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Impacts of digital social media detox for mental health: A systematic review and meta-analysis Ramadhan, Roy N.; Rampengan, Derren D.; Yumnanisha, Defin A.; Setiono, Sabrina BV.; Tjandra, Kevin C.; Ariyanto, Melissa V.; Idrisov, Bulat; Empitu, Maulana
Narra J Vol. 4 No. 2 (2024): August 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i2.786

Abstract

The impact of social media has been significant on various aspects of life, particularly mental health. Growing concerns about the adverse effects of social media use have prompted the exploration of experimental interventions, defined as digital detox interventions. However, it remains unclear whether digital detox interventions are effective for mental health outcomes. The aim of this study was to provide comprehensive insights into the effects of digital detox interventions on various mental health outcomes, including depression, life satisfaction, stress, and mental well-being. Following the PRISMA guidelines, systematic searches were carried out in online databases, including PubMed and ScienceDirect, within the publication range of 2013 and 2023. A total of 2578 titles and abstracts were screened, and 10 studies were included in the analysis. A risk of bias assessment was conducted using RoB 2.0 and the Newcastle-Ottawa scale, while statistical analysis was conducted using RevMan 5.4.1. Our data indicated a significant effect of digital detox in mitigating depression with the standardized mean difference (SMD: -0.29; 95%CI: -0.51, -0.07, p=0.01). No statistically significant effects were discerned in terms of life satisfaction (SMD: 0.20; 95%CI: -0.12, 0.52, p=0.23), stress (SMD: -0.31; 95%CI: -0.83, 0.21, p=0.24), and overall mental well-being (SMD: 0.04; 95%CI: -0.54, 0.62, p=0.90). These data underscore the nuanced and selective influence of digital detox on distinct facets of mental health. In conclusion, digital detox interventions significantly reduce depressive symptoms, suggesting that intentional reduction or cessation of digital engagement may help alleviate contributing factors. However, no statistically significant effects were observed in mental well-being, life satisfaction, and stress. This discrepancy may be due to the complex nature of these constructs, involving various factors beyond the scope of digital detox interventions.
Predicting the risks of stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: A systematic review and meta-analysis Nur, Aqsha; Tjandra, Sydney; Yumnanisha, Defin A.; Keane, Arnold; Bachtiar, Adang
Narra J Vol. 5 No. 1 (2025): April 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i1.2116

Abstract

Macrovascular complications, including stroke, cardiovascular disease (CVD), and peripheral vascular disease (PVD), significantly contribute to morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). The aim of this study was to evaluate the performance of artificial intelligence (AI) models in predicting these complications, emphasizing applicability in diverse healthcare settings. Following PRISMA guidelines, a systematic search of six databases was conducted, yielding 46 eligible studies with 184 AI models. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUROC). Subgroup analyses examined model performance by outcome type, predictor data (lab-only, non-lab, mixed), and algorithm type. Heterogeneity was evaluated using I2 statistics, and sensitivity analyses addressed outliers and study biases. The pooled AUROC for all AI models was 0.753 (95%CI: 0.740–0.766; I2=99.99%). Models predicting PVD achieved the highest AUROC (0.794), followed by cerebrovascular diseases (0.770) and CVD (0.741). Gradient-boosting algorithms outperformed others (AUROC: 0.789). Models with lab-only predictors had superior performance (AUROC: 0.837) compared to mixed (0.759) and non-lab predictors (0.714). External validations reported reduced AUROC (0.725), underscoring limitations in generalizability. AI models show moderate predictive accuracy for T2DM macrovascular complications, with laboratory-based predictors being key to performance. However, the limited external validation and reliance on high-resource data restrict implementation in low-resource settings. Future efforts should focus on non-lab predictors, external validation, and context-appropriate AI solutions to enhance global applicability.