Abstract
Objective
To systematically evaluate the effectiveness of digital health interventions (DHIs) in lung transplant recipients and to investigate their impact on quality of life, psychological health, hospital readmission rates, and adherence, thereby providing evidence-based support for postoperative rehabilitation management.
Methods
This systematic review and meta-analysis was conducted in accordance with the PRISMA guidelines. PubMed, Web of Science, CENTRAL, Scopus, EMBASE, ClinicalTrials.gov, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP databases were searched from inception to August 2025. Randomized controlled trials (RCTs) involving adult lung transplant recipients were included, in which the intervention group received DHIs and the control group received routine follow-up. Primary outcomes included quality of life (overall SF-36 score), depression and anxiety assessed by the Hospital Anxiety and Depression Scale (HADS), hospital readmission rate, and adherence (self-monitoring, medication adherence, and exercise adherence). Pooled analyses were performed using fixed- or random-effects models as appropriate. Publication bias was assessed using funnel plots and Egger’s test.
Results
A total of 11 RCTs involving 1,187 patients were included (610 in the intervention group and 577 in the control group). Compared with routine follow-up, DHIs significantly improved the overall SF-36 score (MD = 3.52, 95% CI 0.61–6.43, P = 0.02), reduced HADS depression scores (MD = − 1.54, 95% CI − 2.23 to − 0.84, P < 0.0001) and anxiety scores (MD = − 1.77, 95% CI − 2.56 to − 0.98, P < 0.0001), and decreased hospital readmission rates (OR = 0.59, 95% CI 0.41–0.83, P = 0.003). Egger’s test indicated potential small-study bias for readmission rates (P = 0.007); however, trim-and-fill analysis identified no missing studies, and the pooled effect estimate remained unchanged. Overall adherence was significantly improved (based on 12 adherence-related outcome measures reported in 9 RCTs; OR = 2.22, 95% CI 1.63–3.02, P < 0.00001), particularly in self-monitoring (OR = 2.15) and medication adherence (OR = 2.48). Exercise adherence showed an improving trend (OR = 1.49) but did not reach statistical significance (P = 0.54). Sensitivity analyses demonstrated robust results.
Conclusions
Digital health interventions significantly improve quality of life, alleviate anxiety and depressive symptoms, enhance adherence, and reduce the risk of hospital readmission in lung transplant recipients. Despite the presence of small-study bias and heterogeneity among interventions, the overall findings were robust. DHIs may serve as an important adjunct to postoperative management following lung transplantation. Future multicenter, large-scale, high-quality RCTs with long-term follow-up are warranted to further confirm their effectiveness.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13019-026-04001-3.
Keywords: Lung transplantation, Digital health interventions, Quality of life, Hospital readmission, Adherence
Introduction
Lung transplantation represents the final therapeutic option for patients with end-stage lung diseases when conventional medical treatments fail, and it has been shown to significantly prolong survival and improve quality of life [1]. Epidemiological data indicate that the global prevalence of end-stage lung diseases—such as chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), and cystic fibrosis (CF)—continues to increase, a trend further exacerbated by population aging in certain regions. Taking IPF as an example, its annual incidence is estimated at approximately 6–17 cases per 100,000 population, with a median survival of only 3–4 years following diagnosis [2, 3]. In recent years, the number of lung transplant procedures worldwide has steadily increased, with nearly 3,000 procedures performed annually in the United States and approximately 4,000 globally. However, long-term outcomes remain suboptimal. According to international registry data, the median survival of adult lung transplant recipients is approximately 5–6 years, extending to only about 6–8 years even among those who survive the first postoperative year [4, 5]. Chronic lung allograft dysfunction (CLAD), with an incidence approaching 50% within five years after transplantation, remains a major contributor to reduced long-term survival [6].
Accumulating evidence suggests that long-term health outcomes following lung transplantation depend not only on surgical techniques and optimization of immunosuppressive regimens but also critically on patients’ health management behaviors [7, 8]. Although most recipients maintain relatively high levels of medication adherence and self-monitoring in the early postoperative period, adherence commonly declines over time as follow-up duration increases. Some patients develop nonadherent behaviors, such as missed doses or discontinuation of monitoring. Psychological factors play a pivotal role in this process. Anxiety and depression are prevalent among lung transplant recipients and not only diminish motivation for self-management but are also closely associated with acute rejection, increased infection risk, reduced quality of life, and higher rates of hospital readmission [7]. The cumulative effects of nonadherence substantially increase healthcare utilization, highlighting the limitations of relying solely on traditional outpatient follow-up to meet the long-term management needs of lung transplant recipients [9].
In recent years, digital health interventions (DHIs) have been increasingly applied to the remote management of lung transplant recipients. These technologies—including remote patient monitoring (RPM), mobile health (mHealth) applications, and wearable devices—enable real-time collection and dynamic feedback of physiological parameters, symptom changes, and medication adherence. Compared with conventional follow-up, such interventions facilitate continuous patient–provider communication, individualized health management strategies, and timely psychological support [10–12]. Evidence from a recent study in kidney transplant recipients suggests that DHIs have the potential to enhance adherence, improve quality of life, and, to some extent, reduce hospital readmission rates and alleviate symptoms of anxiety and depression [13]. Nevertheless, existing studies vary considerably in intervention modalities, duration, and outcome selection, resulting in inconsistent findings. In particular, there is a lack of systematic integration of multidimensional outcomes, including adherence, hospital readmission, quality of life, and psychological health.
Therefore, the present study aimed to systematically evaluate the effectiveness of digital health–based remote interventions in lung transplant recipients through a meta-analysis. The primary outcomes of interest included adherence, hospital readmission rates, quality of life (overall SF-36 score), and psychological health (HADS-A for anxiety and HADS-D for depression), with the goal of providing evidence-based support for long-term care and rehabilitation management following lung transplantation.
Methods
PICOS eligibility criteria
The eligibility criteria were defined according to the PICOS framework: Population (P): Adult patients who had undergone lung transplantation.Intervention (I): The intervention group received digital health interventions (DHIs), including but not limited to remote patient monitoring, mobile health management applications, wearable device–based monitoring, and digital education platforms. These interventions could incorporate personalized feedback, health guidance, or behavioral interventions.Comparison (C): The control group received routine follow-up or standard rehabilitation guidance, without continuous digital health monitoring or personalized feedback.Outcomes (O): Primary outcomes included quality of life (overall SF-36 score), HADS depression score, HADS anxiety score, hospital readmission rate, and adherence (including self-monitoring adherence, medication adherence, and exercise adherence).Study Design (S): Prospective randomized controlled trials (RCTs) that were publicly published and provided complete extractable data.
Inclusion criteria
Studies meeting all of the following criteria were included:1.Prospective randomized controlled trial (RCT) design. 2. Participants were adult lung transplant recipients. 3.The intervention group received digital health interventions, including but not limited to mobile health management applications, remote monitoring systems, wearable devices combined with health guidance, or digital education platforms. 4. The control group received routine follow-up or standard rehabilitation guidance, without personalized digital health monitoring or feedback. 5. At least one of the predefined primary outcomes was reported (quality of life, HADS depression score, HADS anxiety score, hospital readmission rate, or adherence)0.6. Full-text articles published in English or Chinese with sufficient data available for extraction.
Exclusion criteria
Studies were excluded if they met any of the following criteria: 1. Non-original studies, including reviews, meta-analyses, case reports, or conference abstracts.2. Studies without a control group or in which the intervention group did not receive digital health interventions 0.3. Missing or incomplete original data that could not be used for pooled analysis 0.4. Full text unavailable or only abstracts accessible 0.5. Study population not consisting of lung transplant recipients.
Literature search strategy
A comprehensive systematic search was independently conducted by two researchers (the second and third authors). Nine databases were searched: PubMed, Web of Science, the Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, EMBASE, ClinicalTrials.gov, China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP Database.The search strategy combined controlled vocabulary and free-text terms in both English and Chinese, including “lung transplantation,” “digital health,” “mobile health,” “telemonitoring,” “mHealth,” “eHealth,”. Boolean operators were used to construct multiple search combinations. The search period extended from database inception to August 2025.
Study selection and data extraction
Two reviewers independently performed the initial screening of titles and abstracts, followed by full-text review of potentially eligible studies. Data extraction was conducted independently by the same reviewers. Any disagreements were resolved through discussion with a third reviewer until consensus was reached.Extracted data included first author and year of publication, total sample size and group allocation (intervention/control), mean age of participants, detailed descriptions of intervention and control conditions, duration of follow-up, and reported primary outcomes. All extracted information was organized in a standardized format, and the results are summarized in Table 1.
Table 1.
Characteristics of randomized controlled trials (RCTs) investigating digital health interventions in lung transplant recipients
| First author | Year | Sample size (intervention/control) | Age (mean ± SD or range) | Intervention | Control | Follow-up | Outcomes |
|---|---|---|---|---|---|---|---|
| Blumenthal [14] | 2025 | 180 (90/90) | 60 ± 12 | Digital psychological and exercise intervention platform: Fitbit wearable device for real-time activity monitoring + data-driven personalized exercise prescription + weekly remote cognitive-behavioral coping skills training via telephone | Basic digital health education: Fitbit self-monitoring without personalized exercise feedback, routine rehabilitation education | 12 weeks | HADS anxiety/depression, readmission rate |
| Zhou Haiqin [15] | 2019 | 80 (40/40) | Int: 50.85 ± 14.76; Ctrl: 51.85 ± 14.85 | Intelligent home health monitoring app: daily uploads of lung function, weight, blood pressure, and oxygen saturation; real-time monitoring by healthcare team + personalized health guidance via WeChat | Routine follow-up: WeChat health knowledge updates + outpatient visits + paper health record booklet | 3 months | SF-36 QoL, HADS anxiety/depression, readmission rate |
| Finkelstein [16] | 2013 | 65 (30/35) | Int: 37–69; Ctrl: 23–71 | Remote lung function monitoring + intelligent triage system: daily upload of spirometry data and symptom reports, Bayesian algorithm analysis with automated follow-up recommendations | Remote lung function monitoring + nurse-led manual triage: daily uploads reviewed by nurses for follow-up arrangements | 12–24 months | SF-36 QoL, adherence |
| Breuls [17] | 2025 | 90 (48/42) | Int: 61 ± 6; Ctrl: 61 ± 5 | Remote physical activity telecoaching: Fitbit continuous step monitoring + app-based progress feedback + personalized activity goals + biweekly remote health coaching calls | Light telecoaching: Fitbit basic monitoring + app step count display + general activity advice without personalization | 12 months | SF-36 QoL, HADS anxiety/depression |
| Suhling [18] | 2014 | 64 (32/32) | Int: 52 (35.9–57.6); Ctrl: 45 (33.3–53.9) | Tablet-based digital education: iPad preloaded with self-study modules and videos on medication management, adherence strategies, and health education | Conventional face-to-face education by nurses in outpatient settings | 6 months | Adherence, readmission rate |
| Geramita [19] | 2020 | 105 (53/52) | Int: 62 (51–67); Ctrl: 61 (49–66) | Mobile health management app (Pocket PATH®): medication reminders, self-monitoring of vital signs and lung function, trend analysis, and abnormal value alerts | Routine care: paper health records + standard outpatient follow-up | 3.9 years | Adherence, readmission rate |
| Rosenberger [20] | 2017 | 182 (88/94) | Int: 57 ± 13; Ctrl: 58 ± 14 | Mobile health management app (Pocket PATH®): long-term monitoring of medication adherence and vital signs, self-reporting, and abnormal value alerts | Routine care: paper health records + standard outpatient follow-up | 5.7 years | Adherence |
| DeVito Dabbs [21] | 2016 | 201 (99/102) | 62 (51–67) | Mobile health management app (Pocket PATH®): daily recording of lung function, vital signs, and symptoms; automated trend analysis; real-time abnormal alerts with recommendations to contact transplant coordinator | Standard discharge education + paper record without digital monitoring | 12 months | Adherence, readmission rate |
| Guldager [22] | 2020 | 50 (25/25) | Int: 53 ± 12; Ctrl: 54 ± 11 | E-learning platform: online multimedia modules on medication adherence, lifestyle management, and home health monitoring, accessible for repeated learning | Standard patient education: face-to-face teaching during hospitalization or outpatient visits, no online resources | 1 month | Adherence |
| Hume [23] | 2022 | 14 (7/5) | Int: 57 ± 9; Ctrl: 58 ± 4 | Digital exercise behavior intervention: pedometer + app for real-time data synchronization, personalized exercise goal setting, remote feedback, and telephone health coaching | Routine rehabilitation guidance + motivational interviewing without ongoing digital monitoring or personalized feedback | 12 weeks | SF-36 QoL, HADS anxiety/depression, readmission rate |
| Sengpiel [24] | 2010 | 56 (28/28) | Int: 49.5 (33.3–55.8); Ctrl: 48.5 (40.5–55.8) | Bluetooth-enabled remote spirometry system: daily home spirometry with automatic Bluetooth upload to central database, automated abnormal value alerts | Home spirometry with manual upload during outpatient visits for manual review | 6 months | Adherence, HADS anxiety/depression, readmission rate |
Quality assessment
All included studies were randomized controlled trials and were assessed for methodological quality using the Cochrane Risk of Bias 2 (RoB 2) tool. The evaluation covered the randomization process, risk of bias due to deviations from intended interventions, handling of missing outcome data, bias in outcome measurement, and completeness of outcome reporting. Quality assessment was independently performed by two researchers, with discrepancies resolved by discussion. The results of the quality assessment are presented in Table 2.
Table 2.
Risk of bias assessment (RoB 2) for included randomized controlled trials of digital health interventions in lung transplant recipients
| First author (Year) | Randomization process | Bias due to deviations from intended interventions | Missing outcome data | Outcome measurement | Selective reporting | Overall risk of bias |
|---|---|---|---|---|---|---|
| Zhou Haiqin (2019) | Low risk | Some concerns | Low risk | Some concerns | Low risk | Some concerns |
| Finkelstein (2013) | Low risk | Low risk | Some concerns | Low risk | Low risk | Low risk |
| Breuls (2025) | Low risk | Low risk | Low risk | Low risk | Low risk | Low risk |
| Suhling (2014) | Low risk | Some concerns | Low risk | Some concerns | Low risk | Some concerns |
| Geramita (2020) | Low risk | Some concerns | Some concerns | Some concerns | Low risk | Some concerns |
| Rosenberger (2017) | Low risk | Some concerns | Some concerns | Low risk | Low risk | Some concerns |
| DeVito Dabbs (2016) | Low risk | Some concerns | Some concerns | Some concerns | Low risk | Some concerns |
| Guldager (2020) | Some concerns | Some concerns | Low risk | Some concerns | Low risk | Some concerns |
| Hume (2022) | Low risk | Some concerns | Low risk | Some concerns | Low risk | Some concerns |
| Sengpiel (2010) | Low risk | Some concerns | Low risk | Some concerns | Low risk | Some concerns |
| Blumenthal (2025) | Low risk | Some concerns | Some concerns | Some concerns | Low risk | Some concerns |
Statistical analysis
This meta-analysis was performed using RevMan version 5.4 and Stata version 16.0. Continuous outcomes (e.g., quality of life scores and HADS depression and anxiety scores) were expressed as weighted mean differences (MDs) with 95% confidence intervals (CIs). Dichotomous outcomes (e.g., hospital readmission rate and adherence) were reported as odds ratios (ORs) with 95% CIs.Heterogeneity was assessed using the I² statistic and Cochran’s Q test. A random-effects model was applied when substantial heterogeneity was present (I² > 50% or P < 0.10); otherwise, a fixed-effects model was used. Sensitivity analyses were conducted by sequentially excluding individual studies to assess the robustness of the pooled results.Publication bias was evaluated using funnel plots and Egger’s test. When potential publication bias was detected, the trim-and-fill method was applied to estimate the number of potentially missing studies and to calculate adjusted pooled effect estimates.
Results
Literature search and study selection
A total of nine Chinese and English databases were systematically searched, including PubMed, Web of Science, the Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, EMBASE, ClinicalTrials.gov, China National Knowledge Infrastructure (CNKI), Wanfang Data, and the VIP Database.The initial search yielded 1,246 records. After removal of 243 duplicate records, 1,003 studies remained for title and abstract screening. During this stage, 612 records were excluded because they were non-clinical studies, such as reviews, meta-analyses, case reports, animal studies, or conference abstracts. The remaining 391 studies underwent further screening.At the secondary abstract screening stage, 271 records were excluded due to the absence of a control group, inclusion of non–lung transplant populations, or interventions that did not meet the definition of digital health interventions. Consequently, 120 articles were assessed for full-text eligibility.Following full-text review, 109 articles were excluded for reasons including inappropriate intervention modalities (e.g., lack of a digital health component or insufficient intervention intensity), failure to report prespecified outcomes, incomplete data, or unavailability of the full text. Ultimately, 11 randomized controlled trials met the inclusion criteria and were included in the systematic review and meta-analysis. The detailed study selection process is illustrated in Fig. 1.
Fig. 1.
PRISMA flow diagram of study selection process for this review
Characteristics of the included studies
A total of 11 randomized controlled trials (RCTs) involving 1,187 lung transplant recipients were included, with 610 participants allocated to the intervention group and 577 to the control group. The included studies were published between 2010 and 2025 and all compared digital health interventions with routine care.The reporting of outcome measures varied across studies. Quality of life, assessed using the overall SF-36 score, was reported in four studies, involving a total of 247 participants (125 in the intervention group and 122 in the control group). HADS depression scores were reported in five studies, with a total sample size of 418 participants (213 in the intervention group and 205 in the control group). Similarly, HADS anxiety scores were reported in five studies, comprising the same 418 participants (213 in the intervention group and 205 in the control group). Hospital readmission rates were reported in eight studies, including 788 participants (397 in the intervention group and 391 in the control group).Adherence-related outcomes were reported in nine of the 11 included RCTs. Because some studies assessed multiple dimensions of adherence within the same trial (e.g., medication adherence and self-monitoring adherence), a total of 12 adherence-related outcome datasets were included in the adherence meta-analysis. The overall adherence analysis involved 1,313 participants, with 650 in the intervention group and 663 in the control group. Specifically, self-monitoring adherence was reported in five studies (476 participants), medication adherence in five studies (524 participants), and exercise adherence in two studies (97 participants).Detailed characteristics of the included studies—including first author, year of publication, total sample size, mean age of participants (mean ± SD), intervention and control conditions, duration of follow-up, and reported outcomes—are summarized in Table 1.
Risk of bias assessment
All 11 included randomized controlled trials (RCTs) employed explicit randomization procedures. Ten studies were judged to be at low risk of bias with respect to random sequence generation and allocation concealment, whereas one study was rated as having “some concerns” due to insufficient reporting of randomization details.Regarding bias due to deviations from intended interventions, only two studies were assessed as having a low risk of bias because they applied double-blind designs or equivalent intervention conditions between groups. Owing to the nature of digital health interventions, most studies adopted open-label designs in which both participants and investigators were aware of group assignments, potentially introducing behavioral intervention effects.With respect to missing outcome data, most studies reported high follow-up completion rates. However, several trials with longer follow-up durations (e.g., Rosenberger 2017 and Geramita 2020) exhibited relatively higher attrition rates, resulting in an increased risk of bias in this domain.In terms of outcome measurement, studies assessing objective outcomes such as hospital readmission rates were generally judged to be at low risk of bias. In contrast, studies involving self-reported questionnaires, including quality of life measures and HADS scores, were more frequently rated as having “some concerns” due to the potential for measurement bias.All included studies fully reported the prespecified primary and secondary outcomes outlined in their study protocols, and the risk of selective reporting was therefore considered low across all trials. Overall, the methodological quality of the included studies was judged to be generally good. Detailed risk-of-bias assessments are presented in Table 2.
Meta-analysis results
Quality of life (overall SF-36 score)
Four studies reported quality of life outcomes assessed using the overall SF-36 score, involving a total of 247 participants (125 in the intervention group and 122 in the control group). Heterogeneity testing indicated no significant heterogeneity among the included studies (I² = 0%, P = 0.53); therefore, a fixed-effects model was applied.The meta-analysis demonstrated that, compared with the control group, lung transplant recipients receiving digital health interventions achieved a significantly higher overall SF-36 score (MD = 3.52, 95% CI 0.61–6.43, P = 0.02). The forest plot is presented in Fig. 2.
Fig. 2.
Forest plot of the effect of digital health interventions on quality of life assessed by the overall SF-36 score in lung transplant recipients
Sensitivity analysis
A sensitivity analysis was performed using the leave-one-out method, whereby each study was sequentially removed and the pooled effect was recalculated. The results showed that exclusion of any single study did not materially alter the overall findings. The pooled effect estimates remained statistically significant with consistent directions of effect, with MDs ranging from approximately 3.30 to 5.82, and heterogeneity remained unchanged (I² = 0%) across all analyses, indicating robust results. The sensitivity analysis is presented in Fig. 3.
Fig. 3.
Sensitivity analysis of the effect of digital health interventions on quality of life assessed by the overall SF-36 score in lung transplant recipients
HADS depression score
Five studies reported HADS depression scores in lung transplant recipients, involving a total of 418 participants (213 in the intervention group and 205 in the control group). Heterogeneity assessment indicated no significant between-study heterogeneity (I² = 49%, P = 0.10); therefore, a fixed-effects model was applied.The meta-analysis showed that, compared with the control group, patients receiving digital health interventions had significantly lower HADS depression scores (MD = − 1.54, 95% CI − 2.23 to − 0.84, P < 0.0001), indicating that digital health interventions were associated with a reduction in depressive symptoms among lung transplant recipients. The forest plot is shown in Fig. 4.
Fig. 4.
Forest plot comparing digital health interventions with routine care on HADS depression scores among lung transplant recipients
Sensitivity analysis
To assess the robustness of the findings, a sensitivity analysis was conducted using the leave-one-out approach, whereby each study was sequentially excluded and the pooled effect was recalculated. The results demonstrated that removal of any single study did not affect the statistical significance of the pooled estimate, and the direction of effect remained consistent. The pooled MD ranged from approximately − 1.02 to − 1.54, and heterogeneity decreased to 0% (I² = 0%), indicating high stability of the results. The sensitivity analysis is presented in Fig. 5.
Fig. 5.
Sensitivity analysis of the effect of digital health interventions on HADS depression scores in lung transplant recipients
HADS anxiety score
Five studies reported HADS anxiety scores in lung transplant recipients, involving a total of 418 participants (213 in the intervention group and 205 in the control group). Heterogeneity assessment revealed no significant between-study heterogeneity (I² = 20%, P = 0.29); therefore, a fixed-effects model was applied.The meta-analysis demonstrated that, compared with the control group, patients receiving digital health interventions had significantly lower HADS anxiety scores (MD = − 1.77, 95% CI − 2.56 to − 0.98, P < 0.0001), indicating that digital health interventions were associated with alleviation of anxiety symptoms among lung transplant recipients. The forest plot is presented in Fig. 6.
Fig. 6.
Forest plot of the effect of digital health interventions on HADS anxiety scores in lung transplant recipients
Sensitivity analysis
To verify the robustness of the results, a sensitivity analysis was conducted using the leave-one-out method, whereby each study was sequentially excluded and the pooled estimate was recalculated. The results indicated that exclusion of any single study did not alter the statistical significance of the pooled effect, and the direction of the effect remained consistent. The pooled MD ranged from approximately − 1.42 to − 1.77, and heterogeneity decreased to 0% (I² = 0%), suggesting high stability of the findings. The sensitivity analysis is shown in Fig. 7.
Fig. 7.
Sensitivity analysis of the effect of digital health interventions on HADS anxiety scores in lung transplant recipients
Hospital readmission rate
Eight studies reported hospital readmission rates among lung transplant recipients, involving a total of 788 participants (397 in the intervention group and 391 in the control group). Heterogeneity testing showed no significant between-study heterogeneity (I² = 0%, P = 0.98); therefore, a fixed-effects model was applied.The meta-analysis demonstrated that, compared with the control group, patients receiving digital health interventions had a significantly lower hospital readmission rate (OR = 0.59, 95% CI 0.41–0.83, P = 0.003), suggesting that digital health interventions may effectively reduce the risk of hospital readmission in lung transplant recipients. The forest plot is shown in Fig. 8.
Fig. 8.
Forest plot of the effect of digital health interventions on hospital readmission rates in lung transplant recipients
Sensitivity analysis
To assess the robustness of the results, a sensitivity analysis was performed using the leave-one-out method, in which each study was sequentially removed and the pooled estimate was recalculated. The findings indicated that exclusion of any individual study did not alter the statistical significance of the pooled effect, and the direction of the effect remained consistent. The pooled OR remained unchanged at 0.59 across all analyses, with heterogeneity consistently at 0% (I² = 0%), indicating that the results were stable and reliable. The sensitivity analysis is presented in Fig. 9.
Fig. 9.
Sensitivity analysis of the effect of digital health interventions on hospital readmission rates in lung transplant recipients
Adherence
Among the 11 included randomized controlled trials, nine studies reported adherence-related outcomes. Because some trials assessed multiple dimensions of adherence within the same randomized controlled study, a total of 12 adherence-related outcome datasets were included in the meta-analysis. These datasets covered self-monitoring adherence, medication adherence, and exercise adherence, with a cumulative sample size of 1,313 outcome-level observations, including 650 in the intervention group and 663 in the control group. It should be noted that this sample size reflects the total number of adherence outcome entries rather than independent participants, as multiple adherence dimensions were reported within certain trials.Heterogeneity testing revealed no significant between-study heterogeneity (I² = 0%, P = 0.75); therefore, a fixed-effects model was applied. The meta-analysis demonstrated that, compared with the control group, lung transplant recipients receiving digital health interventions exhibited significantly improved overall adherence (OR = 2.22, 95% CI 1.63–3.02, P < 0.00001).Subgroup analyses were performed according to different adherence dimensions. For self-monitoring adherence, five studies were included, involving 252 participants in the intervention group and 224 in the control group. The results indicated that self-monitoring adherence was significantly higher in the intervention group than in the control group (OR = 2.15, 95% CI 1.44–3.22, P = 0.0002). For medication adherence, five studies were included, comprising 273 participants in the intervention group and 251 in the control group. Medication adherence was also significantly improved in the intervention group compared with the control group (OR = 2.48, 95% CI 1.48–4.16, P = 0.0006).For exercise adherence, two studies were included, involving 50 participants in the intervention group and 47 in the control group. Although exercise adherence tended to be higher in the intervention group, the difference did not reach statistical significance (OR = 1.49, 95% CI 0.42–5.26, P = 0.54). The forest plots are presented in Fig. 10.
Fig. 10.
Forest plot of the effects of digital health interventions on adherence outcomes, including self-monitoring adherence, medication adherence, and exercise adherence, in lung transplant recipients
Funnel plot and egger’s test
1 Publication bias assessment for quality of life (overall SF-36 score)
Publication bias for the quality of life outcome (overall SF-36 score) was assessed using funnel plots. Visual inspection of the funnel plot showed an overall symmetric distribution, with only a small number of studies slightly deviating from the symmetry axis, suggesting no evident small-study effects (Fig. 11). Egger’s regression test was further conducted to statistically evaluate publication bias. The results indicated that the intercept (bias) was not statistically significant (P = 0.992), suggesting no significant publication bias for this outcome. Detailed results of Egger’s test are presented in Table 3.
Fig. 11.
Funnel plot assessing publication bias for quality of life measured by the overall SF-36 score in lung transplant recipients
Table 3.
Egger’s test for publication bias in quality of life assessed by the overall SF-36 score
| Std_Eff | | Coef. | Std. Err. | t | P>|t| | [95% Conf. Interval] |
|---|---|---|---|---|---|
| slope | | 0.2882876 | 0.4612907 | 0.62 | 0.596 | −1.696486 2.273061 |
| bias | | 0.0208162 | 1.793855 | 0.01 | 0.992 | −7.697517 7.73915 |
2 Publication bias assessment for HADS depression
Publication bias for the HADS depression outcome was evaluated using funnel plots. Visual inspection revealed an overall symmetric distribution, with studies evenly distributed on both sides of the symmetry axis, indicating no apparent small-study effects (Fig. 12). Egger’s regression test was subsequently performed for statistical assessment. The results showed that the intercept (bias) was not statistically significant (P = 0.767), suggesting no evidence of significant publication bias for this outcome. Detailed results are presented in Table 4.
Fig. 12.
Funnel plot assessing publication bias for HADS depression scores in lung transplant recipients
Table 4.
Egger’s test for publication bias in HADS depression scores
| Std_Eff | | Coef. | Std. Err. | t | P>|t| | [95% Conf. Interval] |
|---|---|---|---|---|---|
| slope | | − 0.3000158 | 0.402252 | −0.75 | 0.510 |
−1.580161 0.9801298 |
| bias | | − 0.5877487 | 1.809478 | −0.32 | 0.767 | −6.346314 5.170817 |
3 Publication bias assessment for HADS anxiety
Publication bias for the HADS anxiety outcome was assessed using funnel plots. Visual inspection of the funnel plot indicated a relatively symmetric distribution, with study points evenly distributed on both sides of the symmetry axis, suggesting no apparent small-study effects (Fig. 13). Egger’s regression test was further conducted for statistical evaluation. The results showed that the intercept (bias) was not statistically significant (P = 0.554), indicating no evidence of significant publication bias for this outcome. Detailed results are presented in Table 5.
Fig. 13.
Funnel plot assessing publication bias for HADS anxiety scores in lung transplant recipients
Table 5.
Egger’s test for publication bias in HADS anxiety scores
| Std_Eff | | Coef. | Std. Err. | t | P>|t| | [95% Conf. interval] |
|---|---|---|---|---|---|
| slope | | − 0.2796771 | 0.2653856 | −1.05 | 0.369 |
−1.124252. 5,648,983 |
| bias | | − 0.794199 | 1.194766 | −0.66 | 0.554 | −4.596479 3.008081 |
4 Publication bias assessment for hospital readmission rate
Publication bias for the hospital readmission outcome was assessed using funnel plots. Visual inspection revealed a certain degree of asymmetry, with several studies deviating toward the left side of the funnel plot, suggesting the potential presence of small-study effects (Fig. 14). Egger’s regression test was further performed for statistical evaluation. The results showed that the intercept (bias) was statistically significant (P = 0.007), indicating a potential risk of publication bias for this outcome. Among the eight included studies, several trials had relatively small sample sizes (fewer than 50 participants per study) and consistently reported significant reductions in hospital readmission rates (OR < 0.6). In addition, the majority of included studies reported positive findings, with few studies presenting null or non-significant results. Some of the small-sample studies were single-center trials with short follow-up durations, which may be associated with limitations in randomization procedures and blinding implementation. Detailed results are presented in Table 6.
Fig. 14.
Funnel plot assessing publication bias for hospital readmission rates in lung transplant recipients
Table 6.
Egger’s test for publication bias in hospital readmission rates
| Std_Eff | | Coef. | Std. Err. | t | P>|t| | [95% Conf. interval] |
|---|---|---|---|---|---|
| slope | | 0.9171209 | 0.0385699 | 23.78 | 0.000 | 0.8227437 1.011498 |
| bias | | − 0.4366677 | 0.1103926 | −3.96 | 0.007 | − 0.7067886 − 0.1665468 |
Trim-and-fill analysis for hospital readmission rate
As noted above, the funnel plot for the hospital readmission outcome exhibited a certain degree of asymmetry, and Egger’s regression test suggested potential publication bias (P = 0.007). To further evaluate the impact of this potential bias, the trim-and-fill method was applied. The analysis indicated that no studies were imputed as missing. Moreover, the direction of the pooled effect estimate remained unchanged after adjustment (Table 7), suggesting that the potential publication bias had a limited influence on the overall results.
Table 7.
Continuity correction and trim-and-fill analysis
| Analysis | OR | 95% CI | Number of missing studies |
|---|---|---|---|
| Original | 0.71 | 0.52–0.98 | – |
| After Trim-and-Fill Adjustment | 0.71 | 0.52–0.98 | 0 |
5 Publication bias assessment for adherence outcomes
Publication bias for adherence-related outcomes was evaluated using funnel plots. Overall, the funnel plot showed a largely symmetric distribution, although a few studies slightly deviated from the symmetry axis (Fig. 15). For self-monitoring adherence, visual inspection of the funnel plot demonstrated an overall symmetric distribution. Egger’s regression test indicated that the intercept (bias) was not statistically significant (P = 0.106), suggesting no evidence of significant publication bias. For medication adherence, the funnel plot also appeared approximately symmetric, and Egger’s regression test showed no statistically significant bias (P = 0.660), indicating the absence of significant publication bias. For exercise adherence, the number of included studies was small (n = 2); therefore, assessment of funnel plot symmetry and the statistical power of Egger’s test were limited. Nevertheless, no apparent evidence of publication bias was observed. Overall, no significant publication bias was detected across adherence-related outcomes. Detailed results are presented in Tables 8, 9 and 10.
Fig. 15.
Funnel plot assessing publication bias for adherence outcomes, including self-monitoring adherence, medication adherence, and exercise adherence, in lung transplant recipients
Table 8.
Egger’s test for publication bias in self-monitoring adherence
| Std_Eff | | Coef. | Std. Err. | t | P>|t| | [95% Conf. Interval] |
|---|---|---|---|---|---|
| slope | | 1.275572 | 0.0802286 | 15.90 | 0.001 | 1.020249 1.530895 |
| bias | | −1.192959 | 0.5203087 | −2.29 | 0.106 |
−2.848813 0.4628958 |
Table 9.
Egger’s test for publication bias in medication adherence
| Std_Eff | | Coef. | Std. Err. | t | P>|t| | [95% Conf. Interval] |
|---|---|---|---|---|---|
| slope | | 1.09954 | 0.0819212 | 13.42 | 0.001 | 0.8388303 1.36025 |
| bias | | − 0.2781656 | 0.5726806 | −0.49 | 0.660 | −2.100691 1.54436 |
Table 10.
Egger’s test for publication bias in exercise adherence
| Std_Eff | | Coef. | Std. Err. | t | P>|t| | [95% Conf. Interval] |
|---|---|---|---|---|---|
| slope | | 1.012286 | - | - | - | - |
| bias | | 0.0547421 | - | - | - | - |
Discussion
In this study, a total of 11 randomized controlled trials involving 1,187 lung transplant recipients were systematically included, and the effectiveness of digital health interventions in this population was comprehensively evaluated through a meta-analysis. The findings demonstrated that digital health interventions were associated with multidimensional improvements in postoperative outcomes, including significant enhancement of quality of life, marked reductions in anxiety and depressive symptoms, a lower risk of hospital readmission, and a substantial improvement in overall adherence.With respect to quality of life, four studies involving 247 participants showed that digital health interventions significantly improved the overall SF-36 score (MD = 3.52, P = 0.02). Sensitivity analyses further confirmed the robustness of this finding. Potential mechanisms underlying this improvement may include the promotion of physical activity through wearable devices and remote feedback, enhanced self-management confidence, and improved health literacy facilitated by digital education platforms, all of which may contribute to better overall health perception and quality of life. For lung transplant recipients, improvements in postoperative quality of life are not only a key indicator of successful rehabilitation but are also closely linked to long-term adherence and survival outcomes. In terms of psychological health, digital health interventions were associated with significant reductions in both depressive (MD = − 1.54, P < 0.0001) and anxiety symptoms (MD = − 1.77, P < 0.0001), with sensitivity analyses indicating stable results. These effects may be attributable to the continuous psychological support and feedback provided by digital health tools, such as remote cognitive behavioral interventions, online health coaching, and peer support, which may reduce feelings of isolation and uncertainty while enhancing patients’ sense of control. By fostering sustained engagement and reassurance, digital health interventions may help alleviate emotional distress commonly experienced by lung transplant recipients during long-term recovery.
Regarding hospital readmission, results from eight studies involving 788 participants demonstrated that digital health interventions significantly reduced the risk of readmission (OR = 0.59, P = 0.003). Interventions such as remote monitoring of lung function, vital signs, and symptom fluctuations may facilitate early detection and timely management of acute rejection, infections, or early manifestations of chronic lung allograft dysfunction, thereby preventing clinical deterioration that necessitates hospitalization. However, publication bias assessment for this outcome revealed funnel plot asymmetry and a statistically significant Egger’s test (P = 0.007), suggesting potential small-study effects. Subsequent trim-and-fill analysis indicated that no studies were imputed as missing and that the adjusted pooled effect remained consistent with the original estimate (OR = 0.71), suggesting that the influence of potential publication bias on this outcome was limited. Nevertheless, the readmission results should be interpreted with appropriate caution.Adherence outcomes were also notably improved with digital health interventions. Based on a meta-analysis of 12 adherence-related outcome datasets derived from nine randomized controlled trials, digital health interventions were associated with a significant improvement in overall adherence (OR = 2.22, 95% CI 1.63–3.02, P < 0.00001), particularly in self-monitoring adherence (OR = 2.15) and medication adherence (OR = 2.48). These benefits may be explained by functionalities inherent to digital tools, such as automated medication reminders, real-time data feedback, trend visualization, and alerts for abnormal values, which can reduce missed doses, delayed intake, or lapses in monitoring. Although exercise adherence showed a favorable trend (OR = 1.49), the difference did not reach statistical significance, likely due to limited sample size and relatively short intervention duration. This finding highlights the need for further optimization of exercise intervention design and long-term adherence support strategies in future studies.From the perspective of intervention characteristics, the digital health interventions included in this review primarily comprised remote physiological monitoring, mobile application–based self-management platforms, wearable device–assisted exercise programs, and digital health education. A shared feature across these interventions was the reinforcement of patient self-management through continuous monitoring and feedback. However, direct comparative evidence regarding the relative effectiveness of different intervention modalities remains limited, underscoring the need for future head-to-head trials.
The observed improvements in quality of life are consistent with findings from previous studies in transplant recipients and patients with chronic respiratory diseases. For example, remote rehabilitation programs incorporating wearable device monitoring and personalized feedback have been shown to improve SF-36 scores and physical functioning in heart transplant recipients [25], while digital exercise interventions in patients with chronic obstructive pulmonary disease have similarly enhanced quality of life and exercise capacity [26]. Proposed mechanisms include increased physical activity facilitated by real-time feedback, strengthened self-management capacity through personalized education and support, and stabilization of daily functioning through reduced hospitalizations and acute events [27, 28]. It is noteworthy that some short-term studies failed to detect significant improvements [28], suggesting that intervention duration, baseline patient characteristics, and adherence levels may modulate intervention effectiveness. Consistent with prior evidence, this study demonstrated a significant reduction in depressive symptoms among lung transplant recipients receiving digital health interventions. Previous randomized trials, such as those conducted by DeVito Dabbs et al. [28], have shown that smartphone-based self-management platforms can reduce depression risk in transplant populations. Similar benefits have been observed in heart transplant recipients receiving rehabilitation programs combined with remote psychological support [29]. Mechanistically, digital health interventions may mitigate depressive symptoms by reducing loneliness and helplessness through continuous communication, while enhancing self-efficacy via personalized goal setting and health feedback [30, 31]. Indirect benefits, including increased physical activity, improved sleep quality, and reduced hospital readmissions, may further contribute to psychological recovery [32]. Digital health interventions also significantly reduced anxiety levels in lung transplant recipients, in line with previous studies in transplant and chronic disease management populations. For instance, Hume et al. reported reduced anxiety scores following remote exercise guidance in lung transplant recipients [23], and prior systematic reviews have demonstrated the effectiveness of digital psychological interventions in alleviating anxiety among patients with cancer and chronic diseases [33]. Potential mechanisms include reduced uncertainty related to graft function and complications through timely monitoring and feedback, enhanced perceived controllability via education and goal setting, and the incorporation of cognitive behavioral therapy–based components into digital platforms to promote adaptive emotional regulation strategies [34, 35].
Although digital health interventions significantly reduced hospital readmission rates, publication bias assessment suggested potential small-study effects. Nevertheless, trim-and-fill analysis indicated that the pooled effect estimate remained robust. Previous studies have similarly reported that remote monitoring can reduce hospitalization risks related to acute rejection, infections, or medication-related adverse events in transplant recipients [36]. For example, Suhling et al. demonstrated that tablet-based education and monitoring reduced readmission events among lung transplant recipients [37]. These effects may be mediated by earlier identification of abnormal physiological changes, timely adjustment of immunosuppressive regimens, and improved patient capacity to recognize early warning signs of complications [37–39]. Overall adherence, particularly self-monitoring and medication adherence, was significantly improved in this study. These findings are consistent with prior randomized trials by Rosenberger et al. [20] and Guldager et al. [22], which demonstrated that mobile application–based reminders and online self-monitoring tools effectively enhanced adherence behaviors. Digital functionalities such as automated reminders, trend analysis, and abnormal value alerts likely play a central role in minimizing forgotten doses and missed monitoring events [40]. Although exercise adherence did not reach statistical significance, the observed trend suggests potential benefit, warranting further investigation with larger samples and longer follow-up durations [41]. Despite its strengths, this study has several limitations. First, although all included studies were randomized controlled trials, most employed open-label designs, and several outcomes—including quality of life, psychological scores, and adherence—were based on self-reported measures, which may introduce subjective bias. Second, publication bias assessment suggested potential small-study effects for hospital readmission outcomes, although trim-and-fill analysis indicated limited impact on the pooled estimate. Third, although 12 adherence-related outcome datasets were included, they originated from only nine trials, and some subgroup analyses—particularly exercise adherence—were based on a small number of studies, resulting in limited statistical power. Additionally, heterogeneity in intervention formats, follow-up duration, and implementation intensity across studies may have influenced certain outcomes.Nevertheless, from a clinical perspective, the present findings support the integration of digital health interventions—particularly those centered on remote monitoring and mobile health platforms—as a complementary strategy to routine outpatient follow-up in the postoperative management of lung transplant recipients. Such interventions may be especially beneficial for patients requiring long-term adherence support and close clinical monitoring.
Conclusions
This systematic review and meta-analysis demonstrated that digital health interventions were consistently associated with improvements in quality of life, reductions in psychological symptoms, enhanced adherence, and a lower risk of hospital readmission among lung transplant recipients. Although potential publication bias and heterogeneity in study design were identified for certain outcomes, sensitivity analyses and trim-and-fill results indicated that the overall findings were robust. Digital health interventions may serve as a valuable adjunct to postoperative rehabilitation and long-term management following lung transplantation. Future multicenter, large-scale, high-quality randomized controlled trials with long-term follow-up are warranted to further confirm their effectiveness.
Supplementary Information
Author contributions
Yibao Deng was responsible for study design, manuscript drafting, and revision, as well as covering publication-related costs. Xiaomin Lin and Ying Lin contributed to case data collection, data analysis, and figure preparation. Wenjie Chen performed manuscript proofreading and acted as the correspondence author. All four authors contributed equally to this work.
Funding
This is a secondary research study and did not receive any form of financial support.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
This study strictly adheres to ethical research principles and has obtained ethical approval from the Ethics Committee of Zhaoqing First People’s Hospital.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.