Currently submitted to: JMIR Biomedical Engineering
Date Submitted: Jul 10, 2019
Open Peer Review Period: Jul 15, 2019 - Sep 9, 2019
(currently open for review)
Validation of a cloud based ecosystem for behavioral and physical monitoring in free-living context
Smartphone usage is widely spreading in the society. Functions and sensors embedded in these devices may play an importnat role in healthcare monitoring and intervention planning. However, the usage of smartphone as a relevant tool for intrapersonal continuous behavioral and physical monotoring is not yet fully supported by adequate studies addressing technical reliability and acceptance.
The objective of this paper is to identify and discuss technical issues that may impact on the wide usage of smartphones as clinical monitoring tools. Main focus is on the quality of the acquired data and the transparency of the data acquisition process to the user daily life.
QuantifyMyPerson (QMP) is a complete architecture for continuous acquisition of smartphone usage features and embedded sensors data. The platform consists of a mobile app running on the smartphone for data acquisition, a backend cloud server for data storing and processing and a web-based dashboard for users' management and data visualization. The data processing is aimed at the extraction of meaningful features for the description of the daily life accoridng ot the existing literature. These features include phone switching on and off, calls, apps usage, GPS and accelerometer data. 12 health subjects installed on their personal smartphones and run the mobile app for and overall period of 7 months. The acquired data were analysed to assess their quality in terms of impact on the daily life of the smartphone (i.e. battery consumption and anomalies in functioning) as well as data integrity. Data integrity was computed as the ratio of app running time and total smartphone working time. Relevance of the selected features in describing changes in daily life was assessed through the computation of a k-NN Global Anomaly Score aimed at detecting days that "differ" from the others with the aim of using the platform to trigger potential modification in daily lifestyle.
The effectiveness of smartphone-based continuous monitoring depends on the acceptability and interoperability of the system to increase users' retention and on integrity of the acquired data. Acceptability was confirmed by the full transparency of the app once installed and the absence of any anomaly in smartphone usage when the mobile app is running. The only negative issue pointed out in acceptability was the battery consumption. Even if the trend of battery drain with and without mobile app running was comparable, the users complain about this. Referring interoperability, the app was successfully installed and run on several brands and models of Android-based smartphones. It has to be pointed out that some smartphone manufacturers implement power saving policies that do not allow a continuous acquisition of data from embedded sensors so impacting on data integrity. Data integrity was 96% on smartphones without power saving policies and 84% overall.
The results of the testing pointed out two main issues that may impact on spreading of continuous behavioural and physical monitoring in free living conditions. On one side there is the battery consumption. However the issue, mainly due to GPS triangulation is limited. It is expected that users motivated to have the app running on their smartphone because of health or wellness related aims may better accept the increasing in battery consumption. On the other side there are the power saving policies of some manufacturing impacting on the continuity of data acquisition. Also this aspect revealed to be not highly relevant in daily life. Because the embedded sensors are re-activated by any event happening on the smartphone, the missing data demonstrated to be related to periods of non usage of the phone in which the activity data would have been anyway null. The key need to have continuous monitoring running on any smartphone to assure no impact in daily habits widely justifies the partial data loss. According to the results of this feasibility test, wide spreading of continuous monitoring on the end user side seem to be fully feasible. Quality of data processing, depending on specific aims of the investigation, as well as usage of the data from the clinical operators is not part of this paper and require further investigation.
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