Comorbidities within mental illnesses in a multiethnic urban population

Effects of internet-based exercise intervention on depression and anxiety: A systematic review and meta-analysis

BACKGROUND

While exercise is acknowledged for its positive effects on depression and anxiety symptoms, the benefits of internet-based exercise on mental health have not been extensively examined. This study seeks to systematically review and quantify the outcomes of high-quality randomized controlled trials (RCTs) that investigate the impact of internet-based exercise on depression and anxiety symptoms.

METHODS

Following the PRISMA 2020 guidelines, we conducted a comprehensive meta-analysis of RCTs. Databases, including Web of Science Core Collection, PubMed, PsycINFO, Medline, BIOSIS Previews, SPORTDiscus, and Education Source, were scoured through in September 2023. After quality assessment and data extraction, the analysis was performed using R. Using random effects models, effect sizes were determined and subsequently represented as standardized mean differences (SMD).

RESULTS

Our analysis incorporated data from 11 RCTs, involving a cohort of 1009 participants. We observed a modest yet significant reduction in depression and anxiety symptoms, with an SMD of -0.44 [95% confidence interval (CI) (-0.63, -0.26), I^2 = 79.3%, P < .01]. Interestingly, the effects were more pronounced in individuals diagnosed with depression, as indicated by an SMD of -0.96 [95% CI (-1.55, -0.37), I^2 = 82%, P < .01]. Furthermore, participants utilizing smartphone applications as part of their intervention reported a meaningful reduction in their symptoms, evidenced by an SMD of -0.52 [95% CI (-0.90, -0.14), I^2 = 87%, P < .01]. Additionally, short-term interventions, specifically those lasting <12 weeks, indicated a notable alleviation in depression symptoms, with an SMD of -0.76 [95% CI (-1.38, -0.14), I^2 = 86%, P < .01].

CONCLUSION

Internet-based exercise interventions yield significant amelioration in depression and anxiety symptoms, with heightened efficacy observed among individuals with depression. Notably, short-term interventions, specifically those under 12 weeks, demonstrate enhanced benefits for depression relief.

Effects of aquatic exercise on mood and anxiety symptoms: A systematic review and meta-analysis

OBJECTIVE

Exercise has beneficial effects on mood and anxiety symptoms. However, the impact of aquatic exercise on mood and anxiety symptoms has not been clearly confirmed. Therefore, this study aimed to synthesize and systematically analyze evidence available on boosting mental health through aquatic exercise.

METHOD

A systematic review and meta-analysis were conducted under the PRISMA 2020 guidelines. PubMed, BIOSIS Previews, PsycINFO, Medline, SPORTDiscus, Education Source, and Web of Science Core Collection (WoSCC) were searched in May 2022. The research included the influence of aquatic exercises on mood and anxiety symptoms. After assessing trial quality and completing data extraction, a meta-analysis was carried out through R software. The results were presented as a standardized mean difference (SMD) and the corresponding 95% confidence interval.

RESULTS

A total of 18 original trials were included. People who received aquatic exercise intervention had a statistically significant reduction in mental disorder symptoms compared with before. The results were aquatic exercise [SMD = -0.77, 95% CI (-1.08, -0.47), I² = 77%, P < 0.01], swimming [SMD = -0.51, 95% CI (-1.14, 0.12), I² = 78%, P < 0.01], aquatic aerobics [SMD = -0.92, 95% CI (-1.32, -0.53), I² = 78%, P < 0.01], moderate intensity [SMD = -0.75, 95% CI (-1.07, -0.43), I² = 67%, P < 0.01], and low intensity [SMD = -1.07, 95% CI (-1.08, -0.47), I² = 85%, P < 0.01].

CONCLUSION

Aquatic exercise could statistically significantly improve mental health. Light aquatic aerobics probably has a better effect on mood and anxiety symptoms. However, given the number and quality of included research, verifying the aforementioned conclusions requires a larger sample of high-quality studies.

Prediction of depression among medical check-ups of 433,190 patients: A nationwide population-based study

Depression is a mental illness that causes significant disturbances in daily life. Depression is commonly associated with low mood, severe health problems, and substantial socioeconomic burden; hence, it is necessary to be able to detect depression earlier. We utilized the medical check-up cohort database of the National Health Insurance Sharing Service in Korea. We split the total dataset into training (70%) and test (30%) sets. Subsequently, five-fold cross validation was performed in the training set. The holdout test set was only used in the last step to evaluate the performance of the predictive model. Random forest algorithm was used for the predictive model. The analysis included 433,190 individuals who had a national medical check-up from 2009-2015, which included 10,824 (2.56%) patients in the depression group. The area under the receiver-operating curve was 0.849. Other performance metrics included a sensitivity of 0.737, specificity of 0.824, positive predictive value of 0.097, negative predictive value of 0.992, and accuracy of 0.780. Our predictive model could contribute to proactively reducing depression prevalence by administering interventions to prevent depression in patients receiving medical check-up. Future studies are needed to prospectively validate the predictability of this model.