Upload
vongoc
View
220
Download
4
Embed Size (px)
Citation preview
Perinatal Summit | July 19, 2016 Perinatal Summit | July 19, 2016
Electronic Screening and Brief Intervention
for substance use in pregnancy
Steven J. Ondersma, PhD Depts. of Psychiatry & Behavioral Neurosciences, and Obstetrics & Gynecology
Deputy Director, Merrill-Palmer Skillman Institute
Wayne State University School of Medicine
Merrill-Palmer Skillman Institute
Acknowledgments & disclosure
I gratefully acknowledge my colleagues (Dace Svikis, Robert Sokol, Kim Yonkers, Emily Grekin, Grace Chang, Golfo Tzilos, Ken Resnicow, Ronald Strickler, James LeBreton, Gregory Goyert, James Janisse, George Divine), lab students and staff (Jessi Beatty, Casey Thacker, Lucy McGoron, Amy Loree, Amy Graham, Ebonie Guyton, Shatoya Rice, Erica Montgomery, Peter Preonas, Erica Hohentanner), the participants who shared their time, the Detroit Medical Center, the Henry Ford Health System, and the Wayne State University Physician’s Group.
Funding for this research is from the NIH (DA000516, DA014621, DA021329, DA018975, DA021668, DA021329, DA029050, AA020056, DA036788) the CDC (CE001078, DP006082), and Joe Young Sr./Helene Lycacki funds from the State of Michigan.
The speaker is part owner of a company marketing authorable computerized intervention software.
1. Why technology matters
2. Empathic technology?
3. Data from randomized trials
WHY TECHNOLOGY MATTERS
• In 1996, 7.5 million children (10% of all children) had one or more parents with a substance use disorder (Huang, Cerbone, & Grfoerer, 1998)
• 16.1% of persons with substance abuse or dependence currently live with one or more of their children
The scale of the problem
Population impact = Effect size X reach
Change in substance use during baseline and after treatment initiation among pregnant women in day treatment
Change is not linear (Ondersma, Winhusen, & Lewis, 2012)
Self-change: How many do it on their own?
25%
75%
With help
Without help
(Bischof, Rumpf, Hapke, Meyer, & John, 2003;
Burman, 1997; Sobell, Ellingstad, & Sobell, 2000)
Non-linear change: My brother Paul
MOTIVATIONAL INTERVIEWING is
a collaborative conversation style for strengthening a person’s own motivation and commitment to change, in part through exploring and resolving ambivalence.
Problem area Effect size (d)
vs. no Tx
Effect size (d)
vs. active Tx
Alcohol (frequency) .25 .09
Alcohol (peak BAC) .53 ---
Drug Use .56 -.01
Diet & Exercise .53 ---
Motivational Interviewing vs. extended interventions (Burke et al., 2003)
The power of personal factors
www.poorlydrawnlines.com
20
29
35 38
13
19
23 24
5
11
15 18
0
5
10
15
20
25
30
35
40
6 Months 12 Months 18 Months 24 Months
Client
Relapse
Rates
Follow-up Points
Low
Medium
High
Slide courtesy of William R. Miller, PhD
Rogerian skill and client outcomes Valle (1981) J Studies on Alcohol 42: 783-790
THE CURIOUS PARADOX is that
when I accept myself just as I am, then I can change.
--Carl Rogers
“We’re encouraging people to become involved in their own rescue.”
Ambivalence: We believe what WE say
Screening, Brief Intervention, & Referral to Treatment (SBIRT) Proactive screening and brief intervention
57% …proportion of participants randomized to the brief
counseling group who actually received the intervention (SIPS trial; Kaner et al., 2013)
4.4
hours per working day
…for a primary care physician to conduct all recommended screening and prevention activities
(Yarnall et al., 2004)
Does anyone have time?
And what do they do while waiting…?
THE GOAL is to turn use of interactive
technology, in the waiting area, into a universal and routine part of prenatal care; and to use that window to deliver evidence-based screening and brief interventions to reduce substance use.
But isn’t that a little cold?
THE FACTORS that make all therapies
effective (i.e., the common factors) are ones that are uniquely human.
Bruce Wampold, 2012
Dr. Clifford Nass Stanford University
1958-2013
Dr. Clifford Nass Stanford University
1958-2013
“…I discovered people were interacting with
computers using the same social rules and expectations that they use when they interact with other
people.”
(New Scientist, 2010)
“Users can be induced to behave as if computers
were human, even though users know that the
machines do not actually possess “selves” or human motivations. We refer to
such assignment of human attitudes, intentions, or motives to non-human
entities as ethopoeia, the classical Greek word for
such attributions. “
(Nass et al., 1993)
Social responses to computers
0
2
4
6
8
10
Positiveaffect
Enjoyment Rating ofcomputer
Willing tocontinue
Generic Flattery
Fogg & Nass, 1997 Mumm & Mutlu, 2011
Rosenthal-von der Pütten et al., 2014
e-SBIRT Electronic screening and brief intervention
with pregnant and postpartum women
Question 1: Can a technology-delivered brief intervention
reduce alcohol use in pregnancy?
PARTICIPANTS Total of 48 pregnant women screening positive for alcohol use risk at intake prenatal care appointment (mean ≈ 12 weeks gestation) Most were African-American and of low to low-moderate SES; few had a history of treatment for alcohol use disorders
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
METHOD Women were screened and randomized to intervention vs. time control conditions immediately following recruitment Follow-up was completed during the postpartum hospital stay, after the participant had slept but before leaving the hospital. Primary outcome = any drinking, past 90 days (TLFB)
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
INTERVENTION The initial 20-minute brief intervention was largely based on MI principles, tailored to current quit status, health beliefs, and reactivity Intervention participants also received three subsequent tailored mailings, each a single page
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
Candace, you said that you had quit drinking even before we talked to you. You made that decision mostly because quitting drinking would improve the health of your baby. Your decision to stop drinking could also save you up to 400 dollars over the course of your pregnancy!
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
ANALYSIS The primary outcome (any drinking in the past 90 days) was examined as a function of experimental condition, using a logistic model controlling for prior drinking. 81.3% of participants were successfully evaluated at follow-up. Loss did not differ between conditions, and was due to miscarriage (44%), delivering outside of the targeted health system (33%), and inability to contact (22%)
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
Variable
Control (n = 24)
Intervention (n = 24)
African-American 21 (88%) 18 (75%)
HS graduate 14 (58%) 18 (75%)
Any public assistance 20 (83%) 19 (79%)
Alcohol use disorder 5 (21%) 7 (29.2)
Prior treatment 0 (0%) 2 (8%)
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
0%
5%
10%
15%
20%
25%
30%
e-SBIRT Control
Any drinking, past 90 days
OR= 3.2 (p = .20)
e-SBIRT for alcohol use in pregnancy: Pilot trial (Ondersma et al., 2015)
A pilot RCT of e-SBIRT for alcohol use in pregnancy
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
e-SBIRT Control
Miscarriage, LBW, or NICU stay
OR= 3.3 (p = .09)
Question 2: Can a technology-delivered brief intervention
reduce tobacco use in pregnancy?
Smoking abstinence for CD-5As intervention vs. control
*
0%
5%
10%
15%
20%
25%
30%
35%
7-day abstinence perbreath CO/self-report
Abstinent per cotinine
Control
e-SBI
e-SBI for smoking in pregnancy (N = 107; Ondersma et al., 2012)
SAMPLE N = 110 primarily African-American pregnant women reporting active smoking, proactively recruited from a Detroit prenatal care clinic
INTERVENTION Intervention was a single 20-minute session following the “5As” approach (Ask, Advise, Assess, Assist, Arrange) plus 5Rs (motivational elements); it included tailored video clips of a physician and women who had quit
*
0%
10%
20%
30%
40%
50%
60%
70%
Called Quitline Talked to MD/RN
e-SBI
Control
Help-seeking following brief intervention
Question 3: Can a technology-delivered brief
intervention reduce postpartum drug use?
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Ease ofuse
Overallliking
Clarity Futureinterest
Rat
ing,
1-5
sca
le
Participant satisfaction (Ondersma, Chase, Svikis, & Schuster, 2005)
SAMPLE Postpartum women in private hospital rooms, after having slept; primarily African-American and low-income, all reporting drug use prior to becoming pregnant.
e-SBIRT for postpartum drug use (Ondersma et al., 2007, 2013)
SAMPLES Postpartum women (N = 107 and N = 143) in private hospital rooms, after having slept; primarily African-American and low-income, all reporting drug use prior to becoming pregnant.
INTERVENTION Based primarily on brief intervention principles; provided information, feedback, and optional goal setting sections with heavy use of synchronous interactivity, reflections, empathy, affirmations, & humor.
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Ease ofuse
Overallliking
Clarity Futureinterest
• 7-day abstinence shows intervention effect at 3 months only (OR = 3.3)
• Hair analysis at 6 months shows advantage for intervention condition (28.9% vs. 7.9% abstinence, p = .018)
*
0%
10%
20%
30%
40%
50%
3 months 6 months
Control Intervention
Abstinence in replication trial (N = 143) (Ondersma, Svikis, Thacker, Beatty, & Lockhart, 2013)
•Ondersma et al., 2012
•110 pregnant women
•Abstinence: 28.6% vs. 15.6%
Smoking in pregnancy
•Ondersma et al., 2007
•107 postpartum women
•Abstinence: 33.3% vs. 16.2%
Postpartum drug use #1
•Ondersma et al., 2014
•143 postpartum women
•Abstinence: 37.3% vs. 13.7%
Postpartum drug use # 2
•Tzilos, Sokol, & Ondersma, 2011
•50 pregnant women
•Birth weight: 3,190 vs. 2,965 gm
Drinking in pregnancy
•Schwartz et al., 2014
•360 primary care patients in NM
•Counselors vs. CIAS
•Software: similar or better results
Person vs. machine
•Ondersma et al., 2015
•48 pregnant women
•Abstinence: 90.0% vs. 73.7% (ns)
•Healthy baby: 21.7%
Drinking in pregnancy II
• Unpublished data from 2 major trials
• Equivalent or better outcomes vs. therapist
Person vs. machine II
•Naar-King et al., 2013
•76 youth with HIV
•Adherence: 97.1% vs. 87.6%
•Undetectable viral load: 52% vs. 38%
Adherence to ART
Promising results in multiple trials
What does the future hold?
Three future challenges:
IMPLEMENTATION, INTEGRATION, & EVALUATION
We need to make technology part of ongoing care
We need information from the patient-facing software to be available to providers
We need to demonstrate system-level improvements
Merrill-Palmer Skillman Institute
Steve Ondersma [email protected]
@steveondersma mpsi.wayne.edu
psychiatry.med.wayne.edu