Smoker-to-Smoker (S2S) Peer Marketing and Messaging to Disseminate Tobacco
Phase: NA
Conditions: Smoking Cessation
Sponsor: University of Massachusetts, Worcester

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
1
 
 
 
Smoker-to-Smoker (S2S) Peer Marketing and 
Messaging to Disseminate Tobacco Interventions 
June 2019 
NCT 03224520 
 
 
 
 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
2
RESEARCH STRATEGY  
A. BACKGROUND  
A.1. Overview 
Building on our PCORI pilot (1IP2P1000582)1,2 and National 
Cancer 
Institute 
(NCI) 
funded 
studies 
(R21CA158968, 
1R01CA129091),2-8 we propose “Smoker-to-Smoker (S2S) Peer 
Marketing 
and 
Messaging 
to 
Disseminate 
Tobacco 
Interventions.” S2S will test two smoker-driven, social marketing innovations to recruit and engage smokers in 
Decide2Quit, an evidence-based, effective “Digital Intervention for Smoking Cessation” (DISC).5,9 These S2S innovations, 
designed to utilize the power of peers and social networks for dissemination are: 
1. Recommender computer tailored health communication (recommender CTHC): Complex machine learning 
algorithms (recommender systems) that uses smokers’ feedback (explicit and implicit) in the current and prior 
studies to adapt its selection of messages to smokers (PCORI IP2P1000582).  
2. Access to peer recruitment toolset: Access to peer recruitment tools to facilitate dissemination of the trial 
(recruitment) – with smokers’ recruiting their peers to increase Decide2Quit access (NCI R21CA158968).  
Thus, this study is a hybrid effectiveness-dissemination study.  First, to test the effectiveness of the recommender CTHC, 
smokers will be recruited online to a randomized trial hosted on Decide2Quit.10 Smokers will be consented and 
randomized to receive either the recommender CTHC (the enhanced intervention) or a standard tailored messaging 
system (the comparison).  Second, to test dissemination (after randomization to the enhanced or comparison messaging 
system), some randomized and enrolled smokers will be further allocated to receive access to peer recruitment tools; and 
these smokers will be encouraged to recruit other smokers in their social network.  Allocation to receive the peer 
recruitment tools occurs in two phases or waves (Wave 1: Half of the enrolled smokers are randomized to receive peer 
recruitment tools, while the others do not receive the tools; and Wave 2: all subsequent smokers who are randomized to 
the effectiveness trial and also report they were peer-recruited are given access to the peer-recruitment tools.  This two-
wave allocation follows principles of respondent driven sampling, as is designed to increase both 1) the Speed of 
dissemination, and 2) the Diversity of smokers recruited.  Note that in a prior NIH-funded pilot study, we found peer 
recruitment was particularly effective for engaging African American smokers. Thus, we will also test the potential of peer 
recruitment to target African American smokers, a disproportionally affected group due to tobacco use, compared with 
online recruitment without peer recruitment, in this dissemination study.11-17 
A.2. Condition impact (smoking) on health of individuals and populations (Criterion 1) 
Smoking is the number one preventable cause of death in the United States (USA).18-22 Yearly, over six million deaths in 
the world are attributable to smoking, including 480,000 in the USA.23 The CDC estimates that for every person who dies 
because of smoking, at least 30 people live with a serious smoking-related illness, including cancer, heart disease, stroke, 
lung diseases, diabetes, and chronic obstructive pulmonary disease (COPD).23 One estimate suggested that the proportion 
of USA health care expenditure attributable to smoking ranges between 6-18% of the budget across different states.24 The 
cost per life year saved from use of pharmacological treatment interventions ranged between US$128 and US$1,450 and 
up to US$4,400 per quality-adjusted life years saved.24 As noted in Section B.3., African Americans are disproportionately 
affected by tobacco and many of their health issues are directly related to their tobacco use.11-17 Thus, reducing tobacco 
use will have considerable health impact, and play an important role in reducing health disparities. 
A.3. Evidence gap (RQ-1) 
Our proposal addresses a key question  — What are the effective strategies for increasing consumer demand for and use 
of proven, individually oriented cessation treatments, including among diverse populations? — raised in the State-of-the-
Science Conference Statement on Tobacco Use.25 We are focused on identifying effective strategies to disseminate and 
improve effectiveness of a DISC — www.decide2quit.org (Decide2Quit). DISCs are health communication programs 
accessible via Internet connections and smart phones. DISCs can include a number of functions designed to support a 
smoker’s cessation attempt. As detailed in Table 5, Decide2Quit includes self-management functions, pushed and tailored 
motivational messages (email or text-messages), online community, and peer support. Several studies, including 
systematic reviews,26-28  have shown that DISCs can be effective. A Cochrane review found a statistically significant effect 
comparing tailored DISCs to usual care or written self-help (RR 1.48, 95% CI 1.11, 2.78).28 Although this evidence is mixed, 
two factors are typically associated with the effectiveness of DISCs.20,26-36 These include the use of CTHC to personalize 
messaging to smokers, and the engagement of smokers (recruitment and repeated system use). In our previous 
randomized trial [1R01CA129091] testing Decide2Quit (n=900), at 6-months, 30% of smokers who received the CTHC 
Acronyms Used 
S2S 
Smoker-to-Smoker 
Decide2Quit 
www.decide2quit.org 
DISC 
Digital Intervention for Smoking Cessation 
CTHC 
Computer-tailored health communication 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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messages reported quitting at 6 months compared to 20% of smokers who received an interactive DISC but no messages 
[odds ratio 1.70 (95% CI 1.03-2.81)].10 We have also demonstrated that the repeated use of Decide2Quit functions was 
significantly associated with six-month, 30-day point prevalence cessation. Using a repeated use measure — an ordinal 
scale of the number of functions used after the first visit to the website (0: use of no functions, 1: use of 1-2 functions, 2: 
> 2-4 functions) — we found a linear association with six-month cessation. For every increase by one in this scale, odds of 
smoking cessation increased (OR= 2.10, 95% CI = 1.03, 4.30).37 In conclusion, DISCs can be an effective resource for 
smokers, but the success depends on the implementation of CTHC, and the longitudinal engagement of smokers.    
Outside healthcare, companies have innovated to use bottom-up, “user-driven” approaches to increase their 
websites’ access and engagement. Peer marketing — engaging one customer to recruit others — has become the 
preferred approach to increase access to their websites. To motivate users to repeatedly use their system, companies like 
Amazon and Netflix Use recommender systems to deliver enhanced personalization (Section B.5).  However, these tools 
have not been rigorously tested for disseminating health interventions in comparative effectiveness studies. In our prior 
studies [PCORI IP2P1000582, NCI R21CA158968], we have developed and demonstrated the efficacy of peer recruitment 
and recommender CTHC to disseminate Decide2Quit — recruit and motivate smokers to repeatedly use the system  — 
and improve its effectiveness. We will rigorously test these tools in a comparative effectiveness study, compared with the 
standard Decide2Quit (online recruitment and standard CTHC). In the RFA, PCORI defines dissemination as the active and 
targeted approach of spreading evidence-based interventions to potential adopters and the target audience through 
determined channels using planned strategies, and its goals as to increase the reach of information, motivation, and 
patients’ ability to use and apply evidence.38-40 Thus, both recruitment and use measures are needed to appropriately 
evaluate our DISC dissemination strategy. If recruitment is unsuccessful, then the intervention’s reach is low. If 
recruitment is successful, but the intervention does not motivate repeated use, then there is low intervention fidelity, 
reducing the patient’s motivation and ability to apply evidence. Beyond measuring dissemination, the primary goal of this 
RFA, we will evaluate our interventions’ effectiveness by measuring six-month cessation (as recommended by prior 
reviews), and measuring reduction in cigarettes, as advised by our patient panel.   
B. SIGNIFICANCE  
Because prior literature and our own preliminary data suggest multiple barriers in the reach of smoking cessation 
interventions, including those online, new approaches to increasing dissemination are needed.41 The majority of smokers 
are not interested in quitting at any given time.42 Even those highly motivated to quit often fail in their attempts.43 Peer 
recruitment and recommender CTHC are unique in that they harness the power of smokers to disseminate a cessation 
intervention (recruit and motivate to repeatedly use), and encourage cessation. In this section, we describe the potential 
impact and reach of DISCs, potential to target those most in need, and the two patient-centric and patient-driven S2S 
innovations (peer recruitment and recommender CTHC), including their theoretical foundations.  
B.1. Impact of DISC (Impact = Reach × Effectiveness) 
Per the RE-Aim framework,44 an intervention’s impact is a product of its reach and effectiveness. Although in-person and 
telephone counseling are effective, they are costly and underused (reducing their reach). DISCs can serve as important 
augmentation for those receiving in-person and telephone counseling (to use in between sessions and for longitudinal 
support). For those who do not have access to these options, DISCs may serve as the only source of tobacco cessation 
support. As noted, Decide2Quit achieved a cessation rate of 30% at six months, much higher than the rate (7%) at which 
smokers quit without support.45 Thus, it is important to innovate and increase dissemination and effectiveness of DISCs.  
B.2. DISCs: Internet, Social Media Use, and Digital divide  
DISCs are health communication programs readily accessible via the Internet and smart phones. Thus, it has considerable 
potential to reach a large and diverse group of smokers.  In 2014, an estimated 87% of Americans had Internet access.46,47 
Digital divide in Internet access has decreased considerably, especially with increasing smart phone use. In 2015, an 
estimated 61% of Whites, 70% of African Americans, and 71% of Hispanics had smart phones. Most of these smart phone 
users had accessed a wide range of functions, including getting information about a health condition (62%) and online 
banking (57%). The use of online functions on smart phones is higher in lower income than higher income users.48 Use of 
social media has also considerably increased.49 Over 74% of USA Internet users use online social networks including 71% 
that use Facebook. Again, smart phones have decreased the digital divide in use of social networks. A higher proportion 
of African Americans (48%) and Hispanics (49%) access social networks on their phones than Whites (36%).48  
 
However, because of this lingering perception that vulnerable populations have little technology access, these 
populations are underrepresented in technology research. It may not be the lack of technology access, but that the 
dissemination strategies have not sufficiently evolved to engage these groups.  

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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B.3. Increasing Reach of DISCs to African American Smokers  
 We focus on African American smokers for several reasons: 1) These smokers suffer disproportionately due to smoking-
related diseases including several cancers, cardiovascular disease, and cerebrovascular disease.11-17 Although they smoke 
fewer cigarettes and start smoking at an older age, these smokers are more likely to die from smoking-related diseases 
than Whites.11 They are less likely to be successful at quitting than White or Hispanic smokers because they are less likely 
to seek cessation support, including DISCs.11-14 2) The standard CTHC in Decide2Quit successfully motivated African 
Americans smokers to quit (Section C.4.1.2). 3) Our NCI funded pilot demonstrated that peer recruitment significantly 
enriched the sample with African American smokers (11-23%) (Figure 4). This increase happened without our tools 
prompting the peer recruitment of African American smokers. In S2S, with our patient advisory panel (See Section F), we 
will further refine our instructions to encourage African American smokers’ recruitment. Thus, we anticipate an even 
higher increase in proportion of African American smokers in S2S.  
B.4. Smoker-to-Smoker (S2S) peer recruitment: Peer-referrals as an online marketing Strategy 
Health information and healthy behaviors can be “infectious” — spreading between social contacts, creating cascading 
effects throughout the network.50,51  For example, over time, smokers in the Framingham cohort were less likely to smoke 
if someone in their network (spouse, sibling) had quit smoking.51 Public health interventions can use these infectious 
effects by using engaged smokers to recruit their peers who smoke.52,53 Peer recruitment leverages current online 
marketing trends. Outside of public health, several marketing groups are using peer recruitment to enhance spread of 
products for a number of reasons. Customers are more likely to trust a peer referral than traditional advertisements.54 
Peer recruited customers are also more profitable than traditional customers.54 There are several examples of successful 
peer-marketing (e.g., Zynga, the online social gaming company) on social media.2,3 To enhance the effectiveness and 
efficiency of these campaigns, companies have developed a more proactive approach, providing customers referral tools 
that allow them to easily refer others to the product (e.g. email referral form).55 As detailed in Section C.4.2.1, the use of 
Facebook referral plugin in our peer recruitment system4 was an example of this approach. Our peer recruitment 
experiment (NCI R21CA158968) to test whether smokers would recruit other smokers was successful. In a one-year period, 
peer recruitment quadrupled our sample —from 190 smokers to 759 smokers.3,56 Peer recruitment also increased 
proportions of not ready-to-quit and African American smokers (11-24%), groups that other recruitment techniques have 
had difficulty encouraging participation.   .   
B.5. Computer-tailored health communication (CTHC): Standard and S2S recommender 
Standard “if-then-else” theory-based CTHC systems: Theory-based CTHC is a tool that is frequently used to support 
behavior change. 57 It builds on the concepts of personal relevance, relatedness, and cultural similarity, constructs of 
multiple behavioral theories including the transtheoretical model, the theory of reasoned action, social cognitive theory, 
and self-determination theory.58-60  Standard CTHC systems use selected variables from patients’ baseline profile and if-
then rules to send tailored messages to specific subsets of patients. 57,61-65  These rules are developed by experts based on 
their knowledge of the targeted population, literature, and health behavior theories. These rules specify how the 
messages should be selected (what messages need to be sent to that patient subset). Table 1 illustrates how a standard 
CTHC system might tailor a message as part of a smoking cessation intervention.66   
Table 1: An Example: Tailored messages addressing weight gain on DISC.* 
John Smith, a 38 year old smoker, has been smoking for 15 years. He has made multiple quit attempts in the 
past, but during each attempt he gained between 10 and 20 pounds. Currently, fear of weight gain is a significant 
barrier to another quit attempt. 
John is trying to quit again and registers on Decide2Quit. For eight weeks, the system sends two tailored 
emails per week to John Smith to help him quit.  
Standard CTHC 
       In this approach, tailoring is based on information that John provides when he registers. For this example, we 
focus on one characteristic only: gender. Since women are typically more concerned about weight gain after quitting, 
67-71 experts have specified that half of the emails sent to women should contain information related to weight gain, 
but only one quarter of the emails sent to men should be focused on weight gain.  After registering on Decide2Quit, 
John receives the first email that targets weight gain support in the second week (3rd message) of the intervention. 
John likes the message and finds the tips it offers useful. He looks forward to receiving similar messages. However, 
the next 5 messages he receives focuses on other topics. The next weight gain message arrives only on week 5.  
John does not think the system helped and fails in his attempt to quit. 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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Recommender CTHC 
In this approach, the selection of the message is based upon the collective-intelligence data, not on preset 
rules.  After registering on Decide2Quit, John visits the weight gain support page on the website (implicit data). The 
system uses this data and selects one of the messages targeting weight gain and sends it to John on Week 2 (3rd 
message). John likes the messages and rates the message highly (explicit data). The system then notes both of these 
items of implicit and explicit feedback and regularly sends messages targeting weight gain to John. The system also 
repeats the message that John rates highly. Because the intervention targeted his needs more specifically, John finds 
these messages useful and succeeds in his attempt to quit. 
*This example was kept simple to be easily understandable. We have not included how the group’s feedback can help John.  
Recommender CTHC systems: New approaches to tailoring based on collective-intelligence may be able to augment 
standard CTHC systems.  Many people already encounter collective-intelligence tailoring as they interact with companies 
like Netflix and Amazon.  These companies use 
a special class of machine learning algorithms 
(recommender systems) to tailor content.  
These systems tailor content based on 
collective-intelligence data (i.e., data derived 
from the behavior of users as they interact 
with the system) in addition to user profiles.72-
74  Collective-intelligence data include implicit 
and explicit user feedback.  Implicit data are 
derived from user actions (e.g., website pages 
and products purchased data).  Explicit data 
consist of self-reported item ratings (e.g., 
ratings provided by users for items like books 
or movies, often on a five-star scale).  
However, in the health promotion arena, 
patients could be asked to rate relevance, 
influence, or other properties of a message or 
product. Using these data, along with user 
demographic characteristics, the algorithms 
underlying the recommender system generate 
personalized item recommendations for each 
user.  As these recommender systems learn 
more about the user, they can continually adapt to improve the recommendations.  
 
The lower portion of Table 1 provides an example of how a recommender system could be implemented to 
provide tailoring as part of smoking cessation intervention.  It shows how applying a recommender approach to health 
promotion could potentially improve upon the tailoring provided by current rule-based CTHC approaches.  The primary 
difference between current and recommender CTHC systems is how the messages are selected.  In standard CTHC, 
message selection is based on preset expert-written if-then-rules. In recommender CTHC, messages selection is based on 
data, i.e., recommender systems learn from the data (patient profiles and implicit and explicit feedback ratings) to select 
the variables and generate the rules that specify how the message will be selected.  As new data about the users are 
collected, these recommender systems have the ability to refine the message selection algorithm. As summarized in Table 
2 and detailed in our paper,66 there are a number of other potential advantages of using recommender CTHC over 
standard CTHC systems. Our pilot experiment (Section 4.2.2) demonstrated that the recommender CTHC performed 
better than Decide2Quit’s standard CTHC, a demonstrated to be effective system, even over a short duration of 30 days. 
 
 
 
 
 
Table 2: Standard CTHC vs. Recommender CTHC66 
Feature 
Standard CTHC 
Recommender CTHC 
Message 
Selection 
(Primary 
Difference)  
Rules-driven: Experts develop 
rules based on the literature 
and theory.  These rules link 
user profiles to the messages, 
selecting messages for a 
patient subset.  
Data-driven: Recommender 
systems derive the tailoring 
rules from the collective-
intelligence data of the 
individual, as well as the 
group.  
Complexity 
(Number of 
variables) 
The number of variables 
incorporated can become 
quickly unmanageable 
dependent on the 
programming team’s skills, 
project’s timeline and budget. 
Recommender systems can 
potentially consider all the 
variables collected in the 
intervention.  
Use of 
theory 
Tailoring limited to theoretical 
constructs 
Augments theory by 
deriving recommendations 
from user data 
Adaptation 
Limited to predicted changes in 
behavior.  
 
System can continuously 
adapt, potentially improving 
with each message 
delivered.  Responds to the 
user’s behavior and to the 
group’s behavior over time. 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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B.6. S2S Theoretical Foundations — Relatedness and personal relevance to increase engagement and motivation:  
Peer recruitment: By providing peer recruitment tools to smokers, S2S facilitates smokers to reach out to their peer 
smokers to participate in a cessation intervention, thereby extending the intervention’s reach. Peer recruitment draw 
strength from the constructs of relatedness, as proposed in self-determination theory (SDT).75 Relatedness between 
individuals — the desire to feel connected to others —  can support behavior change. 75,76 When individuals engage in 
activities that are social in nature, such as providing social support, perceptions of relatedness play an important  role in 
predicting motivation and increasing engagement.77 Thus, both the peer recruitment act, and being peer recruited, might 
motivate the smoker to use the DISC, and quit smoking. 
Recommender CTHC: Recommender CTHC draws strength from personal relevance, relatedness, and cultural similarity, 
constructs of multiple behavioral theories (Table 3). Increasing the personalization of a message, increases the 
relevance and relatedness of 
the message to the user.57 
Personally relevant messages 
are more carefully processed, 
tend to be retained longer, and 
more predictive of behavior 
change.78 By incorporating 
smoker’s feedback, the 
recommender CTHC is able to 
adapt and continuously 
improve the personalization 
of its messages, which can 
increase the DISC’s use, as 
well as cessation outcomes. 
In conclusion, our prior work 
has 
demonstrated 
the 
benefit of each S2S function, 
and in S2S, we will test the 
dissemination potential of 
these functions in a hybrid 
effectiveness-dissemination 
trial.   
C. STUDY DESIGN 
Using a study design that 
includes both randomized 
and 
non-randomized 
elements, we will evaluate 
S2S. Our specific Aims: 
C.1. Specific Aims: (Criterion 
3) 
AIM 1: Implement the hybrid 
effectiveness-dissemination 
trial 
of 
S2S 
enhanced 
functions 
to 
increase 
dissemination (recruitment 
and 
repeated 
use) 
and 
effectiveness of Decide2Quit 
with 1200 smokers (See 
Figures 1a and 1b).  
Table 3: The importance of personalization across multiple theories59 
Methods 
Description 
Theories 
Persuasive 
Communication 
Messages relevant to 
individual’s beliefs  
Persuasion-Communication Matrix, 
Elaboration Likelihood Model, Social 
Cognitive Theory  
Tailoring/ 
Individualization 
Targeting messages based 
on behavior change 
and/or relevance variables 
Transtheoretical Model, Precaution 
Adoption Model, Protection Motivation 
Theory, Persuasion-Communication Matrix 
Figure 1: S2S comparison groups 
     

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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Effectiveness Trial Components (Figure 1a):  
1. 
Recommender computer tailored health communication (recommender CTHC): Complex machine learning 
algorithms (recommender systems) that uses smokers’ feedback (explicit and implicit) in the current and prior 
studies to adapt its selection of messages to smokers.   
2. 
Standard computer tailored health communication (CTHC) using expert-written rules to tailor messages to 
smokers based on a single factor readiness to quit.  
 
Dissemination Study (peer recruitment, Figure 1b):  
1. 
Access to a peer recruitment toolset: to facilitate smokers’ recruiting their peers to increase Decide2Quit access. 
2. 
No peer recruitment:  standard online recruitment using search engine and social media advertisements to recruit 
smokers to Decide2Quit. 
As noted above, as the study is enrolling, we will evaluate the potential of peer recruitment to 1) increase the speed of 
dissemination, and 2) enhance the diversity of smokers recruited.  Related to the Dissemination Study, we have the 
following hypotheses:   
H1: Recruitment  
H1A: Enhancing Diversity:  Peer recruitment will recruit a greater proportion of African American smokers, compared 
to standard online recruitment. 
H1B: Increasing the speed of recruitment:  Peer recruitment will reduce recruitment time (time to recruit each 
participant), compared to standard online recruitment.  
Further, as the study progresses, we will monitor the use of the Decide2Quit.org online system.   
Note that both the recommender CTHC and the peer recruitment tools are designed to increase engagement with 
Decide2Quit.org.  Thus, we propose that the recommender CTHC and peer recruitment tools will be synergistic.  To test 
the potential for increased engagement, we propose the following hypothesis: 
H2:* Increased Engagement (use of Decide2Quit functions over time).   
H2A: Repeated use among those exposed to the fully enhanced group (access to peer recruitment toolset and 
recommender CTHC) will be greater than repeated use among those exposed to: a) peer recruitment toolset only b) 
recommender CTHC with no peer recruitment toolset and c) standard group (no peer recruitment toolset and 
standard CTHC). 
H2B: Repeated use among those exposed to peer recruitment toolset will be greater than repeated use among those 
exposed to the standard group. 
H2C: Repeated use among those exposed to recommender CTHC will be greater than repeated use among those 
exposed to the standard group. 
Thus, we have preliminary hypotheses for dissemination (H1) and engagement (H2).   The main effectiveness outcome 
is described below in Aim 2.   
AIM 2: For the Effectiveness randomized trial, we will recontact participants after six months of enrollment. Compared 
to the standard messaging group, the recommender CTHC is designed to increase smoking cessation rates at follow-up.  
Thus, we hypothesize the following hypotheses:  
H3: * Quit — six month, 7-day point prevalence biochemically verified — and risk reduction in number of cigarettes 
smoked (patient panel recommended outcome).   
H3A: Quit rates among those exposed to the fully enhanced group (peer recruitment toolset and recommender CTHC) 
will be greater than quit rates among those exposed to a) peer recruitment toolset only b) recommender CTHC only 
and c) standard group (no peer recruitment toolset and standard CTHC). 
H3B: Quit rates among those exposed to peer recruitment toolset will be greater than quit rates among those exposed 
to the standard group.  
H3C: Quit rates among those exposed to recommender CTHC will be greater than quit rates among those exposed to 
the standard group. 
H3A, H3B, and H3C will be tested for the risk reduction outcome also. 
*For H3, we will compare all smokers across the groups, and African American smokers across groups, and African 
American and White smokers for assessing heterogeneity of treatment effects.  
 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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TABLE 4: S2S standard and enhanced groups (Criterion 3) 
 
 
Standard 
Enhanced 
 
 
Recruitment 
Tailoring (CTHC) 
Recruitment 
Tailoring (CTHC) 
A 
Fully Enhanced 
S2S 
 
 
Access to peer 
Recruitment 
toolset≠≠≠ 
recommender CTHC*** 
(pushed motivational 
messages via email and text) 
B 
Half Enhanced  
Recommender 
CTHC only 
Standard Online 
recruitment via search 
engine and social 
media advertisements 
(no access to Peer 
Recruitment Toolset) 
 
 
recommender CTHC*** 
(pushed motivational 
messages via email and text) 
C 
Half Enhanced  
Peer 
Recruitment 
toolset only 
 
Standard CTHC*** (pushed 
motivational messages via 
email and text) 
Access to peer 
Recruitment 
toolset≠≠≠ 
 
D 
Standard 
Comparator 
Standard Online 
recruitment via search 
engine and social 
media advertisements 
(no access to Peer 
Recruitment Toolset) 
Standard CTHC*** (pushed 
motivational messages via 
email and text) 
 
 
≠≠≠ Peer recruitment starts with the online recruitment of seeds. Peer recruited smokers will be in the same group as their 
recruiters to minimize contamination. 
***The recommender CTHC adapts to influence ratings provided by current participants in the study (See Section C.4.2.2). Each 
message will include a link to rate the message. Although our standard CTHC does not adapt to this feedback, we will include the 
ratings question in the standard CTHC groups to minimize differences between the two systems. 
 
C.2. Study Design:  This hybrid effectiveness-dissemination trial uses a combination of study designs.  The effectiveness 
study is a two-group randomized trial comparing the recommender CTHC (enhanced intervention) with standard 
messaging (comparison).  For the dissemination trial, allocation occurs in two waves (as described in Figure 1b). Allocation 
to receive the peer recruitment tools occurs in two phases or waves (Wave 1: Half of the enrolled smokers are randomized 
to receive peer recruitment tools, while the others do not receive the tools; and Wave 2: all subsequent smokers who 
are randomized to the effectiveness trial and also report they were peer-recruited are given access to the peer-
recruitment tools.   
  Also as noted above, all Decide2Quit 
functions (Table 5) will be available to all 
groups.  Our goal is to test the 
enhancements 
offered 
by 
peer 
recruitment and recommender CTHC 
(individually and collectively). (RQ-2)  
Online Randomization: In our prior 
technology-assisted randomized trials, 
we have embedded randomization within 
the technology, and we will use a similar 
approach in S2S for the effectiveness trial. As smokers register on the website, they will be allocated to the four 
comparators based on a block-randomization allocation table integrated into the website. Our statistician will generate 
a randomization table; the randomization sequence will be conducted in random blocks of different sizes (8, 12) to ensure 
balance among the groups and reduce predictability of allocation process. Thus, randomization will occur automatically 
at the time of initial registration. For the dissemination study, the initial randomization (see description of Wave 1 
allocation above) will occur after randomization to recommender CTHC or comparison, and will also use the online 
randomization technology.   
Table 5: Major Components of Decide2Quit 
Component 
Description 
Online Community  
Interact with peer smokers through a resource website 
Health Risks 
Information about specific health risks of smoking 
Thinking About 
Quitting 
Helpful ideas and motivational recommendations (e.g: 
interactive calculators assessing triggers, decisional balance) 
Family Tools 
Tips to get help family support, and deal with nagging,  
Provider Tools 
Tips to include healthcare provider in the quit plan  
The Library 
Library about smoking risks and treatments 
Web Resources 
We’ve identified valuable additional websites 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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C.3. Identify, select, recruit, and retain study participants: In Aim 1, we will recruit 1200 smokers online. In S2S, 
recruitment is part of the experiment. Smokers will either be recruited through online advertisements or by peer 
recruitment as further detailed below. (PC-2). 
C.4. Comparators: (RQ-5) 
Smokers in all groups will be given access to all of the primary Decide2Quit functions (Table 5). We describe the 
comparators below.   
 
C.4.1 Standard Functions 
C.4.1.1. Standard Online recruitment via search engine and social media advertisements:  
Smokers will be recruited online using search engine advertisements. These advertisements will be customized to appear 
to smokers searching for quit smoking related search terms online. When smokers click on these advertisements, they will 
be redirected to Decide2Quit, where they will be provided study information and registration instructions. We will use the 
functions provided in the ad managers of the search and social media websites to target ads for smokers. For example, 
the Facebook ad manager allows advertisers to target users based on their interests derived from their profile’s keywords, 
pages they like, and groups they visit. Advertisements will be displayed on the Facebook page of the user.  We will work 
with our patient advisory panel to identify relevant interests, as well as create advertisements with content that will be 
attractive to smokers. We will continuously monitor and refine these advertisements with our patient panel.  
Supportive evidence for online recruitment:  
 We are using online advertisements as our recruitment comparison because it is the current “state-of-the-art” approach 
to recruit smokers, with demonstrated efficacy (RQ-5). Several studies, including our own, have shown that these 
advertisements are successful in recruiting smokers.79,80 Over a five-month period (1R01CA129091-01), we used this 
approach to recruit 300 smokers, 
as well as 100 smokers in 1.5 
months in our PCORI pilot.81 
C.4.1.2 Standard CTHC  
As noted (Section B.4), the 
difference between the standard 
and recommender CTHC is how 
messages are selected. Our study 
goal is to test the ability of the two 
systems 
to 
select 
influential 
messages for individual subjects. 
Thus, both messaging systems 
will use the same message 
database. We plan to send 2 
messages weekly in the first four 
weeks of the intervention. We will 
then send 1 message per week 
until the smoker completes six 
months in the intervention (from 
registration date of a smoker).  
Below, we first describe the 
messaging database and then the 
standard CTHC system.  
The Motivational Messaging database: The messaging database includes 500 messages that were developed in our prior 
RCT and includes both expert-written messages and peer-written messages.82  Expert-written messages (behaviorists, 
physicians, nurses) were developed through an iterative expert group review process.  These messages was informed by 
current guidelines83 and Social Cognitive Theory.84  The current guidelines provided evidence-based content on successful 
cessation strategies.  The Social Cognitive Theory, which incorporates vicarious learning and verbal persuasion, guided the 
writing of the expert messages. Messages reflect theoretical determinants of quitting, such as positive outcome 
Figure 2: Expert vs peer messages: Frequency of codes 
 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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expectations and self-efficacy enhancing small goals.84 Peer-written messages were written by current and former 
smokers responding to an online survey that presented four scenarios tailored by gender, age and readiness-to-quit. These 
messages were then reviewed for use in our system. More details of our methodology to generate peer written messages 
has been previously published.82 Our messaging database includes a comprehensive coding of the messages. We 
developed these codes to facilitate further understanding of what worked and what did not in these messages. These 
codes include constructs from multiple behavioral theories such as the Social Cognitive Theory, the Transtheoretical 
Model, and Theory of Reasoned Action.85 We also coded the messages for content that may be pertinent to a specific 
user, including health and lifestyle status, health issues, and treatment options. Overall, we developed 48 codes divided 
into 8 categories (General Treatments, Behavioral Treatments, Over the Counter and Prescription Treatments, 
Motivations, Health, Socio-Cultural Attributes, Author Attributes, and Author Interaction). A higher proportion of expert-
written messages include codes related to behavioral treatments (49%) and motivations (35%). Peer-written messages 
have more codes related to author attributes and interaction (100%), behavioral treatments (68%), and motivations (52%). 
(Figure 2) Our messages are also categorized by readiness to quit. 
Standard Decide2Quit CTHC System:  
As noted, our comparison standard CTHC is a rule-based (if-then-else) system that tailors messages based on a smoker’s 
readiness to quit. For example, when a smoker logs on to Decide2Quit and indicates his readiness as “not ready to quit”, 
then a message from those categorized for “not ready to quit” smokers will be picked at random and sent to the smoker. 
Similarly, if the smoker indicates his readiness as “set a quit date”, then a message categorized for “set a quit date” 
smokers will be sent to the smoker. The messages can either be sent via emails or text messages.   
Supportive evidence for Standard CTHC system 
We had previously demonstrated this system to be effective in a large nationwide RCT (N=900), compared to a robust 
website control. This website control included such functions as risk, decisional balance, and cessation barrier calculators; 
games linking the chemicals in smoking with their other uses (e.g., formaldehyde is used in both cigarettes and in 
embalming); and a library of informational resources about smoking.10 In the RCT, the messages were sent at the same 
rate as our current plan for the S2S intervention. The messages increased six-month smoking cessation outcomes. Using 
a 6-month, 7-day point prevalence cessation outcome, smokers who received the motivational messages were assessed 
to be more likely to quit than those smokers who received the control website (30% Intervention and 20% comparison, 
Odds Ratio 1.69, 95% CI 1.03-2.8).10  The difference between those who received the CTHC messages and those who did 
not was more pronounced with African Americans (19% versus 3% control). The messages also increased DISC use. 
Compared to non-message days, Decide2Quit use was most likely to occur within 3 days after the motivational message 
(OR=5.4, 95% CI=4.02-7.31). As we also published, the peer messages were most effective in increasing use. Peer messages 
were twice as likely to generate new activity (OR=2.03, 95% CI=1.74-2.35), compared with the expert-written messages,82 
after adjusting for time from registration and smoker characteristics. Hearing from others “just like me” and “in their own 
words” adds value and credibility to smokers regarding how to quit smoking, and successful attempts.  
 
C.4.2. S2S Functions 
C.4.2.1. Access to peer recruitment toolset: 
We will provide smokers access to a peer recruitment toolset to facilitate recruitment of their peer smokers to the study. 
The first wave of smokers who will be given access to the toolset will be referred to as seeds. The peer recruitment toolset 
includes a Facebook website plugin, an online training video, and a recruitment tracker (Appendix A). Our Facebook 
website plugin was developed in our NCI-funded pilot (R21CA15896).3,4 The Facebook plugin will allow smokers to browse 
through their Facebook friends and recruit them by sending private recruitment messages. The online training video will 
teach the recruiter how to use available tools to recruit other smokers from their social network. The recruitment tracker 
will allow smokers to track successful peer recruitment. To encourage recruitment, the system also will email or text a 
weekly recruitment report to the potential recruiter. The basic flow of a single hypothetical smoker through the peer-
referral, registration, and subsequent initiation of new peer-recruitment is described in Figure 3. Once a seed registers on 
Decide2Quit and receives the peer recruitment toolset, we expect the following steps:  
1. The seed consents to be in the study and recruit smokers from his/her network using the peer-recruitment tools 
(by sending a Facebook private message).  
2. The successfully peer-recruited smoker (Wave 1 recruit) registers on the system and consents to then recruit 
other smokers in their social network.  

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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3. The Wave 1 recruit then continues the peer-recruitment chain, recruiting smokers from their social network. 
4. The successfully peer-recruited smoker then registers (Wave 2 recruit). 
5. The waves progress until the target sample size is reached.  
In addition to supporting the smokers’ ability to recruit via Facebook by sending a message, we will also support 
recruitment messaging via emails and text-messaging to further expand reach in this study. 
 
 
 
 
Figure 3: Single hypothetical Peer-referral “Lifecycle” 
 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
12
 
  
 
 In our NCI-funded pilot study, we recruited smokers on Facebook 
to Decide2Quit using online advertisements. These seeds (first 
wave smoker) and peer recruits (subsequent wave smokers) were 
provided the peer recruitment toolset described above to recruit 
other smokers. Smokers were incentivized for up to seven 
successful recruitments and had 30 days to recruit from date of 
registration. Successful peer recruitment was defined as a peer-
recruited smoker completing Decide2Quit registration following a 
referral. Our primary questions were 1) whether smokers will 
recruit other smokers and 2) if this peer recruitment process 
increases the reach of the intervention to harder-to-reach groups, 
including those not ready to quit and minority smokers. In just over 
a year (July 2013 to Sept 2014), peer recruitment quadrupled our 
sample (seeds: 190; peer recruit: 569 smokers) resulting in a total 
sample of 759 smokers. Participants reported being connected to 
6914 smokers [mean 9.1, (SD 5.51), median 8, intra quartile range 
4]. Overall, 15% (n=117) of participants successfully peer recruited 
(54% recruited 7 smokers, 21% recruited 4 to 6 smokers, 18 
recruited 2 to 3, and 24% recruited one smoker). Peer recruitment 
resulted in four waves of participants after the initial wave of seeds (wave 0 to wave 4).  
          Compared to direct recruits, peer recruits were less likely to be ready to quit (74.2% vs. 95.1%). They were also more 
likely to be African American (23.8% vs. 10.8%) (P < 0.01 for all comparisons). (Figure 4) This increase happened without 
our tools prompting the peer recruitment of African American smokers. As noted (Section B.3), we will work with our 
patient advisory panel (See Section F) to further refine our instructions to encourage African American smokers’ 
recruitment. Thus, we anticipate an even higher increase in proportion of African American smokers in S2S.  
          Most participants strongly agreed or agreed that peer recruitment was beneficial to their own quit smoking efforts 
(75.4%), motivated them to get support from those around them to quit smoking (64.4%), did not increase their cravings 
(83.1%), and made them feel like they were being helpful to their family and friends who are smokers (71.2%). We will 
test benefit of peer recruitment to cessation in this study. 
 
C.4.2.2. Recommender CTHC system:  
As detailed above (Section C.4.1.2), the difference between the standard and the recommender CTHC is the approach to 
message selection and not the messages. We will use the same motivational messages as the standard CTHC system. Our 
recommender CTHC system is a hybrid recommender system. Recommender systems can be implemented using either 
a content-based,91 collaborative filtering,92 or hybrid approach.93  Given a sample of rating data, content–based 
recommender systems learn a function to match users to items based on the provided user profile information (e.g, age, 
gender) and the messages’ metadata description. Metadata is defined as data about data; it describes the structure or 
content of particular resource, object, or entity.94 In computer engineering, this assignment of messages to categories is 
referred to as metadata.  Our messages coding by the readiness to quit categories in the comparison standard CTHC is an 
example of the type of metadata that can be used by content-based recommender systems. Content-based 
recommender systems work similarly to standard CTHC systems, but the matching function can be optimized based on 
rating data instead of specified by experts. In contrast to content-based recommender systems, collaborative filtering 
recommender systems match users to items based on past rating history. The simplest examples of this approach are 
nearest neighbor methods.92 These methods match a target user with other users that have given similar ratings to the 
items the users have rated in common. The set of users matched to the target user are referred to as the target user’s 
nearest neighbors. The method then recommends items to the target user that their neighboring users have rated highly. 
Supportive evidence for peer recruitment:  
Figure 4: Increase in proportion of African Americans:  
Seeds vs Peer Recruit in our pilot 
 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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The assumption behind these methods is that if two users are observed to have close agreement on the ratings of a 
sufficiently large number of items, they will likely agree closely on the ratings for the remainder of the items.  
For the recommender CTHC, we chose a hybrid  approach because they merge the strengths of content-based 
and collaborative filtering recommender systems.93 Thus, they can potentially benefit from expert-driven rules (content-
based), and the recommender algorithms. Our hybrid recommender system uses three input data sources to generate 
the recommendations, including the 1) metadata description of the messages, 2) implicit, and 3) explicit feedback data 
(smokers in prior and current study). As explained above, our metadata includes a comprehensive coding of the messages 
(section 4.1.2 and Figure 2). Implicit feedback data are derived from user actions. As our implicit feedback data, we used 
the website return data of 900 smokers that participated in our prior RCT.10 When an email was sent to these smokers, 
we tracked their website usage in the days following the email. Thus, we had data on the frequency at which each 
message promoted use of Decide2Quit, and the characteristics of the 
smokers that received these messages.   
Explicit feedback data consists of self-reported item ratings. Two 
pilot studies were used to generate the explicit feedback data for 
recommender CTHC.95  We first recruited 100 current or former smokers 
to refine the explicit ratings question.  Each subject was asked to provide 
ratings using a 5 point Likert scale of four different aspects of messages: 
influence, emotional response, relevance and preference.  Each subject 
provided ratings for five different randomly selected messages.  Per-message analysis showed a positive correlation 
between the means and variances of the ratings for each question.  Thus, balancing the need to obtain multiple ratings 
per message and the resulting cognitive load, we decided to choose only one question for developing the recommender 
CTHC system. We chose the influence question (Table 6) because it had demonstrated strong predictive validity in a prior 
RCT.96 A second pilot test was performed to collect a larger rating dataset to bootstrap the learning and evaluation of 
collaborative filtering models for the recommender CTHC. 95  We recruited 846 current or former smokers from online 
and local sources to rate the messages on the influence scale. Each smoker was asked to rate 20 messages, resulting in 
16,920 ratings. Several classic and state-of-the art collaborative filtering methods were evaluated for accurate prediction 
methods.  The Bayesian Probabilistic Matrix Factorization (BPMF) was identified as the best single model in our evaluation 
and was used to develop the recommender CTHC. The BPMF model estimates a probability distribution over a joint 
embedding of users and items into complementary latent spaces. The rating a given user supplies for a given item is 
approximated by the expected value of the product of the latent user and item factor vectors representing the user-item 
pair, with the expectation taken over the uncertainty in embeddings.1 In addition to explicit feedback ratings from 
smokers in prior studies, the recommender CTHC is programmed to also use the explicit ratings of smokers receiving the 
messages. Thus, when a smoker is sent an email, we will include a link to rate the message on the influence scale (Table 
6). Although our standard CTHC does not adapt to this feedback, we will include the ratings question in the standard 
CTHC messages to minimize group differences. 
Supportive evidence for recommender CTHC system 
Using PCORI pilot funding (1IP2P1000582),1,2 we tested the efficacy of the recommender CTHC system in a small 
randomized experiment. Our comparison system was the standard Decide2Quit CTHC system. Smokers in both arms were 
emailed daily motivational messages (30 messages) and incentivized to rate messages on the influence scale. We tested 
this in a number of ways: 1) daily ratings of messages using the influence question (Table 6), 2) perceived influence of the 
intervention, and 3) cessation behavior. In all these analyses, the recommender CTHC system performed better. 
Comparing the daily ratings of the two systems, the proportion of days when smokers agreed/strongly agreed (daily rating 
>= 4) that the messages influenced them to quit was significantly (P=.02) higher in the Intervention (74%) than comparison 
(45%). In our assessment of the perceived influence of the intervention, compared with the standard CTHC system, 
smokers strongly agreed or agreed that the recommender CTHC system significantly influenced them to quit smoking and 
use Nicotine Replacement Therapy. Even in the short time span of our study (30-days) and compared with an effective 
rule-based CTHC, the recommender CTHC system demonstrated a higher influence on cessation behavior. Although not 
significant, more intervention smokers reported that they had already quit or set a quit date (39.7% versus 29.7%). At 30-
days, more intervention smokers (36%) stopped smoking for one day or longer than comparison (32%) (P=.70). 
What messages were selected by the recommender CTHC system in the pilot: We conducted a coding-level analysis (see 
codes description in Section 4.1.2) to understand how the message selection differed from the standard CTHC system. As 
noted above, in our pilot experiment, the standard CTHC system tailored based on readiness to quit, and messages in 
Table 6:  Influence Question used in 
recommender CTHC pilot: 
This message influences me to QUIT smoking? 
1. Strongly agree 
2. Agree  
3. Neutral 
4. Disagree  
5. Strongly disagree  

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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each readiness category were picked at random. We identified the top 10 most frequently selected messages by each 
system. We then calculated the proportion of messages with each code, and then calculated the difference in the 
proportions of each code.  Compared to the standard CTHC messages (Figure 5), the recommender CTHC had a higher 
proportion of messages with the codes “Health: Physical Functioning: Smoking Risk” and “Motivations:  Aesthetics”. As 
noted above, messages selected by the recommender CTHC were rated as more influential than the standard CTHC 
messages (see Appendix B for example messages). 
Fig 5: Differences in Message Codes Between top 10 most frequently selected messages (recommender vs. Standard CTHC) 
 
C.5. Aim 1: Conduct the S2S RCT testing (A) peer recruitment and (B) recommender CTHC 
 
In preparation of the study, we will work with our patient advisory panel to refine our recruitment materials 
(advertisements content, study description, peer recruitment instructions, etc.), especially focusing on encouraging the 
peer recruiters to recruit African American smokers. We will work with our biostatistician to help integrate the new 
randomization table in Decide2Quit. As noted (Section C.2.), our program will automatically assign smokers to the groups 
as they register on our site. Our patient-centered measures and analytic plan are described below.    
C.5.1. Patient-centered outcome measures. (RQ-6) 
S2S will include multiple data collection stages (Table 7). We developed our outcome measures based on over 15 years of 
DISC research with smokers (RQ-6).4-6,56,97,98 As noted (Section A.3), to appropriately measure dissemination, we need both 
recruitment and use measures.  We also will test effectiveness in this study. Our primary measures are described below:  
Dissemination Measures (Hypothesis 1): 
H1a:   African-American recruitment:  Related to H1a, we will use the number of African-Americans recruited. 
H1b:  Recruitment time: When smokers register on Decide2Quit, they will be assigned a unique identifier and their 
registration date and time will be recorded. We will compute recruitment time from this data as the time taken to recruit 
each participant from the time that the first participant in the group was recruited. (see equation below).  
𝐑𝐞𝐜𝐫𝐮𝐢𝐭𝐦𝐞𝐧𝐭 𝐭𝐢𝐦𝐞 𝑇𝑖= Xi −X1 
where Xi is date of registration of ith participant;  X1 is the date of registration of 1th participant. 
We also considered number of recruits as a measure. In a “real-world” dissemination project, number of recruits would 
be an ideal measure. However, in a trial with a stated goal of recruiting 600 smokers each in the peer and standard 
recruitment groups and with a time limit, number of recruits would not be a good comparative measure.  Recruitment 
time would allow us to assess differences between the two groups, and we chose this measure. 
H2: Repeated Use:  We use repeated use over other use measures (number of logins) because of the demonstrated 
association with smoking cessation.37 This is an ordinal scale of number of Decide2Quit functions used after the first DISC 
visit (0: no functions used; 1: use of 1-2 functions, 2: > 2 functions used). 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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H3:  Effectiveness Measures:  
 7 
day 
point 
prevalence 
cessation 
biochemically 
verified 
using 
saliva 
NicAlert®: 7 day point prevalence will be 
assessed at six months using: Do you 
currently smoke cigarettes (smoked even 1 
puff in the last 7 days)?100 The Society of 
Nicotine and Tobacco Research have 
recommended this measure for multiple 
reasons.101 Point prevalence can capture 
delayed 
effects 
of 
an 
intervention. 
Continuous 
or 
prolonged 
abstinence 
measures assume that once someone fails 
they are unlikely to return to long-term 
abstinence during the remainder of study. 
A 
psychometric 
analysis 
comparing 
continuous, 
prolonged, 
and 
point 
prevalence outcomes found that point 
prevalence was the measure that had 
highest concurrent validity.101 The 7-day 
window provides an appropriate stringent measure to account for a cross-sectional snapshot.  As recommended by 
reviewers, we will verify quitting using the biochemical measure  — saliva NicAlert® test .102-104 The NicAlert® test (Nymox 
Corporation) is a semi-quantitative method that uses a dipstick to measure the level of cotinine in a sample of saliva. The 
test strip displays the result in seven zones. Each zone represents a range of levels of cotinine/smoking [e.g. zone 0 (0–10 
ng/mL, a nonsmoker) to zone 6 (>1000 ng/mL, a heavy smoker)].  The results will be read as 0-6, and as recommended, 
any value ≥  1 will be considered as tobacco use.105-107 The feasibility of mailed NicAlert testing has been demonstrated.107-
109  We will mail the strips with self-addressed return envelope with clear instructions and a link to a training video. Our 
staff will also be available by phone to help the smokers complete testing.  
Risk reduction or the reduction in the number of cigarettes smoked (PC-3): For the smokers participating in our prior 
studies, reduction in number of cigarettes smoked was an important outcome. In preparing this revision, we further 
confirmed with our patient panel that this was an important outcome. We asked each of them an open-ended question – 
besides quitting what would the most meaningful outcome as a result of participating in a smoking cessation intervention? 
All of them chose risk reduction as their first choice. If needed, we gave them prompts that included: reduction in number 
of cigarettes smoked, ability to be active without feeling short of breath, increased sense of taste and smell, improved 
dental health, and increased energy level in random orders. These prompts were selected from a recent study that 
assessed the top patient-centered outcomes of primary care smokers using focus groups.110 In this study, risk reduction 
or decreased use of tobacco (cutting back, not necessarily quitting) was assessed as the top measure in the patient 
behavior change category. We will calculate risk reduction using the below formula:  
Risk reduction =  Number of cigarettes smoked at follow-up – Number of cigarettes smoked at baseline 
C.6.1. Analytical Plan (IR-4) 
As noted above, our study is powered for our primary effectiveness study.  As the outcome for H3 is the most challenging 
to change, and has a meaningful but modest effect size, H3 requires the most power.  As a results, we will be over-
powered to detect meaningful differences for H1 and H2.  As the analytic plan for H3 is the most complex, we spend the 
majority of our focus on the analytic plan for this hypothesis.  For H1 and H2, we demonstrate that we will be able to 
detect small differences with the large sample size required for H3.   
For the effectiveness trial (H3), to preserve randomization, all primary analyses will be on an intent-to-treat basis (IR-3). 
Secondary analyses will explore dose-response effects among those with variable levels of adherence to the intervention. 
All analyses will be two-sided and alpha error will be set at 0.05. We will begin our analysis by examining univariate 
statistics (means, medians, standard deviations and 95% confidence intervals) and distributions. We will examine the 
balance of participant characteristics by study groups and account for any imbalances in our multivariable analysis. As 
 
 
 
Table 7: Key Data Elements 
 
Search Ad Manager 
Baseline during 
registration 
Every Decide2Quit visit 
Every email message 
6th month Follow-up 
(Online or phone) 
6th month Follow-up 
Mailed) 
Online advertisement stats (no of users who 
saw ads, etc.) 
X 
 
 
 
 
 
Demographics99  
 
X 
 
 
 
 
Participant smoking characteristics (level of 
addiction, allowing smoking at home, quit in 
last 12 months)99  
 
X 
 
 
 
 
Number of cigarettes/day 
 
X 
 
 
X 
 
Readiness to quit 
 
X 
X 
 
 
 
Use of  website 
 
 
X 
 
 
 
Explicit influence ratings 
 
 
 
X 
 
 
7-day point prevalence smoking cessation99  
 
 
 
 
X 
 
Biochemical verification saliva  NicAlert®  
 
 
 
 
 
X 
C.6. Aim 2: Follow participants in the randomized trial for six months testing S2S 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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appropriate, differences in measured characteristics (i.e., demographics and pre-baseline smoking behaviors) by group 
will be tested using chi-square tests of independence (categorical variables), ANOVA (continuous variables) or the 
equivalent non-parametric tests depending on the distribution of the variables. Per best practice, differences in baseline 
characteristics of the intervention and comparison groups will be assessed.111,112  
For the dissemination study (H1), we will compare the speed of recruitment and percent of African-Americans recruited 
in the peer-recruitment and no peer-recruitment groups.  Note that our goal in the dissemination study is to increase 
speed of dissemination and the diversity of recruitment (and dissemination study includes and randomized and non-
randomized wave of recruitment.  Thus, we anticipate that, and hypothesize that, African-Americans will be imbalanced 
(with greater recruitment in the peer recruitment group). We will use a chi-square test to compare the difference in 
proportions (H1A). 
We note below that H2 has a has a limitation, in that it relies both on the randomization of the main effectiveness study, 
and the allocation to peer-recruitment tools (which is, not randomized as required by hypothesis 1).   We also describe 
below our approach to address this limitation. 
H1: Analytic plan, sample size, and power calculation 
H1A Analytic Plan:  To monitor dissemination (H1) through social networks requires a non-randomized allocation for 
those smokers peer-referred.  For H2, we need to consider the Randomized and non-randomized (i.e.: peer referred) 
components of this study. 
Based on our pilot (Section C.4.2.1), we anticipate that peer recruitment will be more effective in recruiting African 
Americans. To test this, we will categorize the smokers as either African Americans or not, and then use the Chi-Square 
statistic to test for differences.   
As a secondary, exploratory analysis, Within the peer recruitment tools group, we will conduct a secondary analysis 
examining differences in demographic characteristic between peer recruited and directly recruited smokers. As noted in 
Section C.4.2.1, peer recruitment resulted in increasing reach of the intervention to harder to reach smokers (not ready 
to quit and African American smokers).   Using data provided by search engine advertisement managers, we will evaluate 
the performance of our online advertisements (number of users registered on Decide2Quit following an advertisement 
on the search engine).  
Sample Size and Power Calculations: In our NCI-funded pilot (R21CA15896),3,4(Section C.4.2.1), peer recruitment 
increased the proportion of African Americans to 23%, compared to 11% in the initial seeds (those recruited by 
advertisements).  Using 10% as the base rate in the non-peer recruitment group (recruitment by online advertisements), 
we estimated sample size requirements varying the proportion in the peer recruitment from 16 to 20%.  With these 
assumptions, we will need 219 smokers in each group to detect difference of 10% (power = 80%, alpha=.05). If we reduce 
the difference to 8% and 6%, we will need 319 and 525 in each group respectively. Given that we will work with our panel 
to encourage recruitment of African American smokers in the peer recruitment group and may see bigger differences 
than our pilot, we will have adequate power particularly with the proposed sample size of 600 in each recruitment 
method. 
H1B Analytic Plan:  For H1B, we will compare mean 
recruitment time between the two types of recruitment 
method using a t-test. .   
Sample Size and Power Calculations: In the NCI pilot 
(R21CA15896),3,4 we estimated that the mean number of 
days to recruit a sample of 700 smokers was 244 days with 
a standard deviation of 81. Assuming that peer 
recruitment proceeds with the same rate and with the 
same standard deviation, we can detect a difference in 
recruitment time as low as 14 days.  Since we expect the 
comparison rate to be much slower, we are adequately powered to detect differences with a sample of 600 (power=0.8). 
See other sample size requirements varying the mean and standard deviation of the number of days to recruit in the 
intervention and comparison (Table 8). Calculations were made in STATA.113 
Table 8: Power Calculations to estimate the minimum 
difference in the mean number of days to recruit 
Intervention 
(Mean) 
Comparison 
Mean 
Standard 
Deviation 
(Both groups) 
Sample 
Size 
Required 
244 
257 
81 
610 
244 
258 
81 
526 
244 
260 
100 
614 
244 
261 
100 
544 
244 
263 
120 
627 
244 
264 
120 
566 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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H2: Analytic plan, sample size, and power calculation 
H2 Analytic Plan:   
We will analyze across the four groups: (A: Fully enhanced; B: Recommender CTHC only; C: Peer recruitment only; D: 
Standard), as we hypothesize there may be an additive effect on engagement of peer recruitment and the recommender 
CTHC system (See section X above). We will use the following generalized linear model, which includes indicators of peer 
recruitment and recommender CTHC and the interaction between the two indicators as independent variables, to test 
H2A:    
𝐸(𝒇(𝒖)) = 𝑏0 + 𝑏1 × 𝑃+ 𝑏2 × 𝑅+ 𝑏3 × 𝑃× 𝑅+ 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑐𝑜𝑛𝑓𝑜𝑢𝑛𝑑𝑒𝑟𝑠 (𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠, 𝑟𝑒𝑎𝑑𝑖𝑛𝑒𝑠𝑠 𝑡𝑜 𝑞𝑢𝑖𝑡) 
 where 𝑢 is the outcome measure (repeated use measure), the function 𝑓(𝑢)depends on the distribution of 𝑢,  
 
P=1    for peer recruitment, =0 for standard recruitment,   R=1 for recommender CTHC, =0 for standard CTHC. 
The adjusted 𝑓(𝑢) (i.e., adjusting for the potential confounders) for each of the 4 groups can be expressed in terms 
of the regression coefficients defined in the box below. 
A 
B 
C 
D 
𝑏0 + 𝑏1 + 𝑏2 + 𝑏3 
𝑏0 + 𝑏2 
𝑏0 + 𝑏1 
𝑏0 
In the model, 𝑏2 + 𝑏3 is the estimated difference between group A (peer recruitment and recommender CTHC) and group 
C (peer recruitment only); 𝑏1 + 𝑏3 is the estimated difference between group A and group B (recommender CTHC only); 
and 𝑏1 + 𝑏2 + 𝑏3 is the estimated difference between group A and group D (standard).  Significant positive values of the 
estimated differences will support H2A. In the event that there is an interaction effect between the two S2S 
enhancements (peer recruitment and recommender CTHC), i.e., b3 is significantly different from zero, H2B and H2C will 
be tested by comparing groups C vs. D (peer recruitment alone vs. standard Decide2Quit; estimated by b1) and groups B 
vs. D (recommender alone vs. standard Decide2Quit; estimated by b2). If there is no interaction effect, i.e. b3 is not 
significantly different from zero the following model will be used to test H2B and H2C: 
𝑬(𝒇(𝒖)) = 𝑎0 + 𝑎1 × 𝑃+ 𝑎2 × 𝑅+ 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑐𝑜𝑛𝑓𝑜𝑢𝑛𝑑𝑒𝑟𝑠 (𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠, 𝑟𝑒𝑎𝑑𝑖𝑛𝑒𝑠𝑠 𝑡𝑜 𝑞𝑢𝑖𝑡). 
In the model, 𝑎1 is the estimated effect of peer recruitment [(A+C) – (B + D)];  𝑎2 is the estimated effect of recommender 
CTHC  [(A+B) – (C+D)]. A significant positive value of 𝑎1 or 𝑎2will support the hypotheses of positive effects of these two 
individual components respectively (H2B and H2C). 
 
We will examine the distribution of the dependent variable u to determine the link function to be used in the 
generalized linear model. In our previous study, we used an ordinal variable for the dependent variable Repeated Use 
with log link function to fit an ordinal logistic regression.37  
We will also conduct a secondary analysis using the influence ratings. As noted above, both standard and 
recommender CTHC will include a link to rate the messages on the influence scale (Table 6). For this analysis, the 
dependent variable for each smoker is the mean of all influence ratings (an influence score) and the independent variable 
is study arm [recommender (A+B) or standard CTHC (C+D)].  We predict that the mean influence score will be higher in 
recommender CTHC than the standard CTHC arm.  For this analysis, we will start with a t-test for differences in mean, but 
acknowledge that the influence scores may not be normally distributed and only approximate a continuous variable. 
Thus, we will use a Wilcoxon rank-sum. As an exploratory evaluation for H2, we will also compare African American 
smokers across the groups, and African American and White smokers to test for heterogeneity of effect. This will provide 
us important data for design future interventions. (HTE) 
 
As noted above,  H2 will rely both on the randomization of the main effectiveness study, and the allocation to peer-
recruitment tools (which is, not randomized as required by hypothesis 1).  Thus,  we need to consider the Randomized 
and non-randomized (i.e.: peer referred) components of this study. To do this, we will adjust the analyses for measured 
covariates that differ between the non-randomized and non-randomized groups from the dissemination study.” 
 
Sample Size and Power Calculations: We used the method published by Whitehead to calculate power for this 
hypothesis.114 As noted above, in our prior RCT (1R01CA129091), we found a linear association with six-month cessation 
using the repeated use scale. For every increase by one in this scale, odds of smoking cessation increased (OR= 2.10, 95% 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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CI = 1.03, 4.30),37 with the current sample size of 300 per group for H2A, we can detect a difference a cumulative odds 
ratio of 1.7.  Thus, our study is adequately powered to measure a reasonable difference in the repeated use measure.  
Power for these hypotheses is driven by H2A, as this analysis is looking at the smaller sample (fully enhanced).  For H2B 
and H2C, power is even greater, as the sample size is 600 per group for each.   
H3: Analytic plan, sample size, and power calculation 
Quitting: We will compare those randomized to enhanced CTHC, against those randomized to standard CTHC. We will 
test the 6-month, 7-day point prevalence cessation as the dependent variable for these analyses. As described in the 
missing data section (C.6.2), we will use a number of approaches to account for missing data, including using a selection 
modeling approach as recommended by experts.115  Using mediation analysis, we will examine the potential mechanisms 
through which we anticipate the intervention may produce a beneficial effect.116  We will categorize the NicAlert® test 
into smokers and not categories, and use the Chi-Square statistic to test for differences. 
Sample Size and Power Calculations:  
We assumed a control cessation rate of 15%,117 a 
two-sided significance level of 0.05. A sample size of 
300 in each group (H3c) will achieve 80% power to 
detect 
a 
difference 
of 
9% 
(quit 
rate 
in 
intervention=24%) in quit rates between the two 
groups, based on a Z-test with pooled variance.   
  
Risk reduction:  As noted above, risk reduction is 
measured by change in cigarettes smoked from 
baseline to follow-up. If risk reduction is normally distributed, we will use identity link function in the generalized linear 
model.  We will also model risk reduction using count regression, using a Poisson or negative binomial regression 
modeling if the variance of the distribution of risk reduction is over dispersed.  
 
Sample Size and Power Calculations:  
We calculated the detectable difference in risk reduction with 300 smokers in each group and the mean in the comparison 
group of 3.3 using standard deviations of 2 and 3 with 80% and 90% power (Table 9). We will have 90% power to detect 
a difference of 0.80 (or smaller) number of cigarette smoked reduction between two groups. This difference is likely to 
be achieved based on the results of our PCORI pilot in which we achieved a reduction of 0.85 (4.15 to 3.3) in 30 days; 
compared to smokers receiving the standard CTHC messages, smokers receiving the recommender CTHC had a higher 
reduction in number of cigarettes at 30 days (Standard CTHC: mean 3.3; S2S adaptive CTHC: 4.15).  
Resources needed to replicate the intervention: As requested by reviewer, we have now added Dr. Bridget Smith to our 
team to help us quantify the resources needed to successfully replicate our intervention. From the perspective of an 
implementing organization, we will compare the costs of the four groups. The research team will track staff time 
associated with each intervention arm, including time for training, recruiting, and administering the different aspects of 
the intervention. In addition, we will estimate development costs of each intervention in our prior NIH and PCORI-funded 
studies and any equipment or supply costs. We will compare the costs of the different intervention arms in multiple 
scenarios in which we examine how costs change based on changes in the components of the intervention and the types 
and amount of staffing provided for implementation. The economic analyses for this will primarily consist of descriptive 
statistics. Using the estimates of costs of supplies, equipment, and staff time and the potential savings that result from 
decreased healthcare costs related to smoking cessation, we will conduct a budget impact analysis to calculate the 
resources needed to implement any particular treatment strategy from the perspective of an implementing and/or 
disseminating organization. We will follow the guidelines outlined for best practices in budget impact analysis.121 We will 
create tables to describe the assumptions of our inputs and outputs of our budget impact analysis, and perform sensitivity 
analyses to examine how changing the assumptions of the model impact the potential costs for an organization 
implementing the intervention.   
C.6.2. Missing data analysis and handling dropouts  
Table 9: Power Calculations to estimate risk reduction using a 
measure of the mean difference in the number of cigarettes smoked 
(baseline to follow-up) 
Power 
Intervention 
(Mean) 
Comparison 
(Mean) 
Both arms 
(Standard 
Deviation)  
Detectable 
difference 
80% 
3.76 
3.3 
2 
0.46 
 
3.99 
3.3 
3 
0.69 
90% 
3.83 
3.3 
2 
0.53 
 
4.10 
3.3 
3 
0.80 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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For our effectiveness study, missing data due to dropouts is only an issue for Hypothesis 3 (H3). For hypothesis 1 and 2, 
missing data is part of the outcome of interest, and will be considered as indicative of no recruitment and lack of use.  For 
hypothesis 3, our primary approach to dealing with missing data is to minimize its occurrence. Drawing upon our prior 
experience, our staff will make multiple attempts to contact the patient and obtain their follow-up (MD-1). We will also 
provide smokers multiple modes (telephone and web) to complete the follow-up (MD-1). However, we recognize that 
there will be missing data. This is especially true in light-touch technology-assisted interventions.115,122,123 As 
recommended in literature, we will take multiple approaches to analyzing the data. Our primary analysis will be an intent-
to-treat analysis where all participants with missing data (MD-4) will be considered smokers (MD-2). We will also conduct 
a complete case analysis. If missing cessation data is greater than 10-20%, experts recommend using selection modeling 
to examine the robustness of findings (MD-3).115 Thus, we will implement selection modeling using inverse probability 
weighting to determine the potential effect of missing data. As a sensitivity analysis, we will compare these results with 
results of complete cases, and last baseline or observation carried over and the multiple imputation method of Hedeker 
et al (MD-5).124 We anticipate missing data in the biochemical verification, and we will use rates of refusal in sensitivity 
analyses.  
 
Addressing multiple outcomes and multiple comparisons:  The statistical literature has reviewed methods for accounting 
for multiple comparisons, and noted that adjustment is controversial and may be over-conservative in some contexts.118-
120 Adjusting for multiple comparisons is highly appropriate in exploratory analyses that are not testing specified 
hypotheses (as example: genome-wide association studies). However, studies that conduct analyses focused on a set of 
specific, pre-analysis-specified hypothesis are conceptually not the same. In these hypothesis-driven studies, the articles 
cited above, would argue that the concept of adjustment for multiple comparisons would be over-conservative.   In this 
project, we are pre-specifying primary and secondary hypotheses, we will present each p-value “as-is” allowing consumer 
of the results to consider the strength of the complete set of findings using his or her preferred way of placing the separate 
findings in context, without imposing a specific, controversial “multiple comparisons” interpretation. 
 
C.6.3. Strengths, Limitations, and Future Opportunities.  
Our proposal has several strengths. S2S is the first study to evaluate smoker-driven innovations to disseminate DISCs. S2S 
is an example of a transdisciplinary innovation. We have combined concepts and theories from behavior change, health 
communication, computer recommender systems, and social marketing together to develop the S2S tools. Patients 
developed several of the S2S tools as content experts (see Section F). The S2S functions have the potential to significantly 
transform the way we disseminate our interventions. By facilitating the spread of the tools from patient to patient, peer 
recruitment provides a naturalistic approach for spreading the intervention reaching hard-to-reach populations that have 
a great need for such tools, leveraging current trends in social networking. One of the challenges with current DISCs is that 
attrition rates are high. 125-128  By continuously adapting to the behavior and needs of the patient, the recommender CTHC 
may continuously engage the patients and reduce attrition rates.  
 In this study, we focus on testing peer recruitment for recruiting African American smokers. While Hispanics or 
Latinos are also disproportionately affected by smoking, we do not have current evidence demonstrating the strategies 
described in this proposal can successfully recruit them. Other strategies such as adaptive peer recruitment may be 
needed for engaging Hispanics in DISCs and we will explore these in future projects. While we will make significant advance, 
future methodological and dissemination opportunities remain. The S2S tools have applications in other health and 
wellness interventions including motivation for weight-loss and compliance. In collaboration with our colleagues at the 
Center for Intelligent Information Retrieval (CIIR) at UMass Amherst, we will develop more complex, machine learning 
algorithms for adaptive recruitment. These algorithms can learn from the recruitment data and adapt to the changes in 
real-time. We will learn several lessons from the S2S study, which can all be used to train the machine learning algorithms. 
We can also develop the recommender CTHC to incorporate contextual and physiological information from smart phones 
and sensors, as a form of patient feedback. On the dissemination front, we envision a large, pragmatic study to fully 
evaluate the integration of S2S into health care systems.  Our team has extensively worked with multiple clinical systems 
integrating behavioral change research with clinical-system tethered Personal Health Records (PHRs).5,129-143  We will 
support a warm-handoff between providers and patients using an integrated EHR/PHR that will automatically flag patients 
that are eligible to be champion recruiters and support automated motivational messaging to them. This should enable 
us to gain the benefits of having providers recruit while not burdening them with additional work. We have conducted 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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several large pragmatic studies integrated with healthcare systems.5,7,8,129,144-146 In our CTSA efforts, we are working to 
create a multi-institutional consortium of Healthcare systems using the same EHR/PHR platform. In conclusion, in planning 
S2S, we have built on our strong efficacy data from current NIH and PCORI -funded research, integrated several 
methodological innovations, and have a clear plan for further dissemination of S2S tools. 
D.  Patient Population  
We will recruit 1200 smokers (400 smokers per group) for this study (Table 10). All current smokers over 19 years of age, 
can read or speak English, and have Internet access at home will be eligible (RQ-3). Our target is any smokers who view 
our advertisements on Google and Facebook and 
register on the website. Thus, screening is not 
applicable for this study. This is a recruitment 
experiment. Thus, we can only estimate the 
characteristics of smokers participating in our study. 
As noted, we will continuously monitor recruitment 
with our patient panel and revise advertisements as 
needed.  
E. Research Team and Environment  
Research Team: We have a multidisciplinary 
research team (computer engineers, behavior 
change, tobacco cessation, clinicians, and patient participants), with an established collaboration record.5-9,147 Drs. 
Sadasivam, Houston, and Allison have collaborated together for over 10 years to develop Decide2Quit,5,7,8,10,37,56 and the 
S2S innovations1,2.3,4 Dr. Allison has over 20 years of research expertise in health disparities and has successfully 
collaborated with Dr. Houston to develop interventions targeting African Americans.96,148. In response to reviewer’s 
critiques, we have recruited Dr. Bridget Smith, a health economist, to help us conduct a budget impact analysis of our 
intervention. Drs. Sadasivam, Houston, and Smith have conducted a similar budget impact analysis of our prior 
intervention.8 Dr. Marlin, our consultant, collaborated with us to develop the recommender CTHC.1,2 Mr Seward is a 
tobacco treatment counselor at the UMass Memorial Health Care system, and has collaborated with us over the last 3 
years providing input on various projects, as well as supporting the recruitment and participation of smokers.149 Dr. 
Sadasivam shadowed Mr. Seward as part of his clinical tobacco treatment specialist training for his career development 
award. Patients will be an integral part of our team, as decision leaders and content developers (see Section F). 
Environment: We will make use of the considerable resources available at the University of Massachusetts Medical School 
(UMMS, see letter of support). UMMS ranks near the top among public medical schools in the Northeast in the amount 
of funding awarded by the NIH. Since 2010 (renewed again in 2015), UMMS is home to the prestigious NIH-funded Clinical 
and Translational Science Award (CTSA) and have established the UMass Center for Clinical and Translational Science 
(UMCCTS). UMCCTS serves as the academic home for clinical and translational scientists across all UMass campuses and 
has considerably enhanced the UMMS research infrastructure by developing innovative core facilities. The mission of the 
UMCCTS is closely tied with the mission of the Quantitative Health Sciences department, academic home of Drs. Sadasivam, 
Houston, and Allison. All three investigators hold leadership position in the UMCCTS. Dr. Allison is a co-leader of the Special 
Resource Population Center (the SPRC) of the UMCCTS. The SPRC was formed to increase engagement of special 
populations in translational research through tailored, culturally responsive strategies. Dr. Houston leads the UMCCTS 
Biomedical Informatics (BMI) Component. Under his leadership, BMI has fostered collaborations between the UMMS 
Division of Health Informatics and Implementation Science, Academic and Research Computing, and the UMass Memorial 
Healthcare Information Systems. This collaboration has resulted in the continued development of multiple core 
infrastructure resources, including the clinical Data Warehouse, the Biomedical Research Informatics Development Group 
(BRIDG), and the Technology Usability lab. Dr. Sadasivam is the director of BRIDG and the Technology Usability lab. Under 
his leadership, the BRIDG has developed a state-of-the-art infrastructure to support eHealth and mHealth research 
projects, and Decide2Quit and the S2S functions were developed, and is currently housed in this infrastructure. We have 
also developed close collaboration with the UMass Amherst Center for Intelligent Information Retrieval (CIIR), a world-
class group of researchers conducting cutting edge information retrieval research. The recommender CTHC development 
was a direct result of this relationship. Dr. Person is a member of Quantitative Methods Core (QMC), the statistical core 
of the UMMCCTS (see details in People and Places resources). In summary, UMass academic units and the UMCCTS 
provide an environment uniquely suited to the innovative work proposed in S2S. 
Table 10. Estimated Final Racial/Ethnic and Gender Enrollment Table 
Race 
Male (N) 
Female (N) 
Total (N) 
American Indian/Alaska Native 
5 
5 
10 
Asian 
60 
58 
118 
Black/African American 
130 
110 
240 
Hawaiian/Pacific Islander 
5 
5 
10 
White 
385 
395 
780 
Multirace 
15 
27 
42 
Ethnicity 
Male (N) 
Female (N) 
Total (N) 
Hispanic (Latino/Latina) 
50 
50 
100 
Non-Hispanic 
550 
550 
1200 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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F.  Engagement Plan  
Engagement: We strongly believe that interventions will fail if they do not fully address patient perspectives and cultural 
or ethnic experiences as part of the full continuum of patient perspectives (PC-1). Incorporating patient perspectives is 
critical to the development of patient-centered interventions that most closely reflect patients’ goals. As noted on the 
Resubmission Letter, reviewers praised our proposal as exemplary in patient-centeredness and patient-engagement. In 
particular, they noted that the S2S functions were driven by patients (peer recruitment, explicit and implicit feedback), 
and that several messages of our motivational messaging system were written by patients. However, reviewers were 
unsure about how we selected members of the patient panel, and their role in the dissemination plan. Reviewers also 
suggested to diversify the panel to include minorities. In response, we have significantly expanded the panel, clarified our 
selection process, and the role of the patients.  In our prior research, patients have participated not just as subjects, but 
as decision leaders and content experts. We will pay them to be a part of the team as research consultants (see budget 
justification). We have found that this model works best as the patients have a bigger ownership, greater motivation and 
assurance to participate in decision-making. We anticipate that our panel will be involved for 10-15 hours per year 
distributed over 8-12 meetings. We detail our approach below.  
Patient selection process: As noted, we will have 5 patients in our panel (3, including one African American, are named in 
the proposal and we will recruit 2 others). These patients were all participants in our current R01 trial (1R01CA190866-
01A1) and demonstrated an interest and commitment to research.  In addition, our staff identified these as star 
participants, i.e., they were able to understand our research needs in a complex study that involves gaming and mHealth 
tools and articulate their critiques. Because of their critiques, we have made several changes to our current R01 trial, 
including modifying our recruitment strategy, our approach to teaching the patients about the study’s mHealth tools, and 
changes to the text messages that will be sent to smokers. We will continue identifying star participants in our current 
study to recruit the two additional patients to our panel, including one more African American smoker.  Mr. Seward, a 
tobacco treatment specialist at the UMass Medical Center, will also serve in the panel. He will bring over 30 years of 
experience counseling smokers in both in-patient and out-patient settings to our panel. Mr. Seward has collaborated with 
us in research, including helping identifying patients for our studies, planning the intervention, as well as evaluating and 
writing manuscripts.149   
1. PLANNING THE STUDY: Stakeholders and patient participants were directly involved in the development of the S2S 
tools and the research question. As noted in Section C.4.1.2 , our motivational CTHC system included messages written by 
smokers.82 In our prior evaluation, we have found that these messages increased longitudinal engagement with the system 
(OR=2.03, 95% CI=1.74, 2.35), compared to expert messages.82 As noted, our S2S recommender systems also uses explicit 
and implicit patient feedback to adapt the personalization of the system. Our research focus of testing new tools to 
increase engagement with the system was also a result of several years of usability evaluations.81 In preparation of this 
study, Dr. Sadasivam also shadowed the tobacco treatment counselors at the UMass Memorial Health Care and interacted 
with smokers. When we assessed barriers to the access of tools such as Decide2Quit, we found that while smokers were 
interested in online tools, they did not use them for several reasons, including lack of awareness of the system, forgetting 
about the system, and lack of motivation. We have over the years designed several approaches to overcome these barriers, 
including provider referral tools in our prior RCT, and the tools being tested in S2S - the peer recruitment and 
recommender CTHC system. As noted in the proposal, our outcome measure (risk reduction) was recommended by 
patients in our prior studies, and confirmed by the panel.  
2. CONDUCTING THE STUDY:   Our patient panel will serve as decision leaders and help us in all phases of the study, 
including helping with 1) Refining our online recruitment strategy (in terms of content or where to place the ads); 2) 
Monitoring our implementation progress and helping refine our approach in case of issues such as slow recruitment; 3) 
Evaluating the data and helping present results to smokers and community-based organizations; and 4) Helping us prepare 
for approaches (EHR integration, community integration)  to disseminate the intervention beyond the scope of the project.  
We will use a semi-structured approach. Our first meeting will establish the roles, duties, and expectations of all involved 
in this partnership. We will focus on building team cohesiveness and highlighting the important contribution the panel will 
make in our study. We will continually stress that the panel’s role is to serve as decision leaders, and not as subjects.   In 
our later meetings, we will have open-ended discussions followed by a structured nominal group technique (NGT) process 
to make decisions. NGT is a highly structured, multi-step, consensus building procedure often used in formative research 
to elicit and prioritize group responses to a specific question. 150,151 We have found that NGT’s worked well to prioritize 
and establish decisions. 7 Our panel will continuously be kept updated using a dashboard available over Web and smart 
phones that we will collaboratively develop with them.  

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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Patients as content experts: As noted above, patients were directly involved in the development of the tools of the 
intervention. With our panel, we will continually monitor and refine our recruitment strategy (content and placement of 
advertisements) to increase the likelihood of success. 
3. DISSEMINATING THE STUDY RESULTS:  
Patients as disseminators: We have a detailed plan for dissemination of the tools, including peer recruitment, integration 
with healthcare organizations, and community organizations (see Dissemination section). Our patient panel will help 
develop lay summaries of our tools and results and help lead these efforts. Peer recruitment is a naturalistic way of 
disseminating tools, and our panel will help us identify places in various communities where we can initiate the peer 
recruitment. Moreover, Dr. Allison is the lead of the Special Population Resource Center, of the UMass Clinical and 
Translational Science Center, as well as the Vice Provost for Health Disparities research. Under his leadership, UMass is 
developing relationships with several local and national community organizations. Dr. Allison (key investigator) will 
continue to support our efforts to identify different groups with whom we can collaborate. We will work with our patient 
panel to help us market our intervention to these groups. We also have several prior and ongoing research in which we 
have established relationship with healthcare organizations and developed tools for provider to refer patients to online 
resources.5,7,8,10 In his role as TTS, Mr. Seward refers smokers to several online tools and he will continue to include 
Decide2Quit in his referrals, as well as identify new avenues for dissemination. In addition, all panel members will be 
invited to serve as authors in the development of manuscripts for peer-reviewed publications.   
4. PRINCIPLES FOR ENGAGEMENT 
• 
Reciprocal Relationships: Our panel will be involved in all aspects of study development. As noted, we will continually 
stress that the panel’s role is to serve as decision leaders, and not as subjects. Through the dashboard and other 
communication channels (e.g., listserv), we will ensure that the panel is constantly updated of new information. Major 
decisions will be undertaken through the NGT process. The panel will serve as authors in any manuscripts we develop 
to reflect their contributions to the study.   
• 
Co-learning: Patients and stakeholders will participate as decision leaders and content experts. Patient perspectives 
are essential in the development, implementation, and dissemination of an intervention. Patient input teaches 
researchers about what outcomes are important and methods that are feasible for the intervention’s target 
population. We will ensure that the patient investigators understand the research process by keeping an open line of 
communication and to thoroughly explain all research processes and protocols.  
• 
Partnership: The time and contributions of patient partners are valued due to the patients’ perspectives. Patient 
investigators can provide researchers with feedback and direction that is more pertinent to the patients’ needs than 
what researchers believe to be beneficial for the population. Patient investigators will receive $50 per meeting.  
• 
Trust, Transparency, Honesty: The patients in our panel have all participated in our research projects.  Thus, we 
already have an ongoing relationship to build on. In our first meeting, our goal is to establish early the value of the 
patient investigators and the significant contribution they are making to the overall intervention. We will emphasize 
the importance of the patients’ viewpoints and personal experiences on helping to design and implement the 
intervention. We will demonstrate how patient input has led to several of our research success. Each patient 
investigator will receive a “reference handbook” summarizing the research process (e.g., the intervention process, 
contact information for the researchers, the time commitment for this partnership, the intervention phases, and tasks 
associated with each research step). Throughout the research process, the researchers will share information about 
the intervention aims. As noted above, we will collaboratively develop a dashboard to continuously update the panel 
on the research progress.   
 
 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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DISSEMINATION AND IMPLEMENTATION POTENTIAL 
A. Describe the potential for disseminating and implementing the results of this research in other settings.  
The Decide2Quit.org digital intervention for smoking cessation and the smoker-to-smoker (S2S) peer recruitment and 
recommender computer-tailored health communication (recommender CTHC) system are all products of over a decade 
of research.  They are integrally tied to the overarching scientific mission of our department of Quantitative Health 
Sciences at UMass Medical School, and we are committed to their ongoing support and dissemination (see support 
letter). Consistent with the Communication and Dissemination RFA, our tools have considerable dissemination potential. 
As noted in the proposal (Section B2), DISCs are health communication programs that can be accessed via the Internet 
and smart phones. As the majority of people in the United States, including vulnerable populations, now have Internet 
connectivity through broadband and smartphones, DISCs have the potential to reach a large and diverse group of smokers. 
The S2S tools also have the potential to be self-sustaining. Peer recruitment promotes a naturalistic approach of spreading 
the intervention from one smoker to another.  By adapting to the needs of the user, the recommender CTHC has the 
potential to continuously engage the user. We will pursue a multipronged dissemination plan extending beyond the 
traditional venues of presentation at scientific conferences and publication in the peer-reviewed literature. Our patient 
panel will be central to this effort — in helping shape the strategy as well as the implementation of the strategy. Our plan 
is detailed below:  
• 
Presentation and Publications: We have an exemplary track record in publishing and presenting results from our 
funded studies. We will diligently apply this experience and skill to disseminate S2S results. In addition to presenting 
our findings at scientific meetings and in peer-reviewed literature, we will also present key findings at community-
based meetings, on our webpage, and social media sites. Our patient panel will be an integral part of this effort.  
• 
Community-based outreach: With our patient panel, we will develop lay summaries and marketing materials and start 
identifying opportunities for continued dissemination of Decide2Quit and S2S tools in community-based settings. As 
noted in Section F, Dr. Allison (key investigator) is the lead of the Special Population Resource Center, of the UMass 
Clinical and Translational Science Center, as well as the Vice Provost for Health Disparities research. Under his 
leadership, UMass is developing relationships with several local and national community organizations ). Dr. Allison 
will continue to support our efforts to identify different groups with whom we can collaborate to disseminate 
Decide2Quit.  
• 
Integration with EHR/PHR and other healthcare settings: We will also take a proactive approach to dissemination of 
the tools leveraging our past research. In our prior RCT, 5,8,10,152 we tested an electronic referral system to market 
Decide2Quit. As smokers were seen by their primary care providers, providers were trained to refer them into 
Decide2Quit website by entering their email address into a secure form. Once e-referred, Decide2Quit website sent 
them up to 10 emails encouraging their participation into the study. This project was highly successful. We had 2166 
referrals from 74 practices, out of which, 672 smokers registered. The next step in this approach is to link the e-referral 
to EHRs around the country. We have several national connections with the Veterans Health Administration (VHA) 
and healthcare systems through our Clinical and Translational Sciences Award. Through these connections, we will 
explore adapting the e-referral system to their environment. In our PCORI pilot (1IP2P1000582), we also experimented 
with identifying smokers in the EHR and mailing them for participation in the study. As the majority of smokers see a 
healthcare provider, this is potentially a powerful approach for disseminating Decide2Quit tools. We will again make 
use of our national connections to assess interest in this approach in various healthcare systems.  
• 
Other social media opportunities: During initial conversations, our patient panel members were excited about the use 
of social media outlets (e.g., Twitter, Facebook) for dissemination and outreach with study participants (see below). 
Dr. Sadasivam is the social media chair of the Society of Behavioral Medicine and has expertise in developing social 
media campaigns. For example, we will create a twitter group and work with our patient panel to develop tweets. In 
previous work,153 we found that Twitter tobacco cessation groups whose tweets had a higher frequency of 
socioemotional support and encouraging/engaging tweets had higher followership. We will use these findings to 
increase the followership of our group. Our study participants will also be invited to subscribe to social media channels 

 
 
 
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PCORI Cycle 1 2016 Research Plan Template                                                                        
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after they complete the study (see below), and we will use these mediums to disseminate study results. We will also 
feature the intervention as part of the social media outreach program of our National Institute on Minority Heath and 
Health Disparities funded Center for Health Equity Intervention (CHEIR). Dr. Allison is the lead of CHEIR, and Drs. 
Sadasivam and Houston play a lead role in shaping the social media outreach of CHEIR.  
• 
Other media opportunities: Dr. Houston’s previous interventions have been incorporated into the Innovations 
Exchange.  Our work have been featured in the in the New York Times,154 the Boston Globe,155 Time Magazine,156  and 
Wall Street Journal.157  We will continue to explore opportunities to have our results featured in these outlets.  
• 
Technological advancements to support dissemination: As noted (Section C.6.3), we will pursue technological 
advancements of the S2S tools to further their dissemination potential. With our colleagues at the Center for 
Intelligent Information Retrieval (CIIR) at UMass Amherst (Dr. Marlin), we will develop targeted peer recruitment 
strategies using algorithms to identify potential “best” recruiters. Instead of pursuing everyone, this will allow 
dedicating resources to further the potential of these recruiters. This will also support targeting those that can best 
reach a vulnerable population. We will continually work to enhance the recommender CTHC algorithm, including 
tailoring the timing of the message delivery, and incorporating additional real-time information from sensors.  
B. Describe possible barriers to disseminating and implementing the results of this research in other settings.  
A primary barrier to dissemination of research results are funding issues. As noted above, Decide2Quit and S2S tools are 
integrally tied to our department’s research mission. Thus, we have a strong commitment towards the dissemination and 
implementation of these tools in other settings. There are also technical barriers to linking the tools to EHRs. Our team 
has several years of experience developing plug-ins for EHRs and we have worked with all the major products, including 
AllScripts and Epic. In addition, we have also developed our tools using a “third-party” Web-service model, similar to how 
credit-card companies develop their products. Thus, any EHR tool can use our Web services to request motivational 
messages for their smokers in a secure manner.  Third, there could be buy-in issues. As noted, our goal is to start preparing 
for dissemination throughout the study years for continued dissemination of our tools. Through the UMass Clinical and 
Translational Sciences Award (Dr. Houston is the leader of the Biomedical Informatics core), we are also continually 
interacting with various healthcare systems and exploring potential opportunities for integrating evidence-based tools in 
their organizations. As we pursue these opportunities, we will also explore the potential of integrating Decide2Quit within 
these organizations. 
C. 
Describe how you will make study results available to study participants after you complete your analyses.  
We will work with our patient panel to tailor the results in a manner that is understandable to study participants. We will 
prepare these for dissemination via multiple formats, including on our website, brochures, social media channels, as well 
as video formats. We will use an iterative approach to developing these results. We will first prepare for dissemination of 
study results using mock data, since the final dataset is only available at the end of year 3. We will prepare different 
versions of the report and continue to refine them with the panel until a suitable format is developed. As noted in the 
proposal, we will use nominal group technique (NGT) process to make panel decisions. After the study data is analyzed, 
we will then prepare the final formats of the reports and use the above channels for dissemination. As participants 
complete the study, they will be invited to subscribe to our web and social media channels for continued updates about 
the study.  
 
In conclusion, our team will pursue multiple opportunities to disseminate the tools tested in this study, and our patient 
panel will lead our marketing efforts, including helping develop the marketing materials and shaping our strategy. Our 
team is committed to the ongoing support and dissemination of these tools.

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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REPLICATION AND REPRODUCIBILITY OF RESEARCH AND DATA SHARING 
1. Describe the ability to reproduce potentially important findings from this research in other data sets and 
populations. 
Transparency of study methods and results is essential for replication of research. The Quantitative Methods Core (QMC) 
and the UMass Clinical and Translation Sciences Center (UMCCTS) have developed a rigorous protocol for ensuring 
reproducibility of UMass studies. Under the guidance of Dr. Sharina Person (our statistician and QMC investigator), we 
will follow this protocol in S2S.  
 
A.1. Documentation of study protocol  
We will develop an overall study protocol that includes an operations manual, information about the study population, 
hypotheses to be tested, data source, and information on primary and derived variables. The analysis plan will expand 
upon the aims and models included within this proposal. Rationale for analytic decision (e.g., exclusion of items, selection 
of a scoring approach) will be documented in detail. The development of the study protocol will be completed and 
available in Year 1 of the study. This protocol will be made available to PCORI staff to circulate as they deem appropriate. 
We will also make the protocol available on demand to individuals with an interest in replicating our findings in a research 
setting or in using the protocol for dissemination of our findings to other healthcare settings and or/validation of our 
approach and resulting measures. All newly developed study documentation materials will be submitted to PCORI with 
the six month progress reports. 
 
A.2. Documentation of study datasets 
Documentation of study data sets will be provided within six months of the end of the final funding year. We will conduct 
ongoing documentation including codebooks, meta-data for the datasets, and programming code used to conduct 
outcomes analyses. Our code will be documented and annotated, so that it can be adapted for use in similar projects. 
 
2. Describe your data management and sharing plan, including how you will make study data sets available in a 
manner that is consistent with applicable privacy, confidentiality and other legal requirements, if requested. 
All datasets will be cleaned and provided upon request in SAS, Stata, and CSV format, accompanied by detailed 
documentation describing all procedures used in dataset creation. We will follow similar procedures established by the 
QMC for sharing datasets, including creating a website that designates the contact person and a request form for the 
dataset. Anyone requesting a dataset will be able to access this form online.   
3. Propose a budget to cover costs of your data-sharing plan, if requested.  
The Department of Quantitative Health Science and the Health Informatics and Implementation Science Division at the 
University of Massachusetts Medical School are committed to this research and will provide support for funding the 
website for data sharing and staff effort. No additional funds for data-sharing are requested. 
 
 
 
 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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PROTECTION OF HUMAN SUBJECTS 
 
Describe the protection of human subjects involved in your research.  
 
1. Risks to the Subjects  
 
A. Human Subjects Involvement and Characteristics  
In Smoker-to-Smoker (S2S) Peer Marketing and Messaging to Disseminate Tobacco Interventions (S2S), we will test the 
dissemination and effectiveness effects of two smoker-driven interventions:  
2. Access to peer recruitment tools: Tools to facilitate smokers’ recruiting their peers to increase Decide2Quit access 
(NCI R21CA158968).  
3. Recommender computer tailored health communication (recommender CTHC): Complex machine learning 
algorithms (recommender systems) that uses smokers’ feedback (explicit and implicit) in the current and prior 
studies to adapt its selection of messages to smokers (PCORI IP2P1000582).  
Our goal is to test the enhancements offered by the peer recruitment and recommender CTHC (individually and 
collectively) over an active comparison group (recruitment using online advertisements and the motivational messages 
tailoring of the standard CTHC system of Decide2Quit).  Human subjects will be involved in the study. In this study, we will 
recruit we will recruit 1200 smokers online (Aim 1 and 2).  
 
Table 1: Compensation Type.  
One month online or telephone follow-up 
$25 
Six month online or telephone follow-up 
$25 
Mailed saliva NicAlert® test 
$50 
 
B. Data Collection  
There are several data sources in this project (see plans for collecting and storing this data in UMass regulated 
environment). The regulated environment is located in the data center and maintained by the UMass Information Services 
department. It provides a secure environment for hosting the applications, as well as the databases of the applications. 
The setup of regulated environment provides the needed security protocols for the regulatory and Federal standards 
required. Access for programmers is restricted through a Virtual Private Network and a secure RSA token and only 
restricted personnel are allowed access to the regulated environment The Regulated environment is secured using 
hardware and software firewalls, along with access restrictions to enforce governmental policies requiring to enforcement. 
Applications and the database are hosted on different servers to provide additional security for the data. Web and mobile 
applications can connect to the application server via HTTPS. HTTPS or secure socket layer (HTTPS) is the current standard 
for secure connectivity to servers on an encrypted channel.  The application sever connects to the database server on a 
secure, password protected port. We have programs continually monitoring these ports to detect any unauthorized access. 
 
 
Table 1: Data Collection 
Data Source 
Data Elements 
Private 
identifiers 
Data Stored In 
Search Ad 
Manager 
Online advertisement stats (no of 
users who saw ads, etc.) 
NO 
Search ad managers (Aggregate data) 
Baseline during 
registration 
Demographics, Participant smoking 
characteristics (allowing smoking at 
Yes 
UMass Regulated Environment 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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home, quit in last 12 months),  
Number of cigarettes/day 
Every 
Decide2Quit  visit 
Readiness to quit,  Use Of 
Decide2Quit 
Yes 
UMass Regulated Environment 
Every message 
(email or text-
messaging) 
Explicit influence ratings, message 
sent date 
Yes 
UMass Regulated Environment 
6th month Follow-
up (Online or 
phone) 
7-day point prevalence smoking 
cessation, number of cigarettes 
Yes 
UMass Regulated Environment; Survey will be 
entered by research staff directly into our 
server.  
6thmonth 
Follow-up 
(mailed) 
 
Biochemical verification saliva  
NicAlert®  
Yes 
Our research staff will receive the mailed 
sample, verify the results and, upload them 
to the database in the UMass Regulated 
Environment. We will then dispose of the 
saliva sample.   
 
 
B.1 UMass Regulated Environment:  
 
C. Potential Risks  
The risks of the study are not high, and thus the safety monitoring plan has been matched to the risk to subjects in this 
study. Risks to participants relate mostly to misinterpretation of what is research and what is loss of confidentiality. The 
major risk is the accidental disclosure of information; however, every precaution will be taken to prevent this and the 
study team has an excellent track record of protection of confidential data. As noted, all data will be stored in the regulated 
environment. All data collected will be stored in a HIPAA compliant regulated environment and access will be only through 
a secure VPN network. All smokers’ related identifiers are encrypted in the database. Our biostatistician will organize data 
security and archiving. In no way will individual participant data be released to the public or cited in a publication. We 
have substantial experience with implementing these methods successfully. All our research staffs are trained in HIPAA 
compliance and will complete all human subjects training.  
 
There may be some stress involved in completing the follow-up survey, but we will emphasize that this participation is 
voluntary. Note that all participants will receive saliva NicAlert® test available over the counter.  The NicAlert detects 
cotinine, the major metabolite of nicotine. Cotinine is the preferred marker for tobacco use because cotinine stays in the 
body much longer than does nicotine. The presence of cotinine in saliva is a reliable indicator for tobacco use within the 
past several days. Every saliva NicAlert® test kit comes with saliva collection containers, which will make it easy for 
participants to mail it to us. We will have a research staff available by phone or email to help the participants complete 
the saliva NicAlert® test. 
 
 
 
II. Adequacy of Protection Against Risks  
A. Recruitment and Informed Consent  
As described above, individuals who are smokers will be recruited for the study. Our investigators (including patient 
investigators) and staff  will be trained in HIPAA compliance and complete all human subjects training.  Smokers will be 
recruited through online advertisements (Facebook and Google) or by peer recruitment. These smokers will be asked to 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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provide online consent. We have conducted multiple online studies and will format our online consent to be easily 
understandable to the participants. 
 
B. Protection against Risk  
We have minimized data collection to only data needed to answer study hypotheses. We have stringent protection against 
breach of confidentiality using secured servers and locked office spaces for data entry at UMMS. The study statistician will 
do periodic checks to ensure that participant confidentiality is protected at all stages of data management and analysis. 
At the start of the study, all study staff will be trained in practices that ensure participants’ confidentiality and privacy. We 
will only share aggregate data with our patient panel.   
 
Specifically, there may be two situations that could accidently reveal the participation in an online study.  Our Facebook 
website plugin will facilitate peer recruitment of smokers to the study. Through the website plugin, smokers will be able 
to post private messages. These private messages are only viewable by the person that the message is sent to. We will 
emphasize with the recruiter the importance of using private messages, and the potential risks of posting messages on 
Facebook through other means. We will make all effort to minimize risks during biochemical verification. The cover for 
the mailing of the sample will only contain a random number and not the participants’ identifier. This random number will 
be stored in another database in the regulated server (not our study database) and be linked via another identifier to the 
participants’ data. Only our research staff and the PI will have access to this random number database. This way, we 
reduce the potential of linking the random number to the participants’ data by others.  
 
Risks for participation in the intervention are not high, and we anticipate that participation may increase cessation. The 
alternative to participation for smokers is not to participate. Although we do not anticipate any adverse health events 
intervention, the participants will be asked to call the project manager and/or IRB at UMMS to report any adverse events 
(AEs) or serious adverse events (SAEs). Again, participants will be informed not to report urgent symptoms (chest pain) to 
the study personnel, specifically not to be reported through the website, but to call their physician or an emergency 
number. Any hospitalizations or deaths discovered by the research team will be promptly reported to the IRB with an 
assessment of whether any study procedures may have contributed to these outcomes. Should any SAEs occur they will 
be reported immediately to the UMMS IRB and to NIH. Actions taken by the IRB in response to SAEs will also be reported 
to NIH, as will reports of changes or amendments to the protocol as a result of an SAE.  
 
As noted, in order to ensure patient-centeredness, we will hire 5 patient Investigators to serve on a patient-expert panel. 
These patients will be considered part of the research team and will give input on research design, implementation and 
dissemination. These patients will paid for their time; they will receive $50/meeting and will be invited to 15 meetings/year. 
However, even though these patients will have completed human subjects training, we will only share with them de-
identified and aggregate data. Our study staff will create these reports for them before every meeting. Our interaction 
with participants is minimal, and all data will remain confidential and only reported in aggregate. There may be some 
stress involved in referrals for the direct and peer recruits, but we will emphasize that this participation is voluntary. The 
study does have the potential to increase smoking cessation, which may likely provide direct health benefits to the 
participant. 
 
 
III. Potential Benefits of the Proposed Research to the Subjects and Others  
The major benefit to smokers is the additional resources to encourage smoking cessation and the potential for supporting 
cessation attempts resulting in quitting smoking and the resulting health benefits.  
 
IV. Importance of the Knowledge to be Gained  

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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This project will help to identify effective strategies for increasing use of cessation treatments. This project is focused on 
the effective dissemination of Decide2Quit, a Digital intervention for Smoking Cessation (DISC). Engaging smokers to DISCs 
has been challenging due to limited reach and limited longitudinal engagement. Even if DISCs succeed in getting smokers 
to access, they do not longitudinally engage with them limiting their effectiveness. The aim of this study is to test two 
methods for increasing dissemination and effectiveness of DISCs.  
 
V. Data and Safety Monitoring Plan 
A. OVERVIEW  
The risks associated with the data collection and participation in this study are low. The primary risk to subjects is the 
accidental disclosure of information; however, every precaution will be taken to prevent this and the study team has an 
excellent track record of protection of confidential data. There are no health or safety risks for participation in the S2S 
study. To that end the purpose of this Data and Safety Monitoring Plan (DSMP) is to specify the procedures and rationales 
of the current study to ensure the safety of participants’ data and the validity and integrity of the data.  
For smokers, the major benefit of participation in the S2S study, is the additional resources to encourage smoking 
cessation and the potential for supporting cessation attempts resulting in quitting smoking and the resulting health 
benefits.  
 
B. OVERSIGHT RESPONSIBILITIES 
Oversight of the trial is provided by the Principal Investigator (PI), Dr. Sadasivam, Dr. Person, the faculty statistician and 
co-investigators, Dr. Thomas Houston and Dr. Jeroan Allison (“co-investigators” throughout).   
 
C. MONITORING PROCEDURES 
Dr. Sadasivam assures that informed consent is obtained prior to performing any research procedures, that all subjects 
meet eligibility criteria, and that the study is conducted according to the IRB-approved research plan.   
 
Study data are accessible at all times for the PI and co-investigators to review. The PI, Dr. Sadasivam, the biostatistician, 
the project manager and the analyst will regularly review database admin reports to inspect for data collection 
completeness. Furthermore study recruitment and retention will be monitored using the aggregated summary reports 
available on the admin site. Project team monitoring includes regular review (monthly) reports which present statistics 
regarding recruitment, retention and general tracking of study participants in a CONSORT type diagram in order to monitor 
study progression. In addition study data will be assessed every three months (once a quarter) to examine completeness 
of survey responses. In addition to these reports, the Decide2Quit website has an Admin Portal which provides real-time 
reports with the following metrics: 
• 
user lists provides data such as the user’s contact information, consent date, randomization arm, their follow-up 
survey status, referral source, registration date, basic demographic characteristics coded into categories, email 
message counts (to track the number of messages sent to them from the system) 
• 
exports of follow-up data which includes all the data collected on the follow-up surveys 
• 
summary reports which provide us an aggregated count of the total number of registered users overall and within 
each randomization arm. In addition, we have summary reports to provide us aggregated counts of users within 
racial/ethnic and gender categories. 
 
All of these reports require login authentication and are only accessible behind the UMMS firewall. The PI ensures all 
protocol deviations and unanticipated events are reported to the IRB according to the applicable regulatory requirements. 
 
D. COLLECTION AND REPORTING OF SAEs AND AEs 
For this study, the following standard AE definitions are used: 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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Adverse event:  Any unfavorable and unintended sign (including an abnormal laboratory finding), symptom or 
disease temporally associated with the use of a medical treatment or procedure, regardless of whether it is 
considered related to the medical treatment or procedure. 
Serious Adverse Event:  Any AE that results in any of the following outcomes: 
• 
Death 
• 
Life-threatening  
• 
Event requiring inpatient hospitalization or prolongation of existing hospitalization 
• 
Persistent or significant disability/incapacity 
AEs are graded according to the following scale: 
Mild:  An experience that is transient, & requires no special treatment or intervention.  The experience does not 
generally interfere with usual daily activities.  This includes transient laboratory test alterations. 
Moderate:  An experience that is alleviated with simple therapeutic treatments.  The experience impacts usual 
daily activities.  Includes laboratory test alterations indicating injury, but without long-term risk. 
Severe:  An experience that requires therapeutic intervention.  The experience interrupts usual daily activities. If 
hospitalization (or prolongation of hospitalization) is required for treatment it becomes an SAE. 
The study uses the following AE attribution scale: 
Not related:  The AE is clearly not related to the study procedures (i.e., another cause of the event is most 
plausible and/or a clinically plausible temporal sequence is inconsistent with the onset of the event).   
Possibly related:  An event that follows a reasonable temporal sequence from the initiation of study procedures, 
but that could readily have been produced by a number of other factors. 
 
Related:  The AE is clearly related to the study procedures.   
Identification of AEs 
As previously stated, given that this is not a medical or therapeutic intervention there are no health or physical safety 
risks associated with participation in the S2S study. Furthermore, contact between the study participants and study 
research staff is extremely limited and occurs primarily via email with some limited contact over the phone.  
Should an AE involving a single participant occur it could potentially be identified is through notification from the 
participant directly to the study staff, PI or UMMS IRB. The only other occasion might be if a participant chose to 
complete a follow-up survey over the phone and the participant informed the study staff of an issue at that time. 
Reporting of Unanticipated Events 
With regard to unanticipated events, our study adheres to the University of Massachusetts Medical School IRB’s SOPs. In 
particular we follow the “SOP HRP-112: Reportable New Information” which states that should our project team become 
aware of any information that represents an unanticipated problem involving risk to subjects or others, we will notify 
the IRB chair or vice-chair within 24 hours of becoming aware of the information for consideration of whether any 
immediate actions are necessary to protect the rights and welfare of subjects in advance of the meeting and then we 
will submit the report to the IRB Committee for review.  

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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Should participants have any questions or concerns they are provided with the contact information for our study team 
as well as the UMass IRB. 
E. MANAGEMENT OF RISKS TO SUBJECTS 
Expected AEs 
As previously stated, there are no health or physical safety risks associated with participation in the S2S study. The 
primary risk to subjects is the accidental disclosure of information or a data security breach of the decide2quit website. 
Our site is located on servers setup as part of the regulated environment maintained by the University of Massachusetts 
Medical School Information Services (UMMS IS). Please reference the Data Management section for a detailed 
description of the UMMS IS servers. These servers are continuously monitored for any suspicious activity. 
Management of AEs 
Should a data breach occur UMMS IS will immediately inform the PI and take necessary steps to fix the breach. The PI 
will work collaboratively with UMMS IS to ascertain the scope of the breach to determine which participants may have 
been affected and what data were leaked. The UMMS IRB would also be notified within 24 hours of the PI becoming 
aware of the breach and a decision would be made as to how to proceed with regards to the study protocol.  
Dose Escalation and Dose-Limiting Toxicities 
Not applicable. 
F. DATA ANALYSIS PLANS 
This study does not involve an intervention which requires safety or efficacy monitoring nor does it have any study 
stopping rules. Ongoing data monitoring as described above in the Monitoring Procedures Section is focused on 
recruitment and retention. In addition we will perform limited checks on overall data quality to ensure that our data 
capture system is operating correctly and that the survey data is written to the database.  
G. PLAN FOR DATA MANAGEMENT 
Compliance of regulatory documents and study data accuracy and completeness will be maintained throughout the 
study duration.  
Our approach to maintaining confidentiality is as follows: 
1) Only collect the necessary Information and ensure that it is as anonymous or de-identified as possible 
a. We have minimized data collection to that which is needed to answer our hypotheses. Or to 
operationally execute our study’s protocol. The only identifiable information collected are email 
(necessary for logging into the system) and phone number also critical for the follow-up data collection. 
Data that is collected solely for the purposes of the protocols operations include first name and last 
name and mailing address. However, we only request the mailing address if a participant indicates that 
they are willing to complete a NicAlert test at their six month follow-up. 
b. To ensure that the study data is held confidential, consented participants will be assigned a study ID 
(identifier).  We will not be collecting Social Security numbers, Date of Birth, or other unique identifiers 
other than those previously mentioned. 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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c. In no way will individual participant data be released to the public or cited in a publication. Our group 
has substantial experience with implementing these methods successfully.  Our interaction with 
participants is minimal, and all data will remain confidential and only reported in aggregate. 
2) Training Staff  
a. All project team members will be trained in practices that ensure participants’ confidentiality and 
privacy. In order to obtain access to any identifiable participant data, staff must be added to our IRB and 
complete CITI training on HIPAA protections and Good Clinical Practices. 
b. Furthermore, research staff are instructed to only access identifiable information when it’s necessary to 
conduct their work on the protocol. For example, if they are conducting follow-up phone calls to 
participants who have not completed the online survey or if they are mailing out gift cards to those who 
have completed the survey. At no time will they download the identifiable data from the secure UMMS 
servers. 
3) Security of our Decide2Quit Website 
a. The data will be encrypted and stored in a HIPAA compliant UMMS regulated environment and access 
will be only through a secure VPN network. The UMMS Regulated Environment provides applications a 
secure network for confidential data.  The Regulated Environment has been securely configured to allow 
application access via the secure socket layer (HTTPS) protocol — a protocol that delivers server 
authentication, data encryption, and message integrity.  The setup of Regulated environment provides 
the needed security protocols for the regulatory and Federal standards required.  The Regulated 
Environment is secured using hardware and software firewalls, along with access restrictions to enforce 
governmental policies requiring to enforcement.  

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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CONSORTIUM CONTRACTUAL ARRANGEMENTS 
 
Describe the proposed research projects that subcontracted organizations will perform. Explain the strengths that these 
partners bring to the overall project. 
 
This study will primarily be conducted at the University of Massachusetts Medical School. We have two aims.  
• 
AIM 1: Implement the hybrid effectiveness-dissemination trial of S2S enhanced functions to increase dissemination 
(recruitment and repeated use) and effectiveness of Decide2Quit with 1200 smokers 
• 
AIM 2: For the Effectiveness randomized trial, we will recontact participants after six months of enrollment. Compared 
to the standard messaging group, the recommender CTHC is designed to increase smoking cessation rates at follow-
up.   
 
As recommended by reviewers, we have added Dr. Bridget Smith (Feinberg School of Medicine at Northwestern University 
in Chicago, IL) to our team to help us quantify the resources needed to successfully replicate our intervention. From the 
perspective of an implementing organization, we will compare the costs of standard Decide2Quit, the peer recruitment 
enhanced Decide2Quit, and the recommender CTHC enhanced Decide2Quit, and the fully enhanced Decide2Quit.  As a 
research associate professor at Northwestern and a research health scientist at the Center of Innovation for Complex 
Chronic Healthcare and formerly the co-director of the Spinal Cord Injury Quality Enhancement Research Initiative (SCI 
QUERI), Dr. Smith has over 15 years of experience in the areas of health economics and outcomes. She has experience 
designing methods and performing economic and statistical analyses. She has extensive experience with cost, primary 
data collection using surveys and medical record reviews, and with studies integrating qualitative and quantitative 
methods and has previously collaborated with Dr. Sadasivam to examine costs associated with a tobacco cessation 
intervention.  Dr. Smith will help the team track staff time associated with each intervention arm, including time for 
training, recruiting, and administering the different aspects of the intervention. In addition, she will help us will estimate 
development costs of each intervention in our prior NIH and PCORI-funded studies and any equipment or supply costs. 
Using best practices outlined budget impact analysis, she will use these information to will create tables to describe the 
assumptions of our inputs and outputs of our budget impact analysis, and perform sensitivity analyses to examine how 
changing the assumptions of the model impact the potential costs for an organization implementing the intervention.   
 
 
 
 
 
 
 
 
 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
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34
 
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hypertension achieve blood pressure control through improved communication with their doctors. Patient Educ 
Couns. 2010;79(2):245-250. 
136. 
Sales AE, Tipton EF, Levine DA, et al. Are co-morbidities associated with guideline adherence? The MI-Plus study 
of Medicare patients. J Gen Intern Med. 2009;24(11):1205-1210. 
137. 
Roblin DW, Houston TK, 2nd, Allison JJ, Joski PJ, Becker ER. Disparities in use of a personal health record in a 
managed care organization. J Am Med Inform Assoc. 2009;16(5):683-689. 
138. 
Levine DA, Allison JJ, Cherrington A, Richman J, Scarinci IC, Houston TK. Disparities in self-monitoring of blood 
glucose among low-income ethnic minority populations with diabetes, United States. Ethn Dis. 2009;19(2):97-
103. 
139. 
Crenshaw K, Shewchuk RM, Qu H, et al. What should we include in a cultural competence curriculum? An 
emerging formative evaluation process to foster curriculum development. Acad Med. 2011;86(3):333-341. 
140. 
Qu H, Houston TK, Williams JH, Gilbert GH, Shewchuk RM. Cognitive mapping tobacco control advice for 
dentistry: a dental PBRN study. Am J Health Behav. 2011;35(2):228-239. 
141. 
Funkhouser E, Houston TK, Levine DA, Richman J, Allison JJ, Kiefe CI. Physician and patient influences on provider 
performance: beta-blockers in postmyocardial infarction management in the MI-Plus study. Circ Cardiovasc Qual 
Outcomes. 2011;4(1):99-106. 
142. 
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patients undergoing major non-cardiac surgery. J Gen Intern Med. 2011;26(2):162-169. 
143. 
Houston TK, Coley HL, Sadasivam RS, et al. Impact of content-specific email reminders on provider participation 
in an online intervention: a dental PBRN study. Stud Health Technol Inform. 2010;160(Pt 2):801-805. 
144. 
Allison JJ, Kiefe CI, Wall T, et al. Multicomponent Internet continuing medical education to promote chlamydia 
screening. American journal of preventive medicine. 2005;28(3):285-290. 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
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145. 
Levine DA, Funkhouser EM, Houston TK, et al. Improving care after myocardial infarction using a 2-year internet-
delivered intervention: the Department of Veterans Affairs myocardial infarction-plus cluster-randomized trial. 
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146. 
Houston TK, Richman JS, Ray MN, et al. Internet delivered support for tobacco control in dental practice: 
randomized controlled trial. Journal of medical Internet research. 2008;10(5):e38. 
147. 
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system for smoking cessation: Pragmatic clinical trial in 174 practices. 41st APHA Annual Meeting and Exposition 
November 02 - 06, 2013; Boston, MA. 
148. 
Houston TK, Allison JJ, Sussman M, et al. Culturally appropriate storytelling to improve blood pressure: a 
randomized trial. Ann Intern Med. 2011;154(2):77-84. 
149. 
DeLaughter LK, Sadasivam SR, Kamberi A, et al. Crave-Out: A Distraction/Motivation Mobile Game to Assist in 
Smoking Cessation. JMIR Serious Games. 2016;4(1):e3. 
150. 
Grant A, Berlin A, Freeman GK. The impact of a student learning journal: a two-stage evaluation using the 
Nominal Group Technique. Med Teach. 2003;25(6):659-661. 
151. 
Miller D, Shewchuk R, Elliot TR, Richards S. Nominal group technique: a process for identifying diabetes self-care 
issues among patients and caregivers. Diabetes Educ. 2000;26(2):305-310, 312, 314. 
152. 
Ray MN, Allison JJ, Coley HL, et al. Variations in tobacco control in National Dental PBRN practices: the role of 
patient and practice factors. Spec Care Dentist. 2013;33(6):286-293. 
153. 
Rocheleau M, Sadasivam RS, Baquis K, et al. An observational study of social and emotional support in smoking 
cessation Twitter accounts: content analysis of tweets. Journal of medical Internet research. 2015;17(1):e18. 
154. 
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http://www.nytimes.com/2011/02/10/health/views/10chen.html?_r=3&ref=health&. Accessed 11/13/2013. 
155. 
Weintraub K. Talk therapy: Storytelling may help what ails her - just as her story has helped others. 2011; 
http://www.boston.com/lifestyle/health/articles/2011/03/21/patients_benefit_from_listening_to_the_stories_
of_those_dealing_with_similar_ailments/. Accessed 11/13/2013. 
156. 
Park A. Storytelling May Help Lower Blood Pressure. 2011; http://healthland.time.com/2011/01/17/story-
telling-may-help-to-lower-blood-pressure/. Accessed 11/13/2013. 
157. 
Gopinath B, Baur LA, Hardy LL, et al. Relationship between a range of sedentary behaviours and blood pressure 
during early adolescence. Journal of human hypertension. 2012;26(6):350-356. 
 
 
 
 
 
 
 

 
 
 
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PCORI Cycle 1 2016 Research Plan Template                                                                        
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APPENDIX  
 
APPENDIX A 
 
 
 
 
 
 
 
 
 
 

 
 
 
PRINCIPAL INVESTIGATOR (SADASIVAM, RAJANI, SADASIVAM): 
 
 
 
PCORI Cycle 1 2016 Research Plan Template                                                                        
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APPENDIX B 
 
 
What's in your wallet? A Message from Your Online Community 
Darcy, a 39-year-old, has thought about the financial savings of quitting 
smoking and say.   Plan to save all the money you spent on cigarettes just 
like you were still buying them and put it away to see how much you can 
save and use it for something great :). 
 Saving to reward yourself for quitting can be really motivating.  
Remember to use the money you save for something you really want and 
not for something you need! 
To learn more, please visit www.decide2quit.org 
 
      Socio-Cultural Attributes-Money 
       Behavioral treatment general 
 
General Treatment-Decide2Quit.org 
Smoking hurts more than just your lungs: A Message from Your Online 
Community. 
 
Tina, a 21-year-old, realizes that when quitting, it's important for women 
to think about longevity of life. Many women have children and want to 
see them grow, but you can't if you die of cancer. Smoking harms nearly 
every organ in the body.  Not only do smokers potentially shorten their 
life but they also are sick more often, take more days off work, and have 
longer hospital stays.   
To learn more, please visit www.decide2quit.org. 
 
Socio-Cultural  Family/Friend Problem 
 
Health:  Physical Functioning - Smoking Risk  
 
General Treatment-Decide2Quit.org 
It's not too late 
Even though smoking really harms your body, it's not too late – 
 you can undo much of the damage by quitting smoking now.  
To learn more, please visit www.decide2quit.org. 
 
Health: Physical Functioning - Smoking Risk  
Health: Physical Functioning-Quit Benefit 
General Treatment-Decide2Quit.org 
How do I love thee? 
Are you concerned about how your smoking affects your family and 
friends?  Until you are ready to quit, try to avoid smoking around your 
loved ones by going outside or not smoking in the car.  Taking small steps 
may help you prepare to quit.      
To learn more, please visit www.decide2quit.org. 
 
Socio-Cultural  Family/Friend Problem  
Behavioral treatments- general  
General Treatment-Decide2Quit.org 
And the list goes on… 
Long term risks of smoking include heart attacks and stroke, cancer of all 
types, osteoporosis, long-term disability, and the need for extended care.  
Think about what your life will be like if you keep smoking.  
To learn more, please visit www.decide2quit.org. 
Health-Medical problems 
Health: Physical Functioning - Smoking Risk  
General Treatment-Decide2Quit.org