BackgroundWhile physical exercise is known to help prevent falls in the elderly, bad weather and long distance between the home and place of exercise represent substantial deterrents for the elderly to join or continue attending exercise programs outside their residence. Conventional modalities for home exercise can be helpful but do not offer direct and prompt feedback to the participant, which minimizes the benefit.ObjectiveWe aimed to develop an elderly-friendly telepresence exercise platform and to evaluate the effects of a 12-week telepresence exercise program on fall-related risk factors in community-dwelling elderly women with a high risk of falling.MethodsIn total, 34 women aged 68-91 years with Fall Risk Assessment scores >14 and no medical contraindication to physical training-based therapy were recruited in person from a senior citizen center. The telepresence exercise platform included a 15-inch tablet computer, custom-made peer-to-peer video conferencing server system, and broadband Internet connectivity. The Web-based program included supervised resistance exercises performed using elastic resistance bands and balance exercise for 20-40 minutes a day, three times a week, for 12 weeks. During the telepresence exercise session, each participant in the intervention group was supervised remotely by a specialized instructor who provided feedback in real time. The women in the control group maintained their lifestyle without any intervention. Fall-related physical factors (body composition and physical function parameters) and psychological factors (Korean Falls Efficacy Scale score, Fear of Falling Questionnaire score) before and after the 12-week interventional period were examined in person by an exercise specialist blinded to the group allocation scheme.ResultsOf the 30 women enrolled, 23 completed the study. Compared to women in the control group (n=13), those in the intervention group (n=10) showed significant improvements on the scores for the chair stand test (95% confidence interval -10.45 to -5.94, P<.001), Berg Balance Scale (95% confidence interval -2.31 to -0.28, P=.02), and Fear of Falling Questionnaire (95% confidence interval 0.69-3.5, P=.01).ConclusionsThe telepresence exercise program had positive effects on fall-related risk factors in community-dwelling elderly women with a high risk of falling. Elderly-friendly telepresence technology for home-based exercises can serve as an effective intervention to improve fall-related physical and psychological factors.Trial RegistrationClinical Research Information Service KCT0002710; https://cris.nih.go.kr/cris/en/search/ search_result_st01.jsp?seq=11246 (Archived by WebCite at http://www.webcitation.org/6zdSUEsmb)

The goal of this paper is to study a distributed version of the gradient temporal-difference (GTD) learning algorithm for multi-agent Markov decision processes (MDPs). The temporal-difference (TD) learning is a reinforcement learning (RL) algorithm which learns an infinite horizon discounted cost function (or value function) for a given fixed policy without the model knowledge. In the distributed RL case each agent receives local reward through a local processing. Information exchange over sparse communication network allows the agents to learn the global value function corresponding to a global reward, which is a sum of local rewards. In this paper, the problem is converted into a constrained convex optimization problem with a consensus constraint. Then, we propose a primal-dual distributed GTD algorithm and prove that it almost surely converges to a set of stationary points of the optimization problem.

No abstract

To evaluate the association between alcohol intake and incident chronic kidney disease measures as well as the sex differences in this association, we analyzed health screening data of 14,190,878 adults who underwent health screening ≥3 times and had glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 and normal proteinuria at baseline. eGFR was calculated with the Chronic Kidney Disease Epidemiology Collaboration equation. Proteinuria was defined as ≥1+ dipstick proteinuria and low eGFR as <60 mL/min/1.73 m2. The risk of incident proteinuria and low eGFR was analyzed with an extended Cox model with alcohol intake level as a time-varying determinant and the annual change of eGFR with generalized linear model. A J-shape association of alcohol intake with the incident proteinuria was observed in men (adjusted hazard ratio [aHR], 0.961, 95% confidence interval [CI], 0.953–0.970 in men drinking alcohol <10 g/day; aHR 1.139, 95% CI, 1.123–1.154 in men drinking alcohol ≥40 g/day, compared with non-drinking men), and a positive association was seen in women (aHR, 1.034, 95% CI, 1.023–1.044 in women drinking alcohol <10 g/day; aHR, 1.094, 95% CI, 1.034–1.158 in women drinking alcohol ≥40 g/day, compared with non-drinking women). In both sexes, an inverse association of alcohol intake with the annual eGFR decline and incident low eGFR was observed. This study observed a beneficial effect of moderate alcohol intake on incident proteinuria in men and a protective effect of alcohol intake of any amount on the annual eGFR decline and incident low eGFR in both sexes. The long-term implications of these observations need to be elucidated with future studies.

Background Cardiorespiratory fitness (CRF), an important index of physical fitness, is the ability to inhale and provide oxygen to the exercising muscle. However, despite its importance, the current gold standard for measuring CRF is impractical, requiring maximal exercise from the participants. Objective This study aimed to develop a convenient and practical estimation model for CRF using data collected from daily life with a wristwatch-type device. Methods A total of 191 subjects, aged 20 to 65 years, participated in this study. Maximal oxygen uptake (VO 2 max), a standard measure of CRF, was measured with a maximal exercise test. Heart rate (HR) and physical activity data were collected using a commercial wristwatch-type fitness tracker (Fitbit; Fitbit Charge; Fitbit) for 3 consecutive days. Maximal activity energy expenditure (aEEmax) and slope between HR and physical activity were calculated using a linear regression. A VO 2 max estimation model was built using multiple linear regression with data on age, sex, height, percent body fat, aEEmax, and the slope. The result was validated with 2 different cross-validation methods. Results aEEmax showed a moderate correlation with VO 2 max ( r =0.50). The correlation coefficient for the multiple linear regression model was 0.81, and the SE of estimate (SEE) was 3.518 mL/kg/min. The regression model was cross-validated through the predicted residual error sum of square (PRESS). The PRESS correlation coefficient was 0.79, and the PRESS SEE was 3.667 mL/kg/min. The model was further validated by dividing it into different subgroups and calculating the constant error (CE) where a low CE showed that the model does not significantly overestimate or underestimate VO 2 max. Conclusions This study proposes a CRF estimation method using data collected by a wristwatch-type fitness tracker without any specific protocol for a wide range of the population.