A Framework for Understanding the Relationship between Social Media Discourse and Mental Health

Mendu, Sanjana; Baglione, Anna N.; Baee, Sonia; Wu, Congyu; Ng, Brandon; Shaked, Adi; Clore, Gerald L.; Boukhechba, Mehdi; Barnes, Laura E.

doi:10.1145/3415215

Cited by 18 publications

(12 citation statements)

References 68 publications

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“…We are preparing for a future deployment of the UT1000 Project that directly addresses these limitations by recruiting (potentially fewer) participants who will commit to longer study periods as well as increasing the number of Fitbits and BEVO Beacons distributed. Another type of data that we did not collect in the UT1000 Project is individual social media data including both the content created and the interaction patterns, which have been utilized to predict personal mental health outcomes in recent studies [ 29 ]. In our conceptual framework of human-centric data modality, social media data would fit in the medium range on both the temporal coverage and the spatial freedom axes, similar to the position of EMAs.…”

Section: Discussionmentioning

confidence: 99%

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts

Fritz

Bastami

et al. 2021

GigaScience

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Background As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users’ daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes. Results To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants’ mood, sleep, behavior, and living environment. Conclusions We were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies.

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Section: Discussionmentioning

confidence: 99%

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts

Fritz

Bastami

et al. 2021

GigaScience

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“…Wearers can use various mood tracking applications, such as MyTherapy, Breathe2Relax, MoodKit, MoodTracker or Daylio as part of QS [ 125 ]. Studies [ 126 , 127 ] show that those who use QS applications feel more in control over their mood, which helps them control their mood and show more confidence, a positive attitude and a better outlook towards their emotional wellbeing. Users can understand their emotional cycles by using mood-tracking applications.…”

Section: Other Considerations For Wearable Technologymentioning

confidence: 99%

Review of Wearable Devices and Data Collection Considerations for Connected Health

Vijayan

Connolly

Condell³

et al. 2021

Sensors

205

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Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

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“…Theory-driven grouping relies heavily on prior literature to categorize participants based on shared characteristics, such as demographics or mental health status. Recent studies that have grouped participants by mental health symptoms (eg, high vs low anxiety and depression) or personality traits (eg, high vs low extraversion) have revealed differences in both social and engagement behaviors between groups [35,36]. Importantly, studies in patients with breast cancer indicate a significant amount of heterogeneity in distress levels and trajectories, such that some patients experience very high levels of distress and mood symptoms, whereas others experience no or relatively low levels of distress throughout treatment [37].…”

Section: Group Participantsmentioning

confidence: 99%

Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study

Baglione¹,

Cai²,

Bahrini³

et al. 2022

JMIR Med Inform

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Background Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success; however, the relationship between mood and engagement among patients with cancer remains poorly understood. A reason for this is the lack of a data-driven process for analyzing mood and app engagement data for patients with cancer. Objective This study aimed to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in patients with breast cancer. Methods We described the steps involved in data preprocessing, feature extraction, and data modeling and prediction. We applied this process as a case study to data collected from patients with breast cancer who engaged with a mobile mental health app intervention (IntelliCare) over 7 weeks. We compared engagement patterns over time (eg, frequency and days of use) between participants with high and low anxiety and between participants with high and low depression. We then used a linear mixed model to identify significant effects and evaluate the performance of the random forest and XGBoost classifiers in predicting weekly mood from baseline affect and engagement features. Results We observed differences in engagement patterns between the participants with high and low levels of anxiety and depression. The linear mixed model results varied by the feature set; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. The accuracy of predicting depressed mood varied according to the feature set and classifier. The feature set containing survey features and overall app engagement features achieved the best performance (accuracy: 84.6%; precision: 82.5%; recall: 64.4%; F1 score: 67.8%) when used with a random forest classifier. Conclusions The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in patients with breast cancer. The ability to leverage both self-report and engagement features to analyze and predict mood during an intervention could be used to enhance decision-making for researchers and clinicians and assist in developing more personalized interventions for patients with breast cancer.

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A Framework for Understanding the Relationship between Social Media Discourse and Mental Health

Cited by 18 publications

References 68 publications

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts

Review of Wearable Devices and Data Collection Considerations for Connected Health

Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study

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