Integration of Sensor-Based and Self-Reported Metrics in a Sleep Diary: A Pilot Exploration.

OBJECTIVES: Discrepancies between sleep diaries and sensor-based sleep parameters are widely recognized. This study examined the effect of showing sensor-based sleep parameters while completing a daily diary. The provision of sensor-based data was expected to reduce variance but not change the mean of self-reported sleep parameters, which would in turn align better with sensor-based data compared to a control diary. METHOD: In a crossover study, 24 volunteers completed week-long periods of control diary (digital sleep diary without sensor-based data feedback) or integrated diary (diary with device feedback), washout, and then the other diary condition. RESULTS: The integrated diary reduced self-reported total sleep time (TST) by <10 minutes and reduced variance in TST. The integrated diary did not impact mean sleep onset latency (SOL) and, unexpectedly, the variance in SOL increased. The integrated diary improved both bias and limits of agreement for SOL and TST. CONCLUSIONS: Integration of wearable, sensor-based device data in a sleep diary has little impact on means, mixed evidence for less variance, and better agreement with sensor-based data than a traditional diary. How the diary impacts reporting and sensor-based sleep measurements should be explored.

Minimal Important Clinical Difference Values Are Not Uniformly Valid in the Active Duty Military Population Recovering from Shoulder Surgery.

BACKGROUND: There are multiple methods for calculating the minimal clinically important difference (MCID) threshold, and previous reports highlight heterogeneity and limitations of anchor-based and distribution-based analyses. The Warfighter Readiness Survey assesses the perception of a military population's fitness to deploy and may be used as a functional index in anchor-based MCID calculations. The purpose of the current study in a physically demanding population undergoing shoulder surgery was to compare the yields of two different anchor-based methods of calculating MCID for a battery of PROMs, a standard receiver operator curve (ROC) -based MCIDs and baseline-adjusted ROC MCIDs. METHODS: All service members enrolled prospectively in a multicenter database with prior shoulder surgery that completed pre- and postoperative PROMs at a minimum of 12 months were included. The PROMs battery included Single Assessment Numeric Evaluation (SANE), American Shoulder and Elbow Surgeons score (ASES), Patient Reported Outcome Management Information System (PROMIS) Physical Function (PF), PROMIS Pain Interference (PI), and the Warfighter Readiness survey. Standard anchor-based and baseline-adjusted ROC MCIDs were employed to determine if the calculated MCIDs were both statistically and theoretically valid (95% confidence interval either completely negative or positive). RESULTS: There were 117 patients (136 operations) identified, comprised of 83% males with a mean age of 35.7 ± 10.4 years and 47% arthroscopic labral repair/capsulorrhaphy. Using the standard, anchor-based ROC MCID calculation, the area under the curve (AUC) for SANE, ASES, PROMIS PF, and PROMIS PI were greater than 0.5 (statistically valid). For ASES, PROMIS PF, and PROMIS PI, the calculated MCID 95% CI all crossed 0 (theoretically invalid). Using the baseline-adjusted ROC MCID calculation, the MCID estimates for SANE, ASES, and PROMIS PI were both statistically and theoretically valid if the baseline score was less than 70.5, 69, and 65.7. CONCLUSION: When MCIDs were calculated and anchored to the results of standard, anchor-based MCID, a standard ROC analysis did not yield statistically or theoretically valid results across a battery of PROMs commonly used to assess outcomes after shoulder surgery in the active duty military population. Conversely, a baseline-adjusted ROC method was more effective at discerning changes across a battery of PROMs among the same cohort.

Low-load blood flow restriction reduces time-to-minimum single motor unit discharge rate.

Resistance training with low loads in combination with blood flow restriction (BFR) facilitates increases in muscle size and strength comparable with high-intensity exercise. We investigated the effects of BFR on single motor unit discharge behavior throughout a sustained low-intensity isometric contraction. Ten healthy individuals attended two experimental sessions: one with, the other without, BFR. Motor unit discharge rates from the tibialis anterior (TA) were recorded with intramuscular fine-wire electrodes throughout the duration of a sustained fatigue task. Three 5-s dorsiflexion maximal voluntary contractions (MVC) were performed before and after the fatigue task. Each participant held a target force of 20% MVC until endurance limit. A significant decrease in motor unit discharge rate was observed in both the non-BFR condition (from 13.13 ± 0.87 Hz to 11.95 ± 0.43 Hz, P = 0.03) and the BFR condition (from 12.95 ± 0.71 Hz to 10.9 ± 0.75 Hz, P = 0.03). BFR resulted in significantly shorter endurance time and time-to-minimum discharge rates and greater end-stage motor unit variability. Thus, low-load BFR causes an immediate steep decline in motor unit discharge rate that is greater than during contractions performed without BFR. This shortened neuromuscular response of time-to-minimum discharge rate likely contributes to the rapid rate of neuromuscular fatigue observed during BFR.

Response Complexity Reduces Errors on a Response Inhibition Task.

OBJECTIVE: The purpose of this investigation was to elucidate the role of button-response complexity to targets in a response inhibition task. BACKGROUND: Response inhibition is the ability to correctly inhibit an overt response to a target. The U.S. military is actively pursuing development of armed, combat robots as a force multiplier, which may present challenges to operators of combat robots in the form of response inhibition errors. METHOD: A total of 15 participants completed two 51-min versions of a modified sustained attention to response task (SART). Participants were outfitted with an electrocardiogram to index heart-rate variability and completed the NASA-Task Load Index (NASA-TLX) to index workload. RESULTS: The results demonstrated that the complex SART reduced errors of commission (4%) and slowed response times (874 ms) to correct Go targets relative to the simple SART (14%, 739 ms). The NASA-TLX did not show differences between the modified SARTs; however, heart-rate variability did demonstrate that Soldiers had an increased autonomic stress response to the complex SART. CONCLUSION: Increasing the behavioral response requirement during a response inhibition task can decrease errors of commission; however, it comes at the cost of slower response times to target stimuli. Heart-rate variability may provide better insight into objective workload relative to subjective measures. APPLICATION: The use of complex behavioral responses may provide a viable option to reduce potential "friendly fire" or collateral damage by Soldiers remotely engaging a target-rich environment.

Differences in muscle contraction onset as determined by ultrasound and electromyography.

INTRODUCTION: We characterize the agreement between the timing of muscle contraction onset detected by surface electromyography (sEMG), fine wire EMG (fwEMG), and motion-mode (M-mode) ultrasound for improved interpretations of clinical outcomes. METHODS: Eighteen healthy adults participated. Differences in contraction onset were compared between sEMG, fwEMG, and M-mode ultrasound collected during concentric contractions of the vastus lateralis and biceps brachii. RESULTS: The mean difference of 13.1 ms (-33.3-59.9) between sEMG and fwEMG was non-significant (intraclass correlation [ICC] = 0.60). Ultrasound was significantly different from surface and fine wire EMG (ICC = 0.65 and ICC = 0.40, respectively), occurring 98.6 ms (72.3-124.9) and 111.7 (60.3-163.0) before sEMG and fwEMG, respectively. Nonparametric interquartile ranges were also wide. CONCLUSIONS: Due to high variability, comparisons between EMG methods should be interpreted with caution. Ultrasound detected onset before either EMG method, which may indicate motion from adjacent muscles during voluntary contractions. Muscle Nerve 59:494-500, 2019.

Determining day-to-day human variation in indirect calorimetry using Bayesian decision theory.

NEW FINDINGS: What is the central question of this study? We sought to understand the day-to-day variability of human indirect calorimetry during rest and exercise. Previous work has been unable to separate human day-to-day variability from measurement error and within-trial human variability. We developed models accounting for different levels of human- and machine-level variance and compared the probability density functions using total variation distance. What is the main finding and its importance? After accounting for multiple levels of variance, the average human day-to-day variability of minute ventilation, CO2 output and O2 uptake is 4.0, 1.8 and 2.0%, respectively. This is a new method to understand human variability and directly enhances our understanding of human variance during indirect calorimetry. ABSTRACT: One of the challenges of precision medicine is understanding when serial measurements taken across days are quantifiably different from each other. Previous work examining gas exchange measured by indirect calorimetry has been unable to separate differential measurement error, within-trial human variance and day-to-day human variance effectively in order to ascertain how variable humans are across testing sessions. We used previously published reliability data to construct models of indirect calorimetry variance and compare these models with methods arising from Bayesian decision theory. These models are conditional on the data upon which they are derived and assume that errors conform to a truncated normal distribution. A serial analysis of modelled probability density functions demonstrated that the average human day-to-day variance in minute ventilation ( V ̇ E ), carbon dioxide output ( V ̇ C O 2 ) and oxygen uptake ( V ̇ O 2 ) was 4.0, 1.8 and 2.0%, respectively. However, the average day-to-day variability masked a wide range of non-linear variance across flow rates, particularly for V ̇ E . This is the first report isolating day-to-day human variability in indirect calorimetry gas exchange from other sources of variability. This method can be extended to other physiological tools, and an extension of this work facilitates a statistical tool to examine within-trial V ̇ O 2 differences, available in a graphical user interface.

The Effect of Soldier Marching, Rucksack Load, and Heart Rate on Marksmanship.

OBJECTIVE: The purpose was to determine if Soldier rucksack load, marching distance, and average heart rate (HR) during shooting affect the probability of hitting the target. BACKGROUND: Infantry Soldiers routinely carry heavy rucksack loads and are expected to engage enemy targets should a threat arise. METHOD: Twelve male Soldiers performed two 11.8 km marches in forested terrain at 4.3 km/hour on separate days (randomized, counterbalanced design). The Rifleman load consisted of protective armor (26.1 kg); the Rucksack load included the Rifleman load plus a weighted rucksack (48.5 kg). Soldiers performed a live-fire shooting task (48 targets) prior to the march, in the middle of the march, and at the end of the march. HR was collected during the shooting task. Data were assessed with multilevel logistic regression controlling for the multiple observations on each subject and shooting target distance. Predicted probabilities for hitting the target were calculated. RESULTS: There was a three-way interaction effect between rucksack load, average HR, and march ( p = .02). Graphical assessment of predicted probabilities indicated that regardless of load, marching increases shooting performance. Increases in shooting HR after marching result in lower probability of hitting the target, and rucksack load has inconsistent effects on marksmanship. CONCLUSION: Early evidence suggests that rucksack load and marching may not uniformly decrease marksmanship but that an inverted-U phenomenon may govern changes in marksmanship. APPLICATION: The effects of load and marching on marksmanship are not linear; the abilities of Soldiers should be continuously monitored to understand their capabilities in a given scenario.

Iterative Assessment of Statistically-Oriented and Standard Algorithms for Determining Muscle Onset with Intramuscular Electromyography.

The onset of muscle activity, as measured by electromyography (EMG), is a commonly applied metric in biomechanics. Intramuscular EMG is often used to examine deep musculature and there are currently no studies examining the effectiveness of algorithms for intramuscular EMG onset. The present study examines standard surface EMG onset algorithms (linear envelope, Teager-Kaiser Energy Operator, and sample entropy) and novel algorithms (time series mean-variance analysis, sequential/batch processing with parametric and nonparametric methods, and Bayesian changepoint analysis). Thirteen male and 5 female subjects had intramuscular EMG collected during isolated biceps brachii and vastus lateralis contractions, resulting in 103 trials. EMG onset was visually determined twice by 3 blinded reviewers. Since the reliability of visual onset was high (ICC(1,1): 0.92), the mean of the 6 visual assessments was contrasted with the algorithmic approaches. Poorly performing algorithms were stepwise eliminated via (1) root mean square error analysis, (2) algorithm failure to identify onset/premature onset, (3) linear regression analysis, and (4) Bland-Altman plots. The top performing algorithms were all based on Bayesian changepoint analysis of rectified EMG and were statistically indistinguishable from visual analysis. Bayesian changepoint analysis has the potential to produce more reliable, accurate, and objective intramuscular EMG onset results than standard methodologies.

Entrainment of vastus medialis complex activity differs between genders.

INTRODUCTION: That the vastus medialis oblique (VMO) is a functional unit of the vastus medialis (VM) is disputed. Delayed VMO activation predicts patellofemoral pain, which has higher rates in women. METHODS: Single MUs and surface electromyogram (EMG) were collected from the VMO and VM of 9 men and 9 women. Men were tested once; women were tested during 5 menstrual phases. Coherence was assessed for motor unit (MU) firings within and between the VM and VMO using multilevel logistic models to determine statistical significance. RESULTS: Compared with women, men have 741% (MU pairs) and 256% (MU-EMG pairs) greater odds of common drive (0-5 Hz) coherent oscillations. MU pairs from the VMO and the dual VM/VMO complex have 228% and 212% greater odds of coherent oscillations in the beta band (15-35 Hz) compared with VM pairs. CONCLUSIONS: The VM and VMO are neurologically different muscles; control of the VM complex is sexually dimorphic.

Maximal force and tremor changes across the menstrual cycle.

PURPOSE: Sex hormones have profound effects on the nervous system in vitro and in vivo. The present study examines the effect of the menstrual cycle on maximal isometric force (MVC) and tremor during an endurance task. METHODS: Nine eumenorrheic females participated in five study visits across their menstrual cycle. In each menstrual phase, an MVC and an endurance task to failure were performed. Tremor across the endurance task was quantified as the coefficient of variation in force and was assessed in absolute time and relative percent time to task failure. RESULTS: MVC decreases 23% from ovulation to the mid luteal phase of the menstrual cycle. In absolute time, the mid luteal phase has the highest initial tremor, though the early follicular phase has substantially higher tremor than other phases after 150 s of task performance. In relative time, the mid luteal phase has the highest level of tremor throughout the endurance task. CONCLUSIONS: Both MVC and tremor during an endurance task are modified by the menstrual cycle. Performance of tasks and sports which require high force and steadiness to exhaustion may be decreased in the mid luteal phase compared to other menstrual phases.

Athlete Health and Human Performance Will Not Improve Without Transdisciplinary Collaboration and Data Sharing in Elite Sport.

There are two largely competing models for an athletics organization at both the collegiate and professional levels: the High Performance Model and the Medical Model. The High Performance Model arises from international Football perspective that places a "Performance Director" at the center of teams supporting the athletes. The Medical Model, supported by both the National Athletic Trainers Association and the NCAA, separates off medical staff (athletic trainers, physical therapists, and physicians, predominantly) and emphasizes the autonomy of medical decisions. The Medical Model has left clinicians in a "medical silo", limiting our ability to care for the individual athletes as holistic people and limits our wider impact in the field of athlete health and injury mitigation. We argue that Medical. Model is consistent with the High Performance Model only if we reject the notion that the "Performance Director" is an administrative person and instead conceptualize this as a "Health and Performance Information Hub" which facilitates transdisciplinary collaboration. This Commentary details how a data broker system can be used to accelerate transdisciplinary collaboration within an athletic organization, leading to better healthcare for athletes and improved team and individual performance. Furthermore, a transdisciplinary organization with data sharing is able to turn real-world data into real-world evidence, enhancing the care and performance of athletes locally as well as facilitating the creation of generalizable knowledge in the area of sports medicine and human performance.

Are Wearable Photoplethysmogram-Based Heart Rate Variability Measures Equivalent to Electrocardiogram? A Simulation Study.

BACKGROUND: Traditional electrocardiography (ECG)-derived heart rate variability (HRV) and photoplethysmography (PPG)-derived "HRV" (termed PRV) have been reported interchangeably. Any potential dissociation between HRV and PRV could be due to the variability in pulse arrival time (PAT; time between heartbeat and peripheral pulse). OBJECTIVE: This study examined if PRV is equivalent to ECG-derived HRV and if PRV's innate error makes it a high-quality measurement separate from HRV. METHODS: ECG data from 1084 subjects were obtained from the PhysioNet Autonomic Aging dataset, and individual PAT dispersions for both the wrist (n = 42) and finger (n = 49) were derived from Mol et al. (Exp Gerontol. 2020; 135: 110938). A Bayesian simulation was constructed whereby the individual arrival times of the PPG wave were calculated by placing a Gaussian prior on the individual QRS-wave timings of each ECG series. The standard deviation (σ) of the prior corresponds to the PAT dispersion from Mol et al. This was simulated 10,000 times for each PAT σ. The root mean square of successive differences (RMSSD) and standard deviation of N-N intervals (SDNN) were calculated for both HRV and PRV. The Region of Practical Equivalence bounds (ROPE) were set a priori at ± 0.2% of true HRV. The highest density interval (HDI) width, encompassing 95% of the posterior distribution, was calculated for each PAT σ. RESULTS: The lowest PAT σ (2.0 SD) corresponded to 88.4% within ROPE for SDNN and 21.4% for RMSSD. As the σ of PAT increases, the equivalence of PRV and HRV decreases for both SDNN and RMSSD. The HDI interval width increases with increasing PAT σ, with the HDI width increasing at a higher rate for RMSSD than SDNN. CONCLUSIONS: For individuals with greater PAT variability, PRV is not a surrogate for HRV. When considering PRV as a unique biometric measure, SDNN may have more favorable measurement properties than RMSSD, though both exhibit a non-uniform measurement error.

Missing Data in Sport Science: A Didactic Example Using Wearables in American Football.

Data are often recorded from athletes to make decisions regarding the mitigation of injuries or the enhancement of performance. However, data collection in the real-world is difficult, and it is common for data to be missing from a particular training session due to equipment malfunction, athlete non-compliance, etc. The statistical community has long recognized that proper handling of missing data is vital to unbiased analyses and decision making, yet most dashboards in sport science and medicine do not recognize the issues introduced by missing data and practitioners are largely unaware that their displays are conveying biased information. The goal of this leading article is to show how real-world data can violate the 'missing completely at random' assumption in an American Football example and then demonstrate some potential imputation solutions which appear to maintain the underlying properties of the data in the presence of missingness. Whether data are aggregated on a dashboard as simple histograms and averages or with higher-level analytics, violating the 'missing completely at random' assumption results in a biased dashboard. Practitioners need to insist that dashboard developers perform missing data analyses and impute data as needed so valid data-driven decisions can be made.

Asking the Right Question Is Key to Getting a Valuable Answer.

Letter to the Editor-in-Chief in response to JOSPT article "The Relationship Between Neck Strength and Sports-Related Concussion in Team Sports: A Systematic Review With Meta-analysis" by Garrett JM et al. J Orthop Sports Phys Ther 2023;53(11):726. doi:10.2519/jospt.2023.0204.

An Exploratory Factor Analysis of the National Institutes of Health Patient-Reported Outcomes Measurement Information System and the Single Assessment Numeric Evaluation in Knee Surgery Patients.

INTRODUCTION: Patient-Reported Outcomes (PROs) have been suggested for use in measuring treatment effectiveness. To minimize patient burden, two approaches have been proposed: An orthopedic-specific Single Assessment Numeric Evaluation (SANE) or computer adaptive testing methods such as the National Institutes of Health (NIH) Patient-Reported Outcomes Measurement Information System (PROMIS). The goal of this work was to examine the constructs measured by the SANE and PROMIS system in a military orthopedic population undergoing knee surgery. MATERIALS AND METHODS: In 732 patients and 2,166 complete observations, the SANE-Knee PRO and PROMIS surveys for Depression, Anxiety, Pain Interference, Sleep Disturbance, and Physical Function were obtained. A correlation matrix between the PROs was calculated, the number of latent factors to extract was determined via parallel plot, and the final principal axis exploratory factor analysis was performed. RESULTS: The parallel plot analysis indicated that two latent factors existed. One latent factor corresponded to measures of psychological distress (PROMIS Sleep Disturbance, Depression, and Anxiety) and the second latent factor corresponded to physical capability (SANE, PROMIS Physical Function, Pain Interference, and Sleep Disturbance). Both PROMIS Physical Function (0.83) and Pain Interference (-0.80) more strongly weigh on the physical capability latent factor than SANE (0.69). CONCLUSIONS: In a knee surgical population, the SANE, PROMIS Physical Function, and PROMIS Pain Interference measure the same human dimension of physical capability; however, PROMIS Physical Function and Pain Interference may measure this construct more effectively in isolation. The SANE may be a more viable option to gauge physical capability when computer adaptive testing is not possible.

The Glenoid Track Paradigm Does Not Reliably Affect Military Surgeons' Approach to Managing Shoulder Instability.

Purpose: To report the frequencies of surgical stabilization procedures performed by military shoulder surgeons and to use decision tree analysis to describe how bipolar bone loss affects surgeons' decision to perform arthroscopic versus open stabilization procedures. Methods: The Military Orthopaedics Tracking Injuries and Outcomes Network (MOTION) database was queried for anterior shoulder stabilization procedures from 2016 to 2021. A nonparametric decision tree analysis was used to generate a framework for classifying surgeon decision making based on specified injury characteristics (labral tear location, glenoid bone loss [GBL], Hill-Sachs lesion [HSL] size, and on-track vs off-track HSL). Results: A total of 525 procedures were included in the final analysis, with a mean patient age of 25.9 ± 7.2 years and a mean GBL percentage of 3.6% ± 6.8%. HSLs were described based on size as absent (n = 354), mild (n = 129), moderate (n = 40), and severe (n = 2) and as on-track versus off-track in 223 cases, with 17% (n = 38) characterized as off-track. Arthroscopic labral repair (n = 428, 82%) was the most common procedure, whereas open repair (n = 10, 1.9%) and glenoid augmentation (n = 44, 8.4%) were performed infrequently. Decision tree analysis identified a GBL threshold of 17% or greater that resulted in an 89% probability of glenoid augmentation. Shoulders with GBL less than 17% combined with a mild or absent HSL had a 95% probability of an isolated arthroscopic labral repair, whereas a moderate or severe HSL resulted in a 79% probability of arthroscopic repair with remplissage. The presence of an off-track HSL did not contribute to the decision-making process as defined by the algorithm and data available. Conclusions: Among military shoulder surgeons, GBL of 17% or greater is predictive of a glenoid augmentation procedure whereas HSL size is predictive of remplissage for GBL less than 17%. However, the on-track/off-track paradigm does not appear to affect military surgeons' decision making. Level of Evidence: Level III, retrospective cohort study.

All MCIDs Are Wrong, But Some May be Useful.

OBJECTIVE: To demonstrate how to apply a baseline-adjusted receiver operator characteristic curve (AROC) analysis for minimum clinically important differences (MCIDs) in an empirical data set and discuss new insights relating to MCIDs. DESIGN: Retrospective study. METHODS: This study includes data from 999 active-duty military service patients enrolled in the United States Military Health System's Military Orthopedics Tracking Injuries and Outcomes Network. Anchored MCIDs were calculated using the standard receiver operator characteristic analysis and the AROC analysis for the Patient-Reported Outcome Measure Information System (PROMIS) Pain Interference and Defense and Veterans Pain Rating Scale (DVPRS). Point estimates where confidence intervals (CIs) crossed the 0.5 identity line on the area-under-the-curve (AUC) analysis were considered statistically invalid. MCID estimates where CIs crossed 0 were considered theoretically invalid. RESULTS: In applying an AROC analysis, the region of AUC and MCID validity for the PROMIS Pain Interference score exists when the baseline score is greater than 61.0 but less than 72.3. For DVPRS, the region of MCID validity is when the baseline score is greater than 5.9 but less than 7.9. CONCLUSION: Baseline values influence not only the MCID but also the accuracy of the MCID. MCIDs are statistically and theoretically valid for only a discrete range of baseline scores. Our findings suggest that the MCID may be too flawed a construct to accurately benchmark treatment outcomes. J Orthop Sports Phys Ther 2022;52(6):401-407. doi:10.2519/jospt.2022.11193.