scholarly journals Perceived user preferences and usability evaluation of mainstream wearable devices for health monitoring

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5350 ◽  
Author(s):  
Yuxi Jia ◽  
Wei Wang ◽  
Dong Wen ◽  
Lizhong Liang ◽  
Li Gao ◽  
...  
Keyword(s):  
Product Design ◽  
Health Monitoring ◽  
Usability Evaluation ◽  
User Preferences ◽  
User Preference ◽  
Heart Health ◽  
Significant Difference ◽  
Health Related ◽  
Fitness Tracking ◽  
Fitness Trackers

Background There are many problems with fitness trackers, such as device usability, which limit their large-scale application, and relevant studies are limited in terms of their sample size and evaluation methods. The purpose of the study was to evaluate the perceived usability of various mainstream fitness trackers on the market, and to learn about user feedback on feature preferences for each device. Methods Trial use of seven mainstream fitness trackers (two smart watches and five smart wristbands) followed by a survey study were applied. The questionnaire was specifically developed for this study, which included two parts (user preferences and device usability in five dimensions). We recruited users to test the devices for at least 30 days and asked experienced users to provide feedback in order to evaluate each device, including the rating and user preference of each device. Results We received 388 valid questionnaires, in which users rated their responses on a five-point Likert scale. (1) User preference: the average user satisfaction was 3.50–3.86 (points), and the rating for willingness to buy averaged between 3.36 and 3.59. More users were willing to wear (58.3–81.3%) and purchase (56.8–83.0%) the devices than were not. The top three general feature preferences were daily activity tracking, heart health monitoring, and professional fitness tracking. The top three health-related feature preferences were heart rate monitoring, daily pedometer, and professional fitness tracking. (2) Usability evaluation: product design was rated from 3.57 to 4.00; durability, 3.63–4.26; ease of use, 3.70–3.90; added features, 3.30–3.83; and user-rated accuracy, 3.44–3.78. A significant difference was observed in the rating of product design and durability among the different devices (p < 0.05) score. Conclusions Users generally had positive subjective intent regarding fitness trackers but were less satisfied with their cost effectiveness. The users preferred health related features such as heart health monitoring, and professional fitness tracking. The rating of most of the current mainstream fitness trackers was fair with some significant differences among the devices. Thus, further improvement is needed.

scholarly journals Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study (Preprint)

2018 ◽  
Author(s):  
Junqing Xie ◽  
Dong Wen ◽  
Lizhong Liang ◽  
Yuxi Jia ◽  
Li Gao ◽  
...  
Keyword(s):  
Heart Rate ◽  
Energy Consumption ◽  
Sleep Duration ◽  
Measurement Accuracy ◽  
Health Indicators ◽  
Wearable Devices ◽  
Health Related ◽  
Fitness Tracking ◽  
Activity State ◽  
Fitness Trackers

BACKGROUND Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. OBJECTIVE The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. METHODS A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). RESULTS Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). CONCLUSIONS At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states.

scholarly journals Identifying and Analyzing Health-Related Themes in Disinformation Shared by Conservative and Liberal Russian Trolls on Twitter

2021 ◽  
Vol 18 (4) ◽  
pp. 2159
Author(s):  
Amir Karami ◽  
Morgan Lundy ◽  
Frank Webb ◽  
Gabrielle Turner-McGrievy ◽  
Brooke W. McKeever ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Health Monitoring ◽  
P Value ◽  
Global Public Health ◽  
Significant Difference ◽  
Public Health Threat ◽  
2016 Election ◽  
Health Related ◽  
Left And Right

To combat health disinformation shared online, there is a need to identify and characterize the prevalence of topics shared by trolls managed by individuals to promote discord. The current literature is limited to a few health topics and dominated by vaccination. The goal of this study is to identify and analyze the breadth of health topics discussed by left (liberal) and right (conservative) Russian trolls on Twitter. We introduce an automated framework based on mixed methods including both computational and qualitative techniques. Results suggest that Russian trolls discussed 48 health-related topics, ranging from diet to abortion. Out of the 48 topics, there was a significant difference (p-value ≤ 0.004) between left and right trolls based on 17 topics. Hillary Clinton’s health during the 2016 election was the most popular topic for right trolls, who discussed this topic significantly more than left trolls. Mental health was the most popular topic for left trolls, who discussed this topic significantly more than right trolls. This study shows that health disinformation is a global public health threat on social media for a considerable number of health topics. This study can be beneficial for researchers who are interested in political disinformation and health monitoring, communication, and promotion on social media by showing health information shared by Russian trolls.

Modeling and quantifying uncertainty in the product design phase for effects of user preference changes

2015 ◽  
Vol 115 (9) ◽  
pp. 1637-1665 ◽  
Author(s):  
Hamid Afshari ◽  
Qingjin Peng
Keyword(s):  
Life Cycle ◽  
Product Design ◽  
Product Life Cycle ◽  
User Preferences ◽  
User Preference ◽  
Design Parameters ◽  
Accurate Estimation ◽  
Product Life ◽  
Content Type ◽  
External Uncertainty

Purpose – The purpose of this paper is to quantify external and internal uncertainties in product design process. The research addresses the measure of product future changes. Design/methodology/approach – Two methods are proposed to model and quantify uncertainty in the product life cycle. Changes of user preferences are considered as the external uncertainty. Changes stemming from dependencies between components are addressed as the internal uncertainty. Both methods use developed mechanisms to capture and treat changes of user preferences. An agent-based model is developed to simulate sociotechnical events in the product life cycle for the external uncertainty. An innovative application of Big Data Analytics (BDA) is proposed to evaluate the external and internal uncertainties in product design. The methods can identify the most affected product components under uncertainty. Findings – The results show that the proposed method could identify product changes during its life cycle, particularly using the proposed BDA method. Practical implications – It is essential for manufacturers in the competitive market to know their product changes under uncertainty. Proposed methods have potential to optimize design parameters in complex environments. Originality/value – This research bridges the gap of literature in the accurate estimation of uncertainty. The research integrates the change prediction and change transferring, applies data management methods innovatively, and utilizes the proposed methods practically.

scholarly journals A User Biology Preference Prediction Model Based on the Perceptual Evaluations of Designers for Biologically Inspired Design

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1860
Author(s):  
Shijian Luo ◽  
Yufei Zhang ◽  
Jie Zhang ◽  
Junheng Xu
Keyword(s):  
Prediction Model ◽  
Product Design ◽  
Linear Regression Analysis ◽  
User Preferences ◽  
User Preference ◽  
Biologically Inspired ◽  
Perceptual Evaluation ◽  
Biological Features ◽  
Cognitive Limitations ◽  
Biologically Inspired Design

Biology provides a rich and novel source of inspiration for product design. An increasing number of industrial designers are gaining inspiration from nature, producing creative products by extracting, classifying, and reconstructing biological features. However, the current process of gaining biological inspiration is still limited by the prior knowledge and experience of designers, so it is necessary to investigate the designer’s perception of biological features. Herein, we investigate designer perceptions of bionic object features based on Kansei engineering, achieving a highly comprehensive structured expression of biological features forming five dimensions—Overall Feeling, Ability and Trait, Color and Texture, Apparent Tactile Sensation, and Structural Features—using factor analysis. Further, producing creative design solutions with a biologically inspired design (BID) has a risk of failing to meet user preferences and market needs. A user preference prediction support tool may address this bottleneck. We examine user preference by questionnaire and explore its association with the perceptual evaluation of designers, obtaining a user preference prediction model by conducting multiple linear regression analysis. This provides a statistical model for identifying the relative weighting of the perception dimensions of each designer in the user preference for an animal, giving the degree of contribution to the user preference. The experiment results show that the dimension “Overall Feeling” of the designer perception is positively correlated with the “like” level of the user preference and negatively correlated with the “dislike” level of the user preference, indicating that this prediction model bridges the gap caused by the asymmetry between designers and users by matching the designer perception and user preference. To a certain extent, this research solves the problems associated with the cognitive limitations of designers and the differences between designers and users, facilitating the use of biological features in product design and thereby enhancing the market importance of BID schemes.

scholarly journals Avoidance of Healthcare Utilization in South Korea during the Coronavirus Disease 2019 (COVID-19) Pandemic

2021 ◽  
Vol 18 (8) ◽  
pp. 4363
Author(s):  
Minjung Lee ◽  
Myoungsoon You
Keyword(s):  
General Population ◽  
South Korea ◽  
Healthcare Utilization ◽  
Online Survey ◽  
Negative Impact ◽  
Sociodemographic Characteristics ◽  
Related Factors ◽  
First Case ◽  
Significant Difference ◽  
Health Related

Avoidance of healthcare utilization among the general population during pandemic outbreaks has been observed and it can lead to a negative impact on population health. The object of this study is to examine the influence of socio-demographic and health-related factors on the avoidance of healthcare utilization during the global outbreak of a novel coronavirus (COVID-19) in 2020. Data were collected through an online survey four weeks after the Korea Centers for Disease Control and Prevention (KCDC) confirmed the first case in South Korea; 1000 subjects were included in the analysis. The logit model for regression was used to analyze the associations between sociodemographic and health-related factors regarding the avoidance of healthcare utilization. Among the participants, 73.2% avoided healthcare utilization, and there was no significant difference in the prevalence of healthcare avoidance between groups with (72.0%) and without (74.9%) an underlying disease. Sociodemographic characteristics (e.g., gender, age, income level, and residential area) were related to healthcare avoidance. Among the investigated influencing factors, residential areas highly affected by COVID-19 (i.e., Daegu/Gyeoungbuk region) had the most significant effect on healthcare avoidance. This study found a high prevalence of healthcare avoidance among the general population who under-utilized healthcare resources during the COVID-19 outbreak. However, the results reveal that not all societal groups share the burden of healthcare avoidance equally, with it disproportionately affecting those with certain sociodemographic characteristics. This study can inform healthcare under-utilization patterns during emerging infectious disease outbreaks and provide information to public health emergency management for implementing strategies necessary to improve the preparedness of the healthcare system.

scholarly journals Quality of life assessments in clinical practice using either the EORTC-QLQ-C30 or the SEIOQL-DW: a randomized study

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Åsa Kettis ◽  
Hanna Fagerlind ◽  
Jan-Erik Frödin ◽  
Bengt Glimelius ◽  
Lena Ring
Keyword(s):  
Quality Of Life ◽  
Clinical Practice ◽  
Significant Difference ◽  
Patient Reported ◽  
Health Related Qol ◽  
Health Related ◽  
Secondary Outcomes ◽  
The Impact ◽  
Over Time

Abstract Background Effective patient-physician communication can improve patient understanding, agreement on treatment and adherence. This may, in turn, impact on clinical outcomes and patient quality of life (QoL). One way to improve communication is by using patient-reported outcome measures (PROMs). Heretofore, studies of the impact of using PROMs in clinical practice have mostly evaluated the use of standardized PROMs. However, there is reason to believe that individualized instruments may be more appropriate for this purpose. The aim of this study is to compare the effectiveness of the standardized QoL-instrument, the European Organization for Research and Treatment of Cancer Quality of Life C-30 (EORTC-QOL-C30) and the individualized QoL instrument, the Schedule for the Evaluation of Individual Quality of Life-Direct Weighting (SEIQoL-DW), in clinical practice. Methods In a prospective, open-label, controlled intervention study at two hospital out-patient clinics, 390 patients with gastrointestinal cancer were randomly assigned either to complete the EORTC-QOL-C30 or the SEIQoL-DW immediately before the consultation, with their responses being shared with their physician. This was repeated in 3–5 consultations over a period of 4–6 months. The primary outcome measure was patients’ health-related QoL, as measured by FACIT-G. Patients’ satisfaction with the consultation and survival were secondary outcomes. Results There was no significant difference between the groups with regard to study outcomes. Neither intervention instrument resulted in any significant changes in health-related QoL, or in any of the secondary outcomes, over time. This may reflect either a genuine lack of effect or sub-optimization of the intervention. Since there was no comparison to standard care an effect in terms of lack of deterioration over time cannot be excluded. Conclusions Future studies should focus on the implementation process, including the training of physicians to use the instruments and their motivation for doing so. The effects of situational use of standardized or individualized instruments should also be explored. The effectiveness of the different approaches may depend on contextual factors including physician and patient preferences.

Wearable energy-efficient fitness tracking system for sports person health monitoring application

2021 ◽  
pp. 1-13
Author(s):  
Dan Xie ◽  
Ming Zhang ◽  
Priyan Malarvizhi Kumar ◽  
Bala Anand Muthu
Keyword(s):  
Health Monitoring ◽  
Energy Efficient ◽  
Tracking System ◽  
Path Loss ◽  
Integrated Approach ◽  
Smartphone Application ◽  
Sensor Nodes ◽  
Engineering Research ◽  
Fitness Tracking ◽  
Monitoring Application

The high potential of wearable physiological sensors in regenerative medicine and continuous monitoring of human health is currently of great interest. In measuring in real-time and non-invasively highly heterogeneous constituents, have a great deal of work and therefore been pushed into creating several sports monitoring sensors. The advanced engineering research and technology lead to the design of a wearable energy-efficient fitness tracking (WE2FT) system for sports person health monitoring application. Instantaneous accelerations are measured against pulses, and specific walking motions can be tracked by this system using a deep learning-based integrated approach of an intelligent algorithm for gait phase detection for the proposed system (WE2FT). The algorithm’s effects are addressed, and the performance has been evaluated. In this study, the algorithm uses a smartphone application to track steps using the Internet of Things (IoT) technology. For this initiative, the central node’s optimal location is measured with the antenna reflectance coefficient and CM3A path loss model (IEEE 802.15.6) among the sensor nodes for energy-efficient communication. The simulation experiment results in the highest performance in terms of energy efficiency and path loss.

scholarly journals Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

2021 ◽  
Vol 11 (3) ◽  
pp. 1064
Author(s):  
Jenq-Haur Wang ◽  
Yen-Tsang Wu ◽  
Long Wang
Keyword(s):  
Social Media ◽  
Feature Fusion ◽  
Relevant Information ◽  
Image Features ◽  
User Preferences ◽  
User Preference ◽  
Late Fusion ◽  
Multimodal Features ◽  
Fusion Methods ◽  
Text Features

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

Experimental Identification of Material Properties of Piezoelectric Patch Transducer

2013 ◽  
Vol 486 ◽  
pp. 205-210
Author(s):  
Zuzana Lašová ◽  
Robert Zemcik
Keyword(s):  
Health Monitoring ◽  
Material Properties ◽  
Element Model ◽  
Experimental Methods ◽  
Optical Microscope ◽  
Piezoelectric Patch ◽  
Experimental Identification ◽  
Substrate Structure ◽  
Significant Difference ◽  
Voltage Signal

This work is focused on identification of material properties of piezoelectric patch transducers used e.g. for structural health monitoring before attaching to the substrate structure. Two experimental methods were concerned. At first two piezoelectric patches were supplied with a pair of collocated strain gauge rosettes. Both transducers were actuated with the same periodical signal. Significant difference in the results for two transducers was found, however it was claimed to be within tolerance by the producer. As an alternative method a measurement in an optical microscope was chosen. The patch was clamped at one side and actuated by a voltage signal. The displacement of the free end was captured by the microscope and processed in a graphical editor. Finally, a finite element model of the transducer was created and its material data were obtained by calibration with experimental data.

Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

2021 ◽  
pp. 1063293X2110195
Author(s):  
Ying Yu ◽  
Shan Li ◽  
Jing Ma
Keyword(s):  
Time Series Prediction ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Service Evaluation ◽  
User Preferences ◽  
User Preference ◽  
Selection Methods ◽  
Qos Prediction ◽  
Qos Performance ◽  
Time Aware

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

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