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 >10%, the Seismofit V.O2peak estimation model needs further improvement to be considered for use in clinical settings.

Accuracy of a Clinical Applicable Method for Prediction of VO2max 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<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.

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

[This corrects the article DOI: 10.2196/35696.].

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): RR2-10.2196/19209.

A Biological Age Model Designed for Health Promotion Interventions: Protocol for an Interdisciplinary Study for Model Development.

BACKGROUND: Actions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span. OBJECTIVE: This interdisciplinary study intends to develop a biological age model and explore its usefulness. METHODS: The model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course. RESULTS: We completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021. CONCLUSIONS: We expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19209.

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

INTRODUCTION: The 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 METHODS: Body 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. RESULTS: At 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 (p<0.001). Number of employees with metabolic syndrome had decreased from 646 at baseline to 557 at follow-up (p = 0.005) and 42% of smokers had quit smoking (p<0.001). CONCLUSION: On 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.

Influence of maximal fat oxidation on long-term weight loss maintenance in humans.

Impaired maximal fat oxidation has been linked to obesity and weight regain after weight loss. The aim was to investigate the relationship between maximal fat oxidation (MFO) and long-term weight loss maintenance. Eighty subjects [means (SD): age, 36(13) yrs; BMI, 38(1) kg/m2] were recruited from a total of 2,420 former participants of an 11- to 12-wk lifestyle intervention. Three groups were established based on percent weight loss at follow-up [5.3(3.3) yr]: clinical weight loss maintenance (CWL), >10% weight loss; moderate weight loss (MWL), 1-10% weight loss; and weight regain (WR). Body composition (dual X-ray absorptiometry) and fat oxidation (indirect calorimetry) during incremental exercise were measured at follow-up. Blood and a muscle biopsy were sampled. At follow-up, a U-shaped parabolic relationship between MFO and percent weight loss was observed (r = 0.448; P < 0.001). Overall differences between CWL, MWL, and WR were observed in MFO (mean [95% confidence interval], in g/min, respectively: 0.46 [0.41-0.52]; 0.32 [0.27-0.38]; 0.45 [0.38-0.51]; P = 0.002), maximal oxygen uptake (V̇o2max, in ml·min-1·FFM-1, respectively; 49 [46-51]; 43 [40-47]; 41 [39-44]; P = 0.007), HAD-activity (in µmol·g-1·min-1, respectively: 123 [113-133]; 104 [91-118]; 97 [88-105]; P < 0.001), muscle protein content of CD36 (in AU, respectively: 1.1 [1.0-1.2]; 0.9 [0.8-1.0]; 0.9 [0.8-0.9]; P = 0.008) and FABPpm (in AU, respectively, 1.0 [0.8-1.2]; 0.7 [0.5-0.8]; 0.7 [0.5-0.9]; P = 0.008), body fat (in %, respectively: 33 [29-38]; 42 [38-46]; 52 [49-55]; P < 0.001), and plasma triglycerides (in mM, respectively: 0.8 [0.7-1.0]; 1.3 [0.9-1.7]; 1.6 [1.0-2.1]; P = 0.013). CWL and WR both had higher MFO compared with MWL, but based on different mechanisms. CWL displayed higher V̇o2max and intramuscular capacity for fat oxidation, whereas abundance of lipids at whole-body level and in plasma was higher in WR.NEW & NOTEWORTHY Impaired maximal fat oxidation has been linked to obesity and weight regain after weight loss. Noteworthy, maximal fat oxidation was equally high after clinical weight loss maintenance and weight regain compared with moderate weight loss. A high maximal fat oxidation after clinical weight loss maintenance was related to higher maximal oxygen updake, content of key proteins involved in transport of lipids across the plasma membrane and ß-oxidation. In contrast, a high maximal fat oxidation after weight regain was related to higher availability of lipids, i.e., general adiposity and plasma concentration of triglycerides.