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All Journal Open Access Indonesia Journal of Social Sciences Eureka Herba Indonesia Open Access Indonesia Journal of Social Sciences
Ni Made Nova Indriani
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Humanities Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences

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Comparative Efficacy and Acute Tolerability of a Standardized Withania somnifera Root Extract Versus Sertraline in Generalized Anxiety Disorder: A Randomized, Double-Blind, Placebo-Controlled Non-Inferiority Trial Ni Made Nova Indriani; Suyong Zhou; Riri Arisanty Syafril Lubis; Jason Willmare
Eureka Herba Indonesia Vol. 6 No. 1 (2025): Eureka Herba Indonesia
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/ehi.v6i1.132

Abstract

Generalized anxiety disorder (GAD) represents a significant psychiatric burden, characterized by chronic hyperarousal and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis. While selective serotonin reuptake inhibitors (SSRIs) like Sertraline are the standard of care, their utility is often compromised by delayed onset and adverse effects, specifically sexual dysfunction. Withania somnifera (Ashwagandha) acts as a GABA-mimetic and adaptogen, yet rigorous head-to-head comparisons against pharmaceutical controls are rare. We conducted an 8-week, randomized, double-blind, placebo-controlled trial involving 150 adults with DSM-5 diagnosed GAD. Participants were randomized (1:1:1) to receive High-Concentration Ashwagandha Root Extract (600 mg/day, standardized to >5% withanolides), Sertraline (50 mg/day), or Placebo. Blinding was maintained using mint-scented desiccants to mask the herb's odor. Efficacy was analyzed using Mixed Models for Repeated Measures (MMRM). Of 150 participants, 138 completed the study. Both Ashwagandha (Mean HAM-A reduction -14.2) and Sertraline (-15.1) demonstrated statistical superiority over Placebo (-5.4; p < 0.001). The difference between active arms was not statistically significant, supporting comparable efficacy. Ashwagandha significantly reduced serum cortisol (-24.3%) and improved GAD-7 scores. Crucially, while Sertraline induced significant sexual dysfunction (worsened ASEX scores, p < 0.001) and nausea (28%), Ashwagandha showed a safety profile indistinguishable from placebo. In conclusion, standardized Withania somnifera extract (600 mg/day) offers anxiolytic efficacy comparable to Sertraline (50 mg/day) with a superior safety profile, specifically devoid of sexual and gastrointestinal adverse effects.
The Algorithmic Sanctuary: Social-Interaction Burnout, Loneliness, and Emotional Attachment to AI Companions Among Young Adults in Indonesia Ni Made Nova Indriani; Immanuel Simbolon; Sophia Lucille Rodriguez
Open Access Indonesia Journal of Social Sciences Vol. 9 No. 3 (2026): Open Access Indonesia Journal of Social Sciences
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/oaijss.v9i3.327

Abstract

Background: Post-pandemic digital life has produced a new affective phenomenon: large-language-model “AI companions” that invite two-way parasocial relationships. Counter-intuitively, the most intense users in urban Indonesia appear to be not the physically isolated but socially active young adults experiencing interaction burnout, who treat AI as a refuge from the judgment costs of a collectivist culture. Yet no integrated model has tested why relational strain translates into machine attachment in a Global-South setting. Objective: To examine whether social-interaction burnout and subjective loneliness predict emotional attachment to AI companions, whether AI parasocial interaction mediates the burnout-attachment path, and whether collectivist judgment apprehension moderates the loneliness-attachment link. Methods: Drawing on Parasocial Interaction Theory, the Computers-Are-Social-Actors paradigm, and emotional-labor theory, this cross-sectional survey of 1,200 young adults recruited through a public organization in Palembang, South Sumatera, Indonesia, measured social-interaction burnout, subjective loneliness, collectivist judgment apprehension, AI parasocial interaction, and emotional attachment to AI using validated Likert scales analysed with reliability, correlation, multiple regression, bootstrap mediation, and moderation. Results: All scales were reliable (α = 0.84–0.91; KMO = 0.96). The model explained 48% of variance in attachment (adjusted R² = 0.486, F = 284, p < 0.001). Parasocial interaction (β = 0.318), loneliness (β = 0.254), and burnout (β = 0.238) were the strongest predictors (all p < 0.001). Parasocial interaction partially mediated the burnout–attachment path (indirect = 0.160, 95% CI [0.132, 0.191]), and judgment apprehension moderated the loneliness–attachment link (β = 0.127, p < 0.001). Conclusion: Findings introduce the “Algorithmic Sanctuary” account of AI companionship and inform digital-wellbeing policy in collectivist societies.
The Algorithmic Sanctuary: Social-Interaction Burnout, Loneliness, and Emotional Attachment to AI Companions Among Young Adults in Indonesia Ni Made Nova Indriani; Immanuel Simbolon; Sophia Lucille Rodriguez
Open Access Indonesia Journal of Social Sciences Vol. 9 No. 3 (2026): Open Access Indonesia Journal of Social Sciences
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/oaijss.v9i3.327

Abstract

Background: Post-pandemic digital life has produced a new affective phenomenon: large-language-model “AI companions” that invite two-way parasocial relationships. Counter-intuitively, the most intense users in urban Indonesia appear to be not the physically isolated but socially active young adults experiencing interaction burnout, who treat AI as a refuge from the judgment costs of a collectivist culture. Yet no integrated model has tested why relational strain translates into machine attachment in a Global-South setting. Objective: To examine whether social-interaction burnout and subjective loneliness predict emotional attachment to AI companions, whether AI parasocial interaction mediates the burnout-attachment path, and whether collectivist judgment apprehension moderates the loneliness-attachment link. Methods: Drawing on Parasocial Interaction Theory, the Computers-Are-Social-Actors paradigm, and emotional-labor theory, this cross-sectional survey of 1,200 young adults recruited through a public organization in Palembang, South Sumatera, Indonesia, measured social-interaction burnout, subjective loneliness, collectivist judgment apprehension, AI parasocial interaction, and emotional attachment to AI using validated Likert scales analysed with reliability, correlation, multiple regression, bootstrap mediation, and moderation. Results: All scales were reliable (α = 0.84–0.91; KMO = 0.96). The model explained 48% of variance in attachment (adjusted R² = 0.486, F = 284, p < 0.001). Parasocial interaction (β = 0.318), loneliness (β = 0.254), and burnout (β = 0.238) were the strongest predictors (all p < 0.001). Parasocial interaction partially mediated the burnout–attachment path (indirect = 0.160, 95% CI [0.132, 0.191]), and judgment apprehension moderated the loneliness–attachment link (β = 0.127, p < 0.001). Conclusion: Findings introduce the “Algorithmic Sanctuary” account of AI companionship and inform digital-wellbeing policy in collectivist societies.
Co-Authors Immanuel Simbolon Jason Willmare Riri Arisanty Syafril Lubis Sophia Lucille Rodriguez Suyong Zhou