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Global Trends and Research Hotspots in Insomnia: A Multi-Dimensional Scientometric Analysis from 2016-2025

S. Ravichandran, P. Rajendran, S. Vivekanandhan

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Indian Journal of Library and Information Sciences 20(3):p 296-306, September-December 2026. | DOI: https://doi.org/10.21088/ijlis.0973.9548.20326.6

How Cite This Article:

S. Ravichandran, P. Rajendran, S. Vivekanandhan. Global Trends and Research Hotspots in Insomnia: A MultiDimensional Scientometric Analysis from 2016-2025. Ind J Lib Inf Sci 2026; 20 (3): 296–306.

Timeline

Received : August 13, 2026         Accepted : September 14, 2026          Published : December 30, 2026

Abstract

Goal: Persistent trouble getting or staying asleep is the hallmark of insomnia, a sleep condition that causes severe daily exhaustion, mood swings, and diminished functioning. It is a sign of underlying problems like stress, anxiety, or bad behaviors rather than a deliberate activity, and it frequently poses serious long-term health hazards. Design/methodology/approach: According to the search results, the query "Insomnia Design/methodology/approach" is related to two different areas: developers utilize Insomnia, an open-source desktop program, to design, debug, and test REST and GraphQL APIs. Approach (Design-First): It enables developers to deal with API specifications (OpenAPI/Swagger) prior to developing code by supporting a "design-first" workflow. Methodology: Spec-Driven Design: OpenAPI YAML specifications with real-time visual previews can be written or imported using the "Design" tab. Finding: Insomnia is a common sleep condition that causes weariness and decreased functioning during the day. It is defined by trouble falling or staying asleep or by waking up too early. Experiencing these symptoms at least three nites a week for three months or more is considered a chronic case. These symptoms are frequently brought on by stress, mental health problems, bad habits, or underlying medical diseases. Research Limitation: Research on insomnia is constrained by a strong emphasis on self-reported, subjective data rather than objective measurements, which leads to varying diagnostic standards between studies. Small sample sizes in neuroimaging, a dearth of long-term longitudinal research, and challenges differentiating between primary insomnia and comorbidities are important limitations. Useful Consequences: From everyday cognitive impairment and safety risks to long-term health decline, insomnia has serious practical ramifications. It results in weariness, mood swings (such as anxiety or despair), and diminished productivity at work. Physically, it raises the risk of diabetes, high blood pressure, and heart disease. Economically, it results in expensive healthcare expenses and lost output. Originality/value: The most common sleep condition, insomnia, affects 10–30% of people and is defined by non-restorative sleep or trouble falling or staying asleep, along with impairment during the day. Its value stems from its high prevalence, enormous economic impact (more than $100 billion annually in the US), and role as a risk factor for both physical and mental health conditions. Its uniqueness as a research topic stems from the growing understanding of it as a systemic, neurobiological disorder rather than just a symptom.


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Data Sharing Statement

There are no additional data available. All raw data and code are available upon request.

Funding

This research received no funding.

Author Contributions

All authors contributed significantly to the work and approve its publication.

Ethics Declaration

This article does not involve any human or animal subjects, and therefore does not require ethics approval.

Acknowledgements

We would like to express our gratitude to the patients, their families, and all those who have contributed to this study.

Conflicts of Interest

No conflicts of interest in this work.


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Cite this article

S. Ravichandran, P. Rajendran, S. Vivekanandhan. Global Trends and Research Hotspots in Insomnia: A MultiDimensional Scientometric Analysis from 2016-2025. Ind J Lib Inf Sci 2026; 20 (3): 296–306.


Licence:

Attribution-Non-commercial 4.0 International (CC BY-NC 4.0)

This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.


Received Accepted Published
August 13, 2026 September 14, 2026 December 30, 2026

DOI: https://doi.org/10.21088/ijlis.0973.9548.20326.6

Keywords

ScientometricInsomniaRGRTime series analysisHighly cited paperRCIVos ViewerRStudio

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Received August 13, 2026
Accepted September 14, 2026
Published December 30, 2026

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Attribution-Non-commercial 4.0 International (CC BY-NC 4.0)

This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.


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