Soccer players’ perceptions on injury risk and prevention strategies

In injury prevention practice, high emphasis has been directed to the implementation and maintenance of evidence-based injury prevention measures into training routines. Despite scientific backing, compliance to injury prevention exercise programs has been observed to be suboptimal in many teams. Thus, understanding the soccer players’ beliefs, knowledge, attitude, and behaviors toward injury prevention exercise programs may unlock some insight toward better player education and compliance of injury prevention approaches implemented in their teams. In this study, we aim to gather preliminary data on Malaysian elite soccer players’ perceptions on injury risk and mechanisms, its means of prevention, and their current practice in prevention of injuries during training and matches. Active players from four elite soccer and amateur leagues (Super League, Premier League, President’s Cup, and Youth Cup) in Malaysia were invited to participate. Forty-five (n = 45) male elite players returned their responses. The most perceived risk factors for injury are muscle impairments and fatigue (91.1%), followed by coordination (88.9%), physical condition (84.4%), and previous injury (77.8%). As per our findings, over 50% of respondents feel adequately informed about injury prevention. However, as a practice, players seem to employ a combination of injury prevention measures that may or may not be supported by comprehensive, evidence-based literature. The findings suggest that there may be conflicting information among the players with regard to the efficacy of one injury prevention practice in comparison to the other. This may be regarded as a call for injury risk and prevention education among players, as well as increase the promotion of evidence-based injury prevention programs in Malaysian professional soccer

Implementation of genomics in medical practice to deliver precision medicine for an Asian population

Whilst the underlying principles of precision medicine are comparable across the globe, genomic references, health practices, costs and discrimination policies differ in Asian settings compared to the reported initiatives involving European-derived populations. We have addressed these variables by developing an evolving reference base of genomic and phenotypic data and a framework to return medically significant variants to consenting research participants applicable for the Asian context. Targeting 10,000 participants, over 2000 Singaporeans, with no known pre-existing health conditions, have consented to an extensive clinical health screen, family health history collection, genome sequencing and ongoing follow-up. Genomic variants in a subset of genes associated with Mendelian disorders and drug responses are analysed using an in-house bioinformatics pipeline. A multidisciplinary team reviews the classification of variants and a research report is generated. Medically significant variants are returned to consenting participants through a bespoke return-of-result genomics clinic. Variant validation and subsequent clinical referral are advised as appropriate. The design and implementation of this flexible learning framework enables a cohort of detailed phenotyping and genotyping of healthy Singaporeans to be established and the frequency of disease-causing variants in this population to be determined. Our findings will contribute to international precision medicine initiatives, bridging gaps with ethnic-specific data and insights from this understudied population

Harnessing technology and molecular analysis to understand the development of cardiovascular diseases in Asia: a prospective cohort study (SingHEART)

BACKGROUND: Cardiovascular disease (CVD) imposes much mortality and morbidity worldwide. The use of "deep learning", advancements in genomics, metabolomics, proteomics and devices like wearables have the potential to unearth new insights in the field of cardiology. Currently, in Asia, there are no studies that combine the use of conventional clinical information with these advanced technologies. We aim to harness these new technologies to understand the development of cardiovascular disease in Asia. METHODS: Singapore is a multi-ethnic country in Asia with well-represented diverse ethnicities including Chinese, Malays and Indians. The SingHEART study is the first technology driven multi-ethnic prospective population-based study of healthy Asians. Healthy male and female subjects aged 21-69 years old without any prior cardiovascular disease or diabetes mellitus will be recruited from the general population. All subjects are consented to undergo a detailed on-line questionnaire, basic blood investigations, resting and continuous electrocardiogram and blood pressure monitoring, activity and sleep tracking, calcium score, cardiac magnetic resonance imaging, whole genome sequencing and lipidomic analysis. Outcomes studied will include mortality and cause of mortality, myocardial infarction, stroke, malignancy, heart failure, and the development of co-morbidities. DISCUSSION: An initial target of 2500 patients has been set. From October 2015 to May 2017, an initial 683 subjects have been recruited and have completed the initial work-up the SingHEART project is the first contemporary population-based study in Asia that will include whole genome sequencing and deep phenotyping: including advanced imaging and wearable data, to better understand the development of cardiovascular disease across different ethnic groups in Asia

Factors predicting patient satisfaction in women with advanced breast cancer: a prospective study

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Leucine-enriched protein feeding does not impair exercise-induced free fatty acid availability and lipid oxidation: beneficial implications for training in carbohydrate-restricted states

Given that the enhanced oxidative adaptations observed when training in carbohydrate (CHO) restricted states are potentially regulated through free fatty acid (FFA) mediated signalling and that leucine rich protein elevates muscle protein synthesis, the present study aimed to test the hypothesis that leucine enriched protein feeding enhances circulating leucine concentration but does not impair FFA availability nor whole body lipid oxidation 56 during exercise. Nine males cycled for 2 h at 70% VO2peak when fasted (PLACEBO) or having consumed a whey protein solution (WHEY) or a leucine enriched whey protein gel (GEL), administered as 22 g 1 hour pre-exercise, 11 g/h during and 22 g thirty minutes post-exercise. Total leucine administration was 14.4 g and 6.3 in GEL and WHEY, respectively. Mean plasma leucine concentrations were elevated in GEL (P= 0.001) compared 60 with WHEY and PLACEBO (375 ± 100, 272 ± 51, 146 ± 14 μmol.L-1 respectively). No differences (P= 0.153) in plasma FFA (WHEY 0.53 ± 0.30, GEL 0.45 ± 0.25, PLACEBO 0.65 ± 0.30, mmol.L-1) or whole body lipid oxidation during exercise (WHEY 0.37 ± 0.26, GEL 0.36 ± 0.24, PLACEBO 0.34 ± 0.24 g/min) were apparent between trials, despite elevated (P= 0.001) insulin in WHEY and GEL compared with PLACEBO (38 ± 16, 35 ± 16, 22 ± 11 pmol.L-1 respectively). We conclude that leucine enriched protein feeding does not impair FFA availability nor whole body lipid oxidation during exercise, thus having practical applications for athletes who deliberately train in CHO restricted states to promote skeletal muscle adaptations

High-resolution digital phenotypes from consumer wearables and their applications in machine learning of cardiometabolic risk markers: cohort study

Background: Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized. Objective: We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk. Methods: We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events. Results: We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers. Conclusions: High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management

The influence of heart disease on characteristics, quality of life, use of health resources, and costs of COPD in primary care settings

<p>Abstract</p> <p>Background</p> <p>To evaluate the influence of heart disease on clinical characteristics, quality of life, use of health resources, and costs of patients with COPD followed at primary care settings under common clinical practice conditions.</p> <p>Methods</p> <p>Epidemiologic, observational, and descriptive study (EPIDEPOC study). Patients ≥ 40 years of age with stable COPD attending primary care settings were included. Demographic, clinical characteristics, quality of life (SF-12), seriousness of the disease, and treatment data were collected. Results were compared between patients with or without associated heart disease.</p> <p>Results</p> <p>A total of 9,390 patients with COPD were examined of whom 1,770 (18.8%) had heart disease and 78% were males. When comparing both patient groups, significant differences were found in the socio-demographic characteristics, health profile, comorbidities, and severity of the airway obstruction, which was greater in patients with heart disease. Differences were also found in both components of quality of life, physical and mental, with lower scores among those patients with heart disease. Higher frequency of primary care and pneumologist visits, emergency-room visits and number of hospital admissions were observed among patients with heart diseases. The annual total cost per patient was significantly higher in patients with heart disease; 2,937 ± 2,957 vs. 1,749 ± 2,120, p < 0.05. Variables that were showed to be independently associated to COPD in subjects with hearth conditions were age, being inactive, ex-smokers, moderate physical exercise, body mass index, concomitant blood hypertension, diabetes, anxiety, the SF-12 physical and mental components and per patient per year total cost.</p> <p>Conclusion</p> <p>Patients with COPD plus heart disease had greater disease severity and worse quality of life, used more healthcare resources and were associated with greater costs compared to COPD patients without known hearth disease.</p