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Mobile health (mHealth) smoking cessation programs are typically designed for smokers who are ready to quit smoking. In contrast, most smokers want to quit someday but are not yet ready to quit. If mHealth apps were designed for these smokers, they could potentially encourage and assist more people to quit smoking. No prior studies have specifically examined the design considerations of mHealth apps targeting smokers who are not yet ready to quit.
To inform the user-centered design of mHealth apps for smokers who were not yet ready to quit by assessing (1) whether these smokers were interested in using mHealth tools to change their smoking behavior; (2) their preferred features, functionality, and content of mHealth programs addressing smoking; and (3) considerations for marketing or distributing these programs to promote their uptake.
We conducted a sequential exploratory, mixed-methods study. Qualitative interviews (phase 1, n=15) were completed with a demographically diverse group of smokers who were smartphone owners and wanted to quit smoking someday, but not yet. Findings informed a Web-based survey of smokers from across the United States (phase 2, n=116). Data were collected from April to September, 2016.
Findings confirmed that although smokers not yet ready to quit are not actively seeking treatment or using cessation apps, most would be interested in using these programs to help them reduce or change their smoking behavior. Among phase 2 survey respondents, the app features, functions, and content rated most highly were (1) security of personal information; (2) the ability to track smoking, spending, and savings; (3) content that adaptively changes with one’s needs; (4) the ability to request support as needed; (5) the ability to earn and redeem awards for program use; (6) guidance on how to quit smoking; and (7) content specifically addressing management of nicotine withdrawal, stress, depression, and anxiety. Results generally did not vary by stage of change for quitting smoking (precontemplation vs contemplation). The least popular feature was the ability to share progress via social media. Relevant to future marketing or distribution considerations, smokers were price-sensitive and valued empirically validated programs. Program source, expert recommendations, and user ratings were also important considerations.
Smokers who are not yet ready to quit represent an important target group for intervention. Study findings suggest that many of these individuals are receptive to using mHealth tools to reduce or quit smoking, despite not having made a commitment to quit yet. The preferences for specific mHealth intervention features, functionality, and content outlined in this paper can aid addiction treatment experts, design specialists, and software developers interested in creating engaging interventions for smokers who want to quit in the future but are not yet committed to this important health goal.
Cigarette smoking is the leading preventable cause of death and illness in the United States [
Whereas most smokers are not ready to quit and are not seeking treatment to quit smoking, most public health smoking interventions and nicotine dependence treatment programs—including smoking cessation apps—are designed for those smokers who are ready to quit in the near term. These programs are typically designed to help people take action but do not necessarily include the support, encouragement, or information smokers need to move from a position of wanting to quit someday to being ready to quit now or to help smokers cut back, but not quit, smoking. As a result, these programs may also have little appeal to smokers who are ambivalent about quitting in the near term. Utilization data are limited but it seems unlikely that smokers who are not actively thinking about quitting smoking are downloading or using cessation apps. For example, in a recent multinational survey of smokers who downloaded a cessation app, 77% were ready to quit in the next month (preparation stage of change) [
In contrast, we believe smokers who are not ready to quit in the near term may be receptive to mHealth tools if these tools were better designed to address their needs and interests, particularly among people who typically use mobile devices already. In prior research, we found precontemplative and contemplative smokers were receptive to both counseling [
Increasing attention is being focused on how to design appealing and effective mHealth programs for smokers who are ready to quit and on identifying smokers’ preferred mHealth features [
The goal of this research was to inform the user-centered design of mHealth tools for smokers who are not yet ready to quit by assessing (1) whether these smokers are interested in using mHealth apps to change their smoking behavior; (2) their preferred features, functionality, and content for these programs; and (3) considerations for marketing or distributing these programs to promote their uptake. To our knowledge, this is the first attempt to delineate these issues in this important target group for nicotine dependence intervention.
We conducted a sequential exploratory, mixed methods study [
Smokers (n=15) were recruited from the Greater Seattle area via Web-based Craigslist ads, community flyers, and from patients of Group Health Cooperative, a large, regional health care system in Washington state. Respondents were eligible if they (1) were 18 to 60 years old; (2) were current smokers interested in quitting someday, but not in the next month; (3) were able to speak and read in English; (4) had medical insurance; and (5) owned a smartphone which they used to access the Internet. Participants were recruited into 3 age categories: 18-29 years old (n=3), 30-39 years old (n=4), and 40 years or older (n=8). Each person participated in a phone interview and received US $50 as a thank you for their time.
The interview guide was designed to elicit participants’ responses regarding their smoking and quit-attempt history, use of smartphones and mHealth apps, and ideal design and content for health-related mHealth apps including apps to help them cut back or quit smoking. Participants were also presented a list of 17 potential features and functions of an app designed to improve their health or help them stop smoking and asked to indicate which they be would be willing to use (yes or no) and why or why not. Items were modified based on a similar scale recently used with smokers and nicotine dependence clinicians [
Quantitative and demographic data were analyzed using descriptive statistics. Interviews were audio recorded, transcribed, and loaded in ATLAS.ti version 7 (ATLAS.ti Scientific Software Development GmbH) for coding and analysis. Qualitative feedback was analyzed using an inductive, conventional content analysis approach [
Participants were demographically diverse: 53% (n=8/15) were female, 27% (n=4/15) were Hispanic or Latino, 60% (n=9/15) were white, and 53% (n=8/15) had only a high school degree or less. Mean age was 37 years (range 19-54). Participants smoked an average of 12 cigarettes per day (range 4-20). Thirteen participants (87%) had previously tried to quit smoking, but none were interested in quitting in the next month. All participants owned and regularly used a smartphone.
Participants overwhelmingly agreed that they would be interested in an mHealth app to assist them in both determining their readiness to quit smoking and providing assistance to help them successfully do so. Many noted that mHealth apps represent a “new approach,” unlike nicotine replacement therapy or quitting cold turkey, methods that several had tried in the past but were not successful. Some participants also noted that an mHealth app that helped them cut back, rather than quit smoking, was particularly appealing. As one participant (P4) stated, such an app, “
Based on the open-ended feedback and feature ratings, emergent themes were organized into three broad categories concerning participants’ recommendations for and perceptions of the utility of an mHealth app. They valued a program that would: (1) address smoking triggers; (2) build self-efficacy and accountability through social support and coaching; and (3) allow them to track their health behavior, set goals, and earn rewards. Key smoking triggers identified included stress, depression, anxiety, and the environment. One participant (P2) described her ideal program as one that would provide answers to questions like:
How would you create a safe environment to quit? How do you make your environment so you can quit? You know what I mean? Like what environment do you need so that you can quit? Like right now I’m talking to you and I didn't want the cigarette.
Several others suggested the app should provide ideas for alternative activities when they have cravings. For example, one (P7) suggested, “
In terms of building self-efficacy and accountability, participants described a fear of failure and belief that they did not possess the tools or ability to successfully quit or cut back on their smoking. In response, all but one said they would use an app that allowed them to talk with a health coach or counselor through private text messaging (short message service, SMS) or secure email built into the app. Two-thirds of participants were interested in receiving support from others. As one participant shared (P11), this type of outside support “
Another person (P2) said they would like “
But others were not interested in peer support or qualified their interest. For example, P7 commented, “
Most participants were also interested in setting health goals, tracking their smoking and cigarette spending, or earning incentives for cutting back or quitting smoking. All of them said they would use a tool to track their smoking. This was seen as a way to help them cut back on their smoking, assess their readiness to quit, and monitor how much they smoke or spend over time.
An app to track how much I’m smoking would be great...like every time you smoke you tap a button and then over the course of a couple of weeks or even months it can tell you the ebb and flow
...where it logs exactly, ‘Hey, you smoked this many cigarettes a day’ and then it turns it into a price...’You smoked $8 a day’ or whatever the case may be. And when you have something to look at and it expresses it to you, when somebody actually points out your fault, you're like, ‘Wow, I need to pay attention to that.’ It would let you know, ‘You know what? You over-smoked today. You’ve overspent.’
Others suggested including features that allow users to track the health impacts of their smoking or quitting,
When asked how they would want to learn about an mHealth app for reducing smoking, most—but not all—participants identified their doctors as trusted informants. These participants described a scenario in which their doctors would introduce the app, but participants would download it themselves from the app store. One participant further described:
That would be so great if (the app recommendation) was on my going-home papers from (my health provider). If I saw (this), I would download it. There's always the quit smoking plan in the back of my after visit summaries, so if there was information about this app, that would be even more incentive than the number to call (to get help quitting).
Participants who were reluctant to get the app from their doctor expressed concerns about cost. Another participant shared that she had not and was unwilling to tell her doctor she smoked, as she feared that her health care costs would go up. But there was a general willingness to download the app from an app store, either based on one’s own interest or at the suggestion of family or friends. One participant felt the app’s popularity could be spread through social networks:
Word of mouth is a great sales (tool) itself. If you really believe in your product or that app. My swamp game that I play, it's the dumbest game in the world, but...I got all my friends playing it just because of word of mouth.
Additional participant preferences for each of the 17 features assessed are presented in
Participant preferences for mHealth features.
Feature domain | Yes |
No |
|
Advice for coping with cravings to smoke that is tailored specifically for you based on your needs or preferences | 14 (93) | 1 (7) | |
Advice for handling stress that is tailored specifically for you based on your needs or preferences | 13 (87) | 2 (13) | |
The ability to talk with a health coach or counselor through private text messaging or secure email built into the app | 14 (93) | 1 (7) | |
Information about the risks of smoking or benefits of quitting | 12 (80) | 3 (20) | |
Information about stop-smoking medications, how they work, or how to get them | 11 (73) | 4 (27) | |
Stories or videos from others talking about how they successfully changed their lifestyle and improved their health | 11 (73) | 4 (27) | |
Social support from people other than your friends or family, like others who are trying to quit smoking or have successfully quit already | 10 (67) | 5 (33) | |
Social support from people other than your friends or family, such as other people trying to change their diet or physical activity | 9 (60) | 6 (40) | |
Social support from friends or family to help you stop smoking | 9 (60) | 6 (40) | |
Social support from friend or family to help you change your diet or physical activitya | 6 (40) | 8 (53) | |
A tool to track how many cigarettes you’ve smoked | 15 (100) | 0 (0) | |
A tool to track how much money you spend on cigarettes or have saved by not smoking | 13 (87) | 2 (13) | |
The ability to get points or credit for using the app and exchange them for rewards | 13 (87) | 2 (13) | |
A tool to track your medication use | 11 (73) | 4 (27) | |
A tool to track your physical activity | 10 (67) | 5 (33) | |
A tool to track your diet | 8 (53) | 7 (47) | |
Information about how to change your diet or physical activitya | 7 (47) | 7 (47) |
aTotals do not add to 100% as 1 participant refused this question.
Survey participants (n=116) were recruited via Craigslist ads and through ResearchMatch.org, a Web-based service funded by the US National Institutes of Health Clinical and Translational Science Award (CTSA) program which matches prescreened volunteers with relevant medical research studies. Advertisements were placed in 21 states representing all US geographic regions, but focused more heavily on states in the southeast and midwest due to higher smoking prevalence in these regions [
Participants were asked about their demographics, smoking and quit attempt history, use of smartphones and mHealth apps, interest in smoking-focused mHealth apps, and reasons they were or were not interested in these tools. Participants were presented a list of 42 specific mobile app features, functionality, and content topics and asked to rate how important or how appealing they found each of them. Item selection was based on a similar scale previously used to assess interest in mHealth app content and features among smoking cessation treatment experts and smokers [
Survey responses were analyzed using SPSS version 22 (IBM Corporation). Descriptive statistics were used to summarize overall results. Preferences for mHealth features, functions, and content were also compared by stage of change (precontemplators vs contemplators) using Pearson chi-square analyses given the categorical nature of the ratings. Multiple comparisons were adjusted using the Bonferroni correction. Write-in comments were coded for common themes and summarized.
Participant demographics and smoking characteristics are presented in
One-third (37.1%, 43/116) had attempted to quit smoking in the past year. Whereas participants were not actively attempting or planning to quit smoking in the near term, most agreed that they would cut back on their smoking if they knew where to find help (59.4%, 69/116) and nearly half said they would quit smoking if they knew where to find help (47.4%, 55/116).
Participants were predominantly Android users (75.0%, 87/116); only 29 (25.0%, 29/116) owned an iPhone. The majority actively used their mobile phones for accessing the Internet (97.4%, 113/116), taking pictures (97.4%, 113/116), sending email (95.7%, 111/116) and text messages (93.1%, 108/116), downloading apps (92.2%, 107/116), playing games (86.2%, 100/116), and listening to music (84.5%, 98/116).
Participant characteristics.
Characteristics | Participants |
|
Female | 84 (72.4) | |
Hispanic or Latino | 8 (6.9) | |
White | 80 (69.0) | |
Black | 19 (16.4) | |
Asian | 5 (4.3) | |
American Indian or Alaska Native | 2 (1.7) | |
Other | 5 (4.3) | |
Decline | 5 (4.3) | |
High school degree or general educational |
48 (41.4) | |
College degree | 58 (50.0) | |
Graduate degree | 8 (6.9) | |
Household income < US $50,000 | 65 (56.1) | |
Electronic cigarettes | 16 (13.8) | |
Cigars or cigarillos | 9 (7.8) | |
Hookah | 14 (12.1) | |
Quit attempt in past year (yes) | 43 (37.1) | |
Precontemplation | 37 (32.2) | |
Contemplation | 78 (67.8) | |
Age, mean (SD) | 38.1 (11.7) | |
Cigarettes per day, mean (SD) | 15.5 (12.9) |
Approximately half (55.2%, 64/116) had used an app to manage 1 or more common health-related issues. Nearly half (45.7%, 53/116) had used a physical activity app. Twenty-five people (21.6%, 25/116) had used an app to track their food, calories, or weight. Eight people (6.9%, 8/116) had used a stress reduction app, 11 (9.5%, 11/116) used an app to track their sleep, 9 (7.8%, 9/116) used an app to help them manage their mood, and 3 people (1.2%, 3/116) had used another health-related app. However, only 4 (3.4%, 4/116) had ever downloaded an app to help them stop smoking.
In contrast, most (75.0%, 87/116) said they would consider downloading an app to help them stop smoking. Among those who said they would not consider this or were unsure whether they would ever consider this, many (44%, 11/25) expressed uncertainty that an app could help them change their behavior. Comments included, “
More people (87.9%, 102/116) were interested in an app that could help them reduce their smoking than stop smoking (75%, 87/116). This interest was driven by a desire to improve one’s health 71.6%, 83/116), protect one’s future health (62.9%, 73/116), and save money (61.2%, 71/116). Similarly, nearly all participants expressed interest in an app that could help them decide “if, when, or how” to quit smoking (90.5%, 105/116). Of these 3 topics, more people were interested in knowing how to quit (51.7%, 60/116) than getting help deciding if they were interested in quitting (11.2%, 13/116) or when would be a good time to quit (26.7%, 31/116). People who already owned health-related apps were more willing to download an app to help them decide if, when, or how to quit smoking than those who did not own health apps (51.8%, 61/116 vs 41.9%, 44/116;
Participants rated the importance of various privacy and security features they would want to see in an app to help them change their smoking behavior (
Perceived importance of privacy and security features. Items are measured on 4-point Likert scale from “not at all” to “very” important.
Feature | Mean (SD) | Rated “very important” |
Rated “not at all important” |
App does not access personal information on phone (eg, contacts, calendar, Facebook) | 3.09 (0.97) | 51 (44) | 8 (6.9) |
App can access personal information on phone, but I can decide which | 3.07 (0.95) | 46 (39.7) | 10 (8.6) |
App is password protected | 2.99 (1.03) | 48 (41.4) | 12 (10.3) |
My data is stored in “the cloud” so I can access from other devices | 2.62 (1.05) | 30 (25.9) | 20 (17.2) |
My data is stored on phone and not in “the cloud” | 2.38 (1.07) | 22 (19.0) | 30 (25.9) |
Participants also rated the relative appeal of a range of potential app functionality (
Participants were asked what type of rewards they would want to receive in exchange for points accumulated from viewing program content or completing tasks. Most (87.0%, 100/115) preferred a gift card or money, 9 people (7.8%, 9/115) wanted nicotine replacement patches, 3 (2.6%, 3/115) preferred free advice from a stop-smoking counselor or doctor, 2 (1.7%, 2/115) were interested in receiving another app of their choosing, and 1 person (0.9%, 1/115) simply wrote “gold.”
There was a significant relationship between stage of change and the appeal of reporting one’s progress on social media (
Perceived appeal of potential app functions. Items were measured on 4-point Likert scale from “not at all” to “very” appealing.
Function domain | Mean (SD) | Rated “very appealing” |
Rated “not at all appealing” |
||
Tracks how much I save by not smoking | 3.34 (0.83) | 63 (54.3) | 2 (1.7) | ||
Tracks how much I spend on smoking | 3.26 (0.88) | 57 (49.1) | 6 (5.2) | ||
Tracks how much I smoke | 3.06 (0.92) | 44 (37.9) | 8 (6.9) | ||
Content adapts over time to my needs or interests | 3.17 (0.75) | 40 (34.5) | 3 (2.6) | ||
Content stays the same and does not change | 2.14 (0.89) | 8 (6.9) | 30 (25.9) | ||
Lets me request support or advice when I need or want it | 3.16 (0.84) | 48 (41.1) | 3 (2.6) | ||
Can get immediate advice after answering a brief survey | 2.92 (0.90) | 34 (29.3) | 8 (6.9) | ||
Includes advice from stop-smoking experts | 2.88 (0.89) | 31 (26.7) | 8 (6.9) | ||
Includes stories from other smokers with support and advice | 2.63 (1.01) | 27 (23.3) | 18 (15.5) | ||
Sends me motivational or supportive messages via text message | 2.63 (0.99) | 24 (20.7) | 18 (15.5) | ||
Let me text or email other smokers for support and advice | 2.60 (1.03) | 27 (23.3) | 20 (17.2) | ||
Can request advice, but may wait 24-48 hours for response | 2.43 (1.03) | 20 (17.2) | 26 (22.4) | ||
Sends me motivational or supportive messages via email | 2.42 (1.00) | 20 (17.2) | 23 (19.8) | ||
Lets me send private messages to my doctor | 2.39 (0.97) | 18 (15.5) | 22 (19.0) | ||
Lets me share my progress with family and friends | 2.18 (1.00) | 13 (11.2) | 35 (30.2) | ||
Lets me video chat with stop-smoking experts | 2.14 (1.05) | 17 (14.7) | 39 (33.6) | ||
Lets me video chat with other smokers | 2.03 (1.04) | 13 (11.2) | 47 (40.5) | ||
Lets me share my progress on Facebook, Twitter, or social mediaa | 1.90 (1.01) | 10 (8.6) | 55 (47.4) | ||
Lets me earn points to redeem for free gifts | 3.45 (0.83) | 74 (63.8) | 3 (2.6) | ||
Lets me earn points or badges to track progress | 3.11 (0.94) | 50 (43.1) | 8 (6.9) |
aSignificant difference by stage of change.
Participants rated the appeal and perceived importance of different content which might be included in an app to help them either reduce their smoking or decide if, when, or how to stop smoking (
Games were considered relatively important among everyone (mean=2.85 out of 4), but overall, fewer people considered these “very important” than considered health related content as “very important” (
Participants were asked what other features they would like to see in an app designed to help them cut back or quit smoking. Twenty three stated they had no additional suggestions. Seventy-seven participants provided written suggestions. Among these, the most common theme (n=15) was the ability to track one’s behavior (cigarettes smoked, purchased), health status (improvements over time), or money (amount spent on cigarettes or saved by not smoking). The second most common themes, each endorsed by 5 people, was the ability to earn rewards by using the program or to somehow distract themselves from smoking. Four respondents requested a place to journal about their experience or record their own positive affirmations and 4 people wanted some type of interaction with other smokers. Suggestions for the latter included stories from other smokers, the ability to get advice from others, and the ability to track others’ milestones without having to personally interact with them. The remaining responses were only endorsed once each and included offering standard treatment content such as information on the risks and benefits of smoking, as well as more controversial suggestions such as providing “electric shock” and including images of smokers’ diseased lungs.
Perceived appeal and importance of content. Items were measured on 4-point Likert scale from “not at all” to “very.”
Content domain | Mean (SD) | Rated “very appealing” |
Rated “not at all appealing” |
|
Guides me “how” to quit | 3.26 (0.84) | 55 (47.4) | 4 (3.4) | |
Helps me cut-back but not quit | 2.95 (0.86) | 34 (29.3) | 5 (4.3) | |
Helps me decide “if” I want to quit | 2.57 (0.97) | 21 (18.1) | 18 (15.5) | |
Helps me manage nicotine withdrawal | 3.35 (0.78) | 61 (52.6) | 1 (0.9) | |
Helps me manage medication side-effects | 2.78 (0.95) | 30 (25.9) | 12 (10.3) | |
Includes information on stop-smoking medications | 2.63 (0.87) | 19 (16.4) | 11 (9.5) | |
Helps manage stressa | 3.40 (0.74) | 61 (52.6) | 18 (15.5) | |
Helps manage anxiety | 3.30 (0.85) | 59 (50.9) | 3 (3.4) | |
Helps manage depression | 3.17 (0.85) | 47 (40.5) | 5 (4.3) | |
Helps manage weight | 2.97 (1.01) | 45 (38.8) | 12 (10.3) | |
Games for fun or distraction from smoking | 2.85 (0.93) | 34 (29.3) | 8 (6.9) |
aSignificant difference by stage of change.
Participants rated the importance of different reputational factors that might influence their decision to use a smoking-related app (
Perceived importance of reputational metrics. Items were measured on 4-point Likert scale from “not at all” to “very” important.
Metrics | Mean (SD) | Rated “very important” |
Rated “not at all important” |
Research tested | 3.22 (0.88) | 56 (48.3) | 4 (3.4) |
Recommended by treatment experts | 3.16 (0.88) | 50 (43.1) | 5 (4.3) |
Highly rated by others | 3.06 (0.95) | 50 (43.1) | 6 (5.2) |
Developed by a trusted source | 3.04 (0.95) | 47 (40.5) | 7 (6.0) |
Recommended by my doctor | 2.70 (1.03) | 34 (29.3) | 15 (12.9) |
Most smokers were not willing to pay much for an app to help them cut back on their smoking or decide if, when, or how to quit (
Maximum price willing to pay for smoking-related mHealth apps.
Maximum willing to pay | App to reduce smoking |
App to decide if, when, or how to quit |
US $0 | 46 (39.7) | 39 (33.6) |
US $1 | 13 (11.2) | 14 (12.1) |
US $2 | 23 (19.8) | 21 (18.1) |
US $5 | 23 (19.8) | 24 (20.7) |
US $10 | 7 (6.0) | 13 (11.2) |
US > $10 | 4 (3.4) | 5 (4.3) |
To our knowledge, this is the first report to assess whether smokers who are not yet ready to quit are interested in using mHealth apps to change their smoking behavior or inform their decisions about smoking. Others have examined technology use among smokers who are not motivated to quit, but did not look at use of smoking cessation apps specifically [
All phase 2 survey participants were smartphone owners and most regularly used apps; however, relatively few had used common mHealth apps and only 3% (4/116) had ever downloaded a cessation app. This supports our contention that smokers who are not yet ready to quit are not likely to proactively download and use traditional cessation-focused smoking apps. Similar results were found in a recent survey of US smokers; only 6% of smartphone owners who smoked and were not motivated to quit had used a cessation app versus 24% of those who were motivated to quit [
As in our prior research with smokers who are not yet ready to quit [
Participants rated their preferences for a variety of potential mHealth features, functions, and content. We suggest that items with a mean score of 3 out of 4 (indicating an average rating of “important or appealing” or “very important or appealing”) reflect items most highly valued. With little exception, these items were also rated as “very” important or appealing by at least 40% of respondents. Using this metric, the app features, functions, and content rated most highly were: (1) security of personal information (eg, password protection and no or limited access to personal information on one’s phone); (2) the ability to track smoking, spending, and savings; (3) content that adaptively changes with one’s needs and preferences; (4) the ability to request support as needed; (5) the ability to earn and redeem awards for program use; (6) guidance on how to quit smoking; and (7) content specifically addressing management of nicotine withdrawal, stress, depression, and anxiety. Many of these themes emerged during the phase 1 interviews as well. With the exception of the security features and incentives, these are standard components of cognitive behavioral nicotine dependence programs [
Participants’ interest in tracking tools was a consistent theme in phases 1 and 2 and was echoed in the phase 2 write-in comments. In fact, tracking was the most common write-in theme endorsed. In addition to tracking the typical financial and smoking metrics, participants suggested they would like to track health changes over time. A similar theme emerged during the phase 1 interviews. As wearable sensors become more advanced and available, we could envision a future system that might track users’ heart rate, pulse, or oxygen saturation as relevant indices of health improvement when one cuts back or quits smoking. Our phase 1 participants indicated this type of feedback would be motivating, although it is worth pointing out that using biologically-based metrics of harm exposure or risk to motivate behavior change, including smoking cessation, has yielded mixed results when empirically studied [
Perhaps equally important to participants’ preferred features and functions was their lack of interest in sharing their progress via social media; nearly half rated this as “not at all” appealing and it received the lowest overall mean score (1.9 out of 4). This finding echoes the opinion of smokers in another recent survey [
Finally, we sought to better understand future marketing or distribution considerations. This will perhaps be the greatest challenge of intervening with precontemplative and contemplative smokers, as it is unclear if smokers who are not yet ready to quit will voluntarily seek out tools to help them decide if or how to quit, even though they expressed interest in these tools in our survey. Whereas our findings do not fully inform how to connect smokers with these tools, it is clear that cost will be a barrier. Sixty percent of respondents said they would be willing to pay but our data suggest the cost should be under US $5. This is consistent with a general trend of price sensitivity for mobile apps. Program source, expert recommendations, empirical validation, and user ratings also appear important considerations for smokers and should be highlighted in promotional materials. Opinions about the role of health care providers in distributing these materials differed between the phase 1 and 2 participants but doctors, health care systems, and insurers could play a role in educating patients about the availability of a relevant mHealth app, even if not directly distributing it.
This work has a number of strengths including the use of a sequential exploratory mixed-methods analysis, nationwide recruitment, inclusion of smokers who are active smartphone users, and its focus on smokers who are not yet ready to quit. The latter makes up the majority of smokers, yet little is known about how best to engage these individuals in treatment or their preferences for using mHealth tools.
The chief limitation of this study is the small sample size, which limits generalizability. Compared with all US smokers assessed via the 2012-2014 National Health Interview Survey (NHIS) [
Finally, we note that the preferences expressed by smokers in this study are based on the features and functionality they expect they would like. User preferences could be different if assessed in reaction to an actual app reflecting these preferred features, particularly if reactions are assessed based on “real-world” user conditions. But the results of this study provide some initial guideposts for developing these tools in the future.
This mixed-methods study confirmed that smokers who are not yet ready to quit are receptive to using mHealth tools to reduce or quit smoking. As such, these smokers represent an important target group for mHealth delivered interventions. In addition to being designed with an understanding of best practice nicotine dependence treatment, mHealth apps should be designed to appeal to smokers who have not yet committed to quitting. Findings from this study can provide insight into how to achieve this goal and may aid addiction treatment experts, design specialists, and software developers interested in creating new public-health focused smoking cessation apps in the future.
mobile Health
Funding for this project was provided by the Group Health Research Institute and the National Institute for Drug Abuse (R34DA034612; J McClure, Principal Investigator). The authors are grateful to the smokers who participated in this research and to Zoe Bermet and Susan Brandzel for their assistance recruiting participants, conducting the key qualitative interviews, and managing study incentives; and to Annie Shaffer for her assistance preparing this manuscript.
None declared.