# Digital multi-modal approaches to subtyping insomnia disorder (DIMOSI): study design, rationale, digital platform, and preliminary baseline characteristics of a national prospective cohort study

**Authors:** Yujing Zhou, Jiyang Pan, Hanrong Cheng, Li Xiao, Wenjing Zhang, Hui Huang, Kezhi Liu, Leqin Fang, Wenbin Ma, Yan Xia, Jinghui Li, Dongsheng Lv, Yanyu Hu, Yi Chang, Zan Wang, Haojuan Tao, Chunrong Zhang, Chenyu Li, Yanhui Peng, Qiying Zhao, Yunshu Zhang, Junhua Mei, Xuehang Wang, Ting Wei, Mingqing Zhou, Yi Zhang, Qiuqiang Chen, Ngan Yin Chan, Bin Zhang, Yun Kwok Wing, Binbin Lei, Jihui Zhang

PMC · DOI: 10.1186/s12888-025-07648-9 · BMC Psychiatry · 2025-12-10

## TL;DR

The DIMOSI study uses a digital platform to explore insomnia subtypes and their effects on mental health through multi-modal assessments in a large cohort.

## Contribution

The study introduces a national digital multi-modal platform for insomnia subtyping and longitudinal tracking in a large prospective cohort.

## Key findings

- The study recruits participants with insomnia disorder for multi-modal assessments using a digital platform.
- Preliminary data show most participants have current insomnia disorder with high insomnia severity scores.
- Longitudinal follow-ups will identify subtypes and their mental health correlates.

## Abstract

Insomnia disorder exhibits complex manifestations and heterogeneous clinical trajectories. Accurate subtyping of insomnia might enhance understanding of its clinical presentations and facilitate precision management. The Digital Multi-modal approaches to Subtyping Insomnia disorder (DIMOSI) study is a national prospective cohort study utilizing multi-modal assessments to explore the subtypes of insomnia disorder, their natural trajectories, and related mental health outcomes.

A total of 4,000 adult participants meeting International Classification of Sleep Disorders, 3rd Edition (ICSD-3) criteria for insomnia disorder will be recruited from community or clinical settings. Eligible participants will be invited to complete the multi-dimensional assessments via a digital platform, including a structured interview, questionnaires, cognitive tasks, sleep-activity diary, physiological characteristics, and ecology momentary assessments, as well as 7-day physical activity and sleep tracking using wearable devices. All participants will be followed up at 6 and 12 months. The primary outcome is the identification of multi-modal subtypes of insomnia disorder and their correlates. Secondary outcomes include the longitudinal trajectories of these subtypes, associated risk factors, and mental health outcomes.

As of June 30, 2025, a total of 2937 patients with insomnia disorder have been recruited, with a mean age of 37.3 years (SD = 12.6), 59.3% from outpatient clinics, and 66.5% female. Among the participants, 2850(97.1%) were suffering from current insomnia disorder, while the mean score of the ISI was 15.5 ± 5.8. A total of 2134 participants (72.7%) wore accelerometers, while 2429 (82.7%) wore wearable EEG monitors for continuous assessments.

The DIMOSI study is a large-scale national prospective cohort investigating insomnia disorder utilizing a self-developed digital multi-modal platform. It integrates comprehensive subjective and objective assessments from 33 centers in China. The current study offers a unique opportunity to explore subtypes of insomnia disorder and their natural course and their correlates through the digital multi-modal platform that provides enriched and comprehensive assessments. It may provide the potential to inform the development of personalized prevention and intervention strategies, ultimately improving patient outcomes.

Clinical Trial Registry Name: Digital Multi-modal approaches to deep phenotyping insomnia disorder. Registration Number: ChiCTR2200056425. Date of Registration: 2022-02-05.

The online version contains supplementary material available at 10.1186/s12888-025-07648-9.

## Full-text entities

- **Diseases:** DIMOSI (MESH:D007319)

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## References

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Source: https://tomesphere.com/paper/PMC12849073