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<p>Online support systems may help address unmet mental health needs after the COVID-19 pandemic. In this uncontrolled, real-world longitudinal pre–post study, we described one-month changes in mental health outcomes among help-seeking users of KOKOROBO, a Japanese severity-guided web-based mental health support platform, which recommends a wait-and-see approach, AI chatbot use, or online-counseling depending on the user’s mental health severity; we explored baseline factors associated with change (<i>n</i> = 3,325). Participants completed measures of depression (PHQ-9), anxiety (GAD-7), insomnia (ISI), and health-related quality of life (EQ VAS) at baseline and one month. Service-use subgroup analyses were descriptive because service use was self-selected and subgroups overlapped. Missing data were addressed using multiple imputation, and multivariable linear regression examined associations between changes and baseline characteristics. At one month, PHQ‑9, GAD‑7, and ISI scores decreased, and EQ VAS increased (PHQ‑9 mean change = − 1.61; 95% CI, − 1.85 to − 1.37). Improvements were generally observed across service-use subgroups. Frequent versus almost no communication with others was associated with more favorable changes across outcomes, whereas communication with family was not associated with change. These findings characterize short-term real-world outcomes; controlled studies are needed to evaluate the effect of the platform and its severity-guided recommendation strategy.</p>

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One-month mental health changes and associated factors among users of an online mental health support system during the COVID-19 pandemic

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Fumikazu Hiyoshi,
  • Takumi Kanata,
  • Mari Oba,
  • Takeshi Fujii,
  • Ryo Iwata,
  • Akira Iino,
  • Keitaro Murayama,
  • Toshiaki Kikuchi,
  • Asuka Yoshimi,
  • Shinsuke Kito,
  • Hironori Kuga,
  • Hideki Oi,
  • Koichiro Watanabe,
  • Norio Ozaki,
  • Tomohiro Nakao,
  • Kazuyoshi Takeda,
  • Kazuyuki Nakagome

摘要

Online support systems may help address unmet mental health needs after the COVID-19 pandemic. In this uncontrolled, real-world longitudinal pre–post study, we described one-month changes in mental health outcomes among help-seeking users of KOKOROBO, a Japanese severity-guided web-based mental health support platform, which recommends a wait-and-see approach, AI chatbot use, or online-counseling depending on the user’s mental health severity; we explored baseline factors associated with change (n = 3,325). Participants completed measures of depression (PHQ-9), anxiety (GAD-7), insomnia (ISI), and health-related quality of life (EQ VAS) at baseline and one month. Service-use subgroup analyses were descriptive because service use was self-selected and subgroups overlapped. Missing data were addressed using multiple imputation, and multivariable linear regression examined associations between changes and baseline characteristics. At one month, PHQ‑9, GAD‑7, and ISI scores decreased, and EQ VAS increased (PHQ‑9 mean change = − 1.61; 95% CI, − 1.85 to − 1.37). Improvements were generally observed across service-use subgroups. Frequent versus almost no communication with others was associated with more favorable changes across outcomes, whereas communication with family was not associated with change. These findings characterize short-term real-world outcomes; controlled studies are needed to evaluate the effect of the platform and its severity-guided recommendation strategy.