scholarly journals Assessing elderly user preference for telehealth solutions in Shenzhen, Hangzhou, Wuhan and Yichang, China (Preprint)

10.2196/27272 ◽  
2021 ◽  
Author(s):  
Nuoya Chen ◽  
Pengqi Liu ◽  
Ping Lv
Keyword(s):  
User Preference ◽  
Elderly User

Elderly user preference analysis for the use of telehealth solutions (Preprint)

2021 ◽  
Author(s):  
Nuoya Chen ◽  
Pengqi Liu ◽  
Ping Lv ◽  
Francesca Spigarelli
Keyword(s):  
Perceived Risk ◽  
Health Management ◽  
Self Care ◽  
The Elderly ◽  
Data Accuracy ◽  
User Preference ◽  
User Group ◽  
Elderly User ◽  
Smart Health ◽  
Elderly Users

BACKGROUND COVID-19 has rendered the elderly as the vulnerable group with higher death rates in the 50+ age group. In the stereotype, the elderly user group do not have the willingness nor the skills in need to adapt to the fast technology progress. Wide use of AI in health monitoring and health management such as the use of smart phone for online consultation, or the use of wearables to monitor key bio-metrics suggests the transformation to a prevention-based healthcare model and homecare. Telehealth solution usage has been accelerated during COVID. Yet the trust of elderly users with telehealth solutions are thin with many prefer face difficulties in reality to access and use such tools. The preference for telehealth solutions among elderly users remain unexplored as companies usually see it as the niche market where users are not active. Past literatures also mostly examine specific sub-population such as students, patients, current and potential users while none specifically explores the preference of the elderly. OBJECTIVE To explore elderly (>=50 years old) user’s preference for smart health solutions in China. The goal is to analyze why users choose telehealth solutions over traditional health solutions based on a model consisting of the benefits (raise health awareness) and risks of using smart health solutions, demographic factors and social influence. METHODS Past literatures on preference on telehealth solutions was checked carefully. The theoretical is built on technology acceptance model, the dual-factor model and health belief model. The questionnaire consists of 31 questions and was distributed offline on pad. The questionnaires were collected from Shenzhen, Hangzhou, Wuhan and Yichang randomly with 390 effective data samples. Subsequently, Stata 16.0 and SPSS 23.0 and was used to analyze data. O-logit rrdered regression and principle component analysis was the main theoretical model used. Data visualization was also used to illustrate factors in a qualitative manner. RESULTS A logit ranking regression model is used to analyze elderly user preference over telehealth solutions. There are three factors related the preference for telehealth solutions, namely the perceived value, the perceived benefits in self-care and health management and perceived risk in trust over data accuracy embedded in telehealth solutions. The model can predict 80% of all preference for telehealth solutions among 390 survey participants. CONCLUSIONS Perceived value of telehealth solutions do not have a statistically important effect on the elderly user's preference for telehealth solutions. Perceived benefits of telehealth solutions in self-care and health management have a positive impact while the perceived risk in trust over data accuracy over data collected from telehealth solutions have a negative impact on the preference for telehealth solutions among elderly user group.

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings

2021 ◽  
Vol 39 (2) ◽  
pp. 1-38
Author(s):  
Gediminas Adomavicius ◽  
Jesse Bockstedt ◽  
Shawn Curley ◽  
Jingjing Zhang
Keyword(s):  
Recommender Systems ◽  
Laboratory Experiments ◽  
User Preference ◽  
Online Retailer ◽  
Product Recommendations ◽  
Preference Ratings ◽  
User Ratings

Prior research has shown a robust effect of personalized product recommendations on user preference judgments for items. Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias users’ preference ratings after item consumption in the direction of the predicted rating. Top-N lists represent another common approach for presenting item recommendations in recommender systems. Through three controlled laboratory experiments, we show that top-N lists do not induce a discernible bias in user preference judgments. This result is robust, holding for both lists of personalized item recommendations and lists of items that are top-rated based on averages of aggregate user ratings. Adding numerical ratings to the list items does generate a bias, consistent with earlier studies. Thus, in contexts where preference biases are of concern to an online retailer or platform, top-N lists, without numerical predicted ratings, would be a promising format for displaying item recommendations.

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