Electronic Screening and Brief Intervention for … Perinatal...Electronic Screening and Brief...

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 s.ondersma@wayne.edu

@steveondersma mpsi.wayne.edu

psychiatry.med.wayne.edu