International Journal of
Environmental Research
and Public Health
Article
Informal Dementia Caregivers: Current Technology Use and
Acceptance of Technology in Care
Daniel Wójcik 1,2,3, *, Katarzyna Szczechowiak 2 , Patrycja Konopka 4 , Mateusz Owczarek 5 , Agata Kuzia 5 ,
Izabela Rydlewska-Liszkowska 1 and Małgorzata Pikala 3
1
2
3
4
5
*
Citation: Wójcik, D.; Szczechowiak,
K.; Konopka, P.; Owczarek, M.; Kuzia,
A.; Rydlewska-Liszkowska, I.; Pikala,
M. Informal Dementia Caregivers:
Current Technology Use and
Acceptance of Technology in Care.
Int. J. Environ. Res. Public Health 2021,
18, 3167. https://doi.org/10.3390/
ijerph18063167
Department of Management and Logistics in Health Care, Medical University of Lodz,
Poland InterDoktorMen Medical University of Lodz, 90-419 Lodz, Poland;
izabela.rydlewska-liszkowska@umed.lodz.pl
Wroclaw’s Alzheimer Center, 53-659 Wroclaw, Poland; szczechowiak@gmail.com
Department of Epidemiology and Biostatistics, Social and Preventive Medicine of the Medical University of
Lodz, 90-752 Lodz, Poland; Malgorzata.pikala@umed.lodz.pl
Institute of Psychology, Polish Academy of Sciences, 00-378 Warsaw, Poland;
patrycja.konopka@sd.psych.pan.pl
Institute of Psychology, University of Wroclaw, 50-527 Wroclaw, Poland; mateusz.owczarek97@onet.pl (M.O.);
kuziaagata@gmail.com (A.K.)
Correspondence: daniel.wojcik@stud.umed.lodz.pl; Tel.: +48-505134649
Abstract: (1) Background: Given the increased social isolation caused by the COVID-19 pandemic, the
challenges faced by informal dementia caregivers have increased. An increasing use of technology,
both in care and dementia clinical trials, depends upon caregivers’ abilities as a user. Accordingly, the
aim of our study was to verify the current technology (smartphone and computer) use and acceptance
in care, regarding socio-demographic variables; (2) Methods: Questionnaires were distributed to
102 dementia caregivers, mostly of patients with moderate dementia; (3) Results: The majority of
participants were women (63%), and large number of them used technological devices such as
a smartphone (91%) or computer (81%). Results revealed differences between age, gender, and
education level on technology acceptance. Interestingly, smartphone use and acceptance seemed to
be feasible, regardless of age, whereas computer use was negatively correlated with age. Technology
was perceived by respondents as most useful for patients’ activities including locomotion, toileting,
and meals; (4) Conclusions: The future of technology use in dementia care should indicate solutions
tailored to individual characteristics such as new technology solutions (GPS trackers, smartphone
apps, dietary intervention, and meal planning apps).
Academic Editor: Izabella Lecka
Received: 17 February 2021
Accepted: 16 March 2021
Keywords: socio-demographic characteristics; informal caregivers; dementia; Alzheimer’s disease; technology
Published: 19 March 2021
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1. Introduction
According to the 2016 World Alzheimer Report, the number of people with dementia
worldwide (46.8 million) will almost double every 20 years in our aging society, and it
is expected to rise to 131.5 million by 2050 [1]. The most common cause of dementia is
Alzheimer’s disease (AD), comprising 60 to 70 percent of all dementia cases [2]. Due to the
Alzheimer’s Association’s 2019 report, AD was responsible for 121,404 deaths in 2017 in the
United States alone, making AD the sixth leading cause of death [3]. It is also noteworthy
that the total estimated worldwide cost of dementia was predicted to rise to a trillion-dollar
disease by 2018 [4]; more importantly, in 2018, over 16 million informal caregivers in the US,
such as spouses, children, and other family members, provided an estimated 18.5 billion
hours of voluntary care to people with dementia, valued at nearly $234 billion [3]. This
shows the scale of the dementia care problem prior to the COVID-19 pandemic. There is
also no doubt that during the pandemic, the elderly, including dementia patients and their
elderly caregivers, are especially vulnerable, raising great concerns for mental well-being
Int. J. Environ. Res. Public Health 2021, 18, 3167. https://doi.org/10.3390/ijerph18063167
https://www.mdpi.com/journal/ijerph
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and physical health outcomes in older adults [5]. Recent studies show that both the viral
infection and the enforced prolonged conditions of social isolation in order to limit the
spreading of the disease have a wide-ranging impact on the elderly’s mental health [6].
Dementia symptoms generally worsen over time, and the patient becomes incapable
of performing daily life activities [7]. Losing the ability to live independently without the
assistance of others, usually relatives, the dementia patient may present different behavioral
symptoms that can strongly affect the caregiver’s life. Therefore, the dementia caregiver’s
quality of life often deteriorates over the course of the disease. Accordingly, the common
potential adverse consequence of caregiving is depression [7] caused by isolation, loneliness,
and exhaustion of care providers [3,8]. Hence, recent findings from a meta-analysis of
17 studies revealed that the aggregate prevalence of depression among AD caregivers was
33.9% and aggregate prevalence of anxiety disorders was 43.6% [9]. Moreover, the level of
stress experienced by informal caregivers seems to be the strongest predictor of a patient’s
admission into a nursing home [8]. At least 60% of dementia caregivers in the US are
female relatives (wives, daughters, daughters-in-law, granddaughters) [10]; the typical
characteristic of informal dementia caregivers is a middle-aged or older female child or
spouse of the patient [11].
Nowadays, there is a pressing demand to provide adequate support and resources in
order to improve the well-being of informal caregivers and make the dementia care challenges less severe. Devices such as smartphones, tablets, and computers can be a helpful
tool in alleviating the caregiver’s psychological burden, encouraging social engagement,
and easing the burden of daily activities [12]. Shreve et al. reported that smartphone technology interventions could address several needs of dementia caregivers such as reducing
the psychological burden and social isolation inherent in caregiving, providing access to
information and resources, and helping to ensure patient’s safety and track the progression
of the disease [13]. There are many forms of internet-based activities that can meet the
caregiver’s needs such as web-based training, psychological and educational programs,
mobile text messaging, video-recording, and chat forums [14]. Furthermore, dementia
technology based on the internet (e.g., smartphone apps, wearables, computer programs,
e-learning, and online platforms) can support care and improve contact with professional
care personnel, enhancing the monitoring of disease progression, identifying emerging
problems, and delivering professional interventions [15,16], which could be a cost-effective
solution in the long-term. In a pilot study, Boessen et al. identified the family caregivers’
requirements and determined the need for reliable information regarding the disease and
contact with fellow family members to be crucial [17].
Furthermore, dementia caregivers’ ease and level of usage in the digital technology
field has become increasingly important in clinical trials of new AD medications. Technology seems to be a key component in recruitment, finding the adequate clinical trial, and
engaging dementia caregivers as the study informants in drug development and other treatment protocols. Technology and internet use can be also helpful in encouraging caregivers
to enroll into trials earlier due to their knowledge about the patient’s early symptoms of
AD. Moreover, some AD clinical trials, including medication trials, emphasize the importance of active data collection using wearable and home-based sensors and apps, online
assessment tools and platforms, electronic medical records, and online technologies, which,
due to the disease characteristics, involve caregivers. The caregiver’s technology use and
acceptance could be crucial factors in home-testing various aspects of patient’s executive
functions, memory and cognition, such as online memory tests, and mobile applications
for health tracking, currently use in AD clinical trials. The examples of such tools are Novel
Assessment of Nutrition and Ageing (NANA) and Cambridge Neuropsychological Test
Automated Battery (CANTAB)—they both have the potential to be self-administered with
the support of a caregiver [15,18]. Therefore, the use of digital technology may be crucial
for monitoring the disease progression, collecting data in response to drug interventions,
and finally reducing costs of the trial [15,18]. However, recent data suggest that access to
the new technologies is not enough to resolve dementia caregivers’ problems and ensure
Int. J. Environ. Res. Public Health 2021, 18, 3167
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effective digital inclusion [19]. Therefore, improving the accessibility of the computational
solutions seems to be an important factor in its effective use in dementia care. Furthermore,
the reasons for technology rejection should also gain adequate attention.
Zwingmann et al. suggested that the social isolation felt by caregivers could be
reduced if support groups were established in more flexible and private settings (e.g.,
telephone- and internet-based, small groups with individual coaching) to counteract the
lack of participation due to personal and service factors (e.g., no time, service difficulties) [20]. This has become even more prevalent due to the increased social isolation
experienced by caregivers during the COVID-19 pandemic.
Regarding the limited literature on technology use in the dementia care field, the
present study aimed to collect the socio-demographic characteristics of informal dementia
caregivers (participants) and care recipients, respondents’ current technology use, the
purpose of using it, the perceived usefulness of technology in patients’ activities of daily
living (ADL), and future acceptance of technology use in care. To our knowledge, it is the
first study aiming to verify such a wide range of variables, hypothesized associations, and
differences between groups. We hope that our findings will help developers, researchers,
and IT companies to produce needed and utile apps and devices that can support informal
dementia caregivers. Ultimately, our interest is to gather data which will be useful in
developing solutions tailored to individual preferences.
2. Materials and Methods
This study was conducted to collect, identify, and verify: (1) socio-demographic
characteristics of informal dementia caregivers and recipients, (2) caregivers’ current
technology use regarding smartphones and computers/PCs/tablets, (3) care recipients’
dependency level based on the ADL scale, (4) informal caregivers’ dementia technology
acceptance regarding the use of smartphones and computers/PCs/tablets in care.
2.1. Study Population and Sampling Procedures
Participant recruitment was conducted in Wroclaw Alzheimer’s Center and partner
hospital’s Dementia Unit between February 2020 and January 2021. Professional personnel
administered information about participation in the study to informal caregivers of all
patients registered and treated at these institutions. Respondents had to meet the following inclusion criteria to participate in the study: (1) currently the primary informal
caregiver (defined as a person providing voluntary care without financial compensation)
of a dementia patient, (2) the ability to speak, read, and write in Polish, (3) willing and
physically able to take part the study, (4) in constant contact with the dementia patient
several times a week. A total of 105 informal caregivers participated in the study; however,
three respondents were excluded due to missing data in the questionnaires. Therefore, the
number of participants included in the study was 102.
The experiment was conducted in accordance with the ethical standards of the 1964
Helsinki declaration and its later amendments. No ethical approval was required from
the institutions because this study involved only non-invasive procedures. Informed
consent was collected from all respondents. All respondents were informed that they
would be asked questions regarding personal information such as socio-demographical
data and patient functioning. Furthermore, respondents were told that participation in the
study was voluntary, questionnaires would be completed anonymously, and they could
withdraw at any time for any reason. Respondents were given the choice of completing
the questionnaires in person (i.e., in sit down sessions with a study psychologist) or
over the phone. Due to the COVID-19 pandemic, all hygienic conditions and sanitary
requirements regarding the safety of participants and study personnel were respected and
carefully followed.
Int. J. Environ. Res. Public Health 2021, 18, 3167
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2.2. Measures
2.2.1. Questionnaires
Questionnaires were performed and/or designed to collect data on (1) socio-demographic
status of informal dementia caregivers and care recipients, (2) caregivers’ current technology
use regarding smartphones and computers/PCs/tablets, (3) care recipients’ dependency
level based on the ADL scale, (4) caregivers’ dementia technology acceptance regarding
the use of smartphones and computers/PCs/tablets in care. Questionnaires took approximately 45 min to complete. We designed a short questionnaire about socio-demographic
information (e.g., caregiver’s age, gender, education, caregiving relationship, and care recipient’s age) and caregiver’s current digital technology use. The questions regarding participant’s
current technology use (smartphone and computer/PC/tablet) were also included.
The Katz index of independence in activities of daily living (ADL) scale was used
to measure the patient’s dependency status [21]. The index ranks the patient’s level of
performance in six functions: bathing, dressing, toileting, transferring, continence, and
feeding, indicating the level of their functional impairment. The caregivers were asked
to score yes or no for independence in each of the six domains. A score of 6 indicates full
function and patient’s independence; 4 indicates moderate impairment; 2 or fewer indicates
severe functional impairment [21]. We added the question “Do you think that technology
could be helpful in care regarding this activity?” to each item of daily activity performed
by the patient. Therefore, the first section of the questionnaire included a brief description
of “technology” assisting in dementia care, defining it as “any internet-based technologies
designed to assist and help in dementia care, such as a computer, tablet, smartphone, GPS
tracker, robot, and electronic device”.
After insightful research and data collection, we designed a questionnaire composed
of the items measuring the technology acceptance construct within Wang et al.’s Unified
Theory of Acceptance and Use of Technology (UTAUT) model [22]. The first section of the
questionnaire referred to the acceptance of computer/PC/tablet use in care, and the second
section measured the acceptance of smartphone use in care. All items were measured on a
5-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). We modify adequately the sentences to fit both (smartphone and computer/PC/tablet) variants and
chose items created by Venkatesh et al. to measure the following: (1) behavioral intention
(BI)—respondents’ intention of future technology use; (2) social influence (SI)—the impact
of significant others’ opinions on the respondents’ behavior toward technology use; (3)
facilitating conditions (FC)—subject’s perception of the degree to which infrastructure
(technical and organizational) supports technology use; (4) effort expectancy (EE)—the
level of ease related to users’ use of technology; (5) performance expectancy (PE)—the
degree to which respondents believe using technology will allow users to perform specific
activities effectively, which seems to be a crucial factor of BI to use the technology. Furthermore, technology acceptance studies suggest that BI is a predictor for actual technology
use [23–25], and SI is crucial for technology user acceptance [26,27].
The questionnaires were previously tested among dementia professionals to estimate
the time needed to complete all items. We divided the collected data into two categories
of variables: independent and dependent. Due to limited data existing in the dementia
caregiving field, the study was designed to examine the associations between the sociodemographic characteristic of caregivers and patients, patient’s ADL, perceived usefulness
of technology (smartphone/computer) in care, current technology use, and technology
acceptance. We also aimed to verify the purpose of technology use in dementia care
reported by informal caregivers. The previous studies reveal that women are more receptive towards technology use in care than men, including the use of tracking devices
in dementia patients [28,29]. We hypothesized that there will be differences between
groups in (1) caregivers’ socio-demographic status (gender, age, and education) on current technology use (smartphone/computer), (2) caregivers’ socio-demographic status
(gender, age, and education) on technology acceptance in dementia care domains; and
associations between (1) caregivers’ socio-demographic status and current technology use
Int. J. Environ. Res. Public Health 2021, 18, 3167
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(smartphone/computer), and technology acceptance (smartphone/computer) in dementia
care, (2) patients’ ADL and dependency level, dementia stage, and time since the first
diagnosis reported by caregivers.
2.2.2. Independent Variables
Dementia caregivers’ socio-demographic information was collected to describe the
respondents. The variables included the caregiver’s age, gender, education, marital status,
caregiving relationship, and care recipients’ age, gender, dementia stage, dependency level,
time since first diagnosis, and ADL.
2.2.3. Dependent Variables
The assessment of study objectives included: (1) the current level and purpose of
technology use (smartphone and computer/PC/tablet); (2) the perceived usefulness of
technology in everyday assistance with patients’ ADL activities; (3) the acceptance of using
technology (computer/PC/tablet and smartphone) in dementia care.
2.3. Data Analysis
The statistical analyses were performed using Statistica 13.1 (StatSoft, Krakow, Poland).
Descriptive statistics including frequency distributions, percentages, means, standard deviations, and medians were used to describe study population characteristics and examine
the current level and purpose of technology use, perceived usefulness of technology in
everyday assistance with patient’s ADL activities, and the acceptance of technology use in
dementia care. Furthermore, t-tests were conducted to study differences in current level
and purpose, perceived usefulness, and acceptance of technology use variables between
groups: (1) male and female respondents, (2) caregivers younger than 65 years old, and caregivers 65 years old and older, and (3) respondents with high school education level or less,
and those with higher education. To examine the correlation between socio-demographic
variables, current technology use, and acceptance of technology use in several domains, we
used Spearman’s rank correlation coefficient, a nonparametric measure of rank correlation,
described using a monotonic function. A significance level of 0.05 was used for the analysis.
3. Results
The study population consisted of 102 informal caregivers of patients with dementia.
The age of respondents ranged from a minimum of 27 to a maximum of 80 years old, with a
mean age of 58.5 years (standard deviation = 11.8; median = 61). The majority of caregivers
were women (63%). The analysis of respondents’ marital status showed 85% declared they
were married, 6% were widowed, and 8% were single/divorced. Most participants had a
high school diploma (53%) or higher level of education (46%); only 1% of respondents had
primary education. Spearman’s rank correlation coefficients showed a medium negative
correlation between the age and education level of participants (−0.43); as the age increases,
the level of education decreases (see Table 1 for full list of correlations).
The t-test analyses showed no significant difference in current smartphone and computer use between caregivers 65+ and younger (Table 2) or gender groups (Table 3). Regarding the socio-demographic characteristics of dementia caregivers and care recipients,
the percentage distribution of caregiving relationship (Figure 1), and care recipients’ dependency level (Figure 2) are presented below.
Int. J. Environ. Res. Public Health 2021, 18, 3167
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Table 1. The correlation between age, education level, current technology use, and technology use acceptance (Spearman’s
rank correlation coefficient).
Spearman’s Rank Correlation Coefficient *
Variable
Age
Age
Current Smartphone Use
Current Computer Use
Education Level
1.00
−0.14
−0.30
−0.43
Current smartphone use
−0.14
1.00
0.56
0.25
Current computer use
−0.30
0.56
1.00
0.41
Computer_Behavioral intention (BI)
−0.25
0.35
0.52
0.49
Computer_Social influence (SI)
−0.13
0.13
0.09
0.05
Computer_Facilitating conditions (FC)
−0.23
0.42
0.62
0.30
Computer_Effort expectancy (EE)
−0.28
0.45
0.51
0.48
Computer_Performance expectancy (PE)
−0.06
0.28
0.33
0.31
Smartphone_Behavioral intention (BI)
0.10
0.42
0.32
0.23
Smartphone_Social influence (SI)
−0.02
0.19
0.00
0.10
Smartphone_Facilitating conditions (FC)
0.07
0.45
0.27
0.14
Smartphone_Effort expectancy (EE)
0.03
0.43
0.34
0.24
Smartphone_Performance expectancy (PE)
0.25
0.33
0.12
0.13
* correlation significant at the level p < 0.05.
Table 2. Difference between age groups in current technology use and technology use acceptance (T-Statistics).
T-Statistics; Grouping Variable: Age Less or Greater than 65 Years
Variable
Mean (<65)
Mean (>65)
t
df
p-Value
SD (<65)
SD (>65)
Current smartphone use
0.90
0.94
−0.61
100
0.54
0.30
0.25
Current computer use
0.83
0.78
0.56
100
0.57
0.38
0.42
Computer_Behavioral intention (BI)
11.47
9.97
2.50
100
0.01 *
2.93
2.53
Computer_Social influence (SI)
6.06
4.94
2.00
100
0.05 *
2.60
2.70
Computer_Facilitating conditions (FC)
12.67
12.09
0.87
100
0.38
3.23
2.80
Computer_Effort expectancy (EE)
15.31
13.38
2.36
100
0.02 *
3.74
4.09
Computer_Performance expectancy (PE)
13.74
12.47
1.50
100
0.14
3.91
4.10
Smartphone_Behavioral intention (BI)
11.81
12.88
−1.53
100
0.13
3.54
2.46
Smartphone_Social influence (SI)
6.34
5.63
1.22
100
0.22
2.81
2.60
Smartphone_Facilitating conditions (FC)
12.59
13.47
−1.49
100
0.14
3.11
1.83
Smartphone_Effort expectancy (EE)
15.41
15.97
−0.72
100
0.47
3.72
3.38
Smartphone_Performance expectancy (PE)
14.84
16.38
−1.82
100
0.07
4.15
3.47
* marked factors statistically significant with p < 0.05.
Table 3. Difference between gender groups in current technology use and technology use acceptance (T-Statistics).
Variable
T-Statistics; Grouping Variable: Male (M) or Female (F)
Mean (M)
Mean (F)
t
df
p-Value
SD (M)
SD (F)
Current smartphone use
0.84
0.95
−1.93
100
0.0568
0.37
0.21
Current computer use
0.76
0.84
−1.01
100
0.3169
0.43
0.37
Computer_Behavioral intention (BI)
10.13
11.52
−2.4
100
0.0183 *
2.82
2.82
Computer_Social influence (SI)
5.39
5.89
−0.91
100
0.3668
2.28
2.87
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Table 3. Cont.
Variable
T-Statistics; Grouping Variable: Male (M) or Female (F)
Mean (M)
Mean (F)
t
df
Computer_Facilitating conditions (FC)
12.13
12.7
−0.9 − 100
−
−
Computer_Effort expectancy (EE)
13.79
15.25
−1.83 − 100
−
Computer_Performance expectancy (PE)
12.61
13.78
−1.45 − 100
−
Smartphone_Behavioral intention (BI)
10.84
12.92
−3.26 −− 100
Smartphone_Social influence (SI)
5.34
6.58
−2.23 −− 100
−
Smartphone_Facilitating conditions (FC)
12.11
13.31
−2.14 − 100
−
Smartphone_Effort expectancy (EE)
13.92
16.58
−3.83 −− 100
−
Smartphone_Performance expectancy (PE)
13.76
16.25
−3.17 − 100
−
* marked factors statistically significant with p < 0.05.
−
p-Value
SD (M)
SD (F)
0.3703
3.45
2.88
0.0698
3.48
4.11
0.1514
3.67
4.14
0.0015 *
3.19
3.07
0.0277 *
2.13
2.99
0.0344 *
3.13
2.5
0.0002 *
4.04
2.94
0.0020 *
3.55
3.98
2% 4%
spousal
children
friend
46%
50%
other
Figure 1. Study population characteristics—caregiving relationship.
11%
independent
mild dependent
13%
9%
21%
moderate dependent
severe dependent
47%
totally dependent
Figure 2. Study population characteristics—care recipient’s dependency level.
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Care recipients’ dementia stage was categorized as early (46%), middle (44%), and
late (6%) stage dementia, with the majority of care recipients being moderately dependent
(47%). The mean time since initial diagnosis was 2.2 years (SD = 1.9) and ranged from less
than a year to a maximum of 10 years. Furthermore, over half (56%) of dementia patients
were female.
In total, 91% of respondents reported they currently use a smartphone, and 81%
currently use a computer (PC/tablet). As shown in Figures 3 and 4, the majority of
participants mostly use these devices to seek general information, including dementia‐
‐
related information.
Dementia care
34%
Working
Contact with health professionals
‐
‐
‐
‐
53%
45%
Contact with relatives/Support
55%
Hobbies/Enterteiment
63%
General information seeking
76%
Figure 3. The purpose of computer use in caregivers.
Dementia care (information/activities)
42%
Working
51%
Contact with health professionals
79%
Contact with relatives/Support
85%
Hobbies/Enterteiment
58%
General information seeking
81%
Figure 4. The purpose of smartphone use in caregivers.
Spearman’s rank correlation coefficient showed no statistically significant association
between age and smartphone use. However,
− a medium negative correlation between age
−
and current computer use was found (−0.30),
showing the older the caregivers were, the
less often they use the computer. Moreover, there was also a small positive correlation
‐
between education level and current smartphone use (0.25), and a medium positive correla- ‐
‐
‐
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tion between education and current computer use (0.41). Furthermore, there was a strong
positive correlation between smartphone and computer use (0.56). There were no more
statistically significant relationships between the socio-demographic variables.
Table 4 shows Spearman’s rank correlation coefficients between ADL score, time since
initial diagnosis, dementia stage, and patient’s dependency level. There was no association
between participants’ socio-demographic variables and patients’ ADL score, ADL scores ‐
in each domain, or the purpose of technology
use in each activity. However, there was a
‐
strong inverse association between patients’ dependency level (in the respondents’ opinion)
and ADL score (−0.51). Moreover, there were medium negative correlations between ‐
ADL score and: (1) time since first diagnosis
(−0.30) and (2) dementia stage (−0.48). ‐
−
Accordingly, the higher the ADL score, the lower patients’ dependency
level, time since
−
initial diagnosis,
and
dementia
stage
were.
On
the
other
hand,
positive
correlations
between
−
patients’ dependency level and: (1) time since the first diagnosis and (2) dementia stage
were observed.
Table 4. The correlation between the Activities of Daily Living (ADL) score, time since the first diagnosis, dementia stage,
and patients’ dependency level (Spearman’s rank correlation coefficient).
Variable
Spearman’s Rank Correlation Coefficient *
Time since First Diagnosis
Dementia Stage
Dependency Level
ADL Score
Time since first diagnosis
1.00
0.48
0.27
−0.30
Dementia stage
0.48
1.00
0.42
−
−0.48
Dependency level
0.27
0.42
1.00
−0.51
ADL score
−0.30
−0.48
−0.51
1.00
−
−
* correlation significant at the level p < 0.05.
−
−
−
We also measured caregivers’ perceived usefulness of technology in helping with
patients’ ADLs, illustrated in Figure 5. The majority of informal dementia caregivers per‐
ceived technology to be most useful in patients’ locomotion, toileting, and meals domains.
39%
25%
16%
24%
15%
12%
Hygiene
Dressing
Toileting Locomotion Continence
Meals
Figure 5. Usefulness of technology perceived by caregivers in helping with ADLs.
Acceptance of technology in dementia care was correlated with respondents’ age,
education level, and current use of technology, including smartphones and computers.
Hence, there were small negative correlations between respondents’ age and BI, SI, and EE
related to computer acceptance in care (see Table 1). Interestingly, the strongest associations ‐
‐
Int. J. Environ. Res. Public Health 2021, 18, 3167
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occurred as strong positive correlations between current computer use and BI (0.52), FC
(0.62), and EE (0.51). There were also statistically significant differences in acceptance of
computers in care between older (>65 years old) and younger (<65 years old) participants;
with the younger participants scoring significantly higher in BI (p = 0.01), SI (p = 0.05), and
EE (p = 0.02) domains (see Table 2). There were no differences between these age groups
regarding smartphone acceptance. On the other hand, the significant differences between
males and females included all smartphone acceptance in care domains, showing higher
scores in women (see Table 3).
The differences between the group of respondents with a high school education
level or less, and those with higher education, are even more interesting (see Table 5).
Respondents with a higher education level used a smartphone (p = 0.003) and computer
(p = 0.000003) significantly more often than those with a high school education or less.
Participants with a higher education also had significantly higher scores in six of the ten
acceptance domains related both to smartphone and computer use in care, than those with
the level of high school education or less.
Table 5. Difference between education level groups in current technology use and technology use acceptance (T-Statistics).
T-Statistics; Grouping Variable: High School Education or Less (HS) and Higher
Education (HE)
Variable
Mean (HE)
Mean (HS)
t
df
p-Value
SD (HS)
SD (HE)
Current smartphone use
1.00
0.84
3.00
100
0.003382 *
0.00
0.37
Current computer use
1.00
0.65
4.93
100
0.000003 *
0.00
0.48
Computer_Behavioral intention (BI)
12.47
9.75
5.37
100
0.000001 *
2.35
2.72
Computer_Social influence (SI)
5.94
5.51
0.80
100
0.423128
2.78
2.58
Computer_Facilitating conditions (FC)
13.66
11.49
3.74
100
0.000303 *
2.16
3.43
Computer_Effort expectancy (EE)
16.60
13.09
4.98
100
0.000003 *
2.90
4.01
Computer_Performance expectancy (PE)
14.66
12.22
3.22
100
0.001748 *
3.94
3.72
Smartphone_Behavioral intention (BI)
12.89
11.51
2.18
100
0.031928 *
2.88
3.46
Smartphone_Social influence (SI)
6.47
5.82
1.19
100
0.236859
2.90
2.62
Smartphone_Facilitating conditions (FC)
13.43
12.38
1.90
100
0.059939
2.28
3.11
Smartphone_Effort expectancy (EE)
16.62
14.71
2.74
100
0.007178 *
3.07
3.83
Smartphone_Performance expectancy (PE)
15.98
14.76
1.54
100
0.126620
4.34
3.63
* marked factors statistically significant with p < 0.05.
4. Discussion
The wide range of variables allows us to examine complex associations between
measured factors and verify several hypotheses regarding technology use. Meanwhile, the
mean age of respondents was 58.5 years, only 31% of them were age 65+. The majority
of participants were women, and a large number. The majority of participants reported
currently using technological devices such as smartphones (91%), and computers (81%).
The main purposes of smartphone use in daily living were contact with relatives/support,
seeking information, and contact with health professionals. These may also have been
influenced by the COVID-19 pandemic, social distancing restrictions, and limited face-toface contact. On the other hand, caregivers use the computer mainly for information seeking
and hobbies/entertainment purposes. These differences between purposes in smartphone
and computer use could help create new apps and programs for dementia caregivers.
The current study confirmed previous findings that the main reason dementia caregivers use a smartphone is to seek support. The literature has consistently shown that,
given the stressful nature of caregiving, internet-based digital tools, online platforms, and
support groups play an important role in regaining a sense of social inclusion and belong-
Int. J. Environ. Res. Public Health 2021, 18, 3167
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ing, reported by caregivers [30] Online support systems, such as the online social support
intervention reported by Dam et al. [31] and/or the videophone psychosocial intervention
conducted by Czaja et al. [32] should therefore be taken into consideration, especially
during the COVID-19 pandemic. Czaja et al. concluded that caregivers who received the
videophone psychosocial intervention reported improvement in their caregiving skills,
a decrease in burden, an increase in perceived social support, and positive perceptions
of the caregiving experience [32], which included most of the difficulties experienced
by caregivers.
Interestingly, since most care recipients were moderately dependent (47%), the technology was perceived by respondents as most useful in patients’: (1) locomotion, (2) toileting,
and (3) meals, based on ADL domains. This highlights the fields of potential dementia care
needs in creating new solutions, and electronic devices such as GPS trackers, smartphone
apps, dietary intervention apps. Accordingly, recent data [33] show that care recipients’
wandering, caregivers’ concern, and caregivers’ smartphone usage can predict informal
caregivers’ BI to use GPS tracking devices. In line with our findings regarding the perceived usefulness of technology in managing patients’ meals and diet, recent data show that
electronic devices such as intelligent voice assistant technology in preparing meals could
be a valuable solution for dementia caregivers even when they do not have a technical
background [34]. The impact of this could be far reaching, as research suggests that dietary
interventions, such as an anti-inflammatory diet [35] and MIND diet - MediterraneanDASH (Dietary Approaches to Stop Hypertension) Intervention for Neurodegenerative
Delay [36], can slow the progression of AD. The large body of evidence shows that, given
the stressful nature of caregiving, internet-based digital tools, online platforms, and support groups play an important role in regaining a sense of social inclusion and belonging,
reported by caregivers [36]. Moreover, our findings show, not surprisingly, the longer time
since the first diagnosis, the higher stage of dementia, and the higher the dependency level
of care recipient were, the lower ADL score was.
As we hypothesized, there were differences between socio-demographic groups in
current smartphone/computer use, but only in the case of education. The respondents
with a higher education level used both smartphones, and computers more frequently.
There were no differences in technology use between men and women, nor between the
younger and 65+ caregivers. However, there were correlations between age, education, and
current technology use, indicating that the older caregivers were, the less often they use
the computer. Furthermore, there was a significant association between smartphone and
computer use. Finally, the higher the level of caregivers’ education, the more often they use
both the smartphone and computer. Interestingly, the level of education decreases as age
increases, which could be an additional factor in technology use difficulties, as mentioned
in previous studies [20].
We hypothesized that technology acceptance in dementia care domains would differ
depending on caregivers’ socio-demographic status (gender, age, and education). The
findings confirmed differences between groups, which may be crucial in providing adequate technology solutions for informal dementia caregivers and meeting their needs
and expectations. Current computer and smartphone use were both positively correlated
with computer and smartphone BI—the domain often used to predict technology user
actual behavior [22]. However, the role of SI, the encouraging function of significant others
influencing user behavior, had no association with age, education level, gender, or current technology use. Furthermore, women and respondents with higher education were
more likely to accept technology in care, which can be a crucial factor in matching new
technology solutions to caregivers and their needs.
We perceive our small sample findings as an introduction to a larger, more widely
measured study. Our study is not exempt from limitations. Therefore, additional sociodemographic information such as household income levels, work/occupation, urban/rural
residency, financial abilities to buy adequate apps and/or devices, and type of dwelling
should be taken into consideration in future studies. Moreover, the missing data of three
Int. J. Environ. Res. Public Health 2021, 18, 3167
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respondents make carefully checking and cautionary data collection the priority. A qualitative analysis of caregivers’ needs regarding dementia care technology could also bring
more data to the research in future perspectives.
5. Conclusions
Given the enormous unpredictability of the COVID-19 pandemic, social distancing,
and face-to-face contact restrictions, the level of challenges faced by informal dementia
caregivers increases. Therefore, the role of technology in caregiving daily activities has
gained considerable significance in the past year. Due to the growing use of technology
in dementia (especially AD), clinical trials (recruitment and participation conducted via
technology use), helpfulness in care, and insufficiency of effective digital inclusion to
improve the well-being of dementia caregivers, there is a pressing demand to identify the
factors responsible for improved accessibility of technology devices. Accordingly, our study
aimed to verify the level of current technology (smartphone and computer) use and future
acceptance of these technology devices in dementia care, as well as socio-demographic
factors of informal caregivers and care recipients.
Our findings reveal that computer use decreases with age; however, this effect of
age did not smartphone use. Moreover, the higher the level of caregivers’ education,
the more often they use both a smartphone and computer. Noteworthy, the level of
education decreased as age increased, which could be an additional factor in technology
use difficulties. An advantage of the study was measuring not only BI of smartphone and
computer use in care as a predictor of future behavior towards technology, but also current
use of these devices. The variables were correlated in both smartphone and computer use.
Furthermore, caregivers over 65 years of age had significantly lower scores in computer
use BI, SI, FC, EE, showing that age can be an important barrier to the acceptance of
computer use in care. There was no such difference with the smartphone use domains,
however. Therefore, interestingly, smartphone use and its acceptance in care seems to be
more feasible, regardless of age. Moreover, the following differences between age, gender,
and education in technology acceptance domains occurred: women were more likely to
accept technology in care; there were significant differences between males and females in
computer BI, and BI, SI, FC, EE, and PE; and respondents younger than 65 had significantly
higher scores in BI, SI, and EE, than those older than 65 years participants.
Our findings highlight the importance of caregiver’s characteristic for technology
developers. The care in the majority of cases concerned moderate dementia patients, and
the technology was perceived by respondents as most useful in patients’ ADLs including
locomotion, toileting, and meals. This shows the potential of developing new technology
solutions such as GPS trackers, smartphone apps, dietary intervention, and meal planning
apps. The types of apps seem crucial. The target population for the new technologies
and devices in care are women and caregivers with higher education. Caregivers use
and accept rather smartphones than computers. Therefore, smartphone apps may be
more desirable in care than computer applications. The differences between purposes
in smartphone and computer use should also be taken into consideration by technology
companies in creating assistive technology for caregivers. While smartphone is more often
used for seeking support and information, the computer seems to be more suitable for
hobbies/entertainment purposes. The role of important others in technology acceptance is
also an important factor in bringing technologies to market. Moreover, the importance of
active data collection using wearable and home-based sensors and apps, on-line assessment
tools and platforms, electronic medical records, and online technologies, which, due to
the disease characteristics, involve caregivers could be beneficial and reduce costs of AD
clinical trials.
In sum, the future of technology use in dementia care should indicate solutions
tailored to individual characteristics and preferences. We hope that our findings will help
to elucidate the specifics of caregivers as technology users.
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Author Contributions: Conceptualization, D.W., K.S., P.K.; methodology, D.W., K.S., P.K.; software,
D.W.; validation, K.S.; formal analysis, D.W.; investigation, D.W., K.S., P.K., M.O., A.K.; resources,
D.W., K.S., P.K.; data curation, M.O., A.K., P.K.; writing—original draft preparation, D.W., K.S., P.K.
writing—review and editing, D.W., K.S., P.K.; supervision, M.P., I.R.-L.; project administration, D.W.,
K.S., M.O., A.K., P.K. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: No ethical approval was obtained because this study involved only non-invasive procedures and collected socio-demographic information and respondents’
opinions on the various subjects.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: This research is conducted within EU-financed InterDoktorMen project (POWR.
03.02.00-00-I027/16).
Conflicts of Interest: The authors declare no conflict of interest.
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