Yuhui Chen

Daily-life benefits of extrinsic interpersonal emotion regulation: An ecological momentary assessment study of depression and healthy controls

Chen, Y., Zuffianò, A., Manfredi, L., Gregori, F., Elliott, R., & López-Pérez, B. (2025)

Translational Issues in Psychological Science

Using ecological momentary assessment over 28 days, this study tracked 52 adults (26 with depression, 26 healthy controls) as they tried to comfort or uplift others in daily life. Although people with depression engaged less in this social regulation, when they did they gained greater boosts to closeness and momentary happiness than healthy controls — yet also experienced stronger emotional costs afterward, with reduced life satisfaction at later time points. The findings highlight both the promise and the complexity of social emotion regulation as a clinical target.

I want you to feel bad: Understanding the role of anger in extrinsic interpersonal affect worsening with ecological momentary assessment

Polias, S., Zuffianò, A., Chen, Y., Manfredi, L., Gregori, F., Bourke, L., & López-Pérez, B. (2025)

Emotion, 25(2), 320–329

When and why do people deliberately try to make others feel worse? This EMA study examined the role of anger in driving extrinsic affect worsening — intentional attempts to worsen another person's emotional state — across daily life. We found that anger predicted both the likelihood and the intensity of affect worsening attempts at the within-person level, with dispositional anger also explaining between-person differences. The findings illuminate the interpersonal dynamics of negative social emotions and the conditions under which people act on them.

Exploring the Potential of Large Language Models to Understand Interpersonal Emotion Regulation Strategies From Narratives

López-Pérez, B., Chen, Y., Li, X., Cheng, S., & Razavi, P. (2025)

Emotion (Washington, D.C.), 25(7), 1653–1667

Can large language models replace human coders in identifying emotion regulation strategies from narratives? Across two studies (N = 2,824 and N = 2,090 responses), this paper evaluated ChatGPT and Claude against human coding of interpersonal emotion regulation strategies. After prompt refinement, both LLMs achieved substantial agreement with human coders (κ > .76), though accuracy varied notably by emotion and strategy type — pointing to both the promise of LLM-assisted coding and the critical importance of prompt engineering and validation.

Distraction over reappraisal strategies in interpersonal emotion regulation: associations with emotional difficulties

López-Pérez, B., Kwon, K., & Chen, Y. (2025)

Cognition and Emotion, 40(2), 345–359

Is it better to distract someone from their feelings or help them reframe the situation? This paper examines whether a dominant tendency to use distraction — rather than reappraisal — when regulating others' emotions is linked to emotional difficulties. In Study 1 (N = 274), greater reliance on distraction over reappraisal predicted more difficulties in identifying and describing feelings and higher personal distress. Study 2 (N = 121) used latent class analysis to identify three regulator profiles: consistent reappraisal users (23%), affective engagement users (48%), and distraction users (19%) — with the distraction class scoring higher on emotion identification difficulties and experiential avoidance. The findings suggest that habitual use of diversion strategies to regulate others' emotions may reflect broader emotional difficulties in the regulator themselves.

Psychological challenges and quality of life in Pakistani parents of children living with thalassemia

Hood, A. M., Chaman, A., Chen, Y., & Mufti, S. (2024)

Journal of Pediatric Nursing, 76, 132–139

Pain Burden Predicts Executive Function in Paediatric Patients with Sickle Cell Disease

Chen, Y., Hood, A. M., Kölbel, M., Stotesbury, H., Kawadler, J., Slee, A., Inusa, B., Pelidis, M., Howard, J., Chakravorty, S., Height, S., Awogbade, M., Kirkham, F. J., & Liossi, C. (2023)

HemaSphere, 7(S1), p. 11