forms to storage of collected data and a key file linking the study ID to the participant will be stored on a university managed secure server.

Follow-up assessments

Participants will be followed up for 6 months, with regular follow-up questionnaires via REDCap for participants to complete every 2 weeks (see figure 1). Participants will also receive a check-in phone call or text message each month to ensure that the participant has no problems with the technologies, though participants can contact researchers at any time if needed.

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Figure 1

Study overview of study devices, variables and collected measures. ACT, Asthma Control Test.

Participants will be offered a digital retail gift card as a token of appreciation for participation in the study—NZ$50 at the beginning of the study and NZ$50 if the participant attends the final 6-month study visit and engaged with the study throughout (see the Withdrawals section). Participants can keep the smart watch at study end.

End of study definition

The end of the study will be when the last participant has completed their final follow-up, 6 months after their enrolment date.

Withdrawals

Participants may be withdrawn from the study at any time for the following reasons:

• When the participant wishes to be withdrawn from the study.

• If the participant experiences a severe adverse event that may be related to study participation.

• Consistent non-adherence to the study procedures defined as non-engagement with two or more study devices for at least 4 weeks or non-completion of two consecutive follow-up measures with no contact with the research team.

• If continuation in the study causes detriment to a patient’s well-being

All withdrawals will be discussed with the study advisory group and fully documented in the participant’s file with a record of the date of withdrawal, duration of participation, reasons for withdrawal and any other relevant details. At the point of withdrawal, participants will be offered the option of having the data that have been collected up until the date of withdrawal removed or continue to be analysed and used in the study.

Patient and public involvement

Patients and members of the public, including people involved in the care of people living with asthma, were involved with the planning and conduct of this research. A study advisory group, Māori advisory group and patient and public involvement group will be set up to inform the study design, recruitment and evaluation of the study and dissemination of results. Meetings with these groups are scheduled two to three times a year or as needed with a focus on understanding patient experience, ensuring cultural appropriateness of study visits and practices to support study recruitment.

Data collection

Medication adherence—digital inhaler sensor

The Hailie sensor (Adherium, August 2023.V—see table 1) is a digital inhaler sensor that records the date and time of each actuation of the inhaler to which the device is attached, and the number of doses used on each occasion. Participants will be provided with a sensor to use with their main reliever and inhaled corticosteroid (ICS)-containing preventer treatments, so each participant will be provided with a maximum of two digital inhalers to use. Participants using multiple inhalers will be assessed on a case-by-case basis to identify which two medications will be monitored.

Participants will be asked to continue to use their inhalers in the usual manner that they were doing prior to study enrolment. No changes will be made to the individual’s asthma-inhaled treatment, which will continue as recommended by their usual asthma healthcare provider, though participants who are on separate ICS and long-acting beta-agonists (LABA) treatments may be changed to a combination ICS/LABA inhaler, so to minimise the number of digital inhaler devices they have to use. This will be discussed with the participant to gain consent, and study clinicians to determine appropriateness before referral to the primary healthcare provider to facilitate the change. Participants on salbutamol will be on the Ventolin brand to ensure compatibility with the Hailie sensor.

For participants on a regular preventer, adherence to their recommended treatment plan will be determined using data from the digital inhaler(s). This will be calculated as a percentage to determine the proportion of doses that were used by the patient relative to that which were recommended at the time of taking. The frequency of use of reliever medication will also be calculated from the digital inhalers. A subset of participants using pMDIs will be offered use of a digital spacer (Trudell Medical Ltd) instead of a Hailie sensor, where digital spacer supply is available and feasible for use. Adherence and inhalation technique range from 0% (poor) to 100% (good) and is based on previous research and recommendations on inhalation errors.

Peak expiratory flow—manual peak flow meter

Day-to-day changes in peak expiratory flow may be an indicator of asthma control.37 38 Participants will be reminded daily to do a daily peak flow at the same time each day by the study app.

Cough—nocturnal cough sensor

Night-time cough has been shown to be a potential marker of overall asthma control.12 Cough sounds will be captured using a nocturnal respiratory symptom monitor (Resmonics).12

Physiological and activity measures—smart watch

The FitBit Inspire V.2 or next available version(s) will be used due to its relatively small size, affordability and acceptability for use in children.39 The device has been used successfully in several other studies requiring health assessments of patients40 41 and are considered a valid and reliable tool for monitoring physical activity and sleep.42 43 Data will be extracted via FitBit Application Programming Interfaces (APIs) to be preprocessed for the data analysis.

Device checks and validation

All devices will be checked on the day of issue to participants to ensure normal functioning of the devices. Any faults or failures of device function will lead to return of the device to the manufacturer or research team for troubleshooting.

Data from retrospective health and environmental datasets

Study outcome measures

Patient-reported outcome measures will be collected via questionnaires hosted either on the custom-built study app or as a link out to REDCap. Participants will be reminded to complete questionnaires when prompted by a push notification and either an email or text message from the research team.

Primary endpoint measures

The primary endpoint is the risk of occurrence of one or more asthma attacks. This will be assessed using two time windows: (a) acute attack risk—risk of an attack within 2 days and (b) medium-term risk—risk of an attack within 14 days. The time window includes the day of measurement of the predictor variable or the following days. The outcome will be measured by self-report.

An attack will be defined as per the criteria in the ATS/ERS definitions for attacks,20 as either severe or moderate:

Severe asthma attacks:

• The use of systemic corticosteroids (tablets, suspension or injection) or an increase from stable maintenance dose, for at least 3 days

• A hospitalisation or ED visit because of asthma, requiring systemic corticosteroids.44

Moderate asthma attacks:

• Deterioration in symptoms, deterioration in lung function or increased rescue bronchodilator use based on digital inhaler data, relative to baseline, for 2 or more days but not severe enough to warrant systemic corticosteroids and/or hospitalisation.

• Use of systemic corticosteroids for less than 3 days.

• ED visits for asthma, not requiring systemic corticosteroids, may be classified as moderate attacks.

Note there is no definition of mild attacks, as recommended by the ATS/ERS task force,20 as these are likely to represent transient losses of asthma control rather than an attack.

Attack data will be collected by patient self-report at the time of the attack. Participants will be asked to report all attacks via a button on their study app as close to the time of the event as reasonably possible, once the participant is stabilised after their attack. The participant will be asked to provide the following detail:

• Date/time of the attack.

• Help sought (GP/hospital/self-management/other).

• Medicines taken (oral steroids/inhalers/nebulisers/other).

• For women, relationship with menstrual cycle (ie, date of last period).

• Sleep position prior to attack

• 24-hour food recall via the Research Food Diary mobile app (Xyris Software (Australia)). This provides a complete diet record with information on the type, portion and brand of all consumed foods. To minimise inaccurate self-reporting, participants with implausible intakes of <500 or >3500 kcal/day will be excluded.

Each attack will be considered a new event if they are separated by 7 days or more of no systemic corticosteroids.20 This information will be confirmed by the participant when prompted by the 2 weekly questionnaires, and the hospitalisation information from National Minimum Dataset and from Pharms data sets.

Secondary Endpoint measures

Secondary endpoint measures will be asthma control, well-being, unscheduled healthcare utilisation, productivity loss and activity impairments and participant acceptability. These are described in table 2.

Data management

The study has a data management plan detailing procedures relating to data governance and data management, including what data are collected; where data are stored and who has access to the data. In summary, all participant data will be deidentified (coded) using a unique participant ID. The participant ID is a randomly generated three-digit number and no personal email addresses or passwords will be used in the study. To preserve data privacy, the participant ID will be used to code all the collected data and the key linking the ID with the participant will only be accessible to the few researchers who are immediately working with the data. This key will be stored on the University secure server and will not be accessible by any heterogeneous data sources or APIs. All participant identifying information will also be removed prior to data processing. All other electronic data will be held on University of Auckland managed secure research drives. All data access will be password protected and accessible only by study investigators, and the immediate research team, with data management as per The University of Auckland data protection policies in line with FAIR (Findable, Accessible, Interoperable, and Reusable) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) principles. As data access requires user sign on, an audit trail for data integrity is available. Physical data (eg, physical copies of consent forms) will be stored in locked cabinets in a secure, access-controlled area of the School of Pharmacy, The University of Auckland.

Statistics and data analysis

The characteristics of the study participants will be summarised using frequencies and percentages for categorical variables and means and SD for continuous variables.

Model development

We will begin by using a traditional statistical approach (regression), which will form the reference analysis. For the regression approach, the primary outcome will be modelled using a mixed effects model to examine the independent associations between each potential risk factor and the outcome of attacks. Medication changes or other factors that may influence the participant’s risk of an asthma attack will be considered in the final model. Input variables will be chosen based on statistical and clinical significance. Results will be reported using adjusted rate ratios and 95% CI. A two-sided p<0.05 will be designated statistically significant.

Following this, we will explore machine learning (ML) based approaches to investigate whether the performance of the model (see below) can be improved over and above the regression approach. Multiple ML algorithms will be explored because of their intrinsic differences with the overall aim of finding the most accurate model. The following ML-based approaches will be used: Decision Trees, Random Forests and Gradient Boosting Machines such as XGBoost (tree-based). Additionally, as data sets are large and diverse, the use of deep learning-based approaches will also be explored. This will include (1): using artificial neural networks; (2): convolution neural networks and (3): LSTMs (Long Short-Term Memory) 45—a type of recurrent neural network, which solve the problem of lack of long-term memory in traditional RNN. There is a risk of overfitting and regularisation will be used to address this. A feature engineering process will be conducted to determine the most relevant features for the models.

The data will be randomly divided into three parts: training, validation and evaluation. The training and validation of the ML models will be done with a ‘training’ set (80% of the data). The ‘evaluation’ set (20% of the data) will be used for determining the performance of the models. The best combination of parameters for the ML models will be identified through a 10-fold cross-validation using the development cohort. This means that participants in the training set will be randomly divided into 10 mutually exclusive and collectively exhaustive subsets, and the algorithm generated will be trained in 10 distinct repeats. In each repeat, the algorithm will be trained on data from nine of the subsets of the participants. For each algorithm, a final model will be obtained by retraining the algorithm on the entire development cohort (all 10-fold combined) using the best parameters and evaluated on the validation cohort.

To enhance ML accuracy, we will integrate human knowledge to reduce data requirements, increase reliability and understanding of the ML. Embeddings, weight sharing, feature engineering/extraction and incorporating invariance will be used to improve model speed and performance for real-life use.46 Predictors will be validated by comparing variable importance to coefficients from the regression, depending on if the regression model fits the data well.

Assessment of safety

Each participant’s health will be evaluated at enrolment via questions about their asthma history, asthma medication, other comorbidities and medications. During the study, the participant will be asked to inform the research team of any changes from baseline in their health or medication. An adverse event describes any sign, symptom or syndrome or illness that newly occurs or worsens during the period of the study and may impair the well-being of the participant. This covers any changes in the laboratory findings of the patient if relevant as well as the need for any unexpected diagnostic or treatment procedures. Events that occur after enrolment into the study and for 14 days after study end will be considered as an adverse event.

All potential adverse events will be documented in the participant’s file, including details of the date of onset, duration, action taken or required, outcome and other relevant details of the event. Identification and documentation of these adverse events do not imply a causal relationship with the study intervention. Participants will provide informed consent at the start of the study to provide permission for the study team to contact the participant’s usual health provider if any clinical issues are identified. All adverse events reported will be discussed with the study advisory group, the probability of a causal relationship with the study intervention determined, and any changes to protocol or actions to be taken documented.

Ethics and dissemination

Ethical approval was obtained from the Southern Health and Disability Ethics Committee (2023 FULL 13541). Participants will only be enrolled once written informed consent is provided. The study is registered with the Australian New Zealand Clinical Trials Registry, registration no. ACTRN12623000764639.

Key results will be disseminated via peer-reviewed publications; national and international conferences in respiratory and in digital health; patient organisations; and public engagement events to share findings with the community and key stakeholders.