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E-mental health (EMH) interventions gain increasing importance in the treatment of mental health disorders. Their outpatient efficacy is well-established. However, research on EMH in inpatient settings remains sparse and lacks a meta-analytic synthesis. This paper presents a meta-analysis on the efficacy of EMH in inpatient settings. Searching multiple databases (PubMed, ScienceGov, PsycInfo, CENTRAL, references), 26 randomized controlled trial (RCT) EMH inpatient studies (n = 6112) with low or medium assessed risk of bias were included. A small significant total effect of EMH treatment was found (g = 0.3). The effect was significant both for blended interventions (g = 0.42) and post-treatment EMH-based aftercare (g = 0.29). EMH treatment yielded significant effects across different patient groups and types of therapy, and the effects remained stable post-treatment. The results show the efficacy of EMH treatment in inpatient settings. The meta-analysis is limited by the small number of included studies. [ABSTRACT FROM AUTHOR]

The "thinking face" is a facial signal used to convey being in thought. For androids, the thinking face may be important to achieve natural human–robot interaction. However, the facial pattern necessary for portraying the thinking face remains unclear and has not yet been investigated in androids. The current study aims to (a) identify the facial patterns when people are engaged in answering complex questions (i.e., thinking face) and (b) clarify whether implementing the observed thinking faces in an android can facilitate natural human–robot interaction. In Study 1, we analyze the facial movements of 40 participants after they are prompted with difficult questions and indicate five facial patterns that corresponded to thinking faces. In Study 2, we further focus on the pattern of furrowing of the brows and narrowing of the eyes among the observed thinking facial patterns and implement this pattern in an android. The results show that thinking faces enhance the perception of being in thought, genuineness, human-likeness, and appropriateness in androids while decreasing eeriness. The free-description data also revealed that negative emotions are attributed to the thinking face. In Study 3, we compared the thinking vs. neutral faces in a question–answer situation. The results showed that the android's thinking face facilitated the perception of being in thought and human-likeness. These findings suggest that the thinking face of androids can facilitate natural human–robot interaction. [ABSTRACT FROM AUTHOR]

Humanlike androids can function as social agents in social situations and in experimental research. While some androids can imitate facial emotion expressions, it is unclear whether their expressions tap the same processing mechanisms utilized in human expression processing, for example configural processing. In this study, the effects of global inversion and asynchrony between facial features as configuration manipulations were compared in android and human dynamic emotion expressions. Seventy-five participants rated (1) angry and happy emotion recognition and (2) arousal and valence ratings of upright or inverted, synchronous or asynchronous, android or human agent dynamic emotion expressions. Asynchrony in dynamic expressions significantly decreased all ratings (except valence in angry expressions) in all human expressions, but did not affect android expressions. Inversion did not affect any measures regardless of agent type. These results suggest that dynamic facial expressions are processed in a synchrony-based configural manner for humans, but not for androids. [ABSTRACT FROM AUTHOR]