Evidence map›Paper›PMID 39737404›Full record

SynthesisFrontiers in oncology2024

Optimal exercise dose-response improves health-related quality of life in cancer survivors: a systematic review and Bayesian network meta-analysis of RCTs.

Zhiyu Xiong, Yuan Yuan, Yong Yang, Bopeng Qiu, Ying Bai, Tao Wang, Junyu Wang, Lin Zhang, Yawen Li

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Effectiveness and safety of inpatient rehabilitation in patients with hematologic malignancies.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Observational
  5. Article
  6. Article
  7. Review
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

9 authors.

Zhiyu Xiong *The School of Physical Education and Health, East China Jiaotong University, Nanchang, China.
Yuan Yuan *The School of Physical Education, Kunsan National University, Kunsan-si, Jeollabuk-do, Republic of Korea.
Yong Yang *Laboratory of Kinesiology and Rehabilitation, School of Physical Education and Sport, Chaohu University, Hefei, China.
Bopeng QiuSchool of Strength and Conditioning Training, Beijing Sport University, Beijing, China.
Ying BaiThe School of Physical Education, Kunsan National University, Kunsan-si, Jeollabuk-do, Republic of Korea.
Tao WangCollege of Physical Education and Health, Southwest University of Science and Technology, Mianyang, China.
Junyu WangThe School of Exercise and Health, Shanghai University of Sport, Shanghai, China.
Lin ZhangDepartment of Rehabilitation, West China Hospital Sichuan University Jintang Hospital, Chengdu, China.
Yawen LiThe School of Electrical & Automation Engineering, East China Jiaotong University, Nanchang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Cancer survivors often face significant health-related quality of life (HRQoL) challenges. Although exercise has been proven to improve HRQoL in cancer survivors, the optimal dose and intensity of exercise for this population has not been fully determined. Adherence to exercise may vary based on exercise intensity, affecting results. This study explored the dose-response relationship of different exercise types and intensities to better understand their impact on HRQoL in cancer survivors. Methods: We searched five databases-PubMed, Embase, the Cochrane Library, Web of Science, and Scopus-from their inception until 1 December 2023. Data analysis was performed using R software with the MBNMA and RJAGS packages. Due to combining data from different scales, effect sizes were reported as standardized mean differences (SMD) with 95% credible intervals (95% CrI). The risk of bias was assessed independently by three reviewers using the RoB2 tool. Results: A total of 48 studies involving 3050 cancer survivors. Across all exercise types, the most beneficial exercise dose was identified to be 850 metabolic equivalents of task (METs)-min/week (SMD: 0.753, 95%Crl: 0.463 to 1.096), with diminishing returns observed beyond 1,100 METs-min/week. Among the various types of exercises, mixed training (MT) emerged as the optimal choice, demonstrating its efficacy at 970 METs-min/week (SMD: 0.883, 95% Crl: 0.455 to 1.345). Aerobic exercise (AE) at a dose of 430 METs-min/week (SMD: 0.681, 95% Crl: 0.206 to 1.099) and resistance training (RT) at 450 METs-min/week (SMD: 0.695, 95% Crl: 0.227 to 1.203) also showed significant benefits. Additionally, mind-body exercises, such as tai chi, qigong, or yoga, exhibited optimal effects at a dose of 390 METs-min/week (SMD: 0.672, 95% Crl: 0.259 to 1.087). Conclusion: Our study sheds light on the intricate relationship between exercise interventions and health-related quality of life in cancer survivors, as elucidated through a systematic review and Bayesian network meta-analysis. The identified optimal exercise dose of 850 METs-min/week resulted in a significant improvement in health-related quality of life, underscoring the importance of regular exercise in cancer survivorship. MT emerged as the most effective modality, closely followed by RT, AE, and MBE. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=493328, identifier CRD42024493328.

Indexed as

Bayesian network meta-analysiscancer survivordose-responseexercisehealth-related quality of life

Identifiers

PMID39737404
PMCPMC11682983

What OpenQuestion holds

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Registered trials

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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.