In addition, compared with traditional care, eHealth interventions showed significant positive effects on several outcome indicators, including quality of life (pooled SMD = 0.49, 95% CI: 0.19 to 0.80, p = 0.002), pelvic floor type I muscle strength (pooled OR = 1.92, 95% CI: 1.30 to 2.82, p = 0.001), pelvic floor type II muscle strength (pooled OR = 2.04, 95% CI: 1.38 to 3.01, p < 0.001), sexual function (pooled SMD = 0.51, 95% CI: 0.29 to 0.73, p < 0.001), satisfaction (pooled OR = 3.93, 95% CI: 2.73 to 5.66, p < 0.001), and self-efficacy (pooled SMD = 2.62, 95% CI: 2.12 to 3.13, p < 0.001).ĮHealth interventions are an effective emerging treatment and preventive modality for female PFD. ![]() The meta-analysis showed that eHealth interventions were not only vital for preventing PFD (pregnant women: pooled OR = 0.25, 95% CI: 0.14 to 0.45, p < 0.001 postnatal women: pooled OR = 0.19, 95% CI: 0.06 to 0.60, p = 0.005), but also for reducing the severity of PFD (pooled SMD = -0.63, 95% CI: -1.20 to -0.06, p = 0.031). ![]() Twenty-four RCTs were included in this meta-analysis that included 3691 women. This study aimed to determine the effectiveness of eHealth interventions in preventing and treating PFD among women.Įleven electronic databases were searched for randomized controlled trials (RCTs) from inception until August 28, 2021. Nevertheless, the effectiveness of eHealth interventions among women with or at risk of pelvic floor dysfunction (PFD) has not been adequately discussed to date. The insights from this predictive model can allow future apps to go beyond current UI-related apps by predicting the time of urination using the most relevant factors that impact voiding frequency.ĮHealth interventions represent a promising novel strategy in pelvic floor management for women. A precise predictive instrument can enable healthcare providers and caregivers to assist people with various forms of UI in reliable, prompted voiding. This research is the first step in developing a machine learning model to predict when a person will need to urinate. The accuracy, precision, recall, and F1 score of the XGB predictive model are 0.70, 0.73, 0.70, and 0.71, respectively. The feature selection steps resulted in nine features considered the most important features affecting UI. Feature selection methods such as lasso regression, decision tree, random forest, and chi-square were used to select the best features, which were then used to train an extreme gradient boosting (XGB) algorithm model to predict the class of the next urination time. The model was built in two steps: (1) feature selection and (2) model training and testing. The study was conducted in two phases: (1) data was prepared for modeling, including missing values, data encoding, and scaling and (2) a classification model was designed with four output classes of the next urination time: 90 min. Other factors, such as age and BMI, were also considered. ![]() ![]() The participants were instructed to record input data (such as the time of consumption and the number of drinks) and output data (i.e., the time the individual urinated). This paper proposes a framework based on machine learning for predicting urination time, which can benefit people with various degrees of UI.Ī total of 850 data points were self-recorded by 51 participants to investigate how different factors impact urination time. UI presents a social, medical, and mental issue with financial consequences. Urinary incontinence (UI) is the inability to completely control the process of releasing urine.
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