# Four night eating behavior subtypes in young and middle-aged adults with type 2 diabetes: insights for targeted public health interventions

## TL;DR

This study identifies four types of night eating behaviors in type 2 diabetes patients in China, offering insights for better public health strategies.

## Contribution

The study introduces four behavioral subtypes of night eating in T2DM patients, enhancing targeted diabetes management approaches.

## Key findings

- Four night eating subtypes were identified: physiological stress-driven, emotional and psychological-driven, social environment-induced, and health cognitive bias-driven.
- Six core factors influencing night eating include motivation, ability, triggers, dietary habits, psychological traits, and sociocultural influences.
- The findings support the use of subtype-specific screening and family-collaborative interventions for diabetes management.

## Abstract

To address the limited evidence for public health interventions targeting night eating behavior (defined as eating between dinner and bedtime or waking to eat during sleep) in young and middle-aged T2DM patients in China, this qualitative interview study explored their real-life experiences, motivations, and influencing factors. Using the FBM, we identified night eating characteristics and developed corresponding behavioral subtypes to inform targeted public health strategies. From October to December 2024, 15 eligible patients were recruited from a tertiary grade A general hospital in China. Semi-structured interviews were conducted, and data analyzed via Colaizzi’s 7-step method. This study elucidated the dynamic night eating process, encompassing pre-eating triggers, psychological conflicts, emotional changes during eating, post-eating experiences, and subsequent coping behaviors. Six core influencing factors were identified (motivation, ability, triggers, dietary habits, psychological traits, sociocultural influences), and based on these, four subtypes were classified: “physiological stress-driven,” “emotional and psychological-driven,” “social environment-induced,” and “health cognitive bias-driven.” The findings provide a localized, targeted basis for community diabetes management (e.g., subtype-specific screening tools, family-collaborative interventions). Furthermore, this study broadens the FBM’s application in chronic disease behavior research and offers refined directions for personalized public health education and comprehensive disease management. It adhered strictly to the EQUATOR Network guidelines, an internationally recognized framework for reporting health research.