Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health Care

Boukhechba, Mehdi; Baglione, Anna N.; Barnes, Laura E.

doi:10.1177/1064804620920494

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…a) Personal Depression and Anxiety Monitoring and Intervention: mHealth Sensing techniques are providing broadly accessible services for individuals with mental disorders, as they can collect user's various indicators anytime and anywhere, as well as deliver timely initial diagnosis and interventions though without scarce clinical resources [17]. A typical…”

Section: A Depression and Anxietymentioning

confidence: 99%

“…For example, to investigate a large group of people such as citizens of a country, Chen et al [54] there are also some side D&Is issues for some problems that may exist in the mobile sensing data [290]. For example, ideally, the input of the data analysis algorithm is continuous and sufficient, while in mHealth Sensing contexts, the data streams collected may be sparse and biased due to some technical issues (e.g., operating system's restrictions on software running in the background) and varying users' usage behaviors (e.g., forgetting to wear the device or run the app); thus overcoming the insufficiency of data and effective modeling is an urgent problem to be solved [17], [291]. Additionally, similar side problems include how to analyze and understand the relationship between the complex dynamics of the health and multimodal factors [292], and how to integrate medical knowledge into algorithms pervasively and effectively [293], [294].…”

Section: Design and Implementation Issues In Data Analysis And Knowledge Discoverymentioning

confidence: 99%

“…Promoting both Personal and Population Health Benefits & Well-Beings: Most of existing mHealth Sensing works solely focus on either individual user's personal health benefit (in Personal Sensing) or population health benefit (in Crowd Sensing) [17], [56], [211], [231]. While the two paradigms have their own advantages and disadvantages (e.g., Crowd Sensing pursues to discover population knowledge, which may conflict with providing personal health benefit to participants as incentives; Personal Sensing pays main attention to providing service for personal health benefit, ignoring that the information of a group of individuals can be leveraged for population research), the organizer could consider combining both of them to make full use of service capacity and collected information of mHealth Sensing apps/systems.…”

Section: Future Directionsmentioning

confidence: 99%

“…Early visionary works [6], [7] proposed the basic framework of mobile health (mHealth) sensing techniques in nowadays that leverage "non-invasive" mobile sensing schemes [8] to collect data for human activities recognition and infer the individual's health status using machine learning algorithms with longitude and real-time sensory data accordingly [4], [9]- [11]. Though there are several works reviewing and surveying the research problems [12]- [16], emerg-ing techniques [17]- [19], system design [20]- [22], and prototyping tools [11], [23], [24] for mobile sensing for health (mHealth Sensing) apps/systems, to the best of our knowledge, the motivations of mHealth Sensing, in addition to tracking individuals' behaviors related to some specific health issues, are not well categorized with respects to two perspectives of modern healthcare as follows.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Wang¹,

Xiong²,

Zhang³

et al. 2021

Preprint

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Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either (a) personalized medicine for individuals or (b) public health for populations, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms – (i) Personal Sensing and (ii) Crowd Sensing paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloudbased data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: (1) Sensing Task Creation & Participation, (2) Health Surveillance & Data Collection, and (3) Data Analysis & Knowledge Discovery. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.

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Section: A Depression and Anxietymentioning

confidence: 99%

Section: Design and Implementation Issues In Data Analysis And Knowledge Discoverymentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Wang¹,

Xiong²,

Zhang³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Further, our findings propose the ability to objectively detect distinct state anxiety experiences, contributing towards the long-term aims of minimizing the burden of anxiety disorders. For example, real-time detection capabilities can support implementing intelligent mobile technologies to guide appropriate and timely interventions to patients outside therapy [8], ultimately supporting to break the vicious cycles of anxiety, therefore; improving the recovery speed or preventing anxiety disorders [56]. Longitudinal analytics on temporal pattern changes across anxiety experiences could estimate risk levels and guide the general public to clinicians on time to receive early support.…”

Section: Hypotheses Novelty and Contributionsmentioning

confidence: 99%

A Multimodal Dataset and Evaluation for Feature Estimators of Temporal Phases of Anxiety

Senaratne

Kuhlmann

Ellis

et al. 2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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Vicious cycles of anxiety responses underlie the onset of increasingly prevalent and highly impairing anxiety disorders and also contribute to their maintenance. Our goal is to evaluate whether different anxiety responses are evident in temporal patterns of physiological and behavioral features. Consequently, we established a rich multimodal-multisensor dataset of cardiac, electrodermal, movement, posture, and speech measures from 95 young adults during two anxiety experiments that induce social anxiety and bug-phobic anxiety. A subset of this dataset is publicly available at "Anxiety Phases Dataset" Figshare repository. We adopted a generalized mixed model approach and found that 10 out of 14 feature trajectories modeled for high-and low-anxiety groups differ significantly at 0.001 level in magnitude, creating at least two temporal phases in both groups. Further differences in magnitude, duration and the number of phases were observed for responses of confrontation, safety behaviors, escape, and avoidance in the high-anxiety group. Our findings contribute to the long-term aim of designing multimodal systems that have great potential to reduce the impacts of anxiety disorders and improve therapy.

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Measuring maladaptive avoidance: from animal models to clinical anxiety

Ball

Gunaydin

2022

Neuropsychopharmacol.

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Avoiding stimuli that predict danger is required for survival. However, avoidance can become maladaptive in individuals who overestimate threat and thus avoid safe situations as well as dangerous ones. Excessive avoidance is a core feature of anxiety disorders, post-traumatic stress disorder (PTSD), and obsessive-compulsive disorder (OCD). This avoidance prevents patients from confronting maladaptive threat beliefs, thereby maintaining disordered anxiety. Avoidance is associated with high levels of psychosocial impairment yet is poorly understood at a mechanistic level. Many objective laboratory assessments of avoidance measure adaptive avoidance, in which an individual learns to successfully avoid a truly noxious stimulus. However, anxiety disorders are characterized by maladaptive avoidance, for which there are fewer objective laboratory measures. We posit that maladaptive avoidance behavior depends on a combination of three altered neurobehavioral processes: (1) threat appraisal, (2) habitual avoidance, and (3) trait avoidance tendency. This heterogeneity in underlying processes presents challenges to the objective measurement of maladaptive avoidance behavior. Here we first review existing paradigms for measuring avoidance behavior and its underlying neural mechanisms in both human and animal models, and identify how existing paradigms relate to these neurobehavioral processes. We then propose a new framework to improve the translational understanding of maladaptive avoidance behavior by adapting paradigms to better differentiate underlying processes and mechanisms and applying these paradigms in clinical populations across diagnoses with the goal of developing novel interventions to engage specific identified neurobehavioral targets.

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Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health Care

Cited by 11 publications

References 22 publications

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

A Multimodal Dataset and Evaluation for Feature Estimators of Temporal Phases of Anxiety

Measuring maladaptive avoidance: from animal models to clinical anxiety

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