Evidence map›Paper›PMID 41918002›Full record

ArticleBMC sports science, medicine & rehabilitation2026

High-intensity interval training versus plyometric training on performance measures among recreational runners: a randomized controlled trial.

Deepali Bidhuri, Sheetal Kalra, Mohammad Miraj, Puneeta Ajmera, Archana Khanna, Msaad Alzhrani, Ahmad Alanazi, Abdul Rahim Shaik, Toufiq Noor, Shalini Mishra and 1 more

Abstract read
In one paragraph

Article in BMC sports science, medicine & rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Deepali BidhuriSchool of Physiotherapy, Delhi Pharmaceutical Sciences and Research University, Pushp Vihar, New Delhi, India.
Sheetal KalraSchool of Physiotherapy, Delhi Pharmaceutical Sciences and Research University, Pushp Vihar, New Delhi, India. sheetalkalra@dpsru.edu.in.
Mohammad MirajDepartment of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, Almajmaah, 11952, Saudi Arabia.
Puneeta AjmeraSchool of Allied Health Sciences, Department of Public Health, Delhi Pharmaceutical Sciences and Research University, Pushp Vihar, New Delhi, India.
Archana KhannaDepartment of Physiotherapy, Sharda University, Greater Noida, Uttar Pradesh, India.
Msaad AlzhraniDepartment of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, Almajmaah, 11952, Saudi Arabia.
Ahmad AlanaziDepartment of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, Almajmaah, 11952, Saudi Arabia.
Abdul Rahim ShaikDepartment of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, Almajmaah, 11952, Saudi Arabia.
Toufiq NoorDental Research Cell, Dr. D. Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth (Deemed to be University), Pune, 411018, India. toufiq.research@gmail.com.
Shalini MishraSchool of Physiotherapy, Delhi Pharmaceutical Sciences and Research University, Pushp Vihar, New Delhi, India.
Mohit BatraSenior Consultant, DFIR Organization, ESEC Forte Technologies Private Limited, Gurgaon, Haryana, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh-Intensity Interval Training (HIIT) and Plyometric training are both well-established methods for improving athletic performance. Despite their popularity, limited experimental evidence directly compares their effects in recreational runners. This study aimed to compare the impact of HIIT and Plyometric training on agility, lower limb power, sprint speed, and functional performance.

methodsA randomized controlled trial was conducted with 60 recreational runners allocated to HIIT (Group A), Plyometric training (Group B), or Control (Group C). Participants underwent supervised exercise sessions three times per week for six weeks. Performance outcomes were assessed at baseline, week 3, and week 6, including agility (T test), lower limb power (vertical jump test), sprint speed (50 m sprint), and functional performance (Figure-8 Hop Test). Baseline characteristics were comparable across groups for age (mean 22.75–23.90 years), BMI (mean 25.34–26.37 kg/m²), and other demographics.

resultsPost-intervention analyses demonstrated significant improvements in Groups A and B compared to the Control group (p < 0.001) across all measured outcomes. Group A (HIIT) showed improvements in agility (12.60 ± 1.67 s), lower limb power (29.75 ± 8.97), sprint speed (3.90 ± 0.70 s), and functional performance (5.91 ± 0.44 s). Group B (Plyometric training) also showed significant improvements versus Control, but no statistically significant differences were observed between Groups A and B (p > 0.05).

conclusionThis study shows that both HIIT and plyometric training effectively improve performance in recreational runners, with no statistically significant differences between the two methods. The findings support equivalence in effectiveness across all measured outcomes. HIIT may be preferred for its time efficiency, but both approaches are valid training options. CLINICAL TRIAL NUMBER REGISTRATION: The trial was registered with the Clinical Trials Registry - India (CTRI) under the registration number CTRI/2024/04/065310, dated 5th April 2024.

Indexed as

AgilityFunctional performanceHigh Intensity Interval TrainingPlyometric trainingSpeed

Identifiers

PMID41918002
PMCPMC13200456

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.