Evidence map›Paper›PMID 41742230›Full record

SynthesisBMC medicine2026

Effects of non-pharmacological interventions on sleep quality in older adults: a systematic review and network meta-analysis of randomized controlled trials.

Jia Song, Heng Dong, Chunying Wang, Yanxi Zheng, Yangzhen Huang, Yilin Wei, Kangkang Zhang, Hua Qing, Zhongbao Xiong, Yangyang Pan and 8 more

Abstract readSystematic ReviewNetwork Meta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Jia Song *School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Heng Dong *School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Chunying WangSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Yanxi ZhengSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Yangzhen HuangSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Yilin WeiSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Kangkang ZhangSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Hua QingSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Zhongbao XiongSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Yangyang PanSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Manwei ChenSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Ruizhe KangSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Bishwajit GhoseInterdisciplinary School of Health Sciences, Univ Ottawa, Ottawa, ON, K1N 6N5, Canada.
Dongfeng TangSchool of Life Course and Population Sciences, King's College London, London, UK.
Ruoxi WangInstitute for Better Health, Trillium Health Partners, Mississauga, Canada.
Chengxu LongDepartment of Global Health and Social Medicine, King's College London, London, UK.
Yunfei LiDepartment of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
Shangfeng TangSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China. sftang2018@hust.edu.cn.

Funding

the National Key Research and Development Program of China 2023YFC3606400the National Natural Science Foundation of China 72374079the Natural Science Foundation of Hubei Province JCZRYB202500352
6 · The paper itself

Abstract

backgroundSleep problems are common among older adults and are associated with a wide range of adverse health outcomes. Concerns about pharmacological treatments have increased interest in non-pharmacological interventions; however, evidence comparing their relative effectiveness remains limited.

methodsA systematic search was conducted in PubMed, Scopus, Embase, Web of Science, Cochrane Library, and CINAHL. Randomized controlled trials (RCTs) evaluating non-pharmacological interventions in adults aged ≥ 60 years published between 2000 and 2024 were included. Network meta-analyses were conducted using random-effects models to estimate standardized mean differences (SMDs) with 95% confidence intervals (CIs). P-scores were used to rank the efficacy of interventions. The protocol was registered in PROSPERO (CRD42024521492).

resultsThirty-four RCTs involving 3078 participants and 21 interventions were included. Eleven interventions significantly improved sleep quality. Cognitive behavioral therapy for insomnia plus positive mood strategies (CBT-I+) showed the largest effect (P-score = 0.99, SMD = - 3.32, 95% CI - 4.59 to - 2.06), followed by cognitive behavioral therapy for insomnia (CBT-I) (P-score = 0.92, SMD = - 2.18, 95% CI - 3.04 to - 1.31). Subgroup analyses indicated that music therapy (MUS) was more effective among participants with PSQI < 10 (SMD = - 1.25, 95% CI - 1.85 to - 0.65), whereas CBT-I+ showed greater effects for those with PSQI ≥ 10 (SMD = - 5.48, 95% CI - 6.80 to - 4.16). By intervention setting, traditional Chinese health-promotion exercise (TCHPE) was more effective in home-based settings (SMD = - 1.55, 95% CI - 2.60 to - 0.50), whereas CBT-I+ showed greater effects in non-home settings (SMD = - 3.31, 95% CI - 4.57 to - 2.06).

conclusionsCBT-I+ was associated with the greatest improvements in sleep quality among older adults, particularly those with baseline PSQI ≥ 10 and in non-home settings. MUS showed greater benefits among older adults with baseline PSQI < 10, and TCHPE showed greater benefits in home-based settings. These findings support stratified, context-specific intervention selection. Given the low GRADE certainty, these results should be interpreted with caution.

Indexed as

Cognitive Behavioral TherapySleep Initiation and Maintenance DisordersSleep QualitySleep Wake DisordersAgedHumansMiddle AgedRandomized Controlled Trials as TopicNetwork meta-analysisNon-pharmacological interventionsOlder adultsSleep quality

Identifiers

PMID41742230
PMCPMC13041094

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.