Effects of Stationary Bikes and Elliptical Machines on Knee Joint Kinematics during Exercise.

Background and Objectives: This study examined the influence of stationary bikes and elliptical machines on knee movement and joint load during exercise. Materials and Methods: Twelve healthy male participants engaged in pedaling exercises on stationary bikes and elliptical machines at speeds of 50 and 70 revolutions per minute (rpm). Knee movement and joint load were assessed using a motion analysis system. Results: The results indicated that elliptical machines induced higher knee joint torque compared to stationary bikes. Notably, peak torque occurred at different joint angles, with stationary bikes reaching an earlier peak at 70°-110° and elliptical machines showing a later peak at 135°-180°. Increased pedaling speed correlated with higher peak knee joint torque on both machines. With the elliptical machine, a higher pedaling frequency correlated with increased peak forces on the knee and ankle joints, as well as vertically. Interestingly, both types of equipment were associated with enhanced peak knee joint torques during high-speed pedaling. Conversely, constant pedaling on elliptical machines limited the ankle angle and could induce inward rotation. Conclusions: This study focused on knee joint torque variations during pedaling on indoor stationary bicycles and elliptical machines. Elliptical machines showed higher peak values of forces and torque, particularly during the propulsive and recovery phases, indicating potential challenges to the knee joint. Notably, peak pedal angles occurred earlier on indoor stationary bicycles, emphasizing the impact of equipment choice on joint kinetics.

A scoping review of fair machine learning techniques when using real-world data.

OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains. METHODS: We conducted a thorough review of techniques for assessing and optimizing AI/ML model fairness in health care when using RWD in health care domains. The focus lies on appraising different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches. RESULTS: We identified 11 papers that are focused on optimizing model fairness in health care applications. The current research on mitigating bias issues in RWD is limited, both in terms of disease variety and health care applications, as well as the accessibility of public datasets for ML fairness research. Existing studies often indicate positive outcomes when using pre-processing techniques to address algorithmic bias. There remain unresolved questions within the field that require further research, which includes pinpointing the root causes of bias in ML models, broadening fairness research in AI/ML with the use of RWD and exploring its implications in healthcare settings, and evaluating and addressing bias in multi-modal data. CONCLUSION: This paper provides useful reference material and insights to researchers regarding AI/ML fairness in real-world health care data and reveals the gaps in the field. Fair AI/ML in health care is a burgeoning field that requires a heightened research focus to cover diverse applications and different types of RWD.

Geographic variation and racial disparities in adoption of newer glucose-lowering drugs with cardiovascular benefits among US Medicare beneficiaries with type 2 diabetes.

BACKGROUND: Prior studies have shown disparities in the uptake of cardioprotective newer glucose-lowering drugs (GLDs), including sodium-glucose cotranwsporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a). This study aimed to characterize geographic variation in the initiation of newer GLDs and the geographic variation in the disparities in initiating these medications. METHODS: Using 2017-2018 claims data from a 15% random nationwide sample of Medicare Part D beneficiaries, we identified individuals diagnosed with type 2 diabetes (T2D), who had ≥1 GLD prescriptions, and did not use SGLT2i or GLP1a in the year prior to the index date,1/1/2018. Patients were followed up for a year. The cohort was spatiotemporally linked to Dartmouth hospital-referral regions (HRRs), with each patient assigned to 1 of 306 HRRs. We performed multivariable Poisson regression to estimate adjusted initiation rates, and multivariable logistic regression to assess racial disparities in each HRR. RESULTS: Among 795,469 individuals with T2D included in the analyses, the mean (SD) age was 73 (10) y, 53.3% were women, 12.2% were non-Hispanic Black, and 7.2% initiated a newer GLD in the follow-up year. In the adjusted model including clinical factors, compared to non-Hispanic White patients, non-Hispanic Black (initiation rate ratio, IRR [95% CI]: 0.66 [0.64-0.68]), American Indian/Alaska Native (0.74 [0.66-0.82]), Hispanic (0.85 [0.82-0.87]), and Asian/Pacific islander (0.94 [0.89-0.98]) patients were less likely to initiate newer GLDs. Significant geographic variation was observed across HRRs, with an initiation rate spanning 2.7%-13.6%. CONCLUSIONS: This study uncovered substantial geographic variation and the racial disparities in initiating newer GLDs.

Association between first-line antidepressant use and risk of dementia in older adults: a retrospective cohort study.

BACKGROUND: Prior studies suggested that antidepressant use is associated with an increased risk of dementia compared to no use, which is subject to confounding by indication. We aimed to compare the dementia risk among older adults with depression receiving first-line antidepressants (i.e., SSRI/SNRI) versus psychotherapy, which is also considered the first-line therapy for depression. METHODS: This retrospective cohort study was conducted using the US Medical Expenditure Panel Survey from 2010 to 2019. We included adults aged ≥ 50 years diagnosed with depression who initiated SSRI/SNRI or psychotherapy. We excluded patients with a dementia diagnosis before the first record of SSRI/SNRI use or psychotherapy. The exposure was the patient's receipt of SSRI/SNRI (identified from self-report questionnaires) or psychotherapy (identified from the Outpatient Visits or Office-Based Medical Provider Visits files). The outcome was a new diagnosis of dementia within 2 years (i.e., survey panel period) identified using ICD-9/ICD-10 codes from the Medical Conditions file. Using a multivariable logistic regression model, we reported adjusted odds ratios (aORs) with 95% confidence intervals (CIs). We also conducted subgroup analyses by patient sex, age group, race/ethnicity, severity of depression, combined use of other non-SSRI/SNRI antidepressants, and presence of underlying cognitive impairment. RESULTS: Among 2,710 eligible patients (mean age = 61 ± 8, female = 69%, White = 84%), 89% used SSRIs/SNRIs, and 11% received psychotherapy. The SSRI/SNRI users had a higher crude incidence of dementia than the psychotherapy group (16.4% vs. 11.8%), with an aOR of 1.36 (95% CI = 1.06-1.74). Subgroup analyses yielded similar findings as the main analyses, except no significant association for patients who were aged < 65 years (1.23, 95% CI = 0.93-1.62), male (1.34, 95% CI = 0.95-1.90), Black (0.76, 95% CI = 0.48-1.19), had a higher PHQ-2 (1.39, 95% CI = 0.90-2.15), and had underlying cognitive impairment (1.06, 95% CI = 0.80-1.42). CONCLUSIONS: Our findings suggested that older adults with depression receiving SSRIs/SNRIs were associated with an increased dementia risk compared to those receiving psychotherapy.

A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes.

Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is crucial to implement effective social risk management strategies at the point of care. Objective: To develop an electronic health records (EHR)-based machine learning (ML) analytical pipeline to address unmet social needs associated with hospitalization risk in patients with T2D. Methods: We identified real-world patients with T2D from the EHR data from University of Florida (UF) Health Integrated Data Repository (IDR), incorporating both contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing instability). The 2015-2020 data were used for training and validation and 2021-2022 data for independent testing. We developed a machine learning analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) and fairness optimization. Results: The study cohort included 10,192 real-world patients with T2D, with a mean age of 59 years and 58% female. Of the cohort, 50% were non-Hispanic White, 39% were non-Hispanic Black, 6% were Hispanic, and 5% were other races/ethnicities. Our iPsRS, including both contextual and individual-level SDoH as input factors, achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial and ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk because of SDoH, that is, the actual 1-year hospitalization rate in the top 5% of iPsRS was 28.1%, ~13 times as high as the bottom decile (2.2% for 1-year hospitalization rate). Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in real word patients with T2D.

Meta-analysis of Association between Newer Glucose-Lowering Drugs and Risk of Parkinson's Disease.

Background: The association between newer classes of glucose-lowering drugs (GLDs) and the risk of Parkinson's disease (PD) remains unclear. Objective: The aim was to examine the effect of newer GLDs on the risk of PD through a meta-analysis of randomized outcome trials. Methods: The methods included randomized placebo-controlled outcome trials that reported PD events associated with three newer classes of GLDs (ie, dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose co-transporter-2 inhibitors) in participants with or without type 2 diabetes. The pooled odds ratio (OR) and 95% confidence interval (CI) were estimated using Peto's method. Results: The study included 24 trials involving 33 PD cases among 185,305 participants during a median follow-up of 2.2 years. Newer GLDs were significantly associated with a lower PD risk (OR: 0.50; 95% CI: 0.25-0.98) than placebo. Conclusion: Newer GLDs may possibly be associated with a decreased risk of PD; however, larger datasets are required to confirm or refute this notion.

Association between first-line antidepressant use and risk of dementia in older adults: a retrospective cohort study.

Background: Prior studies suggested that antidepressant use is associated with an increased risk of dementia compared to no use, which is subject to confounding by indication. We aimed to compare the dementia risk among older adults with depression receiving first-line antidepressants (i.e., SSRI/SNRI) versus psychotherapy, which is also considered the first-line therapy for depression. Methods: This retrospective cohort study was conducted using the US Medical Expenditure Panel Survey from 2010 to 2019. We included adults aged ≥50 years diagnosed with depression who initiated SSRI/SNRI or psychotherapy. We excluded patients with a dementia diagnosis before the first record of SSRI/SNRI use or psychotherapy. The exposure was the patient's receipt of SSRI/SNRI (identified from self-report questionnaires) or psychotherapy (identified from the Outpatient Visits or Office-Based Medical Provider Visits files). The outcome was a new diagnosis of dementia within 2 years (i.e., survey panel period) identified using ICD-9/ICD-10 codes from the Medical Conditions file. Using a multivariable logistic regression model, we reported adjusted odds ratios (aORs) with 95% confidence intervals (CIs). We also conducted subgroup analyses by patient sex, age group, race, severity of depression, combined use of other non-SSRI/SNRI antidepressants, and presence of underlying cognitive impairment. Results: Among 2,710 eligible patients (mean age= 61±8, female=69%, white=84%), 89% used SSRIs/SNRIs, and 11% received psychotherapy. The SSRI/SNRI users had a higher crude incidence of dementia than the psychotherapy group (16.1% vs. 12.7%), with an aOR of 1.39 (95% CI=1.21-1.59). Subgroup analyses yielded similar findings as the main analyses, except no significant association for patients who were black (0.75, 95% CI=0.55-1.02), had a higher PHQ-2 (1.08, 95% CI=0.82-1.41), had concomitant non-SSRI/SNRI antidepressants (0.75, 95% CI=0.34-1.66), and had underlying cognitive impairment (0.84, 95% CI=0.66-1.05). Conclusions: Our findings suggested that older adults with depression receiving SSRIs/SNRIs were associated with an increased dementia risk compared to those receiving psychotherapy.

The AtERF19 gene regulates meristem activity and flower organ size in plants.

Ethylene-responsive factors (ERFs) have diverse functions in the regulation of various plant developmental processes. Here, we demonstrate the dual role of an Arabidopsis ERF gene, AtERF19, in regulating reproductive meristem activity and flower organ size through the regulation of genes involved in CLAVATA-WUSCHEL (CLV-WUS) and auxin signaling, respectively. We found that AtERF19 stimulated the formation of flower primordia and controlled the number of flowers produced by activating WUS and was negatively regulated by CLV3. 35S::AtERF19 expression resulted in significantly more flowers, whereas 35S::AtERF19 + SRDX dominant-negative mutants produced fewer flowers. In addition, AtERF19 also functioned to control flower organ size by promoting the division/expansion of the cells through activating Small Auxin Up RNA Gene 32 (SAUR32), which positively regulated MYB21/24 in the auxin signaling pathway. 35S::AtERF19 and 35S::SAUR32 resulted in similarly larger flowers, whereas 35S::AtERF19 + SRDX and 35S::SAUR32-RNAi mutants produced smaller flowers than the wild type. The functions of AtERF19 were confirmed by the production of similarly more and larger flowers in 35S::AtERF19 transgenic tobacco (Nicotiana benthamiana) and in transgenic Arabidopsis which ectopically expressed the orchid gene (Nicotiana benthamiana) PaERF19 than in wild-type plants. The finding that AtERF19 regulates genes involved in both CLV-WUS and auxin signaling during flower development significantly expands the current knowledge of the multifunctional evolution of ERF genes in plants. The results presented in this work indicate a dual role for the transcription factor AtERF19 in controlling the number of flowers produced and flower organ size through the regulation of genes involved in CLV-WUS and auxin signaling, respectively. Our findings expand the knowledge of the roles of ERF genes in the regulation of reproductive development.

Impact of Contextual-Level Social Determinants of Health on Newer Antidiabetic Drug Adoption in Patients with Type 2 Diabetes.

BACKGROUND: We aimed to investigate the association between contextual-level social determinants of health (SDoH) and the use of novel antidiabetic drugs (ADD), including sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a) for patients with type 2 diabetes (T2D), and whether the association varies across racial and ethnic groups. METHODS: Using electronic health records from the OneFlorida+ network, we assembled a cohort of T2D patients who initiated a second-line ADD in 2015-2020. A set of 81 contextual-level SDoH documenting social and built environment were spatiotemporally linked to individuals based on their residential histories. We assessed the association between the contextual-level SDoH and initiation of SGTL2i/GLP1a and determined their effects across racial groups, adjusting for clinical factors. RESULTS: Of 28,874 individuals, 61% were women, and the mean age was 58 (±15) years. Two contextual-level SDoH factors identified as significantly associated with SGLT2i/GLP1a use were neighborhood deprivation index (odds ratio [OR] 0.87, 95% confidence interval [CI] 0.81-0.94) and the percent of vacant addresses in the neighborhood (OR 0.91, 95% CI 0.85-0.98). Patients living in such neighborhoods are less likely to be prescribed with newer ADD. There was no interaction between race-ethnicity and SDoH on the use of newer ADD. However, in the overall cohort, the non-Hispanic Black individuals were less likely to use newer ADD than the non-Hispanic White individuals (OR 0.82, 95% CI 0.76-0.88). CONCLUSION: Using a data-driven approach, we identified the key contextual-level SDoH factors associated with not following evidence-based treatment of T2D. Further investigations are needed to examine the mechanisms underlying these associations.

Accuracy of a local positioning system for time-series speed and acceleration and performance indicators in game sports.

The objective was to determine the reliability and validity of a local positioning system (LPS) promising high accuracy at reduced product costs. Fifty-five random static positions in a gym (54.8 × 26.0 m) were obtained 10 times via LPS (50 Hz) and measuring tape. An athlete's LPS-derived peak and time-series speed and acceleration during dynamic movements (n = 80) were compared with Vicon (100 Hz). Reliability and validity were assessed via Intraclass and Concordance Correlation Coefficients (ICC/CCC), root mean square errors, Bland-Altman plots, and analysis of variance. ICC3,1 (≥0.999) and CCC (0.387-0.999) were calculated for static positions (errors <0.22 m). CCC for time-series speed and acceleration, and peak speed, acceleration, and deceleration were 0.884-0.902, 0.777-0.854, 0.923, 0.486, and 0.731, respectively. Errors were larger in time-series acceleration (14.37 ± 3.77%) than in speed (11.99 ± 5.78%) (ηp2 = 0.472, p < 0.001) and in peak acceleration (28.04 ± 14.34%) and deceleration (25.07 ± 14.90%) than in speed (7.34 ± 6.07%) (ηp2 = 0.091, p < 0.01). LPS achieved excellent reliability and moderate-to-excellent validity of time-series speed and acceleration. The system accurately measured peak speed but not peak acceleration and deceleration. The system is suitable for analyses based on instantaneous speed and acceleration in game sports (e.g., energy estimations).

Bicontinuous oxide heteroepitaxy with enhanced photoconductivity.

Self-assembled systems have recently attracted extensive attention because they can display a wide range of phase morphologies in nanocomposites, providing a new arena to explore novel phenomena. Among these morphologies, a bicontinuous structure is highly desirable based on its high interface-to-volume ratio and 3D interconnectivity. A bicontinuous nickel oxide (NiO) and tin dioxide (SnO2) heteroepitaxial nanocomposite is revealed here. By controlling their concentration, we fabricated tuneable self-assembled nanostructures from pillars to bicontinuous structures, as evidenced by TEM-energy-dispersive X-ray spectroscopy with a tortuous compositional distribution. The experimentally observed growth modes are consistent with predictions by first-principles calculations. Phase-field simulations are performed to understand 3D microstructure formation and extract key thermodynamic parameters for predicting microstructure morphologies in SnO2:NiO nanocomposites of other concentrations. Furthermore, we demonstrate significantly enhanced photovoltaic properties in a bicontinuous SnO2:NiO nanocomposite macroscopically and microscopically. This research shows a pathway to developing innovative solar cell and photodetector devices based on self-assembled oxides.

A qualitative study of barriers and facilitators to the implementation of a pilot school-based, toothbrushing programme.

BACKGROUND: While supervised toothbrushing programmes have been established in many countries of the world, little is known about different perspectives on their implementation. The aim of the study was to explore stakeholders' barriers and facilitators to implementation of a school-based toothbrushing programme in Taiwan. METHODS: Focus groups and interviews were used to explore the views of elementary school students, teachers, staff, and nurses in a piloted school-based toothbrushing programme. The topic guides were developed according to the Theoretical Domains Framework (TDF) to cover the behavioural factors systematically and comprehensively. Data were analysed with content analysis. RESULTS: Overall, 36 students, 29 teachers/staff, and five school nurses (N = 65) were included. The overarching theme was the importance of habit formation for both staff and children to ensure that toothbrushing as part of the programme was embedded into the school schedule and routine. While children did not necessarily appear to retain the dental knowledge which was taught in the programme, the provision of fluoride toothpaste and toothbrush for their use in schools allowing teachers and staff to choose the timing of the brushing and engaging classmates to supervise each other were found to be key factors. CONCLUSIONS: Implementing a school-based toothbrushing programme with the support of staff and active engagement of children can help children to develop a toothbrushing habit. Classmate-supervised toothbrushing may reduce the burden on teachers and staff to implement the programme.

Regulatory network for FOREVER YOUNG FLOWER-like genes in regulating Arabidopsis flower senescence and abscission.

FOREVER YOUNG FLOWER (FYF) has been reported to play an important role in regulating flower senescence/abscission. Here, we functionally analyzed five Arabidopsis FYF-like genes, two in the FYF subgroup (FYL1/AGL71 and FYL2/AGL72) and three in the SOC1 subgroup (SOC1/AGL20, AGL19, and AGL14/XAL2), and showed their involvement in the regulation of flower senescence and/or abscission. We demonstrated that in FYF subgroup, FYF has both functions in suppressing flower senescence and abscission, FYL1 only suppresses flower abscission and FYL2 has been converted as an activator to promote flower senescence. In SOC1 subgroup, AGL19/AGL14/SOC1 have only one function in suppressing flower senescence. We also found that FYF-like proteins can form heterotetrameric complexes with different combinations of A/E functional proteins (such as AGL6 and SEP1) and AGL15/18-like proteins to perform their functions. These findings greatly expand the current knowledge behind the multifunctional evolution of FYF-like genes and uncover their regulatory network in plants.

Sensor number in simplified insole layouts and the validity of ground reaction forces during locomotion.

Research attempted to validate simplified insoles with a reduced number of sensors to facilitate clinical application. However, the ideal sensor number is yet to be determined. The purpose was to investigate the validity of vertical ground reaction forces in various simplified pressure sensor insoles and to identify an optimal compromise between sensor number and measurement performance. A Kistler force plate (1000 Hz) and 99-sensor Pedar-X insole (100 Hz) obtained force data of 15 participants during walking and jogging. Eight simplified insole layouts (3-17 sensors) were simulated. Layout performances were expressed as Pearson's correlation coefficients (r) with force plate as reference and coefficient of variation. Differences were assessed via repeated-measures ANOVA as partial eta square (ηp2) at p < .05. All layouts correlated with the force plate (r = .70-.99, p < .01). All layout performances were higher in jogging than in walking by r = +.07 ± .04 (ηp2=.28-.66, p < .05). The three- and five-sensor layouts yielded the lowest correlation (r = .70-.88) and the highest coefficient of variation (11-22%). Layout performances improved constantly from 7 to 11 sensors. The optimal compromise between simplification and measurement performance, quantified via change in correlation per sensor number, was found in the 11-sensor layout, recommendable for practical settings to improve monitoring and adjusting protocols.

Ideal Combinations of Acceleration-Based Intensity Metrics and Sensor Positions to Monitor Exercise Intensity under Different Types of Sports.

This study quantified the strength of the relationship between the percentage of heart rate reserve (%HRR) and two acceleration-based intensity metrics (AIMs) at three sensor-positions during three sport types (running, basketball, and badminton) under three intensity conditions (locomotion speeds). Fourteen participants (age: 24.9 ± 2.4 years) wore a chest strap HR monitor and placed three accelerometers at the left wrist (non-dominant), trunk, and right shank, respectively. The %HRR and two different AIMs (Player Load per minute [PL/min] and mean amplitude deviation [MAD]) during exercise were calculated. During running, both AIMs at the shank and PL at the wrist had strong correlations (r = 0.777-0.778) with %HRR; while other combinations were negligible to moderate (r = 0.065-0.451). For basketball, both AIMs at the shank had stronger correlations (r = 0.604-0.628) with %HRR than at wrist (r = 0.536-0.603) and trunk (r = 0.403-0.463) with %HRR. During badminton exercise, both AIMs at shank had stronger correlations (r = 0.782-0.793) with %HRR than those at wrist (r = 0.587-0.621) and MAD at trunk (r = 0.608) and trunk (r = 0.314). Wearing the sensor on the shank is an ideal position for both AIMs to monitor external intensity in running, basketball, and badminton, while the wrist and using PL-derived AIM seems to be the second ideal combination.

Effects of a novel inclined-adaptive footwear on change-of-direction performance in male athletes.

BACKGROUND: Although enhancing change of direction (COD) performance is a crucial factor for improving athletic performance in many sports, few studies have explored its effective methods. RESEARCH QUESTION: This study aimed to investigate the effects of inclined-adaptive footwear (IAF) on force-time characteristics during a COD task. METHODS: Thirteen male team sport athletes were randomly assigned to wear IAF or footwear without adaptive technology to perform a COD60° task at their best effort. A three-dimensional force plate was used to obtain the force-time curve and related parameters at the turning step (plant foot). RESULTS: IAF led to a significantly higher resultant ground reaction force (GRF), horizontal GRF, vertical GRF, and horizontal/vertical ratio during the braking phase, followed by a significantly shorter contact time and higher resultant horizontal GRF and vertical GRF during the propulsive phase. SIGNIFICANCE: This indicated that a greater GRF output, redistributed GRF, and shorter contact time occurred with the IAF. Therefore, IAF has the potential to enhance COD performance for sports involving multi-directional footwork and contribute to the development of new functional footwear.

Impact Position Estimation for Baseball Batting with a Force-Irrelevant Vibration Feature.

In this work we propose a novel method for impact position estimation during baseball batting, which is independent of impact intensity, i.e., force-irrelevant. In our experiments, we mount a piezoelectric vibration sensor on the knob of a wooden bat to record: (1) 3600 vibration signals (waveforms) from ball-bat impacts in the static experiment-30 impacts from each of 40 positions (distributed 1-40 cm from the end of the barrel) and 3 intensities (drop heights at 75, 100, and 125 cm, resp.), and (2) 45 vibration signals from actual battings by three baseball players in the dynamic experiment. The results show that the peak amplitude of the signal in the time domain, and the peaks of the first, second, and third eigenfrequencies (EFs) of the bat all increase with the impact intensity. However, the ratios of peaks at these three EFs (1st/2nd, 2nd/3rd, and 1st/3rd) hardly change with the impact intensity, and the observation is consistent for both the static and dynamic experiments across all impact positions. In conclusion, we have observed that the ratios of peaks at the first three EFs are a force-irrelevant feature, which can be used to estimate the impact position in baseball batting.

Validity of the Favero Assioma Duo Power Pedal System in Maximal-Effort Cycling Tests.

PURPOSE: The pedal-based power meter has its advantages, so it has become a popular monitoring tool in cycling. This study aimed to examine the validity of the Favero Assioma Duo power pedal system (FAD) in comparison with the SRM, which is considered the gold standard under maximal-effort cycling conditions, and a widely used cycling test, the 20-minute Functional Threshold Test. METHODS: Fourteen male adolescent cyclists completed a series of cycling intervals including 5, 15, 30, 60, 240, 600, and 1200 seconds (20-min Functional Threshold Test) with their maximal-effort performance on 2 separate days. Power output data were collected from the FAD and the SRM for analysis. RESULTS: Extremely strong correlations and excellent intraclass correlation coefficients (ICCs) were found between the power output values registered with the FAD and the SRM overall (r > .999, ICC = .996) and each power test (r > .98, ICC > .91). A low bias was found in power tests of longer durations (-3.2% at 240-s test, -3.3% at 600-s test, and -3.1% at 20-min Functional Threshold Test), while the bias augmented in shorter intervals (-2.7% at 5-s test, -3.6% at 15-s test, and -2.6% at 30-s test and -3.3% at 60-s test). A regression equation was proposed as y = -2.943 + 0.976x to diminish the bias (-0.2 W) with increased r value (>.98) and ICC (>.98). CONCLUSION: The FAD appears to be a valid tool for the measures of maximal-effort performance. The recorded power value reflects the true value with proposed regression equation.