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

Ahmed, Sabbir; Khan, Syeda Shabnam; Ahmed, Nova

doi:10.4108/eetpht.9.3663

Cited by 3 publications

(3 citation statements)

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“…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][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%

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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

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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

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Section: Discussionmentioning

confidence: 99%

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

Ahmed,

Hasan,

Islam

et al. 2024

JMIR Res Protoc

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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

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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

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Assessing the Psychosocial Determinants of Mental Health Decline Among Bangladeshi University Students During the COVID-19 Pandemic: A Rapid Systematic Review

Hasan

2024

AJPHN

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Background: The COVID-19 epidemic has profoundly impacted the mental health of university students. This review investigates the psychosocial factors contributing to the mental health decline of Bangladeshi university students, particularly in Dhaka city, with the objective of identifying the primary elements exacerbating their mental health issues during the pandemic. Method: Employing a mixed-methods approach, this review integrates quantitative and qualitative data, adhering to PRISMA guidelines. Data were extracted using MeSH keywords from databases such as PubMed, PsycINFO, EMBASE, Medline, and ARU-E-Library. Out of 822 initially identified publications, 24 met the inclusion criteria. Thematic analysis was used to investigate pertinent data, and studies were appraised using CASP and AXIS tools. Data interpretation involved SPSS and R, utilizing descriptive statistics, t-tests, ANOVA, and regression analyses. Results: The review identified critical factors contributing to mental health deterioration, including financial hardships, academic disruption, social isolation, excessive smartphone use, and career instability. High prevalence rates of anxiety (61.2%), depression (64.7%), and stress (52.4%) were found. Inferential statistics revealed significant differences in mental health outcomes across demographic groups, with female students showing a 20% higher risk of anxiety and those living with parents showing a 15% higher risk of depression. Discussion: The results emphasize the multifaceted nature of the mental health challenges that students encounter. The transition to online learning, financial instability, and social isolation were substantial stressors. The review emphasizes the necessity of legislative changes and targeted mental health interventions. Conclusion: To address the mental health crisis among university students, policymakers must reform frameworks and provide support systems, including flexible learning models and targeted mental health interventions, to ensure students’ long-term well-being and academic success.

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

Cited by 3 publications

References 44 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)

Assessing the Psychosocial Determinants of Mental Health Decline Among Bangladeshi University Students During the COVID-19 Pandemic: A Rapid Systematic Review

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