643
Views
15
CrossRef citations to date
0
Altmetric
Original Articles

Effects of a social-media-based support on premenstrual syndrome and physical activity among female university students in South Korea

ORCID Icon & ORCID Icon
Pages 47-53 | Received 29 Aug 2018, Accepted 06 Dec 2018, Published online: 04 Mar 2019
 

Abstract

Introduction: This study examined the effects of social-media-based support on premenstrual syndrome (PMS) and physical activity among female South Korean university students.

Methods: This quasi-experimental study with an equivalent-control-group pretest–posttest design randomly assigned 64 female students with PMS to the experimental or control group. The experimental group received social-media-based support through a smartphone application, text messaging, and e-mail for one menstrual cycle between September and December, 2016. Descriptive and inferential statistics included a Chi-square test and independent and paired t-tests.

Results: Significant differences emerged between the experimental and control groups in total PMS scores (p = .003), 14 premenstrual symptoms, and physical activity (p = .010).

Conclusions: Female university students with PMS experienced decreased premenstrual symptoms and increased physical activity with social-media-based support, which could be an efficacious, accessible, and widely available nursing intervention to manage PMS and physical activity.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) under Grant number 2013R1A1A1008686.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.