Effects of Age, Gender, and Personality on Individuals’ Behavioral

Intention to Use Health Applications

Andreia Nunes

1,2

, Teresa Limpo

and São Luís Castro

1,2

Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal

Center for Psychology at University of Porto, Porto, Portugal

Keywords: Age, Gender, Personality, Technology Acceptance, Health Applications.

Abstract: Health applications, aimed at helping people with or without diseases to monitor their health, are attracting

the interest of researchers and consumers. The use of health applications may have a short- and long-term

impact on people’s lives by creating early habits to use technology to monitor health, which may prompt the

sustained use of this technology over time. This is especially important for elders as these applications offer

them the possibility to manage their health autonomously. However, elders are resistant to use technology.

One way to improve technology acceptance is by understanding how users’ behavioral intention is influenced

by personal characteristics, preferably before entering in the elderly stage of life. This was the main aim of

this study: we explored the effects of age, gender, and personality on the behavioral intention to use health

applications in younger and older adults (18-39 vs. 40-65 years). Results showed that the effects of personality

on individuals’ behavioral intention was moderated by age in older adults and by gender in younger adults.

These findings seem relevant to promote the current and future use of health applications, helping people to

improve their quality of life and stay healthy throughout the lifespan.

1 INTRODUCTION

During the past years, fast progress and mass

dissemination of mobile devices influenced not only

electronic industry but also consumers’ behaviors and

their life style (Huang and Kao, 2015). Currently,

there are numerous applications for mobile devices.

Many of them are offered for free and are easily

accessible. A very popular group of applications are

“mobile health applications”, known as mHealth.

These applications include several utilities that are

useful to monitor health-related behaviors and

diseases throughout lifetime.

Although one only needs a few seconds to find

dozens of health applications to be used on a daily

basis, little is known about the factors that lead people

to use them. A key question is what makes people

want to use such applications, namely people from

different age groups. Understanding the determinants

of technology acceptance and use is important.

Indeed, the value of technology depends to some

extent on it being used (one may say that technology

is not useful if no one uses it).

Key factors that may influence technology

acceptance are users’ characteristics (Venkatesh et

al., 2012). Factors such as age, gender, and

personality traits may either facilitate or hinder the

adoption of technology. Information on the nature of

the relationship between users’ personal

characteristics and their behavioral intentions to use

technology is useful both for designers and for

marketers. This information may help them to create

applications tailored to the characteristics of targeted

groups (Boudreaux et al., 2014) that may not only

prompt the use of mHealth in the present moment, but

also increase the likelihood of sustained use over

time.

The use of health applications may be especially

valuable for elders. The continuous increase in life

expectancy and the consequent growth of elderly

population gave rise to models of positive ageing

focused on promoting healthy, autonomous, and

high-quality lifestyles (Demiris et al., 2004).

MHealth seems to be very promising to that end by

allowing elders to monitor their health autonomously,

to promote their independent living, and to facilitate

communication with doctors (Czaja, 2015). However,

elders may be reluctant to use technology and may

have difficulties in engaging with it (Young et al.,

2014). Thus, it appears critical to have information on

Nunes, A., Limpo, T. and Castro, S.

Effects of Age, Gender, and Personality on Individuals’ Behavioral Intention to Use Health Applications.

DOI: 10.5220/0006674101030110

In Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2018), pages 103-110

ISBN: 978-989-758-299-8

103

key factors that influence the acceptance of

technology before the elderly stage of life. Promoting

the use of health applications in older and younger

adults, who sooner or later will be in the elderly side

of society, may have a long-term impact on their

future lives by creating early habits to use technology

and promoting their sustained use over time. Also, as

noted by Charness and Boot (2009), the early use of

technology may prevent age-associated impairments

and facilitate a healthy entrance into old age.

Overall, it seems that one way to promote the use

of mHealth applications by elders is by prompting

their use from early on, and by tailoring them to

relatively stable personal characteristics. Grounded

on these ideas, we conducted the present study aimed

to explore the effects of age, gender, and personality

on the behavioral intention to use health applications

in two age groups: younger (18-39 years of age) and

older adults (40-65 years of age).

2 STATE OF THE ART

2.1 Technology Acceptance

Understanding the relationship between consumers’

characteristics and technology use requires

knowledge from multiple disciplines. Among these,

psychology is a critical one. By focusing on the

psychological functioning of consumers, psychology

may help to create useful technologies tailored to

users’ individual needs and characteristics (Demiris

et al., 2004).

Over the last twenty years, several models have

been developed to explain factors influencing

individuals’ acceptance and use of technology

(Venkatesh et al., 2003, Venkatesh et al., 2012, Davis,

1986). These models were inspired by psychological

and sociological theories (e.g., Theory of Reasoned

Action; Fishbein and Ajzen, 1975) aimed to explain

why people behave in a certain way (Venkatesh et al.,

2012), and were based on the premise that there is a

strong relationship between behavioral intentions and

actual behaviors. Two of the most studied technology

acceptance models are the Technology Acceptance

Model (TAM; Davis, 1989, Venkatesh and Davis,

2000, Venkatesh and Bala, 2008) and the Unified

Theory of Acceptance and Use of Technology

(UTAUT; Venkatesh et al., 2003). These models have

been applied in several fields such as education,

organizational settings, or systems engineering

(Huang and Kao, 2015). In general, these models

assume that behavioral intention to use, and effective

use of technology, are influenced by a set of

technology-acceptance determinants, namely

performance expectancy, effort expectancy, social

influence, and facilitating conditions (Venkatesh,

2000).

However, TAM and UTAUT models were more

oriented to organizational settings and addressed

mostly the non-voluntary use of technologies by

workers (e.g., as part of a job task). Only recently did

researchers focus on the acceptance of technology

used by consumers on a voluntary basis (Venkatesh

et al., 2012). To specifically target consumers, the

UTAUT model was recently further developed. This

newer model not only included motivation, price

value, and habits as relevant dimensions to

consumers’ behaviors, but also highlighted the

moderating role of personal characteristics such as

age and gender in the association between

technology-acceptance determinants (viz.,

performance expectancy, effort expectancy, social

influence, facilitation conditions) and people’s

behavioral intentions (Venkatesh et al., 2012).

However, the willingness to use technologies may

also involve other users’ characteristics, such as those

captured by personality traits (Svendsen et al., 2013).

Unfortunately, although users’ personality has

received increased interest from technology

developers over the last years, there has been little

effort to incorporate personality traits into a

comprehensive approach to technology acceptance

(Barnett et al., 2015).

2.2 Personality and Technology

In personality research, many trait models have been

identified. One of the most widely accepted is the

Big-Five personality model. In this model,

personality characteristics are organized into five trait

dimension: Extraversion, Agreeableness,

Conscientiousness, Emotional Stability, and

Openness (Costa and McCrae, 1992). Taken together,

these dimensions capture the essence of personality

with each dimension representing single and unique

human characteristics (John and Srivastava, 1999).

Extraversion refers to sociability, need for

stimulation, and capacity for joy. Agreeableness

refers to the quality of interpersonal orientation along

a continuum from compassion to antagonism.

Conscientiousness refers to the individual’s degree of

organization, persistence, and motivation in task- and

goal-directed behaviors. Emotional Stability refers to

the individual’s disposition in being emotionally

adjusted or not. Openness refers to the need for

variety, novelty, and change.

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Only a few studies examined the effects of the five

personality traits on people’s intention to use

technologies (e.g., Svendsen et al., 2013; Nov and Ye,

2008; Barnett et al., 2015; Pocius, 1991). Individuals

scoring high on extraversion seem to have a higher

degree of interaction with computers (Pocius, 1991).

Those with high scores on agreeableness tend to

cooperate more with others in adopting and use a new

technology (Devaraj et al., 2008). More conscientious

people are more careful when they evaluate the

opportunities offered by technology. Furthermore,

whereas less emotionally stable people tend to be

more reluctant to adopt technological novelties

(Devaraj et al., 2008), those that are more open to

experience are also more prone to accept new

technologies (McElroy et al., 2007).

2.3 Age, Gender, and Technology

Prior models of technology acceptance have barely

considered the direct impact of age and gender on

technology use (Barnett et al., 2015). Instead,

research has focused on how age and gender

moderate the relationship between major

determinants of technology acceptance and the

behavioral intention to use technologies (Venkatesh

et al., 2003).

In the UTAUT model, age was found to be a key

moderator (Venkatesh et al., 2003). For example, the

behavioral intention to use technology was more

strongly determined by performance expectancy in

younger people, and by effort expectancy and

facilitating conditions in older people. Concerning

gender, men’s behavioral intention to use technology

appeared to be more driven by performance

expectancy, whereas women’s intentions were more

influenced by effort expectancy, facilitating

conditions, social influence, and previous experience

with technologies (Venkatesh et al., 2003; Venkatesh

et al., 2012). These studies indicated that, like

personality, age and gender should be taken into

account when examining the role of individual

differences in technology acceptance. As age and

gender seem to display a moderating role in

technology acceptance models, they seem potential

moderators on the relationship between personality

and behavioral intention to use technology. Indeed,

age and gender differences in the big-five dimensions

of personality have already been reported. For

example, older adults were found to be more self-

disciplined and agreeable than younger adults (Soto

et al., 2011); and women scored higher on

agreeableness and lower on emotional stability than

men (Chapman et al., 2008).

2.4 Present Study

As reviewed above, there has been increasing interest

in the study of factors underlying people’s intention

to use technology. Nevertheless, age, gender, and

specially personality traits have received little

attention. This is quite noticeable in the case of

mHealth, despite the evident usefulness of this type

of technologies to support people’s autonomy and

active living (Boudreaux et al., 2014). Here, we

examine the effects of personality, age, and gender on

people’s behavioral intention to use health

applications in two age groups: younger (18-39 years)

and older (40-65 years) adults. We asked two major

research questions. Do age, gender, and personality

influence younger and older adults’ behavioral

intention to use health applications? And do age and

gender moderate the effects of personality traits on

younger and older adults’ behavioral intention to use

health applications?

3 METHODOLOGY

3.1 Participants

Three-hundred eighty-five individuals took part in

this study, all native speakers of European

Portuguese. Forty-five participants were excluded

because they did not respond to at least one item of

the questionnaires, resulting in a total of 340

participants. They were aged between 18 and 65 years

(M = 32.82, SD = 15.27) and 78% were women.

Among all participants, 4% had completed primary

education (4 years), 3% upper primary education (6

years), 5% middle education (9 years), 11%

secondary education (12 years); 49% were attending

university, and 28% held a university degree. In order

to compare the effects of age, we split the sample into

a group of younger adults (n = 205, age range: 18-39

years, M

age

= 20.93, SD

age

= 2.46; 86% women) and a

group of older adults (n = 135, age range: 40-65 years,

age

= 50.87, SD

age

= 6.00; 64% women).

3.2 Procedure and Measures

A booklet including a set of questionnaires was

initially administered to undergraduates in classroom

groups. After completing the questionnaires,

undergraduates were asked to take one booklet and

have it filled by an acquaintance or family member

aged between 40 and 65 years within 15 days. This

booklet included several questionnaires that were part

a larger study on personality and health literacy. Only

Effects of Age, Gender, and Personality on Individuals’ Behavioral Intention to Use Health Applications

105

the measures relevant to the present study are

described here.

To assess the Big-Five dimensions of personality

we used the Ten-Item Personality Inventory (TIPI;

Gosling et al., 2003). TIPI includes two items—a

single word or phrase—per dimension (10 items in

total), and participants are asked to rate the extent to

which each trait applies to themselves using a 7-point

scale (1 = strongly disagree; 7 = strongly agree).

To measure the behavioral intention to use health

applications we used the Behavioral Intention sub-

scale of the Questionnaire of Acceptance of

Technology – Health Applications, adapted from

Cimperman et al. (2016). Participants indicate their

level of agreement with sentences on the potential use

of health applications (e.g., Assuming I had access to

health apps, I would intend to use it.), using a 7-point

scale (1 = strongly disagree; 7 = strongly agree).

4 RESULTS

4.1 Descriptive Statistics and

Correlations

Table 1 presents means and standard deviations for

all predictors and outcome variables, along with the

bivariate correlations between each other across age

groups. In the group of younger adults, men tended to

exhibit higher levels of emotional stability than

women (r = .24), and women tended to show higher

levels of conscientiousness (r = -.15) and more

behavioral intention to use health applications (r = -

.14) than men. In the group of older adults, older

participants tended to exhibit higher levels of

emotional stability (r = .19). Moreover, men tended

to exhibit higher levels of agreeableness (r = .21) and

emotional stability (r = .20) than women.

4.2 Regression Analysis

We conducted two hierarchical multiple regressions:

one to examine the effects of age and personality on

the behavioral intention to use health applications,

and another to examine the effects of gender and

personality on the behavioral intention to use health

applications. Separate analyses were conducted for

younger and older adults (see Table 2 and Table 3 for

the unique contribution of each predictor).

4.2.1 Effects of Age and Personality on

Behavioral Intention

In Step 1, we entered the main effects of age and of

the five personality dimensions (viz., extraversion,

agreeableness, conscientiousness, emotional stability

and openness), which were previously mean centered.

In Step 2, we added the two-way interactions between

age and each personality dimension.

In the group of younger adults, Step 1 showed no

main effects of age and personality on participants’

behavioral intention to use health applications, R

.01, F < 1. The inclusion of interactions between age

and personality dimensions in Step 2 did not result in

any increase in the amount of variance explained, ∆R

= .02, F

change

< 1.

In the group of older adults, there were no main

effects of age and personality in Step 1, R

= .05, F(6,

128) = 1.15, p = .34, but there was a significant

increase in the prediction of behavioral intention to

use health applications with the inclusion of

interactions between age and personality dimensions,

Table 1: Means, standard deviations and correlations for all variables in younger (above the diagonal, n = 205) and older

adults (below the diagonal, n = 135).

Dummy coded, 0 = female, 1 = male. E = Extraversion, A = Agreeableness, C =

Conscientiousness, ES = Emotional Stability, O = Openness, BI = Behavioral Intention. *p < .05. **p < .01. ***p < .001.

Correlations

Measures Age Gender E A C ES O BI M SD

1. Age

.05 -.07 .02 .05 .08 .08 .04 20.93 2.46

2. Gender

.14

.03 -.13 -.15* .24** .06 -.14* 0.14 0.34

3. Extraversion

-.05 -.04 -.14** .02 .12 .38*** .04 4.38 1.53

4. Agreeableness

.004 .21* .01 .29*** .16* .18** .03 6.02 0.83

5. Conscientiousness

-.03 -.07 .15 .30*** .13 .04 .01 5.48 1.20

6. Emotional Stability

.19* .20* .21* .30*** .26** .26*** -.02 3.59 1.21

7. Openness

-.17 .03 .33*** .15 .23** .20*** .08 5.38 1.10

8. Behavioral Intention

-.14 .01 .05 .08 .08 -.01 .16

4.15 1.27

50.87 0.36 4.67 6.22 5.94 4.13 5.24 4.38

SD 6.00 0.48 1.61 0.81 1.14 1.40 1.34 1.12

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∆R

= .09, F

change

(5, 123) = 2.50, p = .03. The final

model including the main effects of age and

personality dimensions, and their respective two-way

interactions explained 14% of the variance in

behavioral intention. Results showed that there was a

significant interaction between age and openness (β =

.20, p = .05). Because the moderator (age) is

continuous, we used the Johnson-Neyman technique

to decompose the interaction. Results revealed that

among participants with 55.12 years or more (22% of

the sample) higher levels of openness were associated

with a stronger behavioral intention to use health

applications, β = .17, t = 1.98, p = .05.

4.2.2 Effects of Gender and Personality on

Behavioral Intention

As before, we conducted hierarchical multiple

regression analysis separately for each age group to

examine the contribution of personality to the

behavioral intention to use health applications and the

moderating role of gender. In Step 1, we entered the

main effects of gender (0 = women, 1 = men) and of

the five personality dimensions, which were

previously mean centered. In Step 2, we added the

two-way interactions between gender and each

personality dimension.

In the group of younger adults, Step 1 results

revealed no main effects of gender and personality on

participants’ behavioral intention to use health

applications, R

= .03, F(6, 198) = 0.91, p = .49. Still,

there was a significant increase in the prediction of

behavioral intention to use health applications with

the inclusion of interactions between gender and

personality dimensions, ∆R

= .07, F

change

(5, 193) =

3.10, p = .01. The final model including the main

effects of gender and personality dimensions, and

their respective two-way interactions explained 10%

of the variance in behavioral intention. Results

indicated that there were significant interactions

between gender and extraversion (β = .25, p = .002),

as well as between gender and emotional stability (β

= -.18, p = .04). Because the moderator (gender) is a

dichotomous variable, we used simple slopes

analyses to decompose the interaction. Results

revealed that these two personality dimensions were

associated with behavioral intention only for male

participants. Specifically, a stronger behavioral

intention to use health applications was found for men

displaying higher levels of extraversion, β = .45, t =

2.75, p = .01, and lower levels of emotional stability,

β = -.35, t = -1.91, p = .05.

In the group of older adults, neither Step 1, R

.05, F(6, 128) = 1.01, p = .42, nor Step 2, ∆R

= .04,

change

(5, 123) = 1.16, p = .33, reached significance.

5 DISCUSSION

Our main research goal was to examine the effects of

age, gender, and personality on the behavioral

intention to use health applications in two age groups

of younger (18-39 years) and older adults (40-65

years).

We formulated two research questions: Do age,

gender, and personality influence younger and older

adults’ behavioral intention to use health

applications? And do age and gender moderate the

effects of personality traits in younger and older

adults’ behavioral intention to use health

applications?

Table 2: Effects of age and personality on participants’ behavioral intention to use health applications across age groups. *p

< .05. **p < .01. ***p < .001.

Younger adults Older adults

B SE β t B SE β t

Constant

3.21 1.18 2.73 3.42 1.25

2.75

Age

0.02 0.05 .04 0.44 0.00 0.02 .002 0.03

Extraversion

0.03 0.07 .03 0.39 0.01 0.06 .01 0.14

Agreeableness

0.01 0.12 .01 0.09 0.12 0.13 .09 0.95

Conscientiousness

-0.003 0.08 -.003 -0.04 0.03 0.09 .03 0.36

Emotional Stability

-0.04 0.08 -.03 -0.45 -0.10 0.08 -.13 -1.34

Openness

0.09 0.10 .07 0.86 0.07 0.08 .09 0.91

Age x Extraversion

0.003 0.04 .01 0.08 0.02 0.01 .18 1.95*

Age x Agreeableness

0.02 0.05 .03 0.36 -0.004 0.03 -.01 -0.16

Age x Conscientiousness

-0.05 0.04 -.10 -1.14 -0.02 0.02 -.12 -1.31

Age x Emotional Stability

0.02 0.03 .05 0.59 0.01 0.01 .10 1.13

Age x Openness

0.04 0.05 .09 0.91

0.02 0.01 .20 1.99*

Effects of Age, Gender, and Personality on Individuals’ Behavioral Intention to Use Health Applications

107

Table 3: Effects of gender and personality on participants’ behavioral intention to use health applications across age groups.

Dummy coded, 0 = female, 1 = male. *p < .05. **p < .01. ***p < .001.

Younger adults Older adults

B SE β t B SE β t

Constant

4.14 0.85 4.85 3.26 1.11

2.95

Gender

-0.28 0.31 -.08 -0.89 0.29 0.23 .12 1.25

Extraversion

-0.07 0.07 -.09 -1.03 -0.04 0.08 -.06 -0.56

Agreeableness

-0.02 0.13 -.01 -0.13 -0.01 0.17 -.01 -0.08

Conscientiousness

-0.07 0.09 -.07 -0.83 0.12 0.11 .13 1.09

Emotional Stability

0.07 0.09 .06 0.77 -0.07 0.10 -.09 -0.71

Openness

0.12 0.10 .11 1.27 0.18 0.10 .21 1.84

Gender x Extraversion

0.57 0.19 .25 3.08** 0.24 0.15 .20 1.61

Gender x Agreeableness

-0.02 0.30 -.01 -0.08 0.49 0.29 .21 1.67

Gender x Conscientiousness

0.19 0.22 .08 0.86 -0.15 0.21 -.09 -0.70

Gender x Emotional Stability

-0.42 0.20 -.18 -2.06* -0.26 0.18 -.18 -1.40

Gender x Openness

-0.20 0.30 -.06 -0.67

-0.20 0.19 -.14 -1.04

5.1 Effects of Age, Gender, and

Personality on Behavioral Intention

Concerning the first research question, we found no

main effects of age, gender, and personality on

individuals’ behavioral intention to use health

applications, neither in younger nor in older adults.

This result should be read carefully as current

research into technology acceptance has barely

considered the unique effects of these variables to

behavioral intention. Instead, age and gender have

been mainly considered as antecedents of

determinants of technology acceptance (e.g., ease of

use) or as moderators of the relationship between

these and behavioral intention (McElroy et al., 2007;

Tarhini et al., 2014). Indeed, Venkatesh et al. (2003)

considered age and gender as important moderators

within the UTAUT model. As for personality,

Svendsen et al. (2013) have already shown that it does

not influence individuals’ behavioral intentions to use

technology directly. Instead, the effect of personality

occurred through other technology-acceptance

determinants, such as perceived usefulness, ease of

use and social norms.

Concerning the second research question, we did

find that age and gender moderated the effects of

personality traits on the behavioral intention to use

health applications. These moderating effects were

different across age groups.

In younger adults, age did not moderate

personality effects on behavioral intention, but it did

in older ones. Specifically, we found that for older

participants (i.e., above 55 years), a higher degree of

openness to experience was associated with a stronger

behavioral intention to use health applications. These

findings are aligned with prior research, showing that

people are more predisposed to accept new

technologies when they report to be more open to new

experiences (McElroy et al., 2007). As shown here,

this relationship seems to be particularly important as

people get older, mainly after 55 years of age.

Gender did not moderate personality effects on

behavioral intention in older adults, but it did in

younger ones. Results showed that men were more

willing to use health applications when they showed

higher levels of extraversion and lower levels of

emotional stability. Previous studies in the

technology domain have already showed that the

effects of personality traits on technology-related

outcomes are moderated by participants’ gender. For

example, such an interaction was reported by Saleem

et al. (2011) in a study focused on computer self-

efficacy. However, few studies have addressed the

moderating role of gender on the relationship between

personality and use of mobile applications in the

health domain. Further studies are needed to

corroborate our findings and deepen knowledge on

their implications to the acceptance of mHealth.

Overall, our results suggest that personal

characteristics are worthy to consider when studying

technology acceptance. Indeed, users’ intention to use

technology seems related to the affinity that they have

for certain types of technology, which is influenced

by personal characteristics such as those here

examined, that is, age, gender, and personality

(Svendsen et al., 2013). As technology acceptance

research suggests that individual differences may

affect the adoption of new technologies (Tsourela and

Roumeliotis, 2015), these findings bring implications

to the design and development of health applications.

The development of new technological solutions

should therefore be tailored to particular segments of

the population. This alignment between applications

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108

and these groups should, at the very least, take into

consideration not only age and gender, but also the

behavioral patterns typical of those groups. Future

studies should continue to pursue this research

avenue, by exploring how other personal

characteristics influence technology acceptance as

people get older and approach the elderly stage of life.

5.2 Limitations and Future Directions

The previously discussed findings should be

considered in view of at least two methodological

limitations, which may guide future research. A first

limitation is that there were more participants in the

younger group. The sample was split at the age of 40

to achieve a relatively large difference between the

mean ages of younger and older groups. However,

this resulted in an unequal sample size per group.

Moreover, due to the recruitment procedure, there

was a larger representation of women than men in our

sample. Future studies should aim to collect larger

samples, with an equivalent number of younger and

older adults, as well as men and women. This would

also allow researchers to use more sophisticated

techniques, such as multiple-group structural

equation modeling, to test and compare different

models of technology acceptance across age groups

and gender. Additionally, because ageing brings

changes in diverse aspects, such as physical health,

perception, cognition, and psychological functioning

(Charness and Boot, 2009), it would be important to

control for these aspects, particularly in older

samples. Along with age, gender and personality,

these personal characteristics may also play a role in

the way that people use or intend to use mHealth. A

second limitation was the lack of measurement of

previous knowledge and actual use of mobile devices

and health applications. This seems to be an important

factor to take into account in future studies. The

previous experience with technologies was also

proposed to influence individuals’ behavioral

intention to use technologies (Venkatesh et al., 2012;

Venkatesh et al., 2003), showing the relevance of

considering this variable when testing technology

acceptance models.

6 CONCLUSIONS

Despite the increased attention to factors influencing

technology acceptance, research examining the role

of personal characteristics is still scarce. Our study

provided additional knowledge on the role of age,

gender, and personality in younger and older adults’

behavioral intention to use mHealth. This knowledge

is useful to develop and adjust technologies to key

characteristics of target groups. With elders

displaying a marked resistance in accepting

technology (Charness and Boot, 2009), the promotion

of mHealth in earlier stages of life seems particularly

important to create habits to use technology. These

habits may promote the sustained use of technology

throughout the lifespan and, at the same time, act as

preventive measures to negative health outcomes.

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