Delivery of a Mental Health Intervention for Chronic Pain Through an Artificial Intelligence–Enabled App (Wysa): Protocol for a Prospective Pilot Study

Gupta, Megha; Malik, Tanya; Sinha, Chaitali

doi:10.2196/36910

Cited by 17 publications

(6 citation statements)

References 97 publications

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“…Users are informed about their rights to exclude data shared with the app for research purposes. Wysa recommends resources based on evidence-based therapies, such as cognitive behavioral therapy (CBT) and mindfulness ( 39 , 40 ). Wysa aims to build mental resilience and promote mental well-being ( 16 ).…”

Section: Methodsmentioning

confidence: 99%

Qualitative analysis of mental health conversational agents messages about autism spectrum disorder: a call for action

Aghakhani,

Carre,

Mostovoy

et al. 2023

Front. Digit. Health

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BackgroundConversational agents (CA's) have shown promise in increasing accessibility to mental health resources. This study aimed to identify common themes of messages sent to a mental health CA (Wysa) related to ASD by general users and users that identify as having ASD.MethodsThis study utilized retrospective data. Two thematic analyses were conducted, one focusing on user messages including the keywords (e.g., ASD, autism, Asperger), and the second one with messages from users who self-identified as having ASD.ResultsFor the sample of general users, the most frequent themes were “others having ASD,” “ASD diagnosis,” and “seeking help.” For the users that self-identified as having ASD (n = 277), the most frequent themes were “ASD diagnosis or symptoms,” “negative reaction from others,” and “positive comments.” There were 3,725 emotion words mentioned by users who self-identified as having ASD. The majority had negative valence (80.3%), and few were positive (14.8%) or ambivalent (4.9%).ConclusionUsers shared their experiences and emotions surrounding ASD with a mental health CA. Users asked about the ASD diagnosis, sought help, and reported negative reactions from others. CA's have the potential to become a source of support for those interested in ASD and/or identify as having ASD.

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Section: Methodsmentioning

confidence: 99%

Qualitative analysis of mental health conversational agents messages about autism spectrum disorder: a call for action

Aghakhani,

Carre,

Mostovoy

et al. 2023

Front. Digit. Health

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“…Most of the interventions described in these reviews are web-based interventions and often include some degree of clinician support. A variety of digital interventions have been designed to support people with chronic pain, including texting interventions [69], the use of smartphone-based chatbots [70,71], and more sophisticated technologies such as virtual reality [72,73]. However, to our knowledge, no published studies have described mHealth interventions developed specifically to support people with chronic pain to reduce their reliance on opioids.…”

Section: Strengths Of the Studymentioning

confidence: 99%

A Digital Video and Text Messaging Intervention to Support People With Chronic Pain During Opioid Tapering: Content Development Using Co-design

Magee¹,

Gholamrezaei²,

McNeilage³

et al. 2022

JMIR Form Res

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Background People living with chronic pain report that tapering prescribed opioids is challenging and more support is needed. In our formative research, consumers indicated that mobile health (mHealth) technology could be an acceptable form of support for opioid tapering and may improve tapering self-efficacy. Objective We aimed to evaluate and improve the content of an mHealth intervention before pilot-testing, based on consumer and clinician feedback. Methods Participants were 12 consumers and 12 clinicians who evaluated an initial draft of a video script and 90 SMS text messages. Consumers and clinicians rated the appropriateness and likely usefulness (consumers) or likely effectiveness (clinicians) of a video script and a random selection of 15 SMS text messages using a 5-point Likert-type scale (1=totally disagree; 5=totally agree). Each draft SMS text message was reviewed by 2 consumers and 2 clinicians. Texts were deemed acceptable for inclusion in the pilot intervention only if the summed participant ratings of text appropriateness and usefulness or effectiveness were ≥8. Participants were also invited to provide open-text feedback on the draft script and SMS text messages. Results Consumers generally agreed that the draft video script and text content were likely to be appropriate (video: mean 4.4, SD 0.52; text: mean 4.3, SD 0.79) and useful (video: mean 4.3, SD 0.65; text: mean 4.2, SD 0.84). Similarly, clinicians generally agreed that the draft video script and text content were likely to be appropriate (video: mean 4.5, SD 0.67; text: mean 4.4, SD 0.81) and effective (video: mean 4.0, SD 0.43; text: mean 4.3, SD 0.76). Overall, 77% (69/90) of the draft texts met the threshold rating for acceptability for inclusion in the pilot test of mHealth intervention by consumers, and 82% (74/90) met the threshold for acceptability by clinicians. Consumers' and clinicians’ ratings were used to rank order the texts. The top 56 draft texts (all meeting the threshold levels of acceptability) were selected for inclusion in the pilot intervention. When consumer or clinician feedback was provided, the texts meeting the criteria for inclusion in the pilot were further revised and improved. Feedback on the video script was also used to further improve the acceptability of the video script before pilot-testing the intervention. Conclusions This study describes the process by which a 28-day mHealth intervention to support patients with chronic pain to taper opioid medications was evaluated and improved before pilot-testing. The mHealth intervention consisted of a 10-minute psychoeducational video about pain and opioid tapering and 56 unique SMS text messages providing information and reassurance (texts delivered twice per day for 28 days). Having established that the content of the mHealth intervention is acceptable to both consumer and clinician groups, the mHealth intervention will be piloted in future research.

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“…We define an intervention as an activity undertaken to determine, prevent, improve, or stabilize a medical condition. Examples of health agents include agents for delivering cognitive behavior therapy [ 1 ], for collecting the medical history [ 2 ], or supporting medication management [ 3 ]. During the COVID-19 pandemic, many CAs have been released within a very short amount of time addressing a diverse set of use cases (e.g., risk assessment, information dissemination, surveillance) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Framework for Guiding the Development of High-Quality Conversational Agents in Healthcare

Denecke

2023

Healthcare

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Evaluating conversational agents (CAs) that are supposed to be applied in healthcare settings and ensuring their quality is essential to avoid patient harm and ensure efficacy of the CA-delivered intervention. However, a guideline for a standardized quality assessment of health CAs is still missing. The objective of this work is to describe a framework that provides guidance for development and evaluation of health CAs. In previous work, consensus on categories for evaluating health CAs has been found. In this work, we identify concrete metrics, heuristics, and checklists for these evaluation categories to form a framework. We focus on a specific type of health CA, namely rule-based systems that are based on written input and output, have a simple personality without any kind of embodiment. First, we identified relevant metrics, heuristics, and checklists to be linked to the evaluation categories through a literature search. Second, five experts judged the metrics regarding their relevance to be considered within evaluation and development of health CAs. The final framework considers nine aspects from a general perspective, five aspects from a response understanding perspective, one aspect from a response generation perspective, and three aspects from an aesthetics perspective. Existing tools and heuristics specifically designed for evaluating CAs were linked to these evaluation aspects (e.g., Bot usability scale, design heuristics for CAs); tools related to mHealth evaluation were adapted when necessary (e.g., aspects from the ISO technical specification for mHealth Apps). The resulting framework comprises aspects to be considered not only as part of a system evaluation, but already during the development. In particular, aspects related to accessibility or security have to be addressed in the design phase (e.g., which input and output options are provided to ensure accessibility?) and have to be verified after the implementation phase. As a next step, transfer of the framework to other types of health CAs has to be studied. The framework has to be validated by applying it during health CA design and development.

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Delivery of a Mental Health Intervention for Chronic Pain Through an Artificial Intelligence–Enabled App (Wysa): Protocol for a Prospective Pilot Study

Cited by 17 publications

References 97 publications

Qualitative analysis of mental health conversational agents messages about autism spectrum disorder: a call for action

Qualitative analysis of mental health conversational agents messages about autism spectrum disorder: a call for action

A Digital Video and Text Messaging Intervention to Support People With Chronic Pain During Opioid Tapering: Content Development Using Co-design

Framework for Guiding the Development of High-Quality Conversational Agents in Healthcare

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