Validity and reliability of seismocardiography for the estimation of cardiorespiratory fitness

BACKGROUND: Low cardiorespiratory fitness (ie, peak oxygen consumption [V.O2peak]) is associated with cardiovascular disease and all-cause mortality and is recognized as an important clinical tool in the assessment of patients. Cardiopulmonary exercise test (CPET) is the gold standard procedure for determination of V.O2peak but has methodological challenges as it is time-consuming and requires specialized equipment and trained professionals. Seismofit is a chest-mounted medical device for estimating V.O2peak at rest using seismocardiography.OBJECTIVE: The purpose of this study was to investigate the validity and reliability of Seismofit V.O2peak estimation in a healthy population.METHODS: On 3 separate days, 20 participants (10 women) underwent estimations of V.O2peak with Seismofit (×2) and Polar Fitness Test (PFT) in randomized order and performed a graded CPET on a cycle ergometer with continuous pulmonary gas exchange measurements.RESULTS: Seismofit V.O2peak showed a significant bias of -3.1 ± 2.4 mL·min-1·kg-1 (mean ± 95% confidence interval) and 95% limits of agreement (LoA) of ±10.8 mL·min-1·kg-1 compared to CPET. The mean absolute percentage error (MAPE) was 12.0%. Seismofit V.O2peak had a coefficient of variation of 4.5% ± 1.3% and an intraclass correlation coefficient of 0.95 between test days and a bias of 0.0 ± 0.4 mL·min-1·kg-1 with 95% LoA of ±1.6 mL·min-1·kg-1 in test-retest. In addition, Seismofit showed a 2.4 mL·min-1·kg-1 smaller difference in 95% LoA than PFT compared to CPET.CONCLUSION: The Seismofit is highly reliable in its estimation of V.O2peak. However, based on the measurement error and MAPE &gt;10%, the Seismofit V.O2peak estimation model needs further improvement to be considered for use in clinical settings.</p

Realidad Aumentada: Diseño y desarrollo de una herramienta didáctica para la industria

La realidad actual y futura en la industria viene de la mano d- • nuevas herramientas tecnológicas, la Realidad Aumentada (RA) es una de ellas. La presente comunicación tiene la finalidad mostrar el proceso de diseño y desarrollo de una herramienta didáctica que será utilizada para capacitar al personal en procesos de evaluación de calidad de sus productos. La capacitación en la industria puede representar un alto coste, ya que requiere espacios, recursos, tiempo e instructores para hacerlo. Sin embargo, los avances tecnológicos y la digitalización están conduciendo a nuevos materiales para la enseñanza en general, y en la industria al rediseño hacia formas de capacitación más económicas, sustentables y seguras. Esta comunicación muestra el proceso de diseño y desarrollo de una aplicación para dispositivo móvil que será utilizada como recurso didáctico para capacitar al personal en procesos de calidad y como herramienta para conducir la evaluación de los productos en una maquiladora del giro automotriz perteneciente al sector industrial de Ciudad Juárez. Se expone el proceso de diseño conducido por un modelo centrado en la experiencia de usuario, la implementación de modelado tridimensional de la pieza, las tecnologías RA utilizadas y resultados de la validación del prototipo con usuarios meta

The effectiveness of body age-based intervention in workplace health promotion:Results of a cohort study on 9851 Danish employees

IntroductionThe aging population emphasize the need for effective health promotion interventions. The workplace is a prioritized setting for health promotion to reach widely within a population. Body age can be used as a health-risk estimate and as a motivational tool to change health behavior. In this study we investigate body age-based intervention including motivational interview and its effect on health, when applied to real life workplace health promotion.Material and methodsBody age-based intervention was performed in 90 companies on 9851 Danish employees from 2011-2017. Metabolic risk factors were assessed, body age score was determined and an individualized motivational interview was conducted at baseline and follow-up. Change in body age score, single risk factors, smoking habits and metabolic syndrome were analyzed. The body age score is a composite score comprising 11 weighted variables. A body age score ≤ 0 is preferred, as this elicit a younger/healthier or equal body age compared to chronological age.ResultsAt 1.3 year follow-up the unhealthiest employees were less likely to participate. Within follow-up participants (39%, n = 3843) body age had improved by a decline in mean body age score of -0.6 and -0.7 years for men and women, respectively (pConclusionOn the basis of this study, we suggest that body age assessment motivates to participate in workplace health promotion, affect high risk behavior such as smoking thus have potential in public health promotion

Accuracy of a Clinical Applicable Method for Prediction of VO ₂max Using Seismocardiography

Cardiorespiratory fitness measured as ˙VO2max is considered an important variable in the risk prediction of cardiovascular disease and all-cause mortality. Non-exercise ˙VO2max prediction models are applicable, but lack accuracy. Here a model for the prediction of ˙VO2max using seismocardiography (SCG) was investigated. 97 healthy participants (18-65 yrs., 51 females) underwent measurement of SCG at rest in the supine position combined with demographic data to predict ˙VO2max before performing a graded exercise test (GET) on a cycle ergometer for determination of ˙VO2max using pulmonary gas exchange measurements for comparison. Accuracy assessment revealed no significant difference between SCG and GET ˙VO2max (mean±95% CI; 38.3±1.6 and 39.3±1.6 ml·min-1·kg-1, respectively. P=0.075). Further, a Pearson correlation of r=0.73, a standard error of estimate (SEE) of 5.9 ml·min-1·kg-1, and a coefficient of variation (CV) of 8±1% were found. The SCG ˙VO2max showed higher accuracy, than the non-exercise model based on the FRIENDS study, when this was applied to the present population (bias=-3.7±1.3 ml·min-1·kg-1, p&lt;0.0001. r=0.70. SEE=7.4 ml·min-1·kg-1, and CV=12±2%). The SCG ˙VO2max prediction model is an accurate method for the determination of ˙VO2max in a healthy adult population. However, further investigation on the validity and reliability of the SCG ˙VO2max prediction model in different populations is needed for consideration of clinical applicability.</p

A Model for Estimating Biological Age from Physiological Biomarkers of Healthy Aging:Cross-sectional Study

BACKGROUND: Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion. OBJECTIVE: This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging. METHODS: Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age. RESULTS: The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes. CONCLUSIONS: Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/1920