Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study
BackgroundThe experience sampling methodology (ESM) has long been considered as the gold standard for gathering data in everyday life. In contrast, current smartphone technology enables us to acquire data that are much richer, more continuous, and unobtrusive than is possible...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-03-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2023/1/e43296 |
| _version_ | 1797734394889240576 |
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| author | Koen Niemeijer Merijn Mestdagh Stijn Verdonck Kristof Meers Peter Kuppens |
| author_facet | Koen Niemeijer Merijn Mestdagh Stijn Verdonck Kristof Meers Peter Kuppens |
| author_sort | Koen Niemeijer |
| collection | DOAJ |
| description |
BackgroundThe experience sampling methodology (ESM) has long been considered as the gold standard for gathering data in everyday life. In contrast, current smartphone technology enables us to acquire data that are much richer, more continuous, and unobtrusive than is possible via ESM. Although data obtained from smartphones, known as mobile sensing, can provide useful information, its stand-alone usefulness is limited when not combined with other sources of information such as data from ESM studies. Currently, there are few mobile apps available that allow researchers to combine the simultaneous collection of ESM and mobile sensing data. Furthermore, such apps focus mostly on passive data collection with only limited functionality for ESM data collection.
ObjectiveIn this paper, we presented and evaluated the performance of m-Path Sense, a novel, full-fledged, and secure ESM platform with background mobile sensing capabilities.
MethodsTo create an app with both ESM and mobile sensing capabilities, we combined m-Path, a versatile and user-friendly platform for ESM, with the Copenhagen Research Platform Mobile Sensing framework, a reactive cross-platform framework for digital phenotyping. We also developed an R package, named mpathsenser, which extracts raw data to an SQLite database and allows the user to link and inspect data from both sources. We conducted a 3-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. As m-Path is already widely used, the ease of use of the ESM system was not investigated.
ResultsData from m-Path Sense were submitted by 104 participants, totaling 69.51 GB (430.43 GB after decompression) or approximately 37.50 files or 31.10 MB per participant per day. After binning accelerometer and gyroscope data to 1 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 sampling frequency in the pilot study was 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 below its target value. This could mostly be ascribed to gaps in the data caused by the operating system pushing away apps running in the background, which is a well-known issue in mobile sensing. Finally, some participants reported mild battery drain, which was not considered problematic for the assessed participants’ perceived user experience.
ConclusionsTo better study behavior in everyday life, we developed m-Path Sense, a fusion of both m-Path for ESM and Copenhagen Research Platform Mobile Sensing. Although reliable passive data collection with mobile phones remains challenging, it is a promising approach toward digital phenotyping when combined with ESM. |
| first_indexed | 2024-03-12T12:43:41Z |
| format | Article |
| id | doaj.art-b00a4150951640e5a443015f87d833e8 |
| institution | Directory Open Access Journal |
| issn | 2561-326X |
| language | English |
| last_indexed | 2024-03-12T12:43:41Z |
| publishDate | 2023-03-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj.art-b00a4150951640e5a443015f87d833e82023-08-28T23:43:09ZengJMIR PublicationsJMIR Formative Research2561-326X2023-03-017e4329610.2196/43296Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance StudyKoen Niemeijerhttps://orcid.org/0000-0002-0816-534XMerijn Mestdaghhttps://orcid.org/0000-0001-5077-861XStijn Verdonckhttps://orcid.org/0000-0002-2199-1072Kristof Meershttps://orcid.org/0000-0002-9610-7712Peter Kuppenshttps://orcid.org/0000-0002-2363-2356 BackgroundThe experience sampling methodology (ESM) has long been considered as the gold standard for gathering data in everyday life. In contrast, current smartphone technology enables us to acquire data that are much richer, more continuous, and unobtrusive than is possible via ESM. Although data obtained from smartphones, known as mobile sensing, can provide useful information, its stand-alone usefulness is limited when not combined with other sources of information such as data from ESM studies. Currently, there are few mobile apps available that allow researchers to combine the simultaneous collection of ESM and mobile sensing data. Furthermore, such apps focus mostly on passive data collection with only limited functionality for ESM data collection. ObjectiveIn this paper, we presented and evaluated the performance of m-Path Sense, a novel, full-fledged, and secure ESM platform with background mobile sensing capabilities. MethodsTo create an app with both ESM and mobile sensing capabilities, we combined m-Path, a versatile and user-friendly platform for ESM, with the Copenhagen Research Platform Mobile Sensing framework, a reactive cross-platform framework for digital phenotyping. We also developed an R package, named mpathsenser, which extracts raw data to an SQLite database and allows the user to link and inspect data from both sources. We conducted a 3-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. As m-Path is already widely used, the ease of use of the ESM system was not investigated. ResultsData from m-Path Sense were submitted by 104 participants, totaling 69.51 GB (430.43 GB after decompression) or approximately 37.50 files or 31.10 MB per participant per day. After binning accelerometer and gyroscope data to 1 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 sampling frequency in the pilot study was 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 below its target value. This could mostly be ascribed to gaps in the data caused by the operating system pushing away apps running in the background, which is a well-known issue in mobile sensing. Finally, some participants reported mild battery drain, which was not considered problematic for the assessed participants’ perceived user experience. ConclusionsTo better study behavior in everyday life, we developed m-Path Sense, a fusion of both m-Path for ESM and Copenhagen Research Platform Mobile Sensing. Although reliable passive data collection with mobile phones remains challenging, it is a promising approach toward digital phenotyping when combined with ESM.https://formative.jmir.org/2023/1/e43296 |
| spellingShingle | Koen Niemeijer Merijn Mestdagh Stijn Verdonck Kristof Meers Peter Kuppens Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study JMIR Formative Research |
| title | Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study |
| title_full | Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study |
| title_fullStr | Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study |
| title_full_unstemmed | Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study |
| title_short | Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study |
| title_sort | combining experience sampling and mobile sensing for digital phenotyping with m path sense performance study |
| url | https://formative.jmir.org/2023/1/e43296 |
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