Evaluating the Impact of Flossing Band Integration in Conventional Physiotherapy for Patellofemoral Pain Syndrome.

Background/Objectives: Patellofemoral Pain Syndrome (PFPS) is prevalent among physically active individuals, highlighting the need for innovative treatment strategies beyond conventional physiotherapy. This study investigates the effectiveness of integrating flossing band therapy with standard physiotherapy, anticipating improved outcomes in pain reduction, functional ability, and patient satisfaction. Methods: A double-blinded randomized controlled trial involved 50 PFPS-diagnosed participants. They were divided into two groups: Standard Physiotherapy Group (SPG) and Flossing Band and Physiotherapy Group (FBPG), each undergoing an 8-week intervention focusing on resistance training supplemented by respective therapies. Assessment metrics included pain (VAS), strength (Dynamometry), lower limb function (LEFS), and PFPS function (AKPS) measured before and after the intervention. Results: Significant enhancements in all outcome measures were noted for both groups, yet the FBPG exhibited notably superior improvements in pain, knee functionality, muscle strength, and lower extremity function. The FBPG demonstrated statistically significant greater efficacy in pain alleviation and strength enhancement. Conclusions: The addition of flossing band therapy to conventional physiotherapy presents a more effective treatment modality for PFPS, suggesting its potential to redefine therapeutic standards. Future studies should delve into the long-term impacts and mechanistic underpinnings of floss band therapy in PFPS management.

The Effects of Acute Deep Seawater Supplementation on Muscle Function after Triathlon.

(1) Background: Trainers and athletes have always sought to reduce the failure of muscle function during long endurance events. However, nowadays, it is a topic that is generating much debate in the scientific field. Currently, deep-sea water (DSW) intake seems to be a suitable hydration alternative for this type of endurance event. Therefore, the aim of this study was to determine whether DSW consumption during a triathlon event could preserve muscle function after exercise. (2) Methods: Nineteen trained male triathletes (age = 39.0 ± 4.25 years; BMI = 23.67 ± 1.81 kg/m2) randomly performed three triathlons, one of them consuming DSW (Totum SPORT 30 AB, Laboratories Quinton International, S.L., Spain), the other consuming isotonic placebo and the last with tap water-hydration. A vertical jump test with countermovement and an isometric muscle strength test were conducted before and after the triathlon test. (3) Results: There was a significant difference between treatment × time during the isometric muscle strength test. Based on the Tukey post hoc analysis, the peak net force decreased statistically in the placebo (p = 0.045) and control conditions (p = 0.026), but not in the experimental condition (p = 0.121). In addition, all of the conditions studied obtained similar results in the countermovement vertical jump after exercise. (4) Conclusions: As a result, consumption of DSW seems to delay the failure of muscle function specifically in isometric exercises but does not improve performance in sports. Thus, DSW does not alter muscle capacity in a negative way; therefore, its consumption may be recommended.

UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises.

Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors' ability to monitor patients' recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient's mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject's position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints.

A spatiotemporal model of firearm ownership in the United States.

Firearm injury is a major public health crisis in the United States, where more than 200 people sustain a nonfatal firearm injury and more than 100 people die from it every day. To formulate policy that minimizes firearm-related harms, legislators must have access to spatially resolved firearm possession rates. Here, we create a spatiotemporal econometric model that estimates monthly state-level firearm ownership from two cogent proxies (background checks per capita and fraction of suicides committed with a firearm). From calibration on yearly survey data that assess ownership, we find that both proxies have predictive value in estimation of firearm ownership and that interactions between states cannot be neglected. We demonstrate use of the model in the study of relationships between media coverage, mass shootings, and firearm ownership, uncovering causal associations that are masked by the use of the proxies individually.

Seawater Hydration Modulates IL-6 and Apelin Production during Triathlon Events: A Crossover Randomized Study.

A triathlon is an endurance event in which athletes need an efficient hydration strategy since hydration is restricted at different stages. However, it seems that seawater intake can be a suitable hydration alternative for this type of endurance event. Therefore, the aim of this study was to evaluate the efficacy of seawater hydration during a triathlon on cytokine production. Fifteen trained male triathletes (age = 38.8 ± 5.62 years old; BMI = 22.58 ± 2.51 kg/m2) randomly performed three triathlons, one of them consuming seawater (Totum SPORT, Laboratories Quinton International, S.L., Valencia, Spain), the other one consuming tap water ad libitum, and the last a physiologic saline solution as placebo. The triathlon consisted of an 800 m swim, a 90 km bike ride, and a 10 km run. Blood samples were taken at rest and after training, where markers of inflammation, hemoglobin, and hematocrit concentration were assessed. While the seawater was not ergogenic, it significantly increased the release of IL-6 and apelin post-exercise. However, no differences were found between the fractalkine, IL-15, EPO, osteonectin, myostatin, oncostatin, irisin, FSTL1, osteocrin, BDNF, and FGF-21 values over those of the placebo group. The present study demonstrates that hydration with seawater stimulates myokine production, which could lead to improved performance recovery after exercise.

Data-Driven Classification of Human Movements in Virtual Reality-Based Serious Games: Preclinical Rehabilitation Study in Citizen Science.

BACKGROUND: Sustained engagement is essential for the success of telerehabilitation programs. However, patients' lack of motivation and adherence could undermine these goals. To overcome this challenge, physical exercises have often been gamified. Building on the advantages of serious games, we propose a citizen science-based approach in which patients perform scientific tasks by using interactive interfaces and help advance scientific causes of their choice. This approach capitalizes on human intellect and benevolence while promoting learning. To further enhance engagement, we propose performing citizen science activities in immersive media, such as virtual reality (VR). OBJECTIVE: This study aims to present a novel methodology to facilitate the remote identification and classification of human movements for the automatic assessment of motor performance in telerehabilitation. The data-driven approach is presented in the context of a citizen science software dedicated to bimanual training in VR. Specifically, users interact with the interface and make contributions to an environmental citizen science project while moving both arms in concert. METHODS: In all, 9 healthy individuals interacted with the citizen science software by using a commercial VR gaming device. The software included a calibration phase to evaluate the users' range of motion along the 3 anatomical planes of motion and to adapt the sensitivity of the software's response to their movements. During calibration, the time series of the users' movements were recorded by the sensors embedded in the device. We performed principal component analysis to identify salient features of movements and then applied a bagged trees ensemble classifier to classify the movements. RESULTS: The classification achieved high performance, reaching 99.9% accuracy. Among the movements, elbow flexion was the most accurately classified movement (99.2%), and horizontal shoulder abduction to the right side of the body was the most misclassified movement (98.8%). CONCLUSIONS: Coordinated bimanual movements in VR can be classified with high accuracy. Our findings lay the foundation for the development of motion analysis algorithms in VR-mediated telerehabilitation.

Quantifying the role of the COVID-19 pandemic in the 2020 U.S. presidential elections.

In the media, a prevalent narrative is that the incumbent United States President Donald J. Trump lost the 2020 elections because of the way he handled the COVID-19 pandemic. Quantitative evidence to support this narrative is, however, limited. We put forward a spatial, information-theoretic approach to critically examine the link between voting behavior and COVID-19 incidence in the 2020 presidential elections. The approach overcomes classical limitations of traditional regression analysis, where it does not require an underlying mathematical model and it can capture nonlinear interactions. From the analysis of county-level data, we uncovered a robust association between voting behavior and prevalence of COVID-19 cases. Surprisingly, such an association points in the opposite direction from the accepted narrative: in counties that experienced less COVID-19 cases, the incumbent President lost more ground to his opponent, now President Joseph R. Biden Jr. A tenable explanation of this observation is the different attitude of liberal and conservative voters toward the pandemic, which led to more COVID-19 spreading in counties with a larger share of republican voters.

Network Dynamics in Elemental Assimilation and Metabolism.

Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by exposures to non-essential elements which may be toxic. In this study, we applied laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to reconstruct longitudinal exposure profiles of essential and non-essential elements throughout prenatal and early post-natal development. We applied cross-recurrence quantification analysis (CRQA) to characterize dynamics involved in elemental integration, and to construct a graph-theory based analysis of elemental metabolism. Our findings show how exposure to lead, a well-characterized toxicant, perturbs the metabolism of essential elements. In particular, our findings indicate that high levels of lead exposure dysregulate global aspects of metabolic network connectivity. For example, the magnitude of each element's degree was increased in children exposed to high lead levels. Similarly, high lead exposure yielded discrete effects on specific essential elements, particularly zinc and magnesium, which showed reduced network metrics compared to other elements. In sum, this approach presents a new, systems-based perspective on the dynamics involved in elemental metabolism during critical periods of human development.

Information Theory and Symbolic Analysis: Theory and Applications.

Symbolic analysis has been developed and used successfully in very diverse fields [...].

Efficacy of Non-Invasive Radiofrequency-Based Diathermy in the Postoperative Phase of Knee Arthroplasty: A Double-Blind Randomized Clinical Trial.

Total knee replacement (TKR) surgery ameliorates knee function and the quality of life of patients, although 20% still experience dissatisfaction due to pain limiting their function. Radiofrequency Diathermy (MDR) has shown improvements in knee osteoarthritis and patellofemoral pain syndrome. As such, this study aims to assess the effects of MDR in the postoperative treatment of TKR patients. Forty-two participants were allocated to an experimental, placebo, or control group. For two weeks, subjects performed daily knee exercises and MDR, knee exercises and placebo MDR, or only knee exercises. Data from the Visual Analogue Scale (VAS), Timed Up-and-Go (TUG) test, Five Times Sit-to-Stand Test (FSST), Western Ontario and McMaster Universities Arthritis Index (WOMAC), physical component summary (PCS), and the mental component summary (MCS) of the SF-12 questionnaire were collected. Group-by-time interaction was significant, with favorable results in the MDR group for VAS (p = 0.009) and WOMAC (p = 0.021). No significant differences were found for TUG, FSST, PCS, or MCS (p > 0.05). In conclusion, the addition of MDR to therapeutic knee exercises obtained better results for knee pain than exercise alone in patients who had recently undergone TKR surgery.

Selection of Embedding Dimension and Delay Time in Phase Space Reconstruction via Symbolic Dynamics.

The modeling and prediction of chaotic time series require proper reconstruction of the state space from the available data in order to successfully estimate invariant properties of the embedded attractor. Thus, one must choose appropriate time delay τ∗ and embedding dimension p for phase space reconstruction. The value of τ∗ can be estimated from the Mutual Information, but this method is rather cumbersome computationally. Additionally, some researchers have recommended that τ∗ should be chosen to be dependent on the embedding dimension p by means of an appropriate value for the time delay τw=(p-1)τ∗, which is the optimal time delay for independence of the time series. The C-C method, based on Correlation Integral, is a method simpler than Mutual Information and has been proposed to select optimally τw and τ∗. In this paper, we suggest a simple method for estimating τ∗ and τw based on symbolic analysis and symbolic entropy. As in the C-C method, τ∗ is estimated as the first local optimal time delay and τw as the time delay for independence of the time series. The method is applied to several chaotic time series that are the base of comparison for several techniques. The numerical simulations for these systems verify that the proposed symbolic-based method is useful for practitioners and, according to the studied models, has a better performance than the C-C method for the choice of the time delay and embedding dimension. In addition, the method is applied to EEG data in order to study and compare some dynamic characteristics of brain activity under epileptic episodes.

[Hyperbaric oxygen therapy efficacy in the treatment of hematuria due to radiation cystitis. Experience in a reference center.] / Eficacia de la oxigenoterapia hiperbárica en el tratamiento de la hematuria por cistitis rádica. Experiencia en un centro de referencia.

OBJECTIVE: To evaluate the efficacy of hyperbaric oxygen therapy (HBOT) in the treatment of hemorrhagic radiation cystitis (HRC). MATERIAL AND METHOD: Retrospective study of patients diagnosed with hematuria secondary to HRC after pelvic radiation from January 2005 to January 2017 who were treated with HBOT. Demographic and clinical variables were collected. A descriptive univariate and multivariate statistical analysis using Cox regression was carried out. The treatment was considered effective when there was a total or partial remission of the hematuria based on the Radiation Therapy Oncology Group (RTOG) scoring schema. Partial remission was defined as the presence of hematuria grade 2 or less. RESULTS: A total of 67 patients with a mean age of 68,6 years (39-87) were included. 65,7% men and 34,3% women. The RT was administered in 64,2% of the cases by urological cause, prostate cancer. The av-erage dose of RT was 75,24 Gy (45-180). The mean from the RT to the HBOT treatment was 55,97 months (4-300) and from the beginning of the hematuria until the treatment was 11,3 months (1-48). Response was observed in 51 (76,1%) patients, total in 50,7% and partial in 25,4% of cases. Patients with a degree of hematuria less than 3, those who were administered more than 30 sessions and those who did not require transfusion or hospital admission, responded significantly bet-ter to treatment with HBOT (p<0.05) according to the univariate and multivariate analysis. No adverse effects related to treatment were reported, only one patient was excluded due to claustrophobia. CONCLUSIONS: Hyperbaric oxygen therapy is an effective and safe treatment for the management of hematuria due to radiological cystitis secondary to radiotherapy. A better response was observed in patients with a lower degree of hematuria and those who could be administered a greater number of sessions.

OBJETIVO: Evaluación la eficacia de la oxigenoterapia hiperbárica (OHB) en el tratamiento de la cistitis rádica hemorrágica (CRH) postradioterapia.MATERIAL Y MÉTODO: Estudio retrospectivo que incluyó a los pacientes diagnosticados de hematuria secundaria a CRH postradioterapia pélvica desde enero de 2005 hasta enero del 2017 tratados con OHB. Se recogieron variables demográficas y clínicas. Se efec-tuó un análisis estadístico descriptivo, univariante y multivariante mediante regresión de Cox. El tratamiento se consideró eficaz cuando se produjo una remisión total o parcial de la hematuria en base a la escala del Grupo Oncológico de Radioterapia (RTOG). La respuesta parcial se definió como la presencia de hematuria grado 2 o menor tras el tratamiento. RESULTADOS: Se recogieron un total de 67 pacientes con una edad media de 68,6 años (39-87). Un 65,7% hombres y 34,3% mujeres. La RT fue administrada en el 64,2% de los casos por causa urológica, la más frecuente fue el cáncer de próstata. La dosis media de RT fue 75,24 Gy (45- 180). La media desde la RT hasta el tratamiento con OHB fue de 55,97 meses (4-300) y desde el inicio de la hematuria hasta el tratamiento de 11,3 meses (1-48). Se observó algún tipo de respuesta en 51 (76,1%) pacientes, siendo una respuesta total en el 50,7% y parcial en el 25,4% de los casos. Los pacientes con un grado de hematuria menor a 3, aquellos que recibieron más de 30 sesiones, y aquellos que no precisaron transfusión ni ingreso hospitalario, respondieron significativamente mejor al tratamiento (p<0,05), según el análisis univariante y multivariante. No fueron reportados efectos adversos relacionados con el tratamiento, solo un paciente fue excluido por claustrofobia. CONCLUSIONES: La terapia con OHB es una tratamiento efectivo y seguro para el manejo de la hematuria por CRH secundaria a radioterapia. Se observó una mejor respuesta en los pacientes con un menor grado de hematuria, en aquellos que recibieron mayor número de sesiones y que no precisaron transfusión ni ingreso hospitalario.

Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings.

Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.

Unequal effects of the national lockdown on mental and social health in Italy.

With the exception of a few countries that chose a different approach, the worldwide reaction to the COVID-19 pandemic was a (longer or shorter) period of national lockdown. While the economic consequences of shutting down national economies were immediately evident, the sociopsychiatric implications of the social confinement of the entire population remain hidden and not fully understood. Italy has been the first European country to be severely impacted by the COVID-19 pandemic, to which it responded through strict lockdown measurements. The results of a timely survey on mental and social health, carried out by students and teachers of a middle school in Rome, might help identify the most vulnerable groups of the population. This evidence could be crucial in conceiving and enacting targeted public health policies to mitigate the consequences of the pandemic on mental health and to prevent intolerance to containment measures in some population segments, which could hamper worldwide efforts in the fight against COVID-19.

An information-theoretic approach to study spatial dependencies in small datasets.

From epidemiology to economics, there is a fundamental need of statistically principled approaches to unveil spatial patterns and identify their underpinning mechanisms. Grounded in network and information theory, we establish a non-parametric scheme to study spatial associations from limited measurements of a spatial process. Through the lens of network theory, we relate spatial patterning in the dataset to the topology of a network on which the process unfolds. From the available observations of the spatial process and a candidate network topology, we compute a mutual information statistic that measures the extent to which the measurement at a node is explained by observations at neighbouring nodes. For a class of networks and linear autoregressive processes, we establish closed-form expressions for the mutual information statistic in terms of network topological features. We demonstrate the feasibility of the approach on synthetic datasets comprising 25-100 measurements, generated by linear or nonlinear autoregressive processes. Upon validation on synthetic processes, we examine datasets of human migration under climate change in Bangladesh and motor vehicle deaths in the United States of America. For both these real datasets, our approach is successful in identifying meaningful spatial patterns, begetting statistically-principled insight into the mechanisms of important socioeconomic problems.

Self-Protection versus Fear of Stricter Firearm Regulations: Examining the Drivers of Firearm Acquisitions in the Aftermath of a Mass Shooting.

Discovering causal mechanisms underlying firearm acquisition can provide critical insight into firearm-related violence in the United States. Here, we established an information-theoretic framework to address the long-disputed dichotomy between self-protection and fear of firearm regulations as potential drivers of firearm acquisition in the aftermath of a mass shooting. We collected data on mass shootings, federal background checks, media output on firearm control and shootings, and firearm safety laws from 1999 to 2017. First, we conducted a cluster analysis to partition States according to the restrictiveness of their firearm-related legal environment. Then, we performed a transfer entropy analysis to unveil causal relationships at the State-level in the Wiener-Granger sense. The analysis suggests that fear of stricter firearm regulations is a stronger driver than the desire of self-protection for firearm acquisitions. This fear is likely to cross State borders, thereby shaping a collective pattern of firearm acquisition throughout the Nation.

Social distancing at health care centers early in the pandemic helps to protect population from COVID-19.

The impact of COVID-19 outbreak has been unequal across Spanish regions. The epidemic wave has been smoother in the Region of Murcia (RM) (6 deaths/100,000 residents). Physical distance from health centers from day 0 is an additional social distancing measure that confers an advantageous starting position in the fight against COVID-19. Late healthcare distancing measures are not as powerful as the early ones.

A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor.

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring.

Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation.

Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.

Phylogeography, Population Structure, and Species Delimitation in Rockhopper Penguins (Eudyptes chrysocome and Eudyptes moseleyi).

Rockhopper penguins are delimited as 2 species, the northern rockhopper (Eudyptes moseleyi) and the southern rockhopper (Eudyptes chrysocome), with the latter comprising 2 subspecies, the western rockhopper (Eudyptes chrysocome chrysocome) and the eastern rockhopper (Eudyptes chrysocome filholi). We conducted a phylogeographic study using multilocus data from 114 individuals sampled across 12 colonies from the entire range of the northern/southern rockhopper complex to assess potential population structure, gene flow, and species limits. Bayesian and likelihood methods with nuclear and mitochondrial DNA, including model testing and heuristic approaches, support E. moseleyi and E. chrysocome as distinct species lineages with a divergence time of 0.97 Ma. However, these analyses also indicated the presence of gene flow between these species. Among southern rockhopper subspecies, we found evidence of significant gene flow and heuristic approaches to species delimitation based on the genealogical diversity index failed to delimit them as species. The best-supported population models for the southern rockhoppers were those where E. c. chrysocome and E. c. filholi were combined into a single lineage or 2 lineages with bidirectional gene flow. Additionally, we found that E. c. filholi has the highest effective population size while E. c. chrysocome showed similar effective population size to that of the endangered E. moseleyi. We suggest that the current taxonomic definitions within rockhopper penguins be upheld and that E. chrysocome populations, all found south of the subtropical front, should be treated as a single taxon with distinct management units for E. c. chrysocome and E. c. filholi.