Para cualquier información sobre este documento contacte con Ángel Carnero Díaz acarnero1@us.es Este archivo ha sido creado el 19/02/2024 por Ángel Carnero Díaz GENERAL INFORMATION ------------------ 1. Dataset title: Effects of instruction on muscle activity and force production during load lifting 2. Authorship: Name: Ángel Carnero Díaz Institution: Universidad de Sevilla Email: acarnero1@us.es ORCID: 0000-0002-4349-5600 Name: Javier Pecci Institution: Universidad de Sevilla Email: javipecci99@gmail.com ORCID: 0000-0003-2842-3067 Name: Matt Greig Institution: Edge Hill university Email: Greigm@edgehill.ac.uk ORCID: 0000-0002-7315-6968 Name: África Calvo-Lluch Institution: Universidad de Sevilla Email: acalllu@upo.es ORCID: 0000-0003-3403-8042 DESCRIPTION ---------- 1. Dataset language: English 2. Abstract: Traditionally, coaches have relied on explicit instruction to enhance motor learning and performance during exercise. However, analogy learning has emerged as an effective alternative for improving motor learning and performance. This study aimed to investigate the effects of analogy (ANA) and explicit (EXP) instructions on performance and muscle activity during a weightlifting task. Twenty novice participants were randomly and counterbalanced instructed through ANA, EXP or control instruction prior to a weightlifting task (i.e., both isometric and dynamic lifting tasks). The experiment spanned three days, encompassing familiarization (day 1), control and experimental condition 1 (day 2), and experimental condition 2 (day 3). Muscle activity, force production, and declared knowledge were assessed using a within-participants comparative design. Participants exhibited significant changes in lower limb electromyography after instruction, regardless of the type (p < 0.05). Additionally, the rate of force development (RFD) in the first 200 ms was notably lower in the EXP group (p < 0.05), while the control instruction showed higher force production than EXP and ANA (p < 0.05). In conclusion, these findings suggest that when seeking maximum force production, no instruction may be the best option, except for rapid force production, in which analogy is not lower than control instruction. Nonetheless, the instruction can produce a reorganization in motor control, favoring a greater participation of the lower limbs. 3. Keywords: Explicit learning, implicit learning, cueing, motor control, coaching, electromyography, force production. 4. Date of data collection (fecha única o rango de fechas): 31/07/2023 5. Publication Date: 19/02/2024 6. Grant information: No aplicable 7. Geographical location/s of data collection: Andalucía, España. ACCESS INFORMATION ------------------------ 1. Creative Commons License of the dataset: Licencia CC-BY (Reconocimiento) 2. Dataset DOI: https://doi.org/10.12795/11441/155782 METHODOLOGICAL INFORMATION ----------------------- 1. Description of the methods used to collect and generate the data: Results are expressed as the mean ± standard deviation (SD). The study utilized a 2 (analogy and explicit) x 1 within-subjects design, assessing variables such as the performance outcome (normalized force output relative to body weight), muscle activity (via electromyography, EMG), and KD metrics. Initially, the Shapiro-Wilk test was employed to evaluate the normality of the continuous variables. Subsequently, differences across the control, ANA, and EXP conditions were analyzed through a 3-level (control, ANA, and EXP) repeated measures ANOVA. In instances of significant interactions, the Tukey post-hoc test was conducted to make pairwise comparisons among the conditions (control vs. ANA, ANA vs. EXP, and control vs. EXP). The eta squared partial (η²p) value was derived as an index of effect size. Moreover, the relative contribution to total muscle activity (%MVC) was scrutinized to understand muscle interactions within each experimental condition. A threshold of 0.05 was established for statistical significance. The statistical analyses were performed using the Jamovi software (The Jamovi project, version 2.3.18). 2. Data processing methods: To measure muscle activity, surface electromyography (EMG) sensors (mDurance Solutions SL, Granada, Spain) were placed at vastus lateralis of the dominant limb (defined as preferred kicking leg), erector spinae in L1 and T9 height and posterior deltoids. Muscle selection was informed by previous electromyographical analysis of the dead-lift exercise (Martín-Fuentes et al., 2020) and electrode placement was informed by the recommendations of Non-Invasive Assessment of Muscles (SENIAM) and the ABC of EMG specifications (Konrad, 2005). A maximal voluntary contraction (MVC) was performed for each muscle to facilitate normalization in subsequent analysis of the isometric and dynamic tasks. MVC Dominant side was confirmed using the Edinburgh inventory (Oldfield, 1971). Total force during the maximum isometric pull was collected using PCE FB-1K S-type load cell (PCE instruments, Germany) rated to 1000 N and a sampling rate up to 1600 Hz. During statistical analysis, force production data were relativized based on the participant's body weight. In addition, to assess values related with knowledge declared, specific questionnaires according to indications in previous works protocol were completed (Abswoude et al., 2019). 3. Software or instruments needed to interpret the data: Excel - Microsoft Jamovi software (The Jamovi project, version 2.3.18). FILE OVERVIEW ---------------------- 1. Explain the file naming conversion, si es aplicable: BBDD_instruction_lifting 2. File list: File name: BBDD_instruction_lifting Description: Excel 4. File format: excel