ArticleMilitary medicine2025
Increased Metabolic Demand During Nighttime Walking in Hilly Forest Terrain While Wearing Night Vision Goggles.
Article in Military medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
introductionFoot-borne soldiers sometimes carry out nighttime operations. It has previously been reported an elevated metabolic demand and impaired walking economy during outdoor walking on a gravel road in darkness wearing night vision goggles (NVG), compared with wearing a headlamp. The aim of the present study was to evaluate the effect of wearing NVG while walking in a hilly forest terrain and compare the results between experienced and inexperienced NVG users. MATERIALS AND
methodsAt nighttime, two different groups, inexperienced (five men and six women) and experienced (nine men) NVG users, walked 1.1 km at a self-selected comfortable pace in a hilly forest. Part I was mainly uphill, and Part II was mainly downhill. Walks were performed wearing a headlamp (light), monocular NVG (mono), binocular NVG (bino), or mono with a 25 kg extra weight (backpack). Walking economy calculated from oxygen uptake in relation to body mass and covered distance (V̇O2 (mL/[kg · km])), heart rate, gait, and walking speed were measured.
resultsIn both groups, walking economy was deteriorated in all three conditions with limited vision (mono, bino, and backpack) compared to the light condition, both during Part I (mono/bino, experienced: +26/+25%, inexperienced: +34/+28%) and Part II (mono/bino, experienced: +44/+46%, inexperienced: +63/+49%). In the backpack condition, the relative change of walking economy was greater for the inexperienced group than the experienced group: Part I (experienced: +46%, inexperienced: +70%), Part II (experienced: +71%, inexperienced: +111%). Concurrently, the step length was shorter in all three conditions with limited vision during Part I (mono/bino/backpack, experienced: -7/-7/-15%, inexperienced: -12/-12/-19%) and Part II (mono/bino/backpack; experienced: -8/-8/-14%, inexperienced: -17/-15/-24%) than in the light condition. The experienced NVG users walked faster during all conditions, but there was no difference in heart rate between groups.
conclusionsDespite that foveal vision using NVG is adequate, it appears that the mechanical efficiency during nighttime walking in hilly terrain was markedly lower while wearing NVG than with full vision, regardless of whether the soldier was an experienced or inexperienced NVG user. Moreover, the walking economy was even more affected when adding the 25-kg extra weight. It is probable that the deteriorated mechanical efficiency was partly due to the shorter step length in all three conditions with limited vision.
Indexed as
Identifiers
What OpenQuestion holds
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.