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mHealth for Alcohol

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

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


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  • mHealth for Development: The Opportunity of Mobile Technology for Healthcare in the Developing World. United Nations Foundation
    http://www.vitalwaveconsulting.com/pdf/mHealth.pdf
  • Mays, D., Cremeens, J., Usdan, S., Martin, R.J., Arriola, K.J. & Bernhard, J.M. (2010). The feasibility of assessing alcohol use among college students using wireless mobile devices: Implications for health education and behavioural research. Health Education Journal, 69(3), 311-320. DOI: 10.1177/0017896910364831
  • Bernhard, 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, 3065-3070. DOI: 10.1016/j.addbeh.2007.04.012
  • Bernhardt, J.M., Usdan, S., Mays, D., Martin, R., 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.
  • 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.
  • 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.
  • 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.
  • Transcript

    • 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