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Introduction

Chronic obstructive pulmonary disease (COPD) is a common chronic lung disease, including emphysema, chronic bronchitis, and others. Currently, the causes of COPD are not yet clear and may be related to external environmental factors, including smoking, second-hand smoke inhalation, polluting dust, harmful chemical gases, air pollution, and other factors.¹ There are also personal factors, such as genetics, diseases, etc.² COPD is more common in people over 40 years of age, possibly due to the decline in physical function and immunity of middle-aged and elderly patients.³ The pathological and physiological characteristics of COPD are persistent airflow limitation, completely irreversible, and persistent malignant development.⁴ Clinical manifestations include dry vomiting, coughing, and sputum production, which worsen with increasing physical activity and seriously affect the patient’s quality of life.⁵ Treatment for COPD is based on improving lung function, alleviating clinical symptoms, and improving quality of life. Common treatment methods include rehabilitation therapy, medication therapy, and home oxygen therapy.⁶ As the disease progresses, some patients may also have pulmonary fibrosis and, if left untreated, there is a risk of developing into pulmonary failure.⁷ Therefore, early identification and diagnosis of such patients and timely treatment of medications are of great significance to improve the patient’s prognosis.

In recent years, ferroptosis, as a new type of programmed cell death, has gradually attracted widespread attention due to its dependence on intracellular Fe ²⁺accumulation and lipid peroxidation.^8,9 Numerous studies have shown that ferroptosis plays a crucial role in the onset and progression of lung diseases.¹⁰ Ferroptosis not only differs morphologically from other forms of cell death, but also exhibits unique mechanisms at the biochemical level. In particular, oxidative stress caused by iron overload and disorders of lipid metabolism is considered an important factor in the induction of ferroptosis.^11,12 These findings provide new opportunities for the treatment of lung diseases by regulating iron metabolism and lipid peroxidation processes, which may effectively promote or prevent ferroptosis, thus improving the therapeutic effect of these diseases.

Transferrin protein 1 (TFR1), also known as CD71 or TFRC, is a type II transmembrane glycoprotein composed of 760 amino acids, exist in the form of a dimer, connected by disulfide bonds on the cell surface. The TFR1 monomer consists of an extracellular C-terminal domain, a transmembrane region, and an intracellular N-terminal domain, where the C-terminal region contains the transferrin (TF) binding site. Each TFR1 monomer can bind 1 molecule of TF and 2 Fe3+ ions, so 1 molecule of TFR1 can bind up to 2 molecule of TF and 4 Fe3+ ions, ultimately delivering iron into the cell in the form of an iron-TF-TFR1 complex.^13,14 Human TFR1 is widely expressed in different tissues and organs. Under physiological conditions, cellular iron absorption is primarily controlled by the plasma membrane protein TFR1, which transports transferrin bound iron into cells through receptor-mediated endocytosis.¹⁵ Therefore, TFR1 is considered a marker protein for ferroptosis. Blocking this process by eliminating TFR1 can prevent ferroptosis.¹⁶ Previous studies have confirmed that high expression of TFR1 in bronchoalveolar lavage fluid (BALF) in asthma patients is associated with impaired lung function, and it is believed that high expression of TFR1 in the sputum is related to the severity of asthma.¹⁷ Down-regulation of TFR1 partially blocked the high secretion of MUC5AC, goblet cell proliferation, and the release of inflammatory factors in COPD model rats, indicating that TFR1 is involved in promoting airway inflammation and airway mucus cell proliferation in COPD.¹⁸ However, TFR1 expression in patients with COPD has not yet been reported.

In the aforementioned study, we have identified ferroptosis involvement in the pathological process of progression of COPD to pulmonary fibrosis in animal models and we found that ferroptosis-related indicators, including GSH and MDA, are correlated with the degree of pulmonary fibrosis. Based on this, this study aims to detect the expression level of TFR1 in COPD patients and analyze its relationship with the severity of COPD and pulmonary fibrosis and to verify the relationship between the expression level of TFR1 and lung injury and pulmonary fibrosis in animal models. The purpose of this study is to provide a reliable biomarker and a potential therapeutic target for patients with COPD progression to pulmonary fibrosis.

Materials and Methods

COPD Patients

97 patients with COPD were included in this study. COPD was diagnosed by pulmonary function test according to the standards of the Global Initiative for Chronic Obstructive Lung Disease (GOLD).¹⁹ Inclusion criteria: (1) FEV1/FVC<0.7 after bronchodilators, and excludes other diseases that may cause limitation of airflow (such as asthma and bronchiectasis). (2) Age over 40 years old, COPD duration ≥ 1 year. Exclusion criteria: (1) Other diseases that cause pulmonary fibrosis: connective tissue diseases (such as rheumatoid arthritis, scleroderma), occupational lung diseases (pneumoconiosis, asbestosis), drug-induced pulmonary fibrosis, or idiopathic pulmonary fibrosis. (2) Combined with asthma, bronchiectasis, active pulmonary tuberculosis, and pulmonary embolism. (3) Serious complications, such as active lung cancer, severe heart failure (NYHA III–IV grade), end-stage renal disease, liver failure, etc. (4) Usage of anti-fibrotic drugs (such as pirfenidone, nintedanib) or immunosuppressants (such as cyclophosphamide, rituximab) within 6 months.

Data including sex, age, smoking index, BMI, CRP, IL-6, ESR, LDH, routine blood examination, lung function, 6 MWT, CAT score, grade mMRC, grade GOLD, frequency of acute exacerbation, and fibrosis score based on HRCT examination were collected from hospital electronic records between January 1, 2022 and December 31, 2024. At the same time, peripheral blood samples were collected from all patients for the detection of serum levels of TFR1 and COL3 using the ELISA assay. This study was approved by the Medical Ethics Committee of the Hunan Provincial People’s Hospital (2024–260).

Mice COPD Model

4-week-old C57BL.6 J mice (18–20g) were purchased from Hunan Slake Jingda Experimental Animal Co., Ltd. (Changsha, China). All animals were fed in the SPF animal facility with a normal day and night cycle. They also had free access to a common diet and water. Mice were randomly divided into 3 groups: control group (n=5), model 1 group (COPD / CSE) (n = 5), and model 2 group (COPD-PF / CSE + LPS) (n = 5 as Supplementary Figure 1. The mice COPD model was constructed as in our previous study.²⁰ All animal experiments were conducted according to the ARRIVE guidelines. The animal study protocol was approved by the Animal Care and Use Committee (ACUC) of Hunan Provincial People’s Hospital, protocol number [2024–260]. The study adhered to the guidelines set by the committee.

ELISA Assay

Human sTfR1 (Soluble Transferrin Receptor1) ELISA kit (EH0386), Mouse TFR (Transferrin Receptor) ELISA kit (EM1400) and Human COL3 (Collagen Type III) ELISA kit (EH2866) were purchased from Fine Test Biotechnology (Wuhan, Hubei, China). COL3 was a biomarker of fibrosis. The levels of TFR1 and COL3 in the serum of COPD patients were measured using an ELISA assay according to the manufacturer’s instructions. The OD450 was measured by Microplate reader. Each sample was calculated on the basis of a standard curve.

Hematoxylin & Eosin Staining

The lung tissue of the mice was fixed in 4% paraformaldehyde for at least 24 hours, followed by gradient dehydration and paraffin embedding. After embedding in paraffin, tissues were cut into sections with a thickness of 4 (m for H&E staining). After dewaxing, staining with hematoxylin and eosin, dehydration, permeabilization, and sealing, observation, and image collection were performed under an inverted microscope (Olympus, Tokyo, Japan). The lung injury score was referred to the previous study, mainly including pulmonary congestion, hemorrhage, neutrophil filtration and aggregation, and alveolar wall thickness and transparent membrane formation.²¹

Masson Trichrome Staining

Paraffin sections were deparaffinized and sequentially stained with Regaud’s hematoxylin staining solution, Masson’s acid eosin solution, and aniline blue. After staining, dehydration, permeabilization, and sealing, observation and image collection were performed under an inverted microscope (Olympus, Tokyo, Japan). The lung injury score was evaluated on the basis of Ashcroft score criteria.²²

IHC Staining

Sections with a thickness of 4 (m obtained from paraffin-embedded lung tissues were deparaffinized, antigen retrieval, blocked, and incubated with primary antibodies against TFR1 (Cat No. 65236-1-Ig, Proteintech, Wuhan, China) with a dilution of 1:200 and (-SMA (Cat No. 14395-1-AP, Proteintech, Wuhan, China) with a dilution of 1:1000. The sections were then incubated with a secondary antibody kit, and observation was performed with a laser scanning microscope (Olympus, Tokyo, Japan).

Statistical Analysis

Parametric data were presented as mean ± standard deviation or mean (range). Nonparametric data were presented as median (interquartile range, IQR). Spearman bivariate correlation analysis was performed between TFR1 and basic demographic information, systemic inflammatory level, and pulmonary function in patients with COPD. The chi-square test was applied to analyze differences in clinical characteristics between the higher TFR1 group and the lower TFR1 group in patients with COPD. Student’s t test was applied to analyze differences between two groups, while variance (ANOVA) was applied to analyze differences between the three groups. Statistical significance was established at P<0.05. SPSS software (version 20.0; SPSS, Inc., Chicago, IL, USA) was used for statistical analysis. GraphPad Prism 8 (GraphPad, San Diego, CA) was used to generate the images.

Results

Characteristics of the COPD Patient

In this study there were 97 patients with COPD. The results of the spearman bivariate correlation analysis between TFR1 and basic demographic information, systemic inflammatory level, lung function in patients with COPD showed that TFR1 levels did not show correlation with gender, age, smoking index, BMI, CRP, IL-6, LDH, WBC, neutrophil count, Hb, EOS%, RV/TLC%, FEV1%pred, mMRC grade. However, TFR1 levels exhibited a positive correlation with FEV1/FVC%, MMEF%, CAT score, GOLD grade, frequency of acute exacerbation and fibrosis score and a negative correlation with lymphocyte count, NLR, DLCO%, DLCO/VA%, 6MWT (see Table 1 for details).

The Frequency of Acute Exacerbation and the Fibrosis Score Differ in COPD Patients with Higher and Lower Levels of TFR1

The TFR1 level of 97 patients was 5.44 ± 0.56 ng/mL. According to the average TFR1 value, they were divided into a low TFR1 group (serum TFR <5.44 ng/mL) and a high TFR1 group (serum TFR1 ≥ 5.44 ng/mL). There were differences in DLCO%, 6 MWT, GOLD grade, frequency of acute exacerbation, and fibrosis score between the two groups (see Table 2 for details). A higher level of TFR1 was associated with a lower percentage of DLCO, a shorter distance of 6 MWT, a higher grade of GOLD, a higher frequency of acute exacerbation and a higher fibrosis score. All of these results suggested that TFR1 was a biomarker positively correlated with the severity of COPD, with higher levels of TFR indicating more severe COPD.

Table 2 Differences of Clinical Characteristics Between Higher TFR1 Group and Lower TFR1 Group in COPD Patients

The TFR1 Level Was Associated with the Frequency of Acute Exacerbation in COPD Patients

The gradual progression of COPD to pulmonary fibrosis is a slow process closely related to repeated acute exacerbation of inflammatory damage. The frequency of acute exacerbation was recorded in one year and all patients were divided into two groups according to the frequency of acute exacerbation, with a frequency of less than or equal to 2 per year recorded as the low-frequency group and more than 2 recorded as the high-frequency group. Representative CT images of patients in the low-frequency and high-frequency groups of acute exacerbation were show in Figure 1A. Then we analyzed the plasma levels of TFR1 and COL3 of the patients in the low-frequency and high-frequency groups and found that the levels of TFR1 and COL3 were significantly lower in the low-frequency group and significantly higher in the high-frequency group (Figure 1B and C). The levels of TFR1 were positively correlated with COL3 (Figure 1D). To predict the frequency of acute exacerbation in COPD, the area under the TFR1 curve was 0.9534 (95% CI:0.9167–0.9901), P<0.0001, the area under the COL3 curve was 0.7289 (95% CI:0.6243–0.8334), P=0.0006, and the area under the fibrosis score curve is 0.7969 (95% CI:0.6851–0.9087), P<0.0001 (Figure 1E).

Figure 1 The TFR1 level was associated with the frequency of acute exacerbation in patients with COPD. (A) Representative CT images of patients in the low-frequency and high-frequency groups of acute exacerbation. (B) Serum TFR1 concentration in the low-frequency and high-frequency groups. (C) Serum COL3 concentration in the low-frequency and high-frequency groups. (D) The spearman analyzes the serum TFR1 concentration and the serum COL3 concentration. (E) ROC cure for TFR1, COL3, and fibrosis scores to predict the frequency of acute exacerbation in COPD. Notes: the low group stands for the low frequency group with a frequency of acute exacerbation less than 2. The high group represents the high-frequency group with an acute exacerbation frequency greater than 2. ***means P < 0.001.

The TFR1 Level Was Correlated with Lung Injury in COPD Mice

To verify the relationship between TFR1 and lung injury, we constructed lung injury models with different degrees of injury. Model 1 was mainly exposed to cigarettes to simulate stable COPD lung injury. Model 2 added an intraperitoneal injection of a combination of LPS exposure to cigarettes to simulate the impact of infection after COPD lung injury, that is, AECOPD. As expected, the Model 1 group showed significant lung injury, while the Model 2 group had more significant lung injury and significant interstitial thickening (Figure 2A and B). And the levels of TFR1 were detected in BALF and plasma. Compared to the control group, TFR1 in both model 1 and model 2 groups increased significantly, and the TFR1 level in model 2 group was higher than in the TFR1 group, indicating that the TFR1 level was related to the degree of lung injury (Figure 2C and D).

Figure 2 The TFR1 level was correlated with lung injury in COPD mice. (A) HE staining of lung tissue. (B) Lung injury scores for the three groups. (C) The content of TFR1 in the BALF. (D) The contents of TFR1 in serum. *means P < 0.05. **means P < 0.01. ***means P < 0.001.

TFR1 Level Correlated with Lung Fibrosis in COPD Mice

There was a positive correlation between TFR1 levels and COL3 pulmonary fibrosis index, as well as CT pulmonary fibrosis score in patients with COPD. Furthermore, the relationship between TFR1 levels and pulmonary fibrosis was also analyzed in COPD mouse models. Through Masson’s trichrome staining, the results showed that varying degrees of pulmonary fibrosis injury were observed in the Model 1 and Model 2 groups, with the Model 2 group showing more significance (Figure 3A and B). Furthermore, TFR1 and a-SMA expression in each group were evaluated by immunohistochemical staining and the results showed that TFR1 was significantly up-regulated in the model groups, with higher positivity in the Model 2 group, consistent with the expression of a-SMA (Figure 3C–E). These results indicated that the level of TFR1 was also closely related to pulmonary fibrosis injury.

Figure 3 The TFR1 level was correlated with lung fibrosis in COPD mice. (A) Masson trichrome staining of lung tissues. (B) Lung fibrosis scores for the three groups. (C and A) IHC staining of TFR1 and α-SMA in lung tissues. (D and E) the positive percentage of TFR1 and α-SMA based on IHC staining. *means P < 0.05. **means P < 0.01. ***means P < 0.001.

Discussion

This study is the first to demonstrate that COPD patients with increased serum TFR1 in COPD patients were related to recurrent acute exacerbations and develop pulmonary fibrosis. TFR1, as a marker for ferroptosis, might serve as a potential indicator for the evaluation of the severity of pulmonary fibrosis in clinical practice in the future.

The objective of the evaluation of COPD is to clarify the severity of the disease, its impact on the patient’s health status, and the risk of certain events (acute exacerbation, hospitalization, and death), while guiding treatment.^23,24 The comprehensive evaluation included symptoms of the disease, the degree of airflow limitation (lung function test), the risk of acute exacerbation, and comorbidities. MMRC is positively correlated with the severity of airflow limitation and lung function impairment, with mMRC greater than 2 serving as the boundary between “mild respiratory distress” and “severe respiratory distress”. A CAT score <10 indicates the need for medical intervention.^25,26 Due to the fact that all of the patients we collected were in the hospital, the mean mMRC values for both the low TFR1 group and the high TFR1 group were greater than 2. The mean values of the CAT score of the patients in both the low TFR1 group and the high TFR1 group were greater than 10. However, it can be seen that the mMRC and CAT scores of the TFR1 high group are higher, which also indicates that the level of the TFR1 group is related to the severity of the disease, although there is no statistical difference. Lung function is another important diagnostic and reference basis for evaluating the condition of COPD. This study examined indicators of lung function such as RV/TLC%, FEV1% pred, FEV1/FVC%, MMEF%, DLCO%, DLCO / VA% and GOLD grade. It also indicated that the levels of TFR1 were related to the lung function. The acute exacerbation of COPD patients is mainly related to infection, and among the infection-related indicators, we observed that TFR1 was only related to the rate of erythrocyte sedimentation and had no relationship with levels of IL-6, C-reactive protein, and LDH. Furthermore, by comparing the differences in inflammatory indicators between high and low levels of TFR1, it was found that there was no significant difference in TFR1 levels and inflammatory indicators. This indicated that the level of TFR1 was associated with lung but not with inflammatory factors.

As COPD progresses and acute exacerbations recur, it can cause airway remodeling and changes in lung interstitial, which eventually develop into pulmonary interstitial fibrosis.^27,28 As a more serious complication in the course of COPD, pulmonary interstitial fibrosis can further aggravate lung function damage as the severity of fibrosis increases.^29,30 Failure to provide timely treatment and control of the disease can lead to respiratory failure or death in patients.³¹ Our results indicated that the level of TFR1 is positively correlated with the frequency of acute exacerbations and the score of lung fibrosis in one year. And it was found that by grouping with increased frequency to observe TFR1 levels and indicators related to fibrosis, patients with more frequent acute exacerbation (more than 2 times per year) had higher levels of TFR1 and COL3. The results of predicting the frequency of acute exacerbations based on the level of TFR1, the level of COL3, and the fibrosis score indicated that TFR1 had greater sensitivity and specificity compared to the level of COL3 and the fibrosis score. Taking into account the difficulty of obtaining lung biopsy tissue from COPD patients, we also observed the association between TFR1 levels and lung injury in a mouse COPD model. As expected, we observed a more intuitive correlation between TFR1 levels and lung injury and fibrosis scores in a mouse COPD model. This result supported the use of TFR1 as a biomarker to predict the degree of lung injury and evaluate pulmonary fibrosis.

With the deepening and enrichment of the research on TFR1, it is now clear that TFR1 is closely related to various tumors and some brain diseases, including Parkinson’s, stroke, and acute brain injury.^32,33 And based on these studies, a series of drugs targeting TFR1 have also been developed for clinical diseases, which currently in the clinical trial stage.^13,34 We can see the broad applications prospects of drugs targeting TFR1 inhibition. However, currently there is very little research on the relationship between TFR1 and COPD. A study found that TTR1+macrophages are involved in the process of pulmonary fibrosis injury, and DFO treatment can work by reducing ferroptosis.³⁵

Conclusion

This study provides some evidence that TFR1 is involved in COPD lung injury, indicating that TFR1 is related to the acute exacerbations of COPD and COPD-associated pulmonary fibrosis, and also provides new evidence for targeted inhibition of TFR1 therapy for COPD. However, only 97 patients were included in the study, and more patients are needed to clarify the specific levels of TFR1 and the cutoff values for grouping. Patients who have not developed pulmonary fibrosis should be set as controls to clarify the level of TFR1 in pulmonary fibrosis. In summary, the prevention and treatment of COPD are crucial and TFR1 is a highly promising therapeutic target. We hope that our research can help improve and improve the prognosis of patients with COPD.

Data Sharing Statement

The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

This study contained COPD patients was approved by the Ethics Committee of Hunan Provincial Hospital, Hunan Normal University (2024-260) and was carried out according to the Declaration of Helsinki guidelines. All patients signed informed consent. The animal study was approved by the Ethics Committee of Hunan Provincial Hospital, Hunan Normal University (2024-107). All methods were performed in accordance with the relevant guidelines and regulations.

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 study was supported by the Hunan Province Natural Science Foundation (2024JJ9279), Hunan Provincial Health Commission Project (D202316006632) and Hunan Province Innovation Guidance Project (2021SK50903).

Disclosure

The authors declare that they have no conflict of interest.

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Introduction

Hematopoietic Stem Cell Transplantation (HSCT) is a well-established curative therapy for a broad spectrum of conditions, particularly malignant hematologic diseases, as well as select nonmalignant congenital and acquired diseases of the hematopoietic system.¹ Since the first transplantation performed more than 6 decades ago,² the use of HSCT has steadily increased. This growth can be attributed to expanding indications, improved patient selection, less toxic conditioning regimens, and enhanced supportive care measures.^2,3 While HSCT has significantly improved survival rates, patients who undergo this procedure remain at heightened risk for a range of late complications, including graft-versus-host disease (GVHD) and disease relapse, as well as unique long-term sequelae related to the transplant itself.⁴

Over the past two decades, accumulating evidence has underscored the critical role of exercise in both the prevention and management of cancer.⁵ Targeted physical activity interventions have demonstrated substantial benefits in improving physical function, quality of life (QoL), and reducing cancer-related fatigue in various patient populations, including those who have undergone HSCT. In fact, the American College of Sports Medicine assigns a high level of evidence to the safety and efficacy of aerobic and strength training for adults during or after HSCT.⁶

Despite these established benefits, numerous barriers often limit patient access to comprehensive cancer rehabilitation services. Socioeconomic factors, transportation challenges, employment obligations, financial costs, and time constraints can all impede patients from receiving consistent in-person exercise-based interventions.^7,8 In this scenario, telemedicine has emerged as a promising strategy to deliver specialized care remotely. By leveraging digital communication tools, telemedicine can enhance access to post-HSCT services, reduce the logistical and financial burdens associated with frequent in-person visits, and maintain close patient monitoring even when individuals are deemed clinically unstable.⁹

Although telemedicine holds considerable promise for optimizing care delivery and long-term management in HSCT populations, its implementation, effectiveness, and overall impact have not been comprehensively explored. This gap in the literature warrants a systematic examination to identify current telemedicine applications, evaluate their feasibility and effectiveness, and highlight areas requiring further investigation. Accordingly, this scoping review aims to map the current landscape of telemedicine-supported exercise interventions in HSCT, assessing their clinical and technical characteristics, feasibility, effectiveness, and gaps in the literature.

Methods

Study Design

This scoping review was conducted following the framework proposed by Arksey and O’Malley,¹⁰ with enhancements suggested by Levac et al¹¹ and the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis.¹² Our methods adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews¹³ (See Table S1).

Information Sources and Search Strategy

A comprehensive search was conducted in the following databases: MEDLINE (via PubMed), SCOPUS, Web of Science, and Embase. We used controlled vocabulary (eg, MeSH terms) and keywords related to telemedicine and hematopoietic stem cell transplantation (eg, “telemedicine”, “telehealth”, “HSCT”). The search included studies published from database inception to July 31, 2024, without language restrictions. Search strategies for each database are detailed in Tables S2–S5.

Study Selection

We included experimental studies, such as randomized and non-randomized clinical trials, as well as quasi-experimental designs (eg, pre-post studies) that were published as peer-reviewed articles, letters, or short communications. The eligible studies reported results on telemedicine-supported exercise interventions pre-, during, and post-HSCT. Additionally, documents reporting any type of healthcare services related to HSCT delivered via telehealth were also included. Conversely, we excluded secondary research studies, including systematic reviews, umbrella reviews, and scoping reviews; however, references within these studies were consulted to identify relevant primary research. Other exclusions included protocols, opinions, case reports, case series, and non-peer-reviewed documents.

The study selection process involved two stages and was conducted using the Rayyan.ai platform. In the first stage, two independent reviewers screened the titles and abstracts of all identified studies. Any disagreements between the reviewers were resolved by a third reviewer. In the second stage, the full texts of selected articles were retrieved and assessed against the predefined eligibility criteria. To ensure consistency in the application of inclusion and exclusion criteria, a pilot screening of 11 documents was conducted prior to the formal selection process. This pilot exercise allowed for standardization of the reviewers’ approach and refinement of the criteria.

Data Charting Process

We developed a standardized data charting form, capturing key information on study design, participant demographics, telemedicine technical features, therapeutic approaches, and outcomes evaluated. This form was piloted on two studies to ensure its clarity and comprehensiveness. Data extraction was conducted independently by two pairs of reviewers. Any discrepancies in data extraction were resolved through discussion or consultation with a third reviewer.

Data Synthesis

Data were synthesized narratively and presented using descriptive statistics and cross-tabulation to illustrate the main findings. We synthesized information on the study designs, technical characteristics of telemedicine interventions, therapeutic approaches used, and outcomes assessed. The results were categorized based on themes identified during the data extraction phase, and tables were generated to provide a detailed summary of the studies included.

Results

Selection Process

We found in our query research 1116 potential papers to be included in the study. After duplicates were removed, 644 reports were screened by title and abstract, 624 of them were excluded and we found 20 relevant documents to the research question. All these studies were then read in detail, resulting in 10 articles to be included in the final study.^14–23 Specific reasons for excluding the remaining full-text articles are provided in Table S6. This process is detailed in Figure 1.

General Characteristics

Table 1 describes the findings regarding the main characteristics of the studies and participants. The included studies were published between 2005 and 2024, with the highest concentration in 2023 (30%, n=3).^20–22 Most studies were conducted in the United States (70%, n=7),^{14,16–18,20,21,23} with the remaining studies carried out in Germany,¹⁵ Australia,¹⁹ and the United Kingdom (10% each).²²

Table 1 Study Design and Participants Characteristics

The majority of studies (90%, n=9) focused on feasibility,^14,16–23 with quasi-experimental designs predominating (60%, n=6).^{14,16,18–20,23} Half of the studies (n=5) included one arm,^{14,18–20,23} while the other half included two arms.^{15–17,21,22} All studies followed prospective designs. Half of the studies (n=5) were single-arm pre-post studies,^{14,18–20,23} one of them being a study that includes dyads of patient-caregiver.²³ Randomized controlled trials (RCTs) were present in 40% of studies.^15,17,21,22 Among these, two RCTs evaluated outcomes across two time points (pre- and post-intervention),^17,21 another across four time points (2 pre-HSCT and 2 post-HSCT),¹⁵ and one across three time points (1 pre-HSCT and 2 post-HSCT).²² The sample sizes were limited, with 8 out of 10 studies (80%) enrolling fewer than 50 participants.^{14,16–21,23} The details on inclusion of patients with autologous (auto) and allogeneic (allo) HSCT as well as patients with GVHD are delineated in Table 1.

Participants Characteristics

Regarding gender distribution, 8 studies (80%) reported a higher proportion of men,^{15–20,22,23} whereas only one study (10%) included more women,¹⁴ and another (10%) had equal representation of men and women.²¹ Age characteristics were reported in various formats. Mean ages were provided 60% of the studies and ranged from 48.8 to 64.7 years.^{14,15,20–23} Median ages were reported in 3 studies (30%) and ranged from 52 to 60.5 years.^16,17,19 Age ranges were described in 5 studies (50%), spanning from as young as 18 to as old as 76.4 years.^15,17–20

Regarding diagnoses, leukemia^15–21,23 and lymphoma^{14,16–21,23} were the most frequently studied conditions, with both being addressed in 8 out of 10 studies (80%). Multiple myeloma followed, appearing in 70% (n=7) of studies.^{14–17,21–23} Most interventions were conducted after HSCT (50%, n=5),^{14,18,19,21,23} while 30% (n=3) were conducted before transplantation^16,17,20 and 20% (n=2) spanned from before to after HSCT.^15,22

Technology Used

As show in Table 2, phone calls and medical devices were the predominant technologies, used in 80% (n=8)^{14–18,20,22,23} and 70% (n=7)^{14,16,17,20–23} of studies, respectively. Videoconferencing was reported in 4 out of 10 studies (40%).^19,20,22,23 Fewer studies incorporated web systems (20%, n=2),^21,23 mobile apps (10%, n=1),²⁰ or SMS (10%, n=1).²⁰ Various medical devices were used, including smart-watches (n=4, 40%),^16,17,20,23 heart-rate monitors (n=2, 20%),^14,22 and gait sensors (n=1, 10%).²¹ Most studies relied on one (30%, n=3)^15,18,19 or two (40%, n=4)^14,16,17,21 technologies, while fewer incorporated three or more (n=3, 30%).^20,22,23

Table 2 Intervention Characteristics

Exercise Program Details

Table 3 summarizes exercises modalities and exercise prescription approaches reported in the reviewed articles. Seven out of 10 studies (70%) reported self-directed exercise programs,^{14–18,20,23} while 30% (n=3) were guided by healthcare providers.^19,21,22 Half of the studies present hybrid exercise delivery (home and clinic) (n=5),^{14,16,17,21,23} with 2 studies (20%) with this delivery of exercise among the whole period of the intervention^15,22 and 3 studies (30%) presenting this modality for the initial sessions.^18–20 Aerobic exercise was the most common modality, included in 80% (n=8) of studies.^{14–17,19,20,22,23} Resistance exercises were implemented in 5 out of 10 studies (50%),^{15,18,19,21,22} whereas flexibility and other exercises, such as mindfulness-based stress management and balance training, appeared in fewer studies.

Table 3 Exercise Description

Exercise program durations ranged from 6 to 15.3 weeks, with some studies reporting unclear durations (n=1, 10%).¹⁵ The most frequently reported session length was 30 minutes, described in 40% (n=4) of studies.^16,17,20,21 Most of the studies reported some way of intensity goals for the exercise programs (n=7, 70%),^{14–17,19,20,22} being HRR the most frequent intensity indicator (n=3, 30%),^14,20,22 followed by MHR (n=2, 20%),^16,17 and the Borg Perceived Exertion Scale (n=2, 20%).^15,19 Only 2 studies (20%) explicitly mentioned the use of any behavioral change theory or technique to guide the exercise intervention.^22,23

Regarding monitoring performance, most studies (n=5, 50%) delivered in a differed way (after the exercise session) by phone-calls (n=4, 30%)^14–16,18 or web-interface connected to medical devices (n=1, 10%).²³ Three studies conducted monitoring during the exercise session using videoconference services (30%).^19,20,22

Outcomes Measured

Functional outcomes were reported in the majority of studies (90%, n=9)^14–22 likely feasibility outcomes which appeared in 90% (n=9).^14,16–23 Quality of life was evaluated in 5 out of 10 studies (50%).^{14,15,17,19,22} Usability outcomes were less common, included in only 20% (n=2) of studies.^19,20 Adverse events were monitored in 60% (n=6) of studies,^{14–16,19,20,22} though no reactions attributable to exercise were reported.

Across the included studies, exercise-based interventions delivered via telemedicine demonstrated varied and meaningful outcomes for patients undergoing hematopoietic stem cell transplantation (HSCT). Significant improvements in physical fitness and functioning were observed, including increases in VO2peak from 14.6 ± 3.1 to 17.9 ± 3.3 mL/kg/min (P < 0.001).²⁰ Sustained improvements in the 6-minute walk test (6MWT) were reported, with mean differences of 79.6 m (95% CI: 28–131) at 3 months and 48.4 m (95% CI: 13–84) at 12 months, along with improvements in sit-to-stand repetitions and handgrip strength.¹⁹ Gait speed and handgrip strength improvements were described as clinically meaningful due to their associations with reduced mortality risk.^18,21 Functional outcomes, such as step counts, also increased significantly during interventions, with one study reporting an increase from 2249 ± 302 steps/day in week 1 to 4975 ± 1377 steps/day in week 8 for participants, while caregivers saw an increase from 8676 ± 3760 to 9838 ± 3723 steps/day during the same period.²³

Interventions positively influenced quality of life and fatigue outcomes. Improvements in quality of life, measured by FACT-G and FACT-BMT, and fatigue, measured by FACIT-F, exceeded minimal important differences during the intervention periods.²² However, declines in quality of life were noted during hospitalization phases, such as autologous stem cell transplantation (ASCT), followed by recovery improvements during rehabilitation phases.²² The exercise group in one study experienced a 15% improvement in fatigue scores compared to a 28% deterioration in the control group (P =0.01–0.03).¹⁵

Home-based aerobic and resistance exercises were frequently implemented and well-accepted, with studies reporting their safety and effectiveness.^15,19 High-intensity interval training programs also yielded significant functional and physiological gains, with observed changes surpassing clinically important thresholds.²⁰ However, one pilot study reported feasibility challenges with its prehabilitation design, limiting its ability to draw definitive conclusions and underscoring the need for refined future interventions.¹⁷

Discussion

Principal Results and Comparison with Prior Work

This review aimed at mapping the current state of research regarding the clinical and technical applications of telemedicine in the context of HSCT. Ten eligible articles reporting experimental results were used for a critical overview of the current landscape and informing future research directions of the telemedicine-supported exercise interventions in patients undergoing HSCT. Although the number of eligible studies was limited, collectively they suggest that telemedicine modalities, ranging from telephone-based consultations to videoconferencing and wearable devices, can effectively support exercise programs for HSCT recipients before, during, and after transplantation (Figure 2). Across these studies, telemedicine-facilitated exercise regimens were generally found to be feasible, acceptable, and safe. Importantly, patients who engaged in these interventions often experienced improvements in functional capacity, including enhanced VO2peak, 6-minute walk test performance, handgrip strength, and 30-second sit-to-stand test scores, as well as better quality of life outcomes.^14–23

Figure 2 Relations between key characteristics of the included studies.

The outcome improvements resulting from telemedicine-supported exercise programs in HSCT patients are analogous to the outcome improvements reported in the comprehensive reviews of in-person exercise interventions conducted in both allo- and auto-HSCT patients.^24–26 A systematic review and meta-analyses of randomized controlled trials assessing impact of physical exercise for patients undergoing hematopoietic stem cell transplantation demonstrated sufficient evidence that recipients of HSCT benefit from physical exercise.²⁷ DeFor et al found that physical exercise provided numerous benefits for HSCT patients, including enhancing both physical and emotional recovery after transplant therapy and potentially speeding up their return to health and functionality following the procedure.²⁸ The review by Morishita et al concluded that physical exercise is beneficial for the physiological, psychological, and psychosocial health of allo-HSCT patients.²⁴ This review recommended encouraging patients to perform physical exercise before, during, and after transplantation, and stated that physical exercise should be integrated into the conditioning and recovery plans for all allo-HSCT patients.²⁴ Those results are very well aligned with the findings from the studies included in this review.

The results of this review are in concordance with the recent report by Gandhi et al⁹ concluding that telemedicine-supported physical activity yields positive results in patients undergoing HSCT. Gandhi et al underscored the potential of telemedicine-supported exercise in frail patients whose functional reserve can be significantly enhanced while following home-based exercise program.⁹ These recommendations are especially relevant in the light of the fact that not only the overall numbers of HSCT are increasing, but HSCTs in adults over 70 years old are increasing at an even greater rate.^24,25 Incorporating telemedicine into exercise interventions will aid in reducing both disability and healthcare utilization among these individuals.

Telemedicine-supported exercise programs have also a potential to address the economic and geographic barriers to access guideline-concordant care. Delamater et al found that fifty million adults reside more than 90 min from the nearest care facility. Access to cancer rehabilitation services is certainly influenced by geography; however, numerous additional factors may limit access to best available care, including sociodemographic status, health insurance and resource availability. Potential alternative strategies to address the transportation challenges for HSCT patients residing in remote location include implementation of telemedicine systems for home-based care delivery.²⁵ Overall, physical exercise delivered via telemedicine provided numerous benefits for HSCT patients, including enhancing both physical and emotional recovery after transplant therapy and potentially speeding up their return to optimal health and functional capacity following HSCT.²⁴ Future approaches for telemedicine-supported exercise in HSCT patients should be enhanced by the recent advances in machine learning optimization of individualized exercise plans,^29,30 predictive analytics utilizing patient-generated data,^31,32 and interfaces with electronic health records to support personalization,^33,34 adherence, efficacy, and safety of the home-based exercise programs.^35,36 Patient engagement in exercise programs can be facilitated by exergaming in virtual reality^37,38 and artificial intelligence-based chatbots^39,40 promoting patient education^41,42 and healthy behaviors.^43,44

Limitations

This review presented some limitations. The included studies were relatively few and predominantly pilot or feasibility trials, limiting the strength of the conclusions and their generalizability. Many studies lacked robust sample sizes and long-term follow-up data, and outcome measures varied considerably across the literature. Additionally, the majority of studies were conducted in high-income countries, which may not reflect global healthcare contexts or resource constraints. Such factors underscore the need for more geographically diverse, large-scale, and standardized research efforts to fully establish the efficacy, cost-effectiveness, and global applicability of telemedicine-based exercise interventions for HSCT patients residing in urban and rural areas. Limited information is available on differences in responses to the telemedicine-supported exercise interventions in patients receiving autologous or allogeneic stem cell transplantation. The impact of telemedicine-supported exercise interventions in patients with GVHD as compared to patients without GVHD has not been systematically studied. Another limitation is that this review did not conduct a formal risk of bias assessment for the included studies. While this could have provided a more thorough evaluation of the methodological rigor of the evidence, this limitation is mitigated by the fact that only peer-reviewed articles and experimental designs were included. These criteria inherently ensure a higher level of methodological quality and reduce the likelihood of significant bias within the included studies.

Conclusions

Telemedicine for patients pre-, post- and during HSCT demonstrated to be feasible, beneficial, acceptable and effective with improvements in quality of life and physical function. However, the evidence base remains limited by small sample sizes, short follow-up periods, and predominantly feasibility-focused designs. Future research should emphasize larger, methodologically robust trials, consistent outcome measures, and include both urban and rural settings in order to establish best practices and guide broader implementation.

Acknowledgments

We would like to express our gratitude to David Villarreal-Zegarra for his invaluable support in managing the Rayyan platform, which facilitated the screening of titles, abstracts, and full-text articles. We also extend our thanks to Stefan Escobar-Agreda for his contributions as a reviewer during the screening and selection process. Their assistance was instrumental in the successful completion of this study.

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 study has been in part supported by the contract HT9425-24-1-0264 from the Congressionally Directed Medical Research Program.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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