The effectiveness of physical activity interventions in improving higher education students' mental health: A systematic review.
Samantha DonnellyKay PennyMary KynnPublished in: Health promotion international (2024)
Traditional interventions aiming to improve students' mental health and quality of life include meditation or canine therapy. The development of physical activity-related interventions has increased over the past decade. We aimed to review all studies using physical activity for improving the mental health and quality of life in higher education students whilst describing the interventions, measurements and effectiveness. A systematic search of six electronic databases including: ProQuest, MEDLINE, Embase, CINAHL, SPORTDiscus and CENTRAL, was conducted following PRISMA guidelines. Randomized or non-randomized controlled trial physical activity-related interventions involving higher education students aiming to improve their mental health and quality of life were included. Searches yielded 58 articles with interventions involving martial arts, sport, mind-body exercises and anaerobic exercises. Psychological measures varied across studies including the State Trait Anxiety Inventory, Beck Depression Inventory and the Perceived Stress Scale. Over half of the studies included in this review (n = 36) were effective in improving students' mental health or quality of life. Findings from our review suggest that interventions aiming to be effective in improving students' mental health quality of life should aim to deliver moderate-vigorous intensity exercises such as dance or Pilates. This systematic review was based on a published protocol in PROSPERO (registration number: CRD42022325975).
Keyphrases
- physical activity
- mental health
- randomized controlled trial
- systematic review
- high school
- sleep quality
- mental illness
- body mass index
- healthcare
- meta analyses
- clinical trial
- depressive symptoms
- microbial community
- study protocol
- gene expression
- quality improvement
- double blind
- machine learning
- risk assessment
- phase iii
- artificial intelligence