Evidence map›Paper›PMID 40745584›Full record

SynthesisBMC geriatrics2025

Effects of different exercise modalities on balance performance in healthy older adults: a systematic review and network meta-analysis of randomized controlled trials.

Shikun Zhang, Guoping Qian, Hong Xu, Meiling Gu, Sujie Mao, Yan'an Wang, Yuqing Zhang, Wensheng Zhou

Abstract readNetwork Meta-AnalysisSystematic Review
In one paragraph

Synthesis in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
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

8 authors.

Shikun ZhangDepartment of Police Physical Education, Jiangsu Police Institute, Nanjing, China.
Guoping QianFaculty of Physical Culture, Gdansk University of Physical Education and Sport, Gdansk, Poland.
Hong XuNanjing Polytechnic Institute, Nanjing, China.
Meiling GuNanjing Tian-zheng Primary School, Nanjing, China.
Sujie MaoHarbin Sport University Heilongjiang, Heilongjiang, China.
Yan'an WangJiangsu Police Institute, Nanjing, China.
Yuqing ZhangJiangsu Police Institute, Nanjing, China.
Wensheng ZhouDepartment of Physical Education, Jiangsu Second Normal University, No.6, Xinhe West Road, Lishui District, Nanjing, China. Vincent714@jssnu.edu.cn.

Funding

Jiangsu Police Institute Education and Teaching Reform Research 2024B01Jiangsu Province University students' innovation and entrepreneurship training program 202410329086Y
6 · The paper itself

Abstract

backgroundExercise has been proven to significantly enhance balance performance in healthy older adults, particularly those living independently in community settings. However, it remains unclear which specific types of exercise are the most effective and which modality yields the best intervention outcomes. We aimed to evaluate the effects of different exercise modalities on four balance-related outcomes-Berg Balance Scale (BBS), Timed Up and Go Test (TUG), gait speed (GS), and one-leg stance with eyes open (OLSEO)-in healthy older adults, using a systematic review and network meta-analysis.

methodsWe searched five databases: Web of Science (WOS), EMBASE, EBSCO, Cochrane Library, and PubMed, with a search cutoff date of March 25, 2024. The search was restricted to randomized controlled trials (RCTs) published in English that investigated the impact of exercise interventions on balance in healthy older adults. Quality was assessed using the PEDro scale, and both traditional and network meta-analyses were performed using Stata 15.1. Surface under the cumulative ranking curve (SUCRA) values were calculated to rank the intervention effects of different exercise modalities.

resultsA total of 93 articles comprising 103 studies with 5,114 participants were included. Overall, the methodological quality of the included studies was relatively high. Pairwise meta-analysis revealed that exercise significantly improved BBS, TUG, OLSEO, and GS (p < 0.05). The network meta-analysis showed that, for improving BBS, augmented reality-based training (AR), aquatic exercise, balance training (BT), comprehensive training (CT), Exergame, functional training, and Otago were significantly more effective than the control group. For improving GS, four exercise modalities-dual-task balance and gait training, balance and gait training, mind-body interventions (MBIs), and CT-were significantly more effective than the control group. For improving OLSEO, dance, Nintendo's Wii Fit Game (Will Fit), and Otago were significantly more effective than the control group. For improving TUG, AR, BT, CT, MBIs, Will Fit, Otago, and resistance training (RT) were significantly more effective than the control group. According to the SUCRA rankings, Exergame was the most effective for improving BBS (SUCRA = 96.3%); CT was the most effective for improving GS (SUCRA = 85.8%); Otago was the most effective for improving OLSEO (SUCRA = 86.8%); and Will Fit was the most effective for reducing TUG test time (SUCRA = 88.5%).

conclusionThis study confirms that exercise improves balance in healthy older adults. Traditional exercise like BT, MBIs, CT, and Otago are recommended, while newer approaches like Exergame, AR, and Will Fit can be cautiously considered.

trial registrationPROSPERO (CRD42024591308).

Indexed as

ExerciseExercise TherapyPostural BalanceAgedHumansRandomized Controlled Trials as TopicBalanceBerg balance scale testGait speedOne-leg stance with eyes openTimed up and go test

Identifiers

PMID40745584
PMCPMC12315335

What OpenQuestion holds

Textmetadata
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.