WH2014 Session: Know your audience predictors of success for a patient centered

KNOW YOUR AUDIENCE PREDICTORS OF

SUCCESS FOR A PATIENT-CENTERED

MARK J. SIEDNER

MASSACHUSETTS GENERAL HOSPITAL CENTER

FOR GLOBAL HEALTH

Know Your Audience: Predictors of success for a pa5ent-‐

centered SMS applica5on to augment HIV linkage to care in rural Uganda

Research Abstract, Wireless Health 2014 Oct 29-‐31, 2014, Bethesda, MD, USA

Mark J. Siedner Massachuse1s General Hospital Center for Global Health

Conflicts of Interest and Funding

•  No financial conflicts of interest to report •  I receive salary and research support from: – Fogarty InternaEonal Center R24 TW 007988

– NaEonal InsEtutes of Health K23 MH 099916 – Harvard Center for AIDS Research

Great PotenEal for Mobile Health in sub-‐Saharan Africa

InternaEonal TelecommunicaEons Union, 2014

-

100

200

300

400

500

600

700

2005 2006 2007 2008 2009 2010 2011 2011 2013 2014*

Fixed-telephone subscriptions

Mobile-cellular subscriptions

Individuals using the Internet

MILL

IONS

Have we harnessed that potenEal?

Keys to Successful mHealth ImplementaEon

 Locally defined and prioriEzed health problem Conceptual framework – How (and if) mobile health can address the problem

–  Is it sufficient to do so?

 Co-‐creaEon (or local creaEon)   Comprehensive evaluaEon by the end-‐user acceptability, feasibility and acceptance

IteraEve assessment (develop pilot efficacy effecEveness implementaEon)

2) Conceptual Framework

ART Eligibility ART Ini5a5on


Lack of Awareness of ART Eligibility (CommunicaEon)




Poverty (TransportaEon Costs)






1. SMS No5fica5on of Laboratory Results





1. SMS No5fica5on of Laboratory Results

2. Transporta5on Reimbursement

3) Co-‐Create

•  Development team – Clinic staff – Clinic database management team – Research team

•  Biomedical

•  Anthropology •  Social scienEsts

– Dimagi Inc (web development company) – Yo! Uganda (content aggregator)

4) End-‐User Acceptability

•  Pre-‐study survey of 50 clinic clients – 100% expressed interest in a cell phone-‐based system of communicaEng clinic informaEon

4) End-‐User Acceptability

“It will save the cost of transport because we come and we find nothing ready for almost four :mes of coming to the clinic and going with no achievement. So it would make us come when we are sure.”

5) IteraEve Assessment

A. Development stage – Dimagi Design

B. Pilot – Clinic and research staff pilot tesEng – Feedback to Dimagi

C. Efficacy Trial

Efficacy Clinical Trial •  Eligibility – Adults in HIV care in Mbarara

– Undergoing “high-‐risk” CD4 count tesEng – Access to a cell phone

Efficacy Clinical Trial •  Study groups – Pre-‐intervenEon period (standard of care) –  IntervenEon period (SMS)

•  Normal laboratory result: single SMS

•  Abnormal laboratory result –  RandomizaEon

»  Direct Message

»  PIN-‐protected Message

»  Coded Message (ABCDEFG) –  Up to 7 daily messages

–  TransportaEon reimbursement (~$6USD)

Efficacy Clinical Trial

Enrollment

• Baseline Survey • CD4 TesEng

CD4 Count Result Outcomes Assessment

• Group DeterminaEon • SMS Scheduling

• Follow-‐up Survey

Efficacy Clinical Trial

Efficacy Trial •  Message DisseminaEon

Efficacy Trial

DAYS TO CLINIC RETURN

Day 14

DAYS TO ART INITIATION

Why did (and didn’t) it work?

•  Secondary analysis •  Predictors of successful response to an SMS-‐based intervenEon for paEent end-‐users in rural Uganda

Predictors of Response

PIN SMS Message + Incen5ve:

49 Abnormal Results

Coded SMS Message + Incen5ve:

43 Abnormal Results

138 Abnormal Results

Direct SMS Message + Incen5ve:

46 Abnormal Results

Randomiza5on

Outcomes of Interest

•  Self-‐reported receipt of at least one SMS •  Accurate idenEficaEon of SMS

•  Appropriate clinic return – Abnormal result: ≤7 days of first SMS

Cohort CharacterisEcs

•  Median age 30 •  55% female

•  Median HH income $100/month

•  60% primary educaEon or less

•  72% successful read a sentence on enrollment

Reported SMS Receipt Accurate SMS Identification

Return to Clinic <7 Days

AOR (95%CI) P-value AOR (95%CI) P-

value AOR (95%CI) P-value





Age <26 REF REF REF 26-32 0.97 (0.30 – 3.13) 0.97 0.33 (0.07 – 1.60) 0.17 0.71 (0.24 – 2.07) 0.53 33-39 1.43 (0.40 – 5.16) 0.59 0.98 (0.18 – 5.27) 0.99 0.72 (0.23 – 2.19) 0.56 ≥40 1.49 (0.41 – 5.45) 0.54 0.24 (0.05 – 1.19) 0.08 0.66 (0.22 – 1.95) 0.45 Female gender 0.95 (0.38 – 2.37) 0.92 1.30 (0.44 – 3.83) 0.63 1.15 (0.52 – 2.52) 0.73 CD4 Result ≤100 REF REF REF 101-350 1.08 (0.39 – 2.96) 0.89 0.51 (0.13 – 1.96) 0.33 0.28 (0.11 – 0.75) 0.011 Read a complete sentence

2.14 (0.85 – 5.39) 0.11 4.54 (1.42 – 14.47) 0.011* 3.81 (1.61 – 9.03) 0.002*

Accessed sample SMS on enrollment†

3.05 (0.76 – 12.21) 0.12 0.63 (0.08 – 4.68) 0.65 4.90 (1.06 – 22.61)

0.04*

Randomized SMS Format Direct REF REF REF PIN 0.76 (0.27 – 2.17) 0.61 0.11 (0.03 – 0.44) 0.002* 0.26 (0.10 – 0.66) 0.005* Coded 1.00 (0.31 – 3.20) 0.99 0.38 (0.08 – 1.80) 0.22 0.58 (0.22 – 1.55) 0.28






2.14 (0.85 – 5.39) 0.11 4.54 (1.42 – 14.47) 0.011* 3.81 (1.61 – 9.03) 0.002*


3.05 (0.76 – 12.21) 0.12 0.63 (0.08 – 4.68) 0.65 4.90 (1.06 – 22.61) 0.04*


†Restricted to par5cipants with an available cellular phone on enrollment






2.14 (0.85 – 5.39) 0.11 4.54 (1.42 – 14.47) 0.011* 3.81 (1.61 – 9.03) 0.002*


3.05 (0.76 – 12.21) 0.12 0.63 (0.08 – 4.68) 0.65 4.90 (1.06 – 22.61)

0.04*


Summary 1

•  Strongest predictor of receipt and response to SMS-‐based mHealth intervenEon was proved literacy on enrollment

•  Cell phone literacy also appears important •  Gender, age, educaEonal a1ainment not predicEve

•  PIN-‐protected messages are challenging •  Coded messages protect privacy without challenging feasibility

Next Steps

•  Clinic wide automated intervenEon in development

•  Post-‐intervenEon effecEveness •  Post-‐intervenEon acceptability

Collaborators and Team

•  MGH CGH/HMS •  Alexander Lankowski •  David Bangsberg •  Jessica Haberer •  Norma Ware •  Richard Holden (IU)

•  MUST •  Yap Boum •  Anthony Wilson •  Bosco Bwana •  Data Santorino •  Winnie Muyindike •  Isaac Aturinda •  Evans Mwesigwa

QuesEons

•  Thank you for your a1enEon

Mark Siedner [email protected]

www.wirelesshealth2014.org

WH2014 Session: Know your audience predictors of success for a patient centered

Healthcare

Transcript of WH2014 Session: Know your audience predictors of success for a patient centered