scholarly journals Privacy Concerns of Overweight Adults to use Wearable Devices for Sustained Health Monitoring

10.29007/mpfc ◽  
2019 ◽  
Author(s):  
Oluwaseyi Ogundele ◽  
Liezel Cilliers
Keyword(s):  
Health Monitoring ◽  
Sampling Technique ◽  
Wearable Devices ◽  
Behavioural Intention ◽  
Third Party ◽  
Research Approach ◽  
Privacy Concerns ◽  
Overweight Adult ◽  
Overweight Adults

The market for wearable devices that can be used for sustained health monitoring purposes is continuously growing within the healthcare sec- tor. However, to function effectively, these devices must collect a large amount of data from the users. There are privacy concerns that may inhibit the behavioural intention of overweight adult to use wearable de- vices for health monitoring in the long term. This study examined the privacy factors influencing the behavioural intention of overweight adult to make use of wearable devices of sustained health monitoring. The study made use of a qualitative research approach with an inter- view design. A purposive sampling technique was used to select and interview twenty overweight adults (aged 18-59 years) who are using wearable devices in East London, South Africa. The Expectation Confirmation Model (ECM) framework was adopted as the underlying re- search theory in this study. Thematic analysis was used to analyse the data provided by participants. The results found that there were 4 levels of privacy concerns among users. Some users were very concerned that their data was collected by the device manufacturing, while others had not concern at all. Some users had privacy concerns, but did not think that the data collected would be useful to a third party and finally some users did have privacy concerns, but indicated that the benefit of using a wearable device outweighed their concerns and they would continue to use the device. The recommendation of the study is that users must educate themselves about what data is collected and how it will be used by third parties.

Secure Health Monitoring in the Cloud Using Homomorphic Encryption

2020 ◽  
pp. 56-92
Author(s):  
Scott Ames ◽  
Muthuramakrishnan Venkitasubramaniam ◽  
Alex Page ◽  
Ovunc Kocabas ◽  
Tolga Soyata
Keyword(s):  
Cloud Computing ◽  
Health Monitoring ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Programming Models ◽  
Privacy Concerns ◽  
Medical Software ◽  
Speed Up ◽  
Data Elements

Extending cloud computing to medical software, where the hospitals rent the software from the provider sounds like a natural evolution for cloud computing. One problem with cloud computing, though, is ensuring the medical data privacy in applications such as long term health monitoring. Previously proposed solutions based on Fully Homomorphic Encryption (FHE) completely eliminate privacy concerns, but are extremely slow to be practical. Our key proposition in this paper is a new approach to applying FHE into the data that is stored in the cloud. Instead of using the existing circuit-based programming models, we propose a solution based on Branching Programs. While this restricts the type of data elements that FHE can be applied to, it achieves dramatic speed-up as compared to traditional circuit-based methods. Our claims are proven with simulations applied to real ECG data.

Unobtrusive Wearable Technology for Health Monitoring

2015 ◽  
pp. 382-395
Author(s):  
James Amor ◽  
Christopher James
Keyword(s):  
Health Monitoring ◽  
Wearable Technology ◽  
Wearable Devices ◽  
Health And Wellness ◽  
Minimal Impact ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Common Device ◽  
Do So

There are a number of situations in the context of health and wellness where it is desirable to monitor a user for a period of time – either for short term assessment or longer term monitoring. It is further desirable, especially for long term monitoring, that the device chosen to do so has a minimal impact on the user. This form of monitoring is unobtrusive monitoring and uses wearable technology to achieve its aims. This chapter presents an overview of unobtrusive monitoring using wearable devices, discusses some common device types and the data that are available and makes some recommendations for factors to consider when choosing or designing a device for unobtrusive monitoring.

Secure Health Monitoring in the Cloud Using Homomorphic Encryption

2015 ◽  
pp. 116-152 ◽  
Author(s):  
Scott Ames ◽  
Muthuramakrishnan Venkitasubramaniam ◽  
Alex Page ◽  
Ovunc Kocabas ◽  
Tolga Soyata
Keyword(s):  
Cloud Computing ◽  
Health Monitoring ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Programming Models ◽  
Privacy Concerns ◽  
Medical Software ◽  
Speed Up ◽  
Data Elements

Extending cloud computing to medical software, where the hospitals rent the software from the provider sounds like a natural evolution for cloud computing. One problem with cloud computing, though, is ensuring the medical data privacy in applications such as long term health monitoring. Previously proposed solutions based on Fully Homomorphic Encryption (FHE) completely eliminate privacy concerns, but are extremely slow to be practical. Our key proposition in this paper is a new approach to applying FHE into the data that is stored in the cloud. Instead of using the existing circuit-based programming models, we propose a solution based on Branching Programs. While this restricts the type of data elements that FHE can be applied to, it achieves dramatic speed-up as compared to traditional circuit-based methods. Our claims are proven with simulations applied to real ECG data.

Unobtrusive Wearable Technology for Health Monitoring

2018 ◽  
pp. 562-577
Author(s):  
James Amor ◽  
Christopher James
Keyword(s):  
Health Monitoring ◽  
Wearable Technology ◽  
Wearable Devices ◽  
Health And Wellness ◽  
Minimal Impact ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Common Device ◽  
Do So

There are a number of situations in the context of health and wellness where it is desirable to monitor a user for a period of time – either for short term assessment or longer term monitoring. It is further desirable, especially for long term monitoring, that the device chosen to do so has a minimal impact on the user. This form of monitoring is unobtrusive monitoring and uses wearable technology to achieve its aims. This chapter presents an overview of unobtrusive monitoring using wearable devices, discusses some common device types and the data that are available and makes some recommendations for factors to consider when choosing or designing a device for unobtrusive monitoring.

scholarly journals A Clustering K-Anonymity Privacy-Preserving Method for Wearable IoT Devices

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Fang Liu ◽  
Tong Li
Keyword(s):  
Internet Of Things ◽  
Wearable Devices ◽  
Third Party ◽  
The Internet ◽  
Specific Data ◽  
Privacy Concerns ◽  
The Third ◽  
Massive Scale ◽  
Iot Devices ◽  
The Internet Of Things

Wearable technology is one of the greatest applications of the Internet of Things. The popularity of wearable devices has led to a massive scale of personal (user-specific) data. Generally, data holders (manufacturers) of wearable devices are willing to share these data with others to get benefits. However, significant privacy concerns would arise when sharing the data with the third party in an improper manner. In this paper, we first propose a specific threat model about the data sharing process of wearable devices’ data. Then we propose a K-anonymity method based on clustering to preserve privacy of wearable IoT devices’ data and guarantee the usability of the collected data. Experiment results demonstrate the effectiveness of the proposed method.

PROGRAM BIMBINGAN UNTUK MENINGKATKAN MOTIVASI BELAJAR SISWA

2017 ◽  
Vol 17 (1) ◽  
pp. 55
Author(s):  
Ismaniar Ismaniar Ismaniar
Keyword(s):  
Experimental Design ◽  
Quantitative Research ◽  
Sampling Technique ◽  
Learning Motivation ◽  
Control Group ◽  
Research Approach ◽  
Purposive Sampling ◽  
Guidance Program ◽  
Quasi Experimental ◽  
11Th Grade

The present study is aimed at developing effective guidance program for increasing student’s learning motivation. The present study applies quantitative research approach with nonequivalent pre-posttest control group quasi-experimental design, and nonrandom-purposive sampling technique. The data were collected using inventory, interview, and documentary study. The study comes up with the main finding that the tested guidance program is proven to be effective for increasing learning motivation students of 11th grade at SMA Kartika XIX-2 Bandung.

The Impact of corporate social responsibility (CSR) and relationship marketing on relationship maintainer and customer loyalty by mediating role of customer satisfaction

2018 ◽  
Vol 4 (4) ◽  
pp. 19-24
Author(s):  
Anam Bhatti ◽  
Sumbal Arif ◽  
Marium Marium ◽  
Sohail Younas
Keyword(s):  
Customer Satisfaction ◽  
Relationship Marketing ◽  
Customer Loyalty ◽  
Sampling Technique ◽  
Research Strategy ◽  
Equation Modeling ◽  
Survey Method ◽  
Research Approach ◽  
Questionnaire Method ◽  
The Impact

CSR has become one of the imperative implements in satisfying customers. The impartial of this research is to calculate CSR, relationship marketing, and customer satisfaction. There is no more study accompanied in Pakistan to quantify the effect of CSR and relationship marketing on the relationship maintainer and customer loyalty. To find out deductive approach and survey method is used as research approach and research strategy respectively. This research design is descriptive and quantitative study. For data, collection questionnaire method with semantic differential scale and seven point scales are adopted. Data has been collected by adopting the non-probability convenience technique as sampling technique and the sample size is 400. For factor confirmatory factor analysis, structure equation modeling and medication analysis, regression analysis Amos software were used. Strong empirical evidence supports that the customer’s perception of CSR performance is highly influenced by the values

The utility of non-formal education in addressing the school dropout phenomenon : a case study of Asifundeni Secondary School in Makonde District Zimbabwe

2020 ◽  
Vol 1 (3) ◽  
pp. 41-69
Author(s):  
Francis Muchenje ◽  
◽  
Pedzisai Goronga
Keyword(s):  
Secondary School ◽  
Formal Education ◽  
Sampling Technique ◽  
School Dropout ◽  
School Level ◽  
Research Approach ◽  
Community Members ◽  
Group Discussions ◽  
Secondary School Level

The study sought to explore students' views on the utility of non-formal education in addressing the school dropout phenomenon at secondary school level. Qualitative research approach was adopted and a case study design was utilised. The population consisted of all the students in the non-formal programme at the school from which a sample of 11 students (2 male and 9 female) was selected through purposive stratified sampling technique. Data were gathered through structured in-depth interviews and focus group discussions. Non-formal education was seen to address the school dropout phenomenon by providing school drop outs with an opportunity to continue their education and hence becomes a form of empowerment. A number of challenges such as lack of adequate tuition in some subjects, lack of conducive learning environment as well as negative perception of non-formal education held by pupils in the formal stream and community members were identified. The study recommends that the Ministry of Primary and Secondary Education should review the staffing situation in schools to ensure the availability of teachers in the various subjects in the non-formal stream. Schools should make an effort to provide appropriate learning facilities for students in the nonformal stream. Furthermore, schools should conscientise their communities on the importance of non-formal education.

Assessment and Prediction of Privacy Concerns on Health Apps (Preprint)

2020 ◽  
Author(s):  
Reham AlTamime ◽  
Vincent Marmion ◽  
Wendy Hall
Keyword(s):  
Data Sharing ◽  
Mobile Applications ◽  
Retention Rate ◽  
Mobile Apps ◽  
Third Party ◽  
Privacy Concerns ◽  
Health Apps ◽  
Mobile Health Apps ◽  
Specific Factors ◽  
And Control

BACKGROUND Mobile apps and IoT-enabled smartphones technologies facilitate collecting, sharing, and inferring from a vast amount of data about individuals’ location, health conditions, mobility status, and other factors. The use of such technology highlights the importance of understanding individuals’ privacy concerns to design applications that integrate their privacy expectations and requirements. OBJECTIVE This paper explores, assesses, and predicts individuals’ privacy concerns in relation to collecting and disclosing data on mobile health apps. METHODS We designed a questionnaire to identify participants’ privacy concerns pertaining to a set of 432 mobile apps’ data collection and sharing scenarios. Participants were presented with 27 scenarios that varied across three categorical factors: (1) type of data collected (e.g. health, demographic, behavioral, and location); (2) data sharing (e.g., whether it is shared, and for what purpose); and, (3) retention rate (e.g., forever, until the purpose is satisfied, unspecified, week, or year). RESULTS Our findings show that type of data, data sharing, and retention rate are all factors that affect individuals’ privacy concerns. However, specific factors such as collecting and disclosing health data to a third-party tracker play a larger role than other factors in triggering privacy concerns. CONCLUSIONS Our findings suggest that it is possible to predict privacy concerns based on these three factors. We propose design approaches that can improve users’ awareness and control of their data on mobile applications

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