Randomized Trial of text4baby in the Military Women’s Population: Evidence

of a Dose-Response Relationship

Presented at:

GW Conference on mHealth in Health Systems in an Era of Healthcare Transformation

Presented by:

W. Douglas Evans, Ph.D.

Sponsored by the Telemedicine and Advanced Technology Research Center (TATRC), US Army Medical and Materiel Command, Grant # W81XWH-10-2-0142

mHealth is many things…

• New channel through which to change behavior

• A new form of behavior (use of mobile phone ) in itself

• A platform for research

• Are mobile devices more effective tools for behavior change than other media? For which behaviors?

• How do we optimize the effectiveness of mobile?

mHealth behavioral theory

• Point of decision aid/prompt

• Cue to action

• Exchange mechanism (build relationship)

• Tailoring/personalization/branding

• Mobile is normative - ‘Everybody’s doing it’

• Need to capture best of existing theories and determine what mobile adds (and doesn’t add)

The text4baby Initiative

• Free mobile SMS service in

English and Spanish

• Messages for pregnant

women and new mothers

• 3 free SMS text messages

each week, tailored to delivery

date/baby’s birth date

Text4baby | Message Content

Sample Content

Health Care Access

Immunization

Nutrition

Prenatal Care

Drugs and Alcohol

Emotional Wellbeing

Smoking Cessation

Labor & Delivery

Breastfeeding

Safe Sleep

Oral Health

Additional topics

Sample Messages

Content developed by

HMHB in collaboration

with HHS, CDC, NICHD,

HRSA, BabyCenter,

physicians and nurses.

You’re not alone. If you

need help, call 1-800-

311-BABY (2229) for

free or low-cost health

care & your local WIC

program.

Congratulations on

your baby’s birth!

Baby's 1st doctor’s visit

should be 2 to 3 days

after leaving the

hospital. Ask your

doctor when to

schedule it.

A seat belt protects you

& your baby. Shoulder

belt goes between your

breasts & lap strap

goes under your belly

(not on or above).

Wear it every time.

Keeping your baby’s

mouth clean is

important even before

she has teeth! Wipe

her gums each day

with a wet washcloth or

use a soft baby

toothbrush.

Text Message Topics Pregnancy Infant

Tobacco Tobacco

Alcohol and drugs Infant feeding/oral health

Nutrition Developmental milestones

Safety Safety

Services: referral, encourage use Services

Infection prevention/screening Immunizations

Medications Postpartum depression

Breastfeeding Infection prevention

Support/bonding/”feel good” Support/crying

Influenza

text4baby in English and Spanish

7

Watch the clip here: http://www.mtv.com/videos/misc/496217/text4baby.jhtml#id=1634555

The text4baby Conceptual Model

• text4baby applies principles from Theory of Planned Behavior (Fishbein & Ajzen, 2010; and Social Cognitive Theory (Bandura, 2004)

Study Objectives

• Conduct a randomized controlled trial (RCT) of text4baby in the military women’s population

– @ Madigan Army Medical Center, Tacoma, WA, USA

• Only examines pregnant women and pre-natal text4baby program

• In this presentation, results of the RCT @ 1) first follow up (FL1) one month after baseline enrollment; and 2) final follow up (FL3) at participant’s postpartum medical appointment

Design & Methods

• Inclusion criteria: 14 weeks or earlier of gestational age, first presentation for pregnancy care, working cell phone

• Randomly assigned into 2 groups: – Standard care

– Text4baby text messaging + standard care

• Enrollees consented, then completed baseline 24-item knowledge, attitudes, and behavior (KAB) instrument – Items based on content of text messages, on 4-point Likert

scale (strongly agree to strongly disagree)

• Clinical outcomes data (e.g., birth weight, depression)

• Data on dosage of text messages received

• Re-interviewed online and/or in person at 3 follow ups: – 4-weeks post baseline (FL1), 28 weeks of gestational age

(FL2), postpartum well-baby visit (FL3)

Data collection and attrition

• Baseline sample was 943 (470 in text4baby, 473 control)

• Completed 459 follow-up surveys, for a 48.7% retention rate – represents attrition from the study not text4baby (99% continued receiving texts)

• Significant difference in those reporting being married 70.31% at BL vs. 76.69% at FL1, p=0.000; and in those currently working or attending school – 63.1% at BL vs. 53.16% at FL1, p=0.0004

• No differences by study condition

Figure 1. Madigan text4baby CONSORT Flow Diagram

Assessed for eligibility (n= 1,078 )

Excluded (n=135)

Not meeting inclusion criteria (n=82)

Declined to participate (n=53)

Other reasons (n=0)

Analyzed (n=230)

Excluded from analysis (n=0)

Lost to follow-up (unable to recontact after

multiple attempts following protocol) (n=243)

Discontinued intervention (give reasons) (n=6)

Allocated to text4baby (n=473)

Received text4baby (n=473)

Did not receive text4baby (n=0)

Lost to follow-up (unable to recontact after

multiple attempts following protocol) (n=241)

Discontinued control (n=0)

Allocated to control (n=470)

Received control (n=470)

Did not receive control (n=0)

Analyzed (n=229)

Excluded from analysis (n=0)

Allocation

Analysis

Follow-Up

Randomized (n=943)

Enrollment

Protocol Details

• Participants assigned to the control condition were excused immediately after completing baseline survey

• Treatment participants were immediately directed to enroll in Text4baby by texting DODBABY (tagged them as participants in our study to a designated SMS short code to receive messages

• This combination of enrollment phrase and short code identified participants as members of the Madigan evaluation study

• Only text4baby participants who were enrolled in the Madigan study were counted in our treatment group

Analysis

• Generalized estimating equations (GEE) logistic regression was used to construct separate models for each outcome over the three follow-up periods

• GEE models both for treatment effects and effects of higher levels of message exposure

• Unadjusted models (no co-variates)

• Adjusted models with age quintile, parity, marital status, and race/ethnicity

• Use multiple imputation (MI) for missing data

Dosage measurement

• We obtained data on the number and timing of text messages delivered to the text4baby participants

• We identified the week of pregnancy at enrollment by assuming a 40-week gestation period and subtracting the total number of days between the participant enrollment and due date recorded at OB/GYN clinic

• Using this calculation we identified the number of messages delivered to each participant during the study period

• This calculated variable was used for the GEE dose-response models presented later

Demographics

0

10

20

30

40

50

60

70

80

90

100

Age group Race Ethnicity

Pe

rce

nt

(%)

Demographics

Control Text4babyn = 943

Results

0

10

20

30

40

50

60

70

80

90

100

Marital Status Sponsor Rank

Pe

rce

nt

(%)

Demographics

Control Text4babyn = 943

0

10

20

30

40

50

60

70

80

Participated in WIC Currently in school/working Parity

Pe

rce

nt

(%)

Demographics

Control Text4babyn = 943

Summary of text4baby messages delivered

(n = 192) Mean Std. Dev. Low High

Total Number of Messages Sent 61.26 43 10 151

Week of Pregnancy When Enrolled 8.15 1.92 4 14

Weeks in Pregnancy Protocol 12.6 10.9 0 33.7

Outcome analysis • text4baby messages targeted knowledge, attitudes &

beliefs about multiple topics • For example: Taking folic acid and prenatal vitamins, about

avoiding smoking and drinking, nutrition, health care and other topics

• At FL1, significant effects of text4baby on attitudes/beliefs targeted by the texts

• Evans, W.D., Nielsen, P., Szekely, D., Wallace, J., Murray, E., Snider, J. (2014). Initial Outcomes From a 4-Week Follow-Up Study of the Text4baby Program in the Military Women’s Population: Randomized Controlled Trial. Journal of Medical Internet Research, 16(5):e13. DOI:10.2196/jmir.3297.

GEE Models –text4baby effects at FL1 OR, (95% CI,) p-value Effect of intervention and time on

strong agreement (unadjusted)

(n=459)

Effect of intervention and time on strong

agreement (fully adjusted) (n=459)

( ATTITUDES

Eating 5 or more fruits and vegetables

per day is important to the health of my

developing baby

1.49 (0.96-2.31), p=0.075 1.47 (0.83- 2.63), p=0.189

Taking a prenatal vitamin is important

to the health of my developing baby

1.68 (0.96-2.94), p=0.069 1.73 (0.80-3.73), p=0.164

I am prepared to be a new mother 1.07 (0.673-1.57), p=0.804 1.28 (0.74-2.23), p=0.555

If I visit my health care provider on a

regular basis, I will be a healthy new

mother

1.52 (1.01-2.31), p=0.046 1.66 (0.98-2.81), p=0.058

If I visit my health care provider on a

regular basis, my baby will be healthy

1.22 (0.83 -1.80), p=0.320 1.32 (0.81-2.16), p=0.268

Smoking will harm the health of my

developing baby

1.63 (0.74-3.61), p=0.226 2.25 (0.64-7.92), p=0.204

Secondhand smoke will not harm the

health of my developing baby (reverse

coded*)

1.14 (0.81-1.58), p=0.450 0.82 (0.47-1.44), p=0.491

Drinking alcohol will harm the health of

my developing baby

2.06 (1.00-4.31), p=0.050 2.19 (0.87-5.52), p=0.095

Taking prenatal vitamins will improve

the health of my developing baby

1.33 (0.84-2.10), p=0.221 1.91 (1.08-3.34), p=0.024

Dosage Analysis

• Following tables present results of unadjusted and adjusted versions of the second set of GEE models

• Here, we examined the effects of high versus low dosage of text4baby, as measured by a median split variable in which the top 50% of the distribution of text message exposure among text4baby intervention participants was compared to the bottom half among that same group

Comparison of High and Low Exposure to text4baby Messages

• * Significant effect on alcohol consumption post-partum as measured by the question “Since you found out about your pregnancy, have you consumed alcoholic beverages?”

• Evans, W.D., Nielsen, P., Szekely, D., Wallace, J., Murray, E., Snider, J. (2015). Dose-Response Effects of the text4baby Mobile Health Program: Randomized Trial. Journal of Medical Internet Research: mHealth uHealth. DOI: 10.2196/mhealth.3909.

Behaviors

Since you found out about your pregnancy, have you consumed alcoholic beverages?

1.344 (0.473, 3.820), P= 0.579 0.212* (0.046, 0.973), P= 0.046

In the last 30 days, did you smoke? 0.938 (0.490, 1.794), P= 0.846 1.271 (0.406, 3.980), P= 0.680

Ate 3 or more servings of fruit a day

0.852 (0.647, 1.122), P= 0.254 0.908 (0.649, 1.271), P= 0.575

Ate 3 or more servings of vegetables a day

0.889 (0.679, 1.164), P= 0.392 0.969 (0.673, 1.394), P= 0.863

Have you ever gone online to search for prenatal care information?

0.992 (0.661, 1.488), P= 0.969 0.893 (0.519, 1.536), P= 0.681

Follow up on drinking quantity

• Asked follow up question about drinking quantity

• At baseline, 97.3% of all participants reported 0 drinks per day; remained fairly constant at 97.6% and 96.0% at FL1 and FL2, with no differences between study conditions

• At FL3, however, the overall rate declined to 55.4%. However, of the 98 respondents who indicated 0 drinks per day, only 39 (39.8%) of them were in the control group and 59 (60.2%) in text4baby

• Evidence of heightened risk perceptions about drinking among text4baby participants

Discussion/Conclusions

• Program holds promise for mHealth interventions as it is a light touch approach (low interaction)

• Need to understand optimal levels of dosage and other factors that affect mHealth intervention outcomes

• Delivering high doses of mHealth interventions has implications in terms of cost, participant burden, and potential ‘wear out’ effects (i.e., over-exposure).

• Identifying optimal mHealth dosages could have potential major benefits for future programs in terms of cost effectiveness and outcomes

Thank you!

• Questions?

• wdevans@gwu.edu

• @douge66