scholarly journals Understanding User Experience: Exploring Participants’ Messages With a Web-Based Behavioral Health Intervention for Adolescents With Chronic Pain

10.2196/11756 ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. e11756 ◽  
Author(s):  
Annie T Chen ◽  
Aarti Swaminathan ◽  
William R Kearns ◽  
Nicole M Alberts ◽  
Emily F Law ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Behavioral Health ◽  
User Experience ◽  
Health Intervention ◽  
Web Based ◽  
Behavioral Health Intervention

scholarly journals Understanding User Experience: Exploring Participants’ Messages With a Web-Based Behavioral Health Intervention for Adolescents With Chronic Pain (Preprint)

2018 ◽  
Author(s):  
Annie T Chen ◽  
Aarti Swaminathan ◽  
William R Kearns ◽  
Nicole M Alberts ◽  
Emily F Law ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Cluster Analysis ◽  
Behavioral Health ◽  
Topic Modeling ◽  
Health Intervention ◽  
Interaction Patterns ◽  
Analysis Method ◽  
Common Component ◽  
Internet Interventions ◽  
Behavioral Health Intervention

BACKGROUND Delivery of behavioral health interventions on the internet offers many benefits, including accessibility, cost-effectiveness, convenience, and anonymity. In recent years, an increased number of internet interventions have been developed, targeting a range of conditions and behaviors, including depression, pain, anxiety, sleep disturbance, and eating disorders. Human support (coaching) is a common component of internet interventions that is intended to boost engagement; however, little is known about how participants interact with coaches and how this may relate to their experience with the intervention. By examining the data that participants produce during an intervention, we can characterize their interaction patterns and refine treatments to address different needs. OBJECTIVE In this study, we employed text mining and visual analytics techniques to analyze messages exchanged between coaches and participants in an internet-delivered pain management intervention for adolescents with chronic pain and their parents. METHODS We explored the main themes in coaches’ and participants’ messages using an automated textual analysis method, topic modeling. We then clustered participants’ messages to identify subgroups of participants with similar engagement patterns. RESULTS First, we performed topic modeling on coaches’ messages. The themes in coaches’ messages fell into 3 categories: Treatment Content, Administrative and Technical, and Rapport Building. Next, we employed topic modeling to identify topics from participants’ message histories. Similar to the coaches’ topics, these were subsumed under 3 high-level categories: Health Management and Treatment Content, Questions and Concerns, and Activities and Interests. Finally, the cluster analysis identified 4 clusters, each with a distinguishing characteristic: Assignment-Focused, Short Message Histories, Pain-Focused, and Activity-Focused. The name of each cluster exemplifies the main engagement patterns of that cluster. CONCLUSIONS In this secondary data analysis, we demonstrated how automated text analysis techniques could be used to identify messages of interest, such as questions and concerns from users. In addition, we demonstrated how cluster analysis could be used to identify subgroups of individuals who share communication and engagement patterns, and in turn facilitate personalization of interventions for different subgroups of patients. This work makes 2 key methodological contributions. First, this study is innovative in its use of topic modeling to provide a rich characterization of the textual content produced by coaches and participants in an internet-delivered behavioral health intervention. Second, to our knowledge, this is the first example of the use of a visual analysis method to cluster participants and identify similar patterns of behavior based on intervention message content.

scholarly journals Development, Feasibility, Acceptability, and Utility of an Expressive Speech-Enabled Digital Health Agent to Deliver Online, Brief Motivational Interviewing for Alcohol Misuse: Descriptive Study

10.2196/25837 ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. e25837
Author(s):  
Maya Boustani ◽  
Stephanie Lunn ◽  
Ubbo Visser ◽  
Christine Lisetti
Keyword(s):  
Alcohol Use ◽  
Behavioral Health ◽  
Digital Health ◽  
Health Intervention ◽  
The Internet ◽  
Conversational Agents ◽  
Promising Alternative ◽  
Expressive Speech ◽  
3 Dimensional ◽  
Behavioral Health Intervention

Background Digital health agents — embodied conversational agents designed specifically for health interventions — provide a promising alternative or supplement to behavioral health services by reducing barriers to access to care. Objective Our goals were to (1) develop an expressive, speech-enabled digital health agent operating in a 3-dimensional virtual environment to deliver a brief behavioral health intervention over the internet to reduce alcohol use and to (2) understand its acceptability, feasibility, and utility with its end users. Methods We developed an expressive, speech-enabled digital health agent with facial expressions and body gestures operating in a 3-dimensional virtual office and able to deliver a brief behavioral health intervention over the internet to reduce alcohol use. We then asked 51 alcohol users to report on the digital health agent acceptability, feasibility, and utility. Results The developed digital health agent uses speech recognition and a model of empathetic verbal and nonverbal behaviors to engage the user, and its performance enabled it to successfully deliver a brief behavioral health intervention over the internet to reduce alcohol use. Descriptive statistics indicated that participants had overwhelmingly positive experiences with the digital health agent, including engagement with the technology, acceptance, perceived utility, and intent to use the technology. Illustrative qualitative quotes provided further insight about the potential reach and impact of digital health agents in behavioral health care. Conclusions Web-delivered interventions delivered by expressive, speech-enabled digital health agents may provide an exciting complement or alternative to traditional one-on-one treatment. They may be especially helpful for hard-to-reach communities with behavioral workforce shortages.

Longitudinal outcomes after brief behavioral health intervention in an integrated primary care clinic.

2012 ◽  
Vol 30 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Bobbie N. Ray-Sannerud ◽  
Diana C. Dolan ◽  
Chad E. Morrow ◽  
Kent A. Corso ◽  
Kathryn E. Kanzler ◽  
...  
Keyword(s):  
Primary Care ◽  
Behavioral Health ◽  
Primary Care Clinic ◽  
Health Intervention ◽  
Integrated Primary Care ◽  
Care Clinic ◽  
Longitudinal Outcomes ◽  
Behavioral Health Intervention

scholarly journals Genomic information as a behavioral health intervention: can it work?

2011 ◽  
Vol 8 (6) ◽  
pp. 659-667 ◽  
Author(s):  
Cinnamon S Bloss ◽  
Lisa Madlensky ◽  
Nicholas J Schork ◽  
Eric J Topol
Keyword(s):  
Behavioral Health ◽  
Health Intervention ◽  
Genomic Information ◽  
Behavioral Health Intervention

scholarly journals Evaluating the impact of integrated behavioral health intervention

Medicine ◽  
2021 ◽  
Vol 100 (34) ◽  
pp. e27066
Author(s):  
Bishnu Bahadur Thapa ◽  
M. Barton Laws ◽  
Omar Galárraga
Keyword(s):  
Behavioral Health ◽  
Health Intervention ◽  
Integrated Behavioral Health ◽  
Behavioral Health Intervention ◽  
The Impact

Primary Care Behavioral Health Intervention Survey

2016 ◽  
Author(s):  
Prerna G. Arora ◽  
Sharon Hoover Stephan ◽  
Kimberly D. Becker ◽  
Lawrence Wissow
Keyword(s):  
Primary Care ◽  
Behavioral Health ◽  
Health Intervention ◽  
Primary Care Behavioral Health ◽  
Behavioral Health Intervention

Home-based Behavioral Health Intervention: Use of a Telehealth Model to Address Poor Adherence to Type-1 Diabetes Medical Regimens

2006 ◽  
Vol 12 (3) ◽  
pp. 370-372 ◽  
Author(s):  
Jennifer W. Adkins ◽  
Eric A. Storch ◽  
Adam B. Lewin ◽  
Laura Williams ◽  
Janet H. Silverstein ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Behavioral Health ◽  
Health Intervention ◽  
Poor Adherence ◽  
Home Based ◽  
Behavioral Health Intervention ◽  
Medical Regimens
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