JMIR Publications

ABSTRACT

Background:

The experience sampling methodology (ESM) has long been considered the gold standard for gathering data in everyday life. On the other hand, new smartphone advancements enable us to acquire data that is far richer, more continuous, and unobtrusive than is possible via ESM. While data from smartphones, known as mobile sensing, can provide useful information, its standalone usefulness is limited when not combined with other sources of information such as ESM.

Objective:

There are currently few mobile apps available that allow researchers to combine ESM and mobile sensing. Furthermore, the majority of these mobile sensing apps are primarily concerned with passive data collection and pay little attention to supplementing the collected data with ESM. In this paper, we present a novel, full-fledged ESM platform with background mobile sensing capabilities.

Methods:

To create an app with both ESM and mobile sensing capabilities, we combined m-Path [26], a versatile and user-friendly platform for ESM, with the CARP Mobile Sensing (CAMS) framework, a reactive cross-platform framework for digital phenotyping [1]. We also developed an R package named mpathsenser [2], which extracts the raw data to an SQLite database and allows the user to link two tables together within a specific time window, among other things. We conducted a three-week pilot study in which we delivered ESM questionnaires while collecting mobile sensing data to evaluate the app’s sampling reliability and perceived user experience. Because m-Path is already widely used, the ease-of-use of the ESM system was not investigated.

Results:

Data from m-Path Sense was submitted by 104 participants, totalling 69.51 GB (430.43 GB after decompression), or around 37.50 files or 31.10 MB per participant per day. After binning accelerometer and gyroscope data to one value per second using summary statistics, the entire SQLite database contained 84,299,462 observations and was 18.30 GB in size. The reliability of the sampling frequency in the pilot study was found to be satisfactory for most sensors based on the absolute number of collected observations. However, the relative coverage rate – the ratio between the actual and expected number of measurements– was well below its target value, meaning fewer measurements were collected than initially anticipated. This could in part be ascribed to gaps in the data caused by the operating system pushing away background running apps, a well-known issue in the mobile sensing community [3,4]. Finally, some participants reported mild battery drain, which was not considered problematic for the assessed participants' perceived user experience.

Conclusions:

To better study behaviour in everyday life, we developed m-Path Sense, a fusion of both m-Path for ESM and CAMS for mobile sensing. Although reliable data collection with mobile phones remains challenging, it is a promising approach towards digital phenotyping when combined with ESM.