Dataset Information

Predicting response to a community-based educational workshop on incontinence among community-dwelling older women: Post hoc analysis of the CACTUS-D trial.

ABSTRACT:

Aims

Our goal was to identify which women participating in an educational workshop on incontinence were most likely to benefit from it.

Methods

We included women aged 65 or older, living in the community, and not treated for incontinence despite reporting urinary leakage at least twice a week. The workshop's aims were to change beliefs about accepting incontinence as a normal part of ageing, explain that incontinence is not irreversible, and that solutions exist. We performed structured interviews at 6 and 12 months to assess impressions of improvement (PGI-I) and changes in both continence (ICIQ-FLUTS) and quality of life (I-QOL).

Results

The analysis included 392 women, 39% aged 80 or older and 57% with daily urinary incontinence. Twelve months after the workshop, 16% of women were "much better" (PGI-I); factors associated with impression of improvement were refusal to believe that incontinence is part of normal ageing at baseline and improvement of urinary symptoms. The median improvement was 4 points on the ICIQ-FLUTS and 8 on the I-QOL. Factors associated with a clinically significant improvement in urinary symptoms were more severe baseline urinary incontinence, obesity, and starting Kegel exercises. Factors associated with a clinically significant improvement in quality of life were a poor urinary quality of life at baseline and an age younger than 81 years.

Conclusions

A short, inexpensive and nonmedical intervention can change the mind-set and behavior of older women with incontinence who are not seeking care. A clinically significant improvement is possible even in women with severe symptoms.

SUBMITTER: Fritel X

PROVIDER: S-EPMC8247852 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveTo systematically review and critically appraise prognostic models for falls in community-dwelling older adults.Eligibility criteriaProspective cohort studies with any follow-up period. Studies had to develop or validate multifactorial prognostic models for falls in community-dwelling older adults (60+ years). Models had to be applicable for screening in a general population setting.Information sourceMEDLINE, EMBASE, CINAHL, The Cochrane Library, PsycINFO and Web of Science for studies published in English, Danish, Norwegian or Swedish until January 2020. Sources also included trial registries, clinical guidelines, reference lists of included papers, along with contacting clinical experts to locate published studies.Data extraction and risk of biasTwo authors performed all review stages independently. Data extraction followed the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Risk of bias assessments on participants, predictors, outcomes and analysis methods followed Prediction study Risk Of Bias Assessment Tool.ResultsAfter screening 11 789 studies, 30 were eligible for inclusion (n=86 369 participants). Median age of participants ranged from 67.5 to 83.0 years. Falls incidences varied from 5.9% to 59%. Included studies reported 69 developed and three validated prediction models. Most frequent falls predictors were prior falls, age, sex, measures of gait, balance and strength, along with vision and disability. The area under the curve was available for 40 (55.6%) models, ranging from 0.49 to 0.87. Validated models' The area under the curve ranged from 0.62 to 0.69. All models had a high risk of bias, mostly due to limitations in statistical methods, outcome assessments and restrictive eligibility criteria.ConclusionsAn abundance of prognostic models on falls risk have been developed, but with a wide range in discriminatory performance. All models exhibited a high risk of bias rendering them unreliable for prediction in clinical practice. Future prognostic prediction models should comply with recent recommendations such as Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.Prospero registration numberCRD42019124021.