This systematic review was conducted to evaluate the effect of heart rate variability biofeedback (HRV BFB) on performance of athletes. Six electronic databases (Springerlink, SportDiscus, Web of Science, PROQUEST Academic Research Library, Google Scholar, and ScienceDirect) and article references were searched. Eligibility criteria were: 1. experimental studies involving athletes randomly allocated among groups (randomized control trial); 2. availability of HRV BFB as a treatment compared to a control condition (CON) that involves regular sport/dance training, a placebo (PLA) or other methods of BFB; 3. performance-related variables such as a dependent index; and, 4. peer-reviewed articles written in English. Out of 660 articles, six studies were included in the systematic review which involved 187 athletes (females: n = 89; males n = 98). Six studies compared HRV BFB with a CON, three studies compared HRV BFB with a PLA, and two studies differentiated HRV BFB with other methods of BFB. Findings support HRV BFB as a potential intervention to improve fine and gross motor function in athletes.

The purpose of this study was to identify the effects of warm-up strategies on countermovement jump performance. Twenty-nine male college football players (age: 19.4 ± 1.1 years; body height: 179.0 ± 5.1 cm; body mass: 73.1 ± 8.0 kg; % body fat: 11.1 ± 2.7) from the Tuzla University underwent a control (no warm-up) and different warm-up conditions: 1. general warm-up; 2. general warm-up with dynamic stretching; 3. general warm-up, dynamic stretching and passive stretching; 4. passive static stretching; 5. passive static stretching and general warm-up; and, 6. passive static stretching, general warm-up and dynamic stretching. Countermovement jump performance was measured after each intervention or control. Results from one way repeated measures ANOVA revealed a significant difference on warm-up strategies at F (4.07, 113.86) = 69.56, p < 0.001, eta squared = 0.72. Bonferonni post hoc revealed that a general warm-up and a general warm-up with dynamic stretching posted the greatest gains among all interventions. On the other hand, no warm-up and passive static stretching displayed the least results in countermovement jump performance. In conclusion, countermovement jump performance preceded by a general warmup or a general warm-up with dynamic stretching posted superior gains in countermovement jump performance.

Complex training (CT) is a strength training intervention performed by completing all the sets of a resistance exercise followed by a series of high-velocity/plyometric exercise/s. The purpose of this novel study was to conduct a meta-analysis on the effect of CT on vertical jump (VJ) performance. Five electronic databases were searched using terms related to CT and the VJ. Studies needed to include randomized trials comparing CT with traditional resistance training (RT)/plyometric training (PLYO)/control (CON) lasting ≥ 4 weeks and the VJ as a dependent variable. Seven studies qualified for the meta-analysis with two studies differentiating VJ performance from CT and RT, two studies comparing VJ performance of CT and PLYO, and two studies establishing the difference in VJ performance between CT and CON. Results indicated similar improvement in VJ performance from CT and RT (p = 0.88). On the other hand, greater VJ performance in CT than PLYO was identified (ES = 0.86; 95% CI 0.24, 1.47; p = 0.01). CT also showed significantly greater enhancement in VJ compared to CON (ES = 1.14; 95% CI 0.60, 1.68; p < 0.01). In conclusion, CT can serve as alternative training from RT in improving VJ performance. On the other hand, CT is a better option in VJ enhancement than PLYO and CON.

Background: Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra–short-term HRV recordings taken by wearable devices have not been examined. Objective: The aims of this study were the following: (1) to compare the validity and reliability of ultra–short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP. Methods: In total, 60 healthy adults were recruited to participate in this study (mean age 22.3 years [SD 3.0 years], mean height 168.4 cm [SD 8.0 cm], mean body weight 64.2 kg [SD 11.5 kg]). A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low–frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis. Results: Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (0.8). The nVLF, nLF, and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient values (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF. Conclusions: Using the PEP app to facilitate a 1-minute ultra–short-term recording is suggested for time-domain HRV indices (SDNN, RMSSD, and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement.

Verbal encouragement (VE) can be used by physical education (PE) practitioners for boosting motivation during exercise engagement. The purpose of this study was to investigate the effects of VE on psychophysiological aspects and physical performance in a PE context. Twenty secondary school male students (age: 17.68 ± 0.51 yrs; height: 175.7 ± 6.2 cm; body mass: 67.3 ± 5.1 kg, %fat: 11.9 ± 3.1%; PE experience: 10.9 ± 1.0 yrs) completed, in a randomized order, two test sessions that comprised a soccer dribbling circuit exercise (the Hoff circuit) either with VE (CVE) or without VE (CNVE), with one-week apart between the tests. Heart rate (HR) responses were recorded throughout the circuit exercise sessions. Additionally, the profile of mood-state (POMS) was assessed pre and post the circuit exercises. Furthermore, rating of perceived exertion (RPE), traveled distance, and physical activity enjoyment (PACES) were assessed after the testing sessions. Furthermore, the CVE trial resulted in higher covered distance, %HRmax, RPE, PACES score, (Cohen’s coefficient d = 1.08, d = 1.86, d = 1.37, respectively; all, p < 0.01). The CNVE trial also showed lower vigor and higher total mood disturbance (TMD) (d = 0.67, d = 0.87, respectively, p < 0.05) and was associated with higher tension and fatigue, compared to the CVE trial (d = 0.77, d = 1.23, respectively, p < 0.01). The findings suggest that PE teachers may use verbal cues during soccer dribbling circuits for improving physical and psychophysiological responses within secondary school students.