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

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.

Authors

Institutions

Publication Details

Journal
BMC Psychiatry
Published
2025-12-10
DOI
https://doi.org/10.1186/s12888-025-07648-9
Primary Topic
Sleep and related disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Chunrong Zhang, Wenjing Zhang, Yunshu Zhang, Kezhi Liu et al.
BMC Psychiatry
Sleep and related disorders
article

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

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

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.

BMC PsychiatryVol. 26(1)
Guangzhou University of Chinese Medicine (CN), Central South University (CN), Shandong University of Aeronautics (CN), Xinjiang Medical University (CN), Harbin Medical University (CN), Chinese University of Hong Kong (HK), Dalian Medical University (CN), Southern University of Science and Technology (CN), Affiliated Hospital of Southwest Medical University (CN), Qinhuangdao Second Hospital (CN), Shanghai University of Traditional Chinese Medicine (CN), Nanfang Hospital (CN), Changji University (CN), Third Affiliated Hospital of Harbin Medical University (CN), Shenzhen Maternity and Child Healthcare Hospital (CN), Inner Mongolia Autonomous Region Meteorological Bureau (CN), Wuhan No.1 Hospital (CN), People's Hospital of Shiyan (CN), Second Affiliated Hospital of Harbin Medical University (CN), First Affiliated Hospital of Dalian Medical University (CN), Guangdong Academy of Medical Sciences (CN), First Bethune Hospital of Jilin University (CN), Hebei Mental Health Center (CN), Sixth Affiliated Hospital of Xinjiang Medical University (CN), Xiamen Chang Gung Hospital (CN), Second Xiangya Hospital of Central South University (CN), Guangdong Provincial People's Hospital (CN), First Affiliated Hospital of Harbin Medical University (CN), First People's Hospital of Chongqing (CN), First Affiliated Hospital of Jinan University (CN), Inner Mongolia Autonomous Region Hospital of Traditional Chinese Medicine (CN), Shenzhen Second People's Hospital (CN), Southern Medical University (CN), Wuhan Sports University (CN), China Medical University (CN), Guangzhou Medical University (CN)
Openalex Percentile: Top 58%
Sleep and related disorders
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.