Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study Article Swipe
Olivia Finnegan
,
R. Glenn Weaver
,
Hongpeng Yang
,
J. W. White
,
Srihari Nelakuditi
,
Zifei Zhong
,
Rahul Ghosal
,
Yan Tong
,
Aliye B. Cepni
,
Elizabeth L. Adams
,
Sarah Burkart
,
Michael W. Beets
,
Bridget Armstrong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1155/2024/5860114
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1155/2024/5860114
Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using score, accuracy, precision, and recall. Model performance was high, with scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2024/5860114
- https://downloads.hindawi.com/journals/hbet/2024/5860114.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399122507
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399122507Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2024/5860114Digital Object Identifier
- Title
-
Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-28Full publication date if available
- Authors
-
Olivia Finnegan, R. Glenn Weaver, Hongpeng Yang, J. W. White, Srihari Nelakuditi, Zifei Zhong, Rahul Ghosal, Yan Tong, Aliye B. Cepni, Elizabeth L. Adams, Sarah Burkart, Michael W. Beets, Bridget ArmstrongList of authors in order
- Landing page
-
https://doi.org/10.1155/2024/5860114Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/hbet/2024/5860114.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/hbet/2024/5860114.pdfDirect OA link when available
- Concepts
-
Mobile device, Computer science, Random forest, Software portability, Machine learning, Artificial intelligence, Accelerometer, Support vector machine, Logistic regression, Sample (material), Programming language, Operating system, Chemistry, ChromatographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.semistructured | 143 |
| abstract_inverted_index.authentication, | 75 |
| abstract_inverted_index.classification. | 203 |
| abstract_inverted_index.opportunistically | 127 |
| abstract_inverted_index.xmlns:mml="http://www.w3.org/1998/Math/MathML" | 220, 233, 252 |
| abstract_inverted_index.id="M1"><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:math> | 221 |
| abstract_inverted_index.id="M2"><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:math> | 234 |
| abstract_inverted_index.id="M3"><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:math> | 253 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5052089004 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I155781252 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.86867573 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |