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

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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.
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Antounetal. BMC Research Notes (2022) 15:38
Acceptability ofsmokers ofaconceptual
cigarette tracker aswearable forsmoking
Jumana Antoun1 , Rana Shehab1, Georges Sakr2, Sani Hlais3, Mariette Awad4 and Maya Romani1*
Objective: The study aims to explore smokers’ acceptance of using a conceptual cigarette tracker like a cigarette fil-
ter 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 moti-
vation 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.
Keywords: Smoking cessation, Smoking reduction, Wearable, Behavior change, TAM model
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Smoking is a public health concern worldwide. Smoking
cessation is challenging for the smoker on many levels,
including the physical and psychological aspects. Inter-
ventions that include both behavioral and pharmacologic
therapies show success rates. Despite all the behavioral
and pharmacological interventions, quit rates are maxi-
mum at 40%. erefore, 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 crav-
ings are the most frequently utilized elements in ciga-
rette smoking cessation apps [1, 35]. Even though these
mechanisms are efficient, relying on user self-reporting
cigarette intake appears burdensome.
Alternative ways to promote automated self-mon-
itoring 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 fol-
low 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 system-
atic review summarized the various attempts to use wear-
able sensors to detect a smoking episode, such as the use
of a lighter, wrist sensors based on hand to mouth prox-
imity, 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.
e cigarette tracker would resemble a cigarette filter, a
small plastic piece that holds the cigarette to reduce the
Open Access
BMC Research Notes
1 Department of Family Medicine, Faculty of Medicine, American
University of Beirut, Beirut, Lebanon
Full list of author information is available at the end of the article
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Antounetal. BMC Research Notes (2022) 15:38
amount of tar smokers inhale. e tracker will be linked
to a smartphone application that follows behavioral
change theories. e study explores smokers’ acceptance
of using a conceptual cigarette tracker like a cigarette fil-
ter for smoking cessation using the Technology Accept-
ance Model (TAM).
Main text
is study is a cross-sectional anonymous survey-based
among smokers presenting to the family medicine clin-
ics 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 (ciga-
rette, Hubble bubble, or electronic cigarettes). Inclusion
criteria included adults aged 18 and above who are cur-
rent smokers. Illiterate patients were excluded as they
needed to read and fill the questionnaire independently.
e 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 ques-
tionnaire. e patients were asked to fill it privately and
drop it in a closed box.
e Institution Review Board granted ethical approval
at the American University of Beirut.
e questionnaire (Additional file 1) included three
sections: (1) a visual of the potential cigarette tracker
prototype and a description of its use, (2) demograph-
ics 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]. Accord-
ing to TAM, perceived ease of use and perceived use-
fulness will determine the attitude and intention to use,
whether the consumer will use the technology [12]. Per-
ceived usefulness is described as “the prospective user’s
subjective probability that using a specific application
system will increase his or her performance” [12]. Per-
ceived ease of use is defined as “the degree to which a
person believes that using a particular system would be
free of effort” [12]. e visibility factor was added as it is
a major human factor that may affect the acceptability of
wearables computers [13].
e research team developed the questions and refined
using frequent iterative meetings among the research
team members (JA, MR, and RS). e research team is an
expert in the domain. MR is a smoking cessation special-
ist. 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 sen-
tence 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 consid-
ered 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 fre-
quencies for categorical variables and means for continu-
ous variables. Linear regression analysis and structural
equational modeling (SEM) were used to test the reliabil-
ity 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. e model has 18
variables, and considering a ratio of 3:1, we need a sam-
ple size of 54. SPSS version 23.0 was used for descriptive
statistics and exploratory factor analysis, and AMOS ver-
sion 21.0 was used for SEM. Statistical significance was
set at p < 0.05.
A total of 45 smokers were included. Table1 shows the
demographics. e mean age was 36.1, with a standard
deviation of 13.7years. e majority (81.4%) achieved a
college or post-graduate degree. \Two-thirds of the par-
ticipants (65.7%) had an income above 1000$ (65.7%).
Table 1 Demographics of the participants
Total N N Percent
Gender 36
Female 15 33.3
Male 21 46.7
Education 43
High school and less 5 11.1
Technical 3 6.7
College 22 48.9
Postgraduate 13 28.9
Monthly Income 38
< 500$ 8 17.8
500–999$ 5 11.1
1000–2000$ 14 31.1
> 2000$ 11 24.4
Mean SD
Age 44 36.1 13.7
Number of daily cigarette
consumption 41 18.1 17.2
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Antounetal. BMC Research Notes (2022) 15:38
e minimum wage is 450 dollars in Lebanon at the
time of conduction of the study [14]. e mean cigarette
intake was 18.1 cigarettes per day (SD 17.2).
Table2 shows the participants’ responses to the vari-
ous 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$. Inter-
estingly, 65% said they would like to try such a tracker.
Two-thirds of the participants perceived the usefulness of
the tracker to reduce the consumed daily cigarettes and
increase the motivation to join a smoking cessation pro-
gram. Only half of the participants agreed with the per-
ceived ease of use of the tracker. Very few (11.9–13.6%)
had negative attitudes towards using the technology.
More people (65.1%) were likely to try the tracker than
definitely buy the tracker (50.0%). A range of 40–50%
of the participants had a neutral attitude towards the
tracker’s visibility and its effect on social acceptance and
Only 6 participants (13.3%) owned a wearable: Apple
watch (1), Fitbit (4) or Polar (1). e participants were split
equally when asked whether a cost of 100$ 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). e 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. e measurements of goodness of fit were as fol-
lows: X2(df = 32) = 43,299, p = 0.088; RMSEA = 0.102,
CFI = 0.940. Only CFI, which is not very sensitive to sam-
ple size, showed goodness of fit.
e hypothesized model is given by:
PU =∼ PU1 +PU2 +PU3 +PU4 +PU5
A=∼ A1+A2
V=∼ V1+V3+V4
Table 2 Participants responses to the acceptance of the cigarette tracker based on the TAM model
Agree (%) Neutral (%) Disagree (%)
Perceived usefulness
A cigarette tracker could help me reduce the number of cigarettes consumed per day 60 20 20
A cigarette tracker could help me stop smoking 40 28.9 31.1
A cigarette tracker could increase my motivation to join a smoking cessation program 64.4 22.2 13.3
A cigarette tracker can help me track my smoking habits 55.6 11.1 33.3
A cigarette tracker can help me improve my health 57.8 15.6 26.7
Perceive ease of use
Using the tracker is simple 51.2 32.6 16.3
Using the tracker is self-explanatory 51.2 23.3 25.6
It is easy to carry the tracker 54.5 20.5 25.0
It is comfortable to use the cigarette tracker 48.8 34.1 17.1
Attitudes towards technology
I like the idea of using a cigarette tracker 47.6 40.5 11.9
Overall, I have positive attitude towards the use of a cigarette tracker 52.3 34.1 13.6
Intention to adopt
I would most probably buy a cigarette tracker 45.2 35.7 19.0
I would definitely buy a cigarette tracker 50.0 28.6 21.4
I would like to try a cigarette tracker 65.1 11.6 23.3
Visibility of the tracker
Cigarette trackers may be socially unacceptable 23.3 39.5 37.2
Cigarette trackers are visible to others 45.2 28.6 26.2
The appearance is aesthetically appealing to me 22.7 52.3 25.0
The use of the tracker will improve my self-image 31.1 44.4 24.4
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Antounetal. BMC Research Notes (2022) 15:38
Figure1 shows the SEM model with the various regres-
sion coefficients. Only perceived usefulness correlated
with intention to adopt with statistical significance. Vis-
ibility was correlated with intention to adopt with a
p-value of 0.061. e SEM model proved the TAM model
relationships except for the non-significant relationship
between the attitude and intention of use.
Smoking cessation is challenging. is cross-sectional
survey-based study aimed to measure smokers’ accept-
ance 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.
ey 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.
e TAM model has been used frequently in the adop-
tion 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’ inten-
tion 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 predic-
tor of attitude and intention [17]. A recent meta-analysis
among users’ acceptance of consumer-oriented health
Fig. 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
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Antounetal. BMC Research Notes (2022) 15:38
information technologies based on TAM has shown that
perceived usefulness has a stronger relationship with atti-
tude 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. is component
was correlated with intention to use yet with a p-value
of 0.06. e social aspect of new technologies has been
tested among smartwatches, smart glasses, smart cloth-
ing, and health and fitness wearable devices [21]. For
example, both perceived usefulness (β = 0.113) and vis-
ibility (β = 0.248) showed a positive effect on the inten-
tion to use smartwatches; nevertheless, only the impact
of visibility reached statistical significance [22]. e 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 avail-
ability of fitness applications [23]. Furthermore, the vis-
ibility and social aspect of technology may be related to
society’s familiarity with the technology. e cigarette
filter is not a new technology used among smokers [24].
is 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
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 scal-
able and can address many smokers who are still reluc-
tant 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.
e 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.
is study asked the participants about their adoption
of a conceptual wearable. ey were given a descrip-
tion of the wearable and a picture of how it will look.
is could have affected their responses and explained
why the study could not establish statistical significance
among the various factors of the TAM model. Further-
more, it was conducted at a single institution in Beirut
and may not generalize to the general population and
different cultures.
TAM: Technology Acceptance Model.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s13104- 022- 05935-2.
Additional le1. Appendix 1: Questionnaire.
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 methodol-
ogy, 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.
Availability of data and materials
The datasets used and/or analysed during the current study available from the
corresponding author on reasonable request.
Ethical approval and consent to participate
Ethical approval was taken from the Institutional Review Board of Ameri-
can University of Beirut. Written informed consent was obtained from all
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Department of Family Medicine, Faculty of Medicine, American University
of Beirut, Beirut, Lebanon. 2 Faculty of Engineering, Saint-Joseph University
of Beirut, Beirut, Lebanon. 3 Faculty of Medicine, Saint-Joseph University of Bei-
rut, Beirut, Lebanon. 4 Maroun Semaan Faculty of Engineering and Architec-
ture, American University of Beirut, Beirut, Lebanon.
Received: 3 December 2021 Accepted: 28 January 2022
Page 6 of 6
Antounetal. BMC Research Notes (2022) 15:38
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Full-text available
Tobacco use contributes to the life and economic losses in developing countries, including China and Pakistan. Effective tobacco-control programs can aid in saving millions of people from such hazards in these countries. This study highlights the usefulness of mobile health (mHealth) and quick response code (QRC) technologies to aid China and Pakistan smokers in cessation and concerned stakeholders of both these countries in controlling the tobacco epidemic. In this pilot study, 1400 English-speaking students were recruited as a convenient sample of participants from China and Pakistan. These participants previously attempted at quitting cigarettes but failed. The technology acceptance model (TAM) was used to examine participants’ acceptance of cessation via mHealth and QRC technologies. TAM constructs included the perceived ease of use (β = .481China, β = .392Pakistan, P < .05) perceived usefulness (β = .412China, β = .361Pakistan, P < 0.05) intention to use (β = .442China, β = .433Pakistan, P < 0.05), actual use (β = .421China, β = .370Pakistan, P < 0.05) and dependent variable of quit smoking (β = .477China, β = .352Pakistan, P < 0.05). The obtained results indicate both China and Pakistan smokers believe mHealth and QRC technologies are easy to use and useful in improving their health beliefs towards smoking cessation. It is suggested that printing anti-tobacco QR codes on cigarette packets in China and adoption of smoking-cessation mHealth applications in Pakistan have the potential to aid the concerned stakeholders of both countries as technology compliance with the WHO MPOWER stratagems and FCTC guidelines on tobacco control.
Full-text available
Globally, cigarette smoking is widespread among all ages, and smokers struggle to quit. The design of effective cessation interventions requires an accurate and objective assessment of smoking frequency and smoke exposure metrics. Recently, wearable devices have emerged as a means of assessing cigarette use. However, wearable technologies have inherent limitations, and their sensor responses are often influenced by wearers’ behavior, motion and environmental factors. This paper presents a systematic review of current and forthcoming wearable technologies, with a focus on sensing elements, body placement, detection accuracy, underlying algorithms and applications. Full-texts of 86 scientific articles were reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines to address three research questions oriented to cigarette smoking, in order to: (1) Investigate the behavioral and physiological manifestations of cigarette smoking targeted by wearable sensors for smoking detection; (2) explore sensor modalities employed for detecting these manifestations; (3) evaluate underlying signal processing and pattern recognition methodologies and key performance metrics. The review identified five specific smoking manifestations targeted by sensors. The results suggested that no system reached 100% accuracy in the detection or evaluation of smoking-related features. Also, the testing of these sensors was mostly limited to laboratory settings. For a realistic evaluation of accuracy metrics, wearable devices require thorough testing under free-living conditions.
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Background The range of benefits associated with regular physical activity participation is irrefutable. Despite the well-known benefits, physical inactivity remains one of the major contributing factors to ill-health throughout industrialized countries. Traditional lifestyle interventions such as group education or telephone counseling are effective at increasing physical activity participation; however, physical activity levels tend to decline over time. Consumer-based wearable activity trackers that allow users to objectively monitor activity levels are now widely available and may offer an alternative method for assisting individuals to remain physically active. Objective This review aimed to determine the effects of interventions utilizing consumer-based wearable activity trackers on physical activity participation and sedentary behavior when compared with interventions that do not utilize activity tracker feedback. Methods A systematic review was performed searching the following databases for studies that included the use of a consumer-based wearable activity tracker to improve physical activity participation: Cochrane Controlled Register of Trials, MEDLINE, PubMed, Scopus, Web of Science, Cumulative Index of Nursing and Allied Health Literature, SPORTDiscus, and Health Technology Assessments. Controlled trials of adults comparing the use of a consumer-based wearable activity tracker with other nonactivity tracker–based interventions were included. The main outcome measures were physical activity participation and sedentary behavior. All studies were assessed for risk of bias, and the Grades of Recommendation, Assessment, Development, and Evaluation system was used to rank the quality of evidence. The guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement were followed. A random-effects meta-analysis was completed on the included outcome measures to estimate the treatment effect of interventions that included an activity tracker compared with a control group. Results There was a significant increase in daily step count (standardized mean difference [SMD] 0.24; 95% CI 0.16 to 0.33; P<.001), moderate and vigorous physical activity (SMD 0.27; 95% CI 0.15 to 0.39; P<.001), and energy expenditure (SMD 0.28; 95% CI 0.03 to 0.54; P=.03) and a nonsignificant decrease in sedentary behavior (SMD −0.20; 95% CI −0.43 to 0.03; P=.08) following the intervention versus control comparator across all studies in the meta-analyses. In general, included studies were at low risk of bias, except for performance bias. Heterogeneity varied across the included meta-analyses ranging from low (I²=3%) for daily step count through to high (I²=67%) for sedentary behavior. Conclusions Utilizing a consumer-based wearable activity tracker as either the primary component of an intervention or as part of a broader physical activity intervention has the potential to increase physical activity participation. As the effects of physical activity interventions are often short term, the inclusion of a consumer-based wearable activity tracker may provide an effective tool to assist health professionals to provide ongoing monitoring and support.
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Background: Worldwide physical activity levels of adults are declining, which is associated with increased chronic disease risk. Wearables and smartphone applications offer new opportunities to change physical activity behaviour. This systematic review summarizes the evidence regarding the effect of wearables and smartphone applications on promoting physical activity. Methods: PubMed, EMBASE and Cochrane databases were searched for RCTs, published since January 2008, on wearables and smartphone applications to promote physical activity. Studies were excluded when the study population consisted of children or adolescents, the intervention did not promote physical activity or comprised a minor part of the intervention, or the intervention was Internet-based and not accessible by smartphone. Risk of bias was assessed by the Cochrane collaboration tool. The primary outcome was changed in physical activity level. Meta-analyses were performed to assess the pooled effect on (moderate-to-vigorous) physical activity in minutes per day and daily step count. Results: Eighteen RCTs were included. Use of wearables and smartphone applications led to a small to moderate increase in physical activity in minutes per day (SMD = 0.43, 95% CI = 0.03 to 0.82; I2 = 85%) and a moderate increase in daily step count (SMD = 0.51, 95% CI = 0.12 to 0.91; I2 = 90%). When removing studies with an unclear or high risk of bias, intervention effects improved and statistical heterogeneity was removed. Conclusions: This meta-analysis showed a small to moderate effect of physical activity interventions comprising wearables and smartphone applications on physical activity. Hence, wearables and smartphone applications are likely to bring new opportunities in delivering tailored interventions to increase levels of physical activity.
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This article examined factors associated with the adoption of smart wearable devices. More specifically, this research explored the contributing and inhibiting factors that influence the adoption of wearable devices through in-depth interviews. The laddering approach was used in the interviews to identify not only the factors but also their relationships to underlying values. The wearable devices examined were a Smart Glass (Google Glass) and a Smart Watch (Sony Smart Watch 3). Two user groups, college students and working professionals, participated in the study. After the participants had the opportunity to try out each of the two devices, the factors that were most important in deciding whether to adopt or not to adopt the device were laddered. For the smart glasses, the most frequently mentioned factor was look-and-feel. For the smart watch, the availability of fitness apps was a key factor influencing adoption. In addition, factors which were linked to image, a personal value, were particularly important across both the student and working groups. This research provides support for the usefulness of the laddering approach to data collection and analysis, and provides some insight into key design criteria to better fit users’ needs and interests.
The purpose of this systematic review was to identify evidence concerning the effectiveness of mobile applications and wearable devices for weight loss in overweight adults. A database search of PubMed and CINAHL yielded 12 eligible articles following the application of inclusion and exclusion criteria. Inclusion criteria consisted of studies primarily pertaining to obesity, inclusion of adult population only (18 years and older), use of experimental study designs only, use of mobile apps or wearable devices as intervention(s), and primary outcome of weight loss. Overall, the research evidence suggests that mobile apps and wearables are effective self-regulating tools for weight loss. Although study design concerns, such as lack of non-intervention comparator groups, prevent a definitive conclusion regarding the relative power of mobile apps and wearables over other self-monitoring methods, evidence indicates that mobile technology can be used as integral tools within overarching weight loss strategies recommended in the primary care setting.
Background: Mobile phone-based smoking cessation support (mCessation) offers the opportunity to provide behavioural support to those who cannot or do not want face-to-face support. In addition, mCessation can be automated and therefore provided affordably even in resource-poor settings. This is an update of a Cochrane Review first published in 2006, and previously updated in 2009 and 2012. Objectives: To determine whether mobile phone-based smoking cessation interventions increase smoking cessation rates in people who smoke. Search methods: For this update, we searched the Cochrane Tobacco Addiction Group's Specialised Register, along with and the ICTRP. The date of the most recent searches was 29 October 2018. Selection criteria: Participants were smokers of any age. Eligible interventions were those testing any type of predominantly mobile phone-based programme (such as text messages (or smartphone app) for smoking cessation. We included randomised controlled trials with smoking cessation outcomes reported at at least six-month follow-up. Data collection and analysis: We used standard methodological procedures described in the Cochrane Handbook for Systematic Reviews of Interventions. We performed both study eligibility checks and data extraction in duplicate. We performed meta-analyses of the most stringent measures of abstinence at six months' follow-up or longer, using a Mantel-Haenszel random-effects method, pooling studies with similar interventions and similar comparators to calculate risk ratios (RR) and their corresponding 95% confidence intervals (CI). We conducted analyses including all randomised (with dropouts counted as still smoking) and complete cases only. Main results: This review includes 26 studies (33,849 participants). Overall, we judged 13 studies to be at low risk of bias, three at high risk, and the remainder at unclear risk. Settings and recruitment procedures varied across studies, but most studies were conducted in high-income countries. There was moderate-certainty evidence, limited by inconsistency, that automated text messaging interventions were more effective than minimal smoking cessation support (RR 1.54, 95% CI 1.19 to 2.00; I2 = 71%; 13 studies, 14,133 participants). There was also moderate-certainty evidence, limited by imprecision, that text messaging added to other smoking cessation interventions was more effective than the other smoking cessation interventions alone (RR 1.59, 95% CI 1.09 to 2.33; I2 = 0%, 4 studies, 997 participants). Two studies comparing text messaging with other smoking cessation interventions, and three studies comparing high- and low-intensity messaging, did not show significant differences between groups (RR 0.92 95% CI 0.61 to 1.40; I2 = 27%; 2 studies, 2238 participants; and RR 1.00, 95% CI 0.95 to 1.06; I2 = 0%, 3 studies, 12,985 participants, respectively) but confidence intervals were wide in the former comparison. Five studies compared a smoking cessation smartphone app with lower-intensity smoking cessation support (either a lower-intensity app or non-app minimal support). We pooled the evidence and deemed it to be of very low certainty due to inconsistency and serious imprecision. It provided no evidence that smartphone apps improved the likelihood of smoking cessation (RR 1.00, 95% CI 0.66 to 1.52; I2 = 59%; 5 studies, 3079 participants). Other smartphone apps tested differed from the apps included in the analysis, as two used contingency management and one combined text messaging with an app, and so we did not pool them. Using complete case data as opposed to using data from all participants randomised did not substantially alter the findings. Authors' conclusions: There is moderate-certainty evidence that automated text message-based smoking cessation interventions result in greater quit rates than minimal smoking cessation support. There is moderate-certainty evidence of the benefit of text messaging interventions in addition to other smoking cessation support in comparison with that smoking cessation support alone. The evidence comparing smartphone apps with less intensive support was of very low certainty, and more randomised controlled trials are needed to test these interventions.
This study was conducted to synthesize existing studies on user acceptance of consumer-oriented health information technologies (CHITs) through a systematic review and meta-analysis. We searched four electronic databases in August 2018 for studies that empirically examined user acceptance of CHITs based on theoretical frameworks of Technology Acceptance Model (TAM). Meta-analysis was used to estimate effect sizes of pairwise relationships among TAM constructs, while subgroup analysis was performed to investigate potential factors that may moderate TAM relationships. Sixty-seven studies were identified and included for analysis. The results show that TAM was a robust model in examining user acceptance of CHITs. The results also identified a number of significant relationships between several antecedents (self-efficacy, subjective norm, trust, perceived behavioral control and facilitating conditions) and the core TAM constructs. In addition, many of the relationships could be moderated by study characteristics such as country of origin, type of user and type of technology. The findings demonstrated that TAM represents a good ground theory for examining factors that influence consumer acceptance of CHITs. Further efforts can be dedicated to contextualize the use of TAM theories in CHIT domain and to further examine factors that are able to moderate the model relationships.
Wearables devices have emerged as rapidly developing technologies that have the potential to change people’s lifestyles and improve their wellbeing, decisions, and behaviours as well as enhance core business processes. However, the adoption of these devices has been relatively slow when compared to mainstream technologies such as smartphones. Hence, manufacturers and designers show a growing interest to understand the influential factors in adopting these technologies. This will help them improve the features and desirability of these devices in order to wow the consumers and win them over. Researchers in various disciplines have studied consumers’ adoption of wearable technologies, such as smart glasses and smartwatches using different theories and methodologies. The goal of this paper is to review and synthesise the literature of consumers’ adoption of wearable technologies, review the applied technology diffusion and adoption theories, identify the influential factors in the adoption decision, and suggest directions for future research.