Smoking-cessation acceptance via mobile health

Abstract

Smoking prevalence is a leading health risk and preventable cause of mortality. As of 1.1 billion global smokers, 80% live in emerging countries. Predictably, 7 million of them will die annually by 2030. Mobile health (referred to as “mHealth”) gets popular in today’s healthcare management uses technology, and to combat smoking peril. Technology, being a societal human system with mutual inter-dependencies among its hardware, software, brainware and support network or net components, is integral to mHealth success. Technology espousal can empower health organizations to assimilate internal and external setups and applications, and the government in lieu of maneuvering healthcare management. Economic, infrastructure constraints, and high entry barriers in healthcare management has made mHealth’s triumph unclear in emerging countries. We designed a mobile application namely “Smokers’ Mirror” to examine mHealth’s leeway on tobacco control in Pakistan. Using, the technology acceptance model (TAM) pertinent in this study context. Students’ centric, time and budget constraints involved, hitherto, the study findings divulge that mHealth is expedient for smoking cessation. Moreover, Pakistani smokers agree using mHealth, believe, it is useful and easy to use. Concluding from the narrative of this study, we advocate mHealth espousal will yield promising upshots, if integrated within Pakistan’s healthcare policy.

Keywords

Smokers’ behavior mHealth technology technology acceptance model smoking cessation healthcare

Rahib Ali is a Ph.D. Candidate at the Harbin Institute of Technology (HIT). He has served as a visiting faculty member at Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (SZABIST) Larkana, Pakistan. His research interests are Personal Management, Organizational Behavior, Electronic Commerce, Services Marketing, Customer Relationship Management, Strategic Marketing Management, Managing Information Systems and Advanced Research Methods.

Professor Ziqiong Zhang has earned Ph.D. from Harbin Institute of Technology (HIT), China. She is Professor in the School of Management at HIT. She has teaching experience of undergraduates in Operations Research. Her research interests are Web Data Mining, E-commerce, and Knowledge Management. She has Editorship (Reviewer) in the International Journal of Hospitality Management. Cornell Hospitality Quarterly.

Mr. Muhammad Bux Soomro is Assistant Professor, Head of Department Computer Science. Program Manager BSCS, Department of Computer Science, SZABIST Larkana Campus, Pakistan. He has M.Phil. in Computer Science Technology from Shah Abdul Latif University (SALU) Pakistan. His research interests are Advanced Analysis of the algorithm, Theory of computation, Theory of Programming Language, Advanced Computer Architecture, Multi-Agent Systems, Capability Maturity Model Integration, Data Mining, Business Process Reengineering, IS-1 (Software Development Processes and Quality Issues), IS-II (Agents in Software engineering and Research).

1 Introduction

Smoking is a leading health risk and preventable cause of mortality Worldwide [1]. Cigarette smoking upsurges from puberty then prevail in later life [2]. Instigating destructive health effects on young cigarette smokers [3 –8]. Globally, 1.1 billion people aging 15 and older are smokers about 80% of them live in low and middle-income countries [9]. Such consequent of smoking menace threats emerging countries to lose 7 million human lives every year by 2030 [10]. It is substantial to design effective smoking cessation interventions in emerging countries. Where tobacco users are more but tobacco quit rate is low [11]. Besides, tobacco companies target adult smokers with broad marketing in these countries [12]. And underutilization of anti-tobacco programs by adolescent smokers contributes to smoking cessation relapse [13]. Pakistan is an emerging country, the majority of its population consists of youth [14]. With growing smoking prevalence in adults, i.e., 9% of females and 36% of the males are smokers in Pakistan. 15% of this populace is the adult male students [15]. According to the World Health Organization’s (WHO) report for the year 2005. A sum of 90, 00,000,000,000 cigarettes consumption was estimated in Pakistan [16] with the rapid increase in youth. Another WHO’s report for the year 2016 reveals that the young adult smokers of 15 years age and older consume 10.5 cigarettes/day in Pakistan [17]. Although, Pakistan is a signatory in the WHO’s Framework Convention on Tobacco Control (FCTC). Which bounds the country to include advising services on tobacco control in the national health and educational programs [18]. Hitherto, such support programs are not effectively practiced in Pakistan [16]. And, the government yearly spends as minimum as $30000 on tobacco control [17]. According to Pakistan Post, smoking-related diseases approximately take 108000 lives every year in the country [19]. Today’s students are tomorrow’s leaders [20]. Creating a smoking-free environment for their health is necessary [21]. Especially in the emerging countries suffering from poor health facilities [22]. Another WHO’^s report for the year 2016. That Pakistani government merely disburses $3.5/person’s health on yearly bases. And, averagely 2.9 doctors are dispensed for 10, 000 rural residents [23]. As smoking prevalence continues to grow in Pakistani adolescents. Adequate measures on tobacco control are required [24] to save the future of the country’s future leaders. Helping young folks to abandon smoking is urgent and challenging. Use of novel methods can give a breakthrough [2]. Such as mHealth technology [25]. Technology is a societal human system, with multiple causal and mutual interdependencies among its hardware, software, brainware and support network or net components [26, 27]. In the study context, mHealth technology has the budding benefits of tobacco control [25 , 28–37]. But, economic and infrastructural constraints, the narrow entrance to medical care and famine of healthcare personnel [38]. Make mHealth triumph unclear in emerging countries [39, 40]. Considering the mHealth’s precincts and prospects. We examine smoking-cessation acceptance via mHealth in Pakistan. We recruited Pakistani students as volunteers in this study. Since they are open to anti-tobacco treatments [24]. The students’ centric, time and budget constraints involved. The mobile application programmed for this study only supports the Andriod operated smartphones. The same mobile application is upgraded to support iOS and Windows operated smartphones for a successive study. Investigating, doctors’ acceptance of mHealth in the healthcare management in Pakistan.

Besides the available theories to examine users’ behavior. Such as the Theory of Planned Behavior (TPB) [41]. Combined TAM and TPB [42]. Theory of Reasoned Action (TRA) [43]. And, Decomposed TPB (DTPB) [44]. We use eminent TAM, pertinent to this study context [45 –49]. While TAM2 [50] will be used for the theoretical framework of the subsequent study.

To curtail the biases in the respondents’ feedback obtained via questionnaire. Only data collection instrument used in the study. We get research partakers’ consent to access their actual use of “Smokers’ Mirror” to compare it with their response.

We have Fig. 1 indicating the workflow of the study from problem identification, programming of a mHealth and to study findings. As a viable solution to combat smoking peril. And, its espousal in the healthcare sector in Pakistan to yield promising outcomes.

Fig.1

Workflow diagram.

2 Related work

Smoking cessation aids are available in the advanced countries. But, their triumph in the emerging countries is less clear [51]. In spite of the available choices for smoking cessation [52]. Studies argue that adolescent smokers do not pursue anti-tobacco treatments comparing to old smokers [53 –55]. In comparison to young smokers between the ages of 18 to 24 years. Old smokers are two times more likely to have used psychological advice from a health professional. Or, used pharmacological e.g. nicotine replacement therapy (NRT) treatments aid for smoking cessation [53]. Although adolescent smokers show interest to quit smoking. But, they fail to quit smoking due to its addiction [10]. Helping young folks to abandon smoking is urgent and challenging. Adopting the novel methods can give a breakthrough in this regard [2]. Such as use of the smartphones and the internet technologies. Current usage of smartphones including phablets is expected to exceed about 50% by 2021 [56]. Enlarging the potential of smartphone usage to reach an outsized number of patients [57]. With mobile-internet, people not only satisfy their communication needs. But, they also search for health-related information [28]. Mobile service used for health, referred to as mHealth is an effective medium to deliver health-related information to its users [25]. At an affordable price [58]. mHealth is useful for patients’ remote diagnoses, nursing, data collection, information dissemination, education provision and for arranging forewarns engagements [25]. The rise of mobile phones [36], mobile-internet [28] and the arrival of mHealth have eased the access to treatments and health-information more than ever [34, 35]. Readily and remote look after of patients increase the leeway of mHealth in current healthcare management [36]. Similarly, health-related information and treatment remedies can be sent to patients via mHealth using reminders [30, 37]. Meanwhile, mHealth applications are helpful to improve user’ experience with information provision, components, and functionalities embedded in it [29]. Young adults accept mHealth usage [59]. With no surprise of being regular seekers of health-related information [2]. A study report indicates, out of 100 health information seekers via mobile applications, 42 are the young adults [60]. Considering the fact that youth in emerging countries lack in knowledge regarding smoking consequences to health and life. Increasing their knowledge is fundamental to aid tobacco control initiatives [37]. Such as, smoking normally reduces one decade from smokers’ lifespan. But, those who quit smoking before the age of 40 can reduce this risk by 90% [61]. mHealth helps smokers to access needed assistance any time throughout the day [31]. Since, the assistance is instantly accessible e.g., support to deal with craving [32]. Comprehensive mobile-based smoking cessation applications are liked by the young adults. Compared to the conventional quitlines and SMS text messaging services [54 , 63]. Smartphone applications have huge scope for smoking cessation [29]. Current smartphone applications for smoking cessation are capable of combining education, social networks support, motivational techniques, quit coaches, quit plans. And include various functionalities which go far from SMS text messaging service only [64]. With budding benefits, mHealth is potentially useful for the emerging countries. In providing health-related education [65]. Including Pakistan [30]. A study suggests that young Pakistani pupil smokers are open to anti-tobacco programs [24]. Ergo, benefiting from the potency of mHealth can be crucial to cut the increasing tobacco epidemic in Pakistan.

3 The programming and use of “Smokers’ Mirror” a mobile application

Considering the unavailability, uncommon acceptance and use of mHealth applications in emerging countries [39, 40]. Due to infrastructural deficits, the narrow entrance to medical care, and famine of healthcare personnel [38]. Designing mHealth applications are challenging. Requiring the involvement of concerned stakeholders, trustworthy institutions, and result oriented research [40]. To overcome the aforementioned obstacles. And, others as mentioned in the introduction and the related work sections of the study. We opt to induce mHealth application. And examine its potency in Pakistan, initially for smoking cessation from smokers’ perspective. Although, mHealth applications contentedly fit to offer customized support for smoking cessation. Hitherto, it is important to discern that to what extent these applications use tailoring. And. if tailoring is user rated [35]. Thus, we conducted preliminary interviews with 20 smokers. Using various mobile applications for smoking cessation. We found, personal profile showing smoking history. Scientific facts on smoking hazards to health and life in visuals. Cost analysis of smoking with time and money. Active support from the health gurus. And, quit plan to smoking as preferred features for a compact smoking cessation application. Consequently, a team of professional Android applications programmers led by Mr. Hassan Nasir working at Eocean Pvt. Ltd. Pakistan. Lead in programming the “Smoker’s Mirror” a mobile application for this study. Although, the same mobile application is examined to curb the tobacco epidemic in China resulting in the effective upshots. However, its acceptance in Pakistan is examined in this study.

We have the active screenshots of the “Smokers’ Mirror” for the login and the dashboard pages. As shown in Fig. 2 indicating the smokers’ preferred features.

Fig.2

Screenshot of the “Smokers’ Mirror” a mobile-app.

4 Literature review

Despite the budding benefits, mHealth encounters numerous challenges. Such as patients’ acceptance of using it [66]. According to a market report conducted in 27 countries in 2014. About assessing the acceptance, and usage of mHealth by the diabetic patients. Study results revealed, only 1.20% of the smartphone-owned patients had managed their disease by using mHealth. And, only 1.70% augmentation in its future usage was expected by 2018 [66]. Many studies have focused to examine user’ acceptance for the technologies. Such as medical records for treatment [67 –70], telemonitoring [71] and web-based ones [72, 73]. It is also important to comprehend the aspects that influence users’ acceptance of mHealth for treatment purpose [74, 75]. Many theories are available and widely used, to explain the components affecting individuals’ acceptance/rejection/continuity to use. Or, to adopt new technologies, i.e., TAM [76]. TAM 2 [50]. TPB [41]. Combined TAM and TPB [42]. TRA [43]. And, Decomposed TPB [44]. Since, mHealth is relatively new with uncertain acceptance in the emerging countries [39 , 77]. TAM is pertinent to use in this study context [45 –49].

5 Theoretical framework

Considering the huge espousal of TAM in the fields of technology acceptance. Including mobile, computer and web-related programs to predict user’ acceptance [45 –49]. More precisely, mHealth for health management [34 , 46]. Like, Hung and Jen used TAM to discover pupils’ intention towards mHealth acceptance for personal health management [48]. Similarly, we opt to use TAM to evaluate smokers’ behavior towards smoking-cessation acceptance via mHealth [58] in Pakistan.

5.1 Technology acceptance model

TAM was first conceived in 1989 by Fred Davis. Explaining that how users comprehend, approach, operate, accept and utilize the technology. Such as computer, mobile and World wide web technology [76]. TAM was derived from TRA that is widely approved in the fields of technology acceptance for healthcare management [46]. And, used to predict user’ acceptance of the technology [45 –49]. TAM has two main cognitive determinants, i.e., perceived usefulness (PU). And perceived ease of use (PEOU) [45]. PU denotes a degree to which users consider of using a technology that they think would improve their job performance. While PEOU refers to the degree to which users believe that using a technology would be easy [46]. These determinants have a direct impact on user’ behavior for intention to use (ITU) [78]. Or, the intention of continual use/actual use (AU) of a technology [75]. If users agree that the alternative technology for health management is easy to use and helpful to self-management of illness. They likely adopt it. Furthermore, if users observe that the technology is actually easy to use. They are likely to perceive it as useful [79].

6 Purpose and prospects of the study

Despite the augmentation in smoking prevalence [15]. Resulting in human losses every year in Pakistan [19]. The government is unserviceable to spend ample funds on tobacco control [17]. Considering the deprived monetarily situations [80]. And, pitiable health infrastructure in the country [81]. Only innovative schemes can give breakthrough on tobacco control [2]. Such as the use of mHealth technology for smoking cessation [29, 58]. The international health policymakers and mavens endorse the use of technology including mHealth. As a viable solution to provide cost-effective health services to the populace [82]. Suggesting, not only it provides health-related services, upsurges life expectancy of the user. But, its adaptation adroit to aid national wealth. Like, in the United States of America, $300 billion savings are conceivable. With $200 billion coming from the chronic disease management [83]. Moreover, the use of technology will empower health organizations to assimilate internal and external setup and applications. Sharing the clinical and non-clinical patient data with the government. That will be used by the concerned stakeholders in lieu of maneuvering health management effectively [82]. Hence, seeking the aforesaid fortes of technology usage. This study scrutinizes smoking-cessation acceptance via mHealth [29, 58] in Pakistan. Since TAM is used for the theoretical framework of this study. The relations of TAM constructs are studied with direct and indirect effects. Finally, besides the confab of study findings. The espousal of mHealth is conferred for the concerned stakeholders to comprehend mHealth prospects. Especially, if it is adopted within the healthcare sector of Pakistan, it will yield the pledged outcomes.

7 Proposition of hypotheses

Discussed above the introduction, related work, literature review and TAM theory in the study context, compelled us to theorize the following hypotheses.

Hypothesis: 1 PEOU of “Smokers’ Mirror” mobile application has significant positive effects on PU

Hypothesis: 2 PU of “Smokers’ Mirror” mobile application has significant positive effects on smokers’ ITU

Hypothesis: 3 PEOU of “Smokers’ Mirror” mobile application has significant positive effects on smokers’ ITU

Hypothesis: 4 ITU of “Smokers’ Mirror” mobile application has significant positive effects on AU

Hypothesis: 5 PU of “Smokers’ Mirror” mobile application has significant positive effects on AU

Hypothesis: 6 PEOU of “Smokers’ Mirror” mobile application has significant positive effects on AU

7.1 Mediation hypotheses

Underneath hypotheses are proposed to find the mediating effects of the study variables. To the best of our knowledge, no prior study using TAM has proposed the mediation hypotheses of TAM constructs. Since mHealth acceptance is indistinct in emerging countries [39 , 77]. To us, ascertaining the mediation hypotheses was important to know the intensification of using TAM. To recommend the concerned stakeholders to comprehend the effective contributors from the users’ perspective while designing the mHealth program.

Hypothesis: 7 ITU of “Smokers’ Mirror” mobile application mediates between PU and AU

Hypothesis: 8 ITU of “Smokers’ Mirror” mobile application mediates between PEOU and AU

Hypothesis: 9 PU of “Smokers Mirror” mobile application mediates between PEOU and ITU

Hypothesis: 10 PU of “Smokers’ Mirror” mobile application mediates between PEOU and AU

Hypothesis: 11 PU and ITU of “Smokers’ Mirror” mobile application together mediate between PEOU and AU

8 Methodology

Based on the convenient sampling method, 750 volunteers from metropolitan cities of Pakistan were recruited for this study. The criterion for recruitment: university students smoking and owning smartphones. Seminars were led by researchers in volunteers’ universities on the importance of mHealth and its usage. At the end of seminars, “Smokers’ Mirror” mobile application was installed on volunteers’ smartphones. And, they suggested to use it during the intervention period.

8.1 Experimental procedure

The data were collected using a self-designed questionnaire. Reliability of the questionnaire items using 5 points Likert scale 1 = strongly agree, to 5 = strongly disagree was inspected in a pilot study. A study shows that smokers revert to smoking in the first ninety days of a cessation program [84]. Therefore, the intervention period for this study was scheduled from 31st December 2017 to 1st April 2018. Getting the volunteers’ consent, their usage of “Smokers’ Mirror” was accessed during the intervention period. Participants were updated with scientific information on smoking hazards to health, life, time, money and other valuables via notification feature of “Smokers’ Mirror”. At the end of the intervention, we found 589 research partakers were using the “Smokers’ Mirror”. The questionnaire was sent to them via the notification feature. Total, 535 research partakers responded to the questionnaire. Of which, 31 survey forms were discarded due to unengaged feedback observed. Where the research partakers erroneously answered to deliberately added question. Ratifying, if they were attentive in attempting the questionnaire. And, 20 survey forms were discarded. Because the respondents rated the same scores against each item. This could bring bias in the study judgments.

8.2 Statistical software

With Statistical Package for Social Sciences (SPSS) v.23 and Analysis of a Moment (AMOS) v.23. The obtained data were statistically examined using the structural equation modeling (SEM) technique to test the study hypotheses.

8.3 Tests parameters

Firstly, we examined the adequacy, convergent validity, discriminant validity and reliability of obtained data with Exploratory Factor Analysis (EFA). Using the initial solution, reproduced correlation matrix and Kaiser-Meyer-Olkin’^s (KMO). We verified sampling adequacy and Bartlett’s test of sphericity. In extraction, maximum likelihood method based on Eigenvalues >1, at 25 maximum iterations were used for convergence. The Promax method for rotation and surpass small coefficient absolute value was set to 0.3. Secondly, to check the validity and reliability of the measurement model. We did a Confirmatory Factor Analysis (CFA). Finally, we found the path coefficients of study variables. And, to assess the mediation hypotheses. We installed a user-defined estimands plugin in AMOS and used bootstrapping [85, 86]. With 2000 samples at a 95% confidence interval.

9 Results and analysis

With absolute values of +1 for skewness, and – 1 for kurtosis. We tested the normality assumption of the obtained data. And found no issues in its normality. In EFA, KMO scores 0.893 with p < 0.001. While all the commonalities extractions were above 0.3. A four-factor model explains 84.54% of the variance. We had less than 2% non-redundant residuals in the study model. We have all the loadings above 0.5 that is evidence of the convergent validity. As evidence of the discriminant validity. We had no strong cross-loadings. The pattern matrix at a minimum. We had all the scores below 0.7 in the correlation matrix. We have a robust reliability. As evidenced by the Cronbach’s Alpha >0.9 of all questionnaire items.

With CFA, we analyzed a freely estimated model across two groups, i.e., male and female. We obtained an adequate goodness of fit, as evidenced by configural invariance test. We have the comparative fit index (CFI) = 0.992, standardized root mean square residual (SRMR) = 0.0181 and root mean square error of approximation (RMSEA) = 0.030. As evidence of the configural invariance of a two groups model. We did a matric invariance test by forcing the fully constrained and the unconstrained models to be equal. And found a chi-square difference Σ² = 8.9, df = 434, p = 1.00. Indicating, two models are invariant. This means, both the male and female smokers are observed using mHealth equally. Because they think that “Smokers’ Mirror” a mobile application is useful and easy to use. Ergo, with negligible difference in choice. Both, the male and female users of “Smokers’ Mirror” accept using it.

In Table 1, we have the convergent validity as evidenced by AVE values all above 0.5 [87]. We have a significant difference between AVE and MSV. With all CR scores above 0.7. We have much robust MaxR(H) values all above 0.9 [88]. We have the discriminant validity based on the square root of AVE values, higher than any inter-factor correlation on this matrix [87]. We compared the unconstrained common method factor model to the zero-constrained common method factor model. To check the shared variance. As evidenced by a common method bias test. We found a chi-square difference with Σ² = 53.9, df = 14, p < 0.001. Indicating a significant shared variance in the study model. This lets us retain the common latent factor (CLF) in the study model.

Table 1
Reliability & validity measures and the correlations among the variables

CR AVE MSV MaxR(H) ITU PU PEOU AU

ITU 0.963 0.898 0.328 0.965 0.947

PU 0.929 0.768 0.335 0.977 0.422 0.876

PEOU 0.955 0.840 0.335 0.985 0.387 0.579 0.917

AU 0.966 0.905 0.328 0.990 0.573 0.529 0.509 0.951

	CR	AVE	MSV	MaxR(H)	ITU	PU	PEOU	AU
ITU	0.963	0.898	0.328	0.965	0.947
PU	0.929	0.768	0.335	0.977	0.422	0.876
PEOU	0.955	0.840	0.335	0.985	0.387	0.579	0.917
AU	0.966	0.905	0.328	0.990	0.573	0.529	0.509	0.951

Note: CR = composite reliability; AVE = average variance extracted; MSV = maximum shared variance; MaxR (H) = maximum reliability; ITU = ITU; PEOU = PEOU; AU = actual use for smoking cessation; ITU = intention to use; PU = perceived usefulness; PEOU = perceived ease of use; AU = actual use. ***p < 0.001.

Table 2 indicates favorable measures of goodness of fit indexes. As evidenced by the established cutoff criteria [89]. We have normally distanced regression weights between 0.00 to 0.05 ranges. With no influential excavated records as evidenced by the cook’s distance test [90].

Table 2

The goodness of fit measures in the model

Measures	χ2	NFI	NNFI (TLI)	CFI	GFI	SRMR	RMSEA	IFI	PCLOSE	DF
Values	49.55	0.995	0.999	0.995	0.986	0.0154	0.006	0.995	0.999	55

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

We have the variable inflation factors (VIF^s) less than 03. And, the tolerances greater than 1. These indicate normal collinearity statistics of independent variables in the study model [91].

We have Fig. 3 showing the validated study model using TAM constructs. And, the direct and indirect path coefficients of the model constructs. These indicate significant positive effects and substantial residuals (r²).

Fig.3

Validated study model using TAM. Note: Represents the direct effects of constructs and used for H: 1 to H: 6. Represents the indirect effects of mediating variable and used for H: 7 to H: 8. Represents the indirect effects of PU between PU and PEOU used for H: 9. Represents the mediating effect of PU between PEOU and AU used for H: 10. Represents the indirect effects of PU and ITU between PEOU and AU used for H: 11.

Table 3

Standardized regression weights with hypothesis reference

Variables Relations			Estimate	S.E.	C.R.	p	H.R
PU	←	PEOU	0.560	0.035	14.856	***	H: 1
ITU	←	PU	0.273	0.056	5.542	***	H: 2
ITU	←	PEOU	0.223	0.052	4.529	***	H: 3
AU	←	ITU	0.381	0.035	10.073	***	H: 4
AU	←	PU	0.212	0.044	5.011	***	H: 5
AU	←	PEOU	0.244	0.041	5.830	***	H: 6

Note: PU = perceived usefulness; PEOU = perceived ease of use; ITU = Intention to use; AU = actual use; S.E = standard error; C.R = composite Reliability; H.R = hypothesis reference.

Figure 4 shows the regression bar chart. Indicating the positive correlations of endogenous variables, i.e., PU, PEOU and ITU on the exogenous variable of AU. Each, with a significant positive impact.

Fig.4

The regression bar chart of the study.

9.1 Hypotheses testing

We have the standardized regression weights in Table 4. Indicating the variables’ relations with β estimates, standard errors, composite reliability scores, p-values and hypothesis references. The positive effects of PU on PEOU (β= 0.560, S.E = 0.035, C.R = 14.856, p = <0.001) significantly support H: 1. The positive effects of PU on ITU (β= 0.273, S.E = 0.056, C.R = 5.542, p = <0.001) significantly support H: 2. The positive effects of PEOU on ITU (β= 0.223, S.E = 0.052, C.R = 4.529, p = <0.001) significantly support H: 3. The positive effects of ITU on AU (β= 0.381, S.E = 0.035, C.R = 10.073, p = <0.001) significantly support H: 4. The positive effects of PU on AU (β= 0.212, S.E = 0.044, C.R = 5.011, p = <0.001) significantly support H: 5. And, the positive effects of PEOU on AU (β= 0.244, S.E = 0.041, C.R = 5.830, p = <0.001) significantly support H: 6. The variance (r²) explained by our model for PU = 0.31, PEOU = 0.27, ITU = 0.19 and AU = 0.44 support the global fit of the study model.

Table 4
Standardized indirect effects of mediating variables with hypothesis reference

Variable Indirect Effects SE-S.E SE-Bias 95% biased corrected confidence Interval Hypothesis

Lower bound Upper bound Reference

PU effects between PEOU and ITU 0.160 0.001 0.001 0.097 0.225 H: 9

PU effects between PEOU and AU 0.115 0.000 0.001 0.060 0.180 H: 10

ITU effects between PU and AU 0.109 0.000 0.001 0.066 0.162 H: 7

ITU effects between PEOU and AU 0.083 0.000 0.000 0.049 0.129 H: 8

PU and ITU effect between PEOU and AU 0.057 0.000 0.000 0.034 0.087 H: 11

Variable	Indirect Effects	SE-S.E	SE-Bias	95% biased corrected confidence Interval	Hypothesis
PU effects between PEOU and ITU	0.160	0.001	0.001	0.097	0.225	H: 9
PU effects between PEOU and AU	0.115	0.000	0.001	0.060	0.180	H: 10
ITU effects between PU and AU	0.109	0.000	0.001	0.066	0.162	H: 7
ITU effects between PEOU and AU	0.083	0.000	0.000	0.049	0.129	H: 8
PU and ITU effect between PEOU and AU	0.057	0.000	0.000	0.034	0.087	H: 11

Note. PU = perceived usefulness; PEOU = perceived ease of use; IT = intention to use; AU = actual use; S.E = standard error; User-defined estimand with bootstrap samples = 2000. **p < 0.01.

Table 4 shows the standardized indirect effects of mediating variables. PU mediates positive and significant effects (β= 0.160, SE = 0.001, Bias = 0.001, lower bound = 0.097, upper bound = 0.225, p = <0.001) between PEOU and ITU. These results significantly support H: 9. PU mediates positive and significant effects (β= 0.115, SE-S.E = 0.001, SE-Bias = 0.001, lower bound = 0.060, upper bound = 0.180, p = <0.001) between PEOU and AU. These results significantly support H: 10. ITU mediates positive and significant effects (β= 0.109, SE-S.E = 0.000, SE-Bias = 0.001, lower bound = 0.066, upper bound = 0.162, p = <0.001) between PU and AU. These results significantly support H: 7. ITU mediates positive and significant effects (β= 0.083, SE-S.E = 0.000, SE-Bias = 0.000, lower bound = 0.049, upper bound = 0.129, p = <0.001) between PEOU and AU. These results significantly support H: 8. PU and ITU mediate positive and significant effects (β= 0.057, SE-S.E = 0.000, SE-Bias = 0.000, lower bound = 0.034, upper bound = 0.087, p = <0.001) between PEOU and AU. These results significantly support H: 11.

10 Discussion

To program, induce and examine mHealth technology in an emerging country like Pakistan. The corollaries of this study have theoretical and practical prospects for concerned stakeholders. As, it is suggested in a study, i.e., healthcare personnel, users, program inventors, and facilitators’ immersion [38]. Accordingly, the study findings are conferred in two parts.

10.1 For users and program inventors

The results of H: 1 to H: 11 of this study are consistent with prior studies. Suggesting that young Pakistani smokers are open to anti-tobacco programs [24]. And, an adaptation of novel methods is crucial for smoking cessation [2]. The findings from H: 1 to H: 6 of smokers’ behavior towards smoking-cessation acceptance via mHealth support previous studies. Signifying the potential of using TAM in the field of technology acceptance [46]. And, to predict users’ behavior towards its acceptance [45 –49]. In particular, mHealth in tobacco control programs [34 , 46]. We programmed “Smokers’ Mirror” a mobile application. And used TAM to discover students’ willingness to accept using it for smoking cessation. Study findings in this context are not surprisingly different from Hung and Jen. Both used TAM to discover pupils’ intention of mHealth acceptance for personal health management [48]. The results from H: 4 to H: 8, and H: 11 of this study validate the previous study. Indicating significant direct and indirect effects of using TAM. In evaluating the effectiveness of smoking-cessation acceptance via mHealth [58]. The result of H: 1 supports study suggestions. That, PU is positively influenced by PEOU. And users of a technology (Smokers’ Mirror a mobile application in our case) are likely to perceive it as useful. Since they find it is easy to use [79]. The result of H: 2 in this study indicates that users consider using mHealth (Smokers’ Mirror a mobile application in our case). Because they believe it is useful. While the counterpart finding of H: 3 next to H: 1 and H: 2 confirms the previous study suggestions. That, users consider mHealth (Smokers’ Mirror a mobile application in our case) as easy to use [46]. In addition, the results of H: 2 to H: 6 validate previous studies. Arguing, PU and PEOU have a direct impact on users’ behavior towards ITU of mHealth [78]. Or, the intention of continual/AU of mHealth [75]. Moreover, the result of H: 6 in this study endorses the previous study. Indicating, users of mHealth (Smokers’ Mirror a mobile application in our case) decide to adopt using it. Since they find it easy to use [79]. Beside the elaboration of direct effects, it is important to acknowledge here. We did not find any erstwhile study, theorizing mediation hypotheses of TAM constructs. Hence, this study is a pioneer in its benevolent to unfold these effects. As evidenced by the H: 7 to H: 11 findings to comprehend the intrinsic potency of the study model. These findings are crucial to concoct effective mHealth ideals.

10.2 For healthcare personnel and facilitators

We achieve favorable end results of “Smokers’ Mirror” a mobile application. As an innovative and feasible program for smoking cessation in Pakistan. These results are consistent with previous studies suggestions. That, only ground-breaking programs can succor on tobacco control [2]. Like, mHealth [29, 58]. Monetarily deprived Pakistan [80]. With pitiable health infrastructure [81]. Despite, facing human losses every year [19]. The country uses the least funds for tobacco control [17]. Thus, Pakistan will benefit from the findings of this study. These findings are potentially consistent with previous studies suggestions. That, the espousal of mHealth in healthcare management is a viable elucidation. That provides cost-effective health services to the populace. And, it will aid to empower the health organizations to assimilate internal and external setup and applications. Through which, the clinical and non-clinical patient data will be shared with the government. That they will be used in lieu of steering effective health management [82]. Moreover, mHealth provides health-related services. And its’ usage upsurges users’ life expectancy. It also adroit to aid national wealth by improvising effective chronic disease management programs [83].

11 Conclusion

The findings of this study reveal that mHealth technology has decent potential for smoking cessation in Pakistan. Reproduced from the theoretical model of the study using TAM. Pakistani smokers accept using it. Because they believe mHealth is useful and easy to use. These findings are crucial for the concerned stakeholders to comprehend mHealth backing in the healthcare sector. Which is not only a cost-effective solution to the problem. But, it contributes to improving users’ lifestyle. Likewise, accenting the gains of mHealth acceptance. Its’ adoption will empower the health organizations to assimilate internal and external setup and applications. These organization then will be able to access patients’ clinical and non-clinical data. And can share this data with the government. So, the concerned stakeholders can efficiently optimize the health management programs. Moreover, the use of mHealth has the potential to save the forgone cost of chronic disease management. Besides, it will aid the national wealth. This is indeed important for Pakistan. Considering, the country’s poor economic, infrastructural conditions, scarcity of health-related funds, and underprivileged health facilities to the public. Resulting in life losses every year due to tobacco consumption. Lastly, the mediation hypothesis in the study model divulges. That, TAM constructs are highly unified with significant positive effects. Hence, it is essential for programmers to weigh the potential of TAM constructs. Especially, when they design mHealth programs from the users’ perspective.

12 Future implications of the study

From the narrative of this study, it is highly expected that the adoption of mHealth will assist Pakistan. Especially, to scheme easily accessible and cost-effective facilities for remote healthcare management. In addition, the limited number of Pakistani doctors will be benefited from the espousal of mHealth in the country. By, giving an active role to patients in their healthcare management [92]. Backed by the scientific evidence of this study. And, analyzing the ongoing usage of “Smokers’ Mirror” a mobile application by Pakistani doctors. It is highly likely that the subsequent study will also reveal akin findings respectively from the doctor’s perspective.

13 Limitations and recommendations

Like every research, this study also has certain limitations. Such as time and budget constraints. Besides, “Smokers’ Mirror” only supports the Android-operated smartphones. This confines the generalizability of this study. To avoid this constraint and to engage a maximum number of smartphones users. Support provision of programmed mobile application for the smartphones running Windows and iOS is considered.

The second limitation refers to the unaccompanied selection of TAM model. To comprehend individuals’ behavior in accepting mHealth. Even, the obtained results are significantly positive to authenticate the levelheadedness of using TAM in the study context. Other models like TAM2, TPB, Combined TAM and TPB, TRA and Decomposed TPB can also be used in future researches. But, it is only valid, once the espousal of mHealth is already cherished.

Thirdly, the results of this study consist of students’ feedback. For future researches, the data can be collected from people working in the public and private sectors. To investigate the leeway of mHealth acceptance from workforce’ perspective.

Lastly, using a questionnaire as the only instrument for data collection. This may have introduced some form of bias in respondents’ retort. Although, researchers have made satisfactory efforts to curtail any bias. By, justifying the anonymity and confidentiality of research partakers. And, by explaining that the data would only be used for academic purposes. Besides, getting the volunteers’ consent. Their mobile application usage was accessed throughout the intervention period. To minimize the probability of any bias occurrence regarding their actual usage of the mobile application and their feedback. However, future researches must focus on multiple sources for data collection to minimize any form of the bias.

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Smoking-cessation acceptance via mobile health

Abstract

Keywords

1 Introduction

3 The programming and use of “Smokers’ Mirror” a mobile application

5 Theoretical framework

5.1 Technology acceptance model

6 Purpose and prospects of the study

7 Proposition of hypotheses

7.1 Mediation hypotheses

8 Methodology

8.1 Experimental procedure

8.2 Statistical software

8.3 Tests parameters

9 Results and analysis

Table 1 Reliability & validity measures and the correlations among the variables CR AVE MSV MaxR(H) ITU PU PEOU AU ITU 0.963 0.898 0.328 0.965 0.947 PU 0.929 0.768 0.335 0.977 0.422 0.876 PEOU 0.955 0.840 0.335 0.985 0.387 0.579 0.917 AU 0.966 0.905 0.328 0.990 0.573 0.529 0.509 0.951

10.1 For users and program inventors

10.2 For healthcare personnel and facilitators

11 Conclusion

12 Future implications of the study

13 Limitations and recommendations

References

Table 1
Reliability & validity measures and the correlations among the variables

CR AVE MSV MaxR(H) ITU PU PEOU AU

ITU 0.963 0.898 0.328 0.965 0.947

PU 0.929 0.768 0.335 0.977 0.422 0.876

PEOU 0.955 0.840 0.335 0.985 0.387 0.579 0.917

AU 0.966 0.905 0.328 0.990 0.573 0.529 0.509 0.951