Mobile App-Based Intervention for Pregnant Women With Stress Urinary Incontinence: Protocol for a Hybrid Effectiveness-Implementation Trial

Self-efficacy with Pelvic floor muscle training mediates the effect of an App-based intervention on improving postpartum urinary incontinence severity among pregnant women: A causal mediation analysis from a randomised controlled trial

BACKGROUND

A pragmatic randomised controlled trial has confirmed the effectiveness of Urinary Incontinence for Women (UIW) app-based intervention in improving postpartum urinary incontinence (UI) severity among pregnant women. However, the causal mechanisms underlying this intervention effect remain unclear.

OBJECTIVE

To examine the mediating role of self-efficacy with pelvic floor muscle training (PFMT) on the effect of the UIW app-based intervention in improving postpartum UI severity.

METHODS

This was a secondary causal mediation analysis of a single-center, 2-arm, unblinded pragmatic randomised controlled trial. Singleton pregnant women without UI before pregnancy aged ≥18 years and between 24 and 28 weeks of gestation were recruited from a tertiary public hospital in China and randomised to receive the UIW app intervention plus oral PFMT instructions (n = 63) or oral PFMT instructions alone (n = 63). The primary outcome was postpartum changes in UI severity at 6 weeks. Changes in self-efficacy with PFMT 2 months after randomisation were a hypothesised mediator. Causal mediation analysis was used to estimate the average causal mediation effect (ACME), average direct effect (ADE), average total effect (ATE), and proportion mediated. A sensitivity analysis was conducted to examine the robustness of the ACME in relation to potential unmeasured confounding.

RESULTS

Data from 103 participants were analyzed. The ATE of UIW app-based intervention on postpartum UI severity was 2.91 points (95 % confidence intervals [CI] 1.69 to 4.12), with ADE of 1.97 points (95 % CI 0.63 to 3.41) and the ACME 0.94 points (95 % CI 0.27 to 1.72). The proportion of ATE mediated by self-efficacy with PFMT was 0.32 (95 % CI 0.08 to 0.67). Sensitivity analysis revealed the robust ACME with respect to the potential effects of unmeasured confounding.

CONCLUSION

An increase in self-efficacy with PFMT partially mediated the effect of the UIW app intervention on improvements in postpartum UI severity.

TRIAL REGISTRATION

The original trial was prospectively registered in the Chinese Clinical Trial Registry under the reference number ChiCTR1800016171 on 16/05/2018. Further details can be accessed at: http://www.chictr.org.cn/showproj.aspx?proj=27455.

The effect of using mobile phone applications for intelligent pelvic floor rehabilitation on elderly female patients with stress urinary incontinence

BACKGROUND

Stress urinary incontinence is prevalent among women and the incidence increases with age.

OBJECTIVE

To explore the effect of intelligent pelvic floor muscle rehabilitation on elderly female patients with incontinence.

METHODS

A total of 209 patients with urinary incontinence who were treated with pelvic floor muscle rehabilitation at Peking University International Hospital from September 2020 to January 2022 were selected by convenient sampling. All subjects were divided into the 50-60 year old patient group (n= 51) and over 60 years old patient group according to age (n= 158). The subjects of different age group were divided into an experimental group and a control group. The patients in the control group received routine nursing and health education, and the patients in the observation group received a combination of mobile application use and smart dumbbells. Based on this, we constructed an intervention model for intelligent, continuous pelvic floor rehabilitation. After 7 and 12 weeks, pelvic floor muscle function knowledge and exercise compliance in the two groups were evaluated. The improvement of urinary incontinence symptoms, pelvic floor muscle strength grades and quality-of-life scales were evaluated.

RESULTS

The results showed that pelvic floor knowledge and exercise compliance in the experimental group were better than in the control group at 7 and 12 weeks after intervention (P< 0.05). There was no significant difference in pelvic floor muscle strength and quality of life between the two groups at 7 weeks after intervention (P> 0.05). However, there was a significant difference in pelvic floor muscle strength and quality of life between the two groups at 12 weeks after intervention (P< 0.05). There was no significant difference between different age groups.

CONCLUSION

The intelligent pelvic floor rehabilitation model that combines a mobile application with smart dumbbells can maintain and strengthen the clinical treatment effect for elderly patients with urinary incontinence.

Effect of a Mobile App-Based Urinary Incontinence Self-Management Intervention Among Pregnant Women in China: Pragmatic Randomized Controlled Trial

BACKGROUND

Urinary incontinence (UI) is a highly prevalent health concern commonly observed during and after pregnancy that can substantially impact women's physical and psychological well-being and quality of life. Owing to its numerous advantages, mobile health may be a promising solution; however, it is unclear whether the app-based intervention can effectively improve UI symptoms during and after pregnancy.

OBJECTIVE

This study aimed to evaluate the effectiveness of the Urinary Incontinence for Women (UIW) app-based intervention for UI symptom improvement among pregnant women in China.

METHODS

Singleton pregnant women without incontinence before pregnancy who were aged ≥18 years and between 24 and 28 weeks of gestation were recruited from a tertiary public hospital in China and were randomly allocated (1:1) to either an experimental group (n=63) or a control group (n=63). The experimental group received the UIW app intervention and oral pelvic floor muscle training (PFMT) instructions, whereas the control group received oral PFMT instructions alone. Neither the participants nor the researchers were blinded to the intervention. The primary outcome was UI severity. The secondary outcomes included quality of life, self-efficacy with PFMT, and knowledge of UI. All data were collected at baseline, 2 months after randomization, and 6 weeks post partum through electronic questionnaires or by checking the electronic medical record system. Data analysis followed the intention-to-treat principle. A linear mixed model was used to examine the intervention effect on primary and secondary outcomes.

RESULTS

Participants in the experimental and control groups were comparable at baseline. Of the 126 overall participants, 117 (92.9%) and 103 (81.7%) women completed follow-up visits at 2 months after randomization and 6 weeks after delivery, respectively. A statistically significant difference in UI symptom severity was observed between the experimental group and control group (2 months after randomization: mean difference -2.86, 95% CI -4.09 to -1.64, P<.001; 6 weeks post partum: mean difference -2.68, 95% CI -3.87 to -1.49, P<.001). For the secondary outcomes, a statistically significant intervention effect on the quality of life, self-efficacy, and UI knowledge was found at the 2-month follow-up (all P<.05) and 6 weeks post partum (all P<.001).

CONCLUSIONS

The app-based UI self-management intervention (UIW) effectively improved UI symptom severity, quality of life, self-efficacy with PFMT, and knowledge of UI during the late pregnancy and early postnatal periods. Larger multicenter studies with a longer postpartum follow-up are required to further extend these findings.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR1800016171; http://www.chictr.org.cn/showproj.aspx?proj=27455.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/22771.

Mitigating urinary incontinence condition using machine learning

Background

Urinary incontinence (UI) is the inability to completely control the process of releasing urine. UI presents a social, medical, and mental issue with financial consequences.

Objective

This paper proposes a framework based on machine learning for predicting urination time, which can benefit people with various degrees of UI.

Method

A total of 850 data points were self-recorded by 51 participants to investigate how different factors impact urination time. 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). Other factors, such as age and BMI, were also considered. 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: <  = 30 min, 31–60 min, 61–90 min, > 90 min. The model was built in two steps: (1) feature selection and (2) model training and testing. 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.

Result

The feature selection steps resulted in nine features considered the most important features affecting UI. The accuracy, precision, recall, and F1 score of the XGB predictive model are 0.70, 0.73, 0.70, and 0.71, respectively.

Conclusion

This research is the first step in developing a machine learning model to predict when a person will need to urinate. A precise predictive instrument can enable healthcare providers and caregivers to assist people with various forms of UI in reliable, prompted voiding. 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.

Persuasive Technology in an mHealth App Designed for Pelvic Floor Muscle Training Among Women: Systematic Review

Background

Pelvic floor muscle training (PFMT) is one of the first-line treatments for stress urinary incontinence among pregnant women. Mobile health (mHealth) technology is potentially effective for delivering PFMT to pregnant women. Persuasive technology in the development of such mobile apps may facilitate behavior change by improving adherence to the exercises. The Capability, Opportunity, and Motivation–Behavior (COM-B) model is potentially useful in selecting the appropriate interventions to be incorporated into the apps.

Objective

This review of mHealth apps for PFMT aims to describe the principles of persuasion used for each app and to propose mHealth app design features based on the COM-B model.

Methods

A systematic literature search was conducted to answer three main research questions: what are the available mHealth apps for PFMT in the published literature, what persuasive strategies were used in their studies how were they mapped to the COM-B model, and how effective were the selected persuasive strategies for PFMT adherence? We searched PubMed, CINAHL, Web of Science, Scopus, and local Malaysian databases such as MyCite and MyMedR for articles reporting mHealth apps used for the delivery of PFMT. We included original articles reporting experimental and cross-sectional studies, including pilot or feasibility trials. Systematic and narrative reviews were excluded. Narrative and thematic syntheses were conducted on the eligible articles based on the research questions. The Cochrane risk of bias tool and the Risk of Bias Assessment Tool for Non-randomized Studies were used to assess study bias.

Results

Of the 169 records from the initial search, 10 (5.9%) articles meeting the selection criteria were included in this review. There were 8 mHealth apps designed for the delivery of PFMT. The Tät, which used 3 categories of persuasive system design, improved PFMT adherence and was cost-effective. Only 1 app, the iBall app, used all categories of persuasive system design, by including social support such as "competition" in its design. The Diário Saúde app was the only app developed using operant conditioning. All apps incorporated Tailoring and Expertise as part of their PSD strategies. Only 3 apps, the Diário Saúde, Tät, and Pen Yi Kang demonstrated improved PFMT adherence.

Conclusions

Persuasive technology used in mobile apps may target desired behavior change more effectively. The persuasive system design can be mapped to the COM-B model to explain its effectiveness on behaviour change outcomes.