scholarly journals Forensic Analysis of Fitbit Versa 2 Data on Android

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1431
Author(s):  
Yung Han Yoon ◽  
Umit Karabiyik
Keyword(s):  
Law Enforcement ◽  
Credit Card ◽  
Smartphone Application ◽  
Forensic Analysis ◽  
New Device ◽  
Related Data ◽  
Mobile Forensics ◽  
Health Related ◽  
Media Message ◽  
Fitness Trackers

The increase in the adoption of wearable fitness trackers has led to their inclusion as valuable evidence used by law enforcement during investigations. The information available in these fitness trackers can be used by law enforcement to prosecute or exonerate an individual. Wearable fitness devices are constantly being released by companies, with new firmware created for each iteration. As technology developers, research and law enforcement must keep pace to take advantage of data that can be used in investigations. The Fitbit line of devices is a popular brand of wearable trackers. This study will investigate what artifacts are generated by the new Fitbit Versa 2 by investigating what data are generated and stored on the smartphone app component of the new device. The artifacts discovered will be related to areas of forensic interest that are relevant to a law enforcement officer or digital forensics practitioner. Previous research and their methodologies used for application and mobile forensics will be used to conduct this research. This study finds the Fitbit Versa 2, and by extension, the Fitbit smartphone application does not store social media message notifications pushed to the tracker by the user’s mobile device. Some credit card information, health-related data, such as heart rate, GPS locations, and other potentially identifying data were found in plaintext. While the exposed data is not enough on its own to pose an immediate serious issue, it can be used as leverage to phish a user for further details.

scholarly journals Would patients undergo postoperative follow-up by using a smartphone application?

BMC Surgery ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Julian Scherer ◽  
Frank Keller ◽  
Hans-Christoph Pape ◽  
Georg Osterhoff
Keyword(s):  
Mobile Application ◽  
Smartphone Application ◽  
Mobile App ◽  
Web Based ◽  
Direct Benefit ◽  
Orthopaedic Practice ◽  
Related Data ◽  
Healthcare Data ◽  
Health Related

Abstract Background eHealth applications have been proposed as an alternative to monitor patients in frequent intervals or over long distances. The aim of this study was to assess whether patients would accept an application on their smartphone to be monitored by their physicians. Methods During September 2017 and December 2017 a survey amongst smartphone users was conducted via paper and web-based questionnaires. Results More than half of the 962 participants (54%) were older than 55 years of age. The majority of the participants (68.7%) would accept a follow-up by a smartphone application obtaining personal healthcare data. 72.6% of all patients older than 55 years of age would use the application. The most prevalent reason against installing the application was data protection. Patients being currently treated in an orthopaedic practice and pedestrians were more eager to accept a follow-up by a mobile app than participants from social media. Conclusion The majority of participants would accept a mobile application, collecting personal health-related data for postoperative follow-up, and saw a direct benefit for the patient in such an application.

Novel Visualization of Large Health Related Data Sets

2015 ◽  
Author(s):  
William E. Hammond ◽  
Vivian L. West ◽  
David Borland ◽  
Igor Akushevich ◽  
Eugenia M. Heinz
Keyword(s):  
Data Sets ◽  
Related Data ◽  
Health Related

scholarly journals Assessment of Dietary Habits, Physical Activity and Lifestyle in Medical University Students

2021 ◽  
Vol 13 (6) ◽  
pp. 3572
Author(s):  
Lavinia-Maria Pop ◽  
Magdalena Iorga ◽  
Iulia-Diana Muraru ◽  
Florin-Dumitru Petrariu
Keyword(s):  
Physical Activity ◽  
Medical Students ◽  
Fast Food ◽  
Dietary Habits ◽  
Healthy Life ◽  
Related Data ◽  
The Third ◽  
Health Related ◽  
Medical University ◽  
Busy Schedule

A busy schedule and demanding tasks challenge medical students to adjust their lifestyle and dietary habits. The aim of this study was to identify dietary habits and health-related behaviours among students. A number of 403 students (80.40% female, aged M = 21.21 ± 4.56) enrolled in a medical university provided answers to a questionnaire constructed especially for this research, which was divided into three parts: the first part collected socio-demographic, anthropometric, and medical data; the second part inquired about dietary habits, lifestyle, sleep, physical activity, water intake, and use of alcohol and cigarettes; and the third part collected information about nutrition-related data and the consumption of fruit, vegetables, meat, eggs, fish, and sweets. Data were analysed using SPSS v24. Students usually slept M = 6.71 ± 1.52 h/day, and one-third had self-imposed diet restrictions to control their weight. For both genders, the most important meal was lunch, and one-third of students had breakfast each morning. On average, the students consumed 1.64 ± 0.88 l of water per day and had 220 min of physical activity per week. Data about the consumption of fruit, vegetables, meat, eggs, fish, sweets, fast food, coffee, tea, alcohol, or carbohydrate drinks were presented. The results of our study proved that medical students have knowledge about how to maintain a healthy life and they practice it, which is important for their subsequent professional life.

Understanding Data Collection Mechanisms Used by Health and Wellness Applications (Preprint)

2021 ◽  
Author(s):  
Ben Philip ◽  
Mohamed Abdelrazek ◽  
Alessio Bonti ◽  
Scott Barnett ◽  
John Grundy
Keyword(s):  
Data Collection ◽  
User Experience ◽  
Health Data ◽  
Domain Model ◽  
Health And Wellness ◽  
Related Services ◽  
Related Data ◽  
Medical Apps ◽  
App Development ◽  
Health Related

UNSTRUCTURED Our objective is to better understand health-related data collection across different mHealth app categories. This would help in developing a health domain model for mHealth apps to facilitate app development and data sharing between these apps to improve user experience and reduce redundancy in data collection. We identified app categories listed in a curated library which was then used to explore the Google Play Store for health/medical apps that were then filtered using our inclusion criteria. We downloaded and analysed these apps using a script we developed around the popular AndroGuard tool. We analysed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects/generates health data. We retrieved 3,251 applications meeting our criteria, and our analysis showed that only 10.7% of these apps requested permission for Bluetooth access. We found 50.9% of the Bluetooth Service UUIDs to be known in these apps, with the remainder being vendor specific. The most common health-related services using the known UUIDs were Heart Rate, Glucose and Body Composition. App permissions show the most used device module/sensor to be the camera (20.57%), closely followed by GPS (18.39%). Our findings are consistent with previous studies in that not many health apps were found to use built-in sensors or peripherals for collecting health data. The use of more peripherals and automated data collection along with integration with other apps could increase usability and convenience which would eventually also improve user experience and data reliability.

Secure Cross-Border Exchange of Health Related Data: The KONFIDO Approach

2019 ◽  
pp. 318-327
Author(s):  
Sotiris Diamantopoulos ◽  
Dimitris Karamitros ◽  
Luigi Romano ◽  
Luigi Coppolino ◽  
Vassilis Koutkias ◽  
...  
Keyword(s):  
Related Data ◽  
Cross Border ◽  
Health Related

scholarly journals Data safe havens to combine health and genomic data: benefits and challenges

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kerina H Jones ◽  
Arron S Lacey ◽  
Brian L Perkins ◽  
Mark I Rees
Keyword(s):  
Association Studies ◽  
Genomic Data ◽  
Population Level ◽  
Data Availability ◽  
Genome Wide Association Studies ◽  
Related Data ◽  
Research Areas ◽  
Individual Privacy ◽  
Access Controls ◽  
Health Related

ABSTRACTObjectivesData safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely. ApproachWe explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data. ResultsThe benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals. ConclusionGenomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.

scholarly journals All Our Data Will Be Health Data One Day: The Need for Universal Data Protection and Comprehensive Consent

10.2196/16879 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16879 ◽  
Author(s):  
Christophe Olivier Schneble ◽  
Bernice Simone Elger ◽  
David Martin Shaw
Keyword(s):  
Health Status ◽  
Data Protection ◽  
Health Data ◽  
Future Health ◽  
Related Data ◽  
Legal Implications ◽  
Health Related ◽  
Behavior Health

Tremendous growth in the types of data that are collected and their interlinkage are enabling more predictions of individuals’ behavior, health status, and diseases. Legislation in many countries treats health-related data as a special sensitive kind of data. Today’s massive linkage of data, however, could transform “nonhealth” data into sensitive health data. In this paper, we argue that the notion of health data should be broadened and should also take into account past and future health data and indirect, inferred, and invisible health data. We also lay out the ethical and legal implications of our model.

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.

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