ArticleSensors (Basel, Switzerland)2024
The Validity and Reliability of the My Jump Lab App for the Measurement of Vertical Jump Performance Using Artificial Intelligence.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 8 papers.
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.
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.
Effectiveness of a Home-based Telehealth Exercise Program Using the Physitrack App on Adherence and Performance in Handball Players: A Randomized Controlled Pilot Study
The Impact of Hyperarch Fascia Training on Ankle Sprain Pain and Exercise Performance in Athletic Men and Women
Who cites it
8 citing papers in PubMed.
- Accuracy of the My Jump Lab App for Two Methods of Single-Leg Countermovement Jump Height Assessment.Journal of clinical medicine · 2026Article
- Anthropometric Characteristics, Neuromuscular Performance and Cardiorespiratory Fitness of Chilean Youth Soccer Players: A Cross-Sectional Study.Journal of functional morphology and kinesiology · 2026Article
- Adaptive Athlete Management in Youth Football: A Developmentally Informed Framework for Applied Practice.Sports (Basel, Switzerland) · 2026Review
- Countermovement jump peak force discriminates low hip bone mineral density in postmenopausal women.Scientific reports · 2026Article
- Velocity- and Power-Oriented Force-Velocity Characteristics Differentiate Competitive Olympic-Style Weightlifting Performance.Journal of functional morphology and kinesiology · 2026Article
- Agreement and reliability of force-plate, smartphone-based, and wearable-sensor countermovement jump assessments for neuromuscular monitoring in male professional volleyball players.Frontiers in physiology · 2026Article
- Validity and Reliability of a Novel AI-Based System in Athletic Performance Assessment: The Case of DeepSport.Sensors (Basel, Switzerland) · 2025Article
- Mobile App-Induced Mental Fatigue Affects Strength Asymmetry and Neuromuscular Performance Across Upper and Lower Limbs.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The countermovement jump (CMJ) is a widely used test to assess lower body neuromuscular performance. This study aims to analyze the validity and reliability of an iOS application using artificial intelligence to measure CMJ height, force, velocity, and power in unloaded and loaded conditions. Twelve physically active participants performed 12 CMJs with external loads ranging from 0% to 70% of their body mass while being simultaneously monitored with a pair of force platforms and the My Jump Lab application. The scores for jump height, mean propulsive force, velocity, and power between devices were compared for validity and reliability purposes. The force platform and the application showed a high association (r > 0.91,
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Registered trials
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.