Caregivers’ Perspectives on Ambient Assisted Living Technologies in

Professional Care Contexts

Julia van Heek, Martina Ziefle and Simon Himmel

Human-Computer Interaction Center, RWTH Aachen, Campus-Boulevard 57, 52064 Aachen, Germany

Keywords: Ambient Assisted Living Technologies, Technology Acceptance, Professional Caregivers, Data Gathering

and Storage, User Diversity.

Abstract: An increasing proportion of older people in need of care presents one of the major challenges within demo-

graphic change. The development of Ambient Assisted Living (AAL) technologies is one option to face the

challenges of rising care needs. Beyond technical and economic aspects, the acceptance of diverse stake-

holders plays a major role for a successful implementation and rollout of those technologies. In particular, it

is questionable whether and to which extent the use of assisting technologies is accepted in professional care

contexts, in particular with respect to gathering and storage of data. Thus, the current study aimed for an in-

vestigation of professional caregivers’ perspectives on the acceptance of AAL technologies in professional

care contexts. In a scenario-based online questionnaire, n = 287 professional caregivers evaluated perceived

benefits, barriers, and acceptance of AAL technologies. Also, they indicated which data can be gathered and

which specific technologies should be used to gather data. Further, data access and data storage were also

under study. The results showed a reserved and critical attitude of professional caregivers towards using

AAL technologies in their everyday working life and allow to analyze trade-offs between permitted gath-

ered data and specific requested technologies in depth.

1 INTRODUCTION

Demographic change entails the development of

more and more older people and people in need of

care representing enormous challenges for today’s

society and especially high burdens for the care

sector (Pickard, 2015; Walker & Maltby, 2012;

Bloom & Canning, 2004). In particular, geriatric and

nursing care institutions suffer badly from a lack of

specialists in combination with higher proportions of

old and diseased people who have to be cared

(Siewert et al., 2010; Shaw et al., 2010; Wild et al.,

2004; Roger et al., 2011). At the same time, there is

a first generation of “old disabled” people due to

medical and technical developments in healthcare

and otherwise also due to the specific historical

background of euthanasia offenses (in particular in

Europe), in which disabled people were systemati-

cally deported or even murdered (Poore, 2007).

Thus, similar to the challenges for geriatric and

nursing care, the sector of care and support of disa-

bled people is also confronted with higher propor-

tions of people in need of care and a simultaneous

lack of care staff (WHO, 2012).

Summarizing, all these care areas face essentially

the same challenges and questions arise how those

challenges can be addressed. Technical innovations

and ideas are increasingly developed in order to

relieve care staff, to enable a longer opportunity to

stay at home for older people, or to enhance safety in

emergencies. Among those technical innovations

and ideas, technical single-case solutions as well as

more complex ambient assisted living systems

(AAL) (Memon et al., 2014; Frank & Labonnote,

2015) exist that detect falls and emergencies, moni-

tor vital parameters, or enable living longer at home

using smart home technology elements (Cheng et al.,

2013; Baig & Gholamhosseini, 2013; Kleinberger et

al., 2007; Rashidi & Mihailidis, 2013).

Besides technical functionality and possibilities,

current research reveals that those systems are rarely

used in real life and especially in professional envi-

ronments (Wichert et al., 2012). The users’ ac-

ceptance is decisive for a sustainable implementa-

tion and usage of innovative technologies and sys-

tems. Hence, diverse stakeholders of AAL technolo-

gies should be addressed and their perceptions, opin-

ions, and ideas should be investigated. Previous

van Heek, J., Zieﬂe, M. and Himmel, S.

Caregivers’ Perspectives on Ambient Assisted Living Technologies in Professional Care Contexts.

DOI: 10.5220/0006691400370048

In Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2018), pages 37-48

ISBN: 978-989-758-299-8

studies indicate differences in AAL acceptance be-

tween disabled people or people in need of care and

the perspective of professional caregivers. Addition-

ally, the caregivers’ acceptance of assistive technol-

ogies is a prerequisite for a successful implementa-

tion of AAL technologies in professional care con-

texts. Therefore, the current study especially focuses

on professional caregivers’ perspectives on specific

AAL technologies, on gathering of data, data access,

storage duration as well as perceived benefits and

barriers. Based on this investigation, it is possible to

integrate the caregivers’ professional perspective

into the design of AAL technologies. This way,

specific technologies can be adapted to the needs

and wishes of professional caregivers and the usage

of AAL technologies in professional care contexts

could potentially be increased.

2 ACCEPTANCE OF AAL

TECHNOLOGIES IN CARE

This section presents the theoretical background of

the current study starting with a short overview of

current AAL technologies and systems. Afterwards,

prestigious and well-known acceptance models as

well as results previous acceptance studies in the

context of AAL technologies for usage in profes-

sional care contexts are introduced.

2.1 Ambient Assisted Living

The term Ambient Assisted Living (AAL) summa-

rizes assisting technologies or systems that contrib-

ute to maintenance of autonomy in everyday life and

are especially applied in care for prevention and

rehabilitation (Kleinberger et al., 2007; Georgieff,

2008). Those technologies cover a broad range of

applications reaching from monitoring and detection

to reminders and smart home functionalities. Here, a

short overview of prototypical examples is given.

In the context of (outdoor) tracking and detection

of positions, Radio Frequency Identification (RFID)

tags are frequently used (Dohr et al., 2010). Further,

different types of monitoring are made possible by

integrating common Information and Communica-

tion Technologies (ICT) (e.g., microphones, move-

ment sensors, or (infra-red) cameras) into people’s

living environments. In particular, it is aimed for

enhancing safety by detection falls and emergencies

in private home environments (Stone & Skubic,

2015) as well as in professional care contexts, e.g.,

hospitals or care institutions (Ni et al., 2012). Be-

sides those safety-relevant functions, other types of

AAL technologies aim for facilitating everyday life

by using automated technologies, e.g. memory aids

or home automation (Costa et al., 2009; Hristova et

al., 2008). Further, also supporting communication

with families, friends, and caregivers by integrating

ICT in home environments is an aim of AAL

(Kleinberger et al., 2007). A further area of AAL

technologies are wearable technologies (e.g., emer-

gency arm strap) worn on the body or integrated in

clothes that are able to communicate with intelligent

AAL systems or smart home environments (Patel et

al., 2012; Memon et al., 2014). Although numerous

systems and technologies are available on the market

(e.g., Essence, 2017; Tunstall, 2017) or focused in

current research projects (e.g., Gövercin et al.,

2016), resounding success of those systems has not

occurred so far, as they are only rarely used in real

life (Wichert et al., 2012) and especially in profes-

sional care contexts (Isern et al., 2010).

On this basis, the question arises for what rea-

sons those existing, assisting, and facilitating tech-

nologies are not widely used in professional

healthcare contexts although they have the potential

to facilitate the professional everyday life? Future

users’ acceptance as well as their perception of us-

age benefits and especially barriers are decisive for a

successful integration of AAL systems in everyday

life. To understand the barriers of AAL usage in

professional contexts, we therefore focused on pro-

fessional caregivers as potential users of these sys-

tems, their perceptions, ideas, wishes, and willing-

ness to adopt home-integrated ICT in this study.

2.2 User-specific Acceptance of AAL

Previous research results revealed that AAL tech-

nologies were mostly evaluated positively and the

necessity and usefulness of technical support were

acknowledged by diverse groups of potential users

(van Heek et al., 2017; Himmel & Ziefle, 2016;

Beringer et al., 2011; Gövercin et al., 2016). Ena-

bling an independent and more autonomous life as

well as a longer staying at the own home for older,

diseased and/or disabled people are strong drivers to

use AAL technologies. In contrast, feelings of isola-

tion (e.g., van Heek et al., 2017a; Sun et al., 2010),

feelings of surveillance, and invasion of privacy

(e.g., Wilkowska & Ziefle, 2012; Wilkowska et al.,

2015; van Heek et al., 2017b) represent the most

frequently mentioned barriers if people were asked

to think about an integration of AAL technologies in

their living environment. In more detail, numerous

focus group (e.g., Demiris et al., 2004; Ziefle et al.,

ICT4AWE 2018 - 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health

2011) and interview studies (e.g., Beringer et al.,

2011) with people aged above 60 were conducted in

order to examine the elderly’s perceptions of AAL

technologies: Similar to the mentioned general posi-

tive perception, the older participants acknowledged

the benefits of staying at home longer, understood

the problematic lack of care staff as well as the

chances and potential of AAL technologies. On the

other side, they expressed fears concerning a de-

pendency on technologies they are not able to con-

trol, a lack of personal contact referring to the con-

cern that care staff will might be substituted by tech-

nologies, and privacy concerns. These mostly quali-

tative gained results have been confirmed by numer-

ous quantitative surveys over the last years (e.g.,

Himmel & Ziefle, 2016).

The perspectives of professional care givers on

integrating AAL technologies in professional care

contexts have rarely been considered in acceptance

research so far, although their perspectives are man-

datory in order to do justice to needs of care and care

itself in professional care contexts. Single studies

focused on caregivers as potential users and on their

perceived concerns regarding in-home monitoring

technologies (Larizza et al., 2014). Other studies

concentrated on requirements and perception of

AAL technology usage as well as the effectiveness

of different technologies, and deriving of guidelines

for design and implementation in the context of

professional care environments (López et al., 2015;

Mortenson et al., 2013).

Although those previous results showed a gen-

eral positive attitude towards AAL technologies,

another comparative study revealed a more critical

and restraint attitude of professional caregivers to-

wards AAL technologies compared to disabled par-

ticipants, relatives of disabled persons, and “not”-

experienced (in terms of professional expertise or in

terms of personal affliction) participants (van Heek

et al., 2017a). This might serve as a starting point

and explanation why AAL technologies are not

widely used in professional care contexts. Hence, it

is of great importance to investigate the perceptions,

wishes, and needs of this specific user group in

depth. To understand the emerging negative attitude

of professional caregivers and the trade-off between

acknowledged benefits and existing perceived barri-

ers, it is necessary to investigate the acceptance of

AAL technologies with a specific and detailed focus

on these users and their usage environments.

For investigating the acceptance of assisting ICT,

well-known and widely spread acceptance models

such as TAM, UTAUT, and their adapted versions

were urgently used in the past years. Against the

background of increasing usage requirements in the

context of care, the existing models of technology

acceptance are not sufficient among others due to

the sensible usage context of care, the models’ view

of acceptance as static technology assessment, and

leaving apart user factors as well as trade-offs be-

tween simultaneously existing benefits and barriers

(Ziefle & Jakobs, 2010).

Therefore, we used interviews specifically tai-

lored to professional caregivers in a first step in

order to identify challenges in care and focus on

perceived benefits as well as perceived barriers of

AAL technology usage. Further, we aimed for an

identification of what technology is exactly allowed

to do and not to do by professional caregivers. On

this basis, we conceptualized an online questionnaire

tailored to professional caregivers needs and wishes

and ensured that all relevant aspects (for this specific

user group) can be quantified.

3 METHOD

In this section, the research design is presented start-

ing with the research questions and aims of the cur-

rent study. Afterwards, the empirical design of the

quantitative study and the sample’s characteristics

are detailed. Our study aimed for an investigation of

professional caregivers’ acceptance of AAL tech-

nologies in professional care contexts including the

following research questions:

1. How do professional caregivers evaluate AAL

technologies and potential benefits as well as

barriers? (RQ 1)

2. Which data can be gathered, which technologies

can be used to gather data, and how is data ac-

cess and storage duration evaluated? (RQ 2)

3. Do user diversity characteristics impact the

acceptance and AAL technology evaluation di-

mensions (benefits, barriers, technologies, data,

data access, storage duration)? (RQ 3)

3.1 Empirical Design

The questionnaire items were developed based on

the findings of previous interview studies. The first

part of the questionnaire addressed demographic

characteristics such as age, gender, education, dura-

tion of professional experience, and care sector (i.e.

geriatric care, nursing care, care/support of disabled

people). In the next part, the participants were asked

to evaluate their technical self-efficacy (using four

items,



= .884; Beier, 1999), their needs for privacy

(using six items,



= .833; Xu et al., 2008; Morton,

Caregivers’ Perspectives on Ambient Assisted Living Technologies in Professional Care Contexts

2013), and their interpersonal trust (using three

items,



= .793; McKnight et al., 2002).

For ensuring that all participants pertain to the

same baseline referred to the evaluation of AAL

technology, a scenario was designed as a very per-

sonal everyday working situation wherein the partic-

ipants should imagine that an AAL system was inte-

grated in their professional working environment. As

technologies of the system, room sensors, ultrasonic

sensors, microphones, and video cameras were in-

troduced and their range of functions and possibili-

ties within the AAL system were explained (e.g.,

alarms (emergencies, falls), automatic opening and

closing of doors and windows, reminders, etc.).

Afterwards, the participants were asked to evalu-

ated potential benefits of the described AAL sys-

tem’s usage within their professional working envi-

ronment (using 14 items,



= .923; based on previ-

ous interview studies’ results). Further, the partici-

pants also assessed potential barriers (using 17

items;



= .861; also based on previous interview

studies’ results). In a next part, the participants

should indicate whether they agreed with gathering

different types of data (using 14 items (data types),



= .856; based on necessary information to realize

technical functions).

Then, the participants were asked to evaluate dif-

ferent technologies to gather data (using 12 items,



= .892; based on technical configurations of AAL

systems). To evaluate the acceptance of the AAL

system, the participants evaluated six different

statements (



= .932; e.g., “I find the described AAL

system useful”). All described items had to be eval-

uated on six-point Likert scales (1 = min: ”I strongly

disagree”; 6 = max: “I strongly agree”) and are pre-

sented in section 4.

Finally, the participants were given opportunity

to reason their opinions on an optional basis and to

provide their feedback concerning the study. Com-

pleting the questionnaire took, on average, 20

minutes. Data was collected online in Germany.

Participants were recruited in online networks as

well as by personal and project contact to care insti-

tutions. Overall, the questionnaire was made availa-

ble for 3 months in spring and summer 2017.

3.2 Sample Description

A total of 287 participants volunteered to participate

in our questionnaire study, which was partly ac-

quired by personal and by direct contact to profes-

sional care institutions. Since only complete data

sets could be used for statistical analyses, a sample

of n=174 remained. The participants were, on aver-

age, 36.3 years old (SD = 11.2; min = 19; max = 68)

and predominantly female (74.7%) (25.3% male).

Most of the participants indicated a completed ap-

prenticeship (42.5%) as their highest educational

level. Further, each 23.0% reported to hold a univer-

sity degree and a university entrance diploma. 7.5%

indicated to hold a secondary school certificate,

while 4.0% reported other certificates.

All participants worked or have worked as pro-

fessional caregivers: 25.9% (n = 45) in geriatric

care, 21.3% (n = 37) in nursing care, and 52.9% (n =

92) in care and support of disabled people. On aver-

age, the caregivers have long-term professional

experience: 43.5% (n = 74) more than 10 years,

41.8% (n = 71) between 3 and 10 years, and only

14.7% (n = 25) have less than 3 years professional

experience.

Referring to attitudinal variables, the participants

reported to have on average a middle technical self-

efficacy (M = 3.4; SD = 0.7; min = 1; max = 6) and

also a middle interpersonal trust (M = 3.5; SD = 0.8;

min = 1; max = 6). The participants’ needs for priva-

cy and data security were on average positive (M =

4.2; SD = 0.9; min = 1; max = 6).

4 RESULTS

Prior to descriptive and inference analyses, item

analyses were calculated to ensure measurement

quality, while a Cronbach’s alpha > 0.7 indicated a

satisfying internal consistency of the scales. Data

was analyzed descriptively, by linear regression

analyses and, with respect to user diversity effects,

by correlation and linear regression analysis. The

level of significance was set at 5%.

First, the results were presented descriptively for

the perception of benefits and barriers as well as the

participants’ evaluation of different technologies,

gathered data, data access, and data storage. In a

second step, the results of a linear regression analy-

sis are presented to analyse which aspect affects the

professional caregivers’ acceptance of AAL tech-

nologies most. Afterwards, the results are analysed

regarding influences of user diversity characteristics.

4.1 General Perception of AAL

This section presents the results concerning per-

ceived benefits and barriers of AAL system usage,

desired applied technologies and data that could be

gathered as well as acceptance of different AAL

technology systems. Thereby, the results initially

ICT4AWE 2018 - 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health

refer to the whole sample of caregivers investigating

RQ 1 and RQ 2.

4.1.1 Perceived Benefits and Barriers (RQ 1)

Figure 1 presents the evaluation of perceived bene-

fits of an AAL system’s usage. Fast Assistance in

emergencies (M = 4.6; SD = 1.2) was perceived as

the most important benefit, followed by increase in

safety for inhabitants (M = 4.3; SD = 1.2). Potential

benefits with regard to care staff (higher control in

everyday working life (M = 4.0; SD = 1.5), relief in

documentation of care (M = 3.9; SD = 1.4), simpli-

fied proof of rendered care (M = 3.9; SD = 1.4),

relief in everyday working life (M = 3.8; SD = 1.4))

were rated only slightly positively. Other potential

benefits such as relief in everyday life (M = 3.8 SD =

1.3), extension of autonomy (M = 3.7; SD = 1.4), or

reduction of dependency (M = 3.5; SD = 1.4) for

inhabitants were also rated only slightly positive or

almost neutrally. The two rather care staff-related

aspects lower fear to be able to do own mistakes (M

= 2.9; SD = 1.5) and measure against crisis in care

(M = 2.9; SD = 1.6) were rated slightly negatively

and were thus not perceived as benefits of AAL

technologies in professional care contexts.

In contrast to the diverse evaluation of benefits

(with accepted and rejected potential benefits), none

of the potential barriers was rejected (see Figure 2).

Therefore, almost all aspects were perceived as solid

barriers of AAL technology usage in professional

care contexts. In detail, items related with privacy

and data security (e.g., invasion in privacy (M = 5.2;

SD = 1.0), data abuse by third parties (M = 4.8; SD

= 1.2), recording of data (M = 4.7; SD = 1.3)) or

with a perceived surveillance (i.e. surveillance by

technology (M = 5.0; SD = 1.1), control by supervi-

sors (M = 4.9; SD = 1.2), control by colleagues (M =

4.6; SD = 1.3)) were rated highest and represent

relevant barriers. Further, other aspects such as fear

of isolation (M = 4.1; SD = 1.4), missing trust in

technical functionality (M = 3.9; SD = 1.4), or inter-

ruption of routines (M = 3.9; SD = 1.3) were evalu-

ated slightly positively. In contrast, handling seems

to be too complex (M = 3.5; SD = 1.3) and confron-

tation with new technology (M = 3.4; SD = 1.4) were

rated neutrally and thus, those aspects were not per-

ceived as notably relevant barriers of AAL technol-

ogy usage.

4.1.2 Data and Specific Technologies (RQ 2)

Besides perceived benefits and barriers of AAL

technology usage in professional care contexts, the

participants were also asked for which data should

Figure 1: Perceived benefits of AAL technology usage.

Figure 2: Perceived barriers of AAL technology usage.

be allowed to be gathered (Figure 3) and which

specific technology should be used to gather data

(Figure 4) to answer RQ 2.

Caregivers’ Perspectives on Ambient Assisted Living Technologies in Professional Care Contexts

Figure 3: Evaluation of type of gathered data.

To gather data related with emergency situa-

tions (emergencies (falls epileptic seizures) (M =

5.3; SD = 0.9), actuation of emergency buttons (care

staff: M = 5.2; SD = 1.0; inhabitants: M = 5.2; SD =

1.0), and cries for help/support M = 5.2; SD = 1.0)

was clearly accepted. Data about fixations (M = 4.1;

SD = 1.6) and rooms (opening windows, doors, …)

(M = 4.0; SD = 1.6) were also allowed to be gath-

ered. Further, gathering the position of inhabitants

(M = 3.6; SD = 1.4) was evaluated neutrally, while

the position of care staff (M = 2.8; SD = 1.5) was

rather rejected. Data about sleeping (M = 3.3; SD =

1.5), care duration (M = 3.0; SD = 1.6), whole care

situations (M = 2.9; SD = 1.6), and times (rooms are

entered or left) (M = 2.9; SD = 1.5) should also not

be gathered due to rather negative values. In con-

trast, to gather data concerning a 24h observation (M

= 2.6; SD = 1.6) and regarding talks during care

= 2.1; SD = 1.4) was clearly rated negatively and

was thus not accepted.

Like the diverse evaluation of the type of gath-

ered data, the specific technologies that should be

used to gather data were also assessed quite differ-

ently (Figure 4). The use of emergency buttons (in-

habitants: M = 5.1; SD = 1.1; care staff: M = 5.1; SD

= 1.2) and fall sensors into the floor (M = 4.8; SD =

1.4) were clearly accepted. Further, fall sensors in

clothes on body of inhabitants (M = 4.3; SD = 1.5)

and room sensors (M = 4.1; SD = 1.6) were rated

positively. To use motion detectors (in rooms: M =

3.4; SD = 1.6; in clothes of inhabitants: M = 3.3; SD

= 1.6) as well as ultrasonic sensors (M = 3.3; SD =

1.5) was marginally rejected. In contrast, infra-red

Figure 4: Evaluation of technologies used to gather data.

cameras (M = 2.5; SD = 1.4), motion detectors in

clothes of care staff (M = 2.5; SD = 1.5), micro-

phones (M = 2.4; SD = 1.4), and cameras (M = 2.2;

SD = 1.3) were evaluated clearly negatively. There-

fore, those technologies were not accepted as tech-

nologies to gather data in professional care contexts.

As a further aspect, the participants assessed the

storage duration and data access after data was gath-

ered (Figure 5). Here, only the most striking descrip-

tive results are reported.

Concerning data access, room data was the own

data type that received slightly positive values and

data access for all supervisors (M = 3.7; SD = 1.7),

direct supervisors (M = 3.9; SD = 1.6), and col-

leagues (M = 4.0; SD = 1.6) was at least tolerated. In

contrast, the negative evaluations showed that posi-

tion data, audio data, and video data should neither

be accessible for all supervisors, direct supervisors,

nor colleagues. Regarding storage duration, the

comparatively positive values showed that all data

types should only be allowed to be evaluated for the

moment (video: M = 3.8; SD = 1.7; audio: M = 3.8;

SD = 1.8; position: M = 4.0; SD = 1.6; room: M =

4.1; SD = 1.5). Storage on a daily basis (video: M =

2.6; SD = 1.5; audio: M = 2.6; SD = 1.6; position: M

= 2.8; SD

= 1.5) and in particular long-term storage

(video: M = 1.9; SD = 1.2; audio: M = 1.8; SD = 1.1;

position: M = 2.1; SD = 1.3) were rejected for all

data types except of room data that received only

almost neutral values for storage on a daily basis (M

= 3.7; SD = 1.7) as well as long-term storage (M =

3.4; SD = 1.8). Thus, storage was most likely toler-

ated with regard to room data.

ICT4AWE 2018 - 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health

Figure 5: Evaluation of storage duration and data access

for different data types.

4.1.3 Acceptance of AAL technologies

Overall, the acceptance of the mentioned AAL tech-

nologies was evaluated rather neutrally (M = 3.6; SD

= 1.3). Comparatively, a system consisting only of

room sensors (M = 4.0; SD = 1.5) received the high-

est evaluation, while a system consisting of all men-

tioned technologies except of a camera was assessed

worst (M = 2.9; SD = 1.4).

In order to analyze, which of the descriptively

presented factors influences the acceptance of AAL

technologies most, a step-wise linear regression

analysis was conducted: here, the acceptance of

AAL technology usage was calculated as dependent

variable, while perceived benefits, perceived barri-

ers, data that is allowed to be gathered, specific type

of technology, data access, and storage duration

represented the independent variables. The calcula-

tion revealed four significant models. The first mod-

el predicted 59.1% (adj. r

= .591) variance of AAL

technology acceptance and was based on the specific

technology that is used to gather data. Thus, the

acceptance of AAL technology usage depends clear-

ly on the specific technologies that are used and

integrated in the system. The second model addi-

tionally contained perceived benefits and explained

+8.5% (adj. r

= .676) variance of AAL technology

acceptance. The third model explained +3.2% (adj.

= .708) variance and was based on the specific

technology, perceived benefits, and additionally on

the type of gathered data. The fourth and final mod-

el explained +2.2% (adj. r

= .730) variance of AAL

technology acceptance and contained besides per-

ceived benefits, specific technology, type of gath-

ered data, also perceived barriers. The other two

integrated dimensions data access and storage dura-

tion were not part of the regression models and did

not influence the acceptance of AAL technologies

significantly. Figure 6 illustrates the final regression

model and displays the regression coefficient



for

all independent variables.

Figure 6: Final regression model based on significantly

influencing variables.

4.2 Impact of Individual

Characteristics (RQ 3)

So far, the results referring to the whole sample of

caregivers have been presented. As user diversity

plays an important role in the acceptance of medical

and assistive technologies (see section 2.2), the re-

sults are further analyzed concerning impacts of

demographic and attitudinal characteristics of the

participants to answer RQ 3.

First, correlation analyses were conducted in or-

der to find out which demographic and attitudinal

characteristics are relevant for the acceptance of

AAL technologies (Figure 7). Starting with demo-

graphic characteristics, the results revealed only

single correlations with the dimensions of AAL

technology usage: Age was not related at all with

one of the other dimensions. Gender correlated only

slightly with perceived barriers of AAL technology

usage (



= -.156; p < .05) and women showed a

Caregivers’ Perspectives on Ambient Assisted Living Technologies in Professional Care Contexts

Figure 7: Results of correlation analysis: user diversity

impacts (dotted line: p<.05; solid line: p<.01).

slightly higher evaluation of perceived barriers than

men. Education was slightly related with perceived

barriers (



= -.202; p < .01) and acceptance of AAL

technologies (



= .196; p < .05). Subsequently con-

ducted multivariate variance analyses, revealed no

significant impact of the demographic characteristics

on the AAL technology usage dimensions.

Referring to attitudinal characteristics, the re-

sults revealed more striking relationships. For inter-

personal trust, there were indeed only single, slight

correlations with the evaluation of specific technol-

ogies (



= .189; p < .05) and acceptance of AAL

technologies (



= .186; p < .05). In contrast, the

results showed numerous and partly also stronger

correlations for the two attitudinal variables privacy

need and technical self-efficacy (TSE). The strongest

correlations of privacy need referred to perceived

barriers (



= .209; p < .01), data access (



= -.301; p

< .01), and storage duration (



= -.228; p < .01)

indicating that participants with higher needs for

privacy showed higher evaluations of perceived

barriers and a more negative attitude towards data

access for other people and a long-term data storage.

Concerning technical self-efficacy, the strongest

relationships referred to the evaluation of specific

technologies (



= .274; p < .01) and acceptance of

AAL technology usage (



= .299; p < .01) indicating

that participants with a higher technical self-efficacy

showed a more positive attitude towards specific

technologies and a higher acceptance of AAL tech-

nologies.

To investigate the impact of user diversity fac-

tors in depth, the attitudinal variables were also

analyzed in multivariate variance analyses. For trust,

the results revealed no significant impact on the

AAL technology usage dimensions. Concerning two

groups with different privacy needs, the analysis

confirmed that data access (F(1,158)=7.076; p < .01)

and storage duration (F(1,158)=6.359; p < .05) are

considered as significantly more critical by people

with high privacy needs (M

access

= 2.7, SD

access

= 0.9;

storage

= 2.9, SD

storage

= 0.9) compared to people with

lower privacy needs (M

access

= 3.2, SD

access

= 1.1;

storage

= 3.3; SD

storage

= 0.7).

Likewise, two groups with a different technical

self-efficacy (TSE) differed significantly regarding

perceived barriers (F(3,149)=7.708; p < .01), the

evaluation of specific technologies (F(1,149)=6.051;

p < .05), and acceptance of AAL technologies

(F(1,149)=6.564; p < .01). The results revealed that

people with a higher TSE (M = 4.1; SD = 0.7) evalu-

ated perceived barriers significantly lower than peo-

ple with a lower TSE (M = 4.5; SD = 0.7). Further,

the results confirmed that people with a higher TSE

tech

= 3.8, SD

tech

= 1.1; M

= 4.0; SD

= 1.3)

had a more positive attitude towards the specific

technologies and showed also a higher acceptance of

AAL technologies than people with a lower TSE

tech

= 3.4, SD

tech

= 0.9; M

= 3.4; SD

= 1.2).

These results show that user groups with different

needs for privacy and a different technical self-

efficacy differed with regard to the evaluation of

AAL technology usage dimensions. Thus, it was

important to find out, whether different dimensions

influence the acceptance of AAL technologies for

the TSE and privacy need groups.

For this purpose, we again conducted regression

analyses separately for the diverse groups. Here,

only the final regression models are reported (Table

1 & 2). Starting with privacy needs (Table 1), the

final model for participants with low privacy needs

explained 61.4% variance of AAL acceptance based

on the evaluation of specific technologies and per-

ceived benefits of AAL technology usage. In con-

trast, the final regression model for participants with

high needs for privacy explained 76.6% of the vari-

ance of AAL technology acceptance based on the

four dimensions: specific technologies, perceived

benefits, type of data, and perceived barriers.

A similar pattern occurred for the two technical

self-efficacy (TSE) groups (Table 2). Here, the re-

gression model for people with a high TSE ex-

plained 73.4% variance of AAL acceptance based on

the dimensions technology and perceived benefits.

ICT4AWE 2018 - 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health

Table 1: Final regression model for participants with low

and high needs for privacy.

Privacy Dimension B SE B  T

high needs technologies .640 .104 .458 6.145

benefits .319 .079 .236 4.019

type of data .448 .112 .282 4.013

barriers

-.279 .099 -.155 -2.836

low needs technology .603 .145 .504 4.148

benefits .364 .089 .269 3.988

However, the regression model for the low TSE

group explained 70.6% variance of AAL technology

acceptance and - similar to the high needs for priva-

cy group – based on the four dimensions: specific

technologies, perceived barriers, type of data, and

perceived benefits.

Table 2: Final regression model for participants with a low

and a high technical self-efficacy (TSE).

TSE Dimension B SE B  T

low technologies .716 .131 .490 5.470

barriers -.333 .125 -.179 -2.660

type of data .389 .126 .247 3.087

benefits .251 .101 .197 2.494

high technology .780 .094 .651 8.274

benefits .606 .112 .425 5.400

5 DISCUSSION

This study revealed insights into caregivers’ per-

spectives on the acceptance of specific AAL tech-

nologies in professional care environments. As pro-

fessional caregivers play a decisive role for the ac-

ceptance of AAL technologies in professional care

contexts, we aimed for a detailed analysis of the

needs and wishes of this specific stakeholder group.

The results provide valuable insights into ac-

ceptance-decisive factors of AAL technologies in

professional care contexts and should be taken into

account for the development, design, and configura-

tion of AAL technologies.

5.1 AAL System Acceptance (RQ 1&2)

The caregivers’ evaluations of AAL technology

acceptance, perceived benefits, and barriers (see RQ

1) differ clearly from previous research results con-

cerning AAL technology acceptance. In contrast to a

general positive evaluation of AAL technologies

found in numerous past studies (e.g., Beringer et al.,

2011; Gövercin et al., 2016), this study’s profession-

al caregivers uncover a very restrained attitude to-

wards AAL technologies and show neutral ac-

ceptance evaluations, if at all. As implied in a pre-

ceding study (van Heek et al., 2017a), the present

study confirms that professional caregivers are much

more critical with regard to the integration of AAL

technologies in their (professional) everyday life

than other stakeholders.

On the one hand, this is expressed by low agree-

ments of potential benefits. Except of a faster assis-

tance in emergencies, all benefits are evaluated only

with rather positive or rather negative i.e. primarily

neutral values. Thus, potential benefits (e.g., meas-

ure against care crisis) are not perceived as real

benefits. This may be due to the applied methodolo-

gy of the scenario-based approach: as previous stud-

ies proved that hands-on experience with AAL tech-

nologies lead to more positive perceptions of usage

motives (Wilkowska et al., 2015), it can be assumed

that professional caregivers would also evaluated

AAL technologies differently if they would have the

chance trying to use those technologies in their pro-

fessional everyday life.

On the other hand, the more negative and critical

attitude of professional caregivers is expressed by

high agreements of barriers: none potential barrier is

rejected and thus, all potential barriers are perceived

as real barriers and severe drawbacks. This pattern

contrasts clearly to previous research results show-

ing a much lower reluctance to AAL systems and a

lower confirmation to the perceived barriers. (e.g.,

van Heek et al., 2017a,b). Within the perceived

barriers especially the aspects of a potential invasion

of privacy, data security concerns, and a feeling of

surveillance are of importance for professional care-

givers. It is a noteworthy finding that the nature of

the seen barriers in the professional caregivers’ per-

spective center around their own professional per-

son. The most severe concern is not to be tracked or

controlled. The patients, the caretakers, for which

caregivers are responsible and which could seriously

profit from AAL Systems are not taken into account.

Further, the study revealed detailed novel in-

sights into the perceptions of professional caregivers

respecting their perspective which data should be

allowed to be gathered and which technology should

be used to gather data, if at all (RQ 2). The results

show clearly that only emergency-related data is

allowed to be gathered. All other data types are

rejected or only just tolerated. This contrasts signifi-

cantly to the functions caregivers want AAL tech-

nologies to undertake (as reported in open comment

fields and interviews), for which gathering different

data types is factually necessary. The evaluation of

technologies that should be used for data collection,

shows similar results: the professional caregivers

only indicate to accept technologies that are partly

already existing (e.g., emergency buttons) or gather

only static, binary data (e.g., room sensors). More

Caregivers’ Perspectives on Ambient Assisted Living Technologies in Professional Care Contexts

complex technologies with a potential higher priva-

cy invasion (e.g., cameras, microphones) are reject-

ed. This is in line with previous research results

(e.g., Himmel et al., 2016) and illustrates the opposi-

tion between the desired technical functionality (that

could support them in caring) and the admitted data

and technology configurations. The evaluation of

data storage and data access confirms the negative

attitude and evaluation of perceived barriers, be-

cause nearly nobody is really allowed to access

gathered data and data should only be processed -

not stored.

As data is not needed to be stored long-term for

most of the functions AAL technologies could un-

dertake, targeted communication strategies focusing

on handling of data (e.g., only processing, not stor-

age) could may help to dismantle perceived barriers

and especially caveats concerning privacy and data

security.

5.2 Diversity of Users Matters (RQ 3)

The integration of user diversity factors into the

analysis of AAL technology acceptance shows that

demographic characteristics of the professional

caregivers are not decisive and did not influence the

caregivers’ acceptance of AAL technologies (see RQ

3). In contrast, the results illustrate that attitudinal

characteristics are more relevant and influence the

perception of AAL technologies.

Technical self-efficacy as well as the caregivers’

need for privacy impact the acceptance and percep-

tions of AAL technologies significantly. Similar to

previous studies (e.g., Ziefle & Schaar, 2010), peo-

ple with a higher technical self-efficacy show a

higher acceptance of AAL technologies which is

influenced by the type of technology and perceived

benefits. In contrast, people with a lower technical

self-efficacy are more restrained concerning the

acceptance of AAL technology. A low technical

self-efficacy affects the evaluation of the type of

technology and perceived benefits but additionally

also the type of gathered data as well as perceived

barriers. A similar pattern was revealed for persons

with different needs for privacy: people with low

needs for privacy show a considerably higher AAL

technology acceptance, influenced by type of tech-

nology and perceived benefits. And vice versa: peo-

ple with high needs for privacy indicate a lower

acceptance influenced by the evaluation of type of

technology, perceived benefits, perceived barriers,

and type of data.

Summarizing, professional caregivers’ technical

expertise and need for privacy contribute to a differ-

ent emphasis referring to the perception of barriers

and caveats (i.e. data gathering, storage, access, and

privacy). This confirms that especially the way AAL

technologies handle data should be focused in future

studies and integrated in communication referring to

AAL technologies in professional environments.

5.3 Limitations and Future Research

Our empirical approach provided valuable insights

into AAL technology acceptance of professional

caregivers in professional care contexts as it focused

on evaluations of specific benefits and barriers as

well as concrete data and technology configurations.

Nevertheless, there are some limitations concerning

the applied method and sample that should be con-

sidered in future approaches.

The present study was a first scenario-based ap-

proach focusing on professional caregivers’ ac-

ceptance of AAL technologies, their perceptions of

benefits and barriers as well as specifically on their

evaluations of technologies and data configurations.

As already mentioned, the applied methodological

approach was based on a scenario and thus, on a

fictional and not on a real AAL system, what proba-

bly influences the evaluations and may lead to an

underestimation of potential benefits and an overes-

timation of potential barriers (Wilkowska et al.,

2015). Thus, we aim for hands-on evaluations of

AAL technologies in future studies focusing on

professional caregivers and respectively usage of

AAL technologies in professional care environ-

ments.

As a first aspect referring to the sample, the

sample size as well as balance of demographic char-

acteristics was sufficient - in particular, referred to

the condition that only professional caregivers were

acquired. The higher proportion of female partici-

pants in the sample represents and fits to the higher

proportion of women working in care institutions

(Simonazzi, 2008). An interesting aspect for future

studies is the investigation of potential care sector

influences (geriatric care, nursing care, care of disa-

bled people) on the acceptance and perception of

AAL technologies in professional contexts due to

different challenges and processes in the respective

sectors. Finally, this study focused German partici-

pants and thus, it represents the perspective of pro-

fessional caregivers of one specific country with a

specific health care system. We assume that the

acceptance of AAL technologies differs with regard

to different countries, their cultures and their specif-

ic healthcare systems. Therefore, we aim for con-

ducting our approach in other countries to be able to

directly compare AAL acceptance depending on

different countries and cultures.

ICT4AWE 2018 - 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health

ACKNOWLEDGEMENTS

The authors thank all participants for their openness

to share opinions on innovative technologies. Fur-

thermore, the authors want to thank Hannah Fahnen-

stich for research assistance. This work was funded

by the German Federal Ministry of Education and

Research project Whistle (16SV7530).

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