A realisation of ethical concerns with smartphone personal health monitoring apps

The pervasiveness of smartphones has facilitated a new way in which owners of devices can monitor their health using applications (apps) that are installed on their smartphones. Smartphone personal health monitoring (SPHM) collects and stores health related data of the user either locally or in a third party storing mechanism. They are also capable of giving feedback to the user of the app in response to conditions are provided to the app therefore empowering the user to actively make decisions to adjust their lifestyle. Regardless of the benefits that this new innovative technology offers to its users, there are some ethical concerns to the user of SPHM apps. These ethical concerns are in some way connected to the features of SPHM apps. From a literature survey, this paper attempts to recognize ethical issues with personal health monitoring apps on smartphones, viewed in light of general ethics of ubiquitous computing. The paper argues that there are ethical concerns with the use of SPHM apps regardless of the benefits that the technology offers to users due to SPHM apps’ ubiquity leaving them open to known and emerging ethical concerns. The paper then propose a need further empirical research to validate the claim

Heart rate monitoring, activity recognition, and recommendation for e-coaching

Equipped with hardware, such as accelerometer and heart rate sensor, wearables enable measuring physical activities and heart rate. However, the accuracy of these heart rate measurements is still unclear and the coupling with activity recognition is often missing in health apps. This study evaluates heart rate monitoring with four different device types: a specialized sports device with chest strap, a fitness tracker, a smart watch, and a smartphone using photoplethysmography. In a state of rest, similar measurement results are obtained with the four devices. During physical activities, the fitness tracker, smart watch, and smartphone measure sudden variations in heart rate with a delay, due to movements of the wrist. Moreover, this study showed that physical activities, such as squats and dumbbell curl, can be recognized with fitness trackers. By combining heart rate monitoring and activity recognition, personal suggestions for physical activities are generated using a tag-based recommender and rule-based filter

Prototyping of a Remote Monitoring System for a medical Personal Area Network using Python

This paper presents a prototype developed in Python of a pervasive mobile health system aimed at monitoring a patient in indoor and outdoor environments continuously. The system is based on a Bluetooth PAN (Personal Area Network), worn by the patient, whose master node, a smartphone, collects information about patient's location and health status and detects emergency situations. These data are sent to a central server through Wi-Fi or GPRSIUMTS, which allows physicians to get access to patient data and configure the PAN sensors remotely using a conventional web browser.Ministerio de Educación y Ciencia TEC2006-12211- C02-01/TC

A low-cost smartphone-based platform for highly sensitive point-of-care testing with persistent luminescent phosphors

Through their computational power and connectivity, smartphones are poised to rapidly expand telemedicine and transform healthcare by enabling better personal health monitoring and rapid diagnostics. Recently, a variety of platforms have been developed to enable smartphone-based point-of-care testing using imaging-based readout with the smartphone camera as the detector. Fluorescent reporters have been shown to improve the sensitivity of assays over colorimetric labels, but fluorescence readout necessitates incorporating optical hardware into the detection system, adding to the cost and complexity of the device. Here we present a simple, low-cost smartphone-based detection platform for highly sensitive luminescence imaging readout of point-of-care tests run with persistent luminescent phosphors as reporters. The extremely bright and long-lived emission of persistent phosphors allows sensitive analyte detection with a smartphone by a facile time-gated imaging strategy. Phosphors are first briefly excited with the phone's camera flash, followed by switching off the flash, and subsequent imaging of phosphor luminescence with the camera. Using this approach, we demonstrate detection of human chorionic gonadotropin using a lateral flow assay and the smartphone platform with strontium aluminate nanoparticles as reporters, giving a detection limit of ?45 pg mL?1 (1.2 pM) in buffer. Time-gated imaging on a smartphone can be readily adapted for sensitive and potentially quantitative testing using other point-of-care formats, and is workable with a variety of persistent luminescent materials

Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone

The number of individuals with mental disorders is increasing and they are commonly found among individuals who avoid social interaction and like to live alone. Amongst such mental health disorders is depression which is both common and serious. The present paper introduces a method to assess the depression level of an individual using a smartphone by monitoring their daily activities. The time domain characteristics from a smartphone acceleration sensor were used alongside a vector machine algorithm to classify physical activities. Additionally, the geographical location information was clustered using a smartphone GPS sensor to simplify movement patterns. A total of 12 features were extracted from individuals’ physical activity and movement patterns and were analyzed alongside their weekly depression scores using the nine-item Patient Health Questionnaire. Using a wrapper feature selection method, a subset of features was selected and applied to a linear regression model to estimate the depression score. The support vector machine algorithm was then used to classify the depression severity level among individuals (absence, moderate, severe) and had an accuracy of 87.2% in severe depression cases which outperformed other classification models including the k-nearest neighbor and artificial neural network. This method of identifying depression is a cost-effective solution for long-term use and can monitor individuals for depression without invading their personal space or creating other day-to-day disturbances

A systematic and integrated review of mobile-based technology to promote active lifestyles in people with Type 2 diabetes

Background: An active lifestyle is important for good Type 2 diabetes management. Mobile-based technology is increasingly being used to promote active lifestyles. Aim: To review studies examining the effectiveness, acceptability and feasibility of mobile-based technology for promoting active lifestyles in people with Type 2 diabetes. Methods: An integrated, systematic review method was used to allow inclusion of a variety of study designs. A comprehensive search of electronic databases including; PubMed, Medline, ScienceDirect and ACM Digital Library was conducted to retrieve studies promoting active lifestyles in people with Type 2 diabetes using mobile-based technology (apps and wearable technology). Studies had to assess; effectiveness, acceptability or feasibility of mobile based technology. Studies were categorised as 1)informing, 2)monitoring, 3)provoking or 4)sustaining behaviour. Results: A total of 9 papers from the 7662 sourced met inclusion criteria; 5 studies used smartphone or tablet apps, 1 a diabetes personal digital assistant, 1 a combination of continuous glucose monitor and accelerometer, 1 a pedometer and 1 a website delivered by a smartphone. The effectiveness of technology was assessed in 6 studies, feasibility examined in 3 studies and acceptability in 4 studies. Most (n=5) of the studies examined the effectiveness of using mobile-based technology to provoke lifestyle. The effectiveness of mobile-based technology in monitoring active lifestyles and the feasibility and acceptability of using mobile-based technology to sustained lifestyle change has not been investigated. Conclusions: To maintain health benefit from active lifestyles future research should explore the feasibility and acceptability of mobile based technology monitoring in sustaining active lifestyles