Study protocol: A comparison of mobile and clinic‐based spirometry for capturing the treatment effect in moderate asthma

Abstract

Several inefficiencies in drug development trial implementation may be improved by moving data collection from the clinic to mobile, allowing for more frequent measurements and therefore increased statistical power while aligning to a patient‐centric approach to trial design. Sensor‐based digital health technologies such as mobile spirometry (mSpirometry) are comparable to clinic spirometry for capturing outcomes, such as forced expiratory volume in 1 s (FEV1); however, the impact of remote spirometry measurements on the detection of treatment effect has not been investigated. A protocol for a multicenter, single‐arm, open‐label interventional trial of long‐acting beta agonist (LABA) therapy among 60 participants with uncontrolled moderate asthma is described. Participants will complete twice‐daily mSpirometry at home and clinic spirometry during weekly visits, alongside continuous use of a wrist‐worn wearable and regular completion of several diaries capturing asthma symptoms as well as participant‐ and site‐reported satisfaction and ease of use of mSpirometry. The co‐primary objectives of this study are (A) to quantify the treatment effect of LABA therapy among participants with moderate asthma, using both clinical spirometry (FEV1_c) and mSpirometry (FEV1_m); and (B) to investigate whether FEV1_m is as accurate as FEV1_c in detecting the treatment effect using a mixed‐effect model for repeated measures. Study results will help inform whether the deployment of mSpirometry and a wrist‐worn wearable for remote data collection are feasible in a multicenter setting among participants with moderate asthma, which may then be generalizable to other populations with respiratory disease.

Study Highlights.

• WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

Inefficiencies in drug development can potentially be addressed by introducing biomarker‐based measures and innovative trial design and analysis approaches. Ultrasonic handheld spirometers with Bluetooth connectivity provide an opportunity to introduce high‐resolution data collection remotely over the course of clinical trials of respiratory conditions, thereby increasing statistical power and reducing the required sample size. This technology has been tested in feasibility studies of asthma and COPD, demonstrating the concordance of in‐clinic and mobile measurements.

• WHAT QUESTION DID THIS STUDY ADDRESS?

The results of prior feasibility studies assessing the concordance between remote and in‐clinic measurements of forced expiratory volume in 1 second (FEV1) need to be replicated in a multicenter trial setting; further, the impact of remote spirometry measurements on the detection of treatment effects has not been investigated. This publication describes the methodology of a study designed to quantify and compare treatment effects in patients with moderate asthma receiving a long‐acting beta agonist therapy.

• WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

Study objectives, endpoints, eligibility criteria, methods of data collection, data quality control procedures, and statistical considerations are described. Alongside these key elements of study implementation, study design considerations are discussed in the context of broader implementation of digital tools in the research process.

• HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

The results from this study, as well as lessons learned during implementation and analysis, will inform deployment of mobile digital measurement tools in future studies.

INTRODUCTION

Drug development remains a time‐consuming and inefficient process, as evidenced by the rising cost and low success rate of moving drug candidates from preclinical to registration. ¹ Drug development programs incorporating surrogate endpoints and biomarkers are associated with a substantially higher rate of approval by the US Food and Drug Administration (FDA) compared to programs that do not use these tools, ² indicating that the adoption of biomarkers is a promising strategy for increasing the likelihood of success. Similarly, increasing costs may be addressed by adopting innovative clinical trial designs, data collection procedures, and statistical approaches. A recent analysis identified the number of patients and clinic visits as the main contributors to the cost of pivotal clinical trials of new therapeutic agents approved by the FDA from 2015 to 2017, ³ both of which may be reduced by moving from in‐clinic to mobile data collection (where possible) while increasing the frequency of measurements. For example, an approach described by Tango ⁴ proposes estimating the efficacy of a new treatment by using a generalized linear mixed effect model that takes into account multiple measures, as opposed to pairwise pre‐post comparisons of baseline and follow‐up timepoints. Such models increase confidence in both the accuracy and precision of the point estimate while reducing the sample size and recruitment duration of the trial, but they rely on the ability to collect data remotely.

Sensor‐based digital health technologies (DHTs), designed to collect health‐related data remotely, became a focus of attention during the coronavirus disease 2019 pandemic because of an urgent need to find alternatives to in‐person clinic visits. ⁵ This need is particularly important for those with existing respiratory conditions ⁵ ; as such, drug development in respiratory disease such as asthma presents a unique opportunity to adopt the innovations described above by collecting digital biomarker data at frequent intervals in remote settings. Asthma is characterized by reversible airway obstruction with diurnal variation, ⁶ plus variability due to environmental exposures including allergens. ⁷ Forced expiratory volume in 1 s (FEV1; the volume of air exhaled during the first second of a single maximal expiration effort in liters) is a well‐established endpoint in randomized trials assessing the efficacy of novel asthma treatments and is traditionally obtained during in‐person clinic visits when participants perform spirometry maneuvers under the supervision of a trained healthcare professional. Recent technological advancements, including the development of ultrasonic handheld spirometers ⁸ with Bluetooth connectivity and data synchronization via smartphones, has led to the availability of mobile spirometry (mSpirometry), which is a sensor‐based DHT procedure undertaken in the remote setting without expert supervision. Several groups have tested mSpirometry against in‐clinic spirometry in interventional studies of asthma and chronic obstructive pulmonary disease (COPD), demonstrating high correlations ⁹ and small mean differences ¹⁰ between the two modalities. On the other hand, Maher et al. ¹¹ reviewed the results of three clinical trials of interstitial lung disease that adopted mSpirometry and noted a range of practical and technical limitations, concluding that this technology requires further optimization in this lung disease.

In 2017, a pre‐competitive industry group analyzed the root causes of drug development failures in immunological disorders, including respiratory diseases, and identified adequate study powering as one solution to increase the probability of successful transitions between phase II and III clinical trials. ¹² Although traditional pre‐post comparisons of in‐clinic measurements have been adopted as the basis for asthma medication approval, ¹³ , ¹⁴ a greater understanding of disease heterogeneity suggests the need to classify patients according to distinct phenotypes and endotypes, ¹⁵ and therefore the need to test interventions in multiple subgroups with narrower eligibility criteria. The resulting increase to study sample size may be addressed by adopting a higher frequency of measurements, as demonstrated by our natural history study of participants with moderate asthma which found that a traditional pre‐post comparison of clinical FEV1 achieved 35.2% statistical power assuming a treatment effect of 0.2, which increased to 75.6% with daily mSpirometry FEV1 measurements. ¹⁶

Previous experience of using mSpirometry in asthma suggests that remote data collection is feasible, and that results of clinic versus mobile measurements are concordant. ¹⁶ We now seek to determine whether previous results can be replicated in a multicenter trial of participants beginning standard‐care therapy for moderate asthma and describe a study protocol designed to quantify and compare the treatment effects detected by clinic versus mSpirometry measurements of FEV1.

METHODS

Objectives and endpoints

The co‐primary objectives of this study are (A) to quantify the treatment effect of long‐acting beta‐agonist (LABA) therapy among participants with moderate asthma, using outcome measures from clinical spirometry (FEV1_c) and mSpirometry (FEV1_m); and (B) to demonstrate that FEV1_m is as accurate as FEV1_c in detecting the treatment effect using a mixed‐effect model for repeated measures. The secondary objectives are to assess adherence to once and twice‐daily mSpirometry; to compare mSpirometry against clinical spirometry on a range of measures in addition to FEV1; to assess diurnal variability in lung function by comparing morning versus evening FEV1_m; and to compare the time to treatment effect determined by FEV1_m versus the Asthma Control Questionnaire (ACQ‐6) ¹⁷ ; and to assess both participant and clinician satisfaction and ease of use of mSpirometry. Exploratory objectives include an exploration of potential temporal associations between the variability of FEV1_m and exacerbations and/or treatment effects; and an exploration of temporal associations between physical activity and vital signs measured using a wrist‐worn sensor‐based DHT and exacerbations and/or treatment effects.

Study design, setting, and participants

This study is a multicenter, single‐arm interventional trial of LABA therapy recruiting 60 participants with uncontrolled moderate asthma who are using inhaled corticosteroids at enrollment but have been deemed a candidate for additional LABA treatment by their physician. Complete eligibility criteria are listed in Table 1. Up to 10 US‐based sites will be involved.

TABLE 1.

Study eligibility criteria.

Inclusion criteria

Exclusion criteria

Individuals 18 years or older Body mass index 18–40 kg/m2 inclusive Clinician‐diagnosed moderate uncontrolled asthma Using a medium to high daily dose of ICS for a minimum of 6 weeks prior to screening Asthma that is not currently using LABA or LAMA (ACQ‐6 score of 0.75 or higher) Note: A 2‐week LABA/LAMA washout period is allowed prior to screening Pre‐bronchodilator FEV1 ≥60% and ≤100% of the predicted normal values at screening A documented positive response to the reversibility test at screening, defined as improvement in FEV1 ≥12% and ≥200 mL over baseline after a SABA standard of care dose. Documented historical reversibility of up to 6 weeks is allowed Judged by a clinician to be in good health based on medical history, physical examination, and vital sign measurements Nonsmokers or exsmokers (including vape or inhaled cannabis) who have stopped smoking more than 1 year ago Women of childbearing potential must have a negative urine pregnancy test before enrolling at baseline day 1 All participants must complete a satisfactory mSpirometry maneuver without assistance during screening period (days negative 7 through 0)

History of life‐threatening asthma, defined as an asthma episode that required intubation and/or was associated with hypercapnia, respiratory arrest, and/or hypoxic seizures or hospitalization (including ED visits) for the treatment of asthma within 3 months prior to screening, or have been hospitalized or have attended the ED for asthma more than twice in the prior 6 months Occurrence of asthma exacerbations or respiratory tract infections within 4 weeks prior to screening History of substance abuse within the 6 months prior to screening, excluding medical or recreational non‐inhaled marijuana Currently taking other biologics to control asthma symptoms (allergy shots are acceptable) Diagnosis of any other airway/pulmonary disease, such as COPD, emphysema, idiopathic pulmonary fibrosis, Churg‐Strauss syndrome, allergic bronchopulmonary aspergillosis cystic fibrosis, bronchiectasis, alpha‐1 antitrypsin deficiency, or restrictive lung disease Clinically unstable participants or history of non‐compliance as assessed by the PI Chronic or acute infection requiring treatment with systemic antibiotics, antivirals, or antifungals within 1 month prior to screening History of neoplastic disease. Exceptions: participants with (1) an adequately treated basal or squamous cell carcinoma or carcinoma in situ of the cervix; (2) localized or regional prostate cancer; or (3) other malignancies which have been successfully treated >5 years prior to screening without evidence of recurrence may participate Participants treated with oral or parenteral corticosteroids in the previous 4 weeks prior to screening Participation in another investigational drug study for 30 days prior to this study or concurrent investigational drug study

Abbreviations: ACQ‐6, Asthma Control Questionnaire; COPD, chronic obstructive pulmonary disease; ED, emergency department; FEV, forced expiratory volume; ICS, inhaled corticosteroids; LABA, long‐acting beta‐agonists; LAMA, long‐acting muscarinic antagonist; PI, principal investigator; SABA, short acting beta agonist.

The study will begin with a screening visit in which informed consent will be obtained and preliminary eligibility assessed, followed by a run‐in period of mSpirometry use at home for up to 7 days. Participants who demonstrate adequate adherence during the run‐in will progress to a baseline visit (day 1) followed by a 1‐week baseline period of remote data collection without LABA treatment (days 1 to 7); and, finally, a 5‐week intervention period with LABA treatment (days 7–43), including remote data collection and six in‐person visits; see Figure 1. The study therefore involves eight in‐person visits over a period of ~7 weeks.

FIGURE 1.

Study flow diagram and schedule of assessments. Black = staff‐administered activities; blue = patient‐reported outcomes; and burgundy = digital health technologies data. ACQ‐6, Asthma Control Questionnaire; ADSD, Asthma Daytime Symptom Diary; ANSD, Asthma Nighttime Symptom Diary; LABA, long‐acting beta agonist.

With diversity in mind, the eligibility criteria do not include an upper age limit, do not refer to binary gender descriptions, and includes a wide body mass index range. Site selection will be driven by multiple factors, including identification of sites from a wide geographic spread with different local populations. The Sponsor will work with each site to develop a site‐specific recruitment plan describing each site's anticipated ability to enroll participants across multiple racial/ethnic groups, supported by data describing the demographic makeup of prior studies. Diversity, equity, and inclusion training will be provided during site training to ensure that site staff are supported during their recruitment efforts. Finally, the Sponsor will monitor the demographic makeup of the sample on a biweekly basis, allowing timely intervention and re‐training if required.

Therapeutic intervention

A site‐based board‐certified co‐investigator will prescribe an appropriate standard‐care LABA dosage to enrolled participants. Although the study involves a therapeutic intervention, the purpose of the study is to compare two different measurement modalities rather than assess the efficacy associated with added LABA therapy to the inhaled corticosteroids which is well‐established. ¹⁸

Study procedures

Screening

Following informed consent and assessment of preliminary eligibility, the screening visit (~day −7) will involve a medical history and physical examination, including vital signs, anthropometrics, documentation of concomitant medications, and collection of sociodemographic data. Three clinical spirometry measurements will be undertaken by a trained clinician according to American Thoracic Society/European Respiratory Society (ATS/ERS) standards. ¹⁹ Participants will then be trained on how to use the mSpirometry device and smartphone app and must complete at least one successful measurement under supervision without assistance in order to proceed in the study. Further details on the two DHTs (mSpirometry and wrist‐worn wearable), as well as the Koneksa smartphone app used for data capture and transmission, are provided below.

Participants will be asked to complete twice‐daily mSpirometry measurements remotely; in order to continue in the study, participants must complete 75% of the measurements. Central review will be used to assess measurement quality according to ATS/ERS standards. ¹⁹

Baseline

The baseline visit (day 1) will include clinical spirometry; mSpirometry performed on‐site but without assistance; and patient‐reported outcomes and questionnaires (ACQ‐6 ¹⁷ on paper, and an in‐house device use questionnaire electronically). A wrist‐worn wearable device will be provided for use on each participant's nondominant wrist for continuous data collection throughout the remainder of the study. Participants will be instructed to complete the Asthma Nighttime Symptom Diary (ANSD) and Asthma Daytime Symptom Diary (ADSD) ²⁰ electronically via the smartphone app prior to each mSpirometry measurement. The baseline visit, and all subsequent visits described below, will be scheduled in the morning such that the mSpirometry measurement taken on‐site will replace the mobile morning measurement for that day.

Intervention period

During the intervention period (days 8–43), participants will self‐administer LABA once or twice daily according to their prescription, allowing 1–3 h to elapse before completing the ANSD or ADSD according to the time of day, followed immediately by mSpirometry testing. Visits will be conducted on days 8, 15, 22, 29, 36, and 43 (end of study), all of which will include clinical spirometry, mSpirometry, and patient‐reported outcomes (ACQ‐6 on paper at every visit; and device use questionnaire electronically on days 22 and 43 only).

Adverse event surveillance

Enquiries regarding potential adverse events will take place at each visit, which will be reported to the institutional review board (IRB) on an expedited or annual basis according to IRB policies.

Study assessments

mSpirometry

Spirometry (both clinical and mobile) requires the participant to inhale as much as possible, then exhale into the mouthpiece as forcefully as possible for as long as possible while wearing a nose clip. Outcome measures include FEV1 and forced vital capacity (FVC; the volume of air exhaled during a single maximal expiration effort in liters).

Participants will be provided with an ultrasonic mSpirometry device ²¹ and charger (Uscom; Kft, Budapest, Hungary; see specifications in Table 2), paired with a provisioned smartphone loaded with the mobile app (Koneksa Health). Participants will be asked to perform mSpirometry twice a day throughout the study, in the morning and in the evening 1–3 h after taking LABA medications. The mobile app allows participants eight opportunities to complete three successful maneuvers. The highest FEV1, and other spirometry parameters, for example, FVC, within each time window will be used for data analysis.

TABLE 2.

Technical specifications of digital health technologies.

	mSpirometer	Wrist‐worn wearable
Make and model	Uscom mSpiro	Empatica EmbracePlus
Sensors	Ultrasound	Accelerometer, photoplethysmography a
Size	9.5 × 7.3 × 4.5 cm	Diameter: 3.2 cm; thickness: 1.5 cm
Form factor	Handheld device	Wrist‐worn watch
Regulatory status	CE Mark; FDA 510(k) 21	CE Mark; FDA 510(k) 22
Data synchronization	Koneksa smartphone app	Koneksa smartphone app
Waterproofing	Splash resistant	Waterproof
Outcome measures of interest	FEV1, FVC	Vital signs: heart rate, heart rate variability Physical activity: gait speed, step count per day, distance walked
Measure type	Point‐in‐time measure	Continuous monitoring
Battery life	6 h b	48 h

Abbreviations: CE, Conformité Européene; FDA, US Food and Drug Administration; FEV, forced expiratory volume; FVC, forced vital capacity.

^a Photoplethysmography (PPG) is an inexpensive and noninvasive optical measurement method that is often used for heart rate monitoring purposes. It uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. ⁴⁰

^b Battery testing demonstrated the loss 4% of charge after 10 measurements. Assuming twice a day use with several expiratory maneuvers performed each time in the morning and in the evening, ~2 weeks of operation is expected with one charge.

Wrist‐worn wearable

A wrist‐worn wearable device (Empatica EmbracePlus ²² ; Milan, Italy; see specifications in Table 2) will be used to collect raw (sample‐level) accelerometry and photoplethysmography signals, which will then be processed by corresponding algorithms (Koneksa Health) to generate study outcome measures (heart rate, heart rate variability, gait speed, steps per day, and distance walked). The device will be placed on each participant's wrist during the baseline visit. Participants will be instructed to remove the device for charging every other day, as well as any periods spent submersed in water, such as a swimming pool, keeping the device in place at all other times.

Smartphone app and site software

The mSpirometry device will be paired with a provided smartphone containing the mobile app (Koneksa Health). The data from each device will be transmitted to the smartphone via Bluetooth, and then synchronized to a cloud‐based platform for analysis. Additionally, the app provides coaching instructions as well as feedback as to whether each expiration maneuver was performed correctly; it also will send reminders to complete maneuvers within the time windows described above. Participants will not be able to view their mSpirometry data during the study.

The wrist‐worn device (EmbracePlus, Empatica) will be paired with the same provisioned smartphone and the data synchronized from the device to the phone using the Empatica Care App. As is the case with mSpirometry data, the data generated by the wrist‐worn device will not be visible to participants.

Monitoring for compliance and quality of measures

The app has a series of quality control features that evaluate each maneuver for common errors: for example, poor effort, mid‐blow cough, and insufficient blow duration. Participants will be notified in real‐time of these errors by means of the app. Upon execution of expiratory maneuvers, the data will be uploaded to the study portal, supported by the Koneksa platform (Koneksa Health), which will be visible to site staff. Additionally, the spirometry data will be monitored for quality (automated quality control of the flow volume loop using ARTIQ.QC software [ARTIQ.QC]), which generates weekly over‐reading reports for each maneuver along with a grading according to the ATS guidelines, as well as risk‐based monitoring assessments of mSpirometry data every 3 weeks. In addition to software‐based quality control, the data will be evaluated by human readers and site staff will be notified of any persistent issues. Flow volume loops in the Koneksa platform for 100% of clinical spirometry measures and ~10% of mobile spirometry measures will be evaluated by trained human readers (TechEd Consultants) in real‐time. Email feedback will be communicated to sites along with participant retraining instructions as needed, to ensure quality data collection throughout the trial. Site staff will be trained on the collection of pulmonary function test parameters prior to study start. Training will include successful submission of system calibration reports and quality control of spirometry output data to a third‐party vendor who will review before releasing all sites for activation. Ongoing refresher training will be offered to ensure quality data collection throughout the trial.

Finally, site staff will use the study portal to review participant adherence in completing remote data collection, including the number of mSpirometry attempts, number of successful mSpirometry maneuvers, the duration of wrist‐worn wearable use, and the completion of electronic questionnaires.

Patient‐reported outcomes and questionnaires

The ACQ‐6 is a validated instrument to assess the most common asthma symptoms; participants are asked to respond to each symptom on a 7‐point scale (0 = no impairment, 6 = maximum impairment) within a 1‐week recall period. ¹⁷

The ADSD and the ANSD are validated instruments to assess daytime and night‐time periods of breathing difficulty, wheezing, shortness of breath, chest tightness, chest pain, and cough. ²⁰

The device use questionnaire is an in‐house survey to assess the likelihood of recommending the mSpirometry device, overall satisfaction with the device, and ease of use; participants are asked to respond to each item on a 5‐point scale. The extended version used during the end of the study visit will include additional items as well as free‐text fields allowing for qualitative feedback (see Supplementary Material S1). A separate version has been developed for site staff (see Supplementary Material S2).

Statistical considerations

Co‐primary objective 1 will be assessed using a mixed‐effects model for repeated measures. The dependent variable will be the change from baseline in morning FEV1. The independent variable of random effect will be participant ID, and independent variables of fixed effect will be study week, an indicator for assessment type to separate FEV1_m and FEV1_c, demographic covariates if required, and an assessment type: study week interaction. The effect of interest is the association between study week and change from baseline in FEV1 for each assessment type (mSpirometry and in‐clinic spirometry), evaluated by examining the simple slopes for study week at each level of assessment type, along with their 95% confidence intervals. The highest FEV1_c value collected during the baseline visit and each of the six follow‐up visits (7 datapoints) will contribute to the model. Similarly, the highest FEV1_m value collected each morning throughout the baseline period (~14 datapoints) and intervention period (~70 datapoints) will contribute to the model.

It was determined a priori that 60 participants would be enrolled in the study with an assumed dropout rate of 10% (n = 6). Considering a sample size of 54, two‐sided alpha of 0.05, and an effect size range of at least 0.15, ²³ , ²⁴ the study will yield a range of 80% statistical power (beta of 0.2) for detecting the LABA treatment effect using pre‐post comparisons.

A description of the abbreviated statistical analysis plan is provided in Supplementary Material S3.

Ethical approval, trial registration, and dissemination plans

The study protocol has been reviewed and approved by Advarra IRB, Columbia, MD (Advarra IRB approval: protocol KH008, approval number 10Jan2023). The study protocol is registered on clinicaltrials.gov (NCT05757908). Study results will be disseminated through peer‐reviewed journals, conferences, and seminar presentations.

The methodology described in this paper is up to date as of July 2023; however, the protocol, statistical analysis plan, and/or other study documents may be amended if deemed necessary by the Sponsor.

DISCUSSION

The recent proliferation of connected, mobile, sensor‐based DHTs provides an opportunity to conduct decentralized or hybrid on‐site/remote clinical trial procedures, offering several potential advantages over traditional on‐site data collection. ²⁵ Importantly, collecting data either continuously or at regular intervals (such as daily) may increase statistical power and therefore reduce the required sample size, ²⁶ potentially addressing a major source of clinical trial inefficiency. Continuous or daily measurements also allow for a greater understanding of day‐to‐day symptom variability not captured in a pre/post design, which is particularly important for outcomes with known diurnal variability, such as lung function in asthma. ²⁷ , ²⁸ The collection of data in the home environment may represent a more accurate reflection of the patients' lived experiences than on‐site measurements, ²⁹ aligns with a patient‐centric approach to trial design and implementation, ³⁰ and is associated with high patient satisfaction. ³¹ Finally, eliminating or reducing the need for in‐clinic visits removes many barriers to trial entry, potentially increasing the rate of recruitment while improving accessibility, diversity, equity, and inclusion. ³² Before adopting sensor‐based DHTs for remote data collection in clinical trials, several considerations must be addressed, including a comparison with reference standard measures ⁹ , ¹⁶ ; assessments of adherence, usability, and patient/clinical staff satisfaction; and questions of logistics, such as staff training, data transfer, and implementation costs. In this paper, a study protocol is described which aims to assess many of these aspects with respect to mSpirometry, using asthma as a disease model.

The study described here is a multicenter, single‐arm interventional trial of LABA therapy among 60 adults with uncontrolled moderate asthma, during which participants will complete twice‐daily mSpirometry and clinic spirometry during weekly visits, along with continuous use of a wrist‐worn wearable. A holistic approach is taken by incorporating several DHT‐derived measures (FEV1 and FVC from mSpirometry; gait speed, steps per day, distance walked, heart rate, and heart rate variability from a wrist‐worn wearable) alongside validated patient‐reported outcomes (asthma symptoms captured in the ACQ‐6, ANSD, and ADSD), allowing for determining appropriate combinations of these DHT‐based assessments in future trials. Finally, systematic data will be collected on DHT adherence and patient/site staff satisfaction, which will guide the improvement of technology deployment in future studies.

Adults with asthma have been identified as an appropriate population in which to assess mSpirometry because it is highly prevalent in the community, ³³ and efficacious medical treatments are widely available (inhaled corticosteroids and LABA therapies ³⁴ ). mSpirometry has been used for remote data collection in prior studies of asthma and COPD ⁹ , ¹⁰ , ¹⁶ ; however, the number of trials conducted to date is limited and none have aimed to compare the ability of mSpirometry and clinic spirometry for capturing the treatment effect. Although prior studies of mSpirometry in asthma and COPD are promising, the results may be device‐, geography‐, and/or study population‐specific. There is a need to replicate the findings of previous studies in an independent dataset, preferably within a multicenter setting to support generalizability of results.

Wearable sensor‐based DHTs, with form factors such as watches or rings, designed to estimate measures of physical activity and vital signs have been the focus of extensive research ³⁵ ; however, there is a need to study their applicability in patients with asthma. Physical exercise often provokes asthma‐related symptoms, reflecting the nature of the disease and/or its insufficient control. Patients with asthma often avoid exercise and lead a sedentary lifestyle; however, current asthma management includes physical activity targets. For example, the Global Initiative for Asthma recommends that people with asthma consistently engage in physical exercise to improve their health ³⁶ which is associated with perceptions of improved quality of life. ³⁷ Additionally, monitoring for physical activity and/or vital sign changes in the context of treatment administration may provide useful information about the overall health trajectory of patients that can be assessed alongside patient‐reported outcomes. ³⁸ Thus, the continuous use of a wrist‐worn wearable has been included in this study in order to compare markers of physical activity and vital signs against the occurrence of asthma exacerbations.

Alongside the strengths of this study described above, several limitations have been identified. First, the sample size of 60 was determined to ensure adequate power for detecting the change in FEV1 from baseline using either spirometry modality (consistent with co‐primary objective 1), rather than a statistical test in which the lower‐bound of the 95% confidence interval of the mean difference between FEV1_m and FEV1_c is compared to published thresholds such as those adopted for the intraclass correlation coefficient. ³⁹ As the purpose of the study was to collect data with which to power future interventional trials, this is not considered a major limitation. Second, although adherence to mSpirometry and the wrist‐worn wearable will be assessed according to published recommendations, ³⁹ this protocol does not include an objective measurement of adherence to LABA. Although the inability to determine whether participants follow the instructions to complete mSpirometry 1–3 h following each LABA dose may be a limitation, the study is not designed to test the efficacy of LABA and thus the lack of LABA adherence data is not considered a major concern. Similarly, the lack of a placebo or other comparator arm is not considered a limitation given the objective of comparing spirometry modalities rather than LABA efficacy. Instead, the 1‐week baseline period prior to starting LABA treatment allows each participant to serve as their own control in terms of assessing the treatment effect while most other analyses will be based on comparisons between different clinical outcome assessments.

In summary, the results from this study will inform whether the deployment of mSpirometry and a wrist‐worn wearable for remote data collection is feasible in a population with moderate asthma in a multicenter setting, providing valuable data with which to design and power large‐scale decentralized or hybrid interventional trials of other respiratory disease treatment/s. Our results regarding participant‐ and site staff‐reported satisfaction and ease of use may also inform the utility of integrating mSpirometry into routine care, where potential advantages include the ability to detect exacerbations early, reduced cost associated with clinic visits, and enhanced safety among patients at risk for airborne illnesses.

AUTHOR CONTRIBUTIONS

E.S.I., J.P.B., J.A.W. wrote the Manuscript; E.S.I., R.K., S.E., A.D.S., T.F.R., D.S., and J.A.W. designed the research; R.K., S.E., J.A.W. performed the research.

FUNDING INFORMATION

The study described in the manuscript was sponsored by Koneksa Health.

CONFLICT OF INTEREST STATEMENT

E.S.I., A.D.S., and J.A.W. are employees of Koneksa health and may own company stock. J.P.B. reports financial interests in Philips, Signifier Medical Technologies, Apnimed, and Koneksa Health, and salary from the non‐profit Digital Medicine Society; her interests were reviewed and are managed by Brigham and Women's Hospital and Mass General Brigham in accordance with their conflicts of interest policy. T.F.R. is on the Advisory Board of Koneksa Health, and received stock options from the company. He is a member of the Board of Directors of the American Thoracic Society, the FDA Science Board, and has been an advisor to other pre‐commercial bio‐pharma companies. He was previously employed by Merck Research Labs, Novartis, Celgene, Covance, Novo Ventures Repertoire Immune Medicines, and Vanderbilt University. D.S. received sponsorship to attend and speak at international meetings, and honoraria for lecturing or attending advisory boards, from AstraZeneca, Boehringer Ingelheim, Chiesi, Cipla, Genentech, GlaxoSmithKline, Glenmark, Menarini, Mundipharma, Novartis, Peptinnovate, Pfizer, Pulmatrix, Teva, Therevance, and Verona. R.K. and S.E. have nothing to disclose.

Supporting information

Data S1

Data S2

Data S3

Izmailova ES, Kilian R, Bakker JP, et al. Study protocol: A comparison of mobile and clinic‐based spirometry for capturing the treatment effect in moderate asthma. Clin Transl Sci. 2023;16:2112‐2122. doi: 10.1111/cts.13615