Introduction

Pelvic floor dysfunction (PFD) is one of the most common gynecological diseases among women worldwide, caused by the weakening of pelvic floor supporting tissue, and consists of a group of degenerative conditions such as urinary incontinence, pelvic organ prolapse, fecal incontinence, sexual dysfunction, and other urogenital symptoms. Disorders of the pelvic floor are known to affect millions of women worldwide. The general population proportion of women with one or more pelvic floor disorders has been reported at 25%, and this markedly increases with age [1]. It has been estimated that the total number of individuals suffering from PFD in developed and developing countries will increase to approximately 43.8 million by 2050 [2]. Many risk factors are associated with PFD progression, including pregnancy, vaginal delivery, age, menopause, chronic cough, obesity, etc. [3]. Among them, it is generally thought that pregnancy and delivery-related pelvic floor trauma are essential risk factors for PFD [4, 5]. PFD can negatively affect the social and physical functions of women, restrict women in their daily activities, impair sexual function, and ultimately reduce their overall quality of life while putting a considerable economic burden on healthcare resources [6, 7].

Effective prevention and treatment are both efficient strategies that are critical to PFD management. PFD is usually treated with conservative methods such as pelvic floor muscle training, bladder retraining, electrostimulation, and lifestyle interventions [8, 9]. Among them, pelvic floor muscle training is not only an effective method to prevent PFD, but also internationally recommended as the first-line treatment for urinary incontinence and pelvic organ prolapse [10, 11]. Despite the existence of evidence-based management approaches to the prevention and treatment of PFD [12, 13], findings from existing literature sources show that the best prevention and management practices for PFD in women have not been routinely enforced in most healthcare settings [14]. Most women experiencing PFD symptoms try to take control of the condition without seeking medical care [15]. According to studies, < 30% of women seek the help of healthcare professionals [16, 17]. Previous researchers have revealed that several reasons for women not seeking professional treatment include stigma and embarrassment, the lack of knowledge about PFD, the high cost of treatment, excessive wait times, limited access to health care services, and concerns about perceived consequences, which hinder the implementation of pelvic floor rehabilitation and are detrimental to individual health and quality of life [18, 19]. As a result, developing innovative strategies or modalities of pelvic floor management for women with or at risk of PFD is critical.

With the rapid advancement of information technology, eHealth has recently attracted considerable attention and is currently being promoted as a way for individuals and healthcare providers to improve health care [20]. It represents a promising method that can reduce barriers for women who do not seek medical care and potentially improve pelvic floor self-management ability and pelvic floor muscle training compliance for women with or at risk of PFD. eHealth, defined as “health services and information delivered or enhanced through the internet and related technologies” [21], includes, for example, teleconsultation, remote monitoring, virtual reality, internet-based interventions, mobile phone apps, videoconferencing, etc. [22, 23]. Through the internet and other electronic-related technologies, eHealth interventions are not limited by the lack of time and space when providing knowledge of disease management and can help women obtain relevant information about pelvic floor management quickly and easily, which is different from the traditional face-to-face intervention. Moreover, due to its anonymity, flexibility, and accessibility, eHealth interventions can also reduce women’s sense of shame and embarrassment associated with seeking professional help, reduce the cost and time, and increase their access to healthcare services [24,25,26].

Some studies have provided evidence that eHealth interventions exert a beneficial influence on women’s pelvic floor symptom management [27, 28], while others could not find any significant improvement [29]. More recently, a systematic review evaluated the efficiency of eHealth interventions in the rehabilitation of female PFD [30]. Nevertheless, there were several important limitations to this systematic review. To begin with, the review included only four related references for analysis. Second, no quantitative summary assessment was performed. Third, the review did not provide evidence to support the effectiveness of eHealth interventions for PFD prevention.

Up to now, a comprehensive review on the effectiveness of eHealth interventions among women with or at risk of pelvic floor disorders has been lacking. Thus, given the limited scope of previous studies, the aim of this meta-analysis was to determine the effectiveness of eHealth interventions in preventing and treating PFD among women compared with traditional care.

Methods

The PRISMA guidelines were followed for this meta-analysis [31], and a protocol was registered on the PROSPERO database (CRD42021287322).

Data sources and searches

We systemically performed a systematic search in 11 electronic databases: PubMed, Web of Science, CINAHL, Embase, PsycINFO, The Cochrane Library databases, Scopus, CNKI, WanFang, VIP databases, and CBM from inception until August 28, 2021. The Medical Subject Headings (MeSH) and keywords were used as follows: “telemedicine,” “telehealth,” “e-health,” “mobile health,” “teleconsultation,” “telecommunications,” “multimedia,” “mobile application,” “smartphone,” “urinary incontinence,” “pelvic organ prolapse,” “uterine prolapse,” “rectocele,” “cystocele,” “fecal incontinence,” “pelvic floor,” “pelvic floor disorders,” and “randomized controlled trial.” The detailed retrieval strategies are available in the Appendix 1. To identify additional records, we also manually searched for potentially eligible publications. In the literature screening process, the results of the searches from different electronic databases were imported into EndNote Version X9, where duplicate studies were deleted. After excluding duplicates, two reviewers independently screened the retrieved studies according to the inclusion and exclusion criteria.

Inclusion and exclusion criteria

We included all studies evaluating the effects of eHealth interventions on women who have been diagnosed with pelvic floor disorders or are at risk of PFD for either prevention or treatment of the disease. Studies were considered for inclusion based on the PICOS framework if the following criteria were met: (1) participants: participants were women who were diagnosed with pelvic floor disorders or at risk of PFD (such as postnatal women or pregnant women); (2) intervention: in the intervention group, participants received any form of eHealth intervention (e.g., distance counseling, mobile applications, videoconferencing, text messaging) to help women treat or prevent PFD through self-management; (3) comparison: traditional care or waiting list control were provided to participants in the control group; (4) outcome: one or more of the following interesting outcomes have been reported (e.g., in prevention studies, the incidence of PFD was assessed as an outcome measure; in treatment studies, the severity of pelvic floor symptoms and the patient’s global impression of improvement were included as outcome indicators; other outcome measures were as follows: quality of life, self-efficacy, satisfaction with the intervention, sexual function, and the rate of qualification for pelvic floor muscle strength) (5) study design: the study was a randomized controlled trial.

The exclusion criteria for studies were as follows: (1) cohort studies, case-control studies, qualitative studies, reviews, conference abstracts, study protocols, or ongoing studies; (2) publications in languages other than English and Chinese; (3) incomplete data; (4) follow-up studies if studies from the same population were published; (5) the outcomes of interest were not reported.

Data extraction

Data from the included studies was extracted and summarized independently by two of the reviewers using a standardized data extraction form. The extracted study information included the first author, year of publication, country, study design, sample size (eHealth/usual care), purpose of administration, study population, mean age, details of the intervention, control content, outcome measures, and data collection time points. The original authors were contacted to obtain any missing data if possible.

Quality assessment

The methodological quality of all individual studies was appraised for study quality using The Cochrane Risk of Bias Assessment Tool by two independent reviewers, with a third researcher was used where discrepancies persisted. The Cochrane Risk of Bias Assessment Tool was used to rate the overall quality of evidence based on six domains: (1) generation of random sequence; (2) concealment of allocation; (3) blinding of participants and personnel; (4) blinding of outcome assessors; (5) adequately addressed incomplete outcome data; (6) selective outcome reporting; (7) other bias (e.g., baseline comparability, early stopping, and possible bias due to funding). All domains were evaluated using the Cochrane criteria to classify the risk of bias as: (1) low risk of bias; (2) high risk of bias; (3) unclear.

Statistical analysis

All the statistical analyses were performed with the Review Manager Software 5.3 and STATA 15.1. According to whether the outcomes were measured with the same scales or different scales, mean difference (MD) and standardized mean difference (SMD) with the 95% confidence interval (CI) were used to analyze continuous variable data. Odds ratio and 95% confidence interval were used for dichotomous outcomes. Statistical heterogeneity among the studies was assessed using the chi-square test and the I2 statistic; if p < 0.10 and/or I2 > 50%, a random effects model would be used because of substantial heterogeneity; otherwise, a fixed-effects model of analysis would be used if heterogeneity between studies was recognized as being low. Subgroup analyses were conducted to determine the effects of different eHealth modalities.

Results

Study selection

A total of 8592 relevant studies were retrieved from the literature search, of which 2334 were considered duplicate literature. After excluding duplicate articles, 6258 titles and abstracts were screened, and 6126 studies were excluded. Thus, 132 of the full-text articles were selected for further consideration, of which 108 were excluded. Finally, 24 RCTs met the inclusion criteria and were included in this meta-analysis [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55]. The study flow diagram is displayed in Fig. 1.

Fig. 1
figure 1

Flow diagram of the study selection process. Abbreviations: CNKI: Chinese National Knowledge Infrastructure; CBM: Chinese Biomedical Literature Database

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Characteristics of the included studies

Selected studies were published between 2013 and 2021, of which 10 studies [32,33,34,35,36, 41, 43, 44, 47, 48] were written in English and the remaining were written in Chinese, originating from 7 countries, including China (15 studies) [37,38,39,40, 42, 45,46,47, 49,50,51,52,53,54,55], Brazil (2 studies) [32, 34], Sweden (2 studies) [33, 44], the US (2 studies) [43, 48], the UK (1 study) [36], Canada (1 study) [35], and The Netherlands (1 study) [41]. The 24 studies included 3691 women who were diagnosed with pelvic floor disorders or at risk of PFD, among whom 1862 were allocated to the eHealth group and 1829 to the control group. Among all the studies, various eHealth technologies were observed, including: mobile phone or tablet applications (7 studies) [32, 33, 36, 38, 40, 41, 45], internet (9 studies) [37, 39, 42, 44, 46, 49,50,51, 55], telemetry device (1 study) [34], video (2 studies) [43, 54], audio (1 study) [47], telephone (2 studies) [52, 53], and mixed technologies, where more than one eHealth modality was reported (2 studies combined telemetry devices with applications) [35, 48]. The characteristics of eligible studies are displayed in Tables 1 and 2.

Table 1 Characteristics and designs of included studies (N = 24)
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Table 2 Characteristics of intervention and control group (N = 24)
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Assessment of the risk of bias for included studies

The risk of bias graph and the risk of bias summary are shown in Figs. 2 and 3. For random sequence generation, 16 studies were evaluated to be at low risk since the methods of random sequence generation were described in detail [32,33,34,35,36, 38,39,40, 44,45,46,47,48,49,50,51]. One of the 24 studies was assessed as high risk because it was grouped according to the odd and even numbers of health care cards [53]. Five studies reported adequate allocation concealment [32, 33, 35, 41, 47], and 18 studies were judged at unclear risk due to insufficient descriptions [34, 37,38,39,40, 42,43,44,45,46, 48,49,50,51,52,53,54,55]. As for blinding of participants and personnel, most included studies (83.3%) were judged to be at an unclear risk of bias. For the blinding of outcome assessment, seven studies were assessed as low risk [32, 34,35,36, 41, 47, 48]. Three studies were rated as having a high risk of attrition bias as the attrition rates were > 15%, and intention-to-treat analysis was not performed [32, 43, 48]. A study was judged to be at high risk of reporting bias because “coordination of the pelvic floor muscles” was given as an outcome indicator in the clinical trial protocol but was not stated in the study [34]. Seventeen studies had an unclear risk of reporting bias due to a lack of detailed information on study protocols and trial registrations for further assessment [37,38,39,40,41,42,43, 45,46,47, 49,50,51,52,53,54,55]. A high risk of reporting other bias was given to two studies because there were baseline differences between the intervention group and control group [32, 41].

Fig. 2
figure 2

Risk of bias graph

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Fig. 3
figure 3

Risk of bias summary

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Effects of eHealth interventions on PFD prevention

The incidence of PFD

Five preventative studies involving 846 participants who were at risk of PFD investigated the incidence of PFD, and we noticed that all of the women included in preventative RCTs were postnatal or pregnant [39, 40, 46, 52, 55].

Three studies involving 579 pregnant women reported the efficiency of eHealth interventions on the incidence of PFD [39, 40, 52]. As no heterogeneity was noted (I2 = 0%) (Fig. 4a), a fixed-effects model was chosen. There was a statistically significant effect of eHealth interventions compared with control groups in preventing the occurrence of PFD for pregnant women (pooled OR = 0.25, 95% CI: 0.14 to 0.45, z = 4.65, p < 0.001).

Fig. 4
figure 4

a Forest plot of the effects of eHealth on the incidence of pelvic floor dysfunction in pregnant women. b. Forest plot of the effects of eHealth on the incidence of pelvic floor dysfunction in postnatal women

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Two studies involving 267 postnatal women reported the incidence of PFD between the eHealth and control groups [46, 55]. A random-effects model was selected for data synthesis because of significant heterogeneity (I2 = 76.3%) across the studies, and a statistically significant difference was found between the two groups (pooled OR = 0.19, 95% CI: 0.06 to 0.60, z = 2.82, p = 0.005) (Fig. 4b).

Effects of eHealth interventions on PFD treatment

The severity of pelvic floor symptoms

Eight studies involving 769 participants examined the efficiency of eHealth interventions on the severity of pelvic floor symptoms [32,33,34, 38, 44, 47, 48, 53]. Owing to the significant heterogeneity (I2 = 92.5%) across the studies, a random-effects model was applied (Fig. 5). The meta-analysis revealed that there was a statistically significant difference in the severity of pelvic floor symptoms between the eHealth and control groups (pooled SMD = -0.63, 95% CI: -1.20 to -0.06, z = 2.15, p = 0.031).

Fig. 5
figure 5

Forest plot of the effects of eHealth on the severity of pelvic floor symptoms

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The patient’s global impression of improvement

We included 5 studies involving 627 participants that reported the patient’s global impression of improvement between the eHealth and control groups [33, 34, 41, 44, 48]. The meta-analysis showed that no significant difference was found in the patient’s global impression of improvement between the two groups (pooled OR = 1.90, 95% CI: 0.64 to 5.59, z = 1.16, p = 0.246) (Fig. 6).

Fig. 6
figure 6

Forest plot of the effects of eHealth on the patient's global impression of improvement

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Effects of eHealth interventions on other outcome measures

Quality of life

Eight studies involving 1089 participants evaluated quality of life [33, 35, 42,43,44, 49, 50, 53]. Due to the significant heterogeneity (I2 = 81.8%), a random-effects model was applied (Fig. 7), and a statistically significant difference was found between the eHealth and control groups (pooled SMD = 0.49, 95% CI: 0.19 to 0.80, z = 3.14, p = 0.002). Compared with the control group, quality of life scores in the eHealth group were higher.

Fig. 7
figure 7

Forest plot of the effects of eHealth on quality of life

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Self-efficacy

We included 3 studies involving 775 participants that assessed self-efficacy between the eHealth and control groups [38, 47, 54]. A random-effects model was applied because of significant heterogeneity (I2 = 78.3%) across the studies, and a statistically significant difference was found between the two groups (pooled SMD = 2.62, 95% CI: 2.12 to 3.13, z = 10.15, p < 0.001) (Fig. 8).

Fig. 8
figure 8

Forest plot of the effects of eHealth on self-efficacy

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Satisfaction with the intervention

We included 5 studies involving 1350 participants that assessed satisfaction with the intervention between the eHealth and control groups [37, 40, 44, 45, 54]. As no heterogeneity was noted (I2 = 0%) (Fig. 9), a fixed-effects model was chosen. A statistically significant difference was observed between the two groups (pooled OR = 3.93, 95% CI: 2.73 to 5.66, z = 7.36, p < 0.001), indicating that the eHealth group had better satisfaction with the intervention than the control group.

Fig. 9
figure 9

Forest plot of the effects of eHealth on satisfaction with the intervention

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Sexual function

Three studies involving 318 participants were included to assess sexual function between the eHealth and control groups [36, 47, 51]. As no heterogeneity was noted (I2 = 0%) (Fig. 10), a fixed-effects model was chosen. A statistically significant difference was observed between the two groups (pooled SMD = 0.51, 95% CI: 0.29 to 0.73, z = 4.52, p < 0.001), indicating that the eHealth group had better sexual function than the control group.

Fig. 10
figure 10

Forest plot of the effects of eHealth on sexual function

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The rate of qualification for pelvic floor muscle strength

Type I muscle strength

Three studies involving 480 participants were included to assess the rate of qualification for type I pelvic floor muscle strength between the eHealth and control groups [47, 51, 55]. A fixed-effects model was selected for data synthesis as the heterogeneity between studies was low (I2 = 4%) (Fig. 11). A statistically significant difference was observed between the two groups (pooled OR = 1.92, 95% CI: 1.30 to 2.82, z = 3.30, p = 0.001).

Fig. 11
figure 11

Forest plot of the effects of eHealth on the rate of qualification for pelvic floor type I muscle strength

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Type II muscle strength

Three studies involving 480 participants were included to assess the rate of qualification for type II pelvic floor muscle strength between the eHealth and control groups [47, 51, 55]. As no heterogeneity was noted (I2 = 0%) (Fig. 12), a fixed-effects model was chosen. The meta-analysis showed that there was a statistically significant difference in the rate of qualification for type II muscle strength between the eHealth and control groups (pooled OR = 2.04, 95% CI: 1.38 to 3.01, z = 3.56, p < 0.001).

Fig. 12
figure 12

Forest plot of the effects of eHealth on the rate of qualification for pelvic floor type II muscle strength

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Subgroup analyses

Subgroup analyses were conducted based on different eHealth modalities (application, telephone, internet, video, audio, telemetry device, and mixed technologies) (Table 3). In subgroup analyses, the results were consistent with the results of the pooled analysis. The results of subgroup analyses showed that the application-based interventions could reduce the severity of PFD (pooled SMD = -1.02, 95% CI: -1.84 to -0.20, z = 2.43, p = 0.015) and improve satisfaction with the intervention (pooled OR = 5.68, 95% CI: 2.05 to 15.75, z = 3.34, p = 0.001) when compared with the control group. Besides, compared with traditional care, the internet-based interventions revealed significant positive effects on several outcome indicators, including quality of life (pooled SMD = 0.47, 95% CI: 0.16 to 0.78, z = 3.00, p = 0.003), satisfaction (pooled OR = 3.74, 95% CI: 2.01 to 6.95, z = 4.17, p < 0.001), pelvic floor type I muscle strength (pooled OR = 1.71, 95% CI: 1.12 to 2.60, z = 2.51, p = 0.012), and pelvic floor type II muscle strength (pooled OR = 1.89, 95% CI: 1.26 to 2.84, z = 3.06, p = 0.002).

Table 3 Impacts of the modality of eHealth interventions: subgroup analyses.
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Discussion

To our knowledge, this is the first systematic review and meta-analysis to summarize the effectiveness of eHealth interventions in the prevention and treatment of female PFD, based on English and Chinese publications. In this study, we included and comprehensively analyzed 24 RCTs from 11 electronic databases, most of which (91.7%) were published within the past 5 years, indicating that the number of women choosing to use mobile-technology health services for pelvic floor intervention has increased recently. The meta-analysis showed that eHealth interventions were vital not only for preventing PFD but also for reducing the severity of PFD. In addition, compared with traditional care, eHealth interventions showed significant positive effects on several outcome indicators, including quality of life, pelvic floor muscle strength, sexual function, satisfaction with the intervention, and self-efficacy.

Our findings showed that, compared with the control group, the intervention group had a lower incidence of pelvic floor dysfunction-related diseases, indicating that eHealth interventions can play a role in preventing PFD in postnatal and pregnant women.

Pregnancy and delivery are independent risk factors for pelvic floor dysfunction. Peripheral nerves, muscles, and connective tissue will be compressed, stretched, or torn during pregnancy and delivery, significantly increasing the risk of pelvic floor injury [56]. Previous studies pointed out that pelvic floor muscle training during the prenatal and postpartum periods is beneficial for the prevention of PFD [57, 58]. As per the information available from UpToDate, women who have no contraindications should perform daily pelvic floor muscle training during pregnancy, and pelvic floor muscle training should be carried out at the appropriate time during postpartum according to the mode of delivery and personal tolerance [59].

Currently, women have several problems with regard to adhering to pelvic floor muscle training, such as forgetting to perform the exercises and a lack of knowledge or skills to perform pelvic floor muscle training [60, 61]. With the emergence of electronic technology, eHealth interventions are beginning to represent a promising novel approach for addressing these issues. Through the internet, apps, and other eHealth-related technologies, women can get reminders and teach themselves about diseases, which improves the accessibility of pelvic floor muscle training [62, 63]. Because pregnancy and childbirth are known as the key periods of pelvic floor muscle recovery, effective eHealth interventions should be provided to postnatal and pregnant women to promote their pelvic floor rehabilitation.

Although the summary results of the randomized control trials revealed that eHealth interventions had no significant effect on the patient’s global impression of improvement, the meta-analysis showed that eHealth interventions improved the severity of pelvic floor disorder symptoms. The discrepancy between these two outcomes can be explained as follows. The patient's global impression of improvement (PGI-I) was based on a single question in which the respondent was asked to recall their pre-treatment symptom condition and compare it with their current status, with responses ranging from "much better" to "very much worse." A previous study has shown that the strength of the association between the patient's global assessments and symptom measures for urinary patients is potentially affected by the recall period [64]. Our findings showed that the intervention duration in the relevant studies that used the patient's global impression of improvement as the outcome measure ranged from 8 weeks to 4 months. It is possible that the data could be affected by recall bias resulting from the long intervention period, which may be the cause of the discrepancy between the patient's global impression of improvement and the severity of symptoms. We found that patients reporting the severity of pelvic floor symptoms included in the study had urinary incontinence, most of whom suffered from stress urinary incontinence. Our results confirmed the effectiveness of eHealth interventions in reducing the severity of urinary incontinence symptoms, which is consistent with earlier studies [65, 66]. The importance of eHealth interventions has been demonstrated [63], and patients can improve their cognition of the disease in real time by using apps and other similar eHealth technologies [67]. Furthermore, the high-efficiency interactive feedback and reminder function not only provides a convenient platform for doctor-patient communication but also improves the compliance of patients with pelvic floor muscle exercise regimes [68]. It should be noted that a prior study has shown that the severity of patients’ urinary incontinence symptoms has an impact on the effectiveness of eHealth interventions in treating urinary incontinence [69]. Individuals with severe urine incontinence show less improvement while using eHealth interventions than patients with minor symptoms. As a result, patients suffering from severe urine incontinence should seek professional medical advice and assistance for appropriate treatment.

Compared with traditional care, eHealth interventions showed significant positive effects on several outcome indicators, including quality of life, pelvic floor muscle strength, sexual function, satisfaction, and self-efficacy of women with or at risk of pelvic floor disorders. Women with or at risk of pelvic floor disease may worry about a series of adverse consequences linked to PFD, including a restricted ability to perform activities, skin rashes, and pruritus in the genital area [66]. A previous survey showed that most individuals would like to gain more knowledge about their pelvic floor status by using the internet, social networking sites, and other online resources [70]. By providing information on lifestyle choices and effective pelvic floor muscle exercises, eHealth interventions enable women to have better capacity and mental reserves to cope with difficulties, thereby promoting women’s self-management of their pelvic floor health in the short and long term. Moreover, healthcare professionals can provide personalized guidance to patients through the platform, which may improve the effectiveness of patients’ rehabilitation training, enhance their pelvic floor muscle strength, and improve their satisfaction with the intervention.

We found that application-based and internet-based interventions were the two main eHealth interventions used, and the results of the subgroup analysis supported the beneficial effects of both intervention types on pelvic floor rehabilitation. To date, there is no consensus on the relative superiority of application-based or internet-based interventions as eHealth modalities [71, 72]. Research indicates that internet-based interventions are considered compatible, and application-based interventions are considered flexible and personalized [71]. Further comparisons of the differences across the various eHealth modalities were difficult because of the limited number of studies included in each group of the subgroup analyses, and the effectiveness of other types of eHealth interventions needs to be further confirmed by more studies.

There were several advantages to this study. First, this review offers a literary resource on the impact of eHealth interventions on female pelvic floor health. As far as we know, only one review has focused on this issue before, and it was limited to qualitative analysis of the results [30]. One article quantitatively evaluated the impact of telemedicine on urinary incontinence, but the number of articles included was insufficient [65]. We expanded the scope of the systematic evaluation, included more studies, and evaluated multiple outcome indicators related to the prevention and treatment of PFD, including the incidence of PFD, pelvic floor muscle strength, satisfaction, etc. Second, we reviewed 11 databases and used a comprehensive search strategy to systematically retrieve the articles. Third, this meta-analysis only included studies that were designed as randomized controlled trials as this is a strict method used to determine causality and ensure scientific effectiveness, and therefore the conclusion is more reliable.

However, this meta-analysis also had some limitations. First, the literature search identified articles published in English and Chinese, potentially resulting in publication bias. Moreover, there was significant heterogeneity among the multiple outcome indicators in this review. This may be related to differences in the different eHealth interventions, including different intervention durations, intensities, frequencies, intervention contents, target populations, and intervention media, which may affect the accuracy of the evidence obtained from the summary analysis of the articles. Furthermore, most studies lacked sufficient details to assess the possible bias in reporting and detection, and the methodological quality of the included studies was limited and had potential bias, which may affect the quality of evidence obtained in this review.

In the future, large-scale and well-designed RCTs are warranted to evaluate the effects of eHealth interventions among women with or at risk of pelvic floor disorders. Researchers should consider the potential impact of behavior change technologies (such as self-monitoring and feedback) and the theoretical framework of the individual application of eHealth resources. To clarify the impact of different intervention components of eHealth on pelvic floor rehabilitation and to facilitate a more comprehensive systematic review in the future, researchers are encouraged to provide the full details of all intervention components and specifically evaluate the different intensities, frequencies, durations, and types of eHealth services. In addition, the influence of eHealth interventions on other pelvic floor disorders, including fecal incontinence and pelvic organ prolapse, should be investigated. Furthermore, evidence-based pelvic floor muscle training parameters should be embedded in apps when developing and designing relevant pelvic floor electronic health applications to perform standardized pelvic floor rehabilitation exercises [73]. During the COVID-19 pandemic, as patients were not permitted to receive face-to-face treatments, eHealth became an important method for pelvic floor management [74]. Since the success of pelvic floor self-management largely depends on individual adherence [63], some principles can be used in eHealth design, such as interestingness and information sharing, which can increase user participation [71]. According to a study, the willingness to use eHealth was linked to computer literacy [75]. Being unfamiliar with electronic medical applications may also affect the enthusiasm of participants. Women, particularly elderly women, should be educated on how to better use eHealth modalities to increase their information literacy.

Conclusions

This meta-analysis demonstrated that eHealth interventions are an effective emerging treatment and preventive modality for female PFD. Higher quality, larger scale, and strictly designed RCTs are required to further evaluate the efficacy of eHealth interventions on pelvic floor management.