"I Felt Grounded and Clear-Headed": Qualitative Exploration of a Mind-Body Physical Activity Intervention on Stress Among College Students During COVID-19.
Ildiko StrehliDonna Harp ZiegenfussMartin E BlockRyan D BurnsYang BaiTimothy A BrusseauPublished in: Inquiry : a journal of medical care organization, provision and financing (2022)
The COVID-19 pandemic affected college students' overall health. The aims of this qualitative inquiry were to provide a more comprehensive understanding of the effectiveness of the mind-body physical activity (MBPA) intervention and to explore the MBPA intervention experiences through the use of journals and photographs (photovoice) of a purposeful sample of 21 college students during the COVID-19 pandemic. An inductive qualitative process was used to explore the data that emerged from photovoice images and journals. Students' experiences were encapsulated in 6 key themes: (1) holistic individual well-being; (2) physical activity as a matter of necessity; (3) mind-body physical activity intervention impacts; (4) broadening strategies for adapting and reacting; (5) systemic effect of stress management changes; and (6) perceiving causes of stress. Participants reflected collective intellectual, physical, and emotional fatigue as obstacles and perceived stress. The quality of COVID-19 related perspectives and stressful experiences are defined from traumatic and overwhelming to higher than normal. Findings from this study contribute to our understanding of the distinctive factors of the COVID-19 era among college students. Health educators should consider the implementation of multilevel and multicomponent MBPA interventions, and our findings highlight the utility of supporting higher education students in a meaningful way.
Keyphrases
- physical activity
- randomized controlled trial
- mental health
- healthcare
- coronavirus disease
- sars cov
- public health
- sleep quality
- systematic review
- body mass index
- stress induced
- spinal cord injury
- quality improvement
- primary care
- deep learning
- depressive symptoms
- machine learning
- health promotion
- respiratory syndrome coronavirus
- optical coherence tomography
- convolutional neural network
- social support
- artificial intelligence
- human health