Efficacy of IoT-based perioperative respiratory rehabilitation for lung resection: Protocol of a randomized controlled trial

Abstract

Background

Breathing rehabilitation is an important method for post-operative recovery in lung cancer patients. However, no studies have yet utilized Internet of Things (IoT) technology to manage the perioperative period for lung cancer surgery patients. This randomized controlled trial (RCT) aims to evaluate the intervention effect of an IoT-based breathing training program on the improvement of lung function trajectories in lung cancer surgery patients.

Methods and analysis

This study adopts a multicenter, randomized, open, parallel-controlled design, aiming to explore the efficacy of perioperative IoT-based breathing rehabilitation management on the post-operative lung function trajectory in lung cancer patients. A total of 340 lung cancer patients undergoing lung resection and treated in our department from September 2024 to September 2026 will be enrolled, with the final number of patients meeting the inclusion and exclusion criteria as the standard. Eligible participants will be randomly divided into two groups at a 1:1 ratio (IoT-based breathing rehabilitation group or breathing exercise group) for perioperative management, with planned lobectomy or sub-lobectomy procedures.

Discussion

The aim is to use lung function trajectories as the primary indicator to evaluate the intervention effect of an IoT-based respiratory training program on the improvement of lung function trajectories in lung cancer surgery patients. Through scientific evaluation, a better understanding of the impact of this respiratory rehabilitation program on patients’ lung function recovery can be achieved, providing scientific evidence for clinical practice and promoting the rehabilitation of lung cancer surgery patients. It is necessary to further verify its role in home management and pre-rehabilitation.

Registration details

China Clinical Trial Registry (ChiCTR2600123294); https://www.chictr.org.cn/showproj.html?proj=304879.

Why carry out this study?

Post-operative breathing rehabilitation is crucial for recovery in lung cancer patients, but a significant unmet need exists for more effective, managed approaches.

No prior studies have investigated the use of Internet of Things (IoT) technology to manage the perioperative period for this specific patient population.

What was the hypothesis of the study?

That an IoT-based breathing training program is more effective at improving the post-operative lung function trajectory in lung cancer surgery patients compared to standard breathing exercises.

What was learned from the study?

The primary outcome will be quantitative data showing the lung function trajectory, which is expected to demonstrate a significant improvement for patients in the IoT-based rehabilitation group versus the control group.

This study will provide the first scientific evidence on the efficacy of IoT-enabled remote management for perioperative respiratory rehabilitation, potentially establishing a new standard of care.

The findings will verify the role of IoT technology not only in post-operative recovery but also in pre-operative (“pre-rehabilitation”) and home-based management, guiding future research and clinical application.

The Internet of Things is a new network model, consisting of a network of devices and objects equipped with sensors, software, electronic devices, and network connections, enabling wireless communication among them and transmitting data to cloud platforms. ¹ Advances in data transmission and network connectivity within the IoT allow the use of sensor-equipped medical devices connected to the internet to wirelessly send, receive, and store real-time health data through cloud platforms. ² Combining IoT with Artificial Intelligence (AI) technology provides analysis for IoT data across various sensor applications. ³ It is predicted that the use of such IoT-based medical platforms will transform patient management, particularly in remote patient monitoring. ⁴ Previous studies have shown that the Internet of Things (IoT) has certain advantages in improving the exercise performance of cancer patients. ⁵

Previous studies have indicated that lung cancer patients often experience a marked decline in respiratory muscle strength, lung volume, and exercise capacity within two days following surgery, which contributes to the development of postoperative pulmonary complications. Maintaining adequate inspiratory muscle strength and exercise capacity has been shown to enhance cough efficacy, decrease sputum retention, and lower the likelihood of additional surgeries stemming from such complications.^6,7 Estimates suggest that postoperative complications affect between 38% and 58% of patients, among which 15% to 25% are respiratory-related—including pulmonary infection, pneumonia, and atelectasis. These issues are associated with prolonged hospital stays, diminished functional outcomes, elevated healthcare expenditures, cancer recurrence, higher 30-day readmission rates, and increased mortality. ⁸ One review also noted that sarcopenia is present in 13.9% to 55.8% of individuals undergoing lung resection, and identified it as a significant predictor of postoperative complications. ⁹ Respiratory muscle training includes a series of exercises aimed at improving thoracic flexibility, correcting poor posture, altering breathing patterns, increasing lung capacity, and enhancing the function of inspiratory and expiratory muscles. ¹⁰ This training method uses resistance exercises to improve respiratory muscle strength and performance, effectively enhancing the respiratory function of both healthy individuals and patients with cardiopulmonary diseases.

To address these issues, respiratory training is widely used in clinical lung function exercises. Incentive spirometry (IS), first proposed by Bartlett et al. ¹¹ in 1973, is one of the most commonly used methods to improve lung function. It involves active inspiratory training, which helps improve lung function, increase respiratory muscle strength, reduce postoperative complications such as atelectasis and pulmonary infections, shorten hospital and catheterization time, and improve quality of life. Millstine et al. ¹² established that interactive, portable electroencephalography can effectively alleviate fatigue, reduce stress, and enhance quality of life among cancer patients undergoing surgical treatment. Cheong et al. ¹³ evaluated an integrated mHealth and IoT system designed to monitor symptoms and nutritional status, along with delivering personalized rehabilitation plans. Li et al. ¹⁴ aims to evaluate the effectiveness of web-based interventions in reducing symptom burden, improving self-management capabilities and self-efficacy, and to provide a reference for clinical practice. Hang et al. ¹⁵ evaluated that the impact of patient self-reported outcomes collected via electronic systems on cancer outcomes can improve quality of life. Their findings indicated that this approach contributed to enhanced physical performance and a decrease in adverse effects in patients undergoing active chemotherapy. However, current clinical respiratory training methods cannot record patients’ training data or generate dynamic visual training reports and data analysis. Patients cannot achieve progressive training and often find it difficult to persist. With the advent of IoT technology, the visualization and comprehensive management of respiratory training have become possible. Both patients and healthcare providers can view exercise progress and various examination reports in real time.

The IoT devices used in this study include an intelligent respiratory trainer, a portable lung function tester, a central monitoring backend, and a patient app. These devices involve functions such as respiratory detection, respiratory training, airway clearance, prescription management, AI voice interaction, and real-time data transmission. The respiratory trainer features both non-flow-dependent threshold load and flow-dependent progressively decreasing resistance functions. It also includes the smart oscillating positive expiratory pressure (OPEP) airway clearance function.

The aim is to use lung function trajectories as the primary indicator to evaluate the intervention effect of an IoT-based respiratory training program on the improvement of lung function trajectories in lung cancer surgery patients. Through scientific evaluation, a better understanding of the impact of this respiratory rehabilitation program on patients’ lung function recovery can be achieved, providing scientific evidence for clinical practice and promoting the rehabilitation of lung cancer surgery patients.

Methods and analysis

Trial design

In this parallel, open-label, multicenter, randomized controlled trial, participants will be randomly assigned to either the breathing exercise group or the IoT-based respiratory rehabilitation group. Patients will be randomly allocated to the experimental or control group using a random number table method.

Pilot study

We conducted a pilot study to assess the feasibility of the research protocol and incorporate patient feedback into the trial design. Based on the findings, appropriate revisions were made to the content and outcome measures of the study protocol.

Participants

Inclusion criteria①Age between 35 and 75 years; ②Planned to undergo minimally invasive unilateral lung resection; ③Preoperative pulmonary function test shows FEV1 actual value greater than or equal to 1.2L and greater than or equal to 60% of the predicted value; ④Capable of using smart devices and filling out electronic questionnaires; ⑤Clear consciousness, no reading, communication, or cognitive impairments, and has signed the informed consent form.

Exclusion criteria①Smoking cessation time is less than 2 weeks before surgery; ② Hospital stay is ≥ 14 days; ③Patients who have previously undergone thoracic surgery; ④Patients scheduled for total pneumonectomy; ⑤History of major diseases, including other malignancies within 5 years, history of angina pectoris, myocardial infarction, or stroke within 6 months; ⑥Presence of severe or uncontrolled systemic diseases, including thyroid diseases, uncontrolled hypertension, coagulation disorders, or active infections, including hepatitis B, hepatitis C, and human immunodeficiency virus (HIV).

The project is a multicenter, open-label, parallel randomized controlled effectiveness study. After being enrolled in the study and completing the baseline assessment, participants will be randomized using a random number table method, stratified according to the type of surgery. The central randomization software will randomly assign patients to either the experimental group or the control group in a 1:1 ratio. Prior to randomization, the recruiting researcher must confirm that the participant meets the eligibility criteria and is officially registered. Once recruitment and data collection at baseline are completed and informed consent for participation in the study is obtained, randomization will proceed. Due to the nature of the intervention, blinding of the researchers and participants is not feasible.

Recruitment procedure

The study flow diagram is presented in Figure 1. Patients in the Department of Thoracic Surgery at 4 hospitals who meet the eligibility criteria will be invited to participate in the RCT study. The thoracic surgeons, nurses and clinical trial staff involved in the recruitment were trained and instructed in the recruitment process in order to maximize the recruitment rate.

Figure 1.

Flow diagram of study participants.

Oral and written information about the study will be provided by the dedicated nurse in an uninterrupted room in the Thoracic Surgery Conference Room. Study information can also be provided to patients in a powerpoint presentation for patients to learn about on site before deciding whether to participate. Patients are advised to spend at least 24 hours considering and discussing participation with relatives or lay representatives before deciding whether to participate in the study, and can withdraw from the study at any time.

Randomization

Participants will be randomly assigned to either the intervention group or the control group at the patient level. Researchers who are not involved in data collection or analysis will use a random number table to allocate participants to the intervention or control group according to a random sequence. After completing baseline measurements, an independent researcher will inform participants of the group to which they have been assigned. The researchers will add patients to the corresponding WeChat groups for the control and intervention groups and obtain their informed consent. If assigned to the intervention group, the researchers will assist them in installing the app on their smartphones and introduce the content related to IoT respiratory rehabilitation. The control group will receive an introduction to respiratory exercise practices.

Blinding

Due to the differences between the IoT-based respiratory rehabilitation group and the respiratory exercise group, it was not feasible to blind the operators and participants; therefore, this study adopted an open-label design. The outcome assessors and data analysts were different individuals, and blinding was implemented. Specifically, the data analysts were kept unaware of the group allocations. IoT-based Respiratory Rehabilitation Interventions.

The IoT device encompasses a centralized monitoring system, an intelligent breathing trainer, a portable pulmonary function device, and a patient-side app, forming a comprehensive management and monitoring system. It is a medical-grade intelligent hardware device with multiple functions. The breathing trainer includes breathing exercises, airway clearance, and data analysis, all managed through AI voice interaction. It supports an automatic training mode and can intelligently adjust resistance, making breathing exercises more convenient for patients. The portable pulmonary function device features both lung function and respiratory muscle strength testing capabilities. Both devices can be connected to the central monitoring platform, with training data automatically collected and uploaded in real-time to the central monitoring platform. They can also connect to the respiratory rehabilitation management system, enabling comprehensive respiratory rehabilitation. Intervention compliance is monitored by researchers who check daily attendance records and send WeChat reminders to those who have not completed the tasks.

A) Comprehensive Respiratory Assessment and Prescription Issuance: On the first day post-operation, a scientific and quantitative assessment will be conducted on enrolled patients, including blood pressure, respiratory muscle strength testing, etc. Based on the assessment results, prescriptions for respiratory muscle training and airway clearance will be issued. These are used to adaptively adjust the intensity of postoperative rehabilitation exercises.

B) Training Phase: After assessing respiratory muscle strength, the rehabilitation doctor will issue prescriptions for respiratory training and airway clearance to the patient. The patient will be informed of the principles and methods of use, receiving one-on-one guidance, and instructions will also be sent through a WeChat group. The first training session will be completed under the supervision of a research nurse. Subsequently, a thoracic surgery nurse will guide the patient in using the intelligent respiratory trainer to complete two sets of respiratory training and one airway clearance session.

C) Postoperative Health Education: From the first day post-operation until discharge, the thoracic surgery nurse will guide and supervise the patient to ensure they correctly master the methods of respiratory muscle training and airway clearance.

D) Real-time Monitoring: Starting from the first day post-operation, the patient’s condition (lung function, respiratory muscle strength) will be continuously monitored through the Internet of Things (IoT) central monitoring system. The medical team can directly intervene at the bedside in response to abnormal alerts. After discharge, real-time monitoring of abnormal respiratory training conditions will be conducted, and intervention can be carried out via interaction in the WeChat group.

E) Inspiratory Muscle Training: This training uses progressive flow-resistance loading, driven by pressure and controlled by flow rate. It includes both non-flow-dependent threshold loading and flow-dependent progressive flow resistance. Starting from the first day post-surgery, patients will complete daily respiratory training tasks, with inspiratory muscle training conducted daily, 2 sets per day, 10 repetitions per set. The initial resistance will be set at 30% of the patient’s initially measured inspiratory muscle strength. The test results will be saved in the software, which will set daily goals based on exercise tolerance, gradually increasing to 100%. The device will personalize adjustments to resistance, training duration, and difficulty based on the patient’s respiratory training performance.

F) Expiratory Muscle Training: Starting from the first day post-surgery, daily respiratory training tasks will be performed, including expiratory muscle training, with 2 sets per day, 10 repetitions per set. The initial resistance will be set at 30% of the patient’s initially measured inspiratory muscle strength. The test results will be saved in the software, which will set daily goals based on exercise tolerance, gradually increasing to 100%. The device will personalize adjustments to resistance, training duration, and difficulty based on the patient’s respiratory training performance.

G) Intelligent Airway Clearance: The respiratory trainer includes an Oscillating Positive Expiratory Pressure (OPEP) function, which combines oscillatory and positive expiratory pressure techniques to aid in mucus clearance. Through intelligent airway clearance, the device helps patients remove respiratory secretions, preventing and reducing the incidence of lung infections, and improving airway cleanliness and comfort. The respiratory trainer adjusts resistance intelligently for airway clearance, with the clearance data being uploaded to the system for visual analysis. Starting from the first day post-surgery, patients will complete 5 minutes of airway clearance daily, with personalized adjustments made based on the patient’s condition.

To ensure standardized implementation, all clinical personnel delivering this intervention completed a mandatory training program conducted by certified specialists from the device provider. Quality control during the trial is maintained through ongoing supervision by this professional team to guarantee protocol adherence.

Control group (breathing exercise group)

A) Postoperative Breathing Exercises: Breathing training involves guiding patients in autonomous breathing exercises, such as pursed-lip breathing, diaphragmatic breathing, and coughing and sputum expulsion training.

1. Choosing the Appropriate Position: The recommended position for the patient is a semi-reclined position with the head of the bed elevated 45° to 60° and the foot of the bed raised by 10°. This position is beneficial for respiratory and circulatory functions and helps reduce tension at the incision site.

2. Pursed-lip Breathing: Inhale through the nose and exhale through the mouth, forming the lips as if blowing a whistle while exhaling. The recommended ratio of inhalation to exhalation time is 1:2, i.e., inhale for 2-3 seconds and exhale for 4-6 seconds, with a frequency of 16 breaths per minute. The key to pursed-lip breathing is to control the exhalation force so that the airflow can tilt a candle flame placed 15-20 cm away from the lips without extinguishing it. This method increases airway resistance, reduces breathing rate, prevents premature collapse and closure of peripheral small airways, facilitates the expulsion of gas from the alveoli, aids in inhaling more fresh air during the next breath, increases tidal volume, enhances exercise endurance, alleviates hypoxia symptoms, and improves lung function.

3. Combining Diaphragmatic Breathing with Pursed-lip Breathing:** The patient should lie flat and relax, adjusting the breathing rhythm. Inhale through the nose, allowing the abdomen to expand with inhalation, while the responsible nurse applies appropriate resistance with both hands. During exhalation, the patient should perform the pursed-lip breathing technique while the abdomen contracts. Train for 10 minutes per session, twice a day.

4. Effective Coughing: (Combined with deep breathing training) Before sputum expulsion, gently cough a few times to loosen the sputum. First, take two deep breaths, then inhale deeply through the nose, hold the breath for 4-5 seconds, and perform one or two short, forceful coughs to expel the sputum.

5. Full-body Breathing Exercises:Raise one arm while inhaling; Press both hands on the abdomen while exhaling; Raise both arms horizontally while inhaling, then lower them while exhaling; Extend both arms forward while inhaling, then press the abdomen while exhaling; Raise both arms vertically while inhaling, then squat while exhaling. Perform these exercises twice a day, for 10-15 minutes per session.

To enhance adherence in the control group, 1) A WeChat group was used for daily follow-up, where patients were required to check in and report their exercise completion. 2) During follow-up assessments, exercise logs were verified. Participants who failed to perform the exercises were excluded from the analysis. We have added this detailed description of the adherence monitoring protocol to the “Control group (Breathing exercise group)”

Concomitant care

During the trial period, participants are permitted to receive all standard perioperative care and medications as prescribed by their clinical team. No specific concomitant care is prohibited, as the interventions (IoT training and breathing exercises) are complementary to standard care.

Data collection procedure

On the day of admission, inform the patient about the purpose of the study and obtain informed consent. After adding the patient on WeChat, add them to a WeChat group. The preoperative questionnaires will be completed with the guidance of the patient at the time of admission. Nurses will guide the patient in completing the questionnaires at various time points. Please refer to Table 1 for an overview of collection of the different outcomes.

1、Lung function data will be collected using a portable spirometer on the day of admission, and at 1 month and 3 months post-discharge.

2、On the day of admission and at 1 month post-discharge, collect data using the MDASI-LC and FACT-L scale. Respiratory muscle strength was collected the first day after surgery and one month after discharge.

3、At 3 months post-discharge, record the incidence of adverse events and overall mortality rate.

Table 1.

Overview of data collection.

​	Baseline (before operation)	The first day after surgery	One month after discharge from the hospital	Three month after discharge from the hospital
Baseline characteristics
Age	√	​	​	​
Gender	√	​	​	​
Education level	√	​	​	​
Smoking status	√	​	​	​
Cancer staging	√	​	​	​
Sign informed consent	√	​	​	​
Pulmonary function data	√	​	√	​
MDASI-LC scale	√	​	√	​
FACT-L Scale	√	​	√	​
Adverse event rate	​	​	​	√
Respiratory muscle strength	√	√	√	​

Outcomes

Baseline characteristics

Researchers designed their own general data questionnaire including age, gender, height, weight, marital status, cultural level, type of medical insurance; annual household income, smoking and so on.

Primary outcome

Lung function will be assessed using a portable spirometer, with data collected at three time points: preoperatively, 1 month post-discharge, and 3 months post-discharge. This data will be used to create lung function trajectories for two groups of patients, which will then be compared and analyzed. The main lung function indicators for comparison include: FVC (Forced Vital Capacity), FEV1 (Forced Expiratory Volume in the first second), and FVC/FEV1 (the ratio of Forced Expiratory Volume in the first second to Forced Vital Capacity) (Table1).

Secondary outcome

1) Symptom scoring at 1 month post-discharge

Symptom scoring will be evaluated using the Chinese version of the M.D. Anderson Symptom Inventory-Lung Cancer Module (MDASI-LC). ¹⁶ The M.D. Anderson Symptom Inventory is a multi-symptom self-assessment scale developed by the University of Texas M.D. Anderson Cancer Center in 2000. It assesses the severity of 13 common symptoms in cancer patients, such as pain, fatigue, and drowsiness, as well as the degree of interference these symptoms cause in six areas of daily life: general activity, work, mood, walking, relationships with others, and enjoyment of life. Each item is scored similarly, ranging from “no interference” (0 points) to “complete interference” (10 points). In 2004, the M.D. Anderson Cancer Center translated the MDASI into Chinese, resulting in the MDASI-C, which has demonstrated good reliability and validity. Therefore, this study will use this scale as the tool for symptom measurement.

2) Patient satisfaction at one month post-discharge

To assess patient satisfaction regarding the use of IoT devices and the overall healthcare services received.

3) Quality of life at 1 month post-discharge

Quality of life will be assessed using the Chinese version of the Functional Assessment of Cancer Therapy-Lung (FACT-L) Version 4.0. ¹⁷ The FACT-L is composed of the FACT-G (General Module) and a lung cancer-specific module. The Chinese version of FACT-L 4.0 was translated and developed by Wan Chonghua and others to evaluate the quality of life in lung cancer patients. The FACT-L consists of 36 items across 5 dimensions, with the FACT-G containing 27 items across 4 dimensions, and the lung cancer-specific module including an additional dimension with 9 items. The scale uses a 5-point Likert scoring method, where higher scores on the items, dimensions, and total scale indicate better quality of life for the patient. The Cronbach’s α coefficient for this scale is 0.805.

Safety indicators

1) Incidence of adverse events and overall mortality ratea at 3-months post-discharge

Adverse events (AEs) and serious adverse events (SAEs) will be recorded in all follow-up visits, using open-ended probes to inquire about potential adverse events from patients to ensure all AEs are documented. Additionally, secondary endpoints (at 1 months) will include a review of the participating hospitals’ medical records to identify any AEs occurring from the time of study enrollment to the 3-month follow-up. An AE is defined as any adverse experience during follow-up that leads to contact with the healthcare system (general practitioner or hospital). If an AE results in hospitalization, prolonged hospitalization, requires re-operation, or if the AE is life-threatening, results in death, permanent disability, or injury, it will be classified as an SAE. SAEs will include lung infections, pulmonary edema, pulmonary embolism, but other AEs meeting the above criteria will also be classified as SAEs. The possibility of early surgery or early exercise and education versus later surgery will be assessed. However, crossover surgeries will be recorded and reported, as they are important when evaluating the clinical applicability of the results. AEs will be classified by the site (knee or other) and recorded and assessed for severity by a review committee, independent of whether there is a causal relationship with the study treatment. The date of contact with the healthcare system will be recorded for all AEs. Additionally, the duration and potential consequences of SAEs will be assessed.

Data analysis

A database will beestablished, and all data will beanalyzed using Excel and SPSS 26.0 statistical software. The significance level will be set at α = 0.05, and a two-tailed test will be employed, with P < 0.05 indicating statistical significance. The primary analysis will follow the intention-to-treat (ITT) principle, including all randomized participants in the groups to which they were originally assigned. A per-protocol analysis will also be conducted as a sensitivity analysis.

Descriptive statistics will be used to analyze the general characteristics of the two groups. The Kolmogorov-Smirnov test will be employed to evaluate the distribution of the data. If the measurement data follow a normal distribution, they will be expressed as mean ± standard deviation (χ̄±s). Differences between groups will be analyzed using an independent samples t-test. For data not following a normal distribution, the median and interquartile range [M (QL, QU)] were used, and the Mann-Whitney U rank-sum test will be employed to analyze the differences between groups. Count data will be expressed as frequency and percentage (%), and comparisons between groups will be conducted using the χ2 test or Fisher’s exact test. This study will employe Linear Mixed Models (LMM) to analyze the longitudinal trajectories of lung function and their influencing factors. The LMM method effectively handles intra-individual correlations, unbalanced time points, and random missing values in repeated measures data, making it suitable for characterizing both the average population trend and inter-individual heterogeneity.

For repeated measures data that that do not follow a normal distribution, the generalized estimating equation was used. For data that follow a normal distribution and meet the assumption of homogeneity of variance, repeated measures ANOVA was used for overall comparisons, provided the Mauchly’s test of sphericity was passed. This analysis will be used to explain the effects of the intervention, time, and interaction on the results. If Mauchly’s test is not passed, the Greenhouse-Geisser correction will typically be applied. If the time effect is statistically significant, the LSD-t test will be usedfor pairwise comparisons between time points.

Ethics

The study applied for a clinical trial at the First Affiliated Hospital of Zhejiang University School of Medicine in August 2024, with the acceptance number IIT20240097C-R1. And in the Chinese clinical trial registry for the record (number:MR-33-24-044200). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki of 1964 and its later amendments. The methodology strictly adhered to the ethical standards set forth by the approving institution. Participants were fully explained the objectives and procedures of the study. They agreed to participate by signing a consent form to participate in the study. Given the non-pharmacological, low-risk nature of the IoT-based breathing training intervention, a formal independent DMC will not be established. Trial safety and data monitoring will be the responsibility of the principal investigator (PI) and the steering committee (see below). Any serious adverse events will be reported immediately to the PI and the institutional ethics committee for review. Any substantial amendments to the protocol will be submitted for approval to the institutional ethics committee. Approved amendments will be communicated to all investigators and will be updated in the trial registry (Medical Research Registration System).

Multicenter quality control

In this multicenter randomized controlled trial, to ensure the consistent and standardized implementation of nursing interventions across all participating centers, a unified standardized protocol has been developed. All centers will utilize intervention equipment, operation manuals, and recording tools uniformly provided by the research team to guarantee identical execution standards. Before the study commences, intervention personnel from each center must undergo centralized training and pass relevant assessments. Throughout the mid and late stages of the study, regular refresher training and coordination meetings will be organized, with two designated research team members responsible for quality supervision, including on-site guidance at each center both at the initiation and during the course of the multicenter study. Should any issues arise during implementation, each center must report them through designated liaisons, after which the research team will issue unified solutions. Unauthorized adjustments are strictly prohibited. All procedures strictly adhere to ethical guidelines, ensuring the reliability and scientific validity of the research data.

Stopping rule

If the intended sample size is not reached at 30 months after recruitment has started at all participating hospitals, the inclusion of patients will stop at 106 patients, which will ensure a power of 80% anticipating 20% loss to follow-up.

Data-management

Personal identifiers will be stored separately from clinical data to ensure confidentiality. Each participant will be assigned a unique identification number. Only the principal researchers and co-principal researchers will have access to participants’ personal data. All data will be stored on password-protected computers and tablets. After the study is completed, all personal identifiers will be deleted. The data will be stored digitally for 5 years until the study is fully completed.

Sample size

The primary outcome measure is lung function trajectory, specifically the Forced Expiratory Volume in one second (FEV1). Using the internationally recognized sample size estimation software G-power, with the parameters set as Effect Size =0.4, α = 0.05, 1-β = 0.95, and 1-Allocation Ratio = 1, it was calculated that136 participants are needed in each group. Assuming a 20% dropout rate, a total of 340 patients will be enrolled (170 in the intervention group and 170 in the control group).

Dissemination plan

This protocol has been prepared in accordance with the Standard Protocol Items: Recommendations for Intervention Trials (SPIRIT) guidelines. The results of the research will be shared through reports, presentations, and publications in peer-reviewed scientific journals. The findings will be reported following the Consolidated Standards of Reporting Trials (CONSORT) guidelines. Any significant modifications to the protocol will be disclosed during the dissemination of the findings.

Perspectives of the study

The results of this randomized controlled trial will provide high-quality evidence on the impact of respiratory rehabilitation, based on IoT technology, for perioperative patients undergoing unilateral lobectomy. The study will verify the effects on lung function trajectories, symptoms, quality of life, incidence of adverse events, and overall mortality. The findings will offer scientific support to healthcare providers and patients.

Discussion

Although this IoT-based respiratory muscle training has been validated in COPD patients, ⁴ its effectiveness in lung cancer patients has not yet been verified, representing the first initiative of its kind in China. An excellent RCT by Liu et al. ¹⁸ showed a six-week inspiratory muscle training and aerobic exercise improves respiratory muscle strength and exercise capacity in lung cancer patients after video-assisted thoracoscopic surgery. We aim to discuss results and clinical applicability of the findings looking at possible effect, possible patterns and correlations between intervention conditions and different types of families. We want to know whether the intelligent respiratory trainer based on the Internet of Things technology can improve the trajectory of lung function of patients through comprehensive monitoring and management of postoperative respiratory rehabilitation of lung cancer patients. Since patients need to continue to exercise at home for one month, controlling the drop rate of the study is a big challenge.

The study has limitations because there are no other similar studies to compare it with, which weakens the validity of the power calculations. In addition, this study is a multi-center study, which is a very important task for the quality control of the study, so we meet regularly to ensure the homogeneity of the study.

However, in reviewing the pre-experiment, the experience of the pilot showed clear differences, which will also be discussed further. The results of this study are intended to be published in national and international peer-reviewed journals and made accessible to any family member in the community through more public and accessible channels. The results of the study will be of great significance for the application of iot in respiratory rehabilitation of patients with perioperative lung cancer. In the future, the Internet of Things respiratory rehabilitation will be further applied to the pre-rehabilitation of lung cancer patients, or promoted to other surgical patients or patients who have undergone preoperative neoadjuvant therapy.

Trial status

The recent protocol was updated to Version 3.0 on August 13, 2024. Participant recruitment commenced on September 1^st, 2024, and is anticipated to conclude on September 1st, 2026.

Supplemental material

Supplemental material - Efficacy of IoT-based perioperative respiratory rehabilitation for lung resection: Protocol of a randomized controlled trial

Supplemental material for Efficacy of IoT-based perioperative respiratory rehabilitation for lung resection: Protocol of a randomized controlled trial by Lin Hang, Jieping Zhang, Zhongjie Lu, Yuan Miao, Yu fang Zeng, and MinMin Zhang in DIGITAL HEALTH.