Factors Influencing Tele-dermatology Adoption among the Lebanese

Youth: A Pilot Study at Saint Joseph University

Nanor Aroutine

, Nabil Georges Badr

and Joumana Yeretzian

Higher Institute of Public Health, Saint Joseph University, Beirut, Lebanon

Keywords: Tele-dermatology, Technology Acceptance Model (TAM), Mobile Medical Application, Skin Cancer,

Consumer Acceptance.

Abstract: The demand and use for Tele-dermatology (TDM) to diagnose skin lesions is rising worldwide. Using the

technology acceptance model, we evaluate the factors influencing the acceptance of Tele-Dermatology to

diagnose skin lesions among the Lebanese students. We complete a pilot study with Lebanese students from

Saint Joseph University of Beirut (USJ). After examining the responses in a descriptive analysis, we develop

some initial hypotheses and proceed to build the statistical model to test them using Smart PLS3. Our findings

show that 64% of the students are ready to use Tele-Dermatology in their everyday life. Most of those students

are females between 18 and 24 years old. Wrapping up our results, information from this study indicates that

marital status is most likely a determinant of intention to use TDM among students – whereby, most single

students are ready now (65%) while most married students are inclined to use it in the future (67%). The study

also suggests that the Lebanese youth prioritize result demonstrability as a factor in their intention to use

TDM. Further, mobile TDM must save them time must be easy to use to be perceived useful.

1 INTRODUCTION

“The two areas that are changing... information

technology and medical technology. Those are the

things that the world will be different from in 20 years

from now than it is today”- Bill Gates

Decentralized, mobile and personalized care has

improved population access to care. Medical mobile

applications are redefining the future of medical

consultation; a trend that started at the turn of the

century caused the line between physical and virtual

care to blur. Before the 2019 COVID-19 pandemic,

physicians and patients started using telehealth to care

for different conditions including cancer, behavioural

health, surgical recovery, substance abuse, home

dialysis and more. These telehealth use cases cover

the full spectrum of care from acute to post-acute and

urgent to managing healthy lifestyles. The demand for

https://orcid.org/0000-0001-6509-1378

https://orcid.org/0000-0001-7110-3718

https://orcid.org/ 0000-0001-5805-4915

https://www.quotemaster.org/author/Bill+Gates

https://gco.iarc.fr/

telehealth, especially for diseases in which the

incidence rate is increasing yearly, such as in the case

with cancer, is on a rise

1.1 Mobile Tele-dermatology

Worldwide, 2020 has seen an estimated 19.3 million

new cancer cases of which more than 8% were skin

related, with loss of life in about 1 in 12 skin cancer

patients (Sung et al, 2021), and an increase of 15% in

skin cancer cases since 2018 (Ferlay, et al. 2018).

Advancements in technology sought to improve

early detection and reduce the mortality rate of the

disease through skin cancer screening for early

detection of suspicious skin lesions which can

potentially lead to skin cancer (Jolliffe et al., 2001).

Mobile Tele-dermatology (TDM) is a mobile medical

application by which patients can get a diagnosis for

their skin lesions, anywhere and anytime (Desai et al.,

2010; Massone et al., 2014). The technology is about

Aroutine, N., Badr, N. and Yeretzian, J.

Factors Inﬂuencing Tele-dermatology Adoption among the Lebanese Youth: A Pilot Study at Saint Joseph University.

DOI: 10.5220/0010746400003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 15-25

ISBN: 978-989-758-552-4; ISSN: 2184-4305

acquiring images of a suspect skin area and presenting

them to a remote attending practitioner for evaluation

(Fig 1.). The technology was initially evaluated in

treating incidences in rural areas (Sáenz et al, 2018),

where dermatology services are commonly managed

by healthcare personnel with minimal specialty

training.

There are two models for processing these images.

One model uses on store and forward mechanisms,

which are less expensive, but require more wait time

for diagnosis and treatment. The alternative uses a

video conference or “synchronous session”, between

the patient and the specialist. The patient gets an

instant treatment plan. The difference lies in the cost

of the two methods, and the diagnosis referral time

(Livingstone & Solomon, 2015; Vidal-Alaball et al.,

2018; Wang et al., 2020a).

Figure 1: Tele-Dermatology (Source: DermEngine).

At the turn of the 21

century, even prior to the

recent COVID-19 pandemic, tele-dermatology

(TDM) had received acceptance among patients,

because of the instantaneous nature of the diagnosis

and management regimen for the condition, and

because it had educational value to the primary care

physician (Eedy and Wootton, 2001). Initially,

diagnostic reliability, image quality and storage

requirements were central notions to the adoption

conversation (Whited, 2006). Web consultations in

dermatology became possible shortly after (Massone

et al, 2008). The continued evolution in technology

improved TDM accuracy and its cost effectiveness

brought the product to the mainstream of practice

(Lee and English, 2018). The main advantages were

prioritization in cancer screening and rapid screening

for trivial conditions (Romero et al, 2018), as

administrative, regulatory, privacy, and

reimbursement policies surrounding this dynamic

field continued to evolve (Lee and English, 2018).

During the Covid-19 pandemic, TDM was seen as

an alternative to in-person visits, as the number of

people using medical applications increased by 50%

compared to 2019 (Koonin, 2020). Many specialists

encouraged patients to perform Telehealth from the

comfort of their homes (McGee et al., 2020), to

minimize unnecessary clinical visits and avoid

endangering their lives through transmission with the

virus (Mostafa & Hegazy, 2020). Therefore, TDM

was considered effective in decreasing the risk of

Covid-19 transmission by minimizing clinical visits

and avoiding overcrowdings of the hospitals both in

private and public facilities (Cartron et al, 2020).

1.2 Motivation

The global TDM market accounted for US$ 4.5

billion in 2019 and is estimated to be US$ 44.8 billion

by 2029 and an anticipated CAGR of 26.0%.

Studies conducted in several countries, such as the

United States (Cartron et al, 2020), United Kingdom

(Nicholson et al, 2020) and Australia (Abbott et al,

2020) have shown how people embraced TDM as a

mobile application to diagnose their skin lesions.

Patients with better health status, those of younger

age and those with less frequent visits to a

dermatologist were more accepting of Tele-

Dermatology (Nicholson et al, 2020); noting that

issues of image quality, privacy concerns, image

storage and retention are indicated as impediments to

wider use (Abbott et al, 2020). In countries where

they have adopted Mobile TDM for skin diagnosis,

users have reported the technology to be a cost-

effective application, which they would prefer using

rather than face-to face clinical visits to reduce cost

and waiting time (Wang et al., 2020b).

In the country of Lebanon, a developing country

where cases of skin cancer have been doubling over

10 years (2006-2016) (Moph, n.d.) there has been

little insight on the adoption of TDM. Telehealth

adoption in Lebanon has been informal and TDM has

not reached mainstream use by practitioners (Shaarani

et al, 2021). We therefore, find it interesting to

investigate this phenomenon and evaluate the factors

that could influence the adoption of mobile medical

application.

In our study, we intend to answer the question:

What are the factors that can influence the intention

to use of TDM in Lebanon?

2 BACKGROUND

Technology acceptance models (TAM) is a

theoretical framework to assess people’s perception

regarding the adoption of new technologies. It is a

HEALTHINF 2022 - 15th International Conference on Health Informatics

tailored representation of factors influencing one’s

intention to use a certain technology, in our case, this

technology is TDM.

Technology Acceptance Model (TAM) has been

used to investigate how perceived usefulness of a

technology and its perceived ease of use, can

influence the users’ intention to use that technology

(Fig. 1) (Davis, 1989; Szajna, 1996; Venkatesh and

Davis, 2000). TAM suggests that an individual’s

technology usage behaviour is determined by the

attitude toward technology usage, predicted by the

extent to which an individual believes that using a

technology will enhance his or her task performance

(perceived usefulness) and the extent to which an

individual believes that using a technology will be

free of significant effort (perceived ease of use).

Figure 2: Simplified Technology Acceptance Model.

Studies using TAM have extended the model to

cover multiple contexts (TAM2, etc.), indicating the

usage and acceptance behaviour towards technology

(Venkatesh et al, 2003). Certain TAM relationships

were found to be significant, whereas others were

inconsistent (Holden & Karsh, 2010). Others have

connected ease of use and usefulness to social impact,

facilitating conditions, attitudes and behaviour of

users in measuring intention to adopt of health

information technologies (Garavand et al, 2016). In

telemedicine, we can find long-standing evidence of

TAM use, for physician acceptance of telemedicine

and the related technologies (Hu, et al, 1999; Chau

and Hu, 2002; Kamal et al, 2020), implying that

acceptance of this technology was a major challenge

in the context of adoption. While the model has been

widely tested, validated and extended in health

informatics applications used by medical

professionals (Ketikidis et al, 2012; Rahimi et al,

2018) they have seldom been applied to the modelling

of consumer (patient) acceptance of health

informatics applications (Rahimi et al, 2018).

https://www.who.int/health-topics/social-determinants-

ofhealth#tab=tab_1

3 RESEARCH MODEL

3.1 Antecedents and Hypotheses

Based on previous evidence with TAM and the

extended model of Venkatesh and Davis (2000), we

developed our model to test the stated hypotheses and

learn about TDM usage and factors of acceptance.

Factors influencing the intention to use are therefore:

Social determinants, medical factors, result

demonstrability, perceived risk, subjective norms,

perceived usefulness and perceived ease of use.

We consider the following variables as latent

variables (Section 4):

SI: Subjective norms (Q: 29 and 30);

SDH: Social determinants (Q: 3 thru 5);

MED: Medical factors (Q: 1, 2, 6 thru 10);

RD: Result demonstrability (Q: 15, 17, 31, 32);

PR: Perceived risk (Q: 18, 20 thru 22, 25, 26, 33 thru

35);

PU: Perceived usefulness (Q: 11 thru 14, 19, 27 and

28);

PEOU: Perceived ease of use (Q: 16); UI: Intention

to Use (Q: 23 and 24).

3.1.1 Social Determinants

We identified social determinants as indicators of

educational level, work and marital status

. The

descriptive analysis found some relevance of

education specialty, and marital status, etc. (Table 2).

The technology acceptance model developed by

Davis et al. (1989) supports education specialty that

may indicate a varied experience with technology.

Consequently, we state our first hypothesis as

H1: Social Determinants such as marital status

and education specialty affect perceived usefulness of

mobile TDM by students

3.1.2 Medical Factors

Medical factors are included to examine the potential

significance of the existence of family cancer history

in the adoption context. Family history of skin cancer,

previous skin cancer removed and presence of moles

larger than 2 mm were considered significant in an

earlier study (Horsham et al., 2016). Therefore, we

included them in the initial model, as they are

contextual to the setting of TDM. We also included

age and gender, as potential indicators of the adoption

dynamics - as it was observed in the descriptive

Factors Inﬂuencing Tele-dermatology Adoption among the Lebanese Youth: A Pilot Study at Saint Joseph University

analysis (Table2) and is also supported by Venkatesh

& Morris (2000). Therefore, we formulate our second

hypothesis as:

H2: Medical factors such as family cancer history,

age and gender affect perceived usefulness of mobile

TDM by students

3.1.3 Result Demonstrability

Supported by Modified TAM (TAM2) model

(Venkatesh and Davis, 2000), the factor of result

demonstrability indicates the effectiveness of the

TDM in the early detection of the onset of the disease,

the user’s trust in technology. In the literature, some

studies reported that users expressed their concern

regarding their privacy if the application were to be

hacked, they also shared their anxiety of waiting for

the final diagnosis, and their doubt about the accuracy

of the image (Abbott et al, 2020). On the other hand,

medical practitioners expressed their concern towards

overloading their system with images, yet 55% of

informants in our study found TDM beneficial for

monitoring and self-examining skin lesions to detect

any suspicious lesions. About 40% of the students in

our study have expressed confidence in the accuracy

of the mobile application compared to face-to-face

diagnosis; and only 34% believed that a suspicious

mole or lesion diagnosis would be understandable

(thus able to provide the value expected). Therefore,

for our model, result demonstrability also relates to

the confidence of the user to receive comprehensible

prognosis – i.e. “the technology has to work, so that it

can be useful” –Survey Question (15, 17, 31 and 32).

Therefore, we formulate our third hypothesis as:

H3: Results Demonstrability (or effectiveness)

indicated by the user’s trust in technology

performance and the perceived ability of mobile TDM

to offer early detection, with accurate information and

an understandable outcome affects perceived

usefulness of mobile TDM by students

3.1.4 Perceived Risk

Perceived risk is an antecedent and a moderator of

user acceptance (Im et al, 2008; Kamal et al, 2020) –

as a moderator, it can either increase the strength of

the correlation or decrease it. In our model, we depict

three indicators for perceived risk:

a) The resistance to use (Im et al, 2008) (Q. 21); and

b) The efficiency of use in the context of selfservice

technology – i.e. saving time (Yang et al., 2019)

and money (Q. 25); and

c) Technology anxiety, learnability and reliability

(Mostafa & Hegazy, 2020) which characterizes

the user’s reluctance to use driven by lack of

familiarity with the technology (Q.33).

Therefore, for our model, we can define perceived

risk as factors of efficiency (saving time and money),

technology anxiety (training, technical assistance and

skill acquisition), presence or absence of facilitating

conditions (here shown as professional assistance

with the required dermatologists support to realize the

benefits from their mobile TDM APP (Gorrepati and

Smith, 2020; Giavina Bianchi et al, 2019). Hence, we

conclude that perceived risk can be conceived as an

antecedent for intention to use, and would have the

potential to moderate the association between

perceived ease of use and intention to use, similarly

between perceived usefulness and intention to use of

mobile TDM by students. We then state our next three

hypotheses as follows:

H4: Perceived risk, indicated by the resistance to

change, efficiency and technology anxiety affects

intention to use of mobile TDM by students

H5: Perceived risk, indicated by resistance to

change, efficiency and technology anxiety moderates

the relationship between perceived ease of use and

intention to use of mobile TDM by students

H6: Perceived risk, indicated by resistance to

change, efficiency and technology anxiety moderates

the relationship between perceived usefulness and

intention to use of mobile TDM by students

3.1.5 Subjective Norms

The descriptive analysis of the survey data indicated

that almost half (47%) of the sampled population

would be influenced by their family and friends to use

TDM. This phenomenon invokes the theory of

planned behaviour (Ajzen, 2011), where subjective

norms (indicated here by social influence) are

believed to shape an individual's behavioural

intentions, here the intention to use TDM. Our

seventh hypothesis therefore posits that:

H7: Subjective norms, indicated by social

influence, affects the intention to use of mobile TDM

by students.

3.1.6 Perceived Ease of Use and Perceived

Usefulness

Lastly, adapted from Venkatesh and Davis (2000) and

based on our background in Section 1, we defined

perceived ease of use as an indicator of user

perception of mobile TDM and perceived usefulness

as the perception of how useful the functionality of

TDM would be for the user. As a result, these two

latent variables directly affect a person’s intention to

HEALTHINF 2022 - 15th International Conference on Health Informatics

use TDM to diagnose skin lesions. In addition,

perceived ease of use directly affects the usefulness of

the application, in other words, if a consumer finds the

application easy to use, then it would be considered

useful. Consequently, the following final three

hypotheses are:

H8: Perceived usefulness, indicated by the

perception that the technology will serve the best

interest of the user, in a rapid, self-examination,

affects intention to use of mobile TDM by students.

H9: Perceived ease of use affects intention to use

of mobile TDM by students

H10: Perceived ease of use affects perceived

usefulness of Mobile TDM by students

We differentiated between two outcomes: (1) “I

will use Mobile TDM when it’s offered to me,” –

which implies readiness now (Q. 23); and (2) “I will

use Mobile TDM in my routine self-skin examination

in the future” – which implies readiness in the future

(Q. 24). The loading of UI1 and UI2 respectively then

assesses these outcomes, to their strengths and

relevance in indicating the “intention to use”.

4 MATERIALS AND METHODS

4.1 Approach and Study Design

We started our study on USJ students, after getting the

approval of the USJ ethics committee in February

2021. This pilot study, completed on May 2021, aims

to inform about assumptions and conditions for a

larger project scope (Hazzi and Maldaon, 2015). The

survey of TDM consumer acceptance was inspired

from an Australian study from Horsham et al. We

reformulated the questionnaire on Google docs. USJ

administration has approached students virtually. All

USJ students from different faculties, received emails

of the self-administered survey, only 89 participated.

After finishing data collection, we extracted the

answers on excel. Demographics were analysed by

descriptive statistics. Since the participant number

was not significantly high, we used SMART PLS 3 to

construct reliability and validity, path coefficient and

then we initiated the structural model to be able to test

for factors that influence the intention to use of the

participants.

4.2 Sample and Participants

We set out to investigate the use of TDM, a form of

telemedicine among the youth in Lebanese

Universities. We conduct this pilot at the University

of Saint Joseph in Beirut Lebanon. Eighty-nine (89)

students took part in this survey. Their demographics

are presented in Table 1.

Table 1: Sample description.

N=89

Gender Male 17%

Female 83%

Age < 20 years 25%

21 - 25 48%

26 - 30 13%

31 - 35 6%

36 - 40 2%

41 - 45 2%

Over 46 3%

Education Business Management 6%

Engineering & Sciences 16%

Humanities 20%

Medical & Health 47%

Political Science & Law 4%

Other 7%

Status Single 90%

Married 10%

The participants (N=89) with an average age of

25, were essentially females (n=74, 82%). Almost

half of the participants were from the Faculty of

Medicine (n=42, 47%). Slightly more than half the

participants work, either full time or part time, (n=52,

58%), while the majority of the participants were

single (n=80, 90%). Only 4 participants had skin

cancer history and only 3 had skin cancer previously

excised.

4.3 Survey Design

We developed a survey questionnaire of 27 questions

with two parts (Appendix) The first part, captured the

demographic and personal information of the

participants and the second explored the TDM context

using a 5-point Likert scale. We collected

demographics information about gender, age, field of

education, marital status as the literature indicated a

possible relation with intention to use (Section 4). We

also captured the prevalence of medical history of

cancer (Section 4.1.2), subjective norms (Section

4.1.5), and factors of risk (Section 4.1.4). We

exported the survey results to excel and normalized

the data in preparation for the descriptive and

statistical stages of the analysis.

Factors Inﬂuencing Tele-dermatology Adoption among the Lebanese Youth: A Pilot Study at Saint Joseph University

4.4 Descriptive Analysis

Indications from the descriptive analysis (Table 2)

show that, both genders are more or less equally ready

to embrace TDM now, but females are more prone to

use it in the future (12% more than males).

Table 2: Descriptive Analysis.

Readiness to Use (N=89) Now Future Intention

Among all participants 64% 57%

Gender

Male 60% 47%

Female 65% 59%

Age

< 20 years 77% 64%

21 - 25 60% 56%

26 - 30 67% 58%

31 - 35 60% 60%

36 - 40 0% 0%

41 - 45 50% 50%

46 - 50 67% 67%

Education

Business Management 60% 60%

Engineering & Sciences 71% 50%

Humanities 67% 67%

Medical & Health 64% 62%

Political Science & Law 50% 25%

Marital Status

Single 65% 56%

Married 56% 67%

Students aged less than 20 years old have reported

the highest intention to use. Engineering & Sciences

students are mostly ready to use TDM now (71%)

while political science students demonstrated the

lowest appetite (25%) Most single students are ready

now (65%), while the majority of married informants

were inclined towards future adoption (67%).

4.5 Statistical Data Analysis

We developed our model (Section 4) using SEM-PLS

via SmartPLS3.0, following the example of Aggelidis

& Chatzoglou (2009), Cepeda-Carrion et al. (2019)

and Kamal et al. (2020). Our sample (n=89) meets the

minimum sample size of 59, using the inverse square

root method (Kock and Hadaya, 2018). For reporting

purposes, we transformed the data captured on a

Likert scale to binary variables, where neutral

answers signified disagreement. That is, we

regrouped the results as either Agree (4-Agree, 5-

Strongly Agree), or Disagree (1-Strongly disagree, 2-

Disagree, 3-Unsure).

5 EVALUATION OF THE MODEL

AND RESULTS

5.1 Technology Acceptance Model

We loaded our model in SmartPLS3.0 and ran the

PLS algorithm. We then reduced the indicator

variables in order to reach convergent validity and

reliability. We accepted only the indicators with

loadings ≥ 0.708 as significant (Hair et al, 2019).

Figure 3 shows our valid model - with outer loading

factors.

Figure 3: Valid Model - with outer loading factors.

The model is of a reflective construct, therefore,

construct validation can be obtained through

Confirmatory Factor Analysis (CFA) (i.e. convergent

and discriminant validity) and reliability testing (i.e.

Cronbach's Alpha) (Hair et al, 2019). We find that the

model has good discriminant validity since the AVE

squared value of each exogenous construct (the value

on the diagonal) exceeds the correlation between this

construct and other constructs - see Fornell-Larcker

Criterion Values in Table 3 (values below the

diagonal).

Table 3: Discriminant Validity.

Subsequently, following Hair et al (2019), we

perform a convergent validity test by looking at the

loading factor value of each indicator against the

construct. We accept the indicators with loadings of

HEALTHINF 2022 - 15th International Conference on Health Informatics

≥ 0.708 as significant (Figure 2) with an AVE value

for each construct > 0.5. Subsequently, we assess the

construct reliability. The reliability test results in

table 4 show that all constructs have composite

reliability and Cronbach's alpha values greater than

0.7 (Hair et al, 2019). In conclusion, all constructs

have met the required validity and reliability.

Table 4: Construct Reliability and Validity.

5.2 Hypothesis Testing

We carried out the effect test using the p value tests

and t-statistics in the partial least squared (PLS)

analysis model of the SmartPLS 3.0 software (Table

5). A pvalue less than 0.05 (typically ≤ 0.05) is

considered statistically significant (Hair et al, 2019).

T-statistics show how many standard errors the

coefficient is away from zero. In our model, the t-

statistics are within the acceptable range. For the

hypotheses where p-values are ≤ 0.05, any t-value

greater than +2 or less than – 2 is acceptable (Hair et

al, 2019). The higher the T-value, the greater the

confidence we have in the coefficient as a predictor,

especially pointing to a significantly high confidence

(t=11.124) in the hypotheses of results

demonstrability – perceived usefulness (H3).

Table 5: Path Coefficient.

6 FINDINGS

6.1 Supported Hypotheses

Four Hypotheses are supported (H1, H3, H4 and

H10):

H1: Social determinants such as marital status

were found as antecedent to perceived usefulness

(p=0.028; t=2.209) with a predictability of 21.1 %

(path coefficient = 0.211). This makes sense as in the

descriptive analysis, we had observed that most single

students are ready now (65%) while most married

students will tend to use it in the future (67%). The

education indicator was is excluded from this finding

since it had to be removed to reach construct validity.

H3: The study results suggested that respondents

linked “results demonstrability” with “perceived

usefulness” of mobile TDM by students (p=0.000)

with a very high level of confidence (t= 11.124) and

with a very strong predictor of 67.2% (path

coefficient = 0.672).

Result demonstrability is indicated by the

effectiveness of mobile TDM to help detect skin

cancer in early stages, where the tool provides a

quality image, for an accurate assessment and

diagnostic by the practitioner reviewing the images.

This finding is aligned with the literature, where

patients have shown their willingness to use a medical

application if it’s beneficial for them. Moreover,

experts believe that comprehensibility and

functionality of the application play an important role

in encouraging people to adopt a certain technology

in their everyday life (Deng et al, 2018).

H4: Perceived risk is a strong antecedent to

intention to use of mobile TDM by students (p=

0.000; t= 3.518) with a predictability of 46.4% (path

coefficient = 0.464).

Hypothesis H4 is therefore supported. Perceived

risk (resistance to use, efficiency and technology

anxiety) predict the outcome by 46.4%. The

informants would use TDM if it is convenient

(loading factor = 0.773), and if it saves time (0.834)

as long as they receive the adequate training (0.776).

In this study, the indicator which weighed the most as

indicator of the risk variable was the technology

anxiety, which means professional assistance could

reduce the anxiety and may influence the behaviour

of the consumer in using TDM.

H10: Perceived ease of use is an antecedent to

perceived usefulness of mobile TDM by students

(p=0.002; t= 3.192) with a predictability of 26.6%

(path coefficient = 0.266). This finding agrees with

the theory of technology acceptance (Venkatesh and

Davis, 2000).

Factors Inﬂuencing Tele-dermatology Adoption among the Lebanese Youth: A Pilot Study at Saint Joseph University

6.2 Other Findings

We can also observe three findings that surprised us:

Medical factors do not appear to relate to the

perceived usefulness of mobile TDM by students

(H2). What is surprising here is that existing

conditions of cancer risk have not persuaded the use

of mobile TDM as a screening tool for early skin

cancer detection. Whilst dermatologists found TDM

useful for triage and diagnosis of most types of skin

conditions followed at primary care (Giavina et al,

2021), the patients use of the technology do not seem

influenced by their condition, in our study.

Perceived usefulness was not found to affect

intention to use (H8) – This is at odds with the

essential TAM theory. In our setting, even if the

application is thought of to be useful, the findings

inform that the Lebanese youth would not develop an

intention to use mobile TDM to diagnose skin lesions.

Similarly, as shown by hypothesis H9, perceived ease

of use was not a factor of intention to use in our

context.

6.3 Relevance of the Model

Our model produced R2 values of .634 and .525 for

perceived usefulness and intention to use

respectively. These moderate to substantial values

reinforce the value of our study and the findings (Hair

et al, 2019). They indicate that 63.4% of the

variability in the outcome in perceived usefulness and

52.5 % of the variability in the outcome in intention

to use may be explained by this study. In other words,

the information included in this study describes the

outcome at a considerable level. These results showed

up in similar studies, where the hypothesized model

accounted for 56 % of the variance in behavioral

intention to use by young users’ of a health

information portal (Tao et al, 2020) and elsewhere

57.1% of the variance in behavioral intention to use

diabetes management apps (Zhang et al, 2019).

7 CONCLUSION AND

LIMITATIONS

Wrapping up our results, the descriptive analysis

followed by a statistical study were successful in

detecting that the intention to use TDM among the

Lebanese youth is considerable – 64% of the

informants have indicated their readiness to use today

and 57% stated their intent to use it in the future.

Information from this study indicates that marital

status is most likely a factor for intention to use of

TDM among students – whereby, most single are

ready now (65%) while most married students will

tend to use it in the future (67%). The study also

informs that the Lebanese youth prioritize result

demonstrability as a factor in their intention to use

TDM. Further, Mobile TDM must save them time

must be easy to use to be perceived useful.

The manuscript has merit in addressing an

interesting topic through an empirical pilot research.

That said, the fact that the sample is composed by

students at a single university is a limitation of the

study. Other limitations are related to the gender

distribution (83% female and 17% male) to the

prevalence of medical and health students (i.e., almost

50% of the sample). This pilot survey adds

observations and findings that should be useful

among researchers and practitioners. A qualitative

study is encouraged to extend this pilot into a deeper

understanding of factors that influence the intention

to use. This work is extensible to a wider population

of different age groups and occupations, in a valuable

comparative of addressable markets and target user

base that could provide insight for manufacturers and

practitioners alike.

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APPENDIX

Survey Questions and Possible Answers

1- Gender: Male, Female

2- Age: < 20 years; 21 – 25; 26 – 30; 31 – 35; 36 – 40; 41 – 45; Above 46

3- Education specialty: Business Management; Engineering & Sciences; Humanities; Medical & Health; Political Science

& Law; Other

4- Work: Part time; Full time; Other or Null

5- Marital status: Single; Married

6- Skin colour: Fair; Medium; Dark

7- Eye colour: Brown; Hazel; Blue; Green; Other or Null

8- Family history of skin cancer: No; Yes

9- Previous skin cancer removed: No; Yes

10- Presence of moles larger than 2 mm: None; Less than 10; 11+

11- Mobile Teledermatology will help me examine my skin more rapidly: Strongly agree; Agree; Unsure; Disagree; Strongly

disagree

12- Mobile Teledermatology will improve my self-skin examination: Strongly agree; Agree; Unsure; Disagree; Strongly

disagree

13- Mobile Teledermatology is useful to diagnose moles on my skin for suspicious lesions: Strongly agree; Agree; Unsure;

Disagree; Strongly disagree

14- Mobile Teledermatology will help save time: Strongly agree; Agree; Unsure; Disagree; Strongly disagree

15- Mobile Teledermatology will help detect skin cancer in early stages: Strongly agree; Agree; Unsure; Disagree; Strongly

disagree

16- Mobile Teledermatology will be easy to use: Strongly agree; Agree; Unsure; Disagree; Strongly disagree

17- A suspicious mole or lesion diagnosis through Mobile Teledermatology will be understandable: Strongly agree; Agree;

Unsure; Disagree; Strongly disagree

18- Mobile Teledermatology users will easily acquire the skills to preform it: Strongly agree; Agree; Unsure; Disagree;

Strongly disagree

19- Mobile Teledermatology will encourage me to examine my skin thoroughly: Strongly agree; Agree; Unsure; Disagree;

Strongly disagree

HEALTHINF 2022 - 15th International Conference on Health Informatics

20- The use of Mobile Teledermatology will change my self-skin examination practice: Strongly agree; Agree; Unsure;

Disagree; Strongly disagree

21- The use of Mobile Teledermatology can fit in my skin examination habit: Strongly agree; Agree; Unsure; Disagree;

Strongly disagree

22- The use of Mobile Teledermatology may interfere with my work: Strongly agree; Agree; Unsure; Disagree; Strongly

disagree

23- I will use Mobile Teledermatology when its offered to me: Strongly agree; Agree; Unsure; Disagree; Strongly disagree

24- I will use Mobile Teledermatology in my routine self-skin examination in the future: Strongly agree; Agree; Unsure;

Disagree; Strongly disagree

25- I will use Mobile Teledermatology if it will save me time: Strongly agree; Agree; Unsure; Disagree; Strongly disagree

26- I will use Mobile Teledermatology if it will save me money: Strongly agree; Agree; Unsure; Disagree; Strongly disagree

27- Mobile Teledermatology will be useful to diagnose skin cancer in general: Strongly agree; Agree; Unsure; Disagree;

Strongly disagree

28- Mobile Teledermatology will be for my best interest: Strongly agree; Agree; Unsure; Disagree; Strongly disagree

29- Health professionals (nurses, physicians...) will welcome the fact that I use Mobile Teledermatolog: Strongly agree;

Agree; Unsure; Disagree; Strongly disagree y

30- My friends and my family will welcome the fact that I use Mobile Teledermatology: Strongly agree; Agree; Unsure;

Disagree; Strongly disagree

31- I will completely trust the diagnosis of the dermatologist based on a photo I’ve sent using Mobile Teledermatology:

Strongly agree; Agree; Unsure; Disagree; Strongly disagree

32- I will rely on the Teledermatology process to supply accurate information about a mole or a spot: Strongly agree; Agree;

Unsure; Disagree; Strongly disagree

33- I will use Mobile Teledermatology if I receive adequate training: Strongly agree; Agree; Unsure; Disagree; Strongly

disagree

34- I will use Mobile Teledermatology if I receive technical assistance when I need it : Strongly agree; Agree; Unsure;

Disagree; Strongly disagree

35- There are health professionals available who will help me with Mobile Teledermatology: Strongly agree; Agree; Unsure;

Disagree; Strongly disagree

Factors Inﬂuencing Tele-dermatology Adoption among the Lebanese Youth: A Pilot Study at Saint Joseph University