30-Days Spirometry Holter - method design and prospective, observational study

doi:10.21203/rs.3.rs-4861783/v1

Download PDF

Article

30-Days Spirometry Holter - method design and prospective, observational study

https://doi.org/10.21203/rs.3.rs-4861783/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 31 Oct, 2024

Read the published version in Scientific Reports →

You are reading this latest preprint version

Background

Asthma underdiagnosis and overdiagnosis remain significant problems for healthcare systems worldwide and indicate considerable pain points with current guidelines and diagnostic methods; therefore, new, targeted approaches seem crucial. This study introduces a novel spirometry-based approach using digital tools for objective asthma diagnosis.

Methods

This was a single-centre (Warsaw Medical University, Poland), prospective cohort study. It included adults with suspected asthma per GINA 2020, without confirmed obstruction in ambulatory spirometry. Patients were equipped and trained with a portable spirometer with built-in manoeuvre quality features AioCare® (HealthUp, Poland). The protocol included twice-daily spirometry examinations over four weeks and symptom reporting in the mobile app. The number of obstructions detected, probability of finding obstruction over time, spirometry values' variability, technical correctness, and reported symptoms were evaluated.

Results

26 patients enrolled (14 females, mean age 37.6 y.o., BMI 24.7 kg/m^2), with a primary outcome of observed obstruction in 42% of participants (3,08 per patient, 95%CI: 0.78–5.37). The detection probability of obstruction plateaued at 100% within the first 21 days of monitoring. Diurnal PEF and FEV1 variability were exceeded (≥ 10%) in 73% and 66% of patients, respectively. 88% of participants performed over half of their tests correctly. 85% of patients reported symptoms at least once.

Conclusions

The Spirometry Holter is a novel and feasible tool for monitoring airway limitation variability in line with GINA guidelines. It shows promise in objectively confirming asthma diagnoses in treatment-naive patients lacking documented prior obstruction.

Health sciences/Health care/Diagnosis

Health sciences/Signs and symptoms/Respiratory signs and symptoms

Asthma

spirometry

pulmonary function testing

home spirometry

diagnostic tool

home monitoring

Asthma represents a significant problem for healthcare systems worldwide. It is one of the most common chronic diseases, and its prevalence continues to rise, especially in urban populations and highly developed countries. According to estimates from the World Health Organization (WHO), approximately 339 million people worldwide have asthma [1]. In a substantial number of cases, asthma has a mild or moderate course. However, the characteristic symptoms of asthma are burdensome for patients and impact their daily functioning, social activities, and professional life. Accurate asthma diagnosis presents diagnostic challenges, especially in primary care practice. This is primarily due to the variability of symptoms and the presence of several disease phenotypes, which differ in their course, further complicating diagnosis during a single visit. In addition to characteristic symptoms, diagnosing asthma requires performing pulmonary function tests, confirming airway limitation and excessive airway flow variability over time [2]. Many physicians opt for empirical treatment in patients without performing pulmonary function tests and without confirmed airway flow variability [3,4]. This may be caused by limited access to spirometry and other specialized tests in primary care. Moreover, due to the variable nature of asthma, longer patient observation and repeated spirometry tests are often necessary. Therefore, despite established diagnostic standards, asthma continues to be frequently either overdiagnosed or underdiagnosed [5,6]. Many observations indicate a higher occurrence of asthma symptoms than reported diagnoses. In children and adults with obstructive symptoms, underdiagnosis can be expected in 40% to as high as 77% of cases [7,8]. Pharmacological therapy should be administered following current guidelines and the best knowledge gathered by research teams. The essence of asthma is the chronic inflammation of the airways, both during symptomatic periods and symptom-free intervals. Therefore, early initiation of chronic asthma control treatment is justified not only to manage asthma symptoms but also to prevent exacerbations and airway remodelling leading to the persistence of obstruction. To achieve this, new methods and biomarkers are needed for more accurate and precise asthma diagnosis to avoid over- and underdiagnosis. In the literature, there is an increasing focus on using digital tools to diagnose and manage asthma, aiming to enhance patient self-control and interactive digital interventions (IDIs) based on data directly collected from the patient's home [9–14]. Various approaches are presented, utilizing symptom diaries, asthma control questionnaires, reminders, and telemedicine consultations with healthcare professionals through various forms of communication such as email, SMS, phone calls, or mobile applications. Monitoring is supported by both "non-digital" and digital personal medical devices, including inhaler attachments, inhalers, peak flow meters, spirometry or FENO devices. Many studies suggest the validity of using digital tools in clinical practice, significantly improving quality of life, better disease control, reduced exacerbations, and fewer unplanned medical visits and hospitalizations [15–18]. The main aim of this study was to describe the new diagnostic method and assess its feasibility and clinical usefulness. We hypothesized that a Spirometry Holter is a feasible method in an outpatient clinic setting, and it will lead to a high detection rate of airway obstructions in the investigated population. Additionally, we explored its role in evaluating excessive airway variability criteria proposed by GINA, presented the relevance of the symptoms and proposed FEV1 diurnal variability cut-off value.

This was an investigator-initiated, single-centre, single-arm, observational, prospective cohort study. It adhered to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by our Institutional Board Review Committee on Human Research (IRB number: KB/16/2021). All participants were fully briefed on the study aims and procedures and provided informed consent.

Spirometry Holter term definition

In the context of this research, the concept of "Spirometry Holter" was introduced, a method using a device designed to measure spirometry and monitor pulmonary function regularly during daily activities. The term "Holter" draws inspiration from a portable device utilised in cardiology, known for continuously recording the heart's electrical activity. It facilitates the detection of transient episodes of abnormal heart rhythms that could not be detected on resting ECG on routine follow-up visits, as defined by the Holter [19]. This concept underscores an analogy between episodes of atrial fibrillation and asthma attacks. Both conditions are often triggered by environmental factors and typically present with symptoms. However, they can also occur without noticeable symptoms, necessitating objective confirmation through independent medical diagnostic tests. For atrial fibrillation, this involves an ECG, while for asthma, spirometry serves as the diagnostic tool.

Participants

Patients were recruited between June 2020 and October 2023 in the outpatient clinic of the Department of Internal Medicine, Pulmonary Diseases and Allergy, Medical University of Warsaw. Inclusion criteria comprised suspected asthma according to GINA 2020 guidelines [20], absence of prior asthma treatment, discontinuation of inhaled corticosteroids for at least 6 weeks, previous spirometry results devoid of obstructive patterns (FEV1/FVC ≥ 5th percentile), age ≥ 18 years, exclusion of other causes of symptoms based on medical history, patient's ability to operate a portable spirometer and a mobile device with internet access, time since the last respiratory tract infection of > 6 weeks, and no prior experience in unsupervised spirometry (to avoid the performance bias). The recruitment flow is presented in Fig. 1.

Equipment

The spirometry tests were conducted using a portable spirometer (AioCare®, HealthUp, Poland), Bluetooth®-connected to the patient's smartphone app. The device complied with ATS/ERS 2019 standards and GDPR. All spirometry data were automatically analyzed for quality and uploaded to a secure online health platform.

Examination protocol

Each patient received a portable spirometer and detailed instructions on proper spirometry techniques. Subsequently, they performed accurate spirometry examinations under the direct supervision of the attending physician in the office setting.

Patients were asked to follow a Spirometry Holter protocol consisting of two spirometry examinations per day, once in the morning and once in the evening, over four weeks, with additional tests prompted by any respiratory symptoms.

Before every examination, patients filled in a symptom questionnaire. Data from this period were presented as a Spirometry Holter report, which the attending physician then reviewed during the follow-up visit. It included all spirometry results, information about compliance to protocol and technical correctness of spirometry examinations, symptoms summary, diurnal PEF variability, diurnal FEV1 variability and difference between max and min FEV1.

The examination protocol is depicted in Fig. 2, and the Spirometry Holter report is in Fig. 3.

Primary and secondary outcomes

The study's primary outcome was defined as the number of participants whose spirometry tests indicated obstruction at least once during any monitoring day. The secondary outcomes included the number and percentage of participants with diurnal PEF larger than 10% (according to GINA), the average diurnal variation in PEF, determined over a 14-day interval, between visit FEV 1 difference counted as a difference between highest and lowest result. Additionally, we analysed the number and percentage of participants who performed at least 50% technically correct spirometry tests and symptoms reported in the app and showed optimal diurnal variability cut-off value for 14-day diurnal FEV1 variability.

Sample Size

The target sample size was estimated based on the primary outcome. In the reference cross-sectional screening nationwide study conducted in Poland using the same mobile spirometer, the prevalence of obstruction was 17% (814 patients) [22].

In the current study, we expected this percentage to be at least twice higher (34%) due to the characteristics of the study population (subjects suspected of having asthma) and multiple measurements on the same patient. The sample size, n = 26, was chosen pragmatically, expecting at least one obstruction to be detected in ≥ 10 participants (38%).

Data analysis

The aggregated results regarding study outcomes and symptoms analysis were reported as a mean and 95% CI, or the counts and/or percentages. The probability of detecting obstruction was estimated for each monitoring day and depicted as a curve scaled to all patients and those with at least one obstruction.

The comparison of the number of days with symptoms reported between groups with at least one or without obstruction was calculated using the chi-squared test with Bonferroni correction. The contingency table presented the relationship between exceeding diurnal variability criteria for PEF and FEV1 over 14 days and detecting at least one obstruction. The ROC analysis was used to calculate the optimal threshold for these parameters.

Twenty-six patients (14 females; 25 Caucasians + 1 mixed ethnicity) with a mean age of 37.6 (32.6–42.5) years and mean BMI of 24.7 (23.6–25.7) kg/m^2 were recruited to the study. One participant claimed active smoking (ID 13). The characteristics of the patients are presented in Table 1.

Table 1

The characteristics of the patients
ID	Age [y]	Sex	BMI [kg/m^2]	days of monitoring	number of examinations	number of obstructions	%correctness of examinations
1	40	F	25.3	31	14	0	100.0
2	66	F	24.6	26	24	0	91.7
3	49	F	20.9	29	49	18	89.8
4	45	M	26.6	24	41	0	85.4
5	38	M	24.9	38	65	0	92.3
6	21	F	22.6	29	30	15	96.7
7	50	F	21.6	29	22	0	63.6
8	44	F	29.3	29	32	4	50.0
9	22	M	27.8	29	28	0	89.3
10	36	M	27.5	30	16	12	81.3
11	25	F	22.7	29	16	0	100.0
12	41	M	23.9	18	40	12	90.0
13	42	M	24.6	24	45	1	86.7
14	43	F	24.0	18	28	0	96.4
15	52	F	21.7	22	32	0	90.6
16	40	M	25.3	32	44	0	70.5
17	22	M	27.5	29	24	0	45.8
18	24	M	28.0	71	56	0	48.2
19	23	M	29.4	15	27	14	63.0
20	49	F	24.7	15	35	0	65.7
21	32	F	21.2	45	45	1	93.3
22	20	F	19.0	27	42	1	71.4
23	48	M	25.8	16	23	0	100.0
24	45	M	24.8	27	48	1	52.1
25	19	F	24.8	23	36	1	72.2
26	41	F	23.1	28	43	0	74.4
Mean (95%CI)	37.6	14 F, 12 M	24.7	28.2	34.8	3.08	79.2

Feasibility

All twenty-six enrolled patients (100%) completed the protocol. The technical correctness of the examination was high for all patients, in particular 23/26 (88%) of them performed > 50% of correct examinations during the monitoring period. The mean percentage of the correct spirometry examination was 79% (95%CI: 72% − 86%). Among incorrect examinations, the following errors occurred the most: repeatability error (in all incorrect examinations, 100%); TPEF > 300 m (in 55% of examinations at least once), BEV > 10% or > 100 ml (in 18% of examinations at least once), EOFE error (in 6% of examinations, at least once), cough (in 11% of examinations, at least once).

Clinical usefulness

The primary outcome, at least one obstruction on any monitoring day, occurred in 11/26 patients (42%). The average number of obstructions per patient was 3.08 (95%CI: 0.78–5.37). Mild obstruction (at least one) was detected in 10/26 (38%) patients and moderate in 3/26 (12%). Moderately severe, severe and very severe obstruction was not detected in any patients. Among 15 patients who performed at least two spirometry examinations for at least one day of monitoring, the norms for diurnal variability of PEF (ΔPEF < 10% on each monitoring day and average ΔPEF in 14 days < 10%) were exceeded in 11/15 (73%) patients. A significant difference in FEV1 between measurements over the monitoring period (norm: ≤ 12% or ≤ 200 ml) was detected in all 26 patients (100%).

A 14-day average diurnal variability of FEV1 larger than 8% was observed in 3/15 patients (20%). The results for the primary and the secondary outcomes are shown in Table 2.

Table 2

Primary and secondary outcomes
Primary outcome
At least one obstruction on any day of monitoring	11/26 (42%)
Secondary outcomes
Average diurnal variation in PEF determined over a 14-day interval (norm: <10%) - #patients who met that criterion (calculated on the subgroup of patients who made spirometry at least twice a day for at least one day during monitoring)	11/15 (73%)
Average diurnal variation in FEV1 determined over a 14-day interval (norm: <8%) - #patients who met that criterion (calculated on the subgroup of patients who made spirometry at least twice a day for at least one day during monitoring)	3/15 (20%)
Significant difference in FEV1 between measurements (norm: ≤ 12% or ≤ 200 ml) - #patients who met that criterion	26/26 (100%)
Technical correctness of the examinations > 50% - #patients (correctness: at least 3 acceptable and repeatable FEV1 and FVC measurements, according to ATS/ERS 2019 Standards)	23/26 (88%)

During the monitoring period, patients reported different symptoms. Specifically, 22/26 (85%) of them reported daily symptoms in at least one day; nocturnal symptoms occurred in 17/26 (65%), activity reduction in 8/26 (31%), and taking a reliever in 6/26 (23%) reported taking a reliever. A summary of the symptom reporting is presented in Table 3.

Table 3

Comparison of reported symptoms in patients with and without obstruction
Number of patients with at least one symptom reported
	Patients with at least one obstruction	Patients without obstruction	P-value
Daytime symptoms	10/11 (91%)	12/15 (80%)	0.832
Nighttime symptoms	8/11 (73%)	9/15 (60%)	0.797
Reduction of activity	4/11 (36%)	4/15 (27%)	0.921
Taking relievers	3/11 (27%)	3/15 (20%)	0.971
Mean %of days of the monitoring period when symptoms reported (95% CI)
	Patients with at least one obstruction	Patients without obstruction	P-value
Daytime symptoms	38.1% (14.3% − 62.0%)	30.3% (10.4% − 50.2%)	0.550
Nighttime symptoms	18.3% (0.0% − 37.7%)	15.4% (0.0% − 32.8%)	0.327
Reduction of activity	3.5% (0.0% − 7.0%)	5.4% (0.0% − 12.0%)	0.751
Taking relievers	2.7% (0.0% − 6.2%)	2.7% (0.0% − 7.1%)	0.673

We estimated the probability of detecting obstruction with each monitoring day by dividing the number of patients who had detected obstruction on that day or before by the total number of patients observed. This calculation ranged from 0 to 30 days. Figure 4 presents the probability curve.

Of the 11 patients who developed at least one obstruction, all occurred during the first 21 days of observation (probability equal to 1). Figure 5 presents the concordance matrixes between the average diurnal variability of PEF and newly proposed FEV1 above a defined threshold and the occurrence of at least one obstruction and corresponding ROC curves.

The concordance between diurnal PEF variability and obstruction detection was 53.3% at a 10% threshold and 86.7% at a 16% threshold, as determined by ROC analysis. The optimal thresholds for PEF and FEV1 were found to be 16% and 8%, respectively, with PEF showing a higher predictive value (AUC 0.851 vs. 0.703 for FEV1).

Asthma underdiagnosis and overdiagnosis remain significant problems for healthcare systems worldwide and indicate considerable pain points with current guidelines and diagnostic methods; therefore, new, targeted approaches seem crucial [5,6]. This is especially important in patients reporting symptoms without objective airway limitation confirmation, who were the focus of our study.

Primary endpoint analysis showed that the new diagnostic method (Spirometry Holter) proposed in our study could detect obstruction in 42% of patients reporting respiratory symptoms without prior confirmation in spirometry and allow the objective diagnosis of asthma. Moreover, the reported symptoms were not significantly related to the evidence of obstruction.

The study showed that the probability of obstruction detection increased in time, with 21 days to detect all obstructions in this cohort and an optimal time of about 10 days to catch most obstructions. Moreover, the study showed that the newly proposed method is feasible to organise in an outpatient clinic environment, and asthma patients can perform spirometry at home, with over 88% of patients achieving over 50% of technically correct examinations. Therefore, the Spirometry Holter emerges as a promising tool for further investigation in cases of indeterminate asthma cases.

GINA Guidelines provide comprehensive criteria and protocols for asthma diagnosis [2]. That includes the medical history of typical, variable respiratory symptoms, documented obstruction and confirmed variable expiratory airflow limitation with various methods differing in sensitivity, specificity, access and invasiveness [23]. Although confirming variable respiratory symptoms is clear, documenting obstruction and confirming variable expiratory airflow limitation is hard to achieve in an outpatient clinic, though mandatory for preventing misdiagnosis.

Therefore, GINA provides a wide range of measures such as MCT, reversibility tests, excessive PEF variability over two weeks, increase in lung functions after four weeks of treatment, excessive variation in lung function between visits, or exercise challenge tests [2].

This study investigated a few measures using a newly proposed structured diagnostic approach. The most valuable diagnostic tool for asthma remains the direct challenge test with methacholine (MCT), followed by the mannitol challenge test [24]. Yurkadul et al. showed 89% diagnostic accuracy for MCT, with the highest negative predictive value (93,5%) [25]. Nevertheless, the method's utilisation seems poor due to its invasiveness and need for an in-hospital setting during the test.

The second diagnostic accuracy was achieved by PEF variability (71%). Diurnal PEF variability over two weeks was measured in our study using the Holter method. The finding of this study showed that using a 10% cut-off value for average diurnal PEF variability, proposed by current guidelines, resulted in many false positive results (9/15, 60%) that may lead to overdiagnosis. Similar results have been shown by L Tilemann, with low specificity, sensitivity and predictive value of diurnal PEF variability compared to the MCT [26].

With all study limitations, our analysis showed that the optimal threshold indicating at least one obstruction in this group of patients was > 16%. Moreover, some studies have shown that PEF poorly correlates with FEV1, especially in children [27]. Therefore, we suspected FEV1, a non-effort-dependent metric, may be a more adequate parameter for airway flow variability. In the additional analysis using an equation originally proposed by GINA for diurnal PEF, we presented a new digital biomarker available in the spirometry Holter method - average diurnal FEV1 variability over 14 days - establishing an optimal threshold for obstruction detection - >8%. Nevertheless, compared to both diurnal PEF variability cut-off values, 10 and 16%, it was less effective in obstruction detection and requires further validation in population studies to be used in clinical practice.

The bronchodilation test in the Yurdakul study achieved only 57% accuracy but was characterised by the highest specificity of all tests (95%). Unfortunately, a direct comparison was impossible because the bronchodilation test was not a part of our study.

Another airflow variability measure described in the literature is excessive variation in lung function between two visits. Although this study's results align with the GINA statement and literature regarding the limited usefulness of excessive variation in lung function between two visits, in this study, 100% of patients who performed Spirometry Holter met this criterion, with a very high false positive rate. Unlike Dean et al., who reported good specificity with only 17% sensitivity [28]. Differences in calculation methods may explain possible bias. We compared maximal and minimal FEV1 from 30 days using our method, and there was no actual difference between the two visits; nevertheless, our approach covers a wider range of spirometry results. Therefore, the current cut-off value for this criterion may need to be further investigated and used cautiously in clinical practice.

This study has several limitations. First, it was a non-controlled, observational study on a relatively small population of patients designed as a feasibility study of a new innovative method. Regarding the lack of healthy control subjects, it was impossible to reliably analyse a method's diagnostic accuracy and directly compare it to other methods proposed by GINA. Secondly, the average age in the investigated population was 37, and the exclusion criteria included the patient's inability to use a digital spirometer with a mobile app. Although digital exclusion may be a substantial limitation of methods utilisation in elderly patients, it is worth noticing that asthma is diagnosed mainly in middle age, with an average of 50 in men and 38 in women [29].

Despite the described limitations, we see our results as highly valuable and, if confirmed, may contribute to developing a new diagnostic approach for this particular group of patients.

Further research should investigate the clinical value of the method, including healthy subjects, to be compared to other measures mentioned by GINA. Additionally, comparisons should include time to asthma diagnosis and the cost-effectiveness of different approaches in a randomized and multicenter manner. A non-inferiority study with the MCT may be desirable regarding the new method's potential place in the asthma diagnostic pathway. Spirometry Holter is a novel tool for monitoring airway limitation variability that is feasible to implement in outpatient clinic settings.

It may be useful in objectively confirming asthma diagnosis in naive to-treatment patients without prior documented obstruction. Further studies are needed to compare its diagnostic accuracy and cost-effectiveness to other methods mentioned in the guidelines.

WHO

World Health Organization

GINA

Global Initiative for Asthma

FEV1

Forced Expiratory Volume in 1 second

FVC

Forced Vital Capacity

IRB

Institutional Review Board

PEF

Peak Expiratory Flow

IDIs

Interactive Digital Interventions

ECG

Electrocardiogram

ATS/ERS

American Thoracic Society/European Respiratory Society

GDPR

General Data Protection Regulation

MCT

Methacholine Challenge Test

ROC

Receiver Operating Characteristic

AUC

Area Under the Curve

BMI

Body Mass Index

BEV

Back Extrapolated Volume

TPEF

Time to Peak Expiratory Flow

EOFE

End of Forced Expiration

SMS

Short Message Service

Ethical Approval

Study adhered to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by our Institutional Board Review Committee on Human Research (IRB number: KB/16/2021). All participants were fully briefed on the study aims and procedures and provided informed consent.

Funding

MS was funded by the COSMOS and HEART.FM projects, which have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement Nos. 788960 and 957532).

Availability of data and materials

The datasets used during the current study are available from the corresponding author upon reasonable request.

Conflict of interest

MB was an employee of Healthup, MS is a contractual employee, and LK is the founder and shareholder of Healthup and the inventor of the AioCare device. All the remaining authors declare no conflict of interest.

5. Literature

Asthma [Internet]. Who.int. Available from: https://www.who.int/news-room/facts-in-pictures/detail/asthma, 15 may 2020
Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention, 2023. Updated July 2023. Available from: www.ginasthma.org
Aaron SD, Vandemheen KL, FitzGerald JM, Ainslie M, Gupta S, Lemière C, et al. Reevaluation of Diagnosis in Adults With Physician-Diagnosed Asthma. JAMA. 2017;317: 269–279.
Gershon AS, Victor JC, Guan J, Aaron SD, To T. Pulmonary function testing in the diagnosis of asthma: a population study. Chest. 2012;141: 1190–1196.
Aaron SD, Boulet LP, Reddel HK, Gershon AS. Underdiagnosis and Overdiagnosis of Asthma. Am J Respir Crit Care Med. 2018;198(8):1012–1020. doi: 10.1164/rccm.201804-0682CI. PMID: 29756989.
Kavanagh J, Jackson DJ, Kent BD. Over- and under-diagnosis in asthma. Breathe (Sheff). 2019;15(1):e20-e27. doi: 10.1183/20734735.0362-2018. PMID: 31031841; PMCID: PMC6481983.
Speight AN, Lee DA, Hey EN. Underdiagnosis and undertreatment of asthma in childhood. Br Med J. 1983;286: 1253–1256.
van Huisstede A, Castro Cabezas M, van de Geijn G-JM, Mannaerts GH, Njo TL, Taube C, et al. Underdiagnosis and overdiagnosis of asthma in the morbidly obese. Respir Med. 2013;107: 1356–1364.
Boros: Boros PW, Maciejewski A, Nowicki MM, Wesołowski S. Comparability of portable and desktop spirometry: a randomized, parallel assignment, open-label clinical trial. Advances in Respiratory Medicine. 2022;90(1):60–7.
Kupczyk M, Hofman A, Kołtowski Ł, et al. Home self-monitoring in patients with asthma using a mobile spirometry system. J Asthma [Internet]. 2021;58(4):505–11. Available from: http://dx.doi.org/10.1080/02770903.2019. 1709864
Sund ZM, Powell T, Greenwood R, et al. Remote daily real-time monitoring in patients with COPD-a feasibility study using a novel
Bell JM, Sivam S, Dentice RL, et al. Quality of home spirometry performance amongst adults with cystic fibrosis. J Cyst Fibros [Internet]. 2022;21(1):84–7. Available from: http://dx.doi.org/10.1016/j.jcf.2021.10.012
Logie K, Welsh L, Ranganathan SC. Telehealth spirometry for children with cystic fibrosis. Arch Dis Child [Internet]. 2020;105(12):1203–5. Available from: http://dx.doi.org/10.1136/archdischild-2019-317147
Mosnaim G, Safioti G, Brown R, et al. Digital Health Technology in Asthma: A Comprehensive Scoping Review. J Allergy Clin Immunol Pract. 2021;9(6):2377–2398.doi:10.1016/j.jaip.2021.02.028
Pool AC, Kraschnewski JL, Poger JM, Smyth J, Stuckey HL, Craig TJ, et al. (2017)Impact of online patient reminders to improve asthma care: A randomized controlled trial. PLoS ONE 12(2): e0170447. https://doi.org/10.1371/journal.pone.0170447
Van den Wijngaart LS, Roukema J, Boehmer ALM, Brouwer ML, Hugen CAC, Niers LEM, et al. A virtual asthma clinic for children: fewer routine outpatient visits, same asthma control. Eur Respir J 2017;50:1700471
Van Den Wijngaart LS, Kievit W, Roukema J, Boehmer AL, Brouwer ML, Hugen CA, et 12 al. The virtual asthma clinic for children: a cost-effectiveness analysis. Pediatr Pulmonol 2016;51:S65.
Cingi C, Yorgancioglu A, Cingi CC, Oguzulgen K, Muluk NB, Ulusoy S, et al. The"physician on call patient engagement trial" (POPET): measuring the impact of a mobile patient engagement application on health outcomes and quality of life in allergic rhinitis and asthma patients. Int Forum Allergy Rhinol 2015;5:487–97
HOLTER NJ, GENERELLI JA. Remote recording of physiological data by radio. Rocky Mt Med J. 1949;46(9):747–51. PMID: 18137532.
Halpin DMG, Criner GJ, Papi A, et al. Global Initiative for the diagnosis, management, and prevention of Chronic Obstructive Lung Disease. The 2020 GOLD Science Committee report on COVID-19 and chronic obstructive pulmonary disease. Am J Respir Crit Care Med [Internet]. 2021;203(1):24–36. Available from: http://dx.doi.org/10.1164/rccm.202009-3533SO
Graham BL, Steenbruggen I, Miller MR, et al. Standardization of Spirometry 2019 Update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am J Respir Crit Care Med. 2019;200(8):e70-e88. doi:10.1164/rccm.201908-1590ST
Jankowski, P., Górska, K., Mycroft, K et al. (2021). The use of a mobile spirometry with a feedback quality assessment in primary care setting – A nationwide cross-sectional feasibility study. Respiratory Medicine, 184(106472), 106472. https://doi.org/10.1016/j.rmed.2021.106472
Sano H, Tomita K, Sano A, Saeki S, Nishikawa Y, Nishiyama O, et al. Accuracy of objective tests for diagnosing adult asthma in symptomatic patients: A systematic literature review and hierarchical Bayesian latent-class meta-analysis. Allergol Int [Internet]. 2019;68(2):191–8. Available from: http://dx.doi.org/10.1016/j.alit.2018.08.013
Romero-Falcón MA, Medina-Gallardo JF, Lopez-Campos JL, Maestre Sánchez MV, Soler Chamorro MJ, Regalado Alvertos E, et al. Evaluation of the diagnostic accuracy of non-specific bronchial provocation tests in the diagnosis of asthma: A randomized cross-over study. Arch Bronconeumol [Internet]. 2023;59(2):76–83. Available from: http://dx.doi.org/10.1016/j.arbres.2022.10.008
Yurdakul AS, Dursun B, Canbakan S, Çakaloğlu A, Çapan N. The assessment of validity of different asthma diagnostic tools in adults. J Asthma [Internet]. 2005;42(10):843–6. Available from: http://dx.doi.org/10.1080/02770900500370981
Tilemann L, Gindner L, Meyer F, Laux G, Szecsenyi J, Schneider A. Diagnostischer Wert der Peak-Flow-Variabilität bei Verdacht auf Asthma bronchiale in der Hausarztpraxis. Dtsch Med Wochenschr [Internet]. 2009;134(41):2053–8. Available from: http://dx.doi.org/10.1055/s-0029-1237554
Solinski M, Dabrowiecki P, Basza M, Gorska K, Korczynski P, Koltowski L. Pef underestimates fev1 and fev1/fvc parameters in children and adult population. Chest [Internet]. 2022;161(6):A455. Available from: http://dx.doi.org/10.1016/j.chest.2021.12.485
Dean BW, Birnie EE, Whitmore GA, Vandemheen KL, Boulet L-P, FitzGerald JM, et al. Between-visit variability in FEV1 as a diagnostic test for asthma in adults. Ann Am Thorac Soc [Internet]. 2018;15(9):1039–46. Available from: http://dx.doi.org/10.1513/annalsats.201803-211oc
Honkamäki J, Hisinger-Mölkänen H, Ilmarinen P, Piirilä P, Tuomisto LE, Andersén H, et al. Age- and gender-specific incidence of new asthma diagnosis from childhood to late adulthood. Respir Med [Internet]. 2019;154:56–62. Available from: http://dx.doi.org/10.1016/j.rmed.2019.06.003

Competing interest reported. Mikołaj Basza was an employee of Healthup, Mateusz Soliński is a contractual employee, and Łukasz Kołtowski is the founder and shareholder of Healthup and the inventor of the AioCare device. All the remaining authors declare no conflict of interest.

Download PDF

Journal Publication

published 31 Oct, 2024

Read the published version in Scientific Reports →

Editorial decision: Revision requested
17 Sep, 2024
Reviews received at journal
16 Sep, 2024
Reviews received at journal
04 Sep, 2024
Reviewers agreed at journal
02 Sep, 2024
Reviewers agreed at journal
31 Aug, 2024
Reviewers invited by journal
27 Aug, 2024
Editor assigned by journal
27 Aug, 2024
Editor invited by journal
20 Aug, 2024
Submission checks completed at journal
16 Aug, 2024
First submitted to journal
05 Aug, 2024

You are reading this latest preprint version

30-Days Spirometry Holter - method design and prospective, observational study

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1. Introduction

2. Methodology

3. Results

4. Discussion

Abbreviations

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1