Effects of a mHealth intervention for alcohol relapse prevention

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[This is a preview of some of the exciting mHealth research being presented at the mHealth Summit on December 3-5, 2012. This abstract and others are candidates for the iMedicalApps-mHealth Summit Research Award]

By:David H. Gustafson, PhD, Fiona M. McTavish, MS, Amy Atwood, PhD, Min-Yuan Chih, MHA, Dhavan Shah, PhD, Michael Boyle, MA, Michael Levy, PhD, iMedicalApps-mHealth Summit Award Finalist

The cost of alcohol use disorders is profound, not only to the individual but to family members and the community at large. Worldwide, the disorders accounted for 3.8% of all deaths in 2004, and the global economic burden in health care, public safety, and workplace costs totals hundreds of billions of dollars per year.

Despite the suffering and other costs associated with alcohol use disorders, the gap between those who need care and those who receive it is greater for alcohol abuse and dependence, at 78.1%, than for all other mental disorders. Of those who do receive treatment, a high proportion, possibly as great as 80%, relapse to heavy or uncontrolled use.

Although most experts consider alcoholism to be a chronic disease, characterized like other chronic illnesses by frequent relapses, patients leaving treatment are not typically offered ongoing support to prevent relapse, even though research indicates that continuing care for alcohol and drug use disorders is associated with better outcomes.

Technology offers one possible way of providing continuing care for alcohol use disorders. Technology has the potential to provide personalized care 24/7 much less expensively than traditional care. This abstract describes a randomized clinical trial of a mobile technology system called A-CHESS (Alcohol – Comprehensive Health Enhancement Support System). A-CHESS was designed to radically improve addiction treatment and continuing care by offering emotional and instrumental support anywhere, at any time. This study tested the hypothesis that a mobile-phone based system (A-CHESS) can reduce risky drinking days over 12 months in patients leaving residential care for alcohol dependence. Preliminary results from our work, including a video, were presented on IMedicalApps previously.

About two weeks before an eligible patient (met the criteria for DSM-IV alcohol dependence) left residential treatment, a study coordinator discussed the study with the patient, including procedures, benefits and risks of participating, and data to be collected. If the patient was willing to take part, written informed consent was then obtained. The study was conducted according to the Declaration of Helsinki and approved by the Institutional Review Board at the University of Wisconsin – Madison. A total of 349 patients consented to participate in the randomized clinical study from February 2010 through June 2011.

Participants were from two treatment agencies, one in the Midwestern U.S. and the other in the Northeastern U.S. Participants had to be at least 18 years old and willing to be randomized. Patients were excluded if they had a psychiatric or medical condition that precluded participating in the study (a history of suicidality, a significant developmental or cognitive impairment that would limit the ability to understand A-CHESS material, or vision problems).

Patients were randomized to one of two groups, a control group that received treatment as usual and a treatment group that received treatment as usual plus A-CHESS. Patients assigned to the A-CHESS group received a smartphone for 8 months. All patients took a pretest and answered survey questions via a telephone interview at 4, 8, and 12 months after leaving residential treatment. Patients in the A-CHESS group also had data about their use of A-CHESS automatically collected.

A-CHESS had digital voice services, text messaging, and Web access. The patient’s counselor could access information about the patient’s use of A-CHESS through a website. Each patient had a unique login that enabled the research team to collect A-CHESS use data automatically in server log files. The server tracked the date and time a patient entered A-CHESS, the service(s) selected, how long the patient used each service, pages viewed, and whether the patient sent or received messages. Table 1 shows A-CHESS services.

Researchers called all study participants to administer surveys at 4, 8, and 12 months after discharge from treatment. The surveys asked questions about risky drinking days, quality of life, treatment services received, and coping behavior. The phone surveys took between 15 and 20 minutes to complete.

It was hypothesized that, compared to the control group, A-CHESS would reduce patients’ risky drinking days during the 12-month intervention and follow-up period (the primary outcome) as well as increase total abstinence. Patients reported the number of risky drinking days they had in the previous 30 days at 4, 8, and 12 months after discharge from residential treatment. Risky drinking days are defined as days on which a patient’s drinking in a 2-hour period exceeded, for men, 4 standard drinks and for women, 3 standard drinks.

Results

For the 12-month intervention and follow-up period, patients in the A-CHESS group reported fewer risky drinking days (M = 1.386) than patients in the control group (M = 2.752), and the difference was significant [t(287.686) = 2.97, p = .003; d = .23]. See Table 2. A-CHESS patients reported fewer risky drinking days than control-group patients at all three time points; the differences were significant at months 4 (p = .020; d = .25) and 12 (p = .032; d = .24) but not month 8 (p = .096; d = .18).

The percentage of patients reporting total abstinence was greater for A-CHESS than control patients at all time points, with significant differences at months 8 (p = .038) and 12 (p = .014) but not at month 4 (p = .132).

Discussion

This randomized trial demonstrates that technology such as A-CHESS can help provide continuing care to people struggling with alcohol use disorders and improve outcomes. Smartphones applications could be a practical and cost-effective way to provide continuing care. While further research needs to be done, (including cost benefits of such systems) this is an encouraging first step into using smartphones technology in alcohol treatment follow-up care.

Acknowledgments

This study was funded by the U.S. National Institute on Alcohol Abuse and Alcoholism (NIAAA) grant R01 AA017192.

David H. Gustafson, PhD – Research Professor of Industrial and Systems Engineering, and Director of the Center for Health Enhancement Systems Studies, at the University of Wisconsin-Madison.

Fiona M McTavish, MS – Researcher, Deputy Director of the Center for Health Enhancement Systems Studies, at the University of Wisconsin-Madison.

Amy Atwood, PhD – Statistician at the Center for Health Enhancement Systems Studies at the University of Wisconsin-Madison.

Ming-Yuan Chih, MHA – PhD Candidate, Industrial and Systems Engineering, University of Wisconsin-Madison.

Dhavan Shah, PhD – Louis A. & Mary E. Maier-Bascom Professor at the University of Wisconsin-Madison. Director of the Mass Communication Research Center in the School of Journalism and Mass Communication.

Michael Boyle, MA – Consult in the Behavioral Health Field, Retired President of Fayette Companies, a behavioral health provider organization in Peoria, Illinois.

Michale Levy, PhD – Licensed Psychologist and Vice President of Clinical Services at Northeast Behavioral Health, Peabody, Massachusetts.

 

 

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