# Exploring the Cognitive and Behavioral Risks and Maintenance Factors of Hikikomori: Protocol for an Ecological Momentary Assessment Study

## TL;DR

This study explores how daily mood, internet use, and social interactions affect the severity of hikikomori, a condition marked by social withdrawal.

## Contribution

This is the first study to use ecological momentary assessment to investigate the dynamic factors maintaining hikikomori symptoms.

## Key findings

- Internet addiction and social anxiety may predict hikikomori symptoms.
- Daily mood and social enjoyment could influence the maintenance of social withdrawal.
- Time-lagged analyses will reveal how these factors interact over time.

## Abstract

Hikikomori is a state of social withdrawal first identified in Japan and is gaining interest globally. Classically, hikikomori is described as a state of isolation within one’s home, though recent conceptualizations have proposed a continuum of severity. Hikikomori frequently shares symptoms with depression, social anxiety, autism, and schizophrenia, as well as internet and gaming disorders. Clinical case studies and cross-sectional studies suggest that dysfunctional emotion regulation, familial support, and internet behaviors are proposed to contribute to the onset and maintenance of a withdrawn state, though they have not been explored longitudinally.

This study aims to investigate affective, behavioral, and cognitive correlates of hikikomori symptoms, and how daily mood, social enjoyment, familial support, and internet usage may maintain a socially withdrawn state.

A minimum of 84 participants aged between 18 and 60 years will complete self-report measures of hikikomori symptoms, internet addiction, depression, anxiety, autism, and fear of offending others before participating in 14 days of ecological momentary assessment surveys. Surveys will be delivered 5 times per day from 8 AM to 10 PM, measuring mood, internet behavior, familial relationships, social interaction frequency, anticipatory and consummatory enjoyment, sleep quality, and physical activity. Participants will repeat the self-report measure of hikikomori symptoms postmonitoring period.

Recruitment began in November 21, 2025. Data collection and analysis are scheduled to be completed by summer 2026, with the results also scheduled to be available by the end of summer 2026. Correlation and multiple regression analyses will investigate whether internet addiction, social anxiety, expressive suppression, fear of offending others, daily mood, internet use, social enjoyment, and familial support predict hikikomori symptoms. Time-lagged network analyses will explore the temporal dynamics of these relationships, and how these differ in those with high and low levels of hikikomori symptoms. Finally, time-lagged logistic regressions will explore which factors predict future social behavior.

This study will be the first to investigate currently proposed mechanisms underlying hikikomori, while also exploring the time-varying relationships between affect and social behavior. The results will provide initial evidence for factors that predict hikikomori symptoms, explore candidate mechanisms underlying hikikomori, and identify potential maintenance factors as targets for intervention.