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.

1 INTRODUCTION

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

https://orcid.org/0000-0002-7163-3492

https://orcid.org/0000-0003-1870-2775

https://orcid.org/0000-0002-6105-4729

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

example of applications referring to activities of daily

living. The main focus lies on the user characteris-

tics—not only the demographic attributes but also

technological self-confidence and previous experi-

ence of users—and 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.

2 THEORETICAL BACKGROUND

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-

rameters.

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-

tics—i.e., factors referring to the users of the consid-

ered technology—have 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.

3 METHOD

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

thereof.

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 technology—was 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 (30–59

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-

tents.

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.

Construct

Items

Perceived ease of

use (3 items;



=.71)

- “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;



=.74)

- “It is useful to get an overview of activities

in one’s life with the help of lifelogging

technologies.”

- “Logging of activities of daily life is only

useful for people with health problems.”

[recoded]

- “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;



=.91)

- “I think it makes sense to record the differ-

ent activities of daily life using lifelogging

technologies.”

- “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

-efficacy

(4 items;



=.81)

- “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

users

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

Self-monitoring

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;



=.08];

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

(Md

male

=20, SD=2.9; Md

female

=17, SD=3.5).

4 RESULTS

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-

ceptance.

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.

5 DISCUSSION

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 influential—at 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

lifelogging.

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 investigated—which 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-

dressed.

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.

6 CONCLUSIONS

The presented empirical study shows that—likewise

many previous technologies—the 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 use—even though not in

a direct way.

User Acceptance of Lifelogging Technologies: The Power of Experience and Technological Self-Efﬁcacy

ACKNOWLEDGEMENTS

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).

REFERENCES

Ajzen, I., 1991. The theory of planned behavior. Organiza-

tional behavior and human decision processes, 50(2),

179–211.

Arber, S., Vandrevala, T., Daly, T., Hampson, S., 2008. Un-

derstanding gender differences in older people's atti-

tudes towards life-prolonging medical technologies.

Journal of Aging Studies, 22(4), 366–375.

Beier, G., 1999. Kontrollüberzeugungen im Umgang mit

Technik [Control convictions in dealing with

technology]. Report Psychologie, 9, 684–693.

Broos, A., 2005. Gender and information and communica-

tion technologies (ICT) anxiety: Male self-assurance

and female hesitation. CyberPsychology & Behavior,

8(1), 21–31.

Caprani, N., Piasek, P., Gurrin, C., O'Connor, N. E., Irving,

K., Smeaton, A. F., 2014. Life-long collections: moti-

vations and the implications for lifelogging with mobile

devices. International Journal of Mobile Human Com-

puter Interaction (IJMHCI), 6(1), 15–36.

Chang, Y. S., Yang, C., 2013. Why do we blog? From the

perspectives of technology acceptance and media

choice factors. Behaviour & Information Technology,

32(4), 371–386.

Chen, Y., Jones, G. J.F., 2012. What do people want from

their lifelogs? In: 6th Irish Human Computer Interac-

tion Conference (iHCI2012), 20-21 June 2012, Galway,

Ireland.

Cho, Y., Park, J., Park, S., Jung, E. S. (2017). Technology

acceptance modeling based on user experience for au-

tonomous vehicles. Journal of the Ergonomics Society

of Korea, 36(2), 87–108.

Climent-Pérez, P., Spinsante, S., Mihailidis, A., Florez-Re-

vuelta, F., 2020. A review on video-based active and

assisted living technologies for automated lifelogging.

Expert Systems with Applications, 139, 112847.

Cohen, J. (1988). Statistical power analysis for the behav-

ioral sciences, Lawrence Erlbaum Associates. New

York, 2

edition.

Davis, F. D., 1989. Perceived Usefulness, Perceived Ease

of Use, and User Acceptance of Information Technol-

ogy. MIS Quarterly, 13(3), 319–340.

Durndell, A., Haag, Z., 2002. Computer self-efficacy, com-

puter anxiety, attitudes towards the Internet and re-

ported experience with the Internet, by gender, in an

East European sample. Computers in Human Behavior,

18(5), 521–535.

Fishbein, M. and Ajzen, I., 1975. Belief, Attitude, Intention

and Behavior: An Introduction to Theory and Research.

Addison-Wesley Publishing Company, Inc., Reading,

MA, USA.

Gurrin, C., Smeaton, A. F., Doherty, A. R., 2014. Lifelog-

ging: Personal big data. Foundations and Trends in In-

formation Retrieval, 8(1), 1–125.

Harvey, M., Langheinrich, M., Ward, G., 2016. Remember-

ing through lifelogging: A survey of human memory

augmentation. Pervasive and Mobile Computing, 27,

14–26.

Hayes, T. L., Abendroth, F., Adami, A., Pavel, M.,

Zitzelberger, T. A., Kaye, J. A., 2008. Unobtrusive as-

sessment of activity patterns associated with mild cog-

nitive impairment. Alzheimer's & Dementia, 4(6), 395–

405.

Heerink, M., 2011. Exploring the influence of age, gender,

education and computer experience on robot ac-

ceptance by older adults. In 2011 6th ACM/IEEE Inter-

national Conference on Human-Robot Interaction

(HRI), pp. 147-148. IEEE.

Jalal, A., Kamal, S., Kim, D., 2014. A depth video sensor-

based life-logging human activity recognition system

for elderly care in smart indoor environments. Sensors,

14(7), 11735–11759.

Kelly, P., Marshall, S. J., Badland, H., Kerr, J., Oliver, M.,

Doherty, A. R., Foster, C., 2013. An ethical framework

for automated, wearable cameras in health behavior re-

search. American journal of preventive medicine, 44(3),

314–319.

Lidynia C., Schomakers EM., Ziefle M., 2019. What Are

You Waiting for? – Perceived Barriers to the Adoption

of Fitness-Applications and Wearables. In: Ahram T.

(eds) Advances in Human Factors in Wearable Tech-

nologies and Game Design. AHFE 2018. Advances in

Intelligent Systems and Computing, vol 795. Springer,

Cham.

McAdams E., Krupaviciute A., Gehin C., Dittmar A., Del-

homme G., Rubel P., Fayn J., McLaughlin J., 2011.

Wearable Electronic Systems: Applications to Medical

Diagnostics/Monitoring. In: Bonfiglio A., De Rossi D.

(eds.) Wearable Monitoring Systems. Springer, Boston.

Miller, L. M. S., Bell, R. A., 2012. Online health infor-

mation seeking: the influence of age, information trust-

worthiness, and search challenges. Journal of aging and

health, 24(3), 525–541.

Morganti, L., Riva, G., Bonfiglio, S., Gaggioli, A., 2013.

Building collective memories on the web: the Nostalgia

Bits project. International Journal of Web Based Com-

munities, 9(1), 83–104.

Mubashir, M., Shao, L., Seed, L., 2013. A survey on fall

detection: Principles and approaches. Neurocomputing,

100, 144–152.

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

O’Hara, K., Tuffield, M.M., Shadbolt, N., 2008. Lifelog-

ging: Privacy and empowerment with memories for

life. IDIS, 1, 155–172.

Poli A., Strazza A., Cecchi S., Spinsante S., 2020. Identifi-

cation Issues Associated with the Use of Wearable Ac-

celerometers in Lifelogging. In: Gao Q., Zhou J. (eds)

Human Aspects of IT for the Aged Population. Technol-

ogies, Design and User Experience. HCII 2020. LNCS,

vol 12207. Springer, Cham.

Rahimi, B., Nadri, H., Afshar, H. L., Timpka, T., 2018. A

systematic review of the technology acceptance model

in health informatics. Applied clinical informatics, 9(3),

604.

Rashidi, P., Cook, D. J., 2009. Keeping the resident in the

loop: Adapting the smart home to the user. IEEE Trans-

actions on systems, man, and cybernetics-part A: sys-

tems and humans, 39(5), 949–959.

Rashidi, P., Mihailidis, A., 2013. A survey on ambient-as-

sisted living tools for older adults. IEEE Journal of Bi-

omedical and Health Informatics, 17(3), 579–590.

Schoeppe, S., Alley, S., Van Lippevelde, W., Bray, N. A.,

Williams, S. L., Duncan, M. J., Vandelanotte, C., 2016.

Efficacy of interventions that use apps to improve diet,

physical activity and sedentary behaviour: a systematic

review. International Journal of Behavioral Nutrition

and Physical Activity, 13(1), 127.

Selke, S., 2016. Lifelogging: Digital self-tracking and Life-

logging-between disruptive technology and cultural

transformation. Springer Fachmedien Wiesbaden.

Shah, M., Mears, B., Chakrabarti, C., Spanias, A., 2012.

Lifelogging: Archival and retrieval of continuously rec-

orded audio using wearable devices. In 2012 IEEE In-

ternational Conference on Emerging Signal Processing

Applications (pp. 99–102). IEEE.

Suzuki, T., Murase, S., Tanaka, T., Okazawa, T., 2007.

New approach for the early detection of dementia by

recording in-house activities. Telemedicine and e-

Health, 13(1), 41–44.

Venkatesh, V., Bala, H., 2008. Technology acceptance

model 3 and a research agenda on interventions. Deci-

sion sciences, 39(2), 273–315.

Venkatesh, V., Davis, F. D., 2000. A theoretical extension

of the technology acceptance model: Four longitudinal

field studies. Management science, 46(2), 186–204.

Wolf, K., Schmidt, A., Bexheti, A., Langheinrich, M.,

2014. Lifelogging: You're wearing a camera?. IEEE

Pervasive Computing, 13(3), 8–12.

Ziefle, M., Schaar, A. K., 2011. Gender differences in ac-

ceptance and attitudes towards an invasive medical

stent. Electronic Journal of Health Informatics, 6(2),

13.

Ziefle, M., Bay, S., 2005. How older adults meet complex-

ity: aging effects on the usability of different mobile

phones. Behaviour & information technology, 24(5),

375–389.

User Acceptance of Lifelogging Technologies: The Power of Experience and Technological Self-Efﬁcacy