A Pilot Trial of a Smoking Cessation App in People Living With HIV (PLWH)
Phase: NA
Conditions: Smoking Cessation, Smoking, Smoking Behaviors, Smoking Reduction, Smoking, Tobacco, Smoking, Cigarette, Hiv, HIV/AIDS
Sponsor: Columbia University

Title: Smoking Cessation Pilot for People Living With HIV (PLWH) 
NCT Number: NCT04808609 
Document Date: 6/1/2021 
 

Stand Alone Protocol: SMOKING CESSATION PILOT – IRB-AAAS6990   
PI: Dr. Rebecca Schnall   
Background:     
Of the approximately one million persons living with HIV (PLWH) in the United States (U.S.), it is 
estimated that between 40-70% smoke cigarettes, at least three times the prevalence (14%) observed 
in the general U.S. adult population. Consequently, PLWH experience substantial tobacco-related 
morbidity and mortality. In PLWH, after achieving and maintaining a suppressed viral load, smoking 
cessation is the next most important health behavior to maximize both quality of life and life expectancy. 
Indeed, PLWH who quit smoking upon entering HIV care gain greater than 5 years of life expectancy as 
compared to those who enter HIV care and continue smoking.1,2        
Given the high prevalence of cigarette smoking among PLWH and the benefit of smoking cessation, 
there is an urgent need for swift action to intervene to reduce tobacco use rates. However, evidence to 
improve tobacco cessation among PLWH is inadequate. Few tobacco cessation interventions have 
been tested among PLWH, and of those which have, there is ‘very low’ quality evidence that tobacco 
cessation interventions were effective in the short-term, and ‘moderate’ quality evidence indicating 
similar outcomes to controls in the long-term.3 Given the few randomized controlled trials (RCTs) 
examining smoking cessation interventions for PLWH, and the major methodological limitations of many 
of these studies (e.g., lack of randomization, comparison conditions, treatment fidelity assessments, 
abstinence verification tests), it is critical to develop evidence-based tobacco cessation interventions to 
address the complex and unique needs of PLWH (e.g., risk factors, treatment needs). Tailored 
cessation studies designed for dissemination and implementation are necessary to focus efforts on 
PLWH who use tobacco products.      
Pharmacotherapy Offered to All Participants. Current treatment guidelines recommend 
pharmacotherapy be offered to all smokers making a quit attempt.4  Pharmacotherapy doubles quit 
rates when compared to placebo,5 and the highest abstinence rates are achieved when 
pharmacotherapy is combined with a behavioral intervention.6 There are also ethical concerns about 
offering a placebo treatment only in a historically mistreated group;7-9 therefore, the interventions would 
likely be less acceptable or feasible if a placebo was part of the study design. As part of this 
intervention, we will provide all participants with NicoDerm CQ Patch as a form of Nicotine     
Replacement Therapy. As all forms of nicotine replacement therapy are over-the-counter medications, 
participants are not required to obtain physician clearance to take them.     
Mobile health (mHealth) is an ideal platform for implementation and dissemination of evidence-based 
strategies.10 mHealth can provide mechanisms for improving the efficiency and effectiveness of care 
provided while reducing administrative burden. mHealth technology can be used for achieving health 
equity in vulnerable groups because it is a widely available and relatively inexpensive tool for health 
behavior change and can be 11 adapted to meet the needs of its end-users.12-15 The emerging evidence 
base on mHealth delivered behavioral interventions supports their promise to improve health disparity 
outcomes.16 Therefore, mHealth interventions such as Lumme proposed through this study is timely, 
relevant, scalable and likely to improve health outcomes in our health disparate study population -- 
PLWH who smoke.      
     
Aim: To conduct a pilot study on the Lumme mobile app in a population of PLWH as a tobacco 
cessation intervention (N=40)  Hypothesis 1:      
Overview of Study Aims, Design and Methods     

Purpose: The purpose of the research study is to find out more about smoking behaviors in people 
living with HIV and the subsequent efficacy of tobacco cessation strategies.     
Study Design: A pilot study will be conducted with 40 PLWH randomized to 2 arms. Participants will be 
randomly assigned to receive Lumme (active) or a control condition (standard smoking cessation 
counseling session). Participants will also receive a survey at baseline and at 12-weeks post-baseline.      
Recruitment: We will recruit PLWH by posting flyers at local clinics and community-based 
organizations throughout New York City. Potential participants will be pre-screened for eligibility over 
the phone by study staff. We will also provide enrolled participants with a study flyer to share with 
potentially eligible individuals.     
Study Populations: Our study participants will consist of 40 individuals who smoke tobacco. Inclusion 
criteria. (1) PLWH; (2) ≥ 18 years of age; (3) own an Android smartphone; (4) understand and read 
English; (5) not pregnant or breastfeeding; (6) permanent contact information; (7) smokes ≥5 cigarettes 
per day for the past 30 days; (8) interested in quitting smoking within 30 days; and (9) blow >5 CO into 
a breath analyzer at baseline. Exclusion criteria. (1) self-report being HIV-negative or unknown status; 
(2) pregnant, breastfeeding, or planning to become pregnant during the study period; (3) planning to 
move within 3 months of enrollment; (4) a positive history of a medical condition that precludes use of 
the nicotine patch; (5) current use of nicotine replacement therapy or other smoking cessation 
medications (e.g., Chantix or Zyban); (6) current enrollment in another smoking cessation program; and  
(7) blows ≤ 5 CO into a breath analyzer at baseline.     
     
Screening: Potential study participants will provide verbal informed consent to complete a phone 
screening that assesses eligibility. If eligible, participants will attend a baseline session described 
below. Those who do not screen eligible will be informed and thanked for their time.      
     
Procedures: Eligible participants up to this point will be scheduled to attend a baseline visit at the     
Columbia University School of Nursing. Prior to arriving, participants will be asked to complete the 
Fagerstrom Test for Nicotine Dependence (FTND) and identify their cigarette type (menthol vs regular). 
Upon arrival to the study site, scheduled participants will be asked to blow into a breath analyzer to 
complete determining study eligibility. Breath samples will be analyzed for exhaled carbon monoxide    
(CO) levels (in ppm) using a breathalyzer (Micro +TM basic Smokerlyzer®). Those identified as ineligible 
will be given a Metrocard to thank them for their time. Those identified as eligible will be given an 
informed consent form and the study staff will read the form to them. Before a participant signs the 
informed consent form, staff will answer any questions. Participants will be given a copy of the informed 
consent form for their records.      
     
After providing written consent, study participants will complete a timeline follow back to track their 
cigarette use for the last 30 days. Afterwards, they will complete a baseline questionnaire that includes 
demographic characteristics, tobacco use history, substance use history, alcohol use history, 
psychosocial factors, pharmacotherapy use, and more (See Table 1). Survey instruments will be 
collected through Qualtrics, a secure, Web-based application designed to support data capture for 
research studies, providing an intuitive interface, audit trails, and automated export. Qualtrics is a free 
service offered through Columbia University Irving Medical Center.      
     
Following the completion of the baseline study instruments, study participants be randomized (1:1) to 
the Lumme arm or the control arm. Participants in both arms will undergo a smoking cessation 
counseling session and be provided with nicotine replacement therapy (NRT) in the form of the 
NicoDerm CQ Patch, under the supervision of a nurse. They will be provided  

NRT sufficient for an 8-week supply in order to follow the three-step program. However, they will be 
encouraged not to begin NRT until 2-weeks after the baseline visit. This will be the quit date set for 
participants in both arms.     
     
Participants who smoke more than 10 cigarettes per day will receive the following dosage: one 21 
milligram patch per day for 4 weeks; one 14 milligram patch per day for 2 weeks; and one 7 milligram 
patch per day for 2 weeks. Participants who smoke 10 or less cigarettes per day will receive the 
following dosage: one 14 milligram patch per day for 6 weeks; and one 7 milligram patch per day for 2 
weeks. This is considered the standard clinical treatment and participants will receive additional 
instructions on usage of the patch. Subjects will be told the risks of using the patch, which include skin 
irritation, itching, dizziness, headache, rapid heartbeat, and nausea. Participants will also be advised 
that they can curb additional cravings with their choice of gum or lozenge available for purchase 
overthe-counter.      
     
For those in the intervention arm, study staff will provide participants with a Misfit Vapor 2 or Skagen 
Falster 2 smartwatch and initiate the Lumme app on the participant’s mobile phone. Both smartwatches 
are compatible with the Lumme app. Participants will be asked to wear the smartwatch and use the app 
for 12 weeks to collect baseline data and improve detection of smoking. Study staff will help users 
create an account with the Lumme app which will passively collect smoking data during the first 2 
weeks, such as when the user smokes (time of day, day of week, before/after eating, after waking 
up/before going to sleep, before driving to/from work, while driving), where the user smokes, and who is 
nearby when the user smokes (based on the seeing the same Bluetooth static address as Lumme does 
not collect identities). Once created, the Lumme app will be paired with the smartwatch so that smoking 
will be detected by the smartwatch and be sent directly to the Lumme app. The smartwatch will detect 
cigarettes smoked throughout the time the user wears the smartwatch, or users can manually enter a 
smoking session. The Lumme app will then be able to reliably predict cravings, target users with 
notifications to prevent individuals from smoking, refine the notifications for each user, and display their 
change in smoking behavior and money saved in a smoking diary. Users will also be able to see their 
quit plan with their assigned quit date 2 weeks after baseline, along with smoking trends, supporting 
tips, and earn badges from the amount of money saved. Data related to participants’ smoking behaviors 
and patterns will be shared with Lumme. The study coordinator will also be able to access smoking 
behaviors and subject information through Lumme’s anonymized online dashboard. At the end of the 
baseline visit, all enrolled participants will be compensated $40 for their participation in the form of a 
pay card.      
     
On the day before their quit date, study staff under the supervision of a nurse will call participants in 
both arms to remind them of their quit date and to start using their NRT NicoDerm CQ patches the 
morning of their quit date. This phone call will consist of the staff reassuring and encouraging the 
participant to stick to their quit date. Specific details for the call will be provided to staff via a phone 
script.    
   
Upon reaching their quit date, participants in the intervention arm will receive personalized feedback in 
the Lumme app. Also, participants in both the control and intervention arm will receive biweekly checkin 
calls from study staff members. Staff will complete these calls in order to 1) track participants reported 
NRT fidelity, 2) provide technology assistance to those in the intervention arm who might experience 
difficulties with the smartwatch or app, and 3) keep participants engaged in the study.   
         
Table 1. Study Measures     
 

Construct     
Measures (if applicable)     
Measurement Time 
Points     
     
     
     
Demographics     Gender, age, education, income, employment, health insurance, housing, time 
since diagnosis with HIV, tobacco, alcohol, substance use, health literacy 
(measured by Short Test of Functional Health Literacy in Adults [S-TOFHLA]),17 
and co-morbid conditions     
Baseline     
Tobacco Use 
History      
Age of initiation, current smoking, daily smoking, types of products used (e.g., 
menthol cigarettes, e-cigarettes), history of quit attempts, prior use of 
quitline/telephone counseling services     
Baseline     
Tobacco   
Cessation     
CO lung/blood concentration, 7-day point prevalence abstinence –Timeline 
follow-back (TLFB)18,19;  number of quit attempts; days of continuous abstinence 
Baseline, 12 weeks     
   
Illicit Substance 
and Alcohol Use   
NIDA ASSIST20,21, Short Michigan Alcoholism Screening Test (SMAST), Drug 
Abuse Screening Test (DAST)     
Baseline     
Health-Related 
Quality of Life     
PROMIS-2922     
Baseline, 12 weeks     
Predictors of     
Tobacco     
Cessation      
Fagerstrom Test for Cigarette Dependence,23 Readiness to quit24      
Baseline, 12 weeks     
Cravings and 
Withdrawal     
Minnesota Withdrawal Scale,25      
Baseline, 12 weeks     
Marker of    
HIV/AIDS immune 
status/ ART 
Adherence     
Case Adherence Index (self-report)26     
Baseline, 12 weeks     
Intervention 
Quality     
Health Information Technology (IT) Usability Evaluation Scale (Health-  
ITUES)27; Post-System Study Usability Questionnaire (PSSUQ)28, Intervention 
ratings     
12 weeks     
Psychosocial 
Factors     
Stress: Perceived Stress Scale (PSS)29,30, Depression: Center for     
Epidemiological Studies Depression scale (CESD score)31, Anxiety: State-Trait 
Anxiety Inventory (STAI score)32, Social support  33     
Baseline, 12 weeks     
Pharmacotherapy  
use     
Nicotine replacement therapy, Bupropion, Varenicline Use and Self-Reported 
Adherence       
Baseline, 12 weeks     
    
At 12 weeks after the baseline visit, participants will attend a 12-week follow-up visit at the study site.   
Or if requested, participants can complete a remote study visit, where they will be mailed a CO monitor 
( by the study team and complete study tasks via video call on Zoom Health with a study team member. 
Study participants will be asked to report a 7-day point prevalence abstinence of smoking cigarettes 
and provide a breath sample using Micro +TM basic Smokerlyzer®. They will also be given a follow-up 
survey that includes questions asked at baseline and process questions dictated by the primary 
outcomes that assess the usability and acceptability of Lumme (see Table 1).     
    
At the 12-week follow-up visit, semi-structured in-depth interviews will take place with participants. The 
in-depth interviews will be approximately 10-30 minutes in length and no additional compensation will 
be provided. The in-depth interviews will be conducted by the study coordinator and research 
assistants, the members responsible for conducting follow-up visits. These interviews aim to fill gaps in 
the literature by taking a qualitative approach to understanding the acceptability of the   

Lumme app as a smoking cessation tool in the intervention arm and learn more about all participants’ 
opinions on the recruitment and enrollment process. Both groups will answer questions including: 1) 
How would you modify the outreach and recruitment process to improve it or make it easier? 2) How 
comfortable were you with the recruitment, screening and enrollment process? 3) How can we improve 
the current screening process? Some of the questions the questionnaire will include for the intervention 
group are: 1) Please describe your general perceptions and expectations of the app, 2) How often did 
you use the app? 3) How helpful was the app for tobacco cessation? 
 
 
The team will adhere to qualitative research processes to ensure the credibility, confirmability, 
dependability, and transferability of the qualitative data from these analyses.34 To support the credibility 
of the data, we will conduct peer debriefing and triangulate findings across multiple data sources 
(surveys). In addition, we will use “member checks,” i.e., sharing of initial data interpretations with 
participants to ensure accurate interpretations. Triangulation of findings, along with reflexivity, will 
enhance the confirmability of the interpretations. The investigators will carefully record an audit trail and 
keep extensive field notes to facilitate transferability of study findings into other contexts.     
At the end of the 12-week follow-up visit, participants will be compensated $60 for their participation. 
Compensation will be provided in the form of pay cards or Amazon gift codes. All participants will also 
receive a Metrocard at each in-person visit.     
     
Operationalization of Outcome Measures: The primary outcome for determining preliminary efficacy 
will be biochemically validated 7-day point prevalence abstinence.      
    
Analysis: This is a pilot study meant to assess preliminary feasibility, acceptability, and efficacy. Given 
the small sample size, the purpose of the arms is simply to monitor for unexpected, gross differences 
between arms.      
To determine acceptability, we will ask participants in both arms how useful each of the intervention 
components are (1 = Not at all useful to 7 = Extremely useful) and whether they would recommend the 
program to a friend (1 = Definitely would not recommend to 7 = Definitely would recommend). For the 
intervention to be deemed acceptable, the mean level for each of these measures would have to be five 
or higher.     
All in-depth interviews will be transcribed verbatim and then coded. The development and application of 
a coding scheme is an integral component of the data analysis process. It enables the systematic 
examination and interpretation of the data related to the primary analytic foci. The coding scheme is 
conceptualized as a multilevel structure. At the highest level are the primary analytic foci coded as 
headings. Specific aspects or dimensions of the headings are assigned core codes. Specific aspects or 
dimensions of the core codes are assigned sub codes. We use ATLAS.ti, a software program for 
qualitative analysis, to facilitate the analysis.    
    
The following 7 steps will be used to develop the coding scheme:     
Step 1: Identify the principal issues discussed by interviewees.     
Step 2: Construct definitions of the primary analytic themes.     
Step 3: Develop and apply core codes and sub-codes to the initial set of interviews.     
Step 4: Develop a provisional coding scheme.     
Step 5: Test and refine the provisional coding scheme.     
Step 6: Reconcile coding differences and construct an updated and final coding scheme.  Step 7: Apply 
the coding scheme to the full data set and assess inter-coder reliability.     
    

After all transcripts have been coded, we will extract and examine the content of text segments linked to 
core codes and sub-codes relevant to understanding the technology acceptance of the Lumme app and 
barriers and facilitators to its use. Based on the coded data, we will propose ways in which certain 
themes are analytically related. A careful examination of the coded text will reveal the associations 
among these themes, and may lead to more refined data searches. Once we establish patterns of 
relationships among themes and issues, we will identify participants’ accounts that support or refute 
these patterns. Identifying and accounting for cases that deviate from an interpretative pattern enable   
us to test and confirm the pattern’s validity and robustness. Schnall has done extensive work  
understanding technology acceptance with particular emphasis on end-users’ needs. 35-38    
    
Efficacy of the intervention will be based on self-reported 7-day point prevalence abstinence at the 
follow-up survey. Lost-to-follow-up participants will be included as smokers. The abstinence rate will be 
calculated.     
    
1. 
Mdodo R, Frazier EL, Dube SR, et al. Cigarette smoking prevalence among adults with hiv compared with 
the general adult population in the united states: Cross-sectional surveys. Annals of Internal Medicine. 
2015;162(5):335-344.     
2. 
Reddy KP, Parker RA, Losina E, et al. Impact of Cigarette Smoking and Smoking Cessation on Life 
Expectancy Among People With HIV: A US-Based Modeling Study. The Journal of Infectious Diseases. 
2016;214(11):1672-1681.     
3. 
Pool ERM, Dogar O, Lindsay RP, Weatherburn P, Siddiqi K. Interventions for tobacco use cessation in 
people living with HIV and AIDS. Cochrane Database of Systematic Reviews. 2016(6).     
4. 
Fiore MC, Smith SS, Jorenby DE, Baker TB. The effectiveness of the nicotine patch for smoking cessation: 
A meta-analysis. JAMA. 1994;271(24):1940-1947.     
5. 
Hughes JR, Goldstein MG, Hurt RD, Shiffman S. Recent advances in the pharmacotherapy of smoking. 
Jama. 1999;281(1):72-76.     
6. 
Stitzer ML. Combined behavioral and pharmacological treatments for smoking cessation. Nicotine & 
Tobacco Research. 1999;1(Suppl_2):S181-S187.     
7. 
Brandt AM. Racism and research: the case of the Tuskegee Syphilis Study. Hastings center report. 
1978;8(6):21-29.     
8. 
Corbie-Smith G. The continuing legacy of the Tuskegee Syphilis Study: considerations for clinical 
investigation. American Journal of the Medical Sciences. 1999;317(1):5-8.     
9. 
Thomas SB, Quinn SC. The Tuskegee Syphilis Study, 1932 to 1972: implications for HIV education and 
AIDS risk education programs in the black community. American journal of public health. 
1991;81(11):1498-1505.     
10. 
mHealth Alliance. Frequently Asked Questions, 2011. 2010; 
http://www.mhealthalliance.org/about/frequently-asked-questions. Accessed August 3, 2011.     
11. 
Cole-Lewis H, Kershaw T. Text Messaging as a Tool for Behavior Change in Disease Prevention and 
Management. Epidemiologic Reviews. 2010;32(1):56-69.     
12. 
Schnall R, Carballo-Dieguez A, Larson E. Can the HIV Home Test Promote Access to Care? Lessons 
Learned from the In-home Pregnancy Test. AIDS Behav. 2014.     
13. 
Ostergren JE, Rosser BRS, Horvath KJ. Reasons for non-use of condoms among men who have sex with 
men: a comparison of receptive and insertive role in sex and online and offline meeting venue. Culture, 
Health & Sexuality. 2010;13(2):123-140.     

14. 
Winetrobe H, Rice E, Bauermeister J, Petering R, Holloway IW. Associations of unprotected anal 
intercourse with Grindr-met partners among Grindr-using young men who have sex with men in Los 
Angeles. AIDS Care. 2014;26(10):1303-1308.     
15. 
Hightow-Weidman L, Muessig K, Bauermeister J, Zhang C, LeGrand S. Youth, Technology, and HIV: Recent 
Advances and Future Directions. Curr HIV/AIDS Rep. 2015;12(4):500-515.     
16. 
Bakken S, Marden S, Arteaga S, et al. Harnessing Consumer Information Technology–Enabled Behavioral 
Interventions as Tools to Advance Health Equity. AJPH. 2019;Vol 109(Supplement 1).     
17. 
Aguirre AC, Ebrahim N, Shea JA. Performance of the English and Spanish S-TOFHLA among publicly 
insured Medicaid and Medicare patients. Patient Educ Couns. 2005;56(3):332-339.     
18. 
Brown RA, Burgess ES, Sales SD, Whiteley JA, Evans DM, Miller IW. Reliability and validity of a smoking 
timeline follow-back interview. Psychology of Addictive Behaviors. 1998;12(2):101.     
19. 
Sobell LC, Sobell MB. Timeline follow-back. In: Measuring alcohol consumption. Springer; 1992:41-72.     
20. 
Ghitza UE, Gore-Langton RE, Lindblad R, Shide D, Subramaniam G, Tai B. Common data elements for 
substance use disorders in electronic health records: the NIDA Clinical Trials Network experience. 
Addiction. 2013;108(1):3-8.     
21. 
Bogenschutz MP, Donovan DM, Adinoff B, et al. Design of NIDA CTN Protocol 0047: screening, 
motivational assessment, referral, and treatment in emergency departments (SMART-ED). The American 
journal of drug and alcohol abuse. 2011;37(5):417-425.     
22. 
Schnall R, Liu J, Cho H, Hirshfield S, Siegel K, Olender S. A Health-Related Quality-of-Life Measure for Use 
in Patients with HIV: A Validation Study. AIDS Patient Care STDS. 2017;31(2):43-48.     
23. 
Fagerstrom K-O, Schneider NGJJobm. Measuring nicotine dependence: a review of the Fagerstrom 
Tolerance Questionnaire. 1989;12(2):159-182.     
24. 
Biener L, Abrams DB. The Contemplation Ladder: Validation of a measure of readiness to consider 
smoking cessation. Health Psychology. 1991;10(5):360-365.     
25. 
Hughes JR, Hatsukami DJAogp. Signs and symptoms of tobacco withdrawal. 1986;43(3):289-294.     
26. 
Mannheimer S, Mukherjee R, Hirschhorn L, et al. The CASE adherence index: A novel method for 
measuring adherence to antiretroviral therapy. 2006;18(7):853-861.     
27. 
Schnall R, Cho H, Liu J. Health Information Technology Usability Evaluation Scale (Health-ITUES) for 
Usability Assessment of Mobile Health Technology: Validation Study. JMIR Mhealth Uhealth. 
2018;6(1):e4.     
28. 
Lewis JR. Psychometric Evaluation of the PSSUQ Using Data from Five Years of Usability Studies. 
International Journal of Human–Computer Interaction. 2002;14(3-4):463-488.     
29. 
Robinson BC. Validation of a Caregiver Strain Index. Journal of Gerontology. 1983;38(3):344-348.     
30. 
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. Journal of health and social 
behavior. 1983:385-396.     
31. 
Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Applied 
psychological measurement. 1977;1(3):385-401.     
32. 
Spielberger CD, Gorsuch RL, Lushene RE. Manual for the state-trait anxiety inventory. 1970.     
33. 
Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. 
Journal of personality assessment. 1988;52(1):30-41.     
34. 
Guba EG. Criteria for assessing the trustworthiness of naturalistic inquiries. Educational Communication 
and Technology Journal. 1981;29(2):75-91.    

35. 
Lee Y, Johnson SB, Trembowelski S, Schnall R, Sheehan B, Bakken S. Cognitive walkthrough evaluation of 
an investigator profiles directory. . Proc AMIA Annu Fall Symp. 2011.    
36. 
Schnall R, Cimino JJ, Bakken S. Development of a prototype continuity of care record with contextspecific 
links to meet the information needs of case managers for persons living with HIV. Int J Med Inform. Aug 
2012;81(8):549-555.    
37. 
Schnall R, Odlum M, Gordon P, Bakken S. Barriers to implementation of a Continuity of Care Record 
(CCR) in HIV/AIDS care. Stud Health Technol Inform. 2009;146:248-252.    
38. 
Sheehan B, Lee Y, Rodriguez M, Tiase V, Schnall R. A comparison of usability factors of four mobile 
devices for accessing healthcare information by adolescents. Appl Clin Inform. 2012;3(4):356-366.