Inspiratory capacity and inhalation techniques evaluated and training by digital therapy comprehensive management platform in COPD patients

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The digital therapy comprehensive management platform has the potential to change this situation. Methods The inspiratory capacity and inhalation techniques of 62 COPD patients were evaluated and trained by a digital therapy comprehensive management platform. Moreover, 60 patients newly diagnosed with COPD and required (pressurized metered dose inhalers) pMDIs were recruited to compare the correct usage rates of inhalation devices after training through self-study based on the instructions, video teaching, and digital therapy comprehensive management platform. Additionally, two cases of using the digital therapy comprehensive management platform for inhalation device training were described. Results The data indicated that peak inspiratory flow (PIF) decreased with the increase of internal resistance of inhalers and positively correlated with maximum inspiratory pressure (MIP), but no significant correlation with forced expiratory volume in one second (FEV1), forced vital capacity (FVC), FEV1% prediction and FEV1/FVC. Usage errors rate of initial evaluation of DPIs was 50%, and decreased to 16.67% after training of digital therapy comprehensive management platform. Among these patients, 50% had insufficient effective inspiratory time, and 16.67% had insufficient inspiratory flow rate. Usage errors rate of initial evaluation of pMDIs was 75%, and decreased to 10% after training. Among these patients, 70% had insufficient effective inhalation time and 25% had hand and mouth incoordination. We also found the most frequency errors were ‘sit up/stand straight & tilt head’, ‘breath out completely before inhalation’, ‘hold breath (for at least 5 s)’, followed by ‘hold breath’ and ‘hand and mouth incoordination’. And the incidence of errors in the digital therapy group was significantly lower than that in self-study group and video teaching group. Conclusion The assessment of combination of checklist with the digital therapy comprehensive management platform is more comprehensive and effective than the checklist alone for evaluation and training of inspiratory capacity and inhalation techniques. The digital therapy integrated management platform is expected to become an important tool for COPD management. Chronic obstructive pulmonary diseases Digital therapy comprehensive management platform Inhalation capacity Inhalation technique Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Inhalation therapy is recommended by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) as the first-line treatment for Chronic Obstructive Pulmonary Disease (COPD) [ 1 ]. Compared with oral and intravenous administration, inhalation therapy has the advantages of rapid effect, high concentration to the lungs, simple operation and less systemic reaction [ 2 – 4 ]. The inhalers currently in common use include pressurized metered dose inhalers (pMDIs), dry powder inhalers (DPIs) and soft mist inhalers (SMIs) [ 4 – 6 ]. pMDIs require slow deep inspirations and hand-mouth coordination, while DPIs require the patients to inhale deeply and forcefully [ 7 – 8 ]. Proper selection and usage of inhalers is an integral part of the management of COPD patients. Due to the complexity and heterogeneity of the structure and operation method of the inhalers, the use error is also widespread. GOLD 2024 indicated that more than two-thirds of patients make at least one error while using an inhaler [ 1 ]. However, there is a lack of a comprehensive management tools that can conduct assessment of inspiratory capacity, training and evaluation of inhalation techniques in clinical practice. The rate of lung deposition of inhaled drugs is determined mainly by inspiratory capacity and internal resistance of inhalation device [ 9 ]. It may lead to insufficient drug deposition if the patient's inspiratory capacity cannot meet the requirements of the devices. Peak inspiratory flow (PIF) refers to the maximum flow rate generated by the patient to overcome the internal resistance of the inhalers, and recommended as an important part of the personalized selection of inhalers [ 10 ]. Optimal PIF is an important condition for DPIs to exert an effective therapeutic effect. However, PIF assessment has not been widely used in clinical work [ 11 ]. Patients can make a variety of errors when using the inhalers [ 12 ]. Clinical studies have found that the correct use rate of inhalation devices is only 38.7–61.4% [ 13 – 14 ]. The incidence of incorrect use of different inhalers is also different. The most common is insufficient exhalation before inhalation, not holding breath after inhalation or insufficient breath-hold time [ 14 – 15 ]. Therefore, inhalation techniques should be trained and evaluated regularly at the beginning of treatment and during treatment. Traditionally, inhalation techniques were assessed by doctors or pharmacist through consultation based on checklists. However, there is a lack of effective detection methods to detect objective data such as PIF, effective inhalation time and hand-mouth coordination. In recent years, digital therapy has been proposed as an effective tool to break this situation [ 16 – 17 ]. The digital therapy comprehensive management platform is equipped with inspiratory capacity assessment, inhalation techniques training and assessment, and compliance management, can be used to measure PIF, guide patients to master inhalation techniques and objectively evaluate patients' inhalation techniques with intelligent inhalation detection device. Therefore, we used digital therapy comprehensive management platform to assess inspiratory capacity and inhalation techniques in patients diagnosed with COPD who had already received inhalation therapy, and compared the correct use rate of inhalers after trained by self-study, video teaching and digital therapy among patients newly diagnosed COPD. Methods Population and devices This is a single-center real-world observational study, the utilized data obtained from retrospective electronic medical record review. The patients with clinical diagnosis of COPD (defined by FEV1/FVC ratio<0.7) were recruited from the respiratory clinic of Zhongshan hospital of Fudan University. Patients received DPIs or pMDIs treatment were eligible of inclusion. Patient with no clinical diagnosis of COPD, with acute cardiovascular disease within 1 month, no documented spirometry, cognitive disabilities, and no documented inhaler regimen were excluded. Demographic characteristics of all participants were collected. This study was approved by the Ethics Committee of Zhongshan Hospital of Fudan University (B2019-142) and all the participants have signed an informed consent. Digital therapy comprehensive management platform The digital therapy comprehensive management platform (Chengxin digital therapy LTD, Tianjin, China) for COPD is based on the "treatment monitoring - review and evaluation - treatment adjustment" recommended by the GOLD. It is driven by software programs and integrates Internet of Things technology, intelligent inhalation resistance simulation devices, airflow monitoring sensors, drug teaching robot, intelligent inhalation techniques sensor and medication recorders, doctor-end software and patient-end mobile programs (Fig. 1 ). The platform can be used for inspiratory capacity assessment/training, inhalation techniques assessment/training, inhalation treatment compliance management, and is a tool for the management of chronic respiratory disease patient. In this study, we mainly used intelligent inhalation resistance simulation device, pressure-flow monitoring sensor, drug teaching robot and intelligent inhalation techniques sensor in the digital therapy platform to evaluate and train the inspiratory capacity and inhalation techniques. Evaluation of inspiratory capacity The intelligent inhalation resistance simulation device and pressure-flow monitoring sensor integrated into digital therapy comprehensive management platform was used to measurement PIF, MIP (maximum inspiratory pressure) and MEP (maximum expiratory pressure). PIF measurement: The patients were instructed to take a relaxed sitting position. Inspiratory process of the patient was collected in real time according to the operation requirements of the inhalers. The test requires at least three measurements. The PIF measurement values should be within 10% error of the best values. MIP measurement: After full exhalation, the maximum force inhalation is maintained for more than 2 seconds, and the average value of the highest inspiratory pressure in one second is taken. MEP measurement: After full inhalation, wrap the lips and clench the mouth without leakage, exhale with maximum force for more than 2 seconds, and take the average value of the highest expiratory pressure in one second. Evaluation of inhalation techniques The patient’s own inhalers were used, otherwise empty demo inhalers with disposable mouthpieces were provided. Install the inhalers into the intelligent inhalation detection device to enable the patient to demonstrate the inhalation process. Combine visual assessment based on checklist with objective data from intelligent inhalation detection to conduct a comprehensive evaluation for the inhalation techniques. If a patient’s techniques deviated from what was described in the checklist, the respiratory therapists informed patients with the errors observed, and guided the patients to watch the instructional video. The demonstration was repeated until the patients confirmed that they had fully mastered, and then the inhalation techniques was assessed again. Comparison of methods for the evaluation of inhalation techniques 60 newly diagnosed COPD patients with pMDIs monotherapy were prospectively enrolled for this comparative study. According to 1:1:1 ratio by computer random table method, they were randomly divided into 3 groups: self-study group, video teaching group, and digital therapy group. In self-study group, the patients learn inhalation techniques according to the drug instructions. In video scanning group, the patients scan the QR codes to watch the video and learn the inhalation techniques. In digital therapy group, the patients watch the instructional video, and then assess the inhalation techniques through a combination of visual evaluation and intelligent inhalation detection device. Until the patients feel that they have fully mastered the skills of inhalation, the pharmacists will evaluate the inhalation techniques according to checklist. If there is at least one error, it is judged that the patient has not mastered the inhalation device. The pharmacist will inform the patients with the errors, and guided the patients to master the techniques. The learn and assessment of inhaler technique was performed in selected study rooms in the pulmonary function room. The outcomes were to compare the accuracy of inhalation techniques. Statistical Analysis GraphPad Prism (v8.0) and SPSS (v27.0) software was used for statistical analysis. Homogeneity of variance was performed before comparison. Student-Newman-Keuls test was performed if each group showed homogeneity of variance, and the results were presented as the mean ± SD. The data were analyzed using the Mann-Whitney test when heteroscedasticity was present and are presented as medians (interquartile range). Pearson's correlation was used to analyze the relevance. A p value < 0.05 was considered statistically significant. Results The correlation analysis of respiratory muscle and PIF with lung function 62 patients who have been diagnosed with COPD were recruited into this study, and the demography and baseline characteristics of the patients are shown in Table 1 . The respiratory muscle function and PIF were assessed, and the correlation with lung function was analyzed. The data indicated that PIF decreases as the internal resistance of the inhaler increases, and PIF significantly positively associated with MIP and MIP% pred (r = 0.596, P = 0.000; r = 0.544, P = 0.00; respectively). No association between PIF and FEV1, FEV1%pred, FVC and FEV1/FVC were observed. The univariate correlations indicated that MIP significantly positively associated with FEV1 and FVC (r = 0.420, P = 0.004; r = 0.449, P = 0.002), and MIP% pred also significantly positively associated with FEV1 and FEV1% pred (r = 0.332, P = 0.026; r = 0.319, P = 0.035). Moreover, both MEP (r = 0.615, P = 0.000; r = 0.328, P = 0.028; r = 0.507, P = 0.000) and MEP% pred (r = 0.318, P = 0.033; r = 0.617, P = 0.000; r = 0.456, P = 0.002) were significantly related to FEV1, FEV1% pred and FEV1/FVC. In addition, it was also found that MIP is positively correlated with MEP (r = 0.295, P = 0.020) and MIP% pred is positively correlated with MEP% pred (r = 0.414, P = 0.001). Table 1 Measurement of respiratory muscle function Characteristic Value Demographics Participants, n 62 Age, mean (SD): Years 66.98(7.61) Female Sex, n (%) 19(29.69) Male Sex, n (%) 45(70.31) BMI, mean (SD):kg/m 2 23.64(3.30) Lung function & Symptom burden FEV1, mean (SD): Liters 1.56 (0.65) FEV1, mean (SD) (%predicted) 58.57 (23.34) FVC, mean (SD): Liters 2.84 (0.83) FEV1/FVC, mean (SD) (%) 55.40 (13.72) Measurement of respiratory muscle function MIP, mean (SD): cmH2O 73.52 (22.92) MIP, mean (SD) (%predicted) 85.47 (24.56) MEP, mean (SD): cmH2O 52.67 (15.43) MEP, mean (SD): (%predicted) 51.43 (18.64) PIF Prevalence with suboptimal PIF, No. (%) 12 (19.36) No resistance, mean (SD): L/min 89.39 (21.60) low/medium resistance, mean (SD): L/min 79.49 (11.08) medium resistance, mean (SD): L/min 63.64 (26.47) Widespread inhalation techniques errors in COPD patients Inhalation techniques of patients have been diagnosed with COPD and have already received DPIs or pMDIs monotherapy treatment, including effective inhalation time, inspiratory flow rate or hand-mouth coordination, were evaluated in 18 patients treated with DPIs and 20 patients with pMDIs by intelligent inhalation sensors. The average age of the 18 patients was 68.61 (6.65) years old. Among them, there were 5 females, accounting for 27.78%. Incorrect rate of initial evaluation of DPIs was 50%. The most frequency errors were ‘insufficient effective inhalation time’, ‘sit up/stand straight & tilt head’ and ‘breath out completely before inhalation’ (Fig. 2 A). The objective data detected by the intelligent inhalation sensor indicated that 50% had insufficient effective inspiratory time, 16.67% had poor inspiratory flow rate, and 5.56% had slow inhalation initiation (Fig. 2 A). But re-evaluation of the errors rate decreased to 16.67% after the intelligent guidance training of drug teaching robot embedded in the digital therapy comprehensive management platform (Fig. 2 B). The average age of the 20 patients was 65.53 (8.44) years old and 6 were female accounting for 30%. The errors rate of initial evaluation of pMDIs was 75%. The most frequency errors also were ‘insufficient effective inhalation time’, ‘sit up/stand straight & tilt head’ and ‘breath out completely before inhalation’, followed by ‘hand-mouth incoordination’ and ‘hold breath (for at least 5 seconds)’ (Fig. 3 A). The objective data detected by the intelligent inhalation sensor indicated that 70% had insufficient effective inhalation time, 25% had hand and mouth incoordination and 15% had no inhalation action. After training by drug teaching robot, the error rate of the final assessment has been reduced to 10% (Fig. 3 B). But there were two patients whose effective inhalation time still failed to meet the standard after undergoing training on inspiratory capacity and inhalation techniques, and their hands and mouth were still uncoordinated. After informing the doctor of this assessment result, the doctor suggested the patients to adjust the treatment plan for pMDI and nebulizers. Thess data indicated that the inhalation errors were widespread among patients with CODP evaluated by combination of visual checklist observation and intelligent inhalation sensors. The use of intelligent inhalation sensors can quantify objective values such as effective inhalation time which visual checklist assessment cannot do. But the rate of errors improved significantly after the training of digital therapy comprehensive management platform. The accuracy comparation of inhalation techniques A total of 60 patients newly diagnosed with COPD ad treated with pMDIs monotherapy participated in this study. As showed in Fig. 4 A, the most frequency errors in self-study group were ‘sit up/stand straight & tilt head’, ‘breath out completely before inhalation’, ‘hold breath (for at least 5 s)’, followed by ‘breath in’, ‘rinse mouth’ and ‘hand and mouth incoordination’. But the frequency of these errors was significantly lower than those in video group and digital therapy group, especially in digital therapy group. However, the training time for the digital therapy group was 1004 ± 233 seconds, and significantly longer than that of the self-study group (277 ± 122 seconds) and the video group (317 ± 129 seconds) (Fig. 4 B). Cases A 67-year-old male patient, who has been diagnosed with COPD for more than 1 year, usually inhaled DPI and complained of dry itching in the throat. We used intelligent inhalation sensors to capture inhalation action, quantify hand-mouth coordination and inhalation time, in conjunction with the visual checklist assessment. The inspiratory capacity and inhalation technique evaluation showed that the PIF was 93.5L/min and the inhalation time was less than 2 second (1.875 s), and indicated that the inhalation speed is too fast and the inhalation time is insufficient. However, after the intelligent guidance and training of the drug teaching robot, the intelligent sensor was used again to evaluate the inhalation technique, indicating that the PIF was 61.2L/min and the inhalation time was 3 seconds, which reached the optimal PIF and inhalation time required by DPI. Another patient, a 65-year-old woman diagnosed with COPD for 3 months, usually inhaled pMDI but reported no significant improvement in dyspnea. Further inspiratory capacity and inhalation technique assessment indicated that PIF was 59.8L/min, the hand and mouth were not coordinated and the effective inhalation time was less than 3 s (1.259 s). After training of drug teaching robot, the inhalation techniques were evaluated again to indicate hand-mouth coordination, and the effective inhalation time was 3.066 s which reached the inhalation time required by pMDI. These two typical cases suggest that if the patient's dyspnea is not well controlled or the patient has local symptoms in the throat during medication, the patient's inspiratory capacity and inhalation techniques should be evaluated in time. Discussion The patient's inhalation capacity can affect the rate of pulmonary deposition of inhaled drug [ 9 ]. Inspiratory capacity may vary among patients with different conditions, affected by age, gender and muscle strength [ 18 – 19 ]. Studies have indicated that patients with COPD have lower PIF compared to the healthy adults [ 19 ]. Women generally have lower PIF than men, and PIF tend to decline with age [ 20 – 21 ]. PIF is also limited by inspiratory pressure, which is generated by the strength and tension of respiratory muscles, and positively correlated with MIP [ 22 – 23 ]. But The application of PIF and MIP in clinical medication guidance and patient management for chronic airway diseases is still relatively limited. Thus, it is particularly important to assess and train the patient's inhalation capacity and select the appropriate inhalation device. So far, In-Check DIAL® and Multi-Function Spirometer System PF810 ® are mainly used to measure patients’inspiratory capacity against internal resistance of inhalers [ 24 – 26 ]. In this study, we used the inspiratory capacity assessment module embedded in the digital therapy comprehensive management platform to quantify the inspiratory capacity of patients through computer-controlled graphical interactive interface software. The results indicated that PIF was positively correlated with MIP and MIP% pred, but not with FEV1, FVC and FEV1%pred. Previous studies have suggested that PIF in COPD patients is positively correlated with FEV1, FVC and FEV1%pred [ 27 ], but there are also studies suggesting that PIF is not correlated with FEV1, FVC and FEV1%pred, and FEV1 is not an independent correlation factor of PIF [ 28 ]. Some researchers have proposed that PIF is mainly aimed at the assessment of inspiratory capacity, but chronic airway diseases are small airway obstruction which has more impact on expiratory capacity, thus it is proposed that FEV1 has no correlation with PIF [ 28 ]. At present, different studies have obtained different conclusions, which may be related to the sample size and the diseases severity. Further studies need to be carried out with the participation of multiple centers and the expansion of the sample size. Inhalation techniques refer to the correct operation of each step when the patient uses the inhalation devices. Improper operation of inhalation devices is directly related to poor disease control. A real-world study of 2935 COPD patients showed that improper use of inhalation devices increased the incidence of acute exacerbations of COPD from 3.3–6.9% [ 29 ]. But wrong operation of inhalation devices is widespread. In an observational study of 3,811 patients with COPD or asthma, device use errors ranged from 49–76%, with critical errors 11–32% [ 30 ]. A multicenter study of elderly COPD patients in Taiwan found there was 65–87.89% error rate in the use of inhalation devices [ 31 ]. So far, there is a lack of objective, convenient and effective assessment methods for inhalation techniques. The traditional assessment of inhalation techniques is mainly visual assessment based on checklist, but it is difficult to visually evaluate objective data during the inhalation process, such as effective inhalation time. In this study, we installed the inhalation device into the intelligent inhalation sensor to allow patients to demonstrate the use of the inhalation device, combined with visual evaluation and objective data detected by the intelligent detect sensor to complete the inhalation technique evaluation. At the same time, patients can also be instructed to demonstrate how to use the inhalation device, ensuring that they have mastered the key steps for correct usage of the inhalation device. In order to improve the current accuracy of inhalation techniques, it is necessary to strengthen the training which can help improve the accuracy of inhalation device operation, increase patient compliance, and improve disease control. But there are many problems with the current methods and effectiveness. Some of DPI inhalation patients with COPD or asthma never received oral instruction, and the quality of training and mastery of inhalation techniques among those patients were unsatisfactory. The preferences of patients for different training methods in order were demonstration teaching, video tutorials, instruction manuals and leaflets, and the demonstration teaching can significantly improve inhalation techniques and compliance [ 32 ]. In this study, we compared the accuracy of inhaler use after the training of instruction manuals, video tutorials and digital therapy platform "teaching-demonstration-evaluation", and found that the patients trained on the digital therapy comprehensive management platform showed a significant improvement in the accuracy of inhalation device use. The advantage of digital therapy is to explain the technical details of the use of inhalation devices to patients through video, and allow patients to demonstrate the use of inhalation devices, combine the checklist and intelligent inhalation sensors to review and evaluate the mastery of inhalation techniques of patients and correct errors in time. Therefore, the digital therapy platform can be used for intelligent inhalation medication guidance, training and technical evaluation in hospitals where with well-developed medical resources. But in areas with poor medical resources, technique training can also be done by repeatedly reading instructions and watching videos to decrease inhalation errors. This study has two limitations. First, the primary limitation was its small sample size and single-center study. However, preliminary data showed promising advantages of the digital therapy comprehensive management platform, suggesting further verification. Second, we only used intelligent detect sensor to conduct inhalation techniques assessment. In fact, it can also record the daily medication frequency of patients, issue medication reminders and guidance to patients, and improve patients' medication compliance. Subsequently, we will conduct further research on this aspect in the following studies. Conclusions The inhalation errors were widespread among patients with COPD. The inhalation techniques can be improved after the training of digital therapy comprehensive management platform. The assessment method based on the checklist combined with intelligent sensors can conduct a more comprehensive and effective assessment of inhalation techniques. The digital therapy comprehensive management platform is expected to become an important tool for COPD management. However, its clinical benefits still require substantiation from clinical trials. Abbreviations COPD Chronic obstructive pulmonary disease GOLD The global initiative for Chronic Obstructive Lung Disease pMDIs Pressurized metered-dose inhalers DPIs Dry powder inhalers PIF Peak inspiratory flow FEV1 Forced expiratory volume in one second FVC Forced vital capacity Declarations Acknowledgements We thank Chengxin (Tianjin, China) for providing the digital therapy comprehensive platform and its working principles. Authors’ contributions Linlin Wang and Ying Gong conceived and designed the study. Linlin Wang analyzed the data and wrote the manuscript. Li Li, Xinyi Tang, Yimeng Lu, Xiaofen Ye and Yufan Li collected the data. Yuanlin Song and Jing Zhang revised the manuscript. All authors reviewed and approved the final manuscript. Funding This study was supported by Shanghai Three-year Action Plan to Strengthen the Construction of Public Health System (2023-2025) (GWVI-11.1-18), National Natural Science Foundation of China (82000087, 82130001), Shanghai Municipal Science and Technology Major Project (ZD2021CY001), Science and Technology Commission of Shanghai Municipality (20Z11901000, 20DZ2261200, 22Y11900800) and Shanghai Municipal Key Clinical Specialty (shslczdzk02201). Availability of data and materials All data generated or analyzed during this study are included in the manuscript. Ethics approval and consent to participate Authors confirmed that all experiments were performed in accordance with relevant guidelines and regulations. This study was approved by the Ethics Committee of Zhongshan Hospital of Fudan University (B2019-142) and all the participants have signed an informed consent. Consent for publication Not applicable. Competing interests The authors have stated explicitly that there are no conflicts of interest regarding this article. References Global Initiative for Chronic Obstructive Lung Disease Global strategy for the diagnosis management and prevention of chronic obstructive pulmonary disease (2024 report). https://goldcopd. org. Mitchell JP. What the pulmonary specialist should know about the new inhalation therapies. Eur Respir J. 2012; 39(4):1054-5. doi: 10.1183/09031936.00210911. Darquenne C. Aerosol deposition in health and disease. J Aerosol Med Pulm Drug Deliv. 2012; 25(3):140-7. doi: 10.1089/jamp.2011.0916. Stephen WS, Charles GT. The History of Therapeutic Aerosols: A Chronological Review. J Aerosol Med Pulm Drug Deliv. 2017; 30(1):20-41. doi: 10.1089/jamp.2016.1297. Nivedita S, David C, Heejun P, Zhou QT. Physical stability of dry powder inhaler formulations. Expert Opin Drug Deliv. 2020; 17(1):77-96. doi: 10.1080/17425247.2020.1702643. Varsha K, Chun YJW, Imco S, Bernhard M, Wietze N, Vishal C, et al. Advances in soft mist inhalers. Expert Opin Drug Deliv,2023; 20(8):1055-1070. doi: 10.1080/17425247.2023.2231850. García-Río F, Soler-Cataluña JJ, Alcazar B, Viejo JL, Miravitlles M. Requirements, Strengths and Weaknesses of Inhaler Devices for COPD Patients from the Expert Prescribers' Point of View: Results of the EPOCA Delphi Consensus. COPD. 2017; 14(6):573-580. doi: 10.1080/15412555.2017.1365120. Chavan V, Dalby R. Novel system to investigate the effects of inhaled volume and rates of rise in simulated inspiratory air flow on fine particle output from a dry powder inhaler. AAPS PharmSci. 2002;4(2):E6. doi: 10.1208/ps040206. Mahler DA, Halpin DMG. Peak Inspiratory Flow as a Predictive Therapeutic Biomarker in COPD. Chest,2021;160(2):491-498. doi: 10.1016/j.chest. Mahler DA. Peak Inspiratory Flow Rate: An Emerging Biomarker in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med,2019 Jun 15;199(12):1577-1579. doi: 10.1164/rccm.201901-0005LE. Pankovitch S, Frohlich M, AlOthman B, Marciniuk J, Bernier J, Paul-Emile D, Bourbeau J, Ross BA. Peak Inspiratory Flow and Inhaler Prescription Strategies in a Specialized COPD Clinical Program: A Real-World Observational Study. Chest. 2025;167(3):736-745. doi: 10.1016/j.chest.2024.09.031. Harb HS, Laz NI, Rabea H, Abdelrahim MEA. Real-life assessment of chronic obstructive pulmonary disease patient performance with different inhalers. Int J Clin Pract. 2021;75(4): e13905. doi: 10.1111/ijcp.13905. W Wei, D Wang, WT Liu, et al. Skills in handling Turbuhaler, Diskus in the west of China. BMC Pulm Med. 2023;23(1):447. doi: 10.1186/s12890-023-02765-1. JKWu, WW Meng, YM Ma, et al. Errors and Adherence to Inhaled Medications in Chinese Adults with COPD. J Gen Intern Med. 2024;39(1):69-76. doi: 10.1007/s11606-023-08378-y. Kocks J, Bosnic-Anticevich S, van Cooten J, et al. Identifying critical inhalation technique errors in Dry Powder Inhaler use in patients with COPD based on the association with health status and exacerbations: findings from the multi-country cross-sectional observational PIFotal study. BMC Pulm Med. 2023; 23(1):302. doi: 10.1186/s12890-023-02566-6. Abidi SR, Rickards T, Woensel WV, Abidi SSR. Digital Therapeutics for COPD Patient Self-Management: Needs Analysis and Design Study. Stud Health Technol Inform.2024; 310:209-213. doi: 10.3233/SHTI230957 Dang A, Arora D, Rane P. Role of digital therapeutics and the changing future of healthcare. J Family Med Prim Care. 2020; 9(5):2207-2213. doi: 10.4103/jfmpc.jfmpc_105_20. Mahler DA. Peak inspiratory flow rate as a criterion for dry powder inhaler use in chronic obstructive pulmonary disease. Ann Am Thorac Soc. 2017;14(7):1103-7 Loh CH, Peters SP, Lovings TM, Ohar JA. Suboptimal inspiratory flow rates are associated with chronic obstructive pulmonary disease and all-cause readmissions. Ann Am Thorac Soc. 2017;14(8):1305–11. Chen HI, Kuo CS. Relationship between respiratory muscle function and age, sex, and other factors. J Appl Physiol (1985). 1989;66(2):943–8. Vidal MB, Pegorari MS, Santos EC, Matos AP, Pinto ACPN, Ohara DG. Respiratory muscle strength for discriminating frailty in community-dwelling elderly: a cross-sectional study. Arch Gerontol Geriatr. 2020; 89:104082. Clark AR, Weers JG, Dhand R. The Confusing World of Dry Powder Inhalers: It Is All About Inspiratory Pressures, Not Inspiratory Flow Rates. J Aerosol Med Pulm Drug Deliv. 2020; 33(1):1-11. doi: 10.1089/jamp.2019.1556. Clark AR. The Role of Inspiratory Pressures in Determining the Flow Rates Though Dry Powder Inhalers; A Review. Curr Pharm Des. 2015;21(27):3974-83. doi: 10.2174/1381612821666150820105800. Clement Clarke International. In-Check DIAL ® G16: Coaching DPI and pMDI inhaler technique. https://www. Haag-streit.com/clement-clarke/products/inhaler- technique/in-check-dial-g16/. Accessed 29 Dec 2023. Alliance Tech Medical. In-Check™ DIAL G16: Inhaler Technique Training and Assessment Tool. https://alliancetechmedical. com/check-dial-training-device/. Accessed 29 Dec 2023. e-LinkCare. UBREATH ® Multi-Function Spirometer System (PF810). https://www.e-linkcare. com/ubreath-multi-function- spirometer-system-pf810-product/. Accessed 24 Jun 2022. JL Hua, XF Ye, Du CL, N Xie, Zhang JQ, M Li, J Zhang. Optimizing inhalation therapy in the aspect of peak inhalation flow rate in patients with chronic obstructive pulmonary disease or asthma. BMC Pulm Med. 2021; 21(1):302. doi: 10.1186/s12890-021-01674-5. Ghosh S, Pleasants RA, Ohar JA, Donohue JF, Drummond MB. Prevalence and factors associated with suboptimal peak inspiratory flow rates in COPD. Int J Chron Obstruct Pulmon Dis. 2019;14:585-595. doi: 10.2147/COPD.S195438. Molimard M, Raherison C, Lignot S, et al. Chronic obstructive pulmonary disease exacerbation and inhaler device handling: real-life assessment of 2935 patients. Eur Respir J. 2017; 49(2):1601794. doi: 10.1183/13993003.01794-2016. Molimard M, Raherison C, Lignot S, Depont F, Abouelfath A, Moore N. Assessment of handling of inhaler devices in real life: an observational study in 3811 patients in primary care. J Aerosol Med. 2003;16(3):249-54. doi: 10.1089/089426803769017613. Liang CY, Chen YJ, Sheu SM, Tsai CF, Chen W. Misuse of inhalers among COPD patients in a community hospital in Taiwan. Int J Chron Obstruct Pulmon Dis. 2018;13:1309-1316. doi: 10.2147/COPD.S158864. Dekhuijzen PNR, Lavorini F, Usmani OS. Patients' perspectives and preferences in the choice of inhalers: the case for Respimat(®) or HandiHaler(®). Patient Prefer Adherence. 2016; 10:1561-72. doi: 10.2147/PPA.S82857. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Aug, 2025 Reviews received at journal 31 Jul, 2025 Reviews received at journal 14 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Submission checks completed at journal 26 Apr, 2025 First submitted to journal 26 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6502020","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449892315,"identity":"6f1234ac-3245-4783-b5d8-3f3ffdde89de","order_by":0,"name":"Linlin Wang","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Wang","suffix":""},{"id":449892316,"identity":"ff0d14e9-d3ce-46bc-8907-50542f6236f3","order_by":1,"name":"Ying Gong","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Gong","suffix":""},{"id":449892317,"identity":"3f00e0f9-b5a7-43e9-b4f8-802659b272bc","order_by":2,"name":"Xinyi Tang","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Tang","suffix":""},{"id":449892318,"identity":"f76c4ed8-f18f-4597-8888-98072f4dffe7","order_by":3,"name":"Yimeng Lu","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China","correspondingAuthor":false,"prefix":"","firstName":"Yimeng","middleName":"","lastName":"Lu","suffix":""},{"id":449892319,"identity":"4bd1653b-232c-43c1-a1b0-243260dc4057","order_by":4,"name":"Xiaofen Ye","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China","correspondingAuthor":false,"prefix":"","firstName":"Xiaofen","middleName":"","lastName":"Ye","suffix":""},{"id":449892320,"identity":"5b077e4d-e595-40ee-95ab-14b7bdc87945","order_by":5,"name":"Yufan Li","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China","correspondingAuthor":false,"prefix":"","firstName":"Yufan","middleName":"","lastName":"Li","suffix":""},{"id":449892321,"identity":"71e46474-7071-42b5-8ba9-9e1473a78c4e","order_by":6,"name":"Li Li","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Li","suffix":""},{"id":449892322,"identity":"7ecf19c6-92b4-472b-b32b-fbe4a8bfcef2","order_by":7,"name":"Jing Zhang","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":449892323,"identity":"a8d19594-458e-4d6d-8aa9-9d5cdfc6d1dc","order_by":8,"name":"Yuanlin Song","email":"data:image/png;base64,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","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China","correspondingAuthor":true,"prefix":"","firstName":"Yuanlin","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2025-04-22 08:23:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6502020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6502020/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82299171,"identity":"2993cc2d-2199-4b59-88b1-56e3fa34f7dd","added_by":"auto","created_at":"2025-05-08 20:31:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":229334,"visible":true,"origin":"","legend":"\u003cp\u003eThe digital therapy comprehensive management platform. A: the platform of inhalation capacity assessment and drug teaching robot. B: breathing trainer. C and D: the intelligent sensors for inhalation techniques and medication recorder.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6502020/v1/298d64eaf3aed01d0a4aaf72.png"},{"id":82298625,"identity":"6f54a9ce-2288-4cf4-9ecb-867c4575984b","added_by":"auto","created_at":"2025-05-08 20:23:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129352,"visible":true,"origin":"","legend":"\u003cp\u003eA: Error frequencies observed in patients with COPD treated by DPIs. B: Comparison of error rates of DPIs before and after training by drug teaching robot.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6502020/v1/a24b33b5b1b05673e7a3ff66.png"},{"id":82298627,"identity":"daea0c31-3820-433b-8fcb-3a393c2b8073","added_by":"auto","created_at":"2025-05-08 20:23:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130187,"visible":true,"origin":"","legend":"\u003cp\u003eA: Error frequencies observed in patients with COPD treated by pMDIs. B: Comparison of error rates of pMDIs before and after training by drug teaching robot.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6502020/v1/27da8e8a8f6d214925b931d8.png"},{"id":82298632,"identity":"bb05db18-b13f-4f30-bb62-28edb9453f06","added_by":"auto","created_at":"2025-05-08 20:23:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167689,"visible":true,"origin":"","legend":"\u003cp\u003eA: The frequency of errors in newly diagnosed with COPD trained by self-study, video and digital therapy. B: The training time of self-study, video and digital therapy.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6502020/v1/38f00e35b93403294810a5ec.png"},{"id":82300042,"identity":"1d3e2807-1383-44c4-891a-0b95dc7405c5","added_by":"auto","created_at":"2025-05-08 20:39:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1403177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6502020/v1/fedbbe4c-0d28-40a4-ac87-cbaede7ebf11.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inspiratory capacity and inhalation techniques evaluated and training by digital therapy comprehensive management platform in COPD patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInhalation therapy is recommended by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) as the first-line treatment for Chronic Obstructive Pulmonary Disease (COPD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Compared with oral and intravenous administration, inhalation therapy has the advantages of rapid effect, high concentration to the lungs, simple operation and less systemic reaction [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The inhalers currently in common use include pressurized metered dose inhalers (pMDIs), dry powder inhalers (DPIs) and soft mist inhalers (SMIs) [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. pMDIs require slow deep inspirations and hand-mouth coordination, while DPIs require the patients to inhale deeply and forcefully [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Proper selection and usage of inhalers is an integral part of the management of COPD patients. Due to the complexity and heterogeneity of the structure and operation method of the inhalers, the use error is also widespread. GOLD 2024 indicated that more than two-thirds of patients make at least one error while using an inhaler [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, there is a lack of a comprehensive management tools that can conduct assessment of inspiratory capacity, training and evaluation of inhalation techniques in clinical practice.\u003c/p\u003e \u003cp\u003eThe rate of lung deposition of inhaled drugs is determined mainly by inspiratory capacity and internal resistance of inhalation device [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It may lead to insufficient drug deposition if the patient's inspiratory capacity cannot meet the requirements of the devices. Peak inspiratory flow (PIF) refers to the maximum flow rate generated by the patient to overcome the internal resistance of the inhalers, and recommended as an important part of the personalized selection of inhalers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Optimal PIF is an important condition for DPIs to exert an effective therapeutic effect. However, PIF assessment has not been widely used in clinical work [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients can make a variety of errors when using the inhalers [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Clinical studies have found that the correct use rate of inhalation devices is only 38.7\u0026ndash;61.4% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The incidence of incorrect use of different inhalers is also different. The most common is insufficient exhalation before inhalation, not holding breath after inhalation or insufficient breath-hold time [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, inhalation techniques should be trained and evaluated regularly at the beginning of treatment and during treatment.\u003c/p\u003e \u003cp\u003eTraditionally, inhalation techniques were assessed by doctors or pharmacist through consultation based on checklists. However, there is a lack of effective detection methods to detect objective data such as PIF, effective inhalation time and hand-mouth coordination. In recent years, digital therapy has been proposed as an effective tool to break this situation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The digital therapy comprehensive management platform is equipped with inspiratory capacity assessment, inhalation techniques training and assessment, and compliance management, can be used to measure PIF, guide patients to master inhalation techniques and objectively evaluate patients' inhalation techniques with intelligent inhalation detection device. Therefore, we used digital therapy comprehensive management platform to assess inspiratory capacity and inhalation techniques in patients diagnosed with COPD who had already received inhalation therapy, and compared the correct use rate of inhalers after trained by self-study, video teaching and digital therapy among patients newly diagnosed COPD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation and devices\u003c/h2\u003e \u003cp\u003e This is a single-center real-world observational study, the utilized data obtained from retrospective electronic medical record review. The patients with clinical diagnosis of COPD (defined by FEV1/FVC ratio\u0026lt;0.7) were recruited from the respiratory clinic of Zhongshan hospital of Fudan University. Patients received DPIs or pMDIs treatment were eligible of inclusion. Patient with no clinical diagnosis of COPD, with acute cardiovascular disease within 1 month, no documented spirometry, cognitive disabilities, and no documented inhaler regimen were excluded. Demographic characteristics of all participants were collected. This study was approved by the Ethics Committee of Zhongshan Hospital of Fudan University (B2019-142) and all the participants have signed an informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDigital therapy comprehensive management platform\u003c/h3\u003e\n\u003cp\u003eThe digital therapy comprehensive management platform (Chengxin digital therapy LTD, Tianjin, China) for COPD is based on the \"treatment monitoring - review and evaluation - treatment adjustment\" recommended by the GOLD. It is driven by software programs and integrates Internet of Things technology, intelligent inhalation resistance simulation devices, airflow monitoring sensors, drug teaching robot, intelligent inhalation techniques sensor and medication recorders, doctor-end software and patient-end mobile programs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The platform can be used for inspiratory capacity assessment/training, inhalation techniques assessment/training, inhalation treatment compliance management, and is a tool for the management of chronic respiratory disease patient. In this study, we mainly used intelligent inhalation resistance simulation device, pressure-flow monitoring sensor, drug teaching robot and intelligent inhalation techniques sensor in the digital therapy platform to evaluate and train the inspiratory capacity and inhalation techniques.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEvaluation of inspiratory capacity\u003c/h3\u003e\n\u003cp\u003eThe intelligent inhalation resistance simulation device and pressure-flow monitoring sensor integrated into digital therapy comprehensive management platform was used to measurement PIF, MIP (maximum inspiratory pressure) and MEP (maximum expiratory pressure).\u003c/p\u003e \u003cp\u003ePIF measurement: The patients were instructed to take a relaxed sitting position. Inspiratory process of the patient was collected in real time according to the operation requirements of the inhalers. The test requires at least three measurements. The PIF measurement values should be within 10% error of the best values.\u003c/p\u003e \u003cp\u003eMIP measurement: After full exhalation, the maximum force inhalation is maintained for more than 2 seconds, and the average value of the highest inspiratory pressure in one second is taken. MEP measurement: After full inhalation, wrap the lips and clench the mouth without leakage, exhale with maximum force for more than 2 seconds, and take the average value of the highest expiratory pressure in one second.\u003c/p\u003e\n\u003ch3\u003eEvaluation of inhalation techniques\u003c/h3\u003e\n\u003cp\u003eThe patient\u0026rsquo;s own inhalers were used, otherwise empty demo inhalers with disposable mouthpieces were provided. Install the inhalers into the intelligent inhalation detection device to enable the patient to demonstrate the inhalation process. Combine visual assessment based on checklist with objective data from intelligent inhalation detection to conduct a comprehensive evaluation for the inhalation techniques. If a patient\u0026rsquo;s techniques deviated from what was described in the checklist, the respiratory therapists informed patients with the errors observed, and guided the patients to watch the instructional video. The demonstration was repeated until the patients confirmed that they had fully mastered, and then the inhalation techniques was assessed again.\u003c/p\u003e\n\u003ch3\u003eComparison of methods for the evaluation of inhalation techniques\u003c/h3\u003e\n\u003cp\u003e60 newly diagnosed COPD patients with pMDIs monotherapy were prospectively enrolled for this comparative study. According to 1:1:1 ratio by computer random table method, they were randomly divided into 3 groups: self-study group, video teaching group, and digital therapy group. In self-study group, the patients learn inhalation techniques according to the drug instructions. In video scanning group, the patients scan the QR codes to watch the video and learn the inhalation techniques. In digital therapy group, the patients watch the instructional video, and then assess the inhalation techniques through a combination of visual evaluation and intelligent inhalation detection device. Until the patients feel that they have fully mastered the skills of inhalation, the pharmacists will evaluate the inhalation techniques according to checklist. If there is at least one error, it is judged that the patient has not mastered the inhalation device. The pharmacist will inform the patients with the errors, and guided the patients to master the techniques. The learn and assessment of inhaler technique was performed in selected study rooms in the pulmonary function room. The outcomes were to compare the accuracy of inhalation techniques.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eGraphPad Prism (v8.0) and SPSS (v27.0) software was used for statistical analysis. Homogeneity of variance was performed before comparison. Student-Newman-Keuls test was performed if each group showed homogeneity of variance, and the results were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. The data were analyzed using the Mann-Whitney test when heteroscedasticity was present and are presented as medians (interquartile range). Pearson's correlation was used to analyze the relevance. A \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe correlation analysis of respiratory muscle and PIF with lung function\u003c/h2\u003e \u003cp\u003e62 patients who have been diagnosed with COPD were recruited into this study, and the demography and baseline characteristics of the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The respiratory muscle function and PIF were assessed, and the correlation with lung function was analyzed. The data indicated that PIF decreases as the internal resistance of the inhaler increases, and PIF significantly positively associated with MIP and MIP% pred (r\u0026thinsp;=\u0026thinsp;0.596, P\u0026thinsp;=\u0026thinsp;0.000; r\u0026thinsp;=\u0026thinsp;0.544, P\u0026thinsp;=\u0026thinsp;0.00; respectively). No association between PIF and FEV1, FEV1%pred, FVC and FEV1/FVC were observed.\u003c/p\u003e \u003cp\u003eThe univariate correlations indicated that MIP significantly positively associated with FEV1 and FVC (r\u0026thinsp;=\u0026thinsp;0.420, P\u0026thinsp;=\u0026thinsp;0.004; r\u0026thinsp;=\u0026thinsp;0.449, P\u0026thinsp;=\u0026thinsp;0.002), and MIP% pred also significantly positively associated with FEV1 and FEV1% pred (r\u0026thinsp;=\u0026thinsp;0.332, P\u0026thinsp;=\u0026thinsp;0.026; r\u0026thinsp;=\u0026thinsp;0.319, P\u0026thinsp;=\u0026thinsp;0.035). Moreover, both MEP (r\u0026thinsp;=\u0026thinsp;0.615, P\u0026thinsp;=\u0026thinsp;0.000; r\u0026thinsp;=\u0026thinsp;0.328, P\u0026thinsp;=\u0026thinsp;0.028; r\u0026thinsp;=\u0026thinsp;0.507, P\u0026thinsp;=\u0026thinsp;0.000) and MEP% pred (r\u0026thinsp;=\u0026thinsp;0.318, P\u0026thinsp;=\u0026thinsp;0.033; r\u0026thinsp;=\u0026thinsp;0.617, P\u0026thinsp;=\u0026thinsp;0.000; r\u0026thinsp;=\u0026thinsp;0.456, P\u0026thinsp;=\u0026thinsp;0.002) were significantly related to FEV1, FEV1% pred and FEV1/FVC. In addition, it was also found that MIP is positively correlated with MEP (r\u0026thinsp;=\u0026thinsp;0.295, P\u0026thinsp;=\u0026thinsp;0.020) and MIP% pred is positively correlated with MEP% pred (r\u0026thinsp;=\u0026thinsp;0.414, P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement of respiratory muscle function\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD): Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.98(7.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale Sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(29.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale Sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(70.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean (SD):kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.64(3.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung function \u0026amp; Symptom burden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1, mean (SD): Liters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56 (0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1, mean (SD) (%predicted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.57 (23.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC, mean (SD): Liters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84 (0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC, mean (SD) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.40 (13.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeasurement of respiratory muscle function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIP, mean (SD): cmH2O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.52 (22.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIP, mean (SD) (%predicted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.47 (24.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEP, mean (SD): cmH2O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.67 (15.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEP, mean (SD): (%predicted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.43 (18.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevalence with suboptimal PIF, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (19.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo resistance, mean (SD): L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.39 (21.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow/medium resistance, mean (SD): L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.49 (11.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emedium resistance, mean (SD): L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.64 (26.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWidespread inhalation techniques errors in COPD patients\u003c/h2\u003e \u003cp\u003eInhalation techniques of patients have been diagnosed with COPD and have already received DPIs or pMDIs monotherapy treatment, including effective inhalation time, inspiratory flow rate or hand-mouth coordination, were evaluated in 18 patients treated with DPIs and 20 patients with pMDIs by intelligent inhalation sensors.\u003c/p\u003e \u003cp\u003eThe average age of the 18 patients was 68.61 (6.65) years old. Among them, there were 5 females, accounting for 27.78%. Incorrect rate of initial evaluation of DPIs was 50%. The most frequency errors were \u0026lsquo;insufficient effective inhalation time\u0026rsquo;, \u0026lsquo;sit up/stand straight \u0026amp; tilt head\u0026rsquo; and \u0026lsquo;breath out completely before inhalation\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The objective data detected by the intelligent inhalation sensor indicated that 50% had insufficient effective inspiratory time, 16.67% had poor inspiratory flow rate, and 5.56% had slow inhalation initiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). But re-evaluation of the errors rate decreased to 16.67% after the intelligent guidance training of drug teaching robot embedded in the digital therapy comprehensive management platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe average age of the 20 patients was 65.53 (8.44) years old and 6 were female accounting for 30%. The errors rate of initial evaluation of pMDIs was 75%. The most frequency errors also were \u0026lsquo;insufficient effective inhalation time\u0026rsquo;, \u0026lsquo;sit up/stand straight \u0026amp; tilt head\u0026rsquo; and \u0026lsquo;breath out completely before inhalation\u0026rsquo;, followed by \u0026lsquo;hand-mouth incoordination\u0026rsquo; and \u0026lsquo;hold breath (for at least 5 seconds)\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The objective data detected by the intelligent inhalation sensor indicated that 70% had insufficient effective inhalation time, 25% had hand and mouth incoordination and 15% had no inhalation action. After training by drug teaching robot, the error rate of the final assessment has been reduced to 10% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). But there were two patients whose effective inhalation time still failed to meet the standard after undergoing training on inspiratory capacity and inhalation techniques, and their hands and mouth were still uncoordinated. After informing the doctor of this assessment result, the doctor suggested the patients to adjust the treatment plan for pMDI and nebulizers.\u003c/p\u003e \u003cp\u003eThess data indicated that the inhalation errors were widespread among patients with CODP evaluated by combination of visual checklist observation and intelligent inhalation sensors. The use of intelligent inhalation sensors can quantify objective values such as effective inhalation time which visual checklist assessment cannot do. But the rate of errors improved significantly after the training of digital therapy comprehensive management platform.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe accuracy comparation of inhalation techniques\u003c/h2\u003e \u003cp\u003eA total of 60 patients newly diagnosed with COPD ad treated with pMDIs monotherapy participated in this study. As showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, the most frequency errors in self-study group were \u0026lsquo;sit up/stand straight \u0026amp; tilt head\u0026rsquo;, \u0026lsquo;breath out completely before inhalation\u0026rsquo;, \u0026lsquo;hold breath (for at least 5 s)\u0026rsquo;, followed by \u0026lsquo;breath in\u0026rsquo;, \u0026lsquo;rinse mouth\u0026rsquo; and \u0026lsquo;hand and mouth incoordination\u0026rsquo;. But the frequency of these errors was significantly lower than those in video group and digital therapy group, especially in digital therapy group. However, the training time for the digital therapy group was 1004\u0026thinsp;\u0026plusmn;\u0026thinsp;233 seconds, and significantly longer than that of the self-study group (277\u0026thinsp;\u0026plusmn;\u0026thinsp;122 seconds) and the video group (317\u0026thinsp;\u0026plusmn;\u0026thinsp;129 seconds) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCases\u003c/h2\u003e \u003cp\u003eA 67-year-old male patient, who has been diagnosed with COPD for more than 1 year, usually inhaled DPI and complained of dry itching in the throat. We used intelligent inhalation sensors to capture inhalation action, quantify hand-mouth coordination and inhalation time, in conjunction with the visual checklist assessment. The inspiratory capacity and inhalation technique evaluation showed that the PIF was 93.5L/min and the inhalation time was less than 2 second (1.875 s), and indicated that the inhalation speed is too fast and the inhalation time is insufficient. However, after the intelligent guidance and training of the drug teaching robot, the intelligent sensor was used again to evaluate the inhalation technique, indicating that the PIF was 61.2L/min and the inhalation time was 3 seconds, which reached the optimal PIF and inhalation time required by DPI. Another patient, a 65-year-old woman diagnosed with COPD for 3 months, usually inhaled pMDI but reported no significant improvement in dyspnea. Further inspiratory capacity and inhalation technique assessment indicated that PIF was 59.8L/min, the hand and mouth were not coordinated and the effective inhalation time was less than 3 s (1.259 s). After training of drug teaching robot, the inhalation techniques were evaluated again to indicate hand-mouth coordination, and the effective inhalation time was 3.066 s which reached the inhalation time required by pMDI. These two typical cases suggest that if the patient's dyspnea is not well controlled or the patient has local symptoms in the throat during medication, the patient's inspiratory capacity and inhalation techniques should be evaluated in time.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe patient's inhalation capacity can affect the rate of pulmonary deposition of inhaled drug [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Inspiratory capacity may vary among patients with different conditions, affected by age, gender and muscle strength [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Studies have indicated that patients with COPD have lower PIF compared to the healthy adults [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Women generally have lower PIF than men, and PIF tend to decline with age [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. PIF is also limited by inspiratory pressure, which is generated by the strength and tension of respiratory muscles, and positively correlated with MIP [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. But The application of PIF and MIP in clinical medication guidance and patient management for chronic airway diseases is still relatively limited. Thus, it is particularly important to assess and train the patient's inhalation capacity and select the appropriate inhalation device. So far, In-Check DIAL\u0026reg; and Multi-Function Spirometer System PF810 \u0026reg; are mainly used to measure patients\u0026rsquo;inspiratory capacity against internal resistance of inhalers [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we used the inspiratory capacity assessment module embedded in the digital therapy comprehensive management platform to quantify the inspiratory capacity of patients through computer-controlled graphical interactive interface software. The results indicated that PIF was positively correlated with MIP and MIP% pred, but not with FEV1, FVC and FEV1%pred. Previous studies have suggested that PIF in COPD patients is positively correlated with FEV1, FVC and FEV1%pred [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], but there are also studies suggesting that PIF is not correlated with FEV1, FVC and FEV1%pred, and FEV1 is not an independent correlation factor of PIF [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Some researchers have proposed that PIF is mainly aimed at the assessment of inspiratory capacity, but chronic airway diseases are small airway obstruction which has more impact on expiratory capacity, thus it is proposed that FEV1 has no correlation with PIF [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. At present, different studies have obtained different conclusions, which may be related to the sample size and the diseases severity. Further studies need to be carried out with the participation of multiple centers and the expansion of the sample size.\u003c/p\u003e \u003cp\u003eInhalation techniques refer to the correct operation of each step when the patient uses the inhalation devices. Improper operation of inhalation devices is directly related to poor disease control. A real-world study of 2935 COPD patients showed that improper use of inhalation devices increased the incidence of acute exacerbations of COPD from 3.3\u0026ndash;6.9% [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. But wrong operation of inhalation devices is widespread. In an observational study of 3,811 patients with COPD or asthma, device use errors ranged from 49\u0026ndash;76%, with critical errors 11\u0026ndash;32% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A multicenter study of elderly COPD patients in Taiwan found there was 65\u0026ndash;87.89% error rate in the use of inhalation devices [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSo far, there is a lack of objective, convenient and effective assessment methods for inhalation techniques. The traditional assessment of inhalation techniques is mainly visual assessment based on checklist, but it is difficult to visually evaluate objective data during the inhalation process, such as effective inhalation time. In this study, we installed the inhalation device into the intelligent inhalation sensor to allow patients to demonstrate the use of the inhalation device, combined with visual evaluation and objective data detected by the intelligent detect sensor to complete the inhalation technique evaluation. At the same time, patients can also be instructed to demonstrate how to use the inhalation device, ensuring that they have mastered the key steps for correct usage of the inhalation device. In order to improve the current accuracy of inhalation techniques, it is necessary to strengthen the training which can help improve the accuracy of inhalation device operation, increase patient compliance, and improve disease control. But there are many problems with the current methods and effectiveness. Some of DPI inhalation patients with COPD or asthma never received oral instruction, and the quality of training and mastery of inhalation techniques among those patients were unsatisfactory.\u003c/p\u003e \u003cp\u003eThe preferences of patients for different training methods in order were demonstration teaching, video tutorials, instruction manuals and leaflets, and the demonstration teaching can significantly improve inhalation techniques and compliance [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this study, we compared the accuracy of inhaler use after the training of instruction manuals, video tutorials and digital therapy platform \"teaching-demonstration-evaluation\", and found that the patients trained on the digital therapy comprehensive management platform showed a significant improvement in the accuracy of inhalation device use. The advantage of digital therapy is to explain the technical details of the use of inhalation devices to patients through video, and allow patients to demonstrate the use of inhalation devices, combine the checklist and intelligent inhalation sensors to review and evaluate the mastery of inhalation techniques of patients and correct errors in time. Therefore, the digital therapy platform can be used for intelligent inhalation medication guidance, training and technical evaluation in hospitals where with well-developed medical resources. But in areas with poor medical resources, technique training can also be done by repeatedly reading instructions and watching videos to decrease inhalation errors.\u003c/p\u003e \u003cp\u003eThis study has two limitations. First, the primary limitation was its small sample size and single-center study. However, preliminary data showed promising advantages of the digital therapy comprehensive management platform, suggesting further verification. Second, we only used intelligent detect sensor to conduct inhalation techniques assessment. In fact, it can also record the daily medication frequency of patients, issue medication reminders and guidance to patients, and improve patients' medication compliance. Subsequently, we will conduct further research on this aspect in the following studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe inhalation errors were widespread among patients with COPD. The inhalation techniques can be improved after the training of digital therapy comprehensive management platform. The assessment method based on the checklist combined with intelligent sensors can conduct a more comprehensive and effective assessment of inhalation techniques. The digital therapy comprehensive management platform is expected to become an important tool for COPD management. However, its clinical benefits still require substantiation from clinical trials.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCOPD \u0026nbsp;Chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eGOLD \u0026nbsp;The global initiative for Chronic Obstructive Lung Disease\u003c/p\u003e\n\u003cp\u003epMDIs \u0026nbsp;Pressurized metered-dose inhalers\u003c/p\u003e\n\u003cp\u003eDPIs \u0026nbsp; \u0026nbsp;Dry powder inhalers\u003c/p\u003e\n\u003cp\u003ePIF \u0026nbsp; \u0026nbsp; Peak inspiratory flow\u003c/p\u003e\n\u003cp\u003eFEV1 \u0026nbsp; Forced expiratory volume in one second\u003c/p\u003e\n\u003cp\u003eFVC \u0026nbsp; \u0026nbsp;Forced vital capacity\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Chengxin (Tianjin, China) for providing the digital therapy comprehensive platform and its working principles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinlin Wang and Ying Gong conceived and designed the study. Linlin Wang analyzed the data and wrote the manuscript. Li Li, Xinyi Tang, Yimeng Lu, Xiaofen Ye and Yufan Li collected the data. Yuanlin Song and Jing Zhang revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Shanghai Three-year Action Plan to Strengthen the Construction of Public Health System (2023-2025) (GWVI-11.1-18), National Natural Science Foundation of China (82000087, 82130001), Shanghai Municipal Science and Technology Major Project (ZD2021CY001), Science and Technology Commission of Shanghai Municipality (20Z11901000, 20DZ2261200, 22Y11900800) and Shanghai Municipal Key Clinical Specialty (shslczdzk02201).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors confirmed that all experiments were performed in accordance with relevant guidelines and regulations. This study was approved by the Ethics Committee of Zhongshan Hospital of Fudan University (B2019-142) and all the participants have signed an informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have stated explicitly that there are no conflicts of interest regarding this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal Initiative for Chronic Obstructive Lung Disease Global strategy for the diagnosis management and prevention of chronic obstructive pulmonary disease (2024 report). https://goldcopd. org.\u003c/li\u003e\n\u003cli\u003eMitchell JP. What the pulmonary specialist should know about the new inhalation therapies. Eur Respir J. 2012; 39(4):1054-5. doi: 10.1183/09031936.00210911.\u003c/li\u003e\n\u003cli\u003eDarquenne C. Aerosol deposition in health and disease. J Aerosol Med Pulm Drug Deliv. 2012; 25(3):140-7. doi: 10.1089/jamp.2011.0916.\u003c/li\u003e\n\u003cli\u003eStephen WS, Charles GT. The History of Therapeutic Aerosols: A Chronological Review. J Aerosol Med Pulm Drug Deliv. 2017; 30(1):20-41. doi: 10.1089/jamp.2016.1297.\u003c/li\u003e\n\u003cli\u003eNivedita S, David C, Heejun P, Zhou QT. Physical stability of dry powder inhaler formulations. Expert Opin Drug Deliv. 2020; 17(1):77-96. doi: 10.1080/17425247.2020.1702643.\u003c/li\u003e\n\u003cli\u003eVarsha K, Chun YJW, Imco S, Bernhard M, Wietze N, Vishal C, et al. Advances in soft mist inhalers. Expert Opin Drug Deliv,2023; 20(8):1055-1070. doi: 10.1080/17425247.2023.2231850.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-R\u0026iacute;o\u003csup\u003e \u003c/sup\u003eF, Soler-Catalu\u0026ntilde;a\u003csup\u003e \u003c/sup\u003eJJ, Alcazar\u003csup\u003e \u003c/sup\u003eB, Viejo\u003csup\u003e \u003c/sup\u003eJL, Miravitlles\u003csup\u003e \u003c/sup\u003eM. Requirements, Strengths and Weaknesses of Inhaler Devices for COPD Patients from the Expert Prescribers\u0026apos; Point of View: Results of the EPOCA Delphi Consensus. COPD. 2017; 14(6):573-580. doi: 10.1080/15412555.2017.1365120.\u003c/li\u003e\n\u003cli\u003eChavan\u003csup\u003e \u003c/sup\u003eV, Dalby R. Novel system to investigate the effects of inhaled volume and rates of rise in simulated inspiratory air flow on fine particle output from a dry powder inhaler. AAPS PharmSci. 2002;4(2):E6. doi: 10.1208/ps040206.\u003c/li\u003e\n\u003cli\u003eMahler\u003csup\u003e \u003c/sup\u003eDA, Halpin\u003csup\u003e \u003c/sup\u003eDMG. Peak Inspiratory Flow as a Predictive Therapeutic Biomarker in COPD. Chest,2021;160(2):491-498. doi: 10.1016/j.chest.\u003c/li\u003e\n\u003cli\u003eMahler DA. Peak Inspiratory Flow Rate: An Emerging Biomarker in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med,2019 Jun 15;199(12):1577-1579. doi: 10.1164/rccm.201901-0005LE.\u003c/li\u003e\n\u003cli\u003ePankovitch S, Frohlich M, AlOthman B, Marciniuk J, Bernier J, Paul-Emile D, Bourbeau J, Ross BA. Peak Inspiratory Flow and Inhaler Prescription Strategies in a Specialized COPD Clinical Program: A Real-World Observational Study. Chest. 2025;167(3):736-745. doi: 10.1016/j.chest.2024.09.031.\u003c/li\u003e\n\u003cli\u003eHarb HS, Laz NI, Rabea H, Abdelrahim MEA. Real-life assessment of chronic obstructive pulmonary disease patient performance with different inhalers. Int J Clin Pract. 2021;75(4): e13905. doi: 10.1111/ijcp.13905.\u003c/li\u003e\n\u003cli\u003eW Wei, D Wang, WT Liu, et al. Skills in handling Turbuhaler, Diskus in the west of China. BMC Pulm Med. 2023;23(1):447. doi: 10.1186/s12890-023-02765-1.\u003c/li\u003e\n\u003cli\u003eJKWu, WW Meng, YM Ma, et al. Errors and Adherence to Inhaled Medications in Chinese Adults with COPD. J Gen Intern Med. 2024;39(1):69-76. doi: 10.1007/s11606-023-08378-y.\u003c/li\u003e\n\u003cli\u003eKocks J, Bosnic-Anticevich S, van Cooten J, et al. Identifying critical inhalation technique errors in Dry Powder Inhaler use in patients with COPD based on the association with health status and exacerbations: findings from the multi-country cross-sectional observational PIFotal study. BMC Pulm Med. 2023; 23(1):302. doi: 10.1186/s12890-023-02566-6.\u003c/li\u003e\n\u003cli\u003eAbidi SR, Rickards T, Woensel WV, Abidi SSR. Digital Therapeutics for COPD Patient Self-Management: Needs Analysis and Design Study. Stud Health Technol Inform.2024; 310:209-213. doi: 10.3233/SHTI230957\u003c/li\u003e\n\u003cli\u003eDang A, Arora D, Rane P. Role of digital therapeutics and the changing future of healthcare. J Family Med Prim Care. 2020; 9(5):2207-2213. doi: 10.4103/jfmpc.jfmpc_105_20.\u003c/li\u003e\n\u003cli\u003eMahler DA. Peak inspiratory flow rate as a criterion for dry powder inhaler use in chronic obstructive pulmonary disease. Ann Am Thorac Soc. 2017;14(7):1103-7\u003c/li\u003e\n\u003cli\u003eLoh CH, Peters SP, Lovings TM, Ohar JA. Suboptimal inspiratory flow rates are associated with chronic obstructive pulmonary disease and all-cause readmissions. Ann Am Thorac Soc. 2017;14(8):1305\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eChen HI, Kuo CS. Relationship between respiratory muscle function and age, sex, and other factors. J Appl Physiol (1985). 1989;66(2):943\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eVidal MB, Pegorari MS, Santos EC, Matos AP, Pinto ACPN, Ohara DG. Respiratory muscle strength for discriminating frailty in community-dwelling elderly: a cross-sectional study. Arch Gerontol Geriatr. 2020; 89:104082.\u003c/li\u003e\n\u003cli\u003eClark AR, Weers JG, Dhand R. The Confusing World of Dry Powder Inhalers: It Is All About Inspiratory Pressures, Not Inspiratory Flow Rates. J Aerosol Med Pulm Drug Deliv. 2020; 33(1):1-11. doi: 10.1089/jamp.2019.1556.\u003c/li\u003e\n\u003cli\u003eClark\u003csup\u003e \u003c/sup\u003eAR. The Role of Inspiratory Pressures in Determining the Flow Rates Though Dry Powder Inhalers; A Review. Curr Pharm Des. 2015;21(27):3974-83. doi: 10.2174/1381612821666150820105800.\u003c/li\u003e\n\u003cli\u003eClement Clarke International. In-Check DIAL\u003csup\u003e\u0026reg;\u003c/sup\u003eG16: Coaching DPI and pMDI inhaler technique. https://www. Haag-streit.com/clement-clarke/products/inhaler- technique/in-check-dial-g16/. Accessed 29 Dec 2023.\u003c/li\u003e\n\u003cli\u003eAlliance Tech Medical. In-Check\u0026trade; DIAL G16: Inhaler Technique Training and Assessment Tool. https://alliancetechmedical. com/check-dial-training-device/. Accessed 29 Dec 2023.\u003c/li\u003e\n\u003cli\u003ee-LinkCare. UBREATH\u003csup\u003e\u0026reg;\u003c/sup\u003e Multi-Function Spirometer System (PF810). https://www.e-linkcare. com/ubreath-multi-function- spirometer-system-pf810-product/. Accessed 24 Jun 2022.\u003c/li\u003e\n\u003cli\u003eJL Hua, XF Ye, Du CL, N Xie, Zhang JQ, M Li, J Zhang. Optimizing inhalation therapy in the aspect of peak inhalation flow rate in patients with chronic obstructive pulmonary disease or asthma. BMC Pulm Med. 2021; 21(1):302. doi: 10.1186/s12890-021-01674-5.\u003c/li\u003e\n\u003cli\u003eGhosh S, Pleasants RA, Ohar JA, Donohue JF, Drummond MB. Prevalence and factors associated with suboptimal peak inspiratory flow rates in COPD. Int J Chron Obstruct Pulmon Dis. 2019;14:585-595. doi: 10.2147/COPD.S195438.\u003c/li\u003e\n\u003cli\u003eMolimard M, Raherison C, Lignot S, et al. Chronic obstructive pulmonary disease exacerbation and inhaler device handling: real-life assessment of 2935 patients. Eur Respir J. 2017; 49(2):1601794. doi: 10.1183/13993003.01794-2016.\u003c/li\u003e\n\u003cli\u003eMolimard M, Raherison C, Lignot S, Depont F, Abouelfath A, Moore N. Assessment of handling of inhaler devices in real life: an observational study in 3811 patients in primary care. J Aerosol Med. 2003;16(3):249-54. doi: 10.1089/089426803769017613.\u003c/li\u003e\n\u003cli\u003eLiang CY, Chen YJ, Sheu SM, Tsai CF, Chen W. Misuse of inhalers among COPD patients in a community hospital in Taiwan. Int J Chron Obstruct Pulmon Dis. 2018;13:1309-1316. doi: 10.2147/COPD.S158864.\u003c/li\u003e\n\u003cli\u003eDekhuijzen PNR, Lavorini F, Usmani\u003csup\u003e \u003c/sup\u003eOS. Patients\u0026apos; perspectives and preferences in the choice of inhalers: the case for Respimat(\u0026reg;) or HandiHaler(\u0026reg;). Patient Prefer Adherence. 2016; 10:1561-72. doi: 10.2147/PPA.S82857.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic obstructive pulmonary diseases, Digital therapy comprehensive management platform, Inhalation capacity, Inhalation technique","lastPublishedDoi":"10.21203/rs.3.rs-6502020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6502020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInhalation therapy is the main pharmaceutical treatment for patients with chronic obstructive pulmonary disease (COPD), but the improper selection and incorrect use of inhalation devices are widespread. The digital therapy comprehensive management platform has the potential to change this situation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe inspiratory capacity and inhalation techniques of 62 COPD patients were evaluated and trained by a digital therapy comprehensive management platform. Moreover, 60 patients newly diagnosed with COPD and required (pressurized metered dose inhalers) pMDIs were recruited to compare the correct usage rates of inhalation devices after training through self-study based on the instructions, video teaching, and digital therapy comprehensive management platform. Additionally, two cases of using the digital therapy comprehensive management platform for inhalation device training were described.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe data indicated that peak inspiratory flow (PIF) decreased with the increase of internal resistance of inhalers and positively correlated with maximum inspiratory pressure (MIP), but no significant correlation with forced expiratory volume in one second (FEV1), forced vital capacity (FVC), FEV1% prediction and FEV1/FVC. Usage errors rate of initial evaluation of DPIs was 50%, and decreased to 16.67% after training of digital therapy comprehensive management platform. Among these patients, 50% had insufficient effective inspiratory time, and 16.67% had insufficient inspiratory flow rate. Usage errors rate of initial evaluation of pMDIs was 75%, and decreased to 10% after training. Among these patients, 70% had insufficient effective inhalation time and 25% had hand and mouth incoordination. We also found the most frequency errors were \u0026lsquo;sit up/stand straight \u0026amp; tilt head\u0026rsquo;, \u0026lsquo;breath out completely before inhalation\u0026rsquo;, \u0026lsquo;hold breath (for at least 5 s)\u0026rsquo;, followed by \u0026lsquo;hold breath\u0026rsquo; and \u0026lsquo;hand and mouth incoordination\u0026rsquo;. And the incidence of errors in the digital therapy group was significantly lower than that in self-study group and video teaching group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe assessment of combination of checklist with the digital therapy comprehensive management platform is more comprehensive and effective than the checklist alone for evaluation and training of inspiratory capacity and inhalation techniques. The digital therapy integrated management platform is expected to become an important tool for COPD management.\u003c/p\u003e","manuscriptTitle":"Inspiratory capacity and inhalation techniques evaluated and training by digital therapy comprehensive management platform in COPD patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 20:23:36","doi":"10.21203/rs.3.rs-6502020/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-25T07:47:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-31T21:52:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T15:46:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317185688694147198910238191153958083352","date":"2025-07-13T07:44:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206593570188857429592523197583001315465","date":"2025-07-10T17:17:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274998106785137595670033667794033535761","date":"2025-06-25T14:18:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T21:30:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T08:59:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-26T10:41:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-04-26T10:40:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c077c9f-1641-4e84-95e1-abfb8c7ffd63","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T09:28:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 20:23:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6502020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6502020","identity":"rs-6502020","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}