Evidence map›Paper›PMID 42516258›Full record

ArticleInternational journal of exercise science2026

Concurrent Exercise Training Adaptations Over 16 Weeks in Sedentary, Middle- Aged Adults With and Without Post-Exercise Peanut Butter Consumption.

Zachary Moon, Rachel Chiella, Erica Otto, Iliana Hernandez, Barrett Knapp, Matthew Bierwirth, Edward K Merritt

Abstract read
In one paragraph

Article in International journal of exercise science, 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

7 authors.

Zachary MoonSouthwestern University, Georgetown, TX, USA.
Rachel ChiellaSouthwestern University, Georgetown, TX, USA.
Erica OttoSouthwestern University, Georgetown, TX, USA.
Iliana HernandezSouthwestern University, Georgetown, TX, USA.
Barrett KnappSouthwestern University, Georgetown, TX, USA.
Matthew BierwirthSouthwestern University, Georgetown, TX, USA.
Edward K MerrittSouthwestern University, Georgetown, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Maximizing training adaptations in athletes requires complex coordination of training and nutrition; however, beneficial health adaptations in middle-aged adults might not require the same complexity. Simple exercise and nutrition strategies might provide meaningful health benefits with minimal burden. Health guidelines recommend concurrent aerobic and resistance exercise several times per week. Middle-aged adults who meet these recommendations might benefit from an inexpensive, readily available post-exercise snack to support cardiorespiratory and strength adaptations. Peanuts are an accessible food source that contain protein and carbohydrates and might provide this. The purpose of this study was to determine whether post-exercise consumption of peanut butter could improve training adaptations in middle-aged adults. We hypothesized that adding peanut butter as a post-exercise nutrition source would improve lean mass, strength, and cardiorespiratory fitness to a greater extent than a carbohydrate-based snack. Seventeen adults (30-55 years), randomized to receive a peanut-containing smoothie (NUT) or an isocaloric, non-peanut carbohydrate-based smoothie (CON) after each workout, completed a 16-week concurrent training program. Body composition, cardiorespiratory fitness, and strength were assessed pre- and post-training. Total lean mass increased 4% with training (

Indexed as

agingcardiorespiratory fitnessnutritionStrength

Identifiers

PMID42516258
PMCPMC13405674

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.