cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25340/r4/yh9oye
title: Related Data for: The effect of beetroot ingestion on high-intensity interval training: A systematic review and meta-analysis

description: Dietary nitrate supplementation has shown promising ergogenic effects on endurance exercise. However, at present there is no systematic analysis evaluating the effects of acute or chronic nitrate supplementation on performance measures during high-intensity interval training (HIIT) and sprint interval training (SIT). The main aim of this systematic review and meta-analysis was to evaluate the evidence for supplementation of dietary beetroot—a common source of nitrate—to improve peak and mean power output during HIIT and SIT. A systematic literature search was carried out following PRISMA guidelines and the PICOS framework within the following databases: PubMed, ProQuest, ScienceDirect, and SPORTDiscus. Search terms used were: ((nitrate OR nitrite OR beetroot) AND (HIIT or high intensity or sprint interval or SIT) AND (performance)). A total of 17 studies were included and reviewed independently. Seven studies applied an acute supplementation strategy and ten studies applied chronic supplementation. The standardised mean difference for mean power output showed an overall trivial, non-significant effect in favour of placebo (Hedges’ g = −0.05, 95% CI −0.32 to 0.21, Z = 0.39, p = 0.69). The standardised mean difference for peak power output showed a trivial, non-significant effect in favour of the beetroot juice intervention (Hedges’ g = 0.08, 95% CI -0.14 to 0.30, Z = 0.72, p = 0.47). The present meta-analysis showed trivial statistical heterogeneity in power output, but the variation in the exercise protocols, nitrate dosage, type of beetroot products, supplementation strategy, and duration among studies restricted a firm conclusion of the effect of beetroot supplementation on HIIT performance. Our findings suggest that beetroot supplementation offers no significant improvement to peak or mean power output during HIIT or SIT. Future research could further examine the ergogenic potential by optimising the beetroot supplementation strategy in terms of dosage, timing, and type of beetroot product. The potential combined effect of other ingredients in the beetroot products should not be undermined. Finally, a chronic supplementation protocol with a higher beetroot dosage (&gt;12.9 mmol/day for 6 days) is recommended for future HIIT and SIT study.
DOI: 10.25919/30x1-rh38
title: NBDL-HXDQPBN3JM11RC
DOI: 10.25919/0fep-z058
title: NBDL-HXDQPBPNHM7RKS
DOI: 10.25919/zj87-8p73
title: NBDL-HXDQPBT2KNWWKB
DOI: 10.25919/56de-5981
title: NBDL-HXDQPBJYQMKW97
DOI: 10.25919/njpe-4s10
title: NBDL-HXDQPBT9HNWF57
DOI: 10.25919/5wg4-y064
title: NBDL-HXDQPBW0MNF56C
DOI: 10.25919/zfgy-3871
title: NBDL-HXDQPBMEMM3VQG
DOI: 10.25919/md5e-3s48
title: NBDL-HXDQPBTPKNX3EV
DOI: 10.25919/7r5s-kf73
title: NBDL-HXDQPBP2KM6VDX
DOI: 10.25919/gs74-fk14
title: NBDL-HXDQPBKRKMHFJF
DOI: 10.25919/8nq2-by25
title: NBDL-HXDQPBQDHM9SJS
DOI: 10.25919/mcz1-rk26
title: NBDL-HXDQPBJ7NMGQNK
DOI: 10.25919/e9ep-jf95
title: NBDL-HXDQPBHKJMF2RY
DOI: 10.6084/m9.figshare.30193648
title: <b>Supplementary materials</b><b> of </b><b>Effects of </b><b><i>Clostridium beijerinckii</i></b><b>-based direct-fed supplementation on growth performance, </b><b>diarrhea frequency</b><b>, </b><b>plasma</b><b> metabolites, and fecal microbiota of dairy calves</b>

description: <b>Supplementary materials</b><b> of </b><b>Effects of </b><b><i>Clostridium beijerinckii</i></b><b>-based direct-fed supplementation on growth performance, </b><b>diarrhea frequency</b><b>, </b><b>plasma</b><b> metabolites, and fecal microbiota of dairy calves</b>
DOI: 10.6084/m9.figshare.30193648.v1
title: <b>Supplementary materials</b><b> of </b><b>Effects of </b><b><i>Clostridium beijerinckii</i></b><b>-based direct-fed supplementation on growth performance, </b><b>diarrhea frequency</b><b>, </b><b>plasma</b><b> metabolites, and fecal microbiota of dairy calves</b>

description: <b>Supplementary materials</b><b> of </b><b>Effects of </b><b><i>Clostridium beijerinckii</i></b><b>-based direct-fed supplementation on growth performance, </b><b>diarrhea frequency</b><b>, </b><b>plasma</b><b> metabolites, and fecal microbiota of dairy calves</b>
DOI: 10.25919/jmbv-wg23
title: NBDL-HXDQPBPRQM7GFN
DOI: 10.25919/z4fq-hq57
title: NBDL-HXDQPBK8KMHMMF
DOI: 10.25919/62h1-mf53
title: NBDL-HXDQPBK2QME25Q
DOI: 10.25919/sahw-gt74
title: NBDL-HXDQPBSMMNP5ZY
DOI: 10.25919/hnyb-h453
title: NBDL-HXDQPBKTQMH8EQ
DOI: 10.25919/x272-cr36
title: NBDL-HXDQPBK6QME4C7
DOI: 10.25919/06mz-ry56
title: NBDL-HXDQPBTKNNX47Z
DOI: 10.25919/tx1z-pz28
title: NBDL-HXDQPBSVJNPXVT
DOI: 10.25919/xbr1-1t64
title: NBDL-HXDQPBQFNM9TF1
DOI: 10.25919/ers1-xz98
title: NBDL-HXDQPBHSPMENEP
DOI: 10.25919/dndx-vz04
title: NBDL-HXDQPBKZNMH0AK
DOI: 10.25919/ag2b-wa37
title: NBDL-HXDQPBRGMNRX5E
DOI: 10.25919/sfdm-qh31
title: NBDL-HXDQPBMZJM06FW
DOI: 10.25919/2k55-dc78
title: NBDL-HXDQPBSRMNPW6E
DOI: 10.25919/k6zj-7z37
title: NBDL-HXDQPBRQJNRN1A
DOI: 10.25340/r4/pf9eyn
title: Related Data for: The effects of nitrate ingestion on high-intensity endurance time-trial performance: A systematic review and meta-analysis

description: Background/Objective Dietary nitrate ingestion extends endurance capacity, but data supporting endurance time-trial performance are unclear. This systematic review and meta-analysis evaluated the evidence for dietary nitrate supplementation to improve high-intensity endurance time-trial performance over 5–30 min on the premise that nitrate may alleviate peripheral fatigue over shorter durations. Methods A systematic literature search and data extraction was carried out following PRISMA guidelines and the PICOS framework within five databases: PubMed, ProQuest, ScienceDirect, Scopus and SPORTDiscus. Search terms used were: (nitrate OR nitrite OR beetroot) AND (high intensity OR all out) AND (time trial or total work done) AND performance. Results Twenty-four studies were included. Fifteen studies applied an acute supplementation strategy (4.1 mmol–15.2 mmol serving on one day), eight chronic supplementation (4.0 mmol–13.0 mmol per day over 3–15 days), and one applied both acute and chronic supplementation (8.0 mmol on one day and over 15 days). Standardised mean difference for time-trial ranging from 5 to 30 min showed an overall trivial effect in favour of nitrate (Hedges'g = 0.15, 95% CI -0.00 to 0.31, Z = 1.95, p = 0.05). Subgroup analysis revealed a small, borderline effect in favour of chronic nitrate intervention (Hedges'g = 0.30, 95% CI -0.00 to 0.59, Z = 1.94, p = 0.05), and a non-significant effect for acute nitrate intervention (Hedges'g = 0.10, 95% CI -0.08 to 0.28, Z = 1.11, p = 0.27). Conclusion Chronic nitrate supplementation improves time-trial performance ranging from 5 to 30 min.
DOI: 10.25919/abs3-za49
title: NBDL-HXDQPBP6KM6XMD
DOI: 10.25919/ygzc-k709
title: NBDL-HXDQPBVXHNZFMQ
DOI: 10.25919/71n3-ax14
title: NBDL-HXDQPBNCGM1ARR
DOI: 10.25919/kybg-fm54
title: NBDL-HXDQPBRHPNRZQ2
DOI: 10.25919/jszg-cx24
title: NBDL-HXDQPBSZJNPM2A
DOI: 10.25919/m0pv-2x51
title: NBDL-HXDQPBR5PNSZ7J
DOI: 10.25919/2s32-xr37
title: NBDL-HXDQPBJ0KMGJXF
DOI: 10.25919/sbwr-qp56
title: NBDL-HXDQPBQHHM9KS9
DOI: 10.25919/vvt4-2a17
title: NBDL-HXDQPBKQNMHWZK
DOI: 10.25919/rtbe-d538
title: NBDL-HXDQPBPTKM7X4X
DOI: 10.25919/yeqz-sy61
title: NBDL-HXDQPBJ8KMG68F
DOI: 10.25919/11s9-ya75
title: NBDL-HXDQPBSHPNP232
DOI: 10.25919/hpwk-vc72
title: NBDL-HXDQPBPWQM4AP5
DOI: 10.25919/4rpg-8719
title: NBDL-HXDQPBNXPM1HG4
DOI: 10.25919/hbtq-zg88
title: NBDL-HXDQPBREGNPCDP
DOI: 10.25919/3yr7-3j75
title: NBDL-HXDQPBVTKNZTMB
DOI: 10.25919/28pk-aj69
title: NBDL-HXDQPBM7JM3NTW
DOI: 10.25919/87pp-5j20
title: NBDL-HXDQPBJ5HMHASB
DOI: 10.25919/vfmj-nw31
title: NBDL-HXDQPBW2GNF62M
DOI: 10.25919/yf0h-6k16
title: NBDL-HXDQPBSPGNPS2P
DOI: 10.25919/rtwr-js57
title: NBDL-HXDQPBV0QNZB3K
DOI: 10.25919/8m6h-0f27
title: NBDL-HXDQPBPYKM7QKD
DOI: 10.25919/gqz7-8328
title: NBDL-HXDQPBKKNMHTH3
DOI: 10.25919/2az5-p913
title: NBDL-HXDQPBHVJMEPBY
DOI: 10.25919/rhnz-wx63
title: NBDL-HXDQPBRKJNRJTT
DOI: 10.25919/4wnz-zy36
title: NBDL-HXDQPBM0GM3YV8
DOI: 10.25919/6h0a-a352
title: NBDL-HXDQPBVVNNZ56Z
DOI: 10.25919/fftq-pp74
title: NBDL-HXDQPBQ1HM9AQ9
DOI: 10.25919/qsfc-nk71
title: NBDL-HXDQPBS9PNQ7T2
DOI: 10.25919/2qbg-6j75
title: NBDL-HXDQPBS0MNQ8VE
DOI: 10.25919/dmwa-qm59
title: NBDL-HXDQPBQCQM9Z05
DOI: 10.25919/zde6-1y36
title: NBDL-HXDQPBP3NM65ZH
DOI: 10.25919/4wwx-7g77
title: NBDL-HXDQPBPQNM7581
DOI: 10.25919/m8n3-en91
title: NBDL-HXDQPBPBNM6PCH
DOI: 10.25919/44qj-m821
title: NBDL-HXDQPBVDHNY6JQ
DOI: 10.25919/f34j-8740
title: NBDL-HXDQPBM5PM3PQ4
DOI: 10.25919/0f2v-jf94
title: NBDL-HXDQPBPMQM7Y15
DOI: 10.25919/v8m4-s379
title: NBDL-HXDQPBNYMM1Q5G
DOI: 10.25919/y5at-0008
title: NBDL-HXDQPBPXHM7N0S
DOI: 10.25919/qhrg-e103
title: NBDL-HXDQPBRWMNS00Y
DOI: 10.25919/gp8s-q471
title: NBDL-HXDQPBHFJMF57E
DOI: 10.25919/hdv4-na88
title: NBDL-HXDQPBKEQMEBZ7
DOI: 10.25919/52g8-mg20
title: NBDL-HXDQPBPHHM7659
DOI: 10.25919/rcns-ja08
title: NBDL-HXDQPBNKJNYF2C
DOI: 10.5281/zenodo.16836077
title: Data

description: Generic Machine Learning Potential for Accelerating Transition State Search in Organic Synthesis

Dataset DOI: 10.5061/dryad.2z34tmpzg

Description of the data and file structure

This reaction database is generated by manuscript“DeePEST-OS: A Generic Machine Learning Potential for Accelerating Transition State Search in Organic Synthesis”.

Files and variables

File: DORTS-9K.h5

Description: contains 9,017 reaction data for the DeePEST-OS model training. The IRC folder includes the geometric structures of five key IRC path points (ts, reactant, product, F_rev_max, F_forw_max), computed via GFN2-xTB, with molecular energies and atomic forces calculated at the wB97M-V/def2-TZVP level. In the NMS, 20 conformers per point were generated using quantum normal modes, and their molecular energies/atomic forces were computed at the wB97M-V/def2-TZVP level. The DeePEST-OS model was trained on all NMS data.User can read the data using the script read_DORT-9K.py.

│├── IRC/│   ├── ts/│   │   ├── rxn1│   │   │   ├──rxn_name│   │   │   ├──symbols│   │   │   ├──coordinates│   │   │   ├──energy│   │   │   └──forces│   │   ├── rxn2│   │   └── ...│   ├── reactant/│   │   ├── rxn1│   │   └── ...│   ├── product/│   │   └── ...│   ├── F_forw/│   │   └── ...│   └── F_rev/│       └── ...│└── NMS/    ├── rxn1    ├── rxn2    └── ...

File: DORTS-9K_deltaML_train.xyz

Description: DeePEST-OS is trained on DORTS-9K_deltaML_train.xyz, where both energy and atomic force labels are the results after Δ-learning based on GFN2-xTB. During training, this dataset is randomly split into a training set and a test set in a 9:1 ratio. For model training parameters, user can refer to the training parameters of the demo model in https://github.com/kaipai-ren/DeePEST-OS/tree/main/examples/model_training.

File: DORTS-9K_deltaML_test.xyz

Description: The test results of DeePEST-OS are based on the DORTS-9K_deltaML_test.xyz dataset, which, together with DORTS-9K_deltaML_train.xyz, constitutes DORTS-9K. The energy and atomic force labels in the dataset are also the results after Δ-learning based on GFN2-xTB.

File: DORTS.tar.gz

Description: contains 65,116 reaction data beyond DORTS-9K. The IRC folder includes the geometric structures of five key IRC path points (ts, reactant, product, F_rev_max, F_forw_max) computed via GFN2-xTB. In the NMS, 20 conformers per point were generated using quantum thermal normal mode sampling. Notably, DORTS.tar.gz only contains geometric structures, without directly providing high-precision molecular energies and atomic force matrices.
DOI: 10.5281/zenodo.17141108
title: Data

description: Generic Machine Learning Potential for Accelerating Transition State Search in Organic Synthesis

Dataset DOI: 10.5061/dryad.2z34tmpzg

Description of the data and file structure

This reaction database is generated by manuscript“DeePEST-OS: A Generic Machine Learning Potential for Accelerating Transition State Search in Organic Synthesis”.

Files and variables

File: DORTS-9K.h5

Description: contains 9,017 reaction data for the DeePEST-OS model training. The IRC folder includes the geometric structures of five key IRC path points (ts, reactant, product, F_rev_max, F_forw_max), computed via GFN2-xTB, with molecular energies and atomic forces calculated at the wB97M-V/def2-TZVP level. In the NMS, 20 conformers per point were generated using quantum normal modes, and their molecular energies/atomic forces were computed at the wB97M-V/def2-TZVP level. The DeePEST-OS model was trained on all NMS data.User can read the data using the script read_DORT-9K.py.

│├── IRC/│   ├── ts/│   │   ├── rxn1│   │   │   ├──rxn_name│   │   │   ├──symbols│   │   │   ├──coordinates│   │   │   ├──energy│   │   │   └──forces│   │   ├── rxn2│   │   └── ...│   ├── reactant/│   │   ├── rxn1│   │   └── ...│   ├── product/│   │   └── ...│   ├── F_forw/│   │   └── ...│   └── F_rev/│       └── ...│└── NMS/    ├── rxn1    ├── rxn2    └── ...

File: DORTS-9K_deltaML_train.xyz

Description: DeePEST-OS is trained on DORTS-9K_deltaML_train.xyz, where both energy and atomic force labels are the results after Δ-learning based on GFN2-xTB. During training, this dataset is randomly split into a training set and a test set in a 9:1 ratio. For model training parameters, user can refer to the training parameters of the demo model in https://github.com/kaipai-ren/DeePEST-OS/tree/main/examples/model_training.

File: DORTS-9K_deltaML_test.xyz

Description: The test results of DeePEST-OS are based on the DORTS-9K_deltaML_test.xyz dataset, which, together with DORTS-9K_deltaML_train.xyz, constitutes DORTS-9K. The energy and atomic force labels in the dataset are also the results after Δ-learning based on GFN2-xTB.

File: DORTS.tar.gz

Description: contains 65,116 reaction data beyond DORTS-9K. The IRC folder includes the geometric structures of five key IRC path points (ts, reactant, product, F_rev_max, F_forw_max) computed via GFN2-xTB. In the NMS, 20 conformers per point were generated using quantum thermal normal mode sampling. Notably, DORTS.tar.gz only contains geometric structures, without directly providing high-precision molecular energies and atomic force matrices.
DOI: 10.25919/fgac-3051
title: NBDL-HXDQPBSFJNQX4A
DOI: 10.25919/10w2-3r59
title: NBDL-HXDQPBR7JNSJBA
DOI: 10.25919/bn1d-fz81
title: NBDL-HXDQPBJJQMKPQQ
DOI: 10.25919/c9zv-0509
title: NBDL-HXDQPBM9PM33VM
DOI: 10.25919/31g2-0c20
title: NBDL-HXDQPBQTKM6ERX
DOI: 10.25919/taax-j008
title: NBDL-HXDQPBV6KNYQRV
DOI: 10.25919/ggy2-5g92
title: NBDL-HXDQPBT8QNWCTK
DOI: 10.25919/vms3-tc75
title: NBDL-HXDQPBTGQNX6HK
DOI: 10.25919/sbat-0g16
title: NBDL-HXDQPBP5HM6K9S
DOI: 10.25919/kre7-9q21
title: NBDL-HXDQPBKYQMH2N7
DOI: 10.25919/sjam-2w52
title: NBDL-HXDQPBJFNMGV0K
DOI: 10.5281/zenodo.16144207
title: Data

description: DeePEST-OS: A Generic Machine Learning Potential for Accelerating Transition State Search in Organic Synthesis

Dataset DOI: 10.5061/dryad.2z34tmpzg

Description of the data and file structure

This reaction database is generated by manuscript“DeePEST-OS: A Generic Machine Learning Potential for Accelerating Transition State Search in Organic Synthesis”.

Files and variables

File: DORTS-9K.h5

Description: contains 9,017 reaction data for the DeePEST-OS model training. The IRC folder includes the geometric structures of five key IRC path points (ts, reactant, product, F_rev_max, F_forw_max), computed via GFN2-xTB, with molecular energies and atomic forces calculated at the wB97M-V/def2-TZVP level. In the NMS, 20 conformers per point were generated using quantum normal modes, and their molecular energies/atomic forces were computed at the wB97M-V/def2-TZVP level. The DeePEST-OS model was trained on all NMS data.User can read the data using the script read_DORT-9K.py.

│├── IRC/│   ├── ts/│   │   ├── rxn1│   │   │   ├──rxn_name│   │   │   ├──symbols│   │   │   ├──coordinates│   │   │   ├──energy│   │   │   └──forces│   │   ├── rxn2│   │   └── ...│   ├── reactant/│   │   ├── rxn1│   │   └── ...│   ├── product/│   │   └── ...│   ├── F_forw/│   │   └── ...│   └── F_rev/│       └── ...│└── NMS/    ├── rxn1    ├── rxn2    └── ...

File: DORTS.tar.gz

Description: contains 65,116 reaction data beyond DORTS-9K. The IRC folder includes the geometric structures of five key IRC path points (ts, reactant, product, F_rev_max, F_forw_max) computed via GFN2-xTB. In the NMS, 20 conformers per point were generated using quantum thermal normal mode sampling. Notably, DORTS.tar.gz only contains geometric structures, without directly providing high-precision molecular energies and atomic force matrices.
DOI: 10.5281/zenodo.16144208
title: Data

description: DeePEST-OS: A Generic Machine Learning Potential for Accelerating Transition State Search in Organic Synthesis

Dataset DOI: 10.5061/dryad.2z34tmpzg

Description of the data and file structure

This reaction database is generated by manuscript“DeePEST-OS: A Generic Machine Learning Potential for Accelerating Transition State Search in Organic Synthesis”.

Files and variables

File: DORTS-9K.h5

Description: contains 9,017 reaction data for the DeePEST-OS model training. The IRC folder includes the geometric structures of five key IRC path points (ts, reactant, product, F_rev_max, F_forw_max), computed via GFN2-xTB, with molecular energies and atomic forces calculated at the wB97M-V/def2-TZVP level. In the NMS, 20 conformers per point were generated using quantum normal modes, and their molecular energies/atomic forces were computed at the wB97M-V/def2-TZVP level. The DeePEST-OS model was trained on all NMS data.User can read the data using the script read_DORT-9K.py.

│├── IRC/│   ├── ts/│   │   ├── rxn1│   │   │   ├──rxn_name│   │   │   ├──symbols│   │   │   ├──coordinates│   │   │   ├──energy│   │   │   └──forces│   │   ├── rxn2│   │   └── ...│   ├── reactant/│   │   ├── rxn1│   │   └── ...│   ├── product/│   │   └── ...│   ├── F_forw/│   │   └── ...│   └── F_rev/│       └── ...│└── NMS/    ├── rxn1    ├── rxn2    └── ...

File: DORTS.tar.gz

Description: contains 65,116 reaction data beyond DORTS-9K. The IRC folder includes the geometric structures of five key IRC path points (ts, reactant, product, F_rev_max, F_forw_max) computed via GFN2-xTB. In the NMS, 20 conformers per point were generated using quantum thermal normal mode sampling. Notably, DORTS.tar.gz only contains geometric structures, without directly providing high-precision molecular energies and atomic force matrices.
DOI: 10.25919/5rx4-yw75
title: NBDL-HXDQPBVRQNZ7GK
DOI: 10.25919/0q7t-yj94
title: NBDL-HXDQPBK9HMEA9V
DOI: 10.25919/6bd1-y894
title: NBDL-HXDQPBNFJM1FHW
DOI: 10.25919/n4cm-3x95
title: NBDL-HXDQPBJSHMKYVV
DOI: 10.25919/rhwc-yp50
title: NBDL-HXDQPBQQNM9PW1
DOI: 10.25919/1y56-sv67
title: NBDL-HXDQPBNZJM1MVW
DOI: 10.25919/12mc-kp30
title: NBDL-HXDQPBRNPNRHXJ
DOI: 10.25919/qnqx-4189
title: NBDL-HXDQPBKSHMHD7V
DOI: 10.25919/hkwz-6816
title: NBDL-HXDQPBVNHNZR9Q
DOI: 10.25919/kzad-mx58
title: NBDL-HXDQPBJRKMKS6F
DOI: 10.25919/s5g3-5153
title: NBDL-HXDQPBTZNNWH4F
DOI: 10.25919/5e12-k595
title: NBDL-HXDQPBMGGM07X8
DOI: 10.25919/a6yc-yq75
title: NBDL-HXDQPBJ2QMGZSQ
DOI: 10.25919/0fa2-6147
title: NBDL-HXDQPBJNHMGDQB
DOI: 10.25919/c1qh-r604
title: NBDL-HXDQPBV2KNZE7B
DOI: 10.25919/q9f0-2886
title: NBDL-HXDQPBQ0QM984N
DOI: 10.25919/mper-x890
title: NBDL-HXDQPBV3NNZ8HZ
DOI: 10.25340/r4/0lxyjk
title: Related Data for: Effects of an isotonic beetroot drink on power output during sprint exercise and jump performance in physically active individuals: A randomized crossover trial

description: Exogenous nitrate ingestion can improve exercise performance. This study investigated whether an isotonic beetroot drink could improve jump and sprint performance in active individuals. Twenty-three physically active participants (17 males, 6 females) (mean ± SD; age: 26 ± 4 years; body mass index: 22.4 ± 1.9 kg/m2) completed a double-blind, randomized, cross-over study where they consumed 570mL of either beetroot juice drink (BR) or isotonic beetroot juice drink (ISO-BR) three hours before performing countermovement (CMJ) and standing broad jump (SBJ) tests and a 30-second all-out sprint on a cycle ergometer. Both drinks contained equal nitrate (12.9 mmol) and carbohydrate (6.1g per 100mL) content but differed in osmolality (BR: 420 mOsm/kg vs. ISO-BR: 315 mOsm/kg). Salivary total nitrate and nitrite concentrations (NOx) 3 hours post-ingestion were significantly higher after consuming ISO-BR than BR. ISO-BR significantly improved peak power output compared to BR by 3.9% (ISO-BR: 11.4 ± 2.5 W/kg vs. BR: 11.0 ± 2.3 W/kg, p = .04) but not time to peak power (ISO-BR: 2.8 ± 1.7 s vs. BR: 2.9 ± 1.6 s, p = .62) or mean power output (ISO-BR: 7.3 ± 1.5 W/kg vs. BR: 7.3 ± 1.5 W/kg, p = .37). There were no significant differences in CMJ or SBJ between trials (p &gt; .05). Sensory evaluation indicated that ISO-BR was preferred by 91% (n = 21) of participants compared with BR (average score; ISO-BR: 5.52 vs. BR: 3.52, p &lt; .05). An ISO-BR drink improved peak power output during sprint cycling but not jump performance compared with BR alone, potentially via increased NOx.
DOI: 10.25919/gdqe-nv38
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