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Abstract

Objective

To assess the diurnal rhythm and variability of lung function in healthy individuals, encompassing both large and small airways.

Methods

A prospective study enrolled 35 healthy adults without a history of smoking. Initial spirometry and a bronchodilation test were performed using the Jaeger spirometer, followed by a seven-day continuous home monitoring using the GOSPT2000. We evaluated repeatability using the intraclass correlation coefficient and agreement through linear regression and Bland–Altman analyses. Circadian rhythm and variability in spirometric measurements were analyzed using the coefficient of variation (CV) and daily variation rate.

Results

The GOSPT2000 demonstrated strong repeatability and high agreement with the Jaeger spirometer. Notable findings included a decrease in nocturnal forced expiratory volume in 1 s (FEV₁), forced vital capacity (FVC), and FEV₃ by 44, 59, and 53 mL, respectively. In contrast, peak expiratory flow at noon showed an increase of 0.143L/min. Small-airway variables, including forced expiratory flow at 50% and 75% of the FVC and maximum midexpiratory flow, showed no significant diurnal variation. The nocturnal CV for large-airway variables was ≤ 4%, while for small-airway variables, it was ≤ 11.89%.

Conclusion

This study has established a spectrum of variability for both large and small airways in healthy populations. The variability of small-airway variables is higher than that of large-airway variables. The investigation into the diurnal rhythms and variability characteristics of both large and small airway variables in the healthy population can serve as a foundation for diagnosing asthma or assessing the efficacy of asthma treatments.

Keywords

Pulmonary function circadian rhythm variation home monitoring portable spirometer

Introduction

Asthma affects more than 330 million people globally and the disease burden is increasing in many countries,^1,2 with high rates of misdiagnosis, uncontrolled symptoms,³ acute attacks, and death. Airflow obstruction follows a circadian pattern,^3–9 contributing to the nocturnal worsening of asthma.⁹ The diurnal variation of lung functions is more prominent in patients with asthma, which is an important criterion for diagnosing asthma.¹ Consecutive spirometry monitoring of large-airway functions (such as forced vital capacity [FVC], forced expiratory volume in 1 s [FEV₁], FEV₁/FVC, FEV₃) and small-airway functions (such as forced expiratory flow at 50% of the FVC [MEF50], MEF25 and maximum mid-expiratory flow [MMEF]) is helpful in diagnosing asthma,^10–12 predicting acute attacks and evaluating therapeutic response.^13–15

The circadian rhythm and variability of lung function in healthy individuals are crucial for interpreting the results of telehome monitoring. Assessed with conventional spirometers, FEV₁ and FEV₁/FVC increased from 9 a.m. until noon and decreased thereafter in healthy individuals.¹⁶ Peak expiratory flow (PEF) has been shown to peak around 4 p.m.^16–18 However, those results from conventional spirometer cannot be directly applied to telehome monitoring because: (1) difference between professional on-site guided and home-based self-supervised operation of spirometers may cause the accuracy bias; (2) trials using conventional spirometry are typically shorter in duration^16–18 compared with telehome monitoring; (3) the influence of environmental factors, such as acclimatization to the hospital environment, disrupted sleep due to spirometry, exposure to outdoor pollutants, and brief cessation of social activities and work, in studies of conventional spirometry remains unclear; and (4) recording of the time of day (such as 4 a.m. and 4 p.m.) in conventional spirometer trials offers limited practicality for real-world, home-based monitoring strategies.^17,18

The purpose of our prospective cohort study was to estimate the circadian rhythm and variability of spirometric variables using a portable spirometer in healthy individuals during a seven-day diurnal and nocturnal home monitoring period with GOSPT2000. The GOSPT2000 spirometer (Shanghai Zhuomiao Electronic Technology Co., Ltd, Shanghai, China) is a product of GoSprio (Monitored Therapeutics Inc., Dublin, OH, USA) approved by the Food and Drug Administration in the United States (K163249). The repeatability, agreement and stability of the results from the portable spirometer and the subjects’ adherence were also evaluated.

Methods

The study has been exempted from the Ethics Committee of Shanghai General Hospital, Shanghai Jiao Tong University.

Chinese Clinical Trial Registry; Website: www.chictr.org.cn; No.: ChiCTR2100050355.

Participant characteristics

The prospective study was approved by the Ethics Committee of Shanghai General Hospital, Shanghai Jiao Tong University (No.2021KY073).

Thirty-five healthy volunteers who consented to participate in the trial and met the following inclusion criteria were enrolled, and homogenized in terms of gender, age, and education degree. The inclusion criteria were as follows: aged 18–65 years; no clinical symptoms within the past eight weeks; normal medical reports (complete blood counts, biochemistry tests, carcinoembryonic antigen, alpha-fetoprotein, B-ultrasonic, electrocardiography and chest high-resolution computed tomography scan) within the past month; no history of smoking; no allergic diseases; and no chronic respiratory diseases.

Subjects were excluded if they had any systemic diseases or symptoms, including unstable cardiovascular status, diabetes, gastroesophageal reflux, nausea, vomiting, abdominal pain, stress urinary incontinence, or if they had undergone surgery of the chest, abdomen, or eye within the previous two weeks. Additionally, those unsuitable for spirometry or with mental diseases and cognitive disorders were also excluded.

Study design and setting

A schematic diagram of the study is presented in Figure 1. We assessed demographics, height, weight and medical history. After confirming the absence of respiratory symptoms (cough, chest tightness, fever, sputum production, breathlessness), subjects underwent spirometry and a bronchodilation test using the hospital spirometer, guided by a trained medical technician in the morning. Subsequently, they were provided with the GOSPT2000 device to take home for pulmonary function monitoring over the next seven consecutive days. The built-in software program prompted users to report any respiratory symptoms before each test. Testing times were set between 8 a.m.–9 a.m. and 8 p.m.–9 p.m.

Figure 1.

Schematic presentation of study design.

Spirometry and bronchodilation test

Spirometry was performed using a Jaeger spirometer (Jaeger Co., Hoechberg, Germany), following the performance criteria recommended by the American Thoracic Society/European Respiratory Society standardization of spirometry.¹⁸ Spirometric variables including PEF, FVC, FEV₁, FEV₃, MEF50, MEF25, and MMEF were expressed as absolute values and as percentages of the predicted value. Ratios such as FEV₁/FVC and FEV₃/FVC were expressed as absolute values.

The bronchodilation test was conducted before and 20 min after the administration of a bronchodilator (Salbutamol 400 ug). An improvement in FEV₁ of less than 12% from predose with an absolute increase of less than 200 mL was defined as negative.

Portable spirometer and home monitoring

The introduction of GOSPT2000 is included in supplemental data.

After seven consecutive days of measurements, we collected data for PEF, FVC, FEV₁, FEV₃, MEF50, MEF25, and MMEF. The back-extrapolated volume (BEV) had to be less than 5% of the FVC or 0.100 L to confirm that FEV₁ resulted from a maximal effort.¹⁸

Diurnal variability was calculated from twice-daily spirometric measurements as the day's highest value minus the day's lowest value, divided by the mean of the day's highest and lowest values, averaged over one week.

Sample size calculation

In this study, we mainly focus on the circadian rhythm of small-airway variables, and we expect a 20% change in day-night differences of small-airway variables to be clinically significant, so 0.20 was used as our effect size (dz). The desired level of significance (alpha) is set at 0.05. The power of the study (1—beta) is 0.80 as commonly used. The noncentrality parameter (δ) was computed as 2.82. The critical t-value was found to be 1.97. The degrees of freedom (df) were calculated as 198. In this case, a sample size of 199 was necessary to achieve a power of 0.80 at a significance level of 0.05, given the effect size of 0.20. Each subject underwent repeated tests for seven consecutive days, resulting in 199/7 = 28.42. Obtaining data for 199 daytime and nighttime measurements necessitates 29 individuals. Anticipating a dropout rate of 20%, we plan to enroll 35 subjects according to the inclusion criteria, and they were homogenized in terms of gender, age, and education degree to ensure the stability of results.

Statistical analysis

Data were analyzed using GraphPad Prism Version 5.01 (GraphPad Software, San Diego, CA, USA) and SPSS Version 21.0.

The normality of distribution was tested with the Shapiro–Wilk test. Normally distributed data were expressed as mean ± standard deviation (SD) or 95% confidence interval (CI). Nonnormally distributed data were expressed as median and interquartile ranges (IQRs). Nonparametric tests were used for comparison between groups. A paired t-test was used to compare the differences between diurnal and nocturnal variables.

Repeatability was assessed using the intraclass correlation coefficient (ICC). The agreement was evaluated through linear regression analysis and Bland–Altman analysis, calculating the mean difference and 95% limits of agreement (LoA).

The coefficient of variation (CV), defined by the standard deviation, relative to the mean was calculated for each continuous variable to demonstrate variability.

T h e d i u r n a l C V = \frac{SD of seven - day diurnal values}{mean of seven - day diurnal values} \times 100 %

\begin{aligned} T h e n o c t u r n a l C V \\ = \frac{SD of seven - day nocturnal values}{mean of seven - day nocturnal values} \times 100 % \end{aligned}

\begin{aligned} T h e i n t r a c o h o r t C V \\ = \frac{SD of values from all participants}{mean of values from all participants} \times 100 % \end{aligned}

T h e s e v e n - d a y C V = \frac{SD of seven - day diurnal and nocturnal values}{mean of seven - day diurnal and nocturnal values} \times 100 %

\begin{aligned} D a i l y v a r i a t i o n r a t e \\ = \frac{2 (Highest value - lowest value of the day)}{(Highest value + lowest value of the day)} \times 100 % \end{aligned}

The threshold for statistical significance for all analysis was set at p < .05.

Results

Demographic and baseline characteristics

All 35 subjects completed Jaeger's spirometry and the seven-day portable spirometer measurement. Three participants were excluded from the data analysis because their FEV₁/FVC were less than 70% on the first GOSPT2000 lung function test. Additionally, one participant was excluded because the BEV was greater than 5% of the FVC and greater than 0.100L, as indicated by the built-in quality control of GOSPT2000 device. This left 31 participants for the final data analysis. The mean age was 36.68 (SD 11.64) years and 41.94% (13 out of 31) were male (Table 1). White blood cells (3.5–9.5 × 10⁹/L), lymphocytes (1.1–3.2 × 10⁹/L), and eosinophils (0.02–0.3 × 10⁹/L) are all within normal ranges for the 31 subjects.

Table 1.

Demographic data and laboratory spirometric variables of subjects.

Variables	M	SD	p-value	Intracohort CV of all the subjects
Age, years	36.68	11.64	> .10	31.74%
Height, cm	168.9	8.791	> .10	5.21%
Weight, kg	65.55	10.38	.0791	15.84%
FVC, L	3.95	0.9438	> .10	23.89%
FEV₁, L	3.259	0.7941	> .10	24.37%
FEV₃, L	3.798	0.9291	> .10	24.46%
PEF^†, L/min	8.135	1.987	> .10	24.43%
MEF50, L	3.856	0.9106	> .10	23.62%
MEF25, L	1.495	0.6424	> .10	42.98%
MMEF, L	3.251	0.8975	> .10	27.61%
FEV₁/FVC, %	82.48	4.898	.0859	5.94%
FEV₃/FVC, %	96.04	2.755	> .10	2.87%
FVC%	99.16	9.201	> .10	9.28%
FEV₁%	96.3	8.937	> .10	9.28%
PEF%^†	103.6	15.7	> .10	15.16%
MEF50%	83.19	17.18	> .10	20.65%
MEF25%	70.82	21.43	> .10	30.26%
MMEF%	79.8	15.59	> .10	19.53%

CV: coefficient of variance; FVC: forced vital capacity; FEV₁: forced expiratory volume in 1 s; FEV₃: FEV in 3 s; MEF50: forced expiratory flow at 50% of forced vital capacity; MEF25: forced expiratory flow at 75% of forced vital capacity; MMEF: forced expiratory flow between 25% and 75%; PEF: peak expiratory flow; SD: standard deviation; MMEF: maximum mid-expiratory flow.

†

n = 31.

No subject discontinued home monitoring with GOSPT2000 during the seven days. Of the 448 sets of spirometry data, only 16 sets (10 sets from one individual) did not confirm to the acceptability and repeatability criteria.

For large-airway variables, the intracohort CV was 9.28% for both FEV₁ and FVC, 5.94% for the FEV₁/FVC ratio and 15.16% for PEF. Among the MEFs, the highest intracohort CV was 30.26% for MEF25, followed by 20.65% for MEF50 and 19.53% for MMEF. FEV₃/FVC ratio exhibited the lowest intracohort CV with 2.87%. All the participants had negative bronchodilation tests.

Repeatability and agreement of GOSPT2000 compared with Jaeger spirometer

FVC and FEV₁ showed excellent repeatability, with ICC values of 0.982 (95% CI [0.963 to 0.991]) for FVC and 0.990 (95% CI [0.980 to 0.995]) for FEV₁. The GOSPT2000-measured values for FVC and FEV₁ were consistent with those obtained from the Jaeger spirometer (ICC > 0.98 for both) (Figure 2A and B). The Bland–Altman plot revealed that the mean FVC measured by GOSPT2000 was 0.058L lower than that measured by Jaeger, with a 95% LoA ranging from −0.006L to 0.121L (Figure 2C). Similarly, the mean FEV₁ measured by GOSPT2000 was 0.043L lower, with a 95% LoA of 0.002L to 0.084L (Figure 2D). Only two FVC values fell outside the LoA, and no FEV₁ measurement was beyond the LoA.

Figure 2.

ICC and Bland–Altman analysis.

Circadian rhythm of large and small airway spirometric variables

As shown in eTable 1, mobile spirometry monitoring confirmed significant diurnal variations in large airway variables. Diurnal results were significantly higher than nocturnal values (Figure 3, p < .001 for FEV₁, FVC, and FEV₃). Compared with daytime data, nocturnal FEV₁, FVC, and FEV₃ decreased by 44, 59, and 53 mL, respectively. Conversely, nocturnal PEF increased by 0.143 L/min (p < .001). No significant day–night differences were observed for the FEV₁/FVC ratio or the small airway variables (p > .05 for all).

Figure 3.

Circadian rhythm of large- and small-airway function variables in GOSPT2000 spirometer.

Variability of large and small airway variables

In healthy individuals undergoing seven-day diurnal and nocturnal home monitoring with GOSPT2000, lower diurnal CVs were observed in large airway variables (2.72% for FVC, 2.57% for FEV₁, 2.18% for FEV₃, 3.52% for PEF) compared to small-airway variables (8.59% for MEF50, 10.91% for MEF25 and 7.19% for MMEF; Figure 4, eTable 2). Notably, two outliers of PEF were identified on the same day in one subject; their exclusion in a sensitivity analysis did not significantly alter the concordance or correlation measures. Additionally, four outliers of PEF across four different days were identified in another subject and were excluded from the final analysis. Nocturnal CVs for large airway variables (2.87% for FVC, 2.76% for FEV₁, 2.59% for FEV₃, and 4.0% for PEF) were also lower than small-airway variables (8.31% for MEF50, 11.89% for MEF25, and 7.22% for MMEF).

Figure 4.

The diurnal CVs and nocturnal CVs of GOSPT2000 spirometry indicators for seven days.

The daily variation rates of small-airway variables were higher than those of large-airway variables. with 3.39 (IQR:2.02–5.13)% for FVC, 2.50 (IQR:2.25–4.72)% for FEV₁, 2.98 (IQR:2.37–4.73)% for FEV₃, 7.20(IQR:5.63–10.15)% for MEF50, 14.04 (IQR:10.55–18.34)% for MEF25, 7.89 (IQR:5.23–10.81)% for MMEF and 4.43 (IQR:3.21–6.00)% for PEF. (Figure 5, eTable 3).

Figure 5.

The daily variation rate of variables by GOSPT2000.

Discussion

This study contributes significantly to the understanding of lung function variability, especially in terms of circadian rhythms and the repeatability of measurements in both large and small airways. The demographic and baseline characteristics of our study provide a solid foundation for interpreting these results. Participants were homogenized by gender, age, and education degree. No bronchial hyperresponsiveness was present, as confirmed by bronchodilation tests. By focusing on a healthy, nonsmoking adult population with an average age of 36.68 years, we ensured a relevant sample that minimizes confounding factors like smoking or preexisting lung diseases.

The study highlights significant diurnal variations in large airway variables, with lower nocturnal values for FEV₁, FVC, and FEV₃, but an increase in nocturnal PEF. There were no significant day–night differences in small-airway variables. Day–night differences in large-airway function are well documented.^3–6 However, most spirometry assessments occur in hospitals with professional oversight and are typically limited to 1–3 days, continuous or otherwise. Our study provides new information on long-term home-based spirometry, enhancing precision with detailed variability data. This aligns with existing literature¹⁹ indicating circadian influences on lung function and suggests that time of day is an important consideration in both clinical and research spirometry. These findings have potential clinical implications, such as timing medication for optimal control, understanding the diurnal pattern of symptoms in respiratory diseases, evaluating asthma control and predicting acute attacks. Besides that, finding additional markers that correlate with symptoms and prognosis is of great clinical significance. For example, a study found airway nanoparticles to correlate with symptoms and lung functions better than spirometry in healthy smokers.²⁰ We believe that circadian rhythm and variability in pulmonary function tests might also prove to be of such value based on further research.

Circadian variation in large-airway function has been recorded in both healthy individuals^6,16 and patients with asthma.^3–5 To minimize circadian variation errors in spirometry,¹⁶ we calculated diurnal and nocturnal CVs for all spirometric variables separately. The diurnal and nocturnal CVs for FVC, FEV₁ and FEV₃ reflect minimal individual differences. The inquiry arises as to whether the criterion for diagnosing asthma in bronchodilator testing, which necessitates an increase in FEV₁ of 12% or more, is reasonable, particularly in light of the study's finding of a nocturnal CV for FEV₁ less than 4%. The slightly higher nocturnal CVs suggest their greater clinical significance in predicting treatment response and acute exacerbation when monitoring variations in airway function simultaneously. Conducting spirometry at relatively fixed time points can minimize circadian rhythm influences, enabling prompt detection of improvements postdiagnostic treatment and providing evidence-based advice for follow-up timings and retesting of pulmonary functions, thereby avoiding hormone overuse and asthma overdiagnosis.

The greater variability in small-airway variables compared to large-airway variables observed in this study is a novel finding. This could be attributed to the inherent differences in the structure and function of large and small airways, as indicated in our previous trial,²¹ age was the main influencing factor of both large and small airway function variability, especially for the small airway function in the evening. When diagnosing asthma, if small-airway variables are included, a relatively higher variability threshold should be adopted. Our previous studies indicate that a 15.26% increase in MMEF or a 26.04% increase in MEF25 during bronchodilation tests can predict a response to antiasthma therapy, as compared to a 3.50% improvement in FEV₁.¹² The elevated daily variation rates could contribute to the higher cutoff values for small-airway variables in treatment response prediction.¹² This monitoring could be particularly helpful for the early clinical detection of those at high risk for asthma. Further detailed studies or specific data on these variables might provide more insights, but the existing evidence points to a notable circadian variation in small-airway functions, which is an important consideration for both clinical diagnosis and management of respiratory conditions like asthma.

Despite international guidelines advocating the use of peak flow meters (PFMs) in patients with poorly controlled asthma symptoms, few physicians educate their patients about them, due to the high intraindividual variability of PEFs and low patient adherence.^22–25 Our study found that the GOSPT2000 offered significantly lower diurnal and nocturnal CVs in PEF measurements compared to those offered by traditional PFMs in healthy subjects,²⁶ indicating its higher stability and reliability.

The study's findings have several implications for clinical practice and future research. The results obtained from telehome monitoring exhibited strong repeatability and high agreement with in-hospital spirometry measurements, which enhances the persuasiveness of our findings. Then comes the implications, first, the observed variability in lung function, especially in the small airways, might aid in the early detection of respiratory diseases. Regular monitoring of lung function using portable spirometers could become an essential part of preventive health check-ups, especially for individuals at risk. Second, understanding individual variability in lung function, particularly in relation to circadian rhythms, could lead to more personalized approaches in treating respiratory diseases. The concept of on-demand asthma treatment, particularly for mild asthma, is garnering increased attention. However, patients often misjudge the timing for starting or stopping on-demand treatment due to a deficient understanding of their symptoms, leading to overtreatment or undertreatment. For instance, adjusting medication timing based on individual diurnal patterns could enhance treatment efficacy. Third, the study's results could inform public health initiatives aimed at lung health, especially in educating the public about the importance of lung function monitoring and the potential benefits of early detection of changes in lung function. By delineating the range of circadian rhythm variations and day–night spirometric variable fluctuations in healthy individuals, we provide a reliable reference for the pathological fluctuation thresholds in asthma patients. What's more, the study opens avenues for further research, especially in exploring the mechanisms underlying the greater variability in small airways and the clinical significance of these findings in the context of early disease detection and management.

While the study provides valuable insights, there are limitations to consider. The sample size, though adequate for this study, could be expanded in future research to include a more diverse population, including the pediatric population. Additionally, extending the monitoring period beyond seven days might provide more comprehensive data on lung function variability.

Conclusions

This study significantly advances our understanding of lung function variability, particularly highlighting the circadian rhythm influences on large and small airway measurements. The inquiry arises as to whether the criterion for diagnosing asthma in bronchodilator testing, which necessitates an increase in FEV₁ of 12% or more, is reasonable, particularly in light of the study's finding of a nocturnal CV for FEV₁ less than 4%. What's more, our findings emphasize greater variability in small-airway variables, suggesting a higher variability threshold for them in asthma diagnosis. These insights offer important clinical implications for early disease detection and personalized treatment strategies, emphasizing the need for regular lung function monitoring. Furthermore, our results reinforce the potential utility of home-based spirometry in providing precise, long-term variability data, crucial for effective asthma management and treatment optimization.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076241254698 - Supplemental material for Circadian rhythm and variability of large and small airway spirometric variables in healthy individuals

Supplemental material, sj-docx-1-dhj-10.1177_20552076241254698 for Circadian rhythm and variability of large and small airway spirometric variables in healthy individuals by Yingying Zhang, Yiting Wu, Xue Zhang, Chengjian Lv, Jingwang Lin, Lei Zhao, Yanmei Lin, Min Zhang and Wuping Bao in DIGITAL HEALTH

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (Grant No. 81873402); Appropriate Technique Application Program of Shanghai Municipal Health System (Grant No. 2019SY042); Scientific and Technological Innovation program funded by Science and Technology Commission of Shanghai Municipality (Grant No. 20Y11902400); Shanghai First People's Hospital clinical research innovation team construction project (Grant No. CTCCR-2021B07); Shanghai First People's Hospital Featured Talent Program (Grant Nos. 02.06.01.21.13 and 02.06.01.21.56). All the grants were awarded to Min Zhang, except No.02.06.01.21.56, which was awarded to Wuping Bao.

Ethics approval

The Ethics Committee of Shanghai General Hospital, Shanghai Jiao Tong University approved this study (No. 2021ky073).

Guarantor

WPB.

Contributorship

MZ, WPB, XZ, and YYZ conceptualized and designed the study. WPB and XZ searched the literature. CJL, JWL, and YYZ acquired the data. WPB, XZ, LZ, YTW, YML, and MZ verified the data. WPB independently did the statistical analyses. MZ verified the statistical analyses. An external mathematical statistician verified all data and statistical analyses. YYZ, YTW, XZ, WPB, and MZ drafted the manuscript. All authors approved the final version of the manuscript.

ORCID iDs

Yiting Wu

Min Zhang

Wuping Bao

Supplemental material

Supplemental material for this article is available online.

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