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The potential of interactive health education for preventive health applications has been widely demonstrated. However, use of mobile apps to promote smoking cessation in hospitalized patients has not been systematically assessed.
This study was conducted to assess the feasibility of using a mobile app for the hazards of smoking education delivered via touch screen tablets to hospitalized smokers.
Fifty-five consecutive hospitalized smokers were recruited. Patient sociodemographics and smoking history was collected at baseline. The impact of the mobile app was assessed by measuring cognitive and behavioral factors shown to promote smoking cessation before and after the mobile app use including hazards of smoking knowledge score (KS), smoking attitudes, and stages of change.
After the mobile app use, mean KS increased from 27(3) to 31(3) (
Our results suggest that a mobile app promoting smoking cessation is well accepted by hospitalized smokers. The app can be used for interactive patient education and counseling during hospital stays. Development and evaluation of mobile apps engaging patients in their care during hospital stays is warranted.
Smoking remains the most common cause of preventable mortality and morbidity in the United States [
National surveys suggest continued need for patient education about smoking hazards [
Hospitalization offers an opportunity to provide smokers with advice, education, and counseling. Acute illness may increase a patient’s motivation and has been described as a teachable moment that providers should not miss [
Interactive health education programs delivered as mobile apps by touch screen tablets or smartphones have been demonstrated to increase knowledge, skills, and self-efficacy levels among patients with asthma, cancer, diabetes, congestive heart failure, chronic obstructive lung disease, and other conditions [
Recent studies have supported the use of mobile apps for smoking cessation [
In a previous study, we assessed the feasibility of promoting smoking cessation in the outpatient setting using an interactive health education program [
We conducted a prospective study of active smokers consecutively admitted to two medicine units at two large urban academic teaching hospitals. Fifty-five consecutive adults aged 18 years or older hospitalized for any reason and who were active smokers and agreed to participate, were enrolled into the study. The study protocol was approved by the institutional review board.
The mobile app employed in this study was based on the COmputer-assisted EDucation system (CO-ED), which was previously described [
Previous studies emphasized importance of usability factors for successful acceptance of computer-assisted education especially in older adults and individuals with limited computer experience [
As this project was undertaken in preparation to wide introduction of tablet-based education for hospitalized patients, patient enrollment procedures were made as close to routine hospital workflow as possible. Hospital unit census was reviewed by a unit nurse on a daily basis to identify hospitalized smokers. Eligible participants were approached by a unit nurse and asked if they are interested to take part in the study. Interested patients were consented by study's research assistant. After obtaining informed consent, the research assistant provided each patient with a set of questionnaires to fill out and then provided a touch screen tablet with which the patient accessed a self-paced interactive education app on hazards of smoking. Patients spent up to 45 minutes using the mobile app independently without research assistant or nurse present. At the end of the 45-minute period, patients were approached by a research assistant again and asked to fill out a post-education survey. A 15 to 20 minute semistructured interview was also conducted at the end of each session so that patients could offer feedback on the feasibility of using the mobile app and ways to improve it.
Prior to system use, participants completed a set of questionnaires with questions about demographics, prior experience with mobile devices, and the Fagerstrom Test for Nicotine Dependence (FTND) [
The KS questionnaire is composed of 34 true or false questions asking basic information about smoking and negative impact of smoking on health. Examples of questions include: “Smoking during pregnancy is linked with a greater chance of miscarriage” and “Smoking only affects the lungs.” An identical questionnaire was completed before and after using the system. A perfect KS on the test is 34. The reliability of the KS scale assessed by the Cronbach’s alpha in our studies including the current one has been 0.75 and higher [
The Process of Smoking Cessation survey measures four different stages of quitting based on the TTM of change [
The Smoking Self-Efficacy questionnaire [
The Decisional Balance Scale [
The Attitudinal Survey assesses patients’ acceptance of the mobile computer-assisted education system and their perceptions of usability, content clarity, and usefulness of the system. The survey, which is composed of 18 items, was developed based on a literature review and critical feedback from experts in the field. The maximum survey score is 72. This survey has been used and validated in our previous studies [
The semistructured interview conducted at the end of the study explored participant opinions on educational content and the app interface. Participants were also asked to highlight mobile app benefits and drawbacks and to suggest areas for improvement. A qualitative thematic analysis was conducted using framework approach [
Data were analyzed using SAS statistical software version 9.2 [
Qualitative interviews were transcribed verbatim. The interview transcripts were independently analyzed by two researchers. A coding scheme was used to reflect themes that emerged from the data following a framework approach in analysis of qualitative data [
Participants’ demographic and socioeconomic status is detailed in
Participant sociodemographic characteristics.
Sociodemographic characteristics | N=55 | % | |
Male | 30 | 55 | |
Female | 25 | 45 | |
Married/Common-law/Partner | 15 | 27 | |
Single/separated/divorced/widowed | 40 | 73 | |
<12 years | 18 | 33 | |
12 years | 24 | 44 | |
>12 years | 13 | 24 | |
None/basic | 31 | 56 | |
Good/advanced | 24 | 44 | |
Employed | 12 | 22 | |
Unemployed | 43 | 78 | |
<20K | 19 | 35 | |
20K-40K | 12 | 22 | |
>40K | 9 | 16 | |
Prefer not to disclose | 15 | 27 | |
Caucasian | 23 | 42 | |
African American | 30 | 55 | |
Other | 2 | 4 |
Women constituted 45.5% (25/55) of enrolled smokers, 55% (30/55) were African Americans, and 42% (23/55) were Caucasians. Approximately 20% (11/55) of the study participants had a full-time employment, 33% (18/55) did not have high school diploma, and 44% (24/55) completed high school. Thirty-five percent (19/55) reported a low income household (<US$20,000 annual income per household). Fifty-six percent (31/55) had only a basic level of computer skills or no computer skills, and 53% (29/55) reported using a computer no more than once per week. Based on the patient chart review, 76% (42/55) of the study subjects were hospitalized for emergency treatment, 14% (8/55) for complex diagnostic procedures, and the rest for surgery or other treatment. Alcohol and drug abuse was the most frequent comorbid condition (20/55, 36%), followed by depression or other emotional problems (19/55, 34%), hypertension (15/55, 27%), heart disease and stroke (12/55, 22%), chronic obstructive pulmonary disease and asthma (12/55, 22%), diabetes (6/55, 11%), and cancer (5/55, 9%).
Smoking history of the study participants is summarized in
Participant smoking history.
Smoking history characteristics (Mean (SD)) | N=55 | % | |
How many cigarettes a day do you smoke in average? | 13.6 (9.1) | ||
How many days have you been already in the hospital? | 3.3 (2.1) | ||
0 (ie, no smoking in the hospital) | 44 | 80 | |
1-2 | 8 | 15 | |
3 | 3 | 5 | |
How many persons in your household smoke? | 2.0 (1.5) | ||
How many years have you smoked cigarettes regularly? | 26.6 (13.7) | ||
How old were you when you started smoking cigarettes? | 18.2 (10.4) | ||
How many times have you SERIOUSLY tried to stop smoking? | 2.4 (2.5) | ||
Never stopped smoking | 15 | 27 | |
Less than a month | 4 | 7 | |
At least one month | 36 | 65 | |
No | 10 | 18 | |
Yes | 45 | 82 | |
Not at all | 4 | 7 | |
Not very seriously | 6 | 11 | |
Fairly seriously | 14 | 25 | |
Very seriously | 31 | 56 |
Smoking knowledge and attitudes before and after the mobile app use.
Cognitive and behavioral factors of smoking cessation | Pretest | Posttest | ||
Knowledge Score Questionnaire (mean±(SDa)) | 27.4 (2.6) | 30.5 (3.1) | 0.0001b | |
I cannot quit smoking | 36 | 18 | 4.6 (0.03)b | |
I have no desire to quit smoking | 18 | 13 | 0.6 (0.43) | |
I would lose a lot in my life if I quit smoking | 15 | 11 | 0.3 (0.57) | |
Health risks of smoking are exaggerated | 20 | 11 | 1.7 (0.19) | |
If I continue to smoke, my risk of dying from smoking-related disease is significantly higher comparing with an average nonsmoker | 89 | 95 | 1.1 (0.30) | |
Positive Affect/Social Situations | 21.3 (5.9) | 20.8 (6.3) | 0.64 | |
Negative Affect Situations | 24.5 (5.0) | 23.4 (5.7) | 0.26 | |
Habitual/Craving Situations | 16.3 (4.9) | 15.9 (5.2) | 0.69 |
aSD: standard deviation.
bPre/post difference is statistically significant.
cHigher the score, more tempted to smoke.
The mean baseline KS was significantly lower among African Americans than among Caucasians (African American KS = 26.7(2.8), range=19.0-31.0; Caucasian KS = 28.4(2.1), range=25.0-33.0;
The mobile app positively affected patient attitudes regarding smoking cessation (
Attitudinal survey (N=55)
Optiona(%) | ||||
Question | 1 | 2 | 3 | 4 |
1. How complicated was it to use the computer? | 2 | 4 | 6 | 88 |
2. Did you have any difficulty moving from one screen to another? | 90 | 10 | 0 | 0 |
3. How difficult was it to use the keyboard/mouse? | 2 | 0 | 10 | 88 |
4. Did you have any difficulty reading text from the computer screen? | 94 | 6 | 0 | 0 |
5. Was the size of the text presented on the screen sufficient? | 94 | 4 | 0 | 2 |
6. Did you like the colors used on the computer screen? | 82 | 14 | 4 | 0 |
7. Did you like the audio/visual content provided by the computer? | 82 | 14 | 4 | 0 |
8. Did you get all the necessary information about using the computer during initial practice session? | 88 | 12 | 0 | 0 |
9. Did you come across any unknown words which were not explained by the computer? | 4 | 4 | 14 | 78 |
10. How difficult were the sentences used in the educational materials? | 2 | 2 | 14 | 82 |
11. How much new information did you get using the computer? | 47 | 41 | 8 | 4 |
12. Did you get any feedback from the computer about your learning progress? | 59 | 31 | 8 | 2 |
13. How frequently did you find the information confusing? | 6 | 10 | 37 | 47 |
14. How frequently did you find educational contents difficult to understand? | 2 | 12 | 20 | 65 |
15. Did you have to wait for new information to come up on the screen? | 2 | 6 | 14 | 78 |
16. Would you like to use this type of computer education in the future? | 76 | 23 | 0 | 2 |
17. Would you advise other patients to use computer education? | 92 | 6 | 2 | 0 |
18. Overall how would you grade this learning experience? | 0 | 8 | 16 | 76 |
aThe following options were used for the questions above (in the ascending order):
#1: Very complicated, Moderately complicated, Slightly complicated, Not complicated at all
#2, #4: Not at all, Very rarely, Frequently, All the time
#3, #10: Very difficult, Moderately difficult, Slightly difficult, Not difficult at all
#5: Fully sufficient, Sufficient almost all the time, Sufficient some of the time, Not sufficient at all
#6, #7: Certainly yes, To a large extent, To some extent, No
#8: All information, Almost all information, Partial information, Very limited information
#9: Very significant amount, Considerable, A few, None
#11: Very significant amount, Considerable, Little, Very little
#12, #15: All the time, Occasionally, Very rarely, Never
#13, #14: Very frequently, Occasionally, Very rarely, Never
#16, #17: Certainly yes, Maybe, Unlikely, No
#18: Needs serious improvement, Satisfactory, Good, Excellent
Scatterplot of post-KS against pre-KS values stratified by race (circles: African Americans, squares: American Indians/Alaska Natives, stars: Caucasians).
Distribution of stages of change before and after the mobile app use (gray bars: before the app use, black bars: after the app use; see detailed description in the text).
To ascertain what factors affected successful improvement in hazards of smoking knowledge after using the mobile app, a multivariate linear regression analysis was performed with pre/post DKS as dependent variable, and baseline KS, age, gender, race, education level, working status, app acceptance, and computer skills level as independent covariates. The analysis demonstrated significant relationship between baseline KS and subsequent knowledge gain after using the mobile app, with lower baseline knowledge scores predicting a higher knowledge gain after using the app (
In order to identify factors affecting patient acceptance of the mHealth education app, a multivariate logistic regression analysis was performed with attitudinal survey score as the dependent variable, and age, gender, race, educational status, computer skills, income, and DKS, as independent covariates. For this analysis, attitudinal survey score and DKS were dichotomized to high/low acceptance and high/low knowledge gain levels correspondingly based on their distribution. The main factor significantly affecting acceptance of the mHealth education app was knowledge gain (DKS). People with higher knowledge gain after using the app were 4.8 times more likely to exhibit more favorable attitudes toward the mobile app (odds ratio (OR)=4.8; 95% confidence interval (CI) (1.1, 20.0)). Other covariates such as age, gender, race, income, education, and computer skills did not appear to significantly affect the mobile app acceptance by the hospitalized smokers.
During the semistructured interviews, participants were asked to share their experiences and provide suggestions in three areas related to the mobile app: content, interface, and value of the program.
Examples of qualitative feedback on the mobile app.
Key quotes from the semi-structured interviews | |||
It gives information you need; I’ve learned a lot; it was very educational to me. I thought I knew it all, but I didn’t know a lot. | I feel I do not know enough, I am not good enough to understand; a couple of questions were little confusing. |
I would like more of the scientific info messages like the number of poisonous elements in a cigarette; include more scary disease related subjects, to show – this is what you are doing to yourself; simpler words, smaller sentences; add video clips; a little animated character like GEICO lizard; more animation would be better; make it into the video game. A super hero stops people on streets and saves them from smoking. Crossword puzzle – all bad words about smoking cigarettes. | |
There was slight pause (delay) between the message and the quiz. It made me skip to the quiz directly; sometimes I had to click couple of times to move to next screen; I hit a wrong button once but I caught up. | I didn’t have any difficulty; just click. | Something other than clapping: for example, Ta-da sound I would make it simple. Simple is the best; may be male voice; may be music on the background; bigger screen; headphones; I would like to be able to go back to the content (message) if I am not sure. Addition of ‘Back’ and ‘Forward’ button would be helpful. | |
I don’t like flipping pages. Paper is cumbersome. Computer is self-contained. I have more control with computer. Quicker and better with computer than learning from brochures; I like Hands On, an interactive part, the computer was better – I would NOT read hand out brochure; computer is much better – you got to see images. Radio is just read, TV got images, but here you have it all. | I think people need to do this program to learn about smoking. I think more people need to do this program to learn what they are breathing into their bodies; kids are using computers. Getting to kids through computer will get the message of hazards of smoking across to them. Get it to schools. | I am going to quit now. I will not pick up a cigarette ever in my life; This is a good program to change people’s minds who smoke. I start thinking about quitting now. This program made me want to quit smoking. |
In this study we demonstrated that delivery of a 45-minute interactive education via a mobile app on hazards of smoking for hospitalized smokers is feasible and associated with a statistically significant increase in hazards of smoking knowledge levels as well as positive changes in patient smoking attitudes, self-efficacy, and readiness for quitting based on stages of the TTM. These positive effects were demonstrated regardless of gender, race, educational level, or computer skills. Significant determinants of successful use and acceptance of the mobile app by hospitalized users were identified.
The study provided important insight on sociobehavioral characteristics of hospitalized smokers understanding of which is essential in developing mobile apps for hospitalized smokers. Majority of them had a long history of tobacco consumption with FTND scores indicating high levels of nicotine dependence. Despite a smoking ban at the hospital premises, 20% (11/55) of the participants indicated that they smoked during their hospital stay. Over three-quarters of these patients were hospitalized for emergency treatment with alcohol and drug abuse being the most frequent comorbid condition (20/55, 36%), followed by depression or other emotional problems (19/55, 34%). Due to high level of distress at admission, the patients were offered mobile app only at the second or third day of their hospital stay. The mean number of smokers in the household of hospitalized smokers was 2.0(1.5). Thus, education about hazards of secondary smoking as well as involvement of household members in a smoking cessation program is necessary for these patients. Despite high levels of nicotine addiction, over 80% of the patients tried to stop smoking in the past and majority stated that they would like to give up smoking in the future. The distribution of stages of change in the hospitalized smokers and in general population differed remarkably. In our study initial distribution of smokers in precontemplation, contemplation and preparation stages was 16%, 44%, and 40% whereas in general population this distribution was reported to be 37%, 47%, and 16%, correspondingly [
Most of our study participants were African Americans, were unemployed, and had low-education and low-income levels. The majority of patients had very limited computer education. At baseline, Caucasians, employed patients, and those with higher-education level demonstrated better knowledge about hazards of smoking but all groups appeared to benefit from this intervention regardless of their background. Differences in baseline knowledge were significant between African Americans and Caucasians; however, after using the mobile app the knowledge scores in both groups increased, and the difference in the mean KS between these two groups became insignificant. Thus, the mobile app helped to decrease racial disparity in health literacy as it pertains to the hazards of smoking knowledge.
The mobile app used in this study aimed to increase patients’ knowledge levels about hazards of smoking and was successful in doing that regardless of race, gender, computer skills, or educational level. The mobile app delivered a very simple interactive curriculum that can meet the educational needs of patients who are able to read at a fifth grade level. It is likely that the consistently positive impact of the mobile app on knowledge gains among the study participants is related to the app’s interface features, such as specifically developed content for low literacy users, one message per screen, question and answer format, voice-over option to address functional illiteracy, and use of illustrations as content anchors. Based on the regression analysis, the main predictor of knowledge gain was the baseline knowledge score. This is anticipated because with higher baseline scores, there is not as much room for knowledge gains resulting in a ceiling effect.
In regard to hospitalized smokers’ readiness for change, our results are consistent with earlier studies showing that the majority of hospitalized smokers are in the contemplation and preparation stages. In a recent study, among hospitalized patients, Katz et al [
Participants’ attitudes toward the mHealth education app in hospital were largely positive. Those who had more knowledge gains scored better on the attitudinal surveys suggesting that they perceived more value and benefits from the mobile app use. The majority of participants reported learning new information from using the mobile app, and more than 90% (51/55) of the participants reported that they would certainly recommend it to other smokers. It is conceivable that additional similarly designed modules incorporating smoking cessation counseling features, in addition to education might provide a feasible and effective approach to deliver smoking cessation counseling to large numbers of hospitalized smokers. Nevertheless, the impact on smoking cessation rates might still be modest in absence of outpatient follow-up [
A strength of this study includes the demonstration of high acceptance of the mobile app by hospitalized smokers who are in greatest need of smoking cessation intervention including minority patients with low socioeconomic status and limited education [
Statistically significant knowledge gain achieved by the mobile app users described in this study concurs with our previous studies [
Multiple studies demonstrated that knowledge and beliefs about smoking are associated with key behaviors such as cessation and intent to quit. [
Our study results concur with previous reports on positive use of hospital-based patient education [
A mobile app provides feasible and effective means to educate patients about the hazards of smoking in a hospital setting. The mobile app has significant potential in facilitating the reduction of racial disparities in health literacy as it pertains to hazards of smoking knowledge. Further research is needed to evaluate the cost-effectiveness and long-term effects of this promising patient engagement and empowerment approach.
confidence interval
COmputer-assisted EDucation
difference in knowledge score
Fagerstrom test for nicotine dependence
knowledge score
odds ratio
standard deviation
theory of planned behavior
transtheoretical model
The authors gratefully acknowledge the support of Daniel Brotman, MD, FHM, FACP, Amy M. Knight, MD, Hanan Aboumatar, MD, and Lee Bone, MPH, RN for valued guidance in several aspects of the project.
Joseph Finkelstein was supported in part by a grant R18HS19313 from the Agency for Healthcare Research and Quality (AHRQ).
Eun Me Cha was partially funded by the Michael & Susan Dell Foundation through resources provided at the Michael & Susan Dell Center for Healthy Living, The University of Texas School of Public Health, Austin Regional Campus.
None declared.