Tobacco control via quick response code and mobile health technologies: Empirical-evidence of the health belief model theory

Abstract

BACKGROUND:

Among the 1.1 billion global smokers about 80% of them live in developing countries, and nearly 7 million of those smokers will die by 2030, including 2 million-plus in China alone. China produces and consumes about one-third of global tobacco products, which affects nearly 80% of China’s total population. Currently, largely-applied programs can aid in saving millions of Chinese citizens from tobacco menaces. Two of such programs are the WHO MPOWER and FCTC programs on tobacco control.

OBJECTIVE:

This study proposes the assimilation and conjoint uses of quick response code (QRC) and mobile health (mHealth) technologies to aid smokers in cessation by improving their health beliefs. The study is also viable to estimate the likelihood that smokers will cut/quit smoking due to their changes in health beliefs.

METHODS:

Since digital technologies play a crucial role in health-care education, ergo, we programmed anti-tobacco QR codes and mHealth application, the conjoint uses of both these two tools aim to improve smokers’ comprehension of tobacco perils and assist them to overcome their perceived barriers related to cessation and attain the perceived benefits of quitting. The health belief model theory was adopted and 600 English-speaking students were recruited as a convenient sample of participants in this study.

RESULTS:

The obtained results suggest that both printing the proposed anti-tobacco codes on cigarette packaging and adoption of mHealth can assist experts in improving the health beliefs of smokers towards smoking-cessation acceptance.

CONCLUSION:

This study will aid experts as technology compliance in accordance with the WHO MPOWER and FCTC programs on tobacco control in China.

Keywords

Health belief model mobile health quick response code technology tobacco control

Rahib Ali is a PhD candidate at the Harbin Institute of Technology (HIT), Harbin, Heilongjiang, China. He has served as a visiting faculty member in SZABIST Larkana Campus, Pakistan. His research and academic interests include Human Resource Management, Leadership, Organizational Behavior, Marketing Principles and Management, Services Marketing, Customer Relationship Management, Strategic Marketing Management, E-Commerce, Managing Information Systems, Advanced Research Methods, and Research Methods for Management.

Professor Ziqiong Zhang has earned both MBA and PhD degrees from the Harbin Institute of Technology (HIT), Harbin, Heilongjiang, China. She is a professor in the School of Management (SoM) at HIT. Her academic and professional expertise include Research Operations, Big Data Mining, E-commerce and Knowledge Management. She has an editorship in the International Journal of Hospitality Management.

Dr Muhammad Bux Soomro has earned his academic degrees, i.e., MSc in Computer Science from Sindh University, and an MPhil from Shah Abdul Latif University (SALU), Pakistan. He is Assistant Professor, Head of Department (HoD) and Program Manager of the BSCS Department at SZABIST Larkana-Campus, Pakistan. His academic and professional expertise are Advanced Analysis of the algorithm, The Theory of Computation, The Theory of Programming Language, Advanced Computer Architecture, Multi-Agent Systems, Capability Maturity Model Integration, Big Data Mining, Business Process Reengineering, IS-1 (Software Development Processes and Quality Issues), and IS-II (Agents in Software Engineering and Research).

Mr Ives Chacourre Wangninanon Gogan is a PhD student in Harbin Institute of Technology (HIT), Harbin, Heilongjiang, China. His research interests include Financial Management, Organizational Behavior, E-Commerce, Marketing Principles and Management, Customer Relationship Management, Strategic Marketing Management, Managing Information Systems and Advanced Research Methods.

Mr Habib Rehman Soomro has done MBA in Finance and Marketing from SZABIST, Pakistan. He is also an MA in Economics from Shah Abdul Latif University (SALU), Pakistan. His academic and professional interests include Financial Management, Economics, Customer Relationship Management, Marketing Management, Strategic Management, and Advanced Research Methods.

1 Introduction

Among the 1.1 billion global smokers over 80% of them live in low and middle-income countries [1], and nearly 7 million of those smokers will die annually by 2030 across the globe [2]. If not controlled, the tobacco prevalence will increase to nearly 10% in emerging countries by 2020 [3], which will kill over seven million people by 2030 [4, 5], including two million-plus in China alone [5, 6]. China produces and consumes about one-third of global tobacco products [7], which affects nearly 80% of China’s total population [8] with more than 356 million active smokers and 740 million people being affected by the second-hand smoking [9]. In this scenario, largely-applied effective programs can aid in saving millions of Chinese citizens from tobacco menace [10]. One such program is the World Health Organization’s Framework Convention on Tobacco Control (WHO FCTC) which includes guidelines for articles 11 and 13, to discourage smokers from smoking via using cigarette packaging itself. The FCTC signatory countries have to print anti-tobacco content (e.g., texts and graphics to display smoking hazards) on cigarette packaging [11], which aims to improve smokers’ health beliefs towards cessation adoption [12, 13]. China is a member of the FCTC program [14] yet still requires effective measures, such as printing anti-tobacco images on cigarette packs [15]. Effective implementation of the FCTC guidelines can prevent nearly 12.8 million deaths and save over 154 million lives in China by 2050 [16].

Partial compliance with the FCTC guidelines [15] makes smokers vulnerable towards tobacco uses [12, 13]. In order to aid smokers in cessation, this study suggests the assimilation and conjoint use of healthcare technologies with cigarette packaging, which can aid tobacco-control experts as technology compliance with the WHO FCTC manifesto. In particular, we propose to print anti-tobacco quick response code (QRC) stickers on cigarette packaging, and the espousal of mobile health (mHealth) to improve smokers’ health beliefs towards cessation. Alike anti-smoking cigarette packaging to educate smokers against tobacco uses [17, 18], both QRC and mHealth technologies have been shown useful tools to benefit health workers in providing health education to patients [19 –22]. Hence, the proposed solution will aid the policymakers of China in compliance with the first four recommendations of the WHO MPOWER stratagems on tobacco control. These guidelines include monitor smokers’ tobacco consumption, protect them from smoking, offer them the help they need to quit smoking, warn them against smoking hazards, enforce prohibitions on tobacco ads, campaigns, patronage, and raise taxes on tobacco products in order to attain smoker cessation[23].

We adopted the health belief model (HBM) theory constructs to design our study model. These constructs include patients’ self-efficacy to participate in health intervention, insertion of effective cues-to-action in an intervention, demonstration of threats related to a destructive habit, provision of assistances that patients need to overcome perceived barriers related to a destructive habit and attain perceived benefits as an encouragement for them to quit that destructive habit [24 –27]. These determinants have been shown effective to include in tobacco-control programs which focus on health education of smokers [21 , 28–31].

2 Related work

2.1 Mobile health technology and tobacco control

Technology with its integral components, i.e., brainware, hardware, software, and support network or net [32], plays a crucial role in the progress of both the developed and emerging countries [33, 34]. Technology espousal can benefit experts in different fields, such as financial markets [35], healthcare industry [36], tourism industry [37], schools and educational systems [38], knowledge management classifications [39], self-organized and self-governed ecosystems [40], and in many more fields to improve organizational performance [41]. Similarly, it benefits tobacco-control experts to aid smokers in cessation via improving their health beliefs [19]. Currently, the use of mobile health (mHealth) technology gets popular in provision and attainment of health-related services [21 , 42–44]. Health experts use mHealth to remotely diagnose patients and collect their medicinal data and manage it for future uses [45]. Similarly, tobacco control experts use mHealth in controlling the tobacco epidemic [46 –48]. Smokers also believe that the use of mHealth is viable [49, 50] to access experts in a hassle-free manner, which is useful to avail tobacco control counseling and pertinent remedies from experts to curb the smoke craving and fallacies related to cessation [51]. In particular, young smokers prefer to use mHealth applications over traditional quit-lines and texting-messaging services for cessation [52]. Since mHealth technology is new, the lack of studies and their effectiveness is unclear in developing countries [53, 54].

2.2 Quick response code technology and tobacco control

QR codes are easy to create, cost-effective and widely used for rapid dissemination of online information to the targeted groups of people [55]. They are generally used for changing social habits in China, such as paying restaurants bills, giving cash as a gift to the wedding couples and even donating to the poor [56]. Subsequently, health workers intend to use QRC technology in healthcare education [20]. However, this study is the first of its kind that highlights the role and usefulness of QR codes in controlling the tobacco epidemic in China.

2.3 Health belief model and tobacco control

Tobacco-control programs designed using the HBM theory can be viable to comprehend a complex behavior of cigarette smoking in smokers [21, 28], and it is easy to estimate the likelihood of smokers towards cessation adoption in such programs [57]. Ergo, we adopted the HBM theory constructs to design a study model, which provides useful insights to comprehend, amend and estimate the participants’ cigarette smoking behavior with respect to change in their health beliefs.

3 The experiment, theoretical framework, literature, and hypotheses proposition

3.1 The programmed mHealth application and QR codes for cessation

Smoking-cessation mHealth applications are promising only if they are designed from the users’ perspective [58]. We conducted workshops in universities in China for two reasons: 1) to recruit participants and, 2) to find and interview those amongst the participants who previously attempted at quitting cigarettes via mHealth applications but failed due to their ineffectiveness. The purpose of interviewing such participants was to obtain their suggestions in order to design a useful and user-defined mHealth application for cessation. We found 40 participants matching with above mentioned criteria and gave them open-ended survey forms in which they suggested that: 1) The user’s personal smoking-profile set and manage cessation, 2) information about names and extents of cigarette toxins and their effects on health, 3) textual and graphical illustrations of tobacco perils, 4) tobacco-control counselling from health experts, 5) and quit-goal functions must be viable to include in a useful mHealth application for cessation.

Besides, we asked the programmers to include four additional functions in the App for the users: 1) the personalized progress report that shows user’s cessation gains, 2) social support from quitters with tobacco-control tips and success stories, 3) users’ ability to scan the programmed anti-smoking QR codes, fetch and display embedded content from those codes on their mobile screens, 4) the push notification function that includes the customized updates, e.g., anti-tobacco content, tips from health experts and the post-intervention questionnaire. Next, we printed those codes on glowing keychains (as a cessation reminder) and gave to participants.

3.2 The experiment in compliance with the WHO FCTC and MPOWER programs

We installed the programmed App on participants’ mobiles and recommended them to consume its functions, scan the programmed codes, fetch embedded content and visualize on their mobile screens when they were susceptible to or craved smoking. These time intervals include early in the morning [59], after lunch and supper [60] and weekend nights since many participants would go to bars/clubs, consume a drink and have the desire to smoke [61]. This recommendation aims to increase participants’ anti-tobacco knowledge by displaying to them the risks of tobacco use. Next, we recommended participants to create their personal smoking-profile in the App to keep a record of daily cigarette consumption in order to evaluate health, life, time and money losses incurred due to smoking. These recommendations aim to daunt participants from smoking in compliance with the WHO FCTC manifesto [11].

We recommend participants to use the quit-plan function of the App to both set and attain cessation goals. Also, participants were recommended to regularly check the push notifications of the App that displayed smokers’ common craving times. These notifications included tobacco control tips, success stories of quitters, experts’ counselling and participants’ progress reports showing health, life, time, and money gains due to cessation, which aims to inspire participants to quit. Finally, we suggested participants connect with health experts (voluntarily available 2 days/week to respond to live calls and messages of participants and available 24/7 to respond to offline emails and messages from participants) to benefit from free and hassle-free cessation counselling and gain appropriate solutions to curb their craving to smoke. A separate account was created for the doctors to login to the App. These recommendations aim to facilitate smoker cessation in compliance with the WHO MPOWER stratagems [23].

3.3 Theoretical framework and literature review

The HBM theory determinants used in tobacco-control programs include smokers’ self-efficacy to quit smoking, effective cues-to-action for smokers to quit smoking, demonstration of perceived threat of smoking to discourage smokers from smoking, evaluation of both perceived barriers related to cessation and perceived benefits of quitting [21, 28].

3.3.1 Self-efficacy and cues-to-action

Sustainable self-efficacy in smokers maintains their internal consistency [62], effective programs include and optimize it [63], which aids smokers in cessation [64]. While cues-to-action such as relational interactions, memos, reminders and healthcare messages from health experts are used as information sources for health education of patients [65]. Tobacco control experts also include pertinent cues-to-action to improve the health beliefs of smokers [21, 28]. In this experiment, the programmed App aims to improve participants’ self-efficacy and the programmed codes work as cues-to-action to improve participants’ health beliefs to quit cigarettes. Both these tools have been shown viable elements to include in tobacco control programs [66 –68].

3.3.2 Perceived threats of smoking

Alike plain-cigarette packaging [12, 13], mHealth has been shown viable aid to improve the health beliefs of smokers towards a smoking cessation behavior [49, 50] similarly QRC technology has been shown useful tool for health experts to improve their patients’ health beliefs [20]. Health programs which provide health counseling from experts are considered effective in aiding smoker cessation [69]. These programs assist smokers to comprehend smoking hazards which discourage them from smoking [31, 70]. Smokers perceive such threats as relative risks, therefore, it is crucial to increase their knowledge with exact facts [71], such as smoking is a fundamental cause of cardiovascular diseases [11]. Both the programmed codes and the App include established evidence about tobacco-led menaces, which aims to increase participants’ comprehension about tobacco risks and discourage them from smoking.

3.3.3 Perceived barriers to non-smoking

Smokers share various barriers in quitting [72], such as craving [73 –75], inattentiveness [76 –78], negative effects [76 , 79–82], lower determination [83], fear of failure/being judged [69], social trolling [84, 85] and cigarette gifting custom [86, 87]. Elimination of such barriers can facilitate smokers in cessation [86 , 89]. The interactive mHealth programs [90] which include the more personalized, non-judgmental and flexible support from experts have been shown effective in controlling the tobacco epidemic [69]. Hence, we included the users’ personal smoking-profile function in the programmed App set and manage cessation. Also, we included contact details of doctors in the programmed codes for participants so that they can have flexible, free, readily available, more personalized tobacco control counseling and pertinent remedies from experts to curb their smoke craving. Also, they can eliminate their misconceptions, such as smoking cessation causes negative effects like depression, laziness, anxiety, inattentiveness and lack of joy. Last, we recommended participants to refuse cigarette gifts from fellow smokers by offering those to consume the programmed codes and App in order to curb social trolling due to cessation.

3.3.4 Perceived benefits of non-smoking

Perceived benefits are the sentiments that push people to take the recommended actions which assist in curbing the destructive habits. Subsequently, it is crucial to improve smokers’ comprehension to both tobacco threats and benefits of quitting with both real and psychological gains to encourage them to quit, such as cessation aids in life [31], health [76 , 91], finances [92] and general well-being of the quitters [76 , 94]. Facilitating smokers to evaluate such benefits motivates them to attain those gains [95, 96]. Hence, we provided participants with their personalized progress report in the programmed App which displayed health, life, time, and money gains due to cessation. This feature aims to facilitate participants compare own gains with fellow users of the App, which aims to motivate them to both set and achieve quit goals in order to live a healthy and wealthy life and become aspirants for others.

3.4 Proposition of hypotheses

Based on the abovementioned previous studies, we theorized the following hypotheses (H).

The conjoint use of QRC and mHealth technologies improve the health beliefs of smokers towards smoking cessation

The conjoint use of QRC and mHealth technologies improves anti-tobacco knowledge of smokers which mediates positive effects on them to quit smoking

The conjoint use of QRC and mHealth technologies assist smokers to overcome perceived barriers related to cessation which mediate positive effects on them to quit smoking

The conjoint use of QRC and mHealth technologies assist smokers to attain cessation benefits which mediate positive effects on them to quit smoking

The assimilation of QRC and mHealth technologies include viable cues-to-action and self-efficacy determinant for smokers to quit smoking

The conjoint use of QRC-mHealth technologies with HBM constructs is viable to estimate smokers’ likelihood of cessation due to their change in health beliefs

4 Study contribution

This HBM designed study explores the conjoint usefulness of both QRC and mHealth technologies to aid smokers in cessation via improving their health beliefs. Hence, it has theoretical and practical implications for the concerned stakeholders of China to curb the tobacco epidemic in the country. In particular, this study will benefit tobacco retailers in China as technology compliance with the WHO FCTC guidelines for cigarette packaging to discourage smokers from smoking. Also, it will benefit the policymakers (e.g., healthcare and tobacco control institutions) in compliance with the first four recommendations of the WHO MPOWER stratagems to facilitate smoker cessation.

5 Methods

5.1 The sampling technique

This is not a clinical trial, however, we obtained permission from “The Ethics Committee of the Harbin Institute of Technology” to conduct this research. Since young smokers prefer to use mHealth applications over traditional quit-lines and texting-messaging services for cessation [52], we recruited 600 English-speaking students as a convenient sample of participants. We conducted seminars in participants’ universities located in three cities in China, i.e., Beijing, Shanghai and Harbin as indicated in Table 1.

Table 1
The demographic information of participants

Convenient Sampling Male (N = 339) Female (N = 153)

Age groups 18–22 104 18–22 68

22–26 97 22–26 43

26–30 61 26–30 29

30–35 77 30–35 13

Educational level Bachelors 99 Bachelors 70

Masters 113 Masters 45

Doctorate 61 Doctorate 27

Post-Doc. 66 Post-Doc 11

Cities Beijing 137 Beijing 69

Shanghai 122 Shanghai 53

Harbin 80 Harbin 31

Convenient Sampling	Male	(N = 339)	Female	(N = 153)
Age groups	18–22	104	18–22	68
	22–26	97	22–26	43
	26–30	61	26–30	29
	30–35	77	30–35	13
Educational level	Bachelors	99	Bachelors	70
	Masters	113	Masters	45
	Doctorate	61	Doctorate	27
	Post-Doc.	66	Post-Doc	11
Cities	Beijing	137	Beijing	69
	Shanghai	122	Shanghai	53
	Harbin	80	Harbin	31

N = number of participants.

5.2 The reliability and validity measures of the QRC-mHealth-HBM intervention

This quasi-experiment includes the pre-post intervention responses from the same group of participants regarding their health beliefs and cigarette smoking consumptions. We used a self-designed questionnaire to obtained participants’ responses using the Likert scale, with 1 = strongly agree to 5 = strongly disagree. We assigned unique codes in questionnaires for the correct identification of respondents and their repeated responses to ensure both the validity and reliability of the study results.

A study suggests that smokers restart smoking within the first three months of a cessation program [97], therefore, we scheduled this intervention from August 1st to October 31st of 2018. In this period, we monitored participants’ consumption of the programmed codes and the App in order to confirm whether or not they properly followed our recommendations to consume both these tools throughout the intervention. On November 1st, we sent a self-designed questionnaire to participants via the push notifications function of the programmed App in order to obtain their post-intervention responses. Out of 600 participants, 551 (92%) responded to our questionnaire, whereas, we discarded 28 questionnaire forms since a significant difference was observed in participants’ online records of the programmed codes and the App consumptions in accordance with our recommendations to participants, and their responses for the same. Furthermore, we discarded 15 questionnaire forms of participants because they incorrectly answered a question that was purposefully added to the questionnaire in order to identify attentive respondents. We also discarded 16 forms as we found that the same answer was given by those participants to each item throughout the questionnaire which indicated their bias.

5.3 The statistical software

We used the Statistical Package for Social Sciences (SPSS) and Analysis of a Moment (AMOS) software v.24 to process and examine the obtained data. We used structural equation modeling (SEM) to develop the study model and to test the hypotheses.

5.4 The statistical tests

Since hypothesis 1 (H1) deals with the examination of changes in the health beliefs and cigarette smoking frequencies of participants, therefore, we used the paired t-test to investigate those changes from the pre-and-post intervention responses. However, due to the occurrence of violation in the normality assumption, we used the Wilcoxon signed-rank test as a pertinent substitute to the paired t-test in order to test H1 [98]. Next, since our model is based on an HBM theory, hence, we used the SEM technique to examine variables’ path coefficients to test the H2 to H5 of this study. This technique is widely adopted in theory-based studies which focus on healthcare education [99]. Last, since H6 deals with the examination of smokers’ likelihood that they would quit cigarettes due to their changes in health beliefs, therefore, we developed a probability estimation model with binary logistics regression in order to test H6, which is in accordance with established cutoffs [100].

5.5 The SEM measures

We conducted the Exploratory Factor Analysis (EFA) which examines the validity of theory-based models [101]. EFA included the adequacy, convergent validity, and discriminant validity and reliability examinations with initial solution method, reproduced correlation matrix, Kaiser-Meyer-Olkin’s (KMO) and Bartlett’s test of sphericity, respectively [101].

Next, we conducted the Confirmatory Factor Analysis (CFA) to examine the reliability measures of study constructs, i.e., path-coefficients, relationships, impacts and mediations. The examination of these measures authenticates the validity of the results of a healthcare study [102]. We used the maximum likelihood method of extraction to examine the convergence effect with eigenvalues >1, maximum iterations at 25, and the Promax method with a surpassing small coefficient of.3 for the data rotation [102]. Last, we installed a user-defined estimand plugin in the AMOS software and bootstrapped the post-intervention data with 2000 samples at 95% of the confidence interval to examine the mediation effects of latent variables [71, 103].

6 Results

6.1 The Wilcoxon signed-rank test

Table 2 shows the Wilcoxon signed-rank test results, i.e., the median differences (md) of study variables as evidenced by the z-scores with significance P < .001. These are: the perceived threats of cigarette smoking (md = 0.84, z = –10.903), perceived benefits of non-smoking (md = 1.22, z = –9.807), perceived barriers to non-smoking (md = –0.68, z = –9.491) and quit smoking (md = 1.66, z = –14.439,). Furthermore, we have 344 cases of perceived threats of the smoking variable with negative ranks which indicate that our intervention has improved participants’ comprehension of tobacco menaces by 70%. We have 341 cases of the perceived benefits of the non-smoking variable with negative ranks which indicate that our intervention has assisted 69% of participants to attain cessation gains. We have 335 cases the perceived barriers to the non-smoking variable with positive ranks which indicate that our intervention has assisted 68% of participants to overcome such barriers. Lastly, we have 359 cases the quit smoking variable with negative ranks which suggest that QRC-mHealth-HBM intervention has facilitated 73% of the study participants in smoking cessation. These results significantly support H1.

Table 2
The Wilcoxon signed-ranks, median differences, and test statistics

Variables and values Pre-Median Post-Median Median difference Ngtv. Ranks Pstv. Ranks Ties z-Test

PTOS 3.30 2.46 0.84 344^d 142^e 6^f –10.903

PBONS 3.44 2.22 1.22 341^g 141^h 10ⁱ –9.807

PBTNS 2.58 3.26 –0.68 150^j 335^k 7^l –9.491

CS 3.66 2.00 1.665 359^a 80^b 53^c –14.439

Variables and values	Pre-Median	Post-Median	Median difference	Ngtv. Ranks	Pstv. Ranks	Ties	z-Test
PTOS	3.30	2.46	0.84	344^d	142^e	6^f	–10.903
PBONS	3.44	2.22	1.22	341^g	141^h	10ⁱ	–9.807
PBTNS	2.58	3.26	–0.68	150^j	335^k	7^l	–9.491
CS	3.66	2.00	1.665	359^a	80^b	53^c	–14.439

PTOS=perceived threat of smoking, PBONS = perceived benefits of non-smoking, PBTNS = perceived barriers to non-smoking, CS = cigarette smoking. Asymp. Sig. (2-tailed, ^***P < 0.0025) N = 492; ^aPosttest CS < Pretest CS; ^bPosttest CS > Pretest CS; ^cPosttest CS = Pretest CS; ^dPosttest PTOS < Pretest PTOS; ^ePosttest PTOS > Pretest PTOS; ^fPosttest PTOS = Pretest PTOS; ^gPosttest PBONS < Pretest PBONS; ^hPosttest PBONS > Pretest PBONS; ⁱPosttest PBONS = Pretest PBONS; ^jPosttest PBTNS < Pretest PBTNS; ^kPosttest PBTNS > Pretest PBTNS; ^lPosttest PBTNS = Pretest PBTNS; ^mNgtv. Ranks = Negative Ranks; ⁿPstv. Ranks = Positive Ranks.

6.2 The structural equation modeling test

In the EFA, KMO scores.932 with P < .001 along with all the commonalities extractions above.3. We also have 01% of the non-redundant residuals and 89% of the variance as explained by study model. We have all the loadings >.5 as evidence of the convergent validity and the zero substantial cross-loadings as evidence of the discriminant validity. The correlation matrix scored <.7 and the Cronbach’s Alpha scored >.9 which indicate the robust reliability of questionnaire items.

We have the configured invariance results indicating the adequate goodness of fit indexes. These include the comparative fit index (CFI = .958), root means of square error of approximation (RMSEA = .031) and standardized mean square residual (SRMR = .0426). We did a matric invariance test by constraining the two models to be equal which produced a significant Chi-square difference with P = 0.664. These results suggest that both freely estimated and constrained models are invariant for male and female participants, which support both validity and reliability of the study constructs.

Table 3 indicates the convergent validity as evidenced by the average variance extracted (AVE) all above.5 in accordance with the established cutoffs [104]. We observed a significant difference between the average and maximum shared variances (MSV) which indicates strong reliability measure as evidenced by composite reliability (CR) value >.7 and maximum reliability MaxR(H) value >.9 [105]. Similarly, we observed the discriminant validity based on the square root of AVE being higher than any inter-factor correlation of variables’ items [104].

Table 3
The reliability, validity and correlation measures of variables

CR AVE MSV MaxR(H) Intervention PTOS PBONS PBTNS CS

Intervention 0.870 0.770 0.598 0.881 0.878

PTOS 0.791 0.654 0.497 0.918 0.705 0.809

PBONS 0.841 0.524 0.355 0.947 –0.557 –0.567 0.724

PBTNS 0.843 0.641 0.598 0.959 0.773 0.658 –0.523 0.801

CS 0.964 0.900 0.557 0.981 0.746 0.704 –0.596 0.740 0.949

	CR	AVE	MSV	MaxR(H)	Intervention	PTOS	PBONS	PBTNS	CS
Intervention	0.870	0.770	0.598	0.881	0.878
PTOS	0.791	0.654	0.497	0.918	0.705	0.809
PBONS	0.841	0.524	0.355	0.947	–0.557	–0.567	0.724
PBTNS	0.843	0.641	0.598	0.959	0.773	0.658	–0.523	0.801
CS	0.964	0.900	0.557	0.981	0.746	0.704	–0.596	0.740	0.949

CR = composite reliability, AVE = average variance extracted, MSV = maximum shared variance, Max(R) = maximum reliability, PTOS = perceived threat of smoking, PBONS = perceived benefits of non-smoking, PBTNS = perceived barriers to non-smoking, CS = cigarette smoking.

We compared the unconstrained common method factor model to the zero constrained common method factor model via conducting a common method bias test and found significant differences in shared variance as evidenced by Chi-square (χ² = 407, df=46 and P<.001). Hence, we retained the common latent factor (CLF) in the model. Table 4 indicates the goodness of fit indexes of study model, which is in accordance with established cutoffs [106]. We conducted the Cook’s distance test and found zero excavated records of the heavily weighted regressing items, which would influence the dependent variable [107]. We have the variable inflation factors <03 and Tolerances >.1 which indicated normal collinear statistics of the independent variables [108].

Table 4

The goodness of fit index measures of study model

Measures	χ ²	NFI	NNFI (TLI)	CFI	GFI	SRMR	RMSEA	IFI	PCLOSE
Values	78.589	.970	.990	.992	.977	.0340	.026	.992	.999

χ²= chi-square test, NFI = normed fit index, NNFI = non-normed fit index, TLI = Tucker-Lewis index, CFI = comparative fit index, GFI = goodness of the fit index, SRMR = standardized root mean square residual, RMSEA = root mean square error of approximation, IFI = incremental fit index.

Table 5 shows the mediation effects of latent variables, i.e., perceived threats of smoking (β= .212, p < .001), perceived barriers (β= .118, p < .001) and perceived benefits (β= .313, p < .001). These results supported H2, H3, and H4, respectively.

Table 5

The indirect effects of mediating variables

Variable	Indirect	(S. E)	Bias	95% Biased Corrected C.I

	Effects (β)			L.B	U.B
PTOS	.212	.001	.001	.129	.327
PBTNS	.118	.001	.001	.072	.176
PBONS	.313	.001	.002	.195	.464

Bootstrapped samples = 2000, p < 0.05, User-defined estimand obtained indirect effects, S.E = standardized error, PTOS = perceived threat of smoking, PBONS = perceived benefits of non-smoking, PBTNS = perceived barriers to non-smoking, C.I = confidence interval, L.B = lower bound, U.B = upper bound.

Figure 2 shows the residuals (r²) explained by latent variables, i.e., cues-to-action (r² = .73), self-efficacy (r² = .60), perceived threats (r² = .52), perceived barriers (r² = .30), perceived benefits (r² = .60) and quit cigarette smoking (r² = .64). These results supported both the local-global fit indexes of the study model as evidenced by the established cutoffs [109]. While Fig. 3 shows the regression lines which indicate significant relationships and impacts among study variables.

Fig. 1

The App screenshots.

Fig. 2

The validated study model with hypotheses references.

Fig. 3

The regression bar-chart of variables.

Table 6 indicates the standardized regression weights (β), significance (P), standard errors (S.E) and composite reliability scores (C.R) of the observed and the latent variables. These include cues-to-action (β= .86, P < .001) and self-efficacy (β= .77, P < .001) with smoking cessation (β= .22, P = .02), perceived threats (β= .719, P < .001) with smoking cessation (β= .239, P < .001), perceived barriers (β= –.544, P < .001) with smoking cessation (β= –.18, P < .001) and perceived benefits (β= .78, P < .001) with smoking cessation (β= .33, P < .001). Furthermore, it indicates the standardized indirect effects of the latent variables with an impact on the outcome variable. These include susceptibility (β= .551, P < .001) and severity (β= .536, P < .001) as the perceived threats of smoking. Improved finance (β= .518, P < .001), general well-being (β= .571, P < .001) and lower chance of diseases (β= .606, P < .001) as the quit benefits. Craving (β= –.229, P < .001), social ostracism (β= –.393, P < .001), inattentiveness (β= –.413, P < .001), lack of joy (β= –.352, P < .001) and negative effects (β= –.428, P < .001) as the barriers related to cessation, as evidenced by the cut (β= .424, P < .001) and quit (β= .397, P < .001) smoking behaviors of participants. These results support H2 to H5, respectively. Similarly, Table 7 shows the standardized indirect effects of the latent variables on the outcome variable.

Table 6

The standardized regression weights

Observed variables	← Latent variables	Estimate	(S.E.)	(C.R.)	P
Perceived Threat	Intervention	.719	.053	12.270	^***
Perceived Barriers	Intervention	–.544	.054	–10.112	^***
Perceived Benefits	Intervention	.775	.058	13.928	^***
Cues to Action	Intervention	.857	.055	18.145	^***
Negative Effect	Perceived Barriers	.787	.063	16.114	^***
Lack of Attention	Perceived Barriers	.759	.061	16.114	^***
Social Ostracism	Perceived Barriers	.723	.066	15.383	^***
Missing of Joy	Perceived Barriers	.647	.060	13.720	^***
Craving	Perceived Barriers	.422	.053	8.754	^***
Lower Disease Threat	Perceived Benefits	.782	.072	15.175	^***
General Wellbeing	Perceived Benefits	.737	.061	15.175	^***
Improved Finance	Perceived Benefits	.668	.059	13.838	^***
Cigarette Smoking	Perceived Threat	.239	.086	3.807	^***
Cigarette Smoking	Intervention	.219	.117	2.302	.021
Cigarette Smoking	Perceived Barriers	–.176	.052	–4.212	^***
Perceived Severity	Perceived Threat	.745	.082	12.827	^***
Perceived Susceptibility	Perceived Threat	.767	.074	12.827	^***
Cigarette Smoking	Perceived Benefits	.328	.082	4.700	^***
Self-efficacy	Intervention	.772	.055	18.145	^***
Cut in Smoking	Cigarette Smoking	.424	.033	11.854	^***
Quit Smoking	Cigarette Smoking	.397	.045	11.988	^***

Sig. ^***p < 0.005.

Table 7

The standardized indirect effects

Variables	Intervention
Perceived Susceptibility	.551
Perceived Severity	.536
Cigarette Smoking	.522
Improved Finance	.518
General Wellbeing	.571
Lower Disease Threat	.606
Craving	–.229
Missing of Joy	–.352
Social Ostracism	–.393
Lack of Attention	–.413
Negative Effects	–.428

Sig. ^***P < 0.005.

6.3 The probability estimation model

Table 8 indicates the intercept-only model based on the results of 370 participants with a likelihood to quit smoking, whereas, it identified 122 participants with the unlikelihood for the same. The prediction accuracy of the model is 75.2% with substantial goodness of fit indexes as evidenced by Chi-square coefficients (χ²=296.584, P < .001), which suggests that this model fits the data more correctly than the null model. Similarly, Table 9 indicates the accuracy of the predictor model with the correct classification of 96 participants who were unlikely to quit smoking. Whereas, it also shows 26 cases as the false negatives, which means that these participants had a likelihood to quit smoking but misclassified by the predictor model. The predictor model has 78.7% of the overall accuracy as evidenced by the correct classification of 122 participants who were unlikely to quit smoking, it also correctly classified 352 of participants who were likely to quit smoking due to their changes in health beliefs. Whereas, it also includes 18 of the false-positive cases, which indicates that these participants were unlikely to quit smoking but misclassified by the predictor model. This model is 95% accurate to correctly classify 370 of participants with a likelihood to quit smoking. Hence, the overall accuracy of the predictor model is 91%.

Table 8
The null model

Observed Predicted cigarette smoking

Reduced Not reduced Percentage correct

Step 0 Reduced 370 0 100.0

Not reduced 120 0 0.00

Overall percentage 75.2

Observed	Predicted cigarette smoking
Step 0	Reduced	370	0	100.0
	Not reduced	120	0	0.00
	Overall percentage			75.2

a. Constant is included in model; b. The cut value is.500.

Table 9

The predictor model

Predicted
Groups		0	1	Percentage correct
Observed	0	96	26	78.7
	1	18	352	95.1
Overall Percentage				91.1

The Cut Value is.500.

Table 10 indicates that every extra unit increased in the perceived knowledge of tobacco hazards decreases the logit of estimated odds by –1.291 units in participants to quit smoking. Subsequently, the odds ratio Exp(β) = .275 indicates that participants with increased knowledge about tobacco menaces are 27.5% more likely to quit smoking. Similarly, every extra unit increased in cessation gains decreases the logit of estimated odds by –1.276 units in participants to quit smoking. Accordingly, the odds ratio Exp(β) = .279 indicates that participants who attained cessation gains are 28% more likely to quit smoking. Furthermore, every extra unit increased in overcoming perceived barriers related to cessation, increases the logit of estimated odds by 1.085 units in participants to quit smoking. Hence, the odds ratio of Exp(β) = .959 indicates that participants who overcame such barriers are 96% more likely to quit smoking. These results support the H6 of this study. Table 11 shows the combined results of all statistical tests.

Table 10

The binary logistic regression weights

Variables	(β)	(S.E.)	Wald	df	Exp(β)	95% C.I. for EXP(β)
						L.B	U.B
Perceived threat	–1.291	.221	34.020	1	.275	.178	.424
Perceived benefits	–1.276	.176	52.671	1	.279	.198	394
Perceived barriers	1.085	.209	26.886	1	2.959	1.963	4.458
Constant	5.252	1.005	27.292	1

Sig. ^***P < 0.001, df = degrees of freedom, S.E = standardized error, L.B = lower bound, U.P = upper bound.

Table 11

The statistical tests and results of variables

Tests	PTOS	PBTNS	PBONS	CS
Wilcoxon signed-rank test	md = 0.84	md = –0.68	md = 1.22	md = 1.665
Structural equation modeling	β= .212	β= .118	β= .313	β= .33
Binary logistic regression	β= –1.291	β= 1.085	β= –1.276	Dependent

PTOS = perceived threat of smoking, PBTNS = perceived barriers to non-smoking, PBONS = perceived benefits of non-smoking, CS = cigarette smoking, Sig. ^***P < 0.005.

6.4 The post-intervention responses

We have the post-intervention responses of participants regarding a usability assessment of the programmed codes and the App to aid them in cessation. The results indicate that 189 (39% ) out of 492 participants have significantly cut their cigarette smoking frequencies, whereas, 163 (33% ) of participants have successfully quit cigarettes. The study participants believed that the conjoint uses of anti-tobacco codes and mHealth App have significantly improved their comprehension about tobacco hazards and the potential benefits of quitting. It also assisted them with the help that needed to overcome perceived barriers related to cessation and attain quit benefits.

In particular, they responded that the personalized smoking-profile function of the programmed App assisted them to overcome perceived barriers, such as their lower determination and fear of failure/being judged which previously stopped them from participation in a cessation program. They responded that their improved comprehension of health, life, time, and money losses due to smoking discouraged them from smoking and encouraged them to gain cessation benefits. In addition, they responded that the quit-goals function of the programmed App assisted them to quit smoking. They were also convinced that their access to anti-tobacco content via QR codes and the push notifications of the APP that displayed on craving times improved their understanding to find tobacco as a real threat, which discouraged them from smoking. Also, participants believed that both the availability of and access to social support, successful quitters and personalized cessation progress motivated them towards cessation. They also responded that their comparison of cessation gains with the programmed App users encouraged them to set and achieve quit goals. Last, the participants responded that connecting with doctors via the programmed codes and App was a hassle-free approach that assisted them attain tobacco control counseling and pertinent remedies in order to curb their smoke craving and to overcome perceived fears related to cessation, such as inattentiveness, aggression, depression, irritation and lazinessin the moods.

7 Discussion

7.1 The study significance

This study highlights the conjoint usefulness of QRC and mHealth technologies to aid smokers in cessation through improving their health beliefs. The study model is designed using the HBM theory constructs [110]. This study has practical implications for the concerned stakeholders of China in controlling the tobacco epidemic. In particular, the study suggests that printing anti-tobacco QR codes on cigarette packaging has the potential to discourage smokers from smoking, which aims to aid tobacco retailers as technology compliance in accordance with the WHO FCTC manifesto [15]. Furthermore, the study will benefit tobacco control experts in compliance with the first four guidelines of the WHO MPOWER stratagems to facilitate smoker cessation [23].

7.2 The statistical measurements of the study model

The reliability and validity measures for the causal model were examined using the SEM technique with the EFA and CFA analyses which supported both the local-global fit indexes as per the established cutoffs [71, 103].

7.3 The perceived threat of smoking

The obtained results regarding the perceived threat of smoking variable suggest that the QRC-mHealth-HBM intervention significantly improved the health beliefs of participants through increasing their knowledge about tobacco menaces which discouraged them from smoking. These results support the argument of the previous studies that it is crucial to improve smokers’ health beliefs [31] in order to attain smoker cessation [28, 111]. The study findings supported the argument of the previous studies that increasing smokers’ anti-tobacco knowledge with exact facts assists in changing their misconceptions to see tobacco threats as relative risks [103, 112], which aids smoker cessation [71 , 112].

7.4 The perceived barriers of non-smoking

The obtained results in this regard suggest that the provision of hassle-free, personalized, free, flexible and non-judgmental tobacco control counseling from health experts make tobacco control programs more viable for smokers to quit cigarettes, these results supported the argument of the previous study [69]. Furthermore, the results suggest that attainment of pertinent remedies from experts assists smokers in curbing their smoke craving and changing their misconceptions related to cessation, such as the potential occurrence of inattentiveness, lack of joy, aggression, irritation, depression, and laziness in the mood. These results support the argument of previous studies [73 –82].

We included the users’ personalized smoking-profile function in the programmed App, subsequently, the obtained results suggest that it has assisted participants to overcome their prior fears which potentially hindered them from participating in a cessation program. Which include smokers’ lower determination and self-efficacy to quit and fear of failure or being judged. These results are consistent with the recommendations of the previous studies that the inclusion of such personalized tobacco control services eases participants to attain desired results of cessation [69, 83]. Also, we recommended participants to refuse cigarette gifts from fellows by offering those to consume the programmed codes and App, subsequently, the obtained findings indicated that our recommendation aided participants to curb social trolling. These results are in accordance with the suggestion of the previous studies that aiding smokers in curbing their potential social isolation due to cessation is an essential attribute to include in the interactive tobacco control programs [86, 87]. Hence, the obtained results draw a conclusion that our recommendation is viable to discourage a cigarette gifting custom, which can aid in curbing the tobacco epidemic [86 , 89].

7.5 The perceived benefits of non-smoking

The obtained results suggest that the comparison of cessation gains with the fellow App users and attainment of social support from successful quitters indicate that both these features of the programmed App have encouraged participants to set and achieve quit goals. Similarly, the quit-goals feature has assisted participants to assess and acquire benefits, e.g., growth in life, health, time and money due to cessation. These results are consistent with the suggestions of the previous studies which emphasize to include such features in tobacco control programs in order to encourage smokers towards cessation [28, 69].

7.6 The self-efficacy and cues-to-action

We programmed the App to strengthen the internal consistency of participants to quit smoking. The observed findings in this regard are consistent with the suggestions of the previous studies that incorporation of the self-efficacy determinant in tobacco control programs assists smokers in managing cessation [62]. While we programmed the anti-tobacco QR codes as useful cues-to-action to increase participants’ knowledge about smoking perils in order to discourage them from smoking. Subsequently, the obtained findings in that regard are consistent with the suggestion of the previous studies that increasing smokers’ anti-tobacco knowledge assists them to eradicate their destructive habit of smoking cigarettes [113 –116]. Furthermore, the observed results validate the argument of the previous studies that tobacco control programs which include both self-efficacy and cues-to-action determinants have been shown effective to facilitate smoker cessation [63, 70]. However, the outcome of this study opposes the argument of the previous studies that the mHealth triumph is uncertain in developing countries [53, 54].

7.7 The health belief model theory and tobacco control

The obtained results in this regard suggest that tobacco control programs designed using the HBM theory constructs are viable to comprehend the complex behavior of cigarette smoking of smokers. These findings validate the suggestions of the previous studies that the incorporation of HBM constructs in anti-smoking interventions is a useful technique to observe, amend and predict the cigarette smoking behavior of smokers [66 –68], which can aid in smoker cessation [16 , 118].

8 Conclusion

This HBM designed study examines the conjoint usefulness of both QRC and mHealth technologies in controlling the tobacco epidemic in China. In particular, it suggests that both printing the anti-tobacco codes on cigarette packaging and adoption of anti-smoking mHealth applications have the potential to aid smokers in cessation via improving their health beliefs. The conjoint use of both these two technologies provides participants with the assistance that they needed to overcome perceived barriers related to cessation and attain benefits of quitting cigarettes. Therefore this study has feasible implications for the concerned stakeholders of China in controlling the tobacco epidemic in the country. Especially, it is viable for tobacco-retailers as technological substitute in compliance with the WHO FCTC manifesto, to discourage smokers from smoking via cigarette packaging itself. Also, this study will benefit to the policymakers in order to facilitate smoker cessation in compliance with the first four recommendations of the WHO MPOWER stratagems on tobacco-control. This study suggests that the provision of tobacco-control advocacy from health experts has assisted participants to curb their smoke cravings, which assists them to overcome their perceived fears related to cessation, such as inattentiveness, anxiety, depression and laziness in the mood. Similarly, the conjoint use of anti-tobacco codes and mHealth application for cessation is an effective approach that can assist experts to comprehend, amend and predict the complex behavior of cigarette smoking in smokers, which can aid in smoker cessation.

8.1 Future implications

This study has shown both the role and usefulness of two technologies, i.e., QRC and mHealth technologies in tobacco control via improving the health beliefs of smokers with healthcare education. Hence, the espousal of both these technologies can assist doctors give patients an active role in their self-healthcare management.

8.2 The study limitations and recommendations

First, this study includes the analysis of repetitive responses obtained from a convenient sample of the 600 English-speaking participants from China. Hence, it is recommended to recruit participants with different demographic factors in future extended studies. Second, the limited budget compelled the researchers to program the App that can only on run on Android phones although the App is being updated so that it can support the iOS and Windows phones in future studies. Third, due to the unavailability of volunteer doctors in China contact details of Pakistani doctors were incorporated in the programmed codes (they graduated from the China medical universities and understand the Chinese language) who offered volunteer tobacco control counseling to the study participants. Hence, it is recommended to include contact details of the local doctors of China for the effective implementation of the proposed solution of this study. Last, the HBM was a solitary theory used in this study, whereas, different theories such as the theory of planned behavior [119, 120] and the health action process approach [121] should be used in extended future studies.

Conflict of interest and funding

None declared.

Footnotes

Appendix

Acknowledgments

We are thankful to Mr Muhammad Hassan Nasir for programming anti-tobacco QR codes and Smokers’ Mirror App, and Dr Abdul Rahman Soomro (MBBS, FCPS, Jinnah Medical University, Karachi, Pakistan) for arranging 11 Chinse-speaking doctors who offered volunteer tobacco control counseling to participants.

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Tobacco control via quick response code and mobile health technologies: Empirical-evidence of the health belief model theory

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1 Introduction

2 Related work

2.1 Mobile health technology and tobacco control

2.2 Quick response code technology and tobacco control

2.3 Health belief model and tobacco control

3 The experiment, theoretical framework, literature, and hypotheses proposition

3.1 The programmed mHealth application and QR codes for cessation

3.2 The experiment in compliance with the WHO FCTC and MPOWER programs

3.3 Theoretical framework and literature review

3.3.1 Self-efficacy and cues-to-action

3.3.2 Perceived threats of smoking

3.3.3 Perceived barriers to non-smoking

3.3.4 Perceived benefits of non-smoking

3.4 Proposition of hypotheses

4 Study contribution

5 Methods

5.1 The sampling technique

5.3 The statistical software

5.4 The statistical tests

5.5 The SEM measures

6 Results

6.1 The Wilcoxon signed-rank test

Table 8 The null model Observed Predicted cigarette smoking Reduced Not reduced Percentage correct Step 0 Reduced 370 0 100.0 Not reduced 120 0 0.00 Overall percentage 75.2

7 Discussion

7.1 The study significance

7.2 The statistical measurements of the study model

7.3 The perceived threat of smoking

7.4 The perceived barriers of non-smoking

7.5 The perceived benefits of non-smoking

7.6 The self-efficacy and cues-to-action

7.7 The health belief model theory and tobacco control

8 Conclusion

8.1 Future implications

8.2 The study limitations and recommendations

Conflict of interest and funding

Footnotes

Appendix

Acknowledgments

References

Table 8
The null model

Observed Predicted cigarette smoking

Reduced Not reduced Percentage correct

Step 0 Reduced 370 0 100.0

Not reduced 120 0 0.00

Overall percentage 75.2