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Renewable sources of electricity generation are continuing to grow strongly around the world, with global capacity expected to more than double by 2030, according to the IEA’s latest medium-term forecast. Led by the rapid rise of solar PV, renewables’ expansion is taking place in a context of supply chain strains, grid integration challenges, financial pressures and policy shifts. 

Renewables 2025, the IEA’s main annual report on the sector, sees global renewable power capacity increasing by 4 600 gigawatts (GW) by 2030 – roughly the equivalent of adding China, the European Union and Japan’s total power generation capacity combined.

Solar PV will account for around 80% of the global increase in renewable power capacity over the next five years – driven by low costs and faster permitting timeframes – followed by wind, hydro, bioenergy and geothermal. Geothermal installations are on course to hit historic highs in key markets, including the United States, Japan, Indonesia and a host of emerging and developing economies. Rising grid integration challenges are renewing interest in pumped-storage hydropower, whose growth is expected to be almost 80% faster over the next five years compared with the previous five.

In emerging economies across Asia, the Middle East and Africa, cost competitiveness and stronger policy support are spurring faster growth of renewables, with many governments introducing new auction programmes and raising their targets. India is on course to become the second-largest renewables growth market globally, after China, and is expected to comfortably reach its ambitious target by 2030.

At the company level, confidence in renewables remains strong. Most major developers have either maintained or raised their 2030 deployment targets compared with last year, reflecting resilience and optimism in the sector. Offshore wind stands apart, however, with a weaker growth outlook – around a quarter lower than in last year’s report – resulting from policy changes in key markets, supply chain bottlenecks and rising costs.

“The growth in global renewable capacity in the coming years will be dominated by solar PV – but with wind, hydropower, bioenergy and geothermal all contributing, too,” said IEA Executive Director Fatih Birol. “Solar PV is on course to account for some 80% of the increase in the world’s renewable capacity over the next five years. In addition to growth in established markets, solar is set to surge in economies such as Saudi Arabia, Pakistan and several Southeast Asian countries. As renewables’ role in electricity systems rises in many countries, policy makers need to play close attention to supply chain security and grid integration challenges.”

The report’s outlook for global renewable capacity growth is revised downward slightly compared with last year, mainly due to policy changes in the United States and in China. The early phase-out of federal tax incentives along with other regulatory changes in the United States lowered our growth expectations for renewables in the US market by almost 50% compared with last year’s forecast. China’s shift from fixed tariffs to auctions is impacting project economics, resulting in a reduction in our forecast for renewables’ growth in the Chinese market.

These adjustments are partly offset by buoyancy in other regions – particularly India, Europe and most emerging and developing economies – where growth prospects have been revised upward due to ambitious new policies, expanded auction volumes, faster permitting and rising deployment of rooftop solar. Corporate purchase power agreements, utility contracts and merchant plants are also a major driver, together accounting for 30% of global renewable capacity expansion to 2030 – doubling their share compared with last year’s forecast.

Solar PV is expected to dominate renewables’ growth between now and 2030, remaining the lowest-cost option for new generation in most countries, while wind power, despite its near-term challenges, is still set for considerable expansion as supply bottlenecks ease and projects move forward, notably in China, Europe and India. Hydropower and other renewable technologies will continue to play important roles in supporting electricity systems and enhancing flexibility.

Global supply chains for solar PV and rare earth elements used in wind turbines remain heavily concentrated in China, underscoring ongoing risks to supply chain security. While new investment to diversify supply chains is taking place in countries around the world, concentration in China for key production segments is set to remain above 90% through 2030.

At the same time, the rapid rise of variable renewables is placing increasing pressure on electricity systems. Curtailment and negative price events are already appearing in more markets, signalling the need for urgent investment in grids, storage and flexible generation. Several countries are beginning to respond with new capacity and storage auctions, but much more will be needed to ensure that variable renewables are integrated in a cost-efficient and secure way.

The role of renewables in transport and heating is expected to rise in the coming years, but only slightly. In the transport sector, their share of energy use is forecast to increase from 4% today to 6% in 2030, driven mainly by renewable electricity for EVs in China and Europe, with biofuels adding growth in Brazil, Indonesia, India and other key markets. Renewables’ share of energy used globally to provide heat for buildings and industry is set to increase from 14% to 18% over the forecast period.

Introduction

With six biologic therapies currently approved for the treatment of moderate-to-severe asthma, there are ample opportunities for patients to switch between biologics to achieve asthma control.^1,2 While suboptimal response is the leading reason for switching respiratory biologics,^3–5 little is known about the characteristics of patients who switch. Identifying these features could help anticipate poor responders. We sought to evaluate switching patterns and patient characteristics associated with switching events between biologics. We hypothesized that patients with higher exacerbation rates at baseline and eosinophil count would be more likely to switch between biologics due to suboptimal response over follow-up.

Methods

Study Population

We analyzed data from adult patients with asthma (International Classification of Diseases (ICD)- 9^th revision: 493.x or 10^th revision: J45) from the Mass General Brigham health system who were on maintenance therapy with inhaled steroids and a long-acting beta agonist and initiated a respiratory biologic between January 2014, shortly before the first anti-IL5 was approved, and June 2023 and evaluated if they switched to another biologic on or before our study end date of June 2024. All patients were seen by an allergist and/or pulmonologist in the MGB Severe Asthma Clinics. We included omalizumab, mepolizumab, benralizumab, and dupilumab. Only one patient initiated reslizumab and so it was not included. We did not include tezepelumab due to its relatively newer introduction into the market and its approval for type-2-low asthma which would both limit switching opportunities. We also excluded patients with other chronic lung diseases, except chronic obstructive pulmonary disease (COPD). To focus on asthma-related use, given that a comorbidity could influence preference for a biologic and switching, we excluded patients with alternate indications for these biologics. These included chronic spontaneous urticaria, atopic dermatitis as well as anyone whose dupilumab was initiated by a dermatologist given the likelihood that this was initiated for the treatment of atopic dermatitis, another leading indication for dupilumab, chronic rhinosinusitis with nasal polyps (CRSwNP) since three of these biologics, but not all, are approved for CRSwNP treatment, eosinophilic granulomatosis with polyangiitis, hypereosinophilic syndrome, eosinophilic esophagitis, and prurigo nodularis. The resulting cohort of patients represent a group of patients with moderate-to-severe asthma who had initiated the respiratory biologic specifically for their asthma and with potentially similar opportunities of being switched to another biologic due to an asthma-related factor. We followed every patient from the date of initiation of their first biologic to the end of the follow up period (June 2024) and evaluated if they switched to an alternate biologic during follow-up.

Chart Reviews/Data Collection

Two authors (AB and AA) reviewed the charts of patients who switched to identify the reasons for switching. For each switching event, we categorized the reason for switch as: 1) for effectiveness (eg suboptimal improvement in asthma symptoms, exacerbations, or lung function), or 2) not for effectiveness (eg due to insurance for payment-related reasons or for patient-specific reasons such as a woman stopping therapy because she found out she was pregnant). In the event that the reason for switching was: 1) not singular- multiple reasons were recorded, 2) unclear- the chart was re-reviewed and the final reason for switching was resolved by consensus, or 3) unreported- the switch was categorized as “not for effectiveness”. We focused on the switches for effectiveness in our analyses.

Statistical Analyses

We presented continuous variables as mean with standard deviation (SD) or median with interquartile range (IQR) and categorical variables as proportions. Using the Wilcoxon rank-sum test for continuous variables and the Chi-square test for categorical variables, we compared the baseline characteristics of switchers at the initiation of their first biologic to the characteristics of non-switchers, and among switchers, those who switched once were compared to those who switched multiple times. Given the preliminary evidence that most patients switched due to effectiveness,^3,4 we evaluated the difference in the reduction in exacerbation rates (incidence rates) using Poisson regression in the one year following the initiation of the first biologic for patients who switched and those who did not switch, adjusting for baseline exacerbations. An asthma exacerbation was defined as an emergency room visit or hospitalization for asthma, wheezing, or bronchospasm, or a new prescription for ≥3 days of oral corticosteroids.⁶ Given that patients who switched over follow up might have differed in systematic ways at baseline from patients who did not switch, we conducted inverse probability weighted analyses (IPTW) which sought to balance switchers and non-switchers on the first biologic initiated and their baseline covariates which included age, sex, race, BMI, smoking status, insurance type, presence of COPD, and LAMA use. These weights were then applied to a model predicting switching with predictors (baseline exacerbations, baseline (most recent) BEC, and maximum BEC in the 3 years prior to therapy initiation). This final model also included the follow-up length from biologic initiation given that the longer the follow-up, the higher the opportunities for switching. A similar model was constructed to compare those who switched once to those who switched two or more times. This retrospective study was approved by the Mass General Brigham IRB (#2021P003227) with a waiver of patient consent. All procedures adhered to the Declaration of Helsinki and data were deidentified and reported in aggregate to assure confidentiality. All analyses were performed using R version 4.1.0 (2021, The R Foundation for Statistical Computing).

Results

We identified 2502 eligible patients. We excluded 835 patients with alternate biologic indications and 56 patients who initiated tezepelumab or reslizumab. Our final study population included 1611 individuals (Figure E1). Omalizumab made up 47% (N=752) of these initiations, dupilumab, 33% (526), mepolizumab, 16% (254), and benralizumab, 5% (79). Baseline characteristics varied by biologic. Patients initiating omalizumab were younger (mean age: 43.6 years). Those on mepolizumab and benralizumab had the highest median blood eosinophil counts (BEC) of 370 and 271 cells/μL respectively. Eighteen percent of mepolizumab users and 11% of dupilumab users had COPD (Table E1).

Fourteen percent (230 patients; 300 switches; Figure E2) switched: 81% switched once; 19% switched ≥2 times. Suboptimal effectiveness accounted for the majority (70%; 162) of these switches. We were unable to identify the reason for one patient who switched from omalizumab. Among those switching due to suboptimal response: multiple demographic factors were associated with switching including higher BMI (30.5 vs 29.3 kg/m², p=0.008), race (p=0.03), and concomitant COPD (18.9% vs 8.6%, p<0.001) (Table 1). Patients who switched had worse asthma control at baseline (2.4 vs 1.5 in year pre-biologic, p<0.001). Higher exacerbation rates at initiation of the first biologic were associated with lower 12-month exacerbation reduction and a higher likelihood of switching (Figure 1a and Table E2). There was a stepwise trend: the worse the baseline exacerbation rate before starting the first biologic, the higher the likelihood of switching multiple times over follow-up. In the year prior to initiation of their first biologic, single biologic users (N=1381) had about 1.4 exacerbations on average compared to 2.3 in those who switched once (N=186), 2.8 in those who switched twice (N=34), and 3.4 in those who switched three times or more (N=10). In weighted analyses, baseline exacerbations (odds ratio, OR: 1.03; 95% Confidence Intervals, CI: 1.01–1.04, p<0.001) and baseline BEC (OR, 2.03 for each 50 cells/mcl increase; 95% CI: 1.20–3.43, p=0.002) remained significant, while the maximum BEC in 3 years was not (OR, 0.91 per 50 cells/mcl increase; 95% CI: 0.80–1.01, p=0.71) (Table 2).

Table 1 Baseline Characteristic Comparing Patients Who Did Not Switch to Patients Who Switched (Used Two or More Biologics)

Table 2 Inverse Probability Treatment-Weighted Analyses Evaluating the Impact of Baseline Exacerbations and Eosinophil Counts on Switching

Figure 1 (a) Exacerbation rates at baseline and in the year following the first biologic stratified by switch status. (b) Exacerbation rates at initiation of second biologic and in the year following the second biologic stratified by switch status. ***p<0.001.

Among switchers, those who used three or more biologics (switched ≥2 times) were similar on multiple clinical characteristics to those who switched only once (Table E3). However, they had more exacerbations at first-biologic start (2.9 vs 2.3 in one-time switchers, p=0.01) and had a higher historical maximum BEC (662 vs 334 cells/μL, p=0.02), though baseline BEC was similar (229 vs 244 cells/μL, p=0.60). When evaluating the improvement after initiation of the second biologic, patients who used only two biologics experienced a 30% reduction in exacerbations in the year following initiation of the second biologic which was statistically significant (rate ratio, RR, 0.70 [0.65–0.75]; Figure 1b and Table E2). In weighted analyses comparing those who switched once to those who switched two or more times, baseline exacerbations remained a significant predictor of switching (OR: 1.02; 95% CI: 1.00–1.05, p=0.04). Neither baseline BEC or maximum BEC in 3 years were significantly different between those who switched once or two or more times (Table 2).

Only 12 of the 526 patients who initiated dupilumab switched due to suboptimal response. For mepolizumab, switchers had lower baseline BEC (235 vs 410 cells/mcL, p= 0.01) and lower maximum BEC in the 3 years prior (441 vs 740 cells/mcL, p= 0.03). This relationship was reversed for omalizumab with switchers having higher BEC (315 vs 143 cells/mcL, p<0.001) (Table E4).

Discussion

We evaluated the switching events in about 1600 patients with moderate-to-severe asthma and no alternate indication for biologic therapy. Fourteen percent of patients switched over the course of follow up and most of these switches were due to suboptimal effectiveness of the initial biologic. About 20% of patients who switched initially switched again a second time. Less than 2% of patients who switched used five biologics over the course of follow up. Patients who switched had higher exacerbation rates in the year prior to initiation of the first biologic. Patients who switched multiple times had higher maximum eosinophil counts in the year and three years prior to initiation of the first biologic compared to those who switched once though recent eosinophil count was similar between both groups. In weighted analyses, the burden of baseline exacerbations remained the top predictor of switching (3% higher risk of switching for each additional exacerbation) and was associated with the frequency of switching (2%). However, while every 50 cell/mcl increase in the most recent eosinophil count at biologic initiation doubled the likelihood that the patient would switch over follow-up, neither the most recent eosinophil count nor the maximum BEC in 3 years differentiated those who switched once vs those who switched two or more times.

Consistent with prior evidence, this study showed that most biologic switches were due to suboptimal effectiveness.^4,5 Switching patterns may serve as proxies for biologic effectiveness. Patients who switched had a higher exacerbation burden and had higher eosinophil counts at baseline. Prior studies have shown that greater asthma severity correlates with higher switching rates.^5,7 Indeed, these patient characteristics might suggest eligibility for alternate biologics. For instance, five of the six currently approved biologics are effective in eosinophilic asthma. Thus, patients with higher eosinophil counts might benefit from these. However, our observing that even at initiation of the first biologic, patients who went on to receive multiple biologics had more baseline exacerbations than their counterparts with fewer exacerbations might suggest we are starting these therapies too late in some patients. In a recent study by Pérez-de-Llano et al, lower baseline exacerbation rates was associated with achieving asthma remission, defined as 0 exacerbations per year, no long-term oral corticosteroid, and percent predicted forced expiratory volume in one second ⩾ 80%, suggesting biologics have a window of opportunity when initiation is likely to yield the best benefits.^6,8 Based on our findings, we propose that patients with at-risk features, such as current smoking, concomitant COPD, or BMI ≥30 kg/m² and experiencing ≥1 asthma exacerbation within a 12-month period despite maximal medication therapy should be considered for initiation of biologic therapy. All patients with ≥2 exacerbations within a 12-month period or ≥1 exacerbation within any 6-month rolling window, should be strongly considered for biologic therapy. However, the need to initiate therapy promptly needs to be balanced with the costs of these therapies. Patients who switched twice had significant improvements in their exacerbation rates in the year following initiation of the second biologic suggesting indeed that switching therapy led to improvements in exacerbations.

This study in a large cohort adds to prior evidence that we might be missing an important window of opportunity if we start therapy only after patients have had a higher burden of exacerbations, but our results should be interpreted with caution. While we reviewed the charts of all patients who switched and were able to confirm they initiated the prior biologic and switched to another, we did not review the charts of those who did not switch and some of these patients may not have initiated the first biologic. However, given that we limited to patients seen in the severe asthma clinics staffed by allergists and pulmonologists, this is likely to be infrequent. Secondly, although we tried to limit to latter years when two or more biologics were on the market, patients whose biologics were later in the study period would have a higher likelihood of switching given the availability of more biologics on the market. However, our finding that patients with higher exacerbation rates are likely to switch between multiple biologics corroborates prior studies and is less likely to change. Thirdly, this is a single health system study and most of our patients were privately insured. Given that patients with public insurance are less likely to initiate biologics, our findings may not be directly translatable to other populations.⁹ Lastly, the numbers of those who switched three or more times was minimal limiting our evaluation of this group. Likewise, we did not have sufficient numbers to appropriately evaluate switching rates across biologics. Thus, we have made within-biologic comparisons.

In summary, we found that patients with higher exacerbation burden and eosinophil counts at initiation of their first biologic were more likely to switch between multiple biologics due to suboptimal control of their asthma. These findings highlight the importance of carefully selecting the first biologic and starting at the most opportune time.

Funding

Dr. Akenroye is supported by the NIH (R00MD015767 and R01HL173055) and by the Brigham and Women’s Hospital Minority Faculty Career Development Award.

Disclosure

The authors have no conflicts of interest relevant to this article.

References

1. Akenroye A, McCormack M, Keet C. Severe asthma in the US population and eligibility for mAb therapy. J Allergy Clin Immunol. 2020;145(4):1295–1297.e6. doi:10.1016/j.jaci.2019.12.009

2. Albers FC, Mullerova H, Gunsoy NB, et al. Biologic treatment eligibility for real-world patients with severe asthma: the IDEAL study. J Asthma. 2018;55(2):152–160. doi:10.1080/02770903.2017.1322611

3. Akenroye A, Zhou G, Jackson JW, Segal J, Alexander GC, Singh S. Incidence of adverse events prompting switching between biologics among adults with asthma: a retrospective cohort study. Allergy. 2022;78:1116–1119. doi:10.1111/all.15564

4. Akenroye A, Ryan T, McGill A, Zhou G, Shier J, Segal J. Switch patterns in a cohort of individuals with asthma who received omalizumab or mepolizumab therapy. J Allergy Clin Immunol. 2022. doi:10.1016/j.jaip.2022.10.047

5. Menzies-Gow AN, McBrien C, Unni B, et al. Real world biologic use and switch patterns in severe asthma: data from the international severe asthma registry and the US CHRONICLE Study. J Asthma Allergy. 2022;15:63–78. doi:10.2147/jaa.s328653

6. Perez-de-Llano L, Scelo G, Tran TN, et al. Exploring definitions and predictors of severe asthma clinical remission after biologic treatment in adults. Am J Respir Crit Care Med. 2024;210(7):869–880. doi:10.1164/rccm.202311-2192OC

7. Scioscia G, Nolasco S, Campisi R, et al. Switching biological therapies in severe asthma. Int J Mol Sci. 2023;24(11):9563. doi:10.3390/ijms24119563

8. Pavord ID. Remission in the world of severe asthma. Am J Respir Crit Care Med. 2024;210(7):855–857. doi:10.1164/rccm.202405-0894ED

9. Akenroye AT, Heyward J, Keet C, Alexander GC. Lower use of biologics for the treatment of asthma in publicly insured individuals. J Allergy Clin Immunol. 2021. doi:10.1016/j.jaip.2021.01.039

Introduction

In a 2019 international study of healthcare carbon footprints, the healthcare sector is hold responsible for 1.6 Gt of CO2 or 4.4% of the global total in 2014 (35.7 Gt).¹ Meanwhile, the Lancet Climate Change Commission declared climate change “the biggest global health threat of the 21^st century” due to its adverse health impacts.² To break this cycle, it is essential to focus on sustainability initiatives within healthcare.³ One crucial action in this regard is the reduction of waste generated during healthcare delivery.^4–6 In recent years, hospitals have predominantly relied on disposable medical products for sterile applications, especially during the outbreak of COVID-19.^7–10 The operating room (OR) is a significant area of interest, as it substantially contributes to a hospital’s footprint through its high levels of waste production, energy consumption, and emissions of anesthetic gases.¹¹ Disposable medical textiles, such as surgical gowns, towels, and drapes, contribute significantly to operating room waste.¹² The circular economy emphasizes maximizing product utility and designing for longevity.¹³ Despite the existence of reusable alternatives, disposables remain dominant, highlighting the need to optimize reusable medical textiles for extended use. Herweyers¹⁴ identified design strategies for long-term reuse by analyzing motivators and barriers from a product, context, and user (PCU) perspective.¹⁵ As product durability, available infrastructure, decision-making routines, and user characteristics (egoccupation and task) all influence the adoption process and market penetration of reusable medical textiles in the healthcare sector.

Attributes That Enhance Behavior Intention and Adoption

The transition to sustainable healthcare practices can be understood through Rogers’ Diffusion of Innovation (DOI) Theory.^16,17 This theory explains how innovations spread through social systems over time, with individuals following an innovation-decision process. Rogers identifies five key attributes that influence 49% to 87% of adoption variance in the persuasion stage: (i) relative advantage over current practices (RA), (ii) compatibility with existing values and needs (C), (iii) simplicity of use (S), (iv) trialability (ease of experimentation) (T), and (v) visibility of results (V).¹⁸ More recent research indicates that Rogers’ five innovation attributes significantly impact the adoption of sustainability innovations and evidence-based practices in healthcare.^19–21 For instance, Yap et al¹² researched expectations about product performance and found that healthcare professionals (HCPs) hesitated to adopt reusable surgical gowns due to concerns about their effectiveness. An important performance indicator for innovative healthcare products is perceived quality. Perceived product quality is influenced by quality cues, which provide information before use.²² In the context of healthcare, supporting products such as hospital gowns should be easy to use and not hindering the complex medical interventions, they should preferable be compatible with the routines HCP is trained for.²³

Trialability, or the ability to test an innovation, enhances adoption likelihood.²⁴ Jilani et al²⁵ demonstrated that trialability significantly increases users’ intention to adopt mHealth apps. Similarly, visibility—especially when respected clinicians advocate for an innovation—fosters peer discussions and accelerates adoption.¹⁷ Opinion leaders, as defined by Rogers,¹⁶ play a crucial role in influencing their peers’ willingness to adopt new practices.²⁶

Aakko and Niinimäki’s²² contextual map of quality perception highlights how product assessment evolves. Initial expectations (quality cues), shaped by intrinsic and extrinsic cues, are refined through user experience.^27,28 While their model assumes post-purchase assessment, HCPs lack direct purchasing authority. Pilot testing enables HCPs to engage with products firsthand, facilitating informed evaluation and alignment with practical needs.

Indeed, although HCPs are part of a broader social system, each undergoes an individual decision-making process regarding innovation adoption. In healthcare organizations, formal procurement decisions are often complex²⁹ and ideally involve input from multiple stakeholders, including HCPs.³⁰ Their acceptance and demand can drive institutional shifts toward reusable alternatives, making HCP’s perceptions and willingness to reuse critical factors in the successful adoption of sustainable practices.³¹ Moreover, hospital administrators and purchasing departments increasingly require evidence-based insights to inform cost-benefit analyses.³² The development of policy and governmental guidelines typically follows the establishment of robust empirical evidence. Consequently, the findings of this study may serve as a foundation for shaping legislative initiatives that promote sustainable healthcare practices.³³

The Product Perspective for Sterile Surgical Gowns

A surgical gown’s function serves a dual purpose: protecting the patient by maintaining a sterile barrier to prevent contamination, as well as protecting the wearer from exposure to blood, bodily fluids, and potential infections.^34–36 According to the European Medical Device legislation³⁷ and the harmonized standard EN13795 for surgical clothing and drapes,³⁸ sterile surgical gowns are Medical Devices³⁹ that are qualified to protect the patient. The protective function of surgical gowns remains unchanged, whether they are disposable or reusable. However, their intrinsic product characteristics vary depending on the brand, product type, and model available on the market. These characteristics—such as wearing comfort, ease of use, trust in protection, and sustainability—play a significant role in a hospital’s purchasing decisions and can be influenced by design.^12,35,40

Comparative studies of reusable and disposable sterile surgical gowns highlight the environmental advantages of reusable options, showing a potential reduction of 66% in greenhouse gas emissions and 84% in solid waste generation.⁴¹ A survey-based study with 80 OR staff members in gynecology compared the comfort and usability of both gown types. The results showed that over 79% of participants rated reusable gowns as equal to or better than disposable ones in terms of ventilation, fit, functionality (standing, sitting, walking), barrier performance, and ease of use.⁴² Additionally, a review by Bolten et al⁴³ assessing the carbon footprint of OR infection prevention found no evidence that reusable and disposable instruments, gowns, and drapes differ in their effectiveness in preventing surgical site infections. Furthermore, McQuerry et al⁴⁰ suggest that reusable gowns may offer enhanced protection and significant cost savings due to their superior durability and sustainability compared to disposable alternatives.

The Context Perspective for Hospital Purchasing Processes

The context of a hospital, and particularly the OR, is highly specialized. Strict hygiene standards are essential, but the ability to act swiftly and without obstruction is equally crucial. Reusable surgical gowns, like disposables, are available in individual or set packaging and must remain readily accessible. After use, however, reusable gowns are collected for laundering, this can either be on-site or by an external provider.⁴⁴ The laundering process involves washing, rinsing and drying with detergents and rinse agents to ensure proper decontamination.⁴¹ Steam sterilization is the most commonly recommended method by manufacturers, typically requiring exposure to 121–135°C for 3–30 minutes, followed by a drying cycle of 1–30 minutes. Additionally, the hospital context necessitates specific procurement procedures, relying on the input of diverse stakeholders.^30,35,40 To ensure an informed decision, a hospital purchasing manager performs thorough analyses before implementing a new practice:^32,45 safety regulations, financial considerations, logistic feasibility, market availability, and ensured delivery are important (extrinsic) context factors.

The User Perspective for Healthcare Professionals

OR personnel, including surgeons and nurses, are key informants on gown suitability. Ideally, products are tested, feedback is gathered, and HCPs advocate for items that enhance performance and efficiency. Yap et al¹² identified that most perioperative staff believes reusable surgical gowns are environmentally friendly. Nevertheless, ambivalence towards transitioning to reusable gowns stemmed from uncertainty in reusable textiles’ safety profile, cost savings, and environmental benefits. These perceptions may prevent successful actual implementation of reusable surgical gowns. One factor that may counter distrust and uncertainty is the observable benefits and successful adoption of a reusable gowns by relevant others. Therefore, identifying innovators and early adopters in the introduction of reusable gowns is key to promoting wider adoption among the early majority, late majority, and laggards.⁴⁶ Personal characteristics, such as occupation, gender, and age, influence HCPs’ willingness to adopt sustainable practices. Research shows that women and younger individuals are more inclined toward environmentally friendly choices.^47,48 Additionally, occupation (doctor vs nurse) and years of employment significantly impact gown preferences.³⁰ Those already engaged in pro-environmental behavior are also more likely to adopt sustainable alternatives.⁴⁹

Research Gap

Research underscores the advantages of reusable gowns over disposables in comfort, usability, protection, and sustainability. Despite HCPs’ interest in sustainability, awareness and experience with reusable alternatives remain limited. Hospitals also require evidence-based guidance for procurement and policy decisions. This study applies the PCU perspective to understand HCPs’ adoption intention of reusable surgical gowns in a high-risk environment, addressing the understudied factors influencing adoption. Additionally, knowledge gaps persist regarding preferred product characteristics, and the role of innovative users and early adopters in influencing hospital decision-making remains unexplored.

Aim & Research Questions

Drawing on the DOI theory¹⁶ and the contextual framework of quality perception,^22,28 this study aims to explore and describe the factors that influence HCPs’ adoption of reusable surgical gowns. The innovation attributes proposed by the DOI theory (RA, C, S, T, and V) are integrated with perceived quality components to examine how specific gown characteristics and contextual hospital factors shape adoption intention. These theoretical constructs are mapped conceptually in Figure 1.

Figure 1 Conceptual model integrating DOI attributes and perceived quality components in the context of reusable surgical gown adoption. Abbreviations: HCPs, healthcare professionals; RA, relative advantage; C, compatibility; S, simplicity; T, trialability; V, visibility.

First, we assess the expectations of HCPs who have not used reusable gowns (non-users). Second, we investigate the impact of trialability and visibility by analyzing feedback from participants in a pilot test with reusable gowns. This research is guided by three key questions in line with the PCU perspective.

RQ1 (product): What do HCPs expect of reusable surgical gowns and how do they evaluate the use of reusable surgical gowns in the operating room?

• What is the importance of surgical gown characteristics in terms of comfort, usability, trust in protection and sustainability according to HCP?
• What are HCP’s initial expectations of reusable surgical gown performance in terms of comfort, usability, trust in protection and sustainability for non-users?
• How do these expectations relate to disposable surgical gown performance expectations ?
• How do the initial expectations of non-users align with the actual evaluations of reusable gowns pilot test participants?

RQ2 (context): Which context factors (financial, logistic, availability, delivery and patient opinion) have an influence on the adoption process of reusable surgical gowns according to HCP?

RQ3 (user): Which user profiles can be considered as possible early adopters of reusable gowns?

• Are there differences in expectations towards reusable gowns characteristics between HCP with different user characteristics (occupation, gender, age, ecological concern, work experience)?
• Does adoption intention of the reusable surgical gown differ between HCP that tried out the gown compared to HCP that did not try out the gown?
• Are there differences in adoption intention of reusable surgical gowns between HCP with different user characteristics (occupation, age, gender, ecological concern and prior experiences)?
• How does expected gown performance (comfort, usability, trust in protection and sustainability) relate HCP’s (un)willingness to use reusable gowns?

Materials and Methods

Figure 2 shows how we plan to achieve insights on the HCPs’ PCU-perceptions of reusable surgical gowns. As not to bias insights in expectation and evaluation, eg through priming mechanisms or awareness creation, the present study utilizes a between subject design.⁵⁰

Figure 2 Overview of the methods and measures used in the between subject design of this study. Abbreviation: HCP, healthcare professionals in group A or B.

One group (HCP A) is surveyed on their expectations (expectation survey) regarding the characteristics of reusable surgical gowns, while another group (HCP B) evaluates (evaluation survey) the same characteristics after participating in a pilot test. Both groups indicate their willingness to adopt reusable gowns in the future. Given the anticipated larger sample size of HCP A, this group will also respond to additional questions assessing their concerns related to the context factors of the procurement process of reusable surgical gowns.

Methods

The expectation survey was distributed, using the snowball sampling technique, to healthcare professionals with experience in working in the operating room in Belgium (HCP A). The researcher contacted OR supervisors by telephone. If the supervisors agreed to participate in the research, they forwarded an e-mail-invitation with hyperlink to their personnel. 22 Supervisors and 2 professional OR associations (for OR nurses and medical specialists) agreed to distribute the survey link via e-mail. The mail contained an online survey link (programmed in Qualtrics, Provo, UT, USA) that was available from the 6^th of October 2022 to the 20^th of December 2022. The evaluation survey was distributed to a smaller sample, only participants in a pilot test with reusable surgical gowns (HCP B) were allow to participate. The pilot tests took place in two Flemish university hospitals (Antwerp and Ghent). Each test took a time span of one week (5 working days) and consisted of one day with the hospital’s regular disposable surgical gowns and four days observation with reusable surgical gowns. The time span was determined in consideration of practical feasibility and availability of the gown supplier. To maximize the variety of disciplines in our sample, each day of observation, one operating room was randomly selected for the pilot test. Two weeks prior to the test, surgeons and nurses were informed about the study’s objectives and provided consent for both observation and survey participation. During the pilot tests, a researcher was present in the operating room to ensure proper use and collection of the reusable gowns. Staff members could revert to disposable gowns if they experienced discomfort. The pilot test encompassed a variety of surgical procedures, including open, laparoscopic, endoscopic, external, minor, and robot-assisted surgeries. Participants represented a broad range of disciplines, such as abdominal surgery, gynaecology, orthopaedics and traumatology, urology, plastic surgery, thoracic and vascular surgery, neurosurgery, ear, nose, and throat, paediatric surgery, oral and maxillofacial surgery, transplant and hepatobiliary surgery, ophthalmology, and cardiac surgery. This wide representation limited the feasibility of discipline-specific statistical analyses. Following the pilot test, participants (HCP B) received an online survey link to evaluate the reusable gowns. The survey remained open from November 21 to December 20, 2022. This study received ethical approval from the Ethics Committee for Social Sciences & Humanities at the University of Antwerp.

Measures

The survey was developed based on the PCU perspectives outlined by Herweyers¹⁴ and the five innovation attributes from the DOI theory of Rogers.¹⁶ While previous studies have applied DOI constructs in healthcare,^51–53 none specifically address the operational context of the OR through surveying. To accommodate this time-sensitive and high-pressure environment, single-item measures were used to minimize respondent burden and optimize completion.⁵⁴ This approach is considered appropriate for applied research where the aim is to explore associations rather than develop a psychometric scale.^55,56 The items were designed to assess how gown characteristics, hospital context, and user demographics relate to healthcare professionals’ intentions to adopt reusable surgical gowns.

The willingness to reuse a surgical gown (dependent variable): to assess the behavioral intention⁵⁷ of HCP A and B, respondents had to indicate their willingness to reuse a surgical gown in both contexts. Responses were captured using a close-ended question with three options: yes, no, or not sure. To deepen our understanding of these responses, we applied methodological triangulation by integrating qualitative data, allowing for richer interpretation of the underlying motivations.⁵⁸ If respondents indicated “no” a branch question would appear on the next survey-page to ask for a motivation why they were not inclined to use a reusable alternative of this product.

Willingness to reuse other medical textile products: Similarly, respondents in both conditions were asked to indicate their willingness to use a reusable alternative for six types of medical textile products: sterile surgical drape, surgical hairnet, surgical mouth mask, warmth jacket (worn by OR personnel), warmth blanket (for patients), and an absorbent pad.

Gown characteristics (product): The study explores four gown characteristics: Usability refers to handling ease, including how easy it is to unpack it, how easy it is to do on and off, how easy it is to dispose it in the correct bin, wearing comfort includes thermal comfort and fit, trust in the protective properties of reusable surgical gowns, and perceived sustainability of these gowns. To assess the gown characteristics, we build on scales used to predict adoption intention and user acceptance.^59,60 Respondents (HCP A) rated the importance of each characteristic item in the expectation survey, using a 5-point Likert scale (1 = not important at all; 2 = not important; 3 = neutral; 4 = important; 5 = very important).^61,62 They also scored characteristics (1 = not good at all; 2 = not good; 3 = neutral; 4 = good; 5 = very good) of their current disposable gowns and characteristic expectations for reusable gown. In the evaluation survey, respondents (HCP B) rated the same characteristics, only for the reusable gowns they had worn during the pilot test.

Factors of influence (context): Likewise, respondents in the expectation survey (HCP A) rated the importance of contextual elements for the adoption intention: financial cost, logistic feasibility, market availability, delivery assurance, and patient opinion on a 5-point Likert scale.

User characteristics: As for user profiles, close-ended questions were included in both surveys: gender, age, occupational group, work experience and prior experience with reusable gowns. If respondents indicated to have prior experience with reusable gowns, a branch question would appear on the next survey-page to ask for a brief context description and their opinion about the use of reusable surgical gowns. For HPC A, additional information was recorded: the frequency of gown use and environmental concern was measured by using the NEP scale consisting of fifteen items, five point Likert scales with a Cronbach’s alpha of 0.742.⁶³

Statistical Analysis

The analyses were performed using IBM SPSS Statistics (Version 28.0; IBM Corp., Endicott, New York, United States). Crosstabs with Chi², paired and independent sample T-tests, and ANOVAS with Bonferroni T-test were used to differentiate significant differences between user groups. P-values less than .01 were considered statistically significant. For the open questions in the survey (unwillingness and prior experience) a qualitative analysis was conducted. Answers were listed in an Excel file (Version 16.0, Microsoft Corp., Redmond, Washington, United States) and open and axial coding was done to identify meaningful themes in participant responses.

Results

For both surveys, the data were cleaned, and incomplete responses were removed from the dataset. The expectation survey (HCP A) yielded 146 fully completed responses, achieving a 63% completion rate.

For the evaluation survey, 124 participants from the pilot test (HCP B) were invited, resulting in 68 completed responses, a 55% response rate and an 87% completion rate. Among these pilot test participants, 13 had worn a disposable gown, while 55 had worn a reusable gown during the test. Only the evaluation responses of the 55 reusable gown wearers will be used in this result section.

Sample Description

Table 1 gives an overview of the respondents’ characteristics. Occupational groups include nurses (scrub nurses, circulating nurses, anesthesia nurses), doctors (surgeons and anesthetists), and others (OR manager, OR coordinator, OR logistics). Of the doctors in HCP A, 35 are surgeon (of which 9 specialists in training), 12 are anesthetist (of which 4 specialists in training) and one is a trainee doctor. Accordingly, the doctors in HCP B, 21 are surgeons (of which 9 specialists in training) and 5 are anesthetists (of which 4 specialists in training). No significant differences between the samples’ demographics were observed.

Table 1 User Characteristics of the Survey Respondents

Regarding ecological concern (n = 146), the majority of respondents (97.9%) scored above 3.5 on the NEP scale, indicating a high level of ecological awareness.

Gown usage frequency among respondents from the expectation survey (HCP A) who wear sterile surgical gowns in the OR (n = 110) varied: 8.2% wore them once a week or less, 21.8% wore them 2–3 days per week, 23.6% wore them 4 days per week, and the majority (40.9%) used them 5 days per week. Only 5.5% reported wearing gowns more than 5 days per week.

On days when respondents wore sterile surgical gowns, they remained in sterile attire for an average of 5.94 hours. They used an average of 3.76 gowns per day (SD = 2.09), with usage ranging from 1 to 10 gowns per day. This corresponds to an average wear time of approximately 1 hour and 35 minutes per gown.

Regarding gown size, most respondents (46.4%) wore extra-large gowns (150 cm length), followed by 35.5% wearing large (130 cm length). A smaller proportion (10.0%) required extra-extra-large (170 cm length), while 3.6% reported wearing other sizes, and 4.5% were unsure of their gown size.

To better understand prior experience with reusable surgical gowns, two open-ended questions were included, addressing the context of use and personal opinions. Responses from both HCP A and HCP B were combined. Most reported experiences with reusable gowns dated back to the past (ranging from the 1980s to 2015), with many respondents indicating that their use occurred during foreign missions or internships in developing countries (eg South Africa, Tanzania, Uganda). Two main types of gowns were frequently mentioned: cotton reusable gowns and parachute-like plastic gowns. While many respondents reported positive experiences regarding comfort and usability, common concerns included issues with permeability, excessive warmth, discomfort, improper sizing, and gown weight.

Test Pilot

During the eight-day pilot test, a total of 89 reusable sterile surgical gowns (standard performance, size medium, 150 cm in length) were used. Based on the average weight of disposable gowns typically used in the hospital, the pilot prevented approximately 16.4 kilograms of waste, excluding packaging.

Expected and Evaluated Gown Characteristics (RQ 1)

HCP A rated the importance of the four gown characteristics in the expectation survey. On a 5-point Likert scale, trust in protective properties was deemed the most important, with an average score of 4.68 (SD: 0.61). This was followed by wearing comfort (4.58; SD: 0.68) and usability (4.44; SD: 0.64). Although sustainability was rated the least important, it still received an average score of 4.10 (SD: 0.79), indicating that respondents also consider sustainability an important characteristic of the gown.

In the expectation survey, HCP A evaluated the characteristics of disposable gowns currently in use (n = 110) alongside their anticipated performance ratings for reusable gowns across the same four characteristics (Table 2). A paired-sample t-test revealed a statistically significant difference in ratings between the disposable gown characteristics and the expectation ratings for reusable gowns. Disposable gowns were rated higher in terms of trust in protective properties, wearing comfort, and usability. In contrast, reusable surgical gowns were anticipated to offer superior sustainability performance.

Table 2 Score for Disposable Surgical Gown Characteristics and Expectation for Reusable Gown Characteristics for HCP A (n = 110). Characteristics are Arranged from Most Important (Top) to Less Important (Bottom)

Looking into user characteristics, an independent samples t-test revealed a significant difference (p = 0.01) in expected gown performance between female and male HCP A for trust in protection. Female respondents have lower expectations (mean: 3.33; SD: 1.038) for trust in the protective properties of a reusable surgical gown than male (mean: 3.85; SD: 0.989). No significant differences in reusable gown expectations were found for occupation, age, and work experience.

In the evaluation survey, participants from the pilot test with reusable gowns (HCP B) rated the characteristics of a reusable gown based on their recent experience during the test. Table 3 presents both the expectation and evaluation scores. On average, scores for trust in protection, usability, and sustainability were higher in the evaluation survey (HCP B) than those reported in the expectation survey (HCP A). However, an independent-samples t-test indicates that only the score for trust in protection is significantly higher. Due to the difference in sample size, a power analysis (α = 0.05) was calculated for the independent samples t-test. Only for trust in protection a power of 0.8 was obtained.

Table 3 Expected (HCP A) vs Evaluated (HCP B) Gown Characteristics for Reusable Gowns. Characteristics are Arranged from Most Important (Top) to Less Important (Bottom)

Context Factors of Influence (RQ 2)

In the expectation survey, HCP A (n = 146) was presented with five factors that could potentially influence their own and, more critically, the hospital’s decision on whether to adopt reusable surgical gowns. These factors, ranked by importance from highest to lowest, were: ensured delivery (mean = 4.32, sd = 0.885), logistical feasibility (mean = 4.07, sd = 0.876), market availability (mean = 4.00, sd = 0.752), financial cost (mean = 3.90, sd = 0.881), and patient opinion regarding reusable gowns (mean = 2.29, sd = 1.037). No significant differences were found for gender, age, occupation, work experience, ecological concerns or prior experience.

Willingness to Reuse Medical Textile Products (RQ 3)

Willingness to use reusable alternatives was assessed for both HCP A and HCP B. In the expectation survey, HCP A showed the highest willingness for reusable warmth jackets, followed by blankets, surgical caps, sterile surgical gowns and drapes. Respondents were more uncertain about absorbent pads and reusable mouth masks. In the evaluation survey, HCP B participants who had worn reusable surgical gowns showed a similar order in willingness to reuse. Table 4 displays willingness to reuse for both groups, with a Chi²-test indicating that HCP B was significantly more willing (p < 0.01) to use a reusable surgical gown than HCP A.

Table 4 Frequency and Percentages of Willingness to Use Reusable Alternative in the Expectation Survey (HCP A) and Participants That Wore a Reusable Gown During the Pilot Test (HCPB)

To assess differences in intention to reuse medical textile products (yes vs no) between doctors and nurses, the sample (HCP A) was limited to these occupations. Crosstab analysis (Table 5) shows that doctors are significantly more willing (p < 0.01) to use reusable warmth blankets. No significant differences were found based on gender, age, work experience, ecological concerns, or prior experience with reusable gowns.

Table 5 Crosstabs of Willingness to Reuse (Yes vs No) and Occupation (Doctor vs Nurse) for HCP A

To assess differences in intention to reuse a surgical gown (yes vs no) between high and low expectations, the sample (HCP A) was split in two groups. Expectations are scored on a 5-point Likert scale, scores 1 to 3 are classified as low and 4 to 5 are classified as high expectations. Crosstab analysis (Table 6) shows that for all gown characteristics, HCP with high expectations are significantly more willing (p < 0.01) to use reusable surgical gowns.

Table 6 Crosstabs of Willingness to Reuse (Yes vs No) and Expected Scores (Low (1–3) vs High (4–5)) for HCP A

Discussion

Healthcare organizations play a crucial role in society but significantly contribute to global carbon emissions. Reusable surgical gowns can help reduce operating room waste and emissions. This study examines their adoption process through two surveys and a pilot test. The research explores factors that may influence the adoption of reusable surgical gowns, drawing upon the DOI’s attributes to describe how gown characteristics (product), hospital factors (context) and HCPs’ personal factors (user) shape adoption behavior. Specifically, the pilot test was designed to allow HCPs to experience reusable surgical gowns firsthand, simulating the innovation attributes trialalibity and visibility.

Product

In this study, relative advantage as an adoption precedent does not emerge for reusable gowns. HCP’s expectations regarding trust in protection, comfort, and usability were significantly lower compared to the current disposable alternative. Consequently, differences in willingness to adopt reusable gowns between HCP A and HCP B are more likely driven by user experiences during the pilot test rather than perceived relative advantages. This low perceived relative advantages for reusable gown could be related to strong repetition-based expectations as the use of surgical gowns is a vital part of the operation routine. According to a study by Verplanken and Wood⁶⁴ habit formation complicates adoption as it reduces the sensitivity to incentives in the regular performance environment, limit the user’s search towards behavioral alternatives and tend to have a confirmation bias about their habitual routine. All these elements maintain the exiting behavior pattern.

Consistent with Lin & Bautista,²⁴ a pilot test or trialability with reusable gowns enables firsthand experience, potentially fostering greater adoption of reusable gowns. While this study is not a before and after comparison, the findings align with the DOI theory, suggesting that trialability facilitates adoption. Through the acquired experience, HCP were able to accurately assess the reusable gowns, leading to a more positive evaluation, while the adoption intention of the other products remained unchanged, HCP B displayed significant higher willingness to use reusable gowns. HCP B also reported a significantly higher evaluation of trust in the protection of a reusable gown compared to what was expected by HCP A.

Context

HCPs prioritize ensured delivery, logistical feasibility, market availability, and financial cost as key purchasing factors. In contrast, patient opinions on reusable surgical gowns are considered relatively unimportant. This difference likely stems from the fact that supply chain and cost factors directly impact HCPs’ daily operations,^65,66 whereas patient preferences do not influence the purchasing and reprocessing process.

This aligns with the innovation attributes of complexity and compatibility, where ease of integration into existing workflows is crucial.^20,67 While patient perceptions contribute to how society evaluates hospital quality and performance, HCPs argue that patients should trust healthcare organizations to ensure high-quality care in equipment-related decisions. Conversely, patient involvement is valued for treatment related decisions.⁶⁸ However, it has widely been discussed in literature that HCPs and patients have different perceptions of care quality.^69,70

In the context of a healthcare organization, HCPs’ preferences can drive institutional shifts, but the formal decision power is embedded in procedures as defined by hospital management. In the benefit of fluent adoption of innovations, hospital managers should encourage their institutions to pursue significantly more rapid-cycle tests of change across various healthcare processes, with the aim of making continuous improvement the norm.^26,29

User

Responses to the ecological concern statements reveal that the sample (HCP A) is highly ecologically aware. According to the DOI theory, the compatibility of sustainable alternatives with their values may lead to greater openness to pilot testing.

In our sample (HCP A), doctors were generally more willing to reuse medical textile products, however, only for the patient blanket this difference was significant. This suggests that doctors may be considered opinion leaders in the adoption process. Hospital management and professional associations are recommended to take a more proactive role in identifying and supporting existing social networks, as well as the opinion leaders within them to facilitate the spread of innovations.²⁶ For example, providing protected time for staff at all levels to network, explore for ideas, and test new ways of working. Additionally, male respondents had significantly higher expectations for trust in the protection of reusable gowns than female respondents. This confidence may also position males more likely to be early adopters, as trust can reduce uncertainty and encourage willingness to reuse. This finding may interfere with previous research in other contexts that found female and younger individuals are more inclined toward environmentally friendly choices.^47,48

Over 50% of respondents (HCP A and B) had prior experience with reusable surgical gowns. However, the majority of this experience dates back to before 2000 or during foreign stays, often in development countries. Unfortunately, often involving low-quality gowns (eg cotton gowns), creating the misconception that reusable gowns are old-fashioned or inferior. Consistent with literature,⁴² reusable gowns are perceived less advantageous, particularly in terms of comfort. To counter this outdated perception, modern, reusable gowns and their reprocessing facilities should be actively showcased, for example, though product testing in the working environment. Additionally, when split into two groups, HCP A who had higher expectations regarding gown characteristics, also demonstrated greater willingness to reuse gowns. Highlighting the importance of raising expectations, for example, through targeted communication campaigns or testimonials. Messages describing what other people are doing (norm-based interventions) are widely used and shown to be effective to encourage certain pro-environmental behavior.⁷¹

Adoption of Other Medical Textile Products

Respondents (HCP A) showed the highest willingness towards using reusable alternatives for textile products such as jackets, blankets, and surgical caps. Familiarity with these types of products commonly washed and reused in the home environment may explain high willingness. The willingness to reuse sterile textile products, such as gowns and drapes, was notably lower. Hesitancy towards changes involving sterile products probably stem for the associated infection and safety risks. Sterility is typically perceived as ensured by using single-use items,⁷² and HCP are trained to avoid risks; when in doubt, they take a new (single-use) product. An even smaller willingness is observed for products like absorbent pads and mouth masks. Concerns about hygiene during the washing process were particularly noted for absorbent pads. HCP express concerns about the hygiene of products that have absorbed bodily fluids like blood and urine during the washing process. Additionally, the reluctance to use reusable mouth masks stems from the perception that ensuring personal hygiene is challenging due to the mask covering the mouth and nose, where breath, saliva, and makeup residues may accumulate.⁷³ A personal item would be preferred. In the sample with participants of the pilot test (HCP B), a similar order emerged, only the willingness to adopt reusable gowns was significantly higher.

Practical Implications

To support the transition towards reusables in healthcare, three practical implications arise from the current study. First, start the adoption process with comprehensive pilot tests. The outdated perception of reusable gowns as inferior highlights the need for more rigorous pilot testing. Willingness to use sterile surgical gowns improved significantly after pilot testing, while willingness to use other reusable alternatives that were not tested, remained the same. Suggesting that tests are helpful to ensure confidence in specific product performance and quality across various reusable items. For example, a pilot test was used to evaluate reusable stainless steel vaginal specula, resulting in overall positive attitude of clinicians⁷⁴ and in another study, computer simulations of process change ideas to improve patient-flow, accelerated adoption, primarily by enabling experimentation and demonstrating trialability.⁵³ Secondly, foster trust in protection properties of reusable products: participants who had participated the pilot test rated their trust in protection significantly higher than those without participation. Doubts related to safety perception of reusable medical textiles are a known barrier.³⁰ Female respondents, however, reported lower trust expectations compared to males. In a literature review,⁴⁷ the authors conclude that males indeed tend to have better knowledge about green issues than females. To bridge this knowledge gap, targeted educational campaigns addressing these concerns could help increase adoption.⁷⁵ Additionally, healthcare education should consider to include safe use of reusables. Lastly, tailored engagement strategies: respondents with better expectations, also reported higher adoption willingness. Campaigns to encourage adoption should highlight reusable products as safe and qualitative alternative for disposables. They can be focused on female HCP as they indicated to have lower expectations and nurses who showed lower willingness to use reusable alternatives. Badawy et al⁷⁶ propose educational and engagement messages to be conveyed via platforms such as social media to inspire HCPs and the broader public. For example in the form of videos, testimonials, and demonstrations to bridge knowledge gaps and align reusable alternatives with HCP’s existing values and needs.

Limitations and Future Research

This study has following limitations: (i) The expectation survey for HCP A used snowball sampling, which may have led to reduced chance of participation, and a possible ecologically biased sample. (ii) The study relies on self-reported data, which can be inaccurate or biased. (iii) For the purpose of this exploratory study and the time-restricted target group of OR-staff, single-item measures can be methodologically appropriate and scientifically defensible.⁵⁴ Nevertheless, further validation would be valuable. For example, a test-retest reliability assessment could be conducted, particularly since the user beliefs examined in this study are expected to remain relatively stable over time.⁷⁷ Additionally, developing a validated single-item scale could enhance content and construct validity, as well as improve predictive power.⁵⁶ (iv) Due to practical constraints, a before-and-after survey was not feasible, a between-subjects comparison was conducted between HCP A and HCP B. The unequal sample sizes reflect the real-world limitations of engaging medical staff, whose time and availability are limited. While the study’s exploratory and descriptive nature allowed for valuable observations, the results cannot be directly attributed to participation in the pilot. Measuring perception changes is necessary to connect pilot test participation with changes in reusable product assessments. We suspect HCP B’s responses reflect recent gown experience, while HCP A’s responses likely rely on past experiences or shared information.

Future research should employ a within-subjects design to assess the impact of pilot tests on reusable products, particularly for hesitant HCPs. This involves surveying staff expectations before and after a pilot test, using an anonymous linking ID for direct comparison. Furthermore, respondents who expressed uncertainty about reusables represent a target group requiring additional attention. Based on the study’s findings, this group likely includes nurses and female HCPs. Additionally, research should support pilot programs to increase familiarity and trialability, with proper test protocols and information dissemination. Further studies are needed to optimize reusable products from a technical perspective, focusing on cleaning and robustness, design attractiveness, and additional functionalities like smart textiles for communication or monitoring. In the healthcare sector, patients are evolving into “clients”, heightening the need to understand patient opinions on the environmental impacts of healthcare delivery and the use of reusable products.

Conclusion

Our study, involving two surveys and a pilot test with reusable surgical gowns, highlights the importance of trialability and visibility in influencing healthcare professionals’ (HCPs) adoption intentions. Participants in the pilot test had a significant higher willingness to use reusable gowns, with 87% of HCPs willing to adopt after the test. Male respondents and doctors appeared more inclined in their responses, showing higher confidence in the gowns’ protective properties and adoption intentions, which may suggest their potential role as opinion leaders, while female respondents and nurses required additional convincing. Opinion leaders can drive the diffusion by advocating for reusable products. The findings point to the possible value of bottom-up readiness strategies and indicate that targeted information and educational insights may be explored to address trust-related concerns and enhance perceptions of reusable gowns as safe and high-quality alternatives. Bottom-up readiness among HCPs can trigger the procurement process, while collaboration with local companies or Work Integration Social Enterprises (WISE) can support the integration and reprocessing of reusable gowns, ultimately improving user satisfaction and driving adoption.

Abbreviations

HCP, Healthcare professional; OR, Operating room; DOI, Diffusion of Innovation; PCU, Product, context, and user; RQ, Research question.

Data Sharing Statement

All data relevant to the analysis are included in the article. The datasets generated during and/or analyzed during the current study are available upon reasonable request to the corresponding author.

Ethical Approval and Information Consent

This study received ethical clearance from the Ethics Committee for Social Sciences & Humanities of the University of Antwerp (SHW_2022_27_1) on October 4, 2022. The participants were informed about the study and written informed consent was obtained before participation in the survey.

Acknowledgments

The authors would like to thank the University hospitals of Antwerp and Ghent for hosting the study, and Cleanlease Medical (Cleanlease, Koudekerk aan den Rijn, Netherlands) for supplying and transporting the reusable gowns for the test pilot. We also extend our gratitude to the respondents for their participation and taking the time to share their insights and experiences. During the preparation of this work, the authors used ChatGPT-4, in order to improve the readability of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was financially supported by the University of Antwerp, Bijzonder Onderzoeksfonds [44104], Belgium and the Flanders Innovation & Entrepreneurship, TETRA funds, Belgium [HBC.2021.1025].

Disclosure

The authors report no conflicts of interest in this work.

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Introduction

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide,¹ with an estimated global prevalence of approximately 10.3% and about 3 million deaths attributed to it annually.^2,3 In China, the prevalence of COPD ranges from 8.4% to 11.8%.^4,5 COPD impairs daily functioning and increases medical expenditures owing to acute exacerbations, imposing a significant disease burden on countries worldwide.^6,7

Pulmonary rehabilitation (PR) can alleviate symptoms of dyspnea in patients with COPD, enhance exercise capacity, and improve quality of life and emotional well-being.^8,9 It has become one of the cost-effective strategies for comprehensive COPD management.¹⁰ However, the widespread application of traditional PR in China remains limited.¹¹ A major challenge is low compliance among patients undergoing long PR treatment cycles, which require at least 6–8 weeks to be effective,¹² particularly during the global COVID-19 pandemic. Factors contributing to low compliance include geographical location, cultural environment, economic status, transportation conditions, and symptom severity.^8,13 The Global Initiative for Chronic Obstructive Lung Disease 2023 guidelines highlighted remotely monitored home-based PR as a strategy for comprehensive COPD management.¹⁴ A multicenter clinical study found that patients participating in remotely monitored PR demonstrated better adherence compared with those in conventional programs,¹⁵ suggesting that this approach may enhance patient compliance.

A previous study found that providing patients with COPD with health education on exercise methods, health regimens, and nutrition, along with PR-related knowledge before initiating PR treatment, significantly improved the completion rate of PR (58.7% vs 75%, p < 0.001).¹⁶ Although patients exhibit varying behavioral choices, no single factor fully explains the behavioral patterns they adopt.¹⁷

Social cognitive theory (SCT), proposed by psychologist Albert Bandura, emphasizes the dynamic interaction between individual behavior, cognition, and environment. By integrating cognitive driving factors such as self-efficacy and outcome expectations with environmental facilitating factors, SCT provides a solid theoretical framework for health promotion.^18,19 Although previous studies have confirmed the sustainability and effectiveness of behavioral interventions based on SCT in improving physical activity levels among COPD patients,^20,21 moreover, this approach provides a framework to explain behavior change and identify barriers and facilitators in home-based activity.²² However, the integration of this theory with PR programs remains insufficient.²³ This highlights a knowledge gap in the field. Specifically, the application of behavior change theory to analyze factors influencing remote home-based PR and to promote its completion rate has not been sufficiently explored.

This study aims to identify specific factors, based on SCT, that affect adherence to remotely monitored home-based PR and to construct a behavioral predictive model for evaluating PR adherence. Furthermore, it seeks to determine effective intervention strategies to improve the completion rate of remotely monitored home-based PR, thereby enhancing the effectiveness of PR treatment for patients with COPD.

Materials and Methods

Study Design and Participants

This cross-sectional survey study aimed to evaluate the correlation between adherence behavior and specific factors in patients with stable COPD eligible for a 12-week remotely monitored home-based PR program, based on SCT. Patients with moderate-to-severe COPD in the stable stage were recruited from May 2021 to November 2023 from the outpatient departments of Respiratory and Critical Care Medicine and Rehabilitation Medicine across three teaching hospitals in Nanjing: The First Affiliated Hospital of Nanjing Medical University, the Geriatric Hospital of Nanjing Medical University, and the Affiliated BenQ Hospital of Nanjing Medical University. Stable COPD is defined as COPD not in an active exacerbation.²⁴ A total of 83 patients met the inclusion and exclusion criteria.

Sample Size Calculation

Based on previous research and preliminary trial results,²⁵ a significance level of α = 0.05 and a power of 1-β = 0.2 were chosen. We conducted a sample size calculation based on one ROC curve power calculation. Using the power.roc.test function from the pROC package in R, assuming an AUC of 0.80, a two-sided significance level of 0.05, and a power of 0.80, the required minimum sample size was estimated to be 26 participants.

Inclusion and Exclusion Criteria

Inclusion criteria: (1) Aged 40–75 years (male or female); (2) Diagnosed according to the Global Initiative for Chronic Obstructive Lung Disease 2021 criteria and in a clinically stable phase, with no respiratory tract infections or acute COPD exacerbations in the past four weeks;²⁶ (3) Be able to complete pulmonary function tests and classified as having moderate-to-severe COPD; (4) Be capable of conducting home-based PR training under remote supervision; (5) Be able to use a smartphone application; (6) Had not participated in other clinical trials in the past six months; (7) Did not engage in routine daily exercise, such as Baduanjin, Tai Chi, or other aerobic exercises; (8) Informed consent and voluntary participation.

Exclusion criteria: (1) Congestive heart failure with New York Heart Association class III or higher; resting heart rate > 100 beats/min; (2) Forced expiratory volume in one second (FEV₁)> 80% predicted or < 25% predicted; (3) Uncontrolled chronic diseases, such as hypertension (resting blood pressure ≥ 160/100 mmHg) or diabetes (random blood glucose > 16.7 mmol/L or glycosylated hemoglobin > 7.0%); (4) Advanced cancer or terminal illness; (5) Unstable ischemic heart disease, left heart failure, or myocardial infarction not stably controlled within six months prior to enrollment, as well as acute coronary syndrome or a history of percutaneous coronary intervention or coronary artery bypass grafting within the past three months; (6) Joint, peripheral vascular, or neurological disorders impairing basic motor ability; (7) Mental or cognitive impairments; (8) Females who were breastfeeding, pregnant, planning to become pregnant during the study period, or of childbearing potential without effective contraceptive methods; (9) Concurrent severe liver or kidney disease, with severe liver disease defined as cirrhosis, portal hypertension, and variceal bleeding, and severe kidney disease including dialysis and kidney transplantation.

Participants adhered to the interventions as depicted in Figure 1.

Figure 1 Research flow diagram.

Ethics

This study was registered with the Chinese Clinical Trial Registry (No. ChiCTR2100042700) at http://www.chictr.org.cn/showproj.aspx?proj=120859. It was approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (No. 2021-SR-024), the Geriatric Hospital of Nanjing Medical University (No. 2021–010), and the Affiliated BenQ Hospital of Nanjing Medical University (No. 2021-KL011).

PR Intervention

All patients with COPD received remotely monitored home-based PR treatment combined with health education. Participants followed a Baduanjin exercise video on a smartphone equipped with the remote rehabilitation application (Sukang R+ Health App, v.5.1.40.0; Chengdu Shangyi Information Technology Co., Ltd., Chengdu, China) while wearing a heart rate monitor (RecoveryPlus H1; Chengdu Shangyi Information Technology Co., Ltd). The PR regimen required a minimum of one hour of cumulative exercise per day, three days per week, for a total of 12 weeks. After completing the treatment, patients were grouped based on their attendance rate. Those with a completion rate of less than 70% were classified into the low completion (LC) group, whereas those with a completion rate of 70% or higher were classified into the high completion (HC) group.²⁷

Outcome Measures

A cross-sectional survey was conducted using one-on-one questionnaires after the completion of the 12-week PR treatment program. Based on SCT, the survey aimed to assess the willingness of patients with COPD to undergo PR treatment. It included various variables, such as demographic characteristics, clinical features, and psychological attributes (Figure 2).¹⁸ The specific evaluation indicators were the Short Form Health Survey (SF-36), Hospital Anxiety and Depression Scale (HADS), Pulmonary Rehabilitation Adapted Index of Self-Efficacy (PRAISE), Outcome Expectations for Exercise Scale (OEE), Montreal Cognitive Assessment (MoCA), and Visual Analog Scale (VAS). The conceptual model diagram is presented in Figure 2.

Figure 2 Model concept diagram.

SF-36

SF-36 is a concise, self-administered questionnaire that assesses eight dimensions of health: physical functioning (PF), role limitations due to physical health (RPH), role limitations due to emotional problems (REP), energy/fatigue (EF), mental health (MH), social functioning (SF), bodily pain, and general health. It also includes a single-item scale for health transition. Each domain is scored on a scale from 0 to 100, with higher scores indicating better health-related quality of life.²⁸

HADS

HADS is a widely used psychological tool designed to detect anxiety and depression in patients within medical settings. It consists of two seven-item subscales—one for anxiety and one for depression—totaling 14 items. Each subscale is scored from 0 to 21, with higher scores indicating more severe symptoms.²⁹

PRAISE

PRAISE is a 15-item questionnaire that measures confidence in adapting to the challenges of PR. Scores range from 15 to 60, with higher scores indicating greater self-efficacy.³⁰

OEE

OEE is a psychological tool used to assess expectations about the outcomes of engaging in regular physical activity. The version used in this study includes 13 items, each scored on a Likert scale, with higher scores indicating stronger outcome expectations.³¹

MoCA

MoCA is a widely used screening tool for mild cognitive impairment. It assesses multiple cognitive domains, including visuospatial and executive functions, memory, language, visual construction skills, abstract thinking, calculation, and orientation. The total score is 30, with a score ≥ 26 considered normal. For individuals with 12 years of education or less, one additional point is added, with the maximum score remaining 30. Scores of 23–25 suggest possible mild cognitive impairment, whereas scores of 19–22 accompanied by difficulties in daily activities typically indicate progression from mild cognitive impairment to dementia. Scores ≤ 18 indicate dementia.³²

VAS

VAS is a psychological measurement tool commonly used to assess pain intensity. It is a simple, unidimensional instrument consisting of a horizontal line, typically 10 cm long, with verbal descriptions at both ends. In this study, it was used to measure willingness to participate in PR. The left end represents “no willingness”, whereas the right end signifies “strong willingness”. Patients mark the point on the line that best reflects their current level of willingness. The distance from the left end to the mark is measured and serves as a quantitative indicator of willingness.^33,34

Statistical Analysis

Statistical analysis was performed using SPSS 26.0 software (IBM Corp). All quantitative variables were tested for normality using the Shapiro–Wilk test. Data with a normal distribution are presented as mean ± standard deviation and analyzed by t–test for intergroup comparisons, while non-normally distributed data are expressed as median (minimum-maximum) and analyzed using Mann–Whitney U–test. Qualitative and ordinal data were presented as relative numbers and analyzed using the chi-square (χ²) test. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the discriminatory power of key indicators for the completion of remote PR treatment. An AUC closer to 1 indicated higher discriminatory performance. Specifically, an AUC > 0.85 signified excellent discrimination, an AUC between 0.70 and 0.85 indicated moderate discrimination, an AUC between 0.50 and 0.70 suggested low discrimination, an AUC of 0.50 implied no discriminative ability, and an AUC < 0.50 indicated indicates poor discrimination (worse than random chance). Indicators with moderate or higher discrimination were selected as candidate predictors. A binary logistic regression method was then used to construct a predictive model by combining candidate predictors, thereby establishing an optimal model. Mediation effect analysis was conducted to evaluate the relationship between variables. A p-value < 0.05 was considered statistically significant.

Results

A total of 83 patients were included in the trial. At 12 weeks, 12 patients declined to sign the survey consent forms and were excluded from the study. Ultimately, all 71 participants completed the survey by the end of the remote PR program, representing 86% of the original 83 patients enrolled in PR. Among them, 44 were in the HC group, and 27 were in the LC group. No significant differences in demographic characteristics were observed between the two groups (all p > 0.05). These characteristics included age, sex, education level, monthly income, smoking index, body mass index, exercise habits, and family history of COPD.

Disease and Psychological Characteristics of Patients

After treatment, no statistically significant differences were observed between the two groups in HADS and SF-36 scale scores for bodily pain, general health, and health transition (all p > 0.05). However, the HC group scored significantly higher than the LC group in PF, RPH, REP, EF, MH, SF, MoCA (all p < 0.05). Significant differences were also observed between the two groups in PRAISE, OEE, and VAS scores (all p < 0.001, Table 1).

Table 1 Patient Characteristics

Screening Key Indicators Between the Two Groups

Significant differences were observed between the two patient groups in the SF-36 scale scores for PF (p = 0.034), RPH (p = 0.009), REP (p = 0.029), EF (p = 0.018), MH (p = 0.013), and SF (p = 0.013). Furthermore, significant differences were found in the PRAISE (p = 0.000), OEE (p = 0.000), MoCA (p = 0.002), and VAS scores (p = 0.000, Table 1 and Figure 3).

Figure 3 Analysis of differences in screening key indicators between high completion group and low completion group (A–J) Differences in the distribution of SF36PF, SF-36RPH, SF-36REP, SF-36EF, SF-36MH, SF-36SF, MoCA, PRAISE, OEE and VAS between high and low completion groups (p<0.05).

Selection of Candidate Indicators for Constructing a Predictive Model

Based on the 10 statistically significant key indicators, ROC curve analysis was conducted to calculate the discriminatory power of each indicator for PR completion. Moderate discrimination was observed for PRAISE (AUC = 0.810, 95% confidence interval [CI]: 0.689–0.931, p < 0.05), OEE (AUC = 0.784, 95% CI: 0.672–0.896, p < 0.05), MoCA (AUC = 0.719, 95% CI: 0.593–0.845, p < 0.05), and VAS (AUC = 0.801, 95% CI: 0.688–0.913, p < 0.05). Low discrimination was observed for PF (AUC = 0.649, 95% CI: 0.519–0.778, p < 0.05), RPH (AUC = 0.671, 95% CI: 0.537–0.805, p < 0.05), REP (AUC = 0.636, 95% CI: 0.500–0.772, p < 0.05), EF (AUC = 0.667, 95% CI: 0.535–0.799, p < 0.05), MH (AUC = 0.676, 95% CI: 0.542–0.811, p < 0.05), and SF (AUC = 0.660, 95% CI: 0.523–0.797, p < 0.05; Table 2).

Table 2 AUC, Sensitivity, and Specificity of Different Variables

Among the 10 key indicators, the PRAISE, OEE, MoCA, and VAS scores demonstrated discriminatory power for determining whether remote PR could be completed. Therefore, these four indicators were selected as candidate indicators for evaluating PR treatment adherence outcomes. Logistic regression analysis was conducted to construct a predictive model combining these indicators. The model showed an AUC of 0.895 (95% CI: 0.812–0.977, p < 0.05), with a sensitivity of 77.8% and a specificity of 93.2%, indicating good discrimination in assessing adherence to remote PR (Figure 4). Additionally, to evaluate the internal validity of the predictive model, we performed a leave-one-out cross-validation (LOOCV) analysis. The corresponding ROC curve demonstrated an AUC of 0.838 (95% CI: 0.732–0.943) based on DeLong’s method, indicating that the model retained good discriminative ability upon internal validation. The prediction formula is as follows:

Figure 4 The subject work characteristic curve and area under the curve of the combined pulmonary rehabilitation adaptation self-efficacy (PRAISE), Outcome Expectations for Exercise Scale (OEE), Montreal cognitive assessment (MoCA), and patient willingness to receive pulmonary rehabilitation treatment (VAS score) model predicting the adherence of remotely monitored home-based pulmonary rehabilitation.

logit(p) = 14.609–0.217(MoCA) – 0.102(PRAISE) – 0.049(OEE) – 0.451(VAS).

In clinical practice, when logit (p) ≥ –0.463, that is, 0.217(MoCA) + 0.102(PRAISE) + 0.049(OEE) + 0.451(VAS) ≤ 15.072, we can assume that the patient will withdraw from the remote PR program.

Analysis of the Mediating Effects of Indicators in the Model

Given that OEE influenced behavioral outcomes through PRAISE, mediation analysis revealed a statistically significant negative mediation effect size of –0.0843 (95% CI: –0.2206 to –0.0230, p < 0.05). Moreover, after controlling for the mediator variable PRAISE, the influence of OEE on behavioral outcomes became statistically insignificant (regression coefficient: –0.0958, 95% CI: –0.1977 to 0.0061, p > 0.05). These findings indicate that PRAISE plays a mediating role in the relationship between OEE and behavioral outcomes and acts as the sole mediator, also known as a complete mediator (Figure 5). In Figure 5, β = −0.0958 corresponds to an odds ratio (OR) of 0.9086 (95% CI: 0.8206–1.0061, p > 0.05), indicating no significant relationship between OEE and compliance. However, β = 0.5864 (p < 0.0001) demonstrates a significant positive association: each one-unit increase in OEE leads to an average increase of 0.5864 units in PRAISE. Additionally, β = −0.1437 (OR = 0.8661, p = 0.006) suggests a significant link between higher PRAISE and improved compliance.

Figure 5 PRAISE plays a mediating role in the impact of OEE on behavioral outcomes. Abbreviations: PRAISE, the Pulmonary Rehabilitation Adapted Index of Self Efficacy; OEE, Expectation of Exercise Results Scale.

Mediation analysis indicated that when MoCA influenced behavioral outcomes through PRAISE, the mediation effect size was –0.1242 (95% CI: –0.2980 to –0.0361, p < 0.05), indicating statistical significance. Furthermore, after controlling for the mediator variable PRAISE, the direct effect of MoCA on behavioral outcomes became statistically insignificant (regression coefficient: –0.1753, 95% CI: –0.3585 to 0.0079, p > 0.05). Therefore, PRAISE serves as a mediating factor in the relationship between MoCA and behavioral outcomes, functioning as the sole intermediary variable, also referred to as complete mediation (Figure 6).

Figure 6 PRAISE plays a mediating role in the impact of cognition on behavioral outcomes. β = −0.1753 (OR = 0.8392, p= 0.0607) suggests no significant association between MoCA and compliance. By contrast, β = 0.8469 (p= 0.0002) indicates that each one-unit increase in MoCA corresponds to an average increase of 0.8469 units in PRAISE, supporting a significant positive relationship. Additionally, β = −0.1467 (OR = 0.8636, p= 0.0037) reveals a significant positive association between higher PRAISE and compliance. Abbreviations: MoCA, Montreal Cognitive Assessment; PRAISE, the Pulmonary Rehabilitation Adapted Index of Self Efficacy.

Assuming PRAISE as a mediator variable for VAS, mediation analysis yielded a statistically significant mediation effect size of –0.1628 (95% CI: –0.7745 to –0.0035, p < 0.05). In addition, after controlling for PRAISE, the impact of VAS on behavioral outcomes remained statistically significant, with a regression coefficient of –0.4309 (95% CI: –0.7110 to –0.1507, p < 0.05). These results suggest that PRAISE acts as a partial mediator in the relationship between VAS and behavioral outcomes (Figure 7).

Figure 7 PRAISE partially mediates the effect of VAS on behavioral outcomes. A significant negative association was observed between high VAS and high compliance (β = −0.4309; OR = 0.6499, p = 0.0026). For each unit increase in VAS, PRAISE scores increased by an average of 1.0077 units (β = 1.0077, p = 0.0056), indicating a strong positive relationship between VAS and PRAISE. Additionally, high PRAISE was significantly associated with high compliance (β = −0.1616; OR = 0.8508, p = 0.0016). Abbreviations: VAS, Visual Analog Scale; PRAISE, the Pulmonary Rehabilitation Adapted Index of Self Efficacy.

Discussion

This study, grounded in SCT, assessed the significance of key research variables on discriminative adherence to remotely monitored home-based PR among patients with moderate-to-severe COPD in a stable phase. Notably, it pioneered the development of a four-variable predictive model based on SCT, incorporating PRAISE, OEE, MoCA, and VAS. The model demonstrated high sensitivity and specificity in discriminating the likelihood of successful completion of remote PR programs. It serves as a valuable assessment tool for identifying patients who may be less capable of effectively participating in remote PR sessions. Furthermore, mediation analysis of the predictors revealed that PRAISE was highly likely to mediate the impact of OEE and MoCA on behavioral outcomes. This finding suggests that PRAISE could be a key intervention target to improve remote PR adherence in COPD patients. Therefore, timely implementation of tailored behavioral interventions might help patients with COPD achieve greater benefits from remote PR.

With the rapid development of the Internet and advanced technology, as well as during the global COVID-19, patients have been severely restricted from receiving traditional rehabilitation treatment in the hospital environment. Therefore, telemedicine has been used globally as a new medical service for self-management of chronic respiratory diseases.³⁵ Adopting a low-cost, safe, effective and user-friendly remote home-based rehabilitation and exercise monitoring smartphone App, patients can be provided with a more scientific exercise training program.³⁶ Traditional Chinese exercise regimen, such as Baduanjin, is an excellent heritage of Chinese health preservation culture. After continuous practice involving relaxation and slow movements, Baduanjin exercise can effectively improve the lung function, exercise endurance, and hospitalization frequency of stable COPD patients.³⁷ However, patients with COPD exhibit a relatively high dropout rate during PR treatment, with the specific reasons remaining unclear. Current research in this field has partially focused on demographic characteristics. Patients aged 60 years and older show a higher PR completion rate compared with those under 60.¹³ Cultural differences, marital status, and economic status are also considered factors influencing PR completion rates.³⁸ Brown et al found that smoking at the time of enrollment was associated with a reduced PR completion rate (adjusted odds ratio: 0.38, 95% CI: 0.16–0.90, p = 0.02).³⁹ Moreover, clinical and psychological characteristics have been identified as factors linked to PR dropout. These include being underweight or obese, experiencing severe dyspnea, shorter six-minute walking distance, frequent exacerbations, and elevated levels of anxiety and depression.^13,38,40,41 Research demonstrates that as little as one month of successful tobacco abstinence yields measurable health improvements. These include enhanced respiratory symptoms and lung function, along with critical metabolic parameter changes such as blood lipid profiles and vitamin D levels.⁴² Preliminary evidence suggests that short-term smoking cessation may improve adherence to PR. This potential effect requires further verification. The presence of a well-developed public medical facility and technical service system is also crucial for ensuring the smooth implementation of PR.⁴³

From a behavioral perspective and based on SCT, this study explored the underlying behavioral factors influencing the choice of remote PR. Among patients with COPD who had similar demographic and disease characteristics, PRAISE, OEE, MoCA, and VAS demonstrated good discriminatory power for the completion of PR, which primarily utilized Baduanjin in remote home settings. Our results showed that PRAISE scores strongly correlate with patient adherence. However, patients with high PRAISE scores may inherently have higher health literacy, rather than directly predicting compliance. PRAISE and OEE scores may not only predict adherence but also indicate that the patient has reached an advanced stage of behavioral change or possesses high health literacy. Thus, the relationship between these variables and adherence is likely bidirectional, not a simple one-way prediction.

PR training, as a behavioral approach to promoting health in patients with COPD, is influenced by a behavioral ecosystem. This ecosystem includes cultural, economic, familial, personal knowledge, and motivational factors. Multiple variables within this system interact at various levels, shaping behavioral patterns.^{12,17,38–41} Understanding the specific mechanisms underlying behavior provides a foundation for influencing or changing these patterns. Within the theoretical framework of behavioral science, identifying key variables that determine behavioral choices for specific actions in particular populations can establish feasible pathways for promoting healthy behavioral patterns.

This study focused on patients with COPD participating in remote PR in our region. After excluding demographic and clinical characteristics previously shown to affect conventional PR completion rates, candidate variables selected based on SCT—including PRAISE, OEE, MoCA, and the VAS score for willingness to receive PR treatment—were better able to explain the reasons for remote PR dropout in this population. Current research has found that intervention through the bystander effect can enhance self-efficacy in patients with COPD during cardiopulmonary exercise testing.⁴⁴ The intervenability of self-efficacy has been methodologically confirmed, suggesting its feasibility as an important target for behavioral interventions aimed at improving PR completion rates.

The interactional determinism theory serves as the core framework of SCT, positing that individual behavior, personal intrinsic factors, and the environment form a dynamic interactive system, thus rejecting unidirectional determinism.^18,19 This study selected four intrinsic factors derived from this theory—PRAISE, OEE, VAS, and MoCA—to assess their impact on PR behavior in patients with COPD. A cross-sectional survey revealed significant correlations between these factors and rehabilitation adherence. Due to the temporal limitations of cross-sectional designs, these indicators cannot infer causal relationships with PR outcomes. However, despite its exploratory nature, these results provide preliminary evidence for future prospective studies.

This study has some limitations, including a small sample size and the refusal of some patients to participate in the cross-sectional survey, which increases the risk of selection bias. Further multicenter, prospective, large-sample cohort studies are needed for validation. Questionnaires were administered only upon completion or withdrawal from remote PR, resulting in the absence of baseline data collection at enrollment and dynamic monitoring throughout the process. Monitoring trends in related variables may help refine the predictive model and enhance its discriminatory power. The study included only patients with COPD who completed or withdrew from remote PR, excluding those who refused participation. However, previous research indicates no significant differences in behavioral factors between those who refused and those who withdrew.⁴⁵ Moreover, the predictive model was based on a 12-week program, which may be inadequate for reflecting outcomes of longer remote home-based rehabilitation interventions. This study revealed through mediation analysis that PRAISE mediates the effect of cognitive function on PR adherence. Nevertheless, since this conclusion is based solely on cross-sectional rather than longitudinal data, its application to mediation analysis is temporally limited. Finally, the self-report tools used in this study, such as PRAISE, OEE, and VAS, may be affected by social desirability bias, especially in samples with high motivation. Future research can reduce such biases by combining objective indicators, such as actual compliance data.

Conclusion

Improving and sustaining health-promoting behaviors are primary objectives of PR for patients with COPD. This study focused on examining the behavioral traits that facilitate the successful completion of remote PR programs. Patients with similar demographic and clinical characteristics showed good correlation between their PRAISE, OEE, MoCA, and VAS scores and adherence to PR. These correlations might predict the completion of remote PR in COPD patients. Notably, self-efficacy was associated with the efficacy of remote home-based intervention strategies. Enhancing self-efficacy may encourage the maintenance of beneficial health behaviors, thereby reducing the substantial burden that COPD imposes on patients and society. This cross-sectional exploratory study proposes a preliminary hypothesis: in specific populations or contexts, research indicators (PRAISE, OEE, VAS, and MoCA) that highly correlate with PR adherence may imply causal relationships. However, this model requires prospective validation and further exploration of adherence dynamics over time.

Abbreviations

AUC, area under the curve; CI, confidence interval; COPD, chronic obstructive pulmonary disease; E/F, energy/fatigue; HADS, Hospital Anxiety and Depression Scale; HC, high-completion; LC, low-completion; MH, mental health; MoCA, Montreal Cognitive Assessment; OEE, Outcome Expectations for Exercise Scale; PF, physical functioning; PR, pulmonary rehabilitation; PRAISE, Pulmonary Rehabilitation Adapted Index of Self-Efficacy; REP, role limitations due to emotional problems; RPH, role limitations due to physical health; SCT, social cognitive theory; SF, social functioning; SF-36, Short Form Health Survey; VAS, Visual Analog Scale.

Data Sharing Statement

The data supporting this study’s findings will be made available upon reasonable request to the corresponding author.

Ethics Approval and Informed Consent

This study was approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (Approval No. 2021-SR-024), the Geriatric Hospital of Nanjing Medical University (Approval No. 2021-010), and the Affiliated BenQ Hospital of Nanjing Medical University (Approval No. 2021-KL011). All participants provided informed consent before enrollment.

Acknowledgments

The authors would like to extend their sincere appreciation to all study participants for their valuable contributions through advisory input and sustained engagement with the research initiative. Special recognition is extended to Minlan Chen and Suzhou Changfeng Pharmatech Inc. for providing expert technical support in implementing remotely monitored Baduanjin exercise interventions throughout the study duration.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was partially supported by Jiangsu Province Elderly Health Research Project (General Program LK2021032) to SY.

Disclosure

The authors declare no known financial or personal conflicts of interest that could have influenced the research outcomes herein.

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