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59. An e-health solution for people with alcohol problems

Author

Gustafson, David H., Boyle, Michael G., Shaw, Bret R., Isham, Andrew, McTavish, Fiona, Richards, Stephanie, Schubert, Christopher, Levy, Michael, and Johnson, Kim

Subjects
E-health -- Research, Alcoholism -- Care and treatment, Technology application, Health
Abstract

Approximately 17 million people in the United States suffer from alcohol use disorders (AUDs) (i.e., alcohol abuse and/or dependence), yet only 10 percent of them receive treatment (Substance Abuse and [...]

Published
2011

62. Intraindividual, Dyadic, and Network Communication in a Digital Health Intervention: Distinguishing Message Exposure from Message Production.

Author

Mi, Ranran Z., Kornfield, Rachel, Shah, Dhavan V., Maus, Adam, and Gustafson, David H.

Subjects
DYADIC communication, INTERPERSONAL communication, CONVALESCENCE, DIGITAL health, SMARTPHONES, HEALTH behavior, COMPULSIVE behavior, EDUCATIONAL attainment
Abstract

Communicating within digital health interventions involves a range of behaviors that may contribute to the management of chronic illnesses in different ways. This study examines whether communication within a smartphone-based application for addiction recovery produces distinct effects depending on 1) the "level" of communication, defined as intraindividual communication (e.g., journal entries to oneself); dyadic communication (e.g., private messaging to other individuals); or network communication (e.g., discussion forum posts to all group members), and 2) whether individuals produce or are exposed to messages. We operationalize these communication levels and behaviors based on system use logs as the number of clicks dedicated to each activity and assess how each category of system use relates to changes in group bonding and substance use after 6 months with the mobile intervention. Our findings show that (1) intraindividual exposure to one's own past posts marginally predicts decreased drug use; (2) dyadic production predicts greater perceived bonding; while dyadic exposure marginally predicts reduced drug use; (3) network production predicts decreased risky drinking. Implications for digital health interventions are discussed. [ABSTRACT FROM AUTHOR]

Published
2022
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69. How underserved breast cancer patients use and benefit from eHealth programs: implications for closing the digital divide

Author

Shaw, Bret, Gustafson, David H., Hawkins, Robert, McTavish, Fiona, McDowell, Helene, Pingree, Suzanne, and Ballard, Denise

Subjects
Online health care information services -- Usage, Online health care information services -- Research, Online health care service, Psychology and mental health, Social sciences
Abstract

This article consolidates insights from 15 years of research examining how the medically underserved use and benefit from an eHealth program, the Comprehensive Health Enhancement Support System (CHESS). The authors outline results from early feasibility tests to determine if the underserved would use CHESS. Distinctive behaviors of underserved populations who have used CHESS are reported with a focus on how online health information and computer-mediated support groups are used. The article then reports on how the underserved benefit from using CHESS. Best practice recommendations for recruitment and training the underserved are offered, and implications for closing the digital divide are discussed. Keywords: eHealth; breast cancer; Internet; Interactive Cancer Communication System; CHESS

Published
2006

78. Exploring the Role of Social Support in Promoting Patient Participation in Health Care among Women with Breast Cancer.

Author

Guan, Mengfei, Han, Jeong Yeob, Shah, Dhavan V., and Gustafson, David H.

Subjects
BREAST cancer patients, SOCIAL support, PATIENT participation, CANCER patient psychology, PHYSICIAN-patient relations, SELF-control, EMOTION regulation, SECONDARY analysis
Abstract

Scholars have adopted Street's (2003) ecological model of communication in medical encounters to investigate the factors promoting patient participation in health care. However, factors demonstrated in the ecological model were bounded in the context of medical care primarily focusing on health care providers and patients. Social factors, such as patients' relationships and supportive communication with others outside the context of health care remain relatively unexplored. To expand the purview of our understanding of factors that influence patient participation, this research integrated social support literature into the research on physician-patient communication and proposed a model which described a process through which social support can enhance patient participation in health care. The data analyzed in this study were a part of two larger clinical trials in which 661 women with breast cancer were recruited from three cancer institutions in the United States. The results from structural equation modeling analysis from cross-sectional and longitudinal data provided strong evidence for the hypotheses predicting that perceived social support was positively associated with health information competence, which in turn fully mediated the association between social support and patient participation in health care. Theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

Published
2021
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82. Potential Influences of the COVID-19 Pandemic on Drug Use and HIV Care Among People Living with HIV and Substance Use Disorders: Experience from a Pilot mHealth Intervention

Author

Hochstatter, Karli R., primary, Akhtar, Wajiha Z., additional, Dietz, Sarah, additional, Pe-Romashko, Klaren, additional, Gustafson, David H., additional, Shah, Dhavan V., additional, Krechel, Sarah, additional, Liebert, Cameron, additional, Miller, Rebecca, additional, El-Bassel, Nabila, additional, and Westergaard, Ryan P., additional

Published
2020
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88. Unraveling uses and effects of an Interactive Health Communication System

Author

Han, Jeong Yeob, Hawkins, Robert P., Shaw, Bret R., Pingree, Suzanne, McTavish, Fiona, and Gustafson, David H.

Subjects
Health education -- Technology application -- Usage, Online health care information services -- Usage -- Influence -- Technology application, Business, Education, Law, Mass communications, Online health care service, Technology application, Influence, Usage
Abstract

By developing a number of measures distinguishing amount, type of content, and when and how that content is used, the current study revealed effective patterns of use that are associated with quality of life benefits during an eHealth intervention. Results generally suggest that the benefits depend on how a patient uses the system, far more than on sheer amount of exposure or even what type of content is chosen. The next generation of eHealth system should focus on providing new and varying content over time, but even more on encouraging intensity of use and long-term commitment to the system., The use of the Internet for health education has reached what many might consider a critical mass. According to the National Cancer Institute's Health Information National Trends Survey (Nelson et [...]

Published
2009

89. iSurvive: An Interpretable, Event-time Prediction Model for mHealth

Author

Dempsey, Walter H., Moreno, Alexander, Scott, Christy K., Dennis, Michael L., Gustafson, David H., Murphy, Susan A., and Rehg, James M.

Subjects
Article
Abstract

An important mobile health (mHealth) task is the use of multimodal data, such as sensor streams and self-report, to construct interpretable time-to-event predictions of, for example, lapse to alcohol or illicit drug use. Interpretability of the prediction model is important for acceptance and adoption by domain scientists, enabling model outputs and parameters to inform theory and guide intervention design. Temporal latent state models are therefore attractive, and so we adopt the continuous time hidden Markov model (CT-HMM) due to its ability to describe irregular arrival times of event data. Standard CT-HMMs, however, are not specialized for predicting the time to a future event, the key variable for mHealth interventions. Also, standard emission models lack a sufficiently rich structure to describe multimodal data and incorporate domain knowledge. We present iSurvive, an extension of classical survival analysis to a CT-HMM. We present a parameter learning method for GLM emissions and survival model fitting, and present promising results on both synthetic data and an mHealth drug use dataset.

Published
2019

90. Don't fumble the treatment handoff; organizations need a systematic approach to ensure clients' smooth transition between levels of care

Author

Gustafson, David H., Resar, Roger, Johnson, Kimberly, and Daigle, John G.

Subjects
Football coaches, Health, Health care industry, Psychology and mental health
Abstract

Legendary Ohio State University football coach Woody Hayes once quoted the great Michigan State University coach Duffy Daugherty as saying, 'There are three things that can happen when you pass [...]

Published
2008

98. Detecting Recovery Problems �Just in Time�: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum

Author

Kornfield, Rachel, Sarma, Prathusha K, Shah, Dhavan V, McTavish, Fiona, Landucci, Gina, Pe-Romashko, Klaren, and Gustafson, David H

Subjects
Male, Online discussion, 020205 medical informatics, Computer science, Word count, Decision tree, Health Informatics, 02 engineering and technology, Machine learning, computer.software_genre, Education, Distance, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, health communication, Humans, supervised machine learning, 030212 general & internal medicine, Health communication, Original Paper, business.industry, Linguistics, social support, self-help groups, Digital health, substance-related disorders, Identification (information), Alternating decision tree, Female, Artificial intelligence, Computational linguistics, business, computer
Abstract

Background: Online discussion forums allow those in addiction recovery to seek help through text-based messages, including when facing triggers to drink or use drugs. Trained staff (or “moderators”) may participate within these forums to offer guidance and support when participants are struggling but must expend considerable effort to continually review new content. Demands on moderators limit the scalability of evidence-based digital health interventions. Objective: Automated identification of recovery problems could allow moderators to engage in more timely and efficient ways with participants who are struggling. This paper aimed to investigate whether computational linguistics and supervised machine learning can be applied to successfully flag, in real time, those discussion forum messages that moderators find most concerning. Methods: Training data came from a trial of a mobile phone-based health intervention for individuals in recovery from alcohol use disorder, with human coders labeling discussion forum messages according to whether or not authors mentioned problems in their recovery process. Linguistic features of these messages were extracted via several computational techniques: (1) a Bag-of-Words approach, (2) the dictionary-based Linguistic Inquiry and Word Count program, and (3) a hybrid approach combining the most important features from both Bag-of-Words and Linguistic Inquiry and Word Count. These features were applied within binary classifiers leveraging several methods of supervised machine learning: support vector machines, decision trees, and boosted decision trees. Classifiers were evaluated in data from a later deployment of the recovery support intervention. Results: To distinguish recovery problem disclosures, the Bag-of-Words approach relied on domain-specific language, including words explicitly linked to substance use and mental health (“drink,” “relapse,” “depression,” and so on), whereas the Linguistic Inquiry and Word Count approach relied on language characteristics such as tone, affect, insight, and presence of quantifiers and time references, as well as pronouns. A boosted decision tree classifier, utilizing features from both Bag-of-Words and Linguistic Inquiry and Word Count performed best in identifying problems disclosed within the discussion forum, achieving 88% sensitivity and 82% specificity in a separate cohort of patients in recovery. Conclusions: Differences in language use can distinguish messages disclosing recovery problems from other message types. Incorporating machine learning models based on language use allows real-time flagging of concerning content such that trained staff may engage more efficiently and focus their attention on time-sensitive issues.

Published
2018

99. An Outcome Study of the CASA-CHESS Smartphone Relapse Prevention Tool for Latinx Spanish-Speakers with Substance Use Disorders

Author

Muroff, Jordana, primary, Robinson, Winslow, additional, Chassler, Deborah, additional, López, Luz M., additional, Lundgren, Lena, additional, Guauque, Claudia, additional, Dargon-Hart, Susan, additional, Stewart, Emily, additional, Dejesus, Diliana, additional, Johnson, Kimberly, additional, Pe-Romashko, Klaren, additional, and Gustafson, David H., additional

Published
2019
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