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. 2023 May 19;29(4):285–292. doi: 10.1097/MCP.0000000000000971

Role of the internet of medical things in care for patients with interstitial lung disease

Gizal Nakshbandi 1, Catharina C Moor 1, Marlies S Wijsenbeek 1
PMCID: PMC10241441  PMID: 37212372

Purpose of review

Online technologies play an increasing role in facilitating care for patients with interstitial lung disease (ILD). In this review, we will give an overview of different applications of the internet of medical things (IoMT) for patients with ILD.

Recent findings

Various applications of the IoMT, including teleconsultations, virtual MDTs, digital information, and online peer support, are now used in daily care of patients with ILD. Several studies showed that other IoMT applications, such as online home monitoring and telerehabilitation, seem feasible and reliable, but widespread implementation in clinical practice is lacking. The use of artificial intelligence algorithms and online data clouds in ILD is still in its infancy, but has the potential to improve remote, outpatient clinic, and in-hospital care processes. Further studies in large real-world cohorts to confirm and clinically validate results from previous studies are needed.

Summary

We believe that in the near future innovative technologies, facilitated by the IoMT, will further enhance individually targeted treatment for patients with ILD by interlinking and combining data from various sources.

Keywords: eHealth, interstitial lung disease, internet of things

INTRODUCTION

Interstitial lung diseases (ILDs) are a heterogeneous group of rare chronic lung diseases, characterized by inflammation and/or fibrosis. ILD can broadly be classified as exposure related, including drug-use, auto-immune related, idiopathic, sarcoidosis, and ultra-rare diseases [1,2]. Some of these diseases are reversible, some have a stable disease course, but many forms of ILD are often progressive and associated with high morbidity and mortality. Symptoms as dyspnea, impaired exercise tolerance, cough and fatigue often lead to an impaired quality of life. Disease course of ILD in individual patients is currently difficult to predict, which makes close monitoring essential. Frequent outpatient clinic visits are needed to follow up on lung function and symptoms, initiate medication, and assess the effects and adverse effects of treatment [3]. These visits can be burdensome for patients with increasing symptoms. Moreover, travel distances are often long, as in many countries care for patients with these rare diseases is organized in expert centers. 

Box 1.

Box 1

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Hence, there has been increasing interesting in innovative (online) technologies to improve care for patients with ILD [4,5]. The internet of medical things (IoMT) is an umbrella term for the multiple layered technological infrastructure that enables the connection of software applications and medical devices with technological healthcare systems [6]. The IoMT enables healthcare professionals to monitor patients from a distance, but also provides patients possibilities for self-monitoring and contributes to inter-colleague communication.

Examples of IoMT for patients with ILD include teleconsultations, home monitoring of physical parameters, symptoms and quality of life, online pulmonary rehabilitation, and self-management programs, digital information provision, but also in-hospital applications such as artificial intelligence (AI) algorithms and online multidisciplinary team meetings. In this review, we will provide an overview of the different applications of the IoMT for patients with ILD (Fig. 1).

FIGURE 1.

FIGURE 1

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Overview of the different applications of the internet of medical things for patients with interstitial lung diseases. Remote applications: wearable technology, online home spirometry, online patient reported outcome measures, online patient portals, online rehabilitation. Outpatient clinic applications: home monitoring platforms, teleconsultations, digital information, social media platforms. In-hospital applications: radiomics, online multidisciplinary team meetings, electronic patient files, multiparameter patient monitor systems.

TELECONSULTATIONS

Teleconsultation refers to the delivery of healthcare services using information and communication technologies at a distance, such as electronic, telephone and video consultations [7,8]. Compared to consultations by telephone, an advantage of video consultation is the ability to see patients and their environment. This can help better assess their physical condition and it enables a more natural face-to-face interaction [8]. Many healthcare professionals specialized in ILD are positive towards the implementation of video consultations for follow up of patients, and believe video consultations can facilitate better access to care, especially for patients that live further away from ILD specialized centers [8]. During the coronavirus disease 2019 (COVID-19) pandemic, outpatient clinic visits have been replaced by video consultations in some centers worldwide [4,8,9▪]. Importantly, video consultations may not be a suitable option for all patients and in all phases of the disease, such as the diagnostic phase, during acute worsening of disease, or following medication changes [8,10,11]. Although the COVID-19 pandemic has boosted the use of video consultations for patients with ILD, broad implementation in daily clinical practice still needs optimization. Recently, a comment from the United States of America (USA) on the application of teleconsultations demonstrated the current legislative barriers. In some parts of the USA, patients have to be in the same state as where their treating healthcare professional holds a license. This hampers the use of teleconsultations across states and may result in situations where patients have to travel for several hours to have a video consultation with their ILD specialist [9▪]. The fact that these laws vary from state to state creates a barrier to achieving uniform and widespread implementation. Besides these legislative issues, technical aspects also play a role. Not all patients are able to conduct teleconsultation due to lack of internet access or compatible devices. Also, teleconsultation can impact the interaction between healthcare professional and patient. Some nuances of patient interaction, or unexpected issues may be attenuated during teleconsultations. Another downside of teleconsultation may be that healthcare professionals lack access to physical parameters, such as lung function and pulse oximetry.

ONLINE HOME MONITORING

Online home monitoring of physical parameters broadens the possibilities of remote care for patients with ILD, and can complement video consultations. The IoMT provides the opportunity to collect and monitor health data of patients with smart devices that are linked to secured online applications. Online home monitoring has the potential to be used throughout the disease course of patients with ILD, for example for close monitoring of disease behavior, guidance of pharmacological and nonpharmacologic treatment decisions, and early detection of disease progression [12▪▪].

Home spirometry

Home spirometry, using a hand-held spirometer, is the most frequently studied home monitoring application for patients with ILD, as forced vital capacity (FVC) is one of the main outcomes used to monitor disease course [13–19]. The first studies, in patients with IPF, used paper diaries to collect home spirometry results, which hampers use in clinical practice [18,20]. Moreover, some studies encountered technical problems, such as connection issues with the spirometers, and large variability of the data [21▪▪,22]. Many of these problems can be overcome by using the IoMT. The use of spirometers linked to an online application facilitates real-time access to lung function results and quality checks of the measurements (Fig. 2a). Studies have confirmed reliability and accuracy of online home spirometry, by comparing results with in-hospital values [13,23,24]. Ongoing trials investigate the role of home spirometry for early detection of acute exacerbations and prediction of disease behavior, and evaluation of response to treatment in different forms of ILD [25–27].

FIGURE 2.

FIGURE 2

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Example of an online home monitoring program for patients with interstitial lung diseases. (a) Online overview of lung function course over time. (b) Online overview of patient reported outcome measurements over time.

Since the COVID-19 pandemic, home spirometry has been increasingly used in daily clinical practice to ensure continuity of care [4,28]. This eventually leads to very large amounts of FVC data being collected, which makes it impossible to manually check all data real-time and provide immediate feedback to the patient. AI has been proposed as one of the solutions to help identify poor quality lung function measures, assist with the interpretation of the home spirometry results, and send automatically generated alerts to healthcare providers in case of relevant FVC decline, though this needs further study in the home setting [29▪▪,30].

Wearables

Hypoxemia is one of the main problems of patients with ILD, and associated with poor prognosis [31,32]. In current practice, the in-hospital 6-min walking test (6MWT) is a tool to assess functional exercise capacity of patients and hypoxemia on exertion. Based on the results, supplemental oxygen therapy may be indicated. Even though the 6MWT provides valuable information on the general health status of the patient, the functional exercise capacity measured with the 6MWT may not be representative for regular daily activity of patients at home. A possibly more representative way to monitor physical activity and hypoxemia during daily activity is by using wearable technology [33].

Wearables are any sort of electronic device that can be worn by the user, and collects data on physiological parameters such as activity, oxygen saturation, or environmental exposure using in-built sensors [34]. A pilot study in ILD showed that it is feasible to use wearable technology for monitoring oxygen saturation and activity at home. In patients not using long-term oxygen therapy, a high correlation was found between the percentage of time spent with an SPO2 below 90% measured with a portable oximeter at home versus in-hospital 6MWT [33]. Moreover, steps per day measured with a pedometer highly correlated with 6MWT. Another pilot study in patients with sarcoidosis showed that wearing an activity tracker was associated with improvement in exercise capacity and reduced fatigue, even without a personal coaching program [35]. Patients had access to their own results which may have stimulated patients to be more active. A study in IPF found a high correlation between daily physical activity and 6MWT, but they also encountered technical and analytical hurdles, such as data loss and high variability in steps per day [36]. The clinical significance of change in physical activity at home, long-term use and adherence has not yet been studied in ILD. Therefore wearable technology assessing activity has currently no role in clinical practice.

Besides patient specific characteristics, wearable technology can be used to collect real-time environmental data. Air pollution is thought to be associated with the development of ILD, but also with disease progression and higher mortality [37,38]. Two studies found a significant correlation between air pollution and acute exacerbation, and lower lung function in patients with IPF [39,40]. These studies were conducted by prospectively measuring air pollution in the residential area by using the geocode of the patient. However, in these studies patients were not tracked real-time. A study in sarcoidosis analyzed local weather, and found a specific seasonal distribution of peak incidence of sarcoidosis in the United States and Europe [41]. Wearable technology may provide the opportunity to more accurately assess impacts of environmental and seasonal effects on ILD.

Patient reported outcome measures

Patient reported outcome measurements (PROMs) are frequently used in the ILD research field to assess and monitor the impact of the disease on patients’ symptoms, quality of life, and functional capacity. The use of PROMs can also hold benefits in daily clinical practice. PROMs can assist with the shift towards more value-based healthcare by efficiently identifying problem areas, which may otherwise be neglected. This could help to better structure regular outpatient clinic visits [42]. In some hospitals, PROMs are included in the electronic patient record, but PROMs can also be integrated in more comprehensive online home monitoring programs alongside collection of physical parameters. Evidence shows that it is not needed to validate existing questionnaires if only minor modifications are made to the original measure, as the psychometric properties of the original PROM will still hold for the digital version [43]. Another benefit of digital PROMS are that results can be visualized over time, which helps interpretation and can stimulate patients’ self-management (Fig. 1b). Importantly, some PROMs are lengthy and may be difficult for patients to complete. Visual analogue scales (VAS) could be a good replacement for longer questionnaires. These short one-item questionnaires have shown good correlation with longer quality of life questionnaires in IPF, and can easily be completed online [44,45].

TELEREHABILITATION

In ILD, pulmonary rehabilitation is associated with improvement of exercise capacity, dyspnea and HRQoL [46]. Telerehabilitation is emerging as an alternative to improve access and adherence to pulmonary rehabilitation programs and has been investigated in several pilot studies in ILD [47]. The potential of telerehabilitation for different chronic lung disease is discussed elsewhere in this issue (Khor and Cox).

DIGITAL INFORMATION

An important function of the IoMT is providing medical information. Hospitals, patient associations, social media groups, and support groups, share disease-specific information and updates on developments in the field of ILD, using different online platforms. Digital information may help gain better insights in disease course, and enhance self-management. Nevertheless, finding reliable online resources can be difficult for patients, due to the extensive quantity and sometimes incorrect information available [48]. Unfortunately, there are currently no methods available for patients to identify online resources that provide good quality information [48]. Recently, two studies evaluated the quality of information available in YouTube videos and found that in the majority of the videos, the quality was not up to standards [49,50▪].

Another, more interactive, way of digital information dissemination are social media channels. Social media platforms can be used for peer support, information exchange, and raising awareness. Different patient organizations are active on social media platforms such as Twitter, Facebook, Instagram, and LinkedIn. These accounts are mainly focused on advocacy, education programs, peer support, and research funding [51,52]. Besides the traditional advantages of connecting patients, social media has also become base for healthcare professionals and researchers to share the latest developments in the ILD research field. However, social media use also has several downsides. Lack of tech savviness, misinformation, and patient privacy are mentioned as potential challenges of use of social media [52].

When it comes to processing digital information, not only patients, but also healthcare professionals struggle with the exorbitant amount of information available. Of note, the medical knowledge doubling time decreased from approximately 50 years in 1950 to several months nowadays [53]. This makes it no surprise that guidance on accurate disease-specific online resources has been identified as important need by ILD healthcare professionals [54]. Currently, different websites are available where ILD healthcare providers can get easy and free access to valuable information, such as pneumotox.com(drug-induced lung diseases) and HPlung.com(listing known exposures associated with hypersensitivity pneumonitis) [55,56]. The maintenance of these websites, which includes technical updates, quality checks and continuous provision of new information is an effortful task. Novel AI tools could possibly help be a solution for automatically identifying and updating high quality information.

IN-HOSPITAL APPLICATIONS OF INTERNET OF MEDICAL THINGS

The IoMT is not limited to remote care, but also concerns in-hospital processes, for example electronic patient records, continuous patient monitoring systems used in intensive care units, and automated systems that assist with inventory management [57]. These applications aim to make healthcare processes in general more efficient and improve patient outcomes.

Multidisciplinary team (MDT) meetings are currently the gold standard for diagnosis of ILD [3,58]. MDTs are sometimes hybrid or completely virtual, which enables discussion of patients between community sites and ILD expert centers. Since the COVID-19 pandemic virtual MDTs are more widely implemented [59]. In Japan, a secured nationwide cloud-based integrated database has been created, in which radiological, clinical and pathological data is collected to facilitate virtual MDTs. This cloud-based database was accessible to all healthcare professionals involved. Solutions like this can improve feasibility of multicenter MDTs as data do not have to be transferred between institutions [60▪▪].

Another field where the IoMT is emerging, is radiology in ILD. Radiomics is the quantitative analysis of imaging data, using automatic or semi-automatic software for the purpose of extracting information from images [61]. Machine learning algorithms have been able to identify ILD in cohorts with at-risk populations [62,63]. A recent study showed the ability of an AI algorithm to detect pulmonary fibrosis on chest radiography with a sensitivity and specificity superior to that of radiologists and pulmonologists [64]. In addition, AI technology can quantify the extent of lung fibrosis, define CT-patterns, and use this information for prediction of lung function decline and mortality [65,66]. To translate this to clinical practice, large CT databases are needed, which require collaboration between institutions and across borders. External validation studies are also needed to confirm these findings in real-world clinical settings [67].

FUTURE STEPS

Although the COVID-19 pandemic has led to an increase in use of technology in healthcare, including for patients with ILD, this has not yet led to widespread implementation of these applications, while both patient and healthcare providers acknowledge the additional value of digital care and home monitoring [28,68]. One of the main challenges for widespread implementation is the lack of reimbursement in many countries, as most funding opportunities for digital solutions during the pandemic were temporary [69]. However, there are some examples of structural reimbursement like in Germany, where some digital health applications are now reimbursed by the health insurance system [70].

Moreover, there are legislation issues that need to be addressed. The IoMT has the ability to link and interconnect healthcare professionals, and can support data sharing. Whereas data are traditionally stored on-site in hospitals, the IoMT facilitates storage of data in secured online clouds. This way it enables data sharing across regional, national, and international borders. Regulatory frameworks entailing cybersecurity, encryption protocols, and legal ownership of data, are required to expand the use of online clouds. This is especially needed for combining data from different digital platforms and for the integration of home monitoring data with in-hospital electronic patient records. In this way, large datasets, with integrated AI algorithms have the potential to optimize care processes for individual patients in the near future. Some applications of the IoMT in ILD are still in its infancy, and need further study in larger real-world cohorts to confirm and clinically validate promising results from pilot studies and translate these to daily clinical practice.

CONCLUSION

In the last decade, the technological developments have rapidly altered the ILD landscape. We expect that in the next 10 years, AI and other innovative technologies facilitated by the IoMT will further enhance individually targeted treatment for patients with ILD by interlinking and combining data from various sources.

Acknowledgements

None.

Financial support and sponsorship

G.N.: Financial grant from Boehringer Ingelheim paid to institution.

C.M.: Financial grant from Boehringer Ingelheim, Astra—Zeneca, Daiichi-Sankyo all paid to institution. Payments for presentations/lectures from Boehringer-Ingelheim and Hoffman—la Roche all paid to institution.

M.W.: Financial grant from Boehringer Ingelheim, Hoffman—La Roche, The Netherlands Organization for Health Research and Development, The Dutch Lung Foundation; The Dutch Pulmonary Fibrosis, all paid to institution.

Consulting fees from Bristol Myers Squibb, Boehringer Ingelheim, Galapagos, Galecto, Hoffman la Roche, Horizon therapeutics, Kinevant Sciences, Molecure, Nerre Therapeutics, Novartis, PureTech Health, and Respivant, all paid to institution.

Support for attending meeting from Boehringer Ingelheim, Hoffman la Roche, Galapagos. Participation on advisory board from Savara, Galapagos, all paid to institution.

Leadership role as Chair of the Idiopathic Interstitial Pneumonia group of the European Respiratory Society, Member of the board of the Netherlands Respiratory Society, Member of the scientific advisory board of the European Idiopathic Pulmonary Fibrosis and related disorders federation, Chair of the educational committee of the European Reference Network for rare Lung Diseases, Advisory board of the Dutch Lung fibrosis and Sarcoidosis patient associations.

Conflicts of interest

None related to the content of this manuscript.

REFERENCES AND RECOMMENDED READING

Papers of particular interest, published within the annual period of review, have been highlighted as:

  • ▪ of special interest

  • ▪▪ of outstanding interest

REFERENCES

  • 1.Wijsenbeek M, Suzuki A, Maher TM. Interstitial lung diseases. Lancet 2022; 400:769–786. [DOI] [PubMed] [Google Scholar]
  • 2.Wijsenbeek M, Cottin V. Spectrum of fibrotic lung diseases. N Engl J Med 2020; 383:958–968. [DOI] [PubMed] [Google Scholar]
  • 3.Raghu G, Remy-Jardin M, Richeldi L, et al. Idiopathic pulmonary fibrosis (an update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med 2022; 205:e18–e47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Nakshbandi G, Moor CC, Wijsenbeek MS. Home monitoring for patients with ILD and the COVID-19 pandemic. Lancet Respir Med 2020; 8:1172–1174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Di Bidino R, Rogliani P, Sebastiani A, et al. Road toward a new model of care for idiopathic pulmonary fibrosis in the Lazio region. Front Med 2022; 9:861076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Springer, Jude Hemanth D, Anitha J, Tsihrintzis GA. Internet of medical things: remote healthcare systems and applications (internet of things). 2021. [Google Scholar]
  • 7.Deldar K, Bahaadinbeigy K, Tara SM. Teleconsultation and clinical decision making: a systematic review. Acta Inform Med 2016; 24:286–292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Morais A, Bugalho A, Drummond M, et al. Teleconsultation in respiratory medicine – a position paper of the Portuguese Pulmonology Society. Pulmonology 2023; 29:65–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9▪.Raghu G, Mehrotra A. Licensure laws and other barriers to telemedicine and telehealth: an urgent need for reform. Lancet Respir Med 2023; 11:11–13. [DOI] [PMC free article] [PubMed] [Google Scholar]; Illustrative comment on the current legislative barriers for the use of video consultations in the USA.
  • 10.Wong AW, Fidler L, Marcoux V, et al. Practical considerations for the diagnosis and treatment of fibrotic interstitial lung disease during the coronavirus disease 2019 pandemic. Chest 2020; 158:1069–1078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Stanziola AA, Salzano A, D’Angelo R, et al. Idiopathic pulmonary fibrosis telemedicine management during COVID-19 outbreak. Open Medicine (Poland) 2022; 17:689–693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12▪▪.Wijsenbeek MS, Moor CC, Johannson KA, et al. Home monitoring in interstitial lung diseases. Lancet Respir Med 2023; 11:97–110. [DOI] [PubMed] [Google Scholar]; This review gives a comprehensive overview on all home monitoring technologies currently studied and/or available for patients with ILD.
  • 13.Moor CC, Mostard RLM, Grutters JC, et al. Home monitoring in patients with idiopathic pulmonary fibrosis: a randomized controlled trial. Am J Respir Crit Care Med 2020; 202:393–401. [DOI] [PubMed] [Google Scholar]
  • 14.Moor CC, Van Leuven SI, Wijsenbeek MS, et al. Feasibility of online home spirometry in systemic sclerosis-associated interstitial lung disease: a pilot study. Rheumatology 2021; 60:2467–2471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Moor CC, van Manen MJG, Tak NC, et al. Development and feasibility of an eHealth tool for idiopathic pulmonary fibrosis. Eur Respir J 2018; 51:1702508. [DOI] [PubMed] [Google Scholar]
  • 16.Moor CC, Wapenaar M, Miedema JR, et al. A home monitoring program including real-time wireless home spirometry in idiopathic pulmonary fibrosis: a pilot study on experiences and barriers. Respir Res 2018; 19:105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Edwards C, Costello E, Cassidy N, et al. Use of the patientMpower App with home-based spirometry to monitor the symptoms and impact of fibrotic lung conditions: longitudinal observational study. JMIR mHealth uHealth 2020; 8:e16158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Russell AM, Adamali H, Molyneaux PL, et al. Daily home spirometry: an effective tool for detecting progression in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 2016; 194:989–997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Johannson KA, Vittinghoff E, Morisset J, et al. Home monitoring improves endpoint efficiency in idiopathic pulmonary fibrosis. Eur Respir J 2017; 50:1602406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Broos CE, Wapenaar M, Looman CWN, et al. Daily home spirometry to detect early steroid treatment effects in newly treated pulmonary sarcoidosis. Eur Respir J 2018; 51:1702089. [DOI] [PubMed] [Google Scholar]
  • 21▪▪.Maher TM, Schiffman C, Kreuter M, et al. A review of the challenges, learnings and future directions of home handheld spirometry in interstitial lung disease. Respir Res 2022; 23:307. [DOI] [PMC free article] [PubMed] [Google Scholar]; This review describes the current technical and analytical hurdles in the use of home spirometry for patients with ILD.
  • 22.Wijsenbeek MS, Bendstrup E, Valenzuela C, et al. Disease behaviour during the peri-diagnostic period in patients with suspected interstitial lung disease: the STARLINER study. Adv Ther 2021; 38:4040–4056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Khan F, Howard L, Hearson G, et al. Clinical utility of home versus hospital spirometry in fibrotic interstitial lung disease: evaluation after INJUSTIS InterimAnalysis. Ann Am Thorac Soc 2022; 19:506–510. [DOI] [PubMed] [Google Scholar]
  • 24.Noth I, Cottin V, Chaudhuri N, et al. Home spirometry in patients with idiopathic pulmonary fibrosis: Data from the INMARK trial. Eur Respir J 2021; 58:2001518. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Kahlmann V, Janssen Bonás M, Moor CC, et al. Design of a randomized controlled trial to evaluate effectiveness of methotrexate versus prednisone as first-line treatment for pulmonary sarcoidosis: the PREDMETH study. BMC Pulm Med 2020; 20:271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Nakshbandi G, Moor CC, Antoniou K, et al. Study protocol of an international patient-led registry in patients with pulmonary fibrosis using online home monitoring: I-FILE. BMC Pulm Med 2023; 23:51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Early detection of acute exacerbation in patients with idiopathic lung fibrosis – a pilot study [updated 27-10-2021. Available at: https://clinicaltrials.gov/ct2/show/NCT03979430. [Google Scholar]
  • 28.Nakshbandi G, Moor CC, Johannson KA, et al. Worldwide experiences and opinions of healthcare providers on eHealth for patients with interstitial lung diseases in the COVID-19 era. ERJ Open Res 2021; 7:00405–2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29▪▪.Giri PC, Chowdhury AM, Bedoya A, et al. Application of machine learning in pulmonary function assessment where are we now and where are we going? Front Physiol 2021; 12:678540. [DOI] [PMC free article] [PubMed] [Google Scholar]; This article dives into the current use and future potential of artificial intelligence algorithms for evaluating in-hospital pulmonary function measurements.
  • 30.Topalovic M, Das N, Burgel PR, et al. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J 2019; 53:1801660. [DOI] [PubMed] [Google Scholar]
  • 31.Xaubet A, Ancochea J, Blanquer R, et al. Diagnosis and treatment of diffuse interstitial lung diseases. Arch Bronconeumol 2003; 39:580–600. [DOI] [PubMed] [Google Scholar]
  • 32.Blanco I, Villaquirán C, Luis Valera J, et al. Peak oxygen uptake during the six-minute walk test in diffuse interstitial lung disease and pulmonary hypertension. Arch Bronconeumol 2010; 46:122–128. [DOI] [PubMed] [Google Scholar]
  • 33.Cardenosa SC, Palomo M, Francesqui J, et al. Home oxygen monitoring in patients with interstitial lung disease. Ann Am Thorac Soc 2022; 19:493–497. [DOI] [PubMed] [Google Scholar]
  • 34.Ferguson T, Olds T, Curtis R, et al. Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. Lancet Digit Health 2022; 4:e615–e626. [DOI] [PubMed] [Google Scholar]
  • 35.Drent M, Elfferich M, Breedveld E, et al. Benefit of wearing an activity tracker in sarcoidosis. J Pers Med 2020; 10:97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Marcoux V, Wang M, Burgoyne SJ, et al. Mobile health monitoring in patients with idiopathic pulmonary fibrosis. Ann Am Thorac Soc 2019; 16:1327–1329. [DOI] [PubMed] [Google Scholar]
  • 37.Winterbottom CJ, Shah RJ, Patterson KC, et al. Exposure to ambient particulate matter is associated with accelerated functional decline in idiopathic pulmonary fibrosis. Chest 2018; 153:1221–1228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Harari S, Raghu G, Caminati A, et al. Fibrotic interstitial lung diseases and air pollution: a systematic literature review. Eur Respir Rev 2020; 29:200093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Johannson KA, Vittinghoff E, Lee K, et al. Acute exacerbation of idiopathic pulmonary fibrosis associated with air pollution exposure. Eur Respir J 2014; 43:1124–1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Johannson KA, Vittinghoff E, Morisset J, et al. Air pollution exposure is associated with lower lung function, but not changes in lung function, in patients with idiopathic pulmonary fibrosis. Chest 2018; 154:119–125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Ramos-Casals M, Kostov B, Brito-Zerón P, et al. How the frequency and phenotype of sarcoidosis is driven by environmental determinants. Lung 2019; 197:427–436. [DOI] [PubMed] [Google Scholar]
  • 42.Kalluri M, Luppi F, Vancheri A, et al. Patient-reported outcomes and patient-reported outcome measures in interstitial lung disease: where to go from here? Eur Respir Rev 2021; 30:210026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Coons SJ, Gwaltney CJ, Hays RD, et al. Recommendations on evidence needed to support measurement equivalence between electronic and paper-based patient-reported outcome (PRO) measures: ISPOR ePRO Good Research Practices Task Force report. Value Health 2009; 12:419–429. [DOI] [PubMed] [Google Scholar]
  • 44.Moor C, Mostard RLM, Grutters JC, et al. Patient-reported outcomes in idiopathic pulmonary fibrosis; validity and reliability of visual analogue scales. Eur Respir J 2021; 58: (Suppl 65): A3749. [Google Scholar]
  • 45.Scallan C, Strand L, Hayes J, et al. R-scale for pulmonary fibrosis: a simple, visual tool for the assessment of health-related quality of life. Eur Respir J 2022; 59:2100917. [DOI] [PubMed] [Google Scholar]
  • 46.Dowman L, Hill CJ, May A, et al. Pulmonary rehabilitation for interstitial lung disease. Cochrane Database Syst Rev 2021. CD006322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Cox NS, Oliveira CC, Lahham A, et al. Pulmonary rehabilitation referral and participation are commonly influenced by environment, knowledge, and beliefs about consequences: a systematic review using the Theoretical Domains Framework. J Physiother 2017; 63:84–93. [DOI] [PubMed] [Google Scholar]
  • 48.Fisher JH, O’Connor D, Flexman AM, et al. Accuracy and reliability of internet resources for information on idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 2016; 194:218–225. [DOI] [PubMed] [Google Scholar]
  • 49.Goobie GC, Guler SA, Johannson KA, et al. YouTube videos as a source of misinformation on idiopathic pulmonary fibrosis. Ann Am Thorac Soc 2019; 16:572–579. [DOI] [PubMed] [Google Scholar]
  • 50▪.Lashari BH, Chan V, Shoukat U, et al. YouTube as a source of patient education in idiopathic pulmonary fibrosis: a media content analysis. J Community Hosp Intern Med Perspect 2019; 9:98–102. [DOI] [PMC free article] [PubMed] [Google Scholar]; This article gives a comprehensive overview of the potential of social media, but also highlights challenges.
  • 51.Grewal JS, Kawano-Dourado L, Ryerson CJ. The opportunities and challenges of social media in interstitial lung disease: a viewpoint. Respir Res 2021; 22:247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Kochan A, Ong S, Guler S, et al. Social media content of idiopathic pulmonary fibrosis groups and pages on Facebook: cross-sectional analysis. JMIR Public Health Surveill 2021; 7:e24199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Densen P. Challenges and opportunities facing medical education. Trans Am Clin Climatol Assoc 2011; 122:48–58. [PMC free article] [PubMed] [Google Scholar]
  • 54.Alfaro TM, Wijsenbeek MS, Powell P, et al. Educational aspects of rare and orphan lung diseases. Respir Res 2021; 22:92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Barnes H, Lu J, Johannson KA. Development of a knowledge-sharing website for hypersensitivity pneumonitis exposures. ATS Sch 2022; 3:460–467. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56. Camus P. The drug-induced respiratory disease website. Available at: https://www.pneumotox.com/drug/index/. [Google Scholar]
  • 57.Al-Turjman F, Nawaz MH, Ulusar UD. Intelligence in the internet of medical things era: a systematic review of current and future trends. Comput Commun 2020; 150:644–660. [Google Scholar]
  • 58.Cottin V, Martinez FJ, Smith V, Walsh SLF. Multidisciplinary teams in the clinical care of fibrotic interstitial lung disease: current perspectives. Eur Respir Rev 2022; 31:220003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Tikellis G, Corte TJ, Teoh AKY, et al. Barriers and facilitators to best care for idiopathic pulmonary fibrosis in Australia. Respirology 2022; 27:76–84. [DOI] [PubMed] [Google Scholar]
  • 60▪▪.Fujisawa T, Mori K, Mikamo M, et al. Nationwide cloud-based integrated database of idiopathic interstitial pneumonias for multidisciplinary discussion. Eur Respir J 2019; 53:1802243. [DOI] [PMC free article] [PubMed] [Google Scholar]; This article shows the capability of new machine learn algorithms to identify fibrosis on HRCT.
  • 61.Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48:441–446. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Furukawa T, Oyama S, Yokota H, et al. A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases. Respirology 2022; 27:739–746. [DOI] [PubMed] [Google Scholar]
  • 63.Barnes H, Humphries SM, George PM, et al. Machine learning in radiology: the new frontier in interstitial lung diseases. Lancet Digit Health 2023; 5:e41–e50. [DOI] [PubMed] [Google Scholar]
  • 64.Nishikiori H, Kuronuma K, Hirota K, et al. Deep-learning algorithm to detect fibrosing interstitial lung disease on chest radiographs. Eur Respir J 2023; 61:2102269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Walsh SLF, Mackintosh JA, Calandriello L, et al. Deep learning-based outcome prediction in progressive fibrotic lung disease using high-resolution computed tomography. Am J Respir Crit Care Med 2022; 206:883–891. [DOI] [PubMed] [Google Scholar]
  • 66.Humphries SM, Mackintosh JA, Jo HE, et al. Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis. Respirology 2022; 27:1045–1053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Walsh SLF, Humphries SM, Wells AU, et al. Imaging research in fibrotic lung disease; applying deep learning to unsolved problems. Lancet Respir Med 2020; 8:1144–1153. [DOI] [PubMed] [Google Scholar]
  • 68.Nakshbandi G, Moor C, Aerts J, et al. eHealth in pulmonary fibrosis: an international survey among healthcare providers and patients. Eur Respir J 2020; 56: (Suppl 64): 788. [Google Scholar]
  • 69.Fahy N, Williams GA, Habicht T, et al. Use of digital health tools in Europe: Before, during and after COVID-19 [Internet]. Copenhagen (Denmark): European Observatory on Health Systems and Policies; 2021. [PubMed] [Google Scholar]
  • 70.Lantzsch H, Panteli D, Martino F, et al. Benefit assessment and reimbursement of digital health applications: concepts for setting up a new system for public coverage. Front Public Health 2022; 10:832870. [DOI] [PMC free article] [PubMed] [Google Scholar]

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