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Acceptability of smokers of a conceptual cigarette tracker as wearable for smoking reduction

Abstract

Objective

The study aims to explore smokers' acceptance of using a conceptual cigarette tracker like a cigarette filter for smoking cessation using the Technology Acceptance Model (TAM). Smokers presenting to the family medicine clinics at a tertiary care center were asked to complete an anonymous questionnaire.

Results

A total of 45 participants were included. Two-thirds of the smokers reported that they would like to try such a tracker and perceived its usefulness in reducing the number of daily cigarettes consumed and increasing the motivation to join a smoking cessation program. A range of 40–50% of the participants had a neutral attitude towards the visibility of the tracker and its effect on social acceptance and self-image. The structural equation model with latent variables path analysis showed that only perceived usefulness correlated to the intention to adopt with statistical significance. Visibility was correlated with intention to adopt with a marginal p-value of 0.061. Driven by perceived usefulness, smokers may buy or try a cigarette tracker for smoking reduction or cessation.

Introduction

Smoking is a public health concern worldwide. Smoking cessation is challenging for the smoker on many levels, including the physical and psychological aspects. Interventions that include both behavioral and pharmacologic therapies show success rates. Despite all the behavioral and pharmacological interventions, quit rates are maximum at 40%. Therefore, there is still room to develop new interventions. Mobile applications have shown promising results with a range of 13–24% quit rate [1, 2]. Using checklists and recording the number of puffs that one has consumed and coping mechanisms to resist cravings are the most frequently utilized elements in cigarette smoking cessation apps [1, 3,4,5]. Even though these mechanisms are efficient, relying on user self-reporting cigarette intake appears burdensome.

Alternative ways to promote automated self-monitoring are needed to reduce users’ burden of inputting behavioral data. Wearables are possible tools that could support the automated self-monitoring of smoking. Wearable trackers have been used to help consumers follow a healthy lifestyle, increase their physical activity [6] and decrease in weight [7], especially when combined with a smartphone application [8, 9]. In 2019, a systematic review summarized the various attempts to use wearable sensors to detect a smoking episode, such as the use of a lighter, wrist sensors based on hand to mouth proximity, respiratory signals based on a belt sensor, acoustic signals based on throat sensors, and others [10].

Our research team is working on a new prototype of a cigarette tracker based on heat and pressure sensors. The cigarette tracker would resemble a cigarette filter, a small plastic piece that holds the cigarette to reduce the amount of tar smokers inhale. The tracker will be linked to a smartphone application that follows behavioral change theories. The study explores smokers' acceptance of using a conceptual cigarette tracker like a cigarette filter for smoking cessation using the Technology Acceptance Model (TAM).

Main text

Methods

This study is a cross-sectional anonymous survey-based among smokers presenting to the family medicine clinics at the American University of Beirut in Lebanon. All patients were approached to participate in the research at the triage station if they were current smokers (cigarette, Hubble bubble, or electronic cigarettes). Inclusion criteria included adults aged 18 and above who are current smokers. Illiterate patients were excluded as they needed to read and fill the questionnaire independently. The nurse introduced the research, explained as needed, and asked if they would like to participate. If they agree, they were provided with the informed consent and questionnaire. The patients were asked to fill it privately and drop it in a closed box.

The Institution Review Board granted ethical approval at the American University of Beirut.

Questionnaire

The questionnaire (Additional file 1) included three sections: (1) a visual of the potential cigarette tracker prototype and a description of its use, (2) demographics including gender, age, level of education, monthly income, number of daily cigarette consumption, ever use of wearables and accepted cost of such a tracker, and (3) questions related to the acceptance of the tracker using the TAM model framework.

TAM is a commonly used model to explain users' acceptance of new technology in healthcare [11]. According to TAM, perceived ease of use and perceived usefulness will determine the attitude and intention to use, whether the consumer will use the technology [12]. Perceived usefulness is described as “the prospective user’s subjective probability that using a specific application system will increase his or her performance” [12]. Perceived ease of use is defined as “the degree to which a person believes that using a particular system would be free of effort” [12]. The visibility factor was added as it is a major human factor that may affect the acceptability of wearables computers [13].

The research team developed the questions and refined using frequent iterative meetings among the research team members (JA, MR, and RS). The research team is an expert in the domain. MR is a smoking cessation specialist. RS is a behavioral counseling smoking cessation nurse in the smoking cessation program. JA is a specialist in health informatics. SH finally reviewed the content from a participant's perspective, and few grammatical and sentence structural changes were done. Participants were asked to answer the questions using a Likert scale from1 to 7. Participants strongly disagreed with the statement if they scored between 1 and 3. A score of 4 was considered a neutral position. A score of 5–7 was considered a strong agreement with the statement.

Statistical analysis and sample size calculation

Descriptive data of the demographics and the various acceptance model questions were performed with frequencies for categorical variables and means for continuous variables. Linear regression analysis and structural equational modeling (SEM) were used to test the reliability and validity of the framework using AMOS. For SEM, the suggested minimum for sample size ranges from 3 to 20 times the number of variables. The model has 18 variables, and considering a ratio of 3:1, we need a sample size of 54. SPSS version 23.0 was used for descriptive statistics and exploratory factor analysis, and AMOS version 21.0 was used for SEM. Statistical significance was set at p < 0.05.

Results

A total of 45 smokers were included. Table 1 shows the demographics. The mean age was 36.1, with a standard deviation of 13.7 years. The majority (81.4%) achieved a college or post-graduate degree. \Two-thirds of the participants (65.7%) had an income above 1000$ (65.7%). The minimum wage is 450 dollars in Lebanon at the time of conduction of the study [14]. The mean cigarette intake was 18.1 cigarettes per day (SD 17.2).

Table 1 Demographics of the participants

Table 2 shows the participants’ responses to the various questions related to the acceptance of the cigarette tracker based on the TAM model. Participants would accept the tracker's price to be between 20 and 50$. Interestingly, 65% said they would like to try such a tracker. Two-thirds of the participants perceived the usefulness of the tracker to reduce the consumed daily cigarettes and increase the motivation to join a smoking cessation program. Only half of the participants agreed with the perceived ease of use of the tracker. Very few (11.9–13.6%) had negative attitudes towards using the technology. More people (65.1%) were likely to try the tracker than definitely buy the tracker (50.0%). A range of 40–50% of the participants had a neutral attitude towards the tracker's visibility and its effect on social acceptance and self-image.

Table 2 Participants responses to the acceptance of the cigarette tracker based on the TAM model

Only 6 participants (13.3%) owned a wearable: Apple watch (1), Fitbit (4) or Polar (1). The participants were split equally when asked whether a cost of 100$ for the tracker would hinder them from buying the tracker. Women (62.5%) were more likely to report that 100$ may prevent them from buying the tracker than men (37.5%), X2(1, N = 33) = 6.945, p = 0.013. When asked about the accepted cost of the tracker, participants proposed a range of 20–50$ as an acceptable price of the tracker.

A structural equation model with latent variables path analysis was performed to predict adoption or intention (INT). The latent variables are perceived usefulness (PU), perceived ease of use (PEU), attitude (A), and visibility (V). Intention to adopt was computed as the sum of the three intention questions. Question V2 was put in the model; however, its p-value > 0.05 and was removed from the final model. The measurements of goodness of fit were as follows: X2(df = 32) = 43,299, p = 0.088; RMSEA = 0.102, CFI = 0.940. Only CFI, which is not very sensitive to sample size, showed goodness of fit.

The hypothesized model is given by:

$$ PU = \sim {\text{PU}}1{ } + {\text{ PU}}2{ } + {\text{ PU}}3{ } + {\text{ PU}}4{ } + {\text{ PU}}5 $$
$$ PEU = \sim {\text{PEU}}1{ } + {\text{ PEU}}2{ } + {\text{ PEU}}3{ } + {\text{ PEU}}4 $$
$$ A = \sim A1 + A2 $$
$$ V = \sim V1 + V3 + V4 $$
$$ INT\sim PU + PEU + A + V $$

Figure 1 shows the SEM model with the various regression coefficients. Only perceived usefulness correlated with intention to adopt with statistical significance. Visibility was correlated with intention to adopt with a p-value of 0.061. The SEM model proved the TAM model relationships except for the non-significant relationship between the attitude and intention of use.

Fig. 1
figure 1

SEM model showing the various regression coefficients among the various variables. All regression coefficients are significant at p < .05 except *p-value = 0.7 and **p-value = 0.06

Discussion

Smoking cessation is challenging. This cross-sectional survey-based study aimed to measure smokers' acceptance to the use of a conceptual cigarette tracker in the form of a cigarette filter for smoking cessation/reduction using the TAM model as a framework. Two-thirds of the smokers would try the tracker. Smokers had a positive attitude towards the tracker and its perceived usefulness. They were less positive about its ease of use and neutral about the visibility of the tracker and its effect on social acceptance and self-image. Perceived usefulness was the most important predictor of the use of the tracker.

The TAM model has been used frequently in the adoption of technology in healthcare [11] and for smoking cessation [15]. Similar to our study, perceived usefulness was also an important predictor of the use of a potential SMS-assisted smoking cessation program [15]. In a study in China and Pakistan, both perceived ease of use and perceived usefulness positively impacted users' intention to use mHealth technologies for smoking cessation [16]. Regarding the use of wearables, similar to our study results, a study among 927 people who purchased their smartwatch or smartband in South Korea has also shown that perceived usefulness was the most influential predictor of attitude and intention [17]. A recent meta-analysis among users’ acceptance of consumer-oriented health information technologies based on TAM has shown that perceived usefulness has a stronger relationship with attitude and behavioral intention than perceived ease of use [18]. Characteristics of the technology, the context, the user, perceived benefits and risks, and social factors may influence the adoption of health and fitness wearables [19, 20].

Visibility of the technology and social factors such as social norms and image regulation were lumped into one component in this study analysis. This component was correlated with intention to use yet with a p-value of 0.06. The social aspect of new technologies has been tested among smartwatches, smart glasses, smart clothing, and health and fitness wearable devices [21]. For example, both perceived usefulness (β = 0.113) and visibility (β = 0.248) showed a positive effect on the intention to use smartwatches; nevertheless, only the impact of visibility reached statistical significance [22]. The effect of visibility on technology adoption may be related to the context and type of technology. While the look-and-feel of a smart glass was the most frequently mentioned factor for adoption, other factors beyond visibility were mentioned for smartwatch adoption, such as the availability of fitness applications [23]. Furthermore, the visibility and social aspect of technology may be related to society's familiarity with the technology. The cigarette filter is not a new technology used among smokers [24]. This could have also contributed to the lack of ease of use on the adoption of the cigarette tracker. Only few (less than 25%) participants had concerns that the tracker may not be simple, self-explanatory, easy, or comfortable to carry.

Future implications

From a theoretical point of view, this cigarette tracker would be innovative and add to the list of behavioral interventions intended for smoking cessation. It is scalable and can address many smokers who are still reluctant to set a quit date. Furthermore, this study shed light on marketing e-health programs or wearables where the consumer is more likely to use the technology if they perceive its usefulness. Finally, qualitative studies could better understand the perspective of those who do not intend to use the cigarette trackers.

Conclusion

The use of a conceptual cigarette tracker for smoking cessation may be acceptable by smokers. Smokers are interested in the usefulness and benefits of the tracker. Visibility and social acceptance of the tracker may play a lesser role in their adoption.

Limitations

This study asked the participants about their adoption of a conceptual wearable. They were given a description of the wearable and a picture of how it will look. This could have affected their responses and explained why the study could not establish statistical significance among the various factors of the TAM model. Furthermore, it was conducted at a single institution in Beirut and may not generalize to the general population and different cultures.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

TAM:

Technology Acceptance Model

References

  1. Regmi K, Kassim N, Ahmad N, Tuah NA. Effectiveness of mobile apps for smoking cessation: a review. Tob Prev Cessation. 2017;3:12.

    Google Scholar 

  2. Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y. Mobile phone-based interventions for smoking cessation. Cochrance Database Syst Rev. 2016;4:CD06611.

    Google Scholar 

  3. Turner-McGrievy GM, Hales SB, Schoffman DE, Valafar H, Brazendale K, Weaver RG, et al. Choosing between responsive-design websites versus mobile apps for your mobile behavioral intervention: presenting four case studies. Transl Behav Med. 2017;7(2):224–32.

    Article  Google Scholar 

  4. West R, Raw M, McNeill A, Stead L, Aveyard P, Bitton J, et al. Health-care interventions to promote and assist tobacco cessation: a review of efficacy, effectiveness and affordability for use in national guideline development. Addiction. 2015;110(9):1388–403.

    Article  Google Scholar 

  5. Ubhi HK, Michie S, Kotz D, van Schayck OC, Selladurai A, West R. Characterising smoking cessation smartphone applications in terms of behaviour change techniques, engagement and ease-of-use features. Transl Behav Med. 2016;6(3):410–7.

    Article  Google Scholar 

  6. Brickwood K-J, Watson G, O’Brien J, Williams AD. Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis. JMIR Mhealth Uhealth. 2019;7(4):e11819.

    Article  Google Scholar 

  7. Cheatham SW, Stull KR, Fantigrassi M. Motel. The efficacy of wearable activity tracking technology as part of a weight loss program: a systematic review. J Sports Med Phys Fitness. 2018;58(4):534–48.

    Article  Google Scholar 

  8. Gal R, May AM, van Overmeeren EJ, Simons M, Monninkhof EM. The effect of physical activity interventions comprising wearables and smartphone applications on physical activity: a systematic review and meta-analysis. Sports Med Open. 2018;4(1):1–15.

    Article  Google Scholar 

  9. Wang E, Abrahamson K, Liu PJ, Ahmed A. Can mobile technology improve weight loss in overweight adults? a systematic review. West J Nurs Res. 2020;9:747–59.

    Article  Google Scholar 

  10. Imtiaz MH, Ramos-Garcia RI, Wattal S, Tiffany S, Sazonov E. Wearable sensors for monitoring of cigarette smoking in free-living: a systematic review. Sensors. 2019;19(21):4678.

    Article  Google Scholar 

  11. Gücin NÖ, Berk ÖS. Technology acceptance in health care: an integrative review of predictive factors and intervention programs. Procedia Soc Behav Sci. 2015;195:1698–704.

    Article  Google Scholar 

  12. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319–39.

    Article  Google Scholar 

  13. Cherrylyn BH. Human factors to acceptability of wearable computers. Int J Multimedia Ubiquitous Eng. 2013;8(3):103–13.

    Google Scholar 

  14. Lebanon Minimum Wage, Labor Law, and Employment Data Sheet Lebanon Minimum Wage Rate. 2020: minimum-wage.org; https://www.minimum-wage.org/international/lebanon.

  15. Andrews L, Drennan J, Tossan V, Cacho-Elizondo S, editors. Using TAM to examine consumer acceptance of a mobile phone assisted smoking cessation program in Australia. Conference proceedings-39th EMAC Conference: The Six Senses-The Essentials of Marketing; 2010: Copenhagen Business School.

  16. Ali R, Zhang Z, Soomro MB. Smoking-cessation acceptance via mobile health and quick response code technologies: empirical evidence of a pilot study from China and Pakistan. Curr Psychol. 2021;40(12):6085–97.

    Article  Google Scholar 

  17. Park E, Kim KJ, Kwon SJ. Understanding the emergence of wearable devices as next-generation tools for health communication. Inf Technol People. 2016;29(4):717–32.

    Article  Google Scholar 

  18. Tao D, Wang T, Wang T, Zhang T, Zhang X, Qu X. A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies. Comput Hum Behav. 2019;104:106147.

    Article  Google Scholar 

  19. Canhoto AI, Arp S. Exploring the factors that support adoption and sustained use of health and fitness wearables. J Mark Manag. 2017;33(1–2):32–60.

    Article  Google Scholar 

  20. Kim Y-S, Kim M, editors. Exploring the asthetic factor on wearble fitness technolgoy fitness acceptance model. 2017 Global Fashion Management Conference at Vienna; 2017.

  21. Kalantari M. Consumers’ adoption of wearable technologies: literature review, synthesis, and future research agenda. Int J Technol Mark. 2017;12(3):274–307.

    Article  Google Scholar 

  22. Chuah SH-W, Rauschnabel PA, Krey N, Nguyen B, Ramayah T, Lade S. Wearable technologies: the role of usefulness and visibility in smartwatch adoption. Comput Hum Behav. 2016;65:276–84.

    Article  Google Scholar 

  23. Adapa A, Nah FFH, Hall RH, Siau K, Smith SN. Factors influencing the adoption of smart wearable devices. Int J Hum Comput Interact. 2018;34(5):399–409.

    Article  Google Scholar 

  24. Thrasher JF, Abad-Vivero EN, Moodie C, O’Connor RJ, Hammond D, Cummings KM, et al. Cigarette brands with flavour capsules in the filter: trends in use and brand perceptions among smokers in the USA, Mexico and Australia, 2012–2014. Tobacoo Control. 2016;25(3):275–83.

    Article  Google Scholar 

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Authors and Affiliations

Authors

Contributions

JA contributed to the design and concept of the study, methodology, formal analysis, and drafting of the manuscript. RS was involved in methodology, data acquisition, and review of the final manuscript; GS was involved in the data analysis and review of the final manuscript; SH was involved in the methodology, data analysis, and review of the final manuscript; MA was involved in the methodology and final review of the manuscript; MR was involved in the methodology, results’ interpretation and final review of the methodology. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Maya Romani.

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Ethical approval was taken from the Institutional Review Board of American University of Beirut. Written informed consent was obtained from all participants.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1.

Appendix 1: Questionnaire.

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Antoun, J., Shehab, R., Sakr, G. et al. Acceptability of smokers of a conceptual cigarette tracker as wearable for smoking reduction. BMC Res Notes 15, 38 (2022). https://doi.org/10.1186/s13104-022-05935-2

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Keywords

  • Smoking cessation
  • Smoking reduction
  • Wearable
  • Behavior change
  • TAM model