User Acceptance of Lifelogging Technologies: The Power of
Experience and Technological Self-Efficacy
Wiktoria Wilkowska
, Julia Offermann-van Heek
and Martina Ziefle
Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, 52074 Aachen, Germany
Keywords: Lifelogging Technology, Technology Acceptance, Technological Self-Efficacy, Lifelogging Experience.
Abstract: Today, innovations in the field of lifelogging technology and its assistance in everyday life enable different
users to gain an overview of different areas of their lives. Especially for older and frail people, lifelogging
offers useful solutions that allow them to stay longer in their private environment and maintain their autonomy.
Although lifelogging is already used in many contexts, opinions of users on the different lifelogging applica-
tions and the influence of user characteristics on their acceptance still remain underexplored. In this study, we
investigate the acceptance of lifelogging technology for activities of daily living and examine the impact of
user characteristics on its key determinants according to the Technology Acceptance Model, which is used as
a theoretical background. For data collection we used a quantitative online survey and took opinions of N=209
German adults into consideration in the statistical analyses. Our findings demonstrate that an already existing
experience with lifelogging is the main influencing factor for user acceptance: High levels of the experience
and technological self-efficacy in handling of the technology significantly enhance the acceptance of lifelog-
ging for activities of daily living, while age and gender shape the acceptance indirectly. This study contributes
to the user acceptance research of lifelogging in private environments, and our findings deepen the under-
standing of how adoption of lifelogging technologies is shaped by different users.
In the today’s world, lifelogging technology has be-
come pervasive, taking over more and more areas of
life. Digital self-tracking, as described by Selke
(2016), enables the collection, storage, retrieval, and
sophisticated analyses of information about a per-
son’s life and behaviour. The growth of information
acquisition, along with the range of information that
can be gathered, is almost limitless, but users mostly
gather information that is relevant to their main inter-
ests and needs (O’Hara et al., 2008).
Lifelogging applications are used in many con-
texts, for instance for self-monitoring, fun, improve-
ment of well-being, and/or performance. The fields of
application are thus diverse and relate to both private
and professional areas of life. While in the early days
in particular younger people used lifelogging, the
fields of application became increasingly interesting
for older users as well. Especially in the area of am-
bient systems, where information and communication
technology is able to monitor people’s activities, de-
tect emergencies, and recognise their behaviour devi-
ations, lifelogging applications have a great potential
to support ageing. This technology thus gains more
and more importance for older and frail individuals,
assisting them in their necessities and processes of the
everyday life. However, the availability of advanced
technical solutions does not equate an active use:
Much more user acceptance of the lifelogging solu-
tions is an essential precondition for an appropriate
meeting of the care needs of older adults. Yet, in the
area of technology acceptance little is known about
(i) who is using or not using which lifelogging appli-
cations and why, (ii) what users think about the dif-
ferent lifelogging applications, and (iii) how the user
characteristics affect the technology’s adoption.
Hence, the present empirical study investigates
the acceptance of lifelogging technology through the
Wilkowska, W., Heek, J. and Ziefle, M.
User Acceptance of Lifelogging Technologies: The Power of Experience and Technological Self-Efficacy.
DOI: 10.5220/0010436400260035
In Proceedings of the 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2021), pages 26-35
ISBN: 978-989-758-506-7
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
example of applications referring to activities of daily
living. The main focus lies on the user characteris-
ticsnot only the demographic attributes but also
technological self-confidence and previous experi-
ence of usersand their impact on the accepted use.
In the following, we firstly provide a theoretical back-
ground and briefly outline relevant findings in related
research. We then describe the method applied in this
study and present the outcomes of the statistical anal-
yses before discussing the findings and critically re-
flecting on the study.
In the following, previous research on developments
in the field of lifelogging applications as well as on
their perception and acceptance is presented.
2.1 Lifelogging Technology for
Assistance in Everyday Life
Lifelogging refers to a recording of everyday life and
can be realized by different variants of digital self-
tracking (Selke, 2016). It enables digital recordings in
different levels of detail and for different reasons by
collecting, archiving, observing, and reflecting
health-related physiological and behavioural data
(Gurrin et al., 2014). The technical realizations as
well as the application contexts of lifelogging are di-
verse and cover a broad spectrum, ranging from as-
sisting lifelogging applications for older people or
people in need of care up to sportive applications,
which are predominantly used by younger people.
The latter aims at a tracking (and improvement) of
physical activity and eating habits, enabling also
game-based competitions (Schoeppe et al., 2016).
Being realised as wearable (e.g., McAdams et al.,
2011) or ambient-installed systems (e.g., Rashidi and
Cook, 2009), diverse sensors (e.g., Poli et al., 2020),
audio-based technologies (e.g., Shah et al., 2012), or
video-based technologies (Climent-Perez et al., 2020)
can be used to monitor and track health-related phys-
iological and behavioural data (Rashidi and Mihai-
lidis, 2013). Besides sportive motivation, tracking
and analysis of different activities of daily living
(ADL) collected by lifelogging applications are also
useful from a medical diagnostic and preventive per-
spective, and can be realized in private environments
as well as in professional care institutions. Such life-
logging technologies can provide support and assis-
tance for older and frail people as well as for their
caregivers (e.g., Jalal et al., 2014, Climent-Perez et
al., 2020). This way, security-relevant functions like
fall detection can be realized (e.g., Mubashir et a.,
2013), activities and movements can be monitored
(Rashidi and Cook, 2009; Suzuki et al., 2007), and
changes in movements or behaviours can be identi-
fied as indicators for specific clinical pictures (Hayes
et al., 2008).
In addition, lifelogging can serve as a human
memory augmentation as it allows us to capture digi-
tal snapshots of the different moments of our lives and
store this information (Harvey et al., 2016). A study
of Chen and Jones (2012) investigated intentions that
potential users have for lifelogging and revealed that
besides purposes of sharing memories, the most de-
sired lifelogging functions and applications refer to
emotional purposes (reminiscing), task-based pur-
poses (recollecting or extracting specific information
for re-use or evidence), and well-being supporting
purposes (analysing and comparing current life pat-
tern, exercises, work-related and financial processes).
These examples show that the potential of lifelog-
ging applications is high, as they assist and motivate
people to live a healthier lifestyle, while they are also
able to relieve tasks in the everyday life and increase
the autonomy for older people (in need of care). Sim-
ultaneously, the daily usage of technologies tracking
everyday activities and health-related, personal infor-
mation also entails scepticism due to concerns about
data security and privacy (Kelly et al., 2013, Lidynia
et al., 2018; Wolf et al., 2014).
2.2 User Factors Influencing
Technology Acceptance
Based on the trade-off between the enormous poten-
tial and the existing concerns in terms of the daily use
of lifelogging applications, user acceptance of these
applications has to be examined in detail. Previous re-
search on the acceptance of lifelogging technologies
used for tracking activities of daily living showed that
reminding functions (Morganti et al., 2013) as well as
collecting and sharing information with related peo-
ple (Caprani et al., 2014) represent the most relevant
motives to use lifelogging. In contrast, an unauthor-
ized forwarding to third parties and a perceived loss
of control over sensitive data were identified to be the
major barriers for the everyday use of lifelogging ap-
plications (Lidynia et al., 2018). Beyond these in-
sights, lifelogging technology acceptance for tracking
activities of daily living has not been systematically
investigated based on the well-known acceptance pa-
The Theory of Reasoned Action (Fishbein and
Ajzen, 1975) and the Theory of Planned Behavior
(Ajzen, 1991) represent two relevant and influential
User Acceptance of Lifelogging Technologies: The Power of Experience and Technological Self-Efficacy
models for the prediction of factors promoting or re-
ducing acceptance. Within both models, a strong re-
lationship has been postulated between an individ-
ual’s intention towards a behaviour and the actual be-
haviour. The behavioural intention is thereby im-
pacted by the individual factors or personal attitudes.
Based on these models, Davis (1989) set up the Tech-
nology Acceptance Model (TAM) continuing the re-
lationship between the intention towards a behaviour
and the actual behaviour. Beyond that, the model as-
sumes that two key components, the perceived use-
fulness and perceived ease of use, significantly influ-
ence the attitude towards using, which is closely re-
lated with the behavioural intention to use and, thus,
with the actual use of a technology (Davis, 1989). The
perceived ease of use refers to an individual’s percep-
tion of how difficult/easy it will be to learn to use the
technology, while the perceived usefulness relates to
an individual’s idea of how useful the technology is
for improving processes. Research on health-related
technologies applied and adapted the TAM in various
ways, confirming both key acceptance determinants
as useful predictors for the acceptance of an innova-
tive technology (Rahimi et al., 2018).
Beyond that, specific individual user characteris-
ticsi.e., factors referring to the users of the consid-
ered technologyhave been regarded in the technol-
ogy acceptance research as well. In a first step, Davis
(1989) postulated so-called external variables as po-
tential influencing factors on the acceptance key com-
ponents, perceived ease of use and perceived useful-
ness. Later, the users’ age, gender, and previous ex-
perience have been integrated into the acceptance re-
search and the respective acceptance models (e.g.,
Venkatesh and Davis, 2000; Venkatesh and Bala,
2008). These individual user characteristics have
been proven to be relevant influencing parameters for
the user acceptance of information and communica-
tion technologies in various contexts, such as gender
with regard to the invasive medical technology
(Ziefle and Schaar, 2011) and life prolonging technol-
ogies (Arber et al., 2008), or age (Ziefle and Bay,
2005) and previous experience (Venkatesh and Bala,
2008). As a further factor, the technological self-effi-
cacy has been examined in previous research (Beier
1999). Several studies showed that female users ex-
pressed a lower perceived control, stronger fears,
lower self-confidence, as well as less use of, and ex-
perience with, computers compared to male users
(e.g., Broos, 2005, Durndel and Haag, 2002).
In the next section, we describe the used method
and study design providing all details on how we op-
erationalised the research questions of the study.
Considering the lifelogging technology with its po-
tential of a comprehensive digital self-monitoring in
different areas of life, the present research focuses on
user acceptance of lifelogging applied for the activi-
ties of daily living. The lifelogging applications refer
to both basic activities, such as personal care, dietary
intake, mobility, and instrumental activities, like for
instance medicine intake, food preparation, money
management, etc.
We adopted a mixed-method approach to investi-
gate the research questions. In the first step, qualita-
tive interviews (N=14) were conducted to explore the
general knowledge of, and attitudes towards, already
existing lifelogging technologies. The valuable find-
ings of the individual interviews were then validated
by a quantitative survey (N=209). Note: Due to space
restrictions, in this paper we address only the quanti-
tative findings.
3.1 Research Questions (RQ)
In terms of the adoption of lifelogging for ADL, the
main question of this study is to examine to what ex-
tent the particular characteristics of the user signifi-
cantly affect his/her accepted use (RQ 1). To under-
stand which user profiles favour and which profiles
impede the use, can be very valuable not only for the
research but especially for the mercantile purposes.
The second research topic relates to the question
whether, and to what extent, the potential users intend
to use lifelogging for the different activities of daily
living (RQ 2), e.g., to monitor their nutrition or com-
munication habits. And also, it is of interest how the
user acceptance constructs interrelate (RQ 3) and
therefore validate the established technology ac-
ceptance models in the context of lifelogging.
3.2 Study Design
The quantitative study investigated the users’ adop-
tion of lifelogging technology for the activities of
daily living (ADL) and examined the influence of dif-
ferent user characteristics on their attitudes toward
technology acceptance and on the key components
As dependent variables, we examined the follow-
ing key determinants of the technology acceptance:
Perceived ease of use (PEU),
Perceived usefulness (PU),
Attitude toward using (AT), and
Intention to use (ItU) the ADL applications.
ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health
Table 1 summarizes all items for the acceptance
variables PEU, PU, and AT used in the survey, while
the intention to use lifelogging technology for ADL
is elaborated in detail in the results-part (Figure 4).
In addition, technological self-efficacy (TSE)
referring to the perceived competence when interact-
ing with, or handling of, a technologywas included
as one of the main study constructs (see Table 1). Ac-
cording to a significant difference between the male
and female participants in the general handling of
technology (see Section 3.4), TSE was considered as
one of the user characteristics, the impact of which
was statistically tested.
As independent variables, i.e., user characteris-
tics, we used the following:
Age: young (<30 years), middle-aged (3059
years), and older adults (60 years);
Gender: male vs. female users;
Lifelogging experience (LLE: with experience
vs. no experience); and
Technological self-efficacy (TSE: high vs. low).
The described research variables are depicted in
Figure 1; the user characteristics are thereby summa-
rized as the external variables in the research design.
Figure 1: Research design of the study adopted from TAM
(Davis, 1989): The grey frame encompasses the study con-
3.3 Quantitative Data Collection
The quantitative data of the present study were col-
lected by a standardized online survey, structured in
three main parts.
In the first part we collected the demographic data
of the participants, including age, gender, education,
health status and place of residence. To gain infor-
mation about their perceived self-efficacy in using
technology, participants were asked to respond to
questions referring to technological self-confidence
according to Beier (1999).
The second part of the survey started with a short
explanation of the term “lifelogging”. Also, examples
of different contexts of possible application fields
were given, among others health monitoring, location
and presence detection, performance measurement at
work, consumption tracking, etc. Here, the partici-
pants’ knowledge of, and experience with, lifelogging
technologies in their everyday lives was investigated.
If they actively used lifelogging technologies, they
additionally answered questions regarding the type of
lifelogging used and their motives for using it.
In the third part, we asked the participants to en-
vision logging of activities of daily living for their
own use, for instance taking medication, making
phone calls, cleaning of the living spaces, showering,
walking/jogging, etc. A short scenario described the
ADL application as well as the types of sensors used
to log the data. After that, participants were asked to
share their opinions regarding technology acceptance,
Table 1: Items used in the online survey for the assessment
of acceptance in lifelogging applications recording ADL.
Perceived ease of
use (3 items;
- “With the help of lifelogging technologies,
data of my daily activities can be collected
with little effort.”
- “I expect the lifelogging application to be
easy to use.”
- “The handling of the lifelogging technolo-
gies should be intuitive.”
Perceived usefulness
(4 items;
- “It is useful to get an overview of activities
in one’s life with the help of lifelogging
- “Logging of activities of daily life is only
useful for people with health problems.”
- “With the help of lifelogging, parts of daily
life can be optimized.”
- “With the help of specific lifelogging to
record activities of daily life, health prob-
lems can be partly identified.”
Attitude toward
using lifelogging
(3 items;
- “I think it makes sense to record the differ-
ent activities of daily life using lifelogging
- “I evaluate the use of lifelogging technolo-
gies to record activities of daily life nega-
tively.” [recoded]
- “I consider it beneficial to record activities
using lifelogging.”
Technical self
(4 items;
- “I can solve quite a few of the technical
problems I face on my own.”
- “Technical devices are often inscrutable
and difficult to control.” [recoded]
- “Even when there are obstacles, I can still
solve a technical problem.”
- “Most technical problems are so compli-
cated that there is little point in dealing
with them.” [recoded]
User Acceptance of Lifelogging Technologies: The Power of Experience and Technological Self-Efficacy
including aspects of technology acceptance such as
the perceived ease of use, the perceived usefulness,
and their general attitude towards the ADL applica-
tion. Table 1 summarizes the relevant constructs and
items used in the study. The assessment scales pro-
vided the forced choice format for the responses rang-
ing from 1 (=’totally disagree’) to 6 (=’totally agree’).
To avoid any biases, the items were alternated be-
tween positive and negative items. For the statistical
analyses, we transformed all scales of the used con-
structs to 100 points to better compare the results.
3.4 Data Analyses
In this study, the relevant aspects of technology ac-
ceptance and data resulting from logging of ADL are
reported by means of descriptive statistics, like mean
(M), median (Md), and standard deviations (SD). Per-
centages (%) of the examined sample are given to re-
port proportions. To statistically compare the means
for the different user groups, f-tests were calculated.
Multiple analysis of variance (MANOVA) tests for
statistical effects of the examined user factors on the
key determinants of the technology acceptance; effect
sizes were calculated by eta squared (η
) according to
Cohen (1988). For correlative analyses, Pearson’s
product-moment correlation (r) was calculated for
continuous variables, Spearman’s rank order correla-
tion (
) for dichotomous variables. The level of sta-
tistical significance (p) was set at the conventional
level of 5%.
3.5 Participants
The target population for this study consisted of
N=209 adults between 18 and 79 years of age (M=37,
SD=15.1) and 54% of them were female (n=112). The
vast majority enjoyed good or very good health (94%;
n=196), 19% reported chronic disease or a physical
impairment (n=39), and 22% stated to regularly take
medication (n=46). Moreover, 56% of the respond-
ents (n=117) stated to already actively use lifelogging
technologies; Table 2 summarizes the users’ experi-
ence regarding the used technology, their motives and
the context of use. According to the outcomes, partic-
ipants use apps in their smartphones most frequently
for health monitoring, location detection and con-
sumption tracking, and they report self-monitoring
and fun as the main motives for the lifelogging usage.
As opposed to that, cameras are used the least and the
performance measurement at the workplace as well as
the use for comparison reasons are the less preferred
options for logging one’s own data.
Table 2: Used technologies, contexts of use and usage mo-
tives among the participants who use lifelogging in their
daily life (N=117).
Proportion of
Used technology
Fitness wristband
Smartphone app
Personal computer (manual entry)
Stationary and portable cameras
12.9% (n=27)
37.8% (n=79)
13.9% (n=29)
5.3% (n=11)
Context of usage
Health monitoring
Location and presence detection
Performance measurement (work)
External “memory”
Consumption tracking
37.3% (n=78)
39.7% (n=83)
8.6% (n=18)
18.2% (n=38)
28.2% (n=59)
Usage motives
Fun / Interest in the subject matter
Improvement of performance
Improvement of well-being
Comparison with others
Financial reasons
31.1% (n=65)
40.2% (n=84)
21.5% (n=45)
16.3% (n=34)
4.8% (n=10)
9.1% (n=19)
The outcome revealing that more than half of the
examined sample used lifelogging technologies led to
the analysis of how the subjects perceived their gen-
eral technological self-efficacy. Respondents reached
on average M=18.4 (SD=3.5) from 24 possible points
on the TSE scale. The results in technological self-
efficacy significantly differed depending on the par-
ticipants’ gender [F(1,208)=18.02, p.001;
but less so for the different age groups [F(2,208)=1.7,
n.s.]. Given this finding, we assigned high and low
levels of technological self-efficacy according to the
gender-related median-splitting of the sample
=20, SD=2.9; Md
=17, SD=3.5).
In this section, we firstly examine the influence of the
external variables on the acceptance of lifelogging
technology and present then the results of intended
use of lifelogging in different activities of daily liv-
ing. Eventually, correlations between the relevant re-
search variables are displayed.
4.1 Effects of the External Variables on
the Acceptance
In a multiple analysis of variance, we included all in-
dependent variables (age groups, gender, TSE, LLE)
and tested their impact on the acceptance criteria
PEU, PU, and AT.
ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health
The analysis revealed a moderate effect of the
technological self-efficacy [F(3,184)=3.9, p=.010;
=.06] and showed that participants with a high level
of TSE (M=83.3, SD=11.3) perceived the lifelogging
applications for ADL as easier to use than participants
with a low level in TSE (M=79.3, SD=13). At the
same time, the overall scores for perceived usefulness
(low: M=65, SD=13.6; high: M=62.7, SD=17.2) and
the attitude toward using lifelogging (low: M=62.4,
SD=16.5; high: M=62.1, SD=22) were reversed for
persons with high and low levels of TSE. The means
are depicted in Figure 2.
Figure 2: Effect of technological self-efficacy on ac-
In addition, the MANOVA revealed a significant
impact of the existing lifelogging experience on the
acceptance [F(3,184)=4.1, p=.008; η
=.06]. This
moderate effect with the resulting means is showed in
Figure 3 and discloses that participants with experi-
ence reach in all acceptance criteria higher means
(PEU: M=84.4, SD=10.4; PU: M=67.2, SD=14.7; AT:
M=67, SD=17.8) than participants without experience
(PEU: M=77.5, SD=13.5; PU: M=59.5, SD=15.6; AT:
M=56.1, SD=19.8).
Figure 3: Effect of lifelogging experience on acceptance.
4.2 Intention to Use Lifelogging
Technology for ADL
In the next step, we present the results of intention to
use lifelogging technology. Participants of the online
survey were asked to indicate on a four-point Likert
scale whether they would permit (=4) or reject (=1)
the lifelogging for different activities of daily living.
The resulting means are summarized in Figure 4.
As can be seen there, the opinions in our sample were
not very distinct. Most of the activities were rather
slightly rejected (means < 2.5). Only two activities,
i.e., the medication intake (M=2.9, SD=1.2) and the
mobility behaviour (M=2.9, SD=1.2), were on aver-
age slightly permitted and for the preparation of
meals resulted a neutral opinion (M=2.5, SD=1.2). By
contrast, logging of body care and hygiene was most
clearly rejected by the respondents (M=1.5, SD=1).
Figure 4: Means resulting for the intention to use lifelog-
ging technology for different activities of daily living.
4.3 Correlations between the Relevant
Research Variables
Given the above outcomes, we provide now an over-
view of correlative relationships between the relevant
research variables.
Considering firstly the correlations between the
user characteristics (=external variables) and the user
acceptance, lifelogging experience positively corre-
lated with all key criteria (PEU:
=.26, p.001; PU:
=.25, p.001; AT:
=.29, p.001) and the techno-
logical self-efficacy moderately shaped the PEU
(r=.30, p.001). Among the demographic variables,
age negatively correlated with the user acceptance
criteria (PEU: r=.20, p.003; PU: r=.20, p.004;
AT: r=.15, p.026). Additionally, the self-efficacy
(r=.23, p.001) and the experience (r=.25, p.001)
decreased with increasing age. Even though the cor-
relations were rather weak, these results make clear
that age is an important carrier for the technology
User Acceptance of Lifelogging Technologies: The Power of Experience and Technological Self-Efficacy
adoption. Gender correlated directly “only” with PU
=.19, p.006), however, it also significantly
shaped the self-efficacy (
=.38, p.001) thus indi-
rectly interrelating with the other acceptance criteria.
All these correlations are provided in Figure 5.
Figure 5: Correlations between user factors and the key ac-
ceptance criteria (N=209; ** p.01, * p.05).
Next, we specify the correlative relationships be-
tween the beliefs and attitudinal constructs. PEU and
PU resulting from this study were positively associ-
ated (r=.34, p.001), but their connections to the atti-
tude toward using lifelogging varied considerably in
strength: While PEU only moderately (r=.25, p.001)
correlated with the general attitude toward using life-
logging technology, PU was very strongly connected
to it (r=.81, p.001).
Following Technology Acceptance Model (Davis,
1989), the general attitude toward using the technol-
ogy significantly affects the intention to use it. Figure
6 depicts the correlation coefficients to all activities
of daily living enquired in the survey. As it is evident,
AT was significantly associated with using lifelog-
ging for different ADL. A positive AT correlated
strongly with the intention to use lifelogging for the
mobility behaviour (r=.63, p.001), shopping (r=.58,
p.001), and for preparation of meals (r=.56, p.001).
Figure 6: Correlations between the attitude toward using
lifelogging and different ADL (N=209; **p.001).
The smallest correlation coefficient resulted between
the AT and lifelogging for body hygiene (r=.31,
p.001), suggesting a weaker relationship between
the variables. Overall, the higher the attitude the
higher was the intention to use lifelogging for differ-
ent activities of daily living.
The presented research investigated the adoption of
lifelogging technology, examining the influence of
different user factors on the key predictors of user ac-
ceptance. Using technology acceptance constructs ac-
cording to TAM (Davis, 1989), the study analysed
how external variables (i.e., user characteristics) af-
fect the users’ attitudes, and how the key acceptance
criteria are related to the intention of using lifelogging
technologies for the activities of daily living. In the
following, we discuss the key results and the limita-
tions of this study.
5.1 Key Results
In reference to the first research question (RQ1), our
findings show that the most influential external fac-
tors, referring to the user characteristics, are the tech-
nological self-efficacy and lifelogging experience. In
comparison, the demographic variables of age and
gender are less influentialat least these variables do
not directly affect the user’s acceptance.
According to the results, high levels of gender-
specific self-efficacy cause the users to perceive life-
logging as easier to use, but this does not apply to the
perceived usefulness of, and the general attitude to-
ward, using lifelogging for ADL. This outcome indi-
cates that high competence in handling of lifelogging
technology influences the users’ acceptance in the
way of an easy interaction with the technology, even
though this perceived competence does not signifi-
cantly affect the attitude toward lifelogging or its use-
fulness. To the contrary, the experience with lifelog-
ging affects user acceptance throughout. In concrete
terms, having the experience makes users perceive
the lifelogging applications as significantly more use-
ful and easier to use, and these users are generally
more positive about using this technology for the ac-
tivities of daily living. This outcome corroborates
previous findings referring, for example, to the assis-
tive social robots (Heerink, 2011), blog assistance be-
haviours (Chang and Yang, 2013) or even autono-
mous vehicles (Cho et al., 2017).
According to the correlation analyses, among the
demographic variables age is weakly associated with
ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health
user acceptance. The negative coefficients indicate
that with increasing age users perceive the technology
as less easy to use and less useful, with their positive
attitude toward using the technology diminishing.
Although several other studies have found a signifi-
cant impact of age on the user acceptance of technol-
ogy in different contexts (e.g., Heerink, 2011; Miller
and Bell, 2012), according to our analyses the influ-
ence of age plays no significant role in the context of
Interestingly, we observe in our findings also the
missing impact of gender on the acceptance of the
lifelogging technology for the activities of daily liv-
ing. At the same time, user acceptance is significantly
affected by the technological self-efficacy, the levels
of which were assigned to the participants on the basis
of the gender-specifically varying medians. This ex-
citing result suggests that even though gender itself
does not directly influence acceptance ratings, it indi-
rectly diverges the attitudes of the potential users,
playing thus an important role regarding their ac-
ceptance or rejection of the technology.
Referring to the second research question (RQ2)
of this study, we can state that the overall intention to
log different activities of the daily life is rather reluc-
tant. According to our results, there is no activity
among those investigatedwhich would be enthusi-
astically used by the survey participants. While on av-
erage people still permit to monitor their medication
intake and mobility behaviour, they clearly reject the
observation of their body hygiene habits. The remain-
ing activities, like shopping, cleaning, communica-
tion or nutrition, are less interesting for lifelogging
among the respondents.
Furthermore, the key predictors of technology ac-
ceptance, PEU and PU, are both positively connected,
being in accordance with the TAM (Davis, 1989).
The attitude toward using lifelogging is very strongly
correlated especially with the perceived usefulness
and is also positively associated with the intention to
use lifelogging for the different activities of daily liv-
ing. These findings are in line with previous research
on the technology acceptance (RQ3).
Summing up, from our results follows that the key
to make an efficient use of lifelogging for ADL lies
in trying it out and making the own experience on the
applications, strengthening at the same time the own
technological self-efficacy. Among the user charac-
teristics, age and gender are less influential but shape
the using behaviour indirectly: The hurdle seems to
be higher, the older are the users and women ap-
proach lifelogging with less technological confidence
than men. Conversely, this means that in the less tech-
nically adept user groups communication and market-
ing strategy on the potential and the benefits of life-
logging should be anew elaborated to overall optimise
the adoption.
5.2 Limitations and Future Research
Before concluding, some limitations of the research
and also the potential for future studies should be ad-
Firstly, due to the structure of our sample the risk
of a beta-error regarding the impact of age on using
lifelogging for ADL represents an issue. We used an
arbitrary division of the test persons to the respective
age groups, trying to depict young (n=112), middle-
aged (n=76), and older parts of the society and tech-
nology users at the same time. However, the group
sizes varied greatly: Especially the proportion of the
adults aged 60 years and above was comparably small
(n=21), so that statistical validity is questionable; this
could lead to a missing disclosure of statistically con-
siderable differences.
In addition, the focus on the use of lifelogging
technology reaches primarily an already selected
group of people, who are most probably familiar with
technological innovations. To meet the needs of less
technology-savvy persons, and thereby increase the
representativeness of the findings, future research has
to extend the radius of the addressees up to surveys
with traditional paper and pencil data collection in ad-
dition to the online survey method.
The presented empirical study shows thatlikewise
many previous technologiesthe use of lifelogging
technologies for the activities of daily living broadly
depends on the perceived usefulness and an easy use,
which shape the user’s general attitude and the inten-
tion to use them. However, user characteristics deci-
sively influence the acceptance of this technology.
The previous experience with lifelogging and the
technological self-efficacy significantly affect the
user acceptance, but also the carrying variables age
and gender shape the actual useeven though not in
a direct way.
User Acceptance of Lifelogging Technologies: The Power of Experience and Technological Self-Efficacy
We thank all survey respondents for participation and
sharing their opinions on aspects referring to their ac-
ceptance of lifelogging technologies in their everyday
living. Kind thanks also to Linda Engelmann for the
assistance in data collection. This work resulted from
the project PAAL (Privacy Aware and Acceptable
Lifelogging services for older and frail people) and
was funded by the German Federal Ministry of Edu-
cation and Research (16SV7955).
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