Correspondence: Living systematic reviews

Telehealth in the rehabilitation of female pelvic floor dysfunction: a systematic literature review

Introduction and hypothesis

The pandemic caused by coronavirus disease 2019 (COVID-19) increased the awareness and efforts to provide care from distance using information technologies. We reviewed the literature about the practice and effectiveness of the rehabilitation of the female pelvic floor dysfunction via telehealth regarding symptomatology and quality of life and function of pelvic floor muscles (PFM).

Methods

A bibliographic review was carried out in May 2020 in the databases: Embase, Medline/PubMed, LILACS and PEDro. A total of 705 articles were reviewed after the removal of duplicates. The methodological quality of the articles was evaluated by the PEDro scale. Two authors performed data extraction into a standardized spreadsheet.

Results

Four studies were included, two being randomized controlled trials. Among the RCTs, only one compared telehealth with face-to-face treatment; the second one compared telehealth with postal treatment. The other two studies are follow-up and cost analysis reports on telehealth versus postal evaluation. Data showed that women who received the intervention remotely presented significant improvement in their symptoms, such as reducing the number of incontinence episodes and voiding frequency, improving PFM strength and improving quality of life compared to women who had the face-to-face treatment.

Conclusions

Telehealth promoted a significant improvement in urinary symptoms, PFM function and quality of life. Telehealth is still emerging, and more studies are needed to draw more conclusions. The recommendations of the governmental authorities, physical therapy councils and corresponding associations of each country also need to be considered.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00192-020-04588-8.

Semi-Automated evidence synthesis in health psychology: current methods and future prospects

The evidence base in health psychology is vast and growing rapidly. These factors make it difficult (and sometimes practically impossible) to consider all available evidence when making decisions about the state of knowledge on a given phenomenon (e.g., associations of variables, effects of interventions on particular outcomes). Systematic reviews, meta-analyses, and other rigorous syntheses of the research mitigate this problem by providing concise, actionable summaries of knowledge in a given area of study. Yet, conducting these syntheses has grown increasingly laborious owing to the fast accumulation of new evidence; existing, manual methods for synthesis do not scale well. In this article, we discuss how semi-automation via machine learning and natural language processing methods may help researchers and practitioners to review evidence more efficiently. We outline concrete examples in health psychology, highlighting practical, open-source technologies available now. We indicate the potential of more advanced methods and discuss how to avoid the pitfalls of automated reviews.