Presentation given at the 2010 American Public Health Association Annual Meeting on mHealth for Substance Use Measurement and Prevention

1. mHealth for Measuring and
Controlling Substance Use
Jay M. Bernhardt, PhD, MPH
Director, Center for Digital Health and Wellness
Professor and Chair, Health Education and Behavior
University of Florida
Stuart Usdan, PhD
Associate Professor, Health Education
University of Alabama
Monica Webb, MS
Doctoral Research Associate, University of Florida

2. Mobile Health = “mHealth”
• mHealth is considered a segment of eHealth
– Devices include mobile phones, smart phones,
PDAs, laptops, tablets, and other wireless tools
– Uses wireless connectivity (like SMS, MMS,
Bluetooth, GSM/GPRS/3G, WiFi, WiMAX, etc.)
– Tools store, transmit, and enable various eHealth
data content, applications, and services
– Accessed by health workers, patients or consumers
Adapted from http://www.vitalwaveconsulting.com/pdf/mHealth.pdf

3. Mobile > Phone
• Phone
• Clock
• Calculator
• Calendar
• Text messaging (SMS & MMS)
• Wireless internet (mobile web)
• Wireless connectivity
(bluetooth, etc)
• PC/Enterprise synchronization
• MP3 player
• GPS receiver
• Applications (apps)
Freakingnews.com

4. Change in Internet Use by Age, 2000-2010

5. Home Broadband vs. Mobile
• Home broadband access in US
– All Adults: 60% 50-64: 56% 65+: 26%
• % of Adults Own Cellphone Wireless Internet
– 18-29 90% 84%
– 30-49 88% 64%
– 50-64 82% 49%
– 65+ 57% 20%

6. Project HAND (2003-2005)
• HAND: Handheld Assisted Networked Diary
• Study Purpose:
– To assess feasibility of using wireless devices
(smart phones) for measuring alcohol use
– To compare mobile data collection to gold-
standard data collection approaches
– To pilot test using tailored text messages for
reducing alcohol use and negative consequences
• Funding Agency: NIAAA

7. Project HAND (2003-2005)
• Design:
– Multiple experiments held during study period
– Undergraduate students; Moderate drinkers
– Multiple campuses in Southeastern US
• Measures:
– HAND: Administered by smartphone app
– TLFB: Timeline Follow-Back (retrospective/paper)
– Diary: Paper-based daily alcohol diary
– Other scales & measures: context/consequences

8. Welcome! Today is
Monday. When you
answer the following
questions, think
about YESTERDAY as
the past 24 hours.
(Next to continue.)
Next 
• Administered on
Palm i705 and Palm
m125 handheld
computers
• Reminders via
alarms, SMS, email,
and RA calls
Project HAND (2003-2005)

9. Feasibility of Mobile Data Collection
• MD average completion rate = 85.8 (SD 19.6)
• PB average completion rate = 97.6 (SD 3.0)
• 85% encountered challenges completing the
daily assessment and contacted technical
support an average of 3.61 (SD 1.99) times.
• 94.2% reported receiving a reminder
• Overall, participants found the assessments
easy to complete, easy to read, and enjoyed
using the MD to complete daily assessments.

10. Mobile Data Collection vs. TLFB
HAND TLFB
Number of drinking days 132 153
Mean total number of
alcoholic drinks reported
22.6 (SD = 17.6) 23.7 (SD = 21.6) t= 0.418, p = .679
Mean number of drinks per
drinking day
6.15 (SD = 2.79) 5.67 (SD = 3.41) t = 1.34, p = .191
Mean number of heavy
drinking episodes
2.13 (SD = 1.90) 2.15 (SD = 1.77) t = -0.110, p = .913
• Both methods obtained similar levels of
reported alcohol consumption
• Data collected daily decreases recall bias
compared to longer-term retrospective

11. Comparing HAND to DSD for Alcohol-Related Variables
Variable
Total Drinks
b (95% CI)
Drinking Days
b (95% CI)
Drinks/
Drinking Day
b (95% CI)
Assessment (1 = HAND, 0 = DSD) -0.15 (-2.52, 2.21) -0.09 (-0.37, 0.19) 0.50 (-0.41, 1.41)
Baseline TFLB
Log (1+total drinks)a 7.45† (6.06, 8.90) - -
Drinking days - 0.18† (0.14, 0.23) -
Drinks/drinking day - - 0.66† (0.49, 0.83)
Gender (1 = male, 0 = female) -2.97* (-5.41, -0.54) -0.25 (-0.53, 0.04) -0.58 (-1.52, 0.37)
Age 0.42 (-0.38, 1.35) 0.14* (0.03, 0.24) 0.29 (-1.52, 0.37)
Null model likelihood ratio test
χ2 = 90.9, 9 df,
p < .001
χ2 = 105.41, 9 df,
p < .001
χ2 = 50.07, 9 df,
p < .001
Notes: TLFB = Timeline Followback. a Natural log transformation was applied because the baseline total drinks
variable was strongly kurtotic.

12. Tailored Text Message Pilot
• Participants assigned to Handheld-only (HH)
or Handheld-plus-messaging (HHM)
• Recoded their alcohol consumption for the
previous day on the handheld computer each
day throughout the 2-week study period
• Baseline included TLFB assessment, Alcohol
Consequences Expectancies Scale (ACES), and
the Alcohol Consequences Self-Efficacy Scale
(ACSES)

13. Variable
HHM
Marginal
Means
HH
Marginal
Means
p
Baseline to follow-up surveys
Alcohol consumed, total drinks during study period 22.07 27.52 .35
Drinking days 4.52 3.98 .49
Drinks per drinking day 4.86 6.41 .02*
Negative consequences 2.77 2.36 .68
Negative consequences per day 0.61 0.72 .71
Notes: Follow-up and handheld surveys gathered data for the same study period. HHM = handheld plus messaging; HH = handheld
only. *Difference between groups at p = .05 level.
Mean difference from baseline to follow-up

14. Scale
HHM
Marginal
Means
HH
Marginal
Means
p
ACES 2.55 2.60 .58
Positive a 4.20 4.10 .40
Negative 1.94 2.03 .42
Trouble 1.33 1.58 .02*
Overindulgence 2.21 2.49 .08§
Emotional 2.86 2.33 .07§
Physical harm 1.87 1.78 .55
ACSES 4.07 3.95 .29
Trouble a 4.67 4.37 .06§
Academic/hangover a 4.04 3.70 .06§
Notes: HHM = handheld plus messaging; HH = handheld only; ACES = Alcohol Consequences Expectancy Scale; ACSES = Alcohol
Consequences Self-Efficacy Scale. a Higher scores are positive; for others, high scores are negative.
§ Difference between groups at p > .05 – p = .10; *difference between groups at p = .05 level.
Mean follow-up scores and group differences

15. Project HAND Implications
• Data collected via MD can be less time consuming
and lead to more cost efficient data analysis.
• MD data collection and delivery allows for real
time, personalized responses for risk behaviors.
• MD assessment applied over a longer period of
time may be more comparable to the cost of PB
assessments.
• Research is needed to examine alcohol
assessment and intervention capabilities of MDs,
in particular investigating their use over longer
study periods.

16. For more information on Project HAND
• Mays D, Cremeens J, Usdan S, Martin R, Arriola KJ,
Bernhardt JM. (2010). The feasibility of assessing
alcohol use among college students using wireless
mobile devices: Implications for health behavior
research. Health Education Journal.
• Bernhardt, J.M., Usdan, S., Mays, D., Arriola, K.J.,
Martin, R.J., Cremeens, J., & Arriola, K.J. (2009).
Alcohol assessment among college students using
wireless mobile technology. Journal of Studies on
Alcohol and Drugs, 70, 771-775.
• Arriolla, K.J., Usdan, S., Mays, D., Weitzel, J.A.,
Cremeens, J., Martin, R., Borba, C.P.C., &
Bernhardt, J.M. (2009). Reliability and validity of
the Alcohol Consequences Expectations Scale,
American Journal Health Behavior, 33, 504-512.
• Mays, D., Bernhardt, J.M. et al. (2009).
Development and Validation of the Retrospective
Alcohol Context Scale, American Journal of Drug
and Alcohol Abuse, 35, 109-114.
• Usdan, S., Martin, R.J., Mays, D., Cremeens, J.,
Aungst-Weitzel, J. & Bernhardt, J. (2008). Self-
reported consequences of intoxication among
college students: Implications for harm reduction
approaches to high-risk drinking. Journal of Drug
Education, 38, 4, 377-387.
• Bernhardt, J.M., Usdan, S., Mays, D., Arriola, K.J.,
Martin, R.J., Cremeens, J., McGill, T., & Weitzel, J.A.
(2007). Alcohol assessment using wireless
handheld computers: A pilot study. Addictive
Behaviors, 32, 12, 3065-3070.
• Weitzel, J.A., Bernhardt, J.M., Usdan, S., Mays, D.,
& Glanz, K. (2007). Using wireless handheld
computers and tailored text messaging to reduce
negative consequences of drinking alcohol. Journal
of Studies on Alcohol and Drugs, 68, 534-537.
• Bernhardt, J.M., Usdan, S.L., & Burnett, A. (2005).
Using handheld computers for daily alcohol
assessment: Results from a pilot study. Journal of
Substance Use, 10, 347-353.

17. Growing Evidence for SMS-Based
Behavioral Interventions
• Lewis & Kershaw, Epidemiol Rev, 2010:
– 12 Studies (5 disease prev; 7 disease mgmt)
– Of 9 sufficiently powered studies, 8 had evidence to support
SMS as behavior change tool
• Fjeldsoe, Marshall, Miller, Am J Prev Med, 2009
– Reviewed 14 studies (4 prevention, 10 self mgmt)
– Positive change outcomes observed in 13 of 14
• Positive short-term effects on behaviors
• Larger and longer-term studies needed

18. mHealth 1.0 to 2.0 and beyond…

19. Thank you
jaybernhardt@ufl.edu
twitter.com/jaybernhardt
jaybernhardt.com