A wearable and mobile intervention delivery system for individuals with panic disorder

Cruz, Luís A. da Silva; Rubin, Jonathan; Abreu, Rui; Ahern, Shane; Bobrow, Daniel G.

doi:10.1145/2836041.2836058

Cited by 21 publications

(25 citation statements)

References 16 publications

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“…Across the different system functions, the most prevalent therapy cited was cognitive behavioral therapy (CBT) -13 papers based their designs on CBT [12,31,41,53,89,107,130,131,144,147,157,176,182]. Other therapies cited include for example: Respiratory Biofeedback-assisted Therapy [43], Virtual Reality Exposure Therapy [29,87], Interpersonal Social Rhythm Therapy [110,117] and Emotion Regulation Therapy [72,113] There is space for more engagement with existing treatment methods or emotion regulation theories. This said, not all design explorations need to be based on an existing therapy method.…”

Section: Analysis Of Reviewed Work System Functionsmentioning

confidence: 99%

HCI and Affective Health

Sanches

Janson

Karpashevich

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

151

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In the last decade, the number of articles on HCI and health has increased dramatically. We extracted 139 papers on depression, anxiety and bipolar health issues from 10 years of SIGCHI conference proceedings. 72 of these were published in the last two years. A systematic analysis of this growing body of literature revealed that most innovation happens in automated diagnosis, and self-tracking, although there are innovative ideas in tangible interfaces. We noted an overemphasis on data production without consideration of how it leads to fruitful interventions. Moreover, we see a need to promote ethical practices for involvement of people living with affective disorders. Finally, although only 16 studies evaluate technologies in a clinical context, several forms of support and intervention illustrate how rich insights are gained from evaluations with real patients. Our findings highlight potential for growth in the design space of affective health technologies.

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Section: Analysis Of Reviewed Work System Functionsmentioning

confidence: 99%

HCI and Affective Health

Sanches

Janson

Karpashevich

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

151

View full text Add to dashboard Cite

show abstract

“…A wearable and mobile intervention solution, introduced by L. Cruz et al, has the objective of reducing symptoms of panic disorder by guiding the PwD on performing breathing and relaxation exercises; in this case, anxiety was one of the three more severe symptoms identified [18].…”

Section: Related Work On Anxiety Detectionmentioning

confidence: 99%

“…PAMID used automatic audio analysis to detect disruptive behaviours from PwD to prevent anxiety episodes that could escalate [23]. To support that approach, the audio was used to provide information about different levels of contexts, such as speech, activities [18], and environmental sound events [12].…”

Section: Related Work On Anxiety Detectionmentioning

confidence: 99%

Prototypical System to Detect Anxiety Manifestations by Acoustic Patterns in Patients with Dementia

Hernandez-Cruz¹,

García-Constantino²,

Beltrán-Márquez³

et al. 2019

EAI Endorsed Transactions on Pervasive Health and Technology

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INTRODUCTION: Dementia is a syndrome characterised by a decline in memory, language, and problem-solving that affects the ability of patients to perform everyday activities. Patients with dementia tend to experience episodes of anxiety and remain for extended periods, which affects their quality of life.OBJECTIVES: To design AnxiDetector, a system capable of detecting patterns of sounds associated before and during the manifestation of anxiety in patients with dementia. METHODS:We conducted a non-participatory observation of 70 diagnosed patients in-situ, and conducted semi-structured interviews with four caregivers at a residential centre. Using the findings from our observation and caregiver interviews, we developed the AnxiDetector prototype and tested this in an experimental setting where we defined nine classes of audio to represent two groups of sounds: (i) Disturbance which includes audio files that characterise sounds that trigger anxiety in patients with dementia, and (ii) Expression which includes audio files that characterise sounds expressed by the patients during episodes of anxiety. We conducted two experimental classifications of sounds using (i) a Neural Network model trained and (ii) a Support Vector Machine model. The first evaluation consists of a binary discriminating between the two groups of sounds; the second evaluation discriminates the nine classes of audio. The audio resources were retrieved from publicly available datasets. RESULTS:The qualitative results present the views of the caregivers on the adoption of AnxiDetector. The quantitative results from our binary discrimination show a classification accuracy of 98.1% and 99.2% for the Deep Neural Network and Support Vector Machine models, respectively. When classifying the nine classes of sound, our model shows a classification accuracy of 92.2%. Whereas, the Support Vector Machine model yielded an overall classification accuracy of 93.0%. CONCLUSION: In this paper, we presented the outcomes from an observational study in-site at a residential care centre, qualitative findings from interviews with caregivers, the design of AnxiDetector, and preliminary qualitative results of a methodology devised to detect relevant acoustic events associated with anxiety in patients with dementia. We conclude by signalling future plans to conduct in-situ validation of the effectiveness of AnxiDetector for anxiety detection.

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“…While models customized for the individual have higher accuracy, some efforts have achieved good performance using general models, such as in [20], by normalizing physiological data. Finally, most studies make a binary classification with the two states (stress and no-stress) [18,22,26]. In [17] authors distinguish between 3 levels of stress (low, moderate and high perceived stress).…”

Section: Visual Analysismentioning

confidence: 99%