A Rule Mining and Bayesian Network Analysis to Explore the Link Between Depression and Digital Behavioral Markers of Games App Usage

Ahmed, Sabbir; Hasan, Tanvir; Rahman, M.; Ahmed, Nova

doi:10.31234/osf.io/d93zu

Cited by 3 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our previous pilot studies in Bangladesh on the relation of app usage with depression [ 35 , 73 ] and loneliness [ 75 ], classifying depressed and nondepressed students [ 33 ] and with and without loneliness [ 74 ], showed promising models solely based on resource-insensitive [ 33 ] app usage behavioral markers. Incorporating app usage rhythmic features and also the MTL framework by leveraging the similarities among the symptoms’ prediction tasks so that tasks do not hurt one another’s performance may help researchers and developers in developing more robust models to predict the symptoms of psychological problems solely through app usage data.…”

Section: Discussionmentioning

confidence: 99%

“…For each app usage event, there are data on the app name, package name, and timestamp of the event, which we will use to extract behavioral markers. The app ( Figure 1 ) was used in our previous studies to explore different research problems, including students’ academic results [ 70 - 72 ], depression [ 33 - 35 , 73 ], and loneliness [ 74 , 75 ], showing the app’s reliability and validity.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

Ahmed,

Hasan,

Islam

et al. 2024

JMIR Res Protoc

Self Cite

View full text Add to dashboard Cite

Background Understanding a student’s depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data. Objective The main objective of this study is to explore whether there exist statistically significant rhythms in resource-insensitive app usage behavioral markers and predict depressive symptoms through these marker-based rhythmic features. Another objective of this study is to understand whether there is a potential link between rhythmic features and depressive symptoms. Methods Through a countrywide study, we collected 2952 students’ raw app usage behavioral data and responses to the 9 depressive symptoms in the 9-item Patient Health Questionnaire (PHQ-9). The behavioral data were retrieved through our developed app, which was previously used in our pilot studies in Bangladesh on different research problems. To explore whether there is a rhythm based on app usage data, we will conduct a zero-amplitude test. In addition, we will develop a cosinor model for each participant to extract rhythmic parameters (eg, acrophase). In addition, to obtain a comprehensive picture of the rhythms, we will explore nonparametric rhythmic features (eg, interdaily stability). Furthermore, we will conduct regression analysis to understand the association of rhythmic features with depressive symptoms. Finally, we will develop a personalized multitask learning (MTL) framework to predict symptoms through rhythmic features. Results After applying inclusion criteria (eg, having app usage data of at least 2 days to explore rhythmicity), we kept the data of 2902 (98.31%) students for analysis, with 24.48 million app usage events, and 7 days’ app usage of 2849 (98.17%) students. The students are from all 8 divisions of Bangladesh, both public and private universities (19 different universities and 52 different departments). We are analyzing the data and will publish the findings in a peer-reviewed publication. Conclusions Having an in-depth understanding of app usage rhythms and their connection with depressive symptoms through a countrywide study can significantly help health care professionals and researchers better understand depressed students and may create possibilities for using app usage–based rhythms for intervention. In addition, the MTL framework based on app usage rhythmic features may more accurately predict depressive symptoms due to the rhythms’ capability to find subtle differences. International Registered Report Identifier (IRRID) DERR1-10.2196/51540

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

Ahmed,

Hasan,

Islam

et al. 2024

JMIR Res Protoc

Self Cite

View full text Add to dashboard Cite

show abstract

“…For each app usage event, there are data on the app name, package name, and timestamp of the event, which we will use to extract behavioral markers. The app (Figure 1) was used in our previous studies to explore different research problems, including students' academic results [70][71][72], depression [33][34][35]73], and loneliness [74,75], showing the app's reliability and validity.…”

Section: Retrieval Of App Usage Behavioral Markersmentioning

confidence: 99%

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study (Preprint)

Ahmed,

Hasan,

Islam

et al. 2023

Preprint

View full text Add to dashboard Cite

BACKGROUND Understanding a student’s depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data. OBJECTIVE The main objective of this study is to explore whether there exist statistically significant rhythms in resource-insensitive app usage behavioral markers and predict depressive symptoms through these marker-based rhythmic features. Another objective of this study is to understand whether there is a potential link between rhythmic features and depressive symptoms. METHODS Through a countrywide study, we collected 2952 students’ raw app usage behavioral data and responses to the 9 depressive symptoms in the 9-item Patient Health Questionnaire (PHQ-9). The behavioral data were retrieved through our developed app, which was previously used in our pilot studies in Bangladesh on different research problems. To explore whether there is a rhythm based on app usage data, we will conduct a zero-amplitude test. In addition, we will develop a cosinor model for each participant to extract rhythmic parameters (eg, acrophase). In addition, to obtain a comprehensive picture of the rhythms, we will explore nonparametric rhythmic features (eg, interdaily stability). Furthermore, we will conduct regression analysis to understand the association of rhythmic features with depressive symptoms. Finally, we will develop a personalized multitask learning (MTL) framework to predict symptoms through rhythmic features. RESULTS After applying inclusion criteria (eg, having app usage data of at least 2 days to explore rhythmicity), we kept the data of 2902 (98.31%) students for analysis, with 24.48 million app usage events, and 7 days’ app usage of 2849 (98.17%) students. The students are from all 8 divisions of Bangladesh, both public and private universities (19 different universities and 52 different departments). We are analyzing the data and will publish the findings in a peer-reviewed publication. CONCLUSIONS Having an in-depth understanding of app usage rhythms and their connection with depressive symptoms through a countrywide study can significantly help health care professionals and researchers better understand depressed students and may create possibilities for using app usage–based rhythms for intervention. In addition, the MTL framework based on app usage rhythmic features may more accurately predict depressive symptoms due to the rhythms’ capability to find subtle differences. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/51540

show abstract

Is There Any Relation Between Smartphone Usage and Loneliness During the COVID-19 Pandemic?: A Study by Exploring Two Objective App Usage Datasets

Ahmed

Khan

Ahmed

2023

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

BACKGROUND: Though smartphone is popular and loneliness is higher among the youth, in low-and-middle income countries (LMICs) such as Bangladesh, the relation of loneliness with actual app usage is unexplored amid pandemic. Also, the studies conducted in developed countries are limited by exploration of some app categories. METHODS: We conducted two studies in Bangladesh: in 2020 (N1=100) and 2021 (N2=105). We collected participant’s ULS-8 score and 7 days’ actual app usage. We extracted app usage behavioral data from 1.69 million events and did semi-partial and partial correlation analyses. RESULTS: Our analysis did not present any significant relation which may indicate a negative impact on loneliness. However, we found higher usage of Social Media, Communication, Education, Books, and Shopping apps and higher entropy of Browser apps had significant (q<.05) relation with lower loneliness. CONCLUSION: Smartphone may not negatively impact loneliness. Instead, some app categories can play a role to mitigate loneliness.

show abstract

A Rule Mining and Bayesian Network Analysis to Explore the Link Between Depression and Digital Behavioral Markers of Games App Usage

Cited by 3 publications

References 9 publications

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study (Preprint)

Is There Any Relation Between Smartphone Usage and Loneliness During the COVID-19 Pandemic?: A Study by Exploring Two Objective App Usage Datasets

Contact Info

Product

Resources

About