Vol.:(0123456789)
1 3
Quality of Life Research (2020) 29:1721–1730
https://doi.org/10.1007/s11136-020-02418-4
Quality oflife inpeople withdementia living innursing homes:
validation ofaneight‑item version oftheQUALIDEM forintensive
longitudinal assessment
StefanJunge1 · PaulGellert1 · JulieLorraineO’Sullivan1· SebastianMöller2· Jan‑NiklasVoigt‑Antons2 ·
AdelheidKuhlmey1 · JohannaNordheim1
Accepted: 6 January 2020 / Published online: 18 January 2020
© The Author(s) 2020
Abstract
Purpose Our aim was to examine whether quality of life which was repeatedly assessed over time is related with the com-
prehensive assessment of quality of life (QoL) and thereby to validate a brief QoL assessment.
Method This longitudinal study used a comprehensive assessment of quality of life at baseline (QUALIDEM; 37 items) to
validate an eight-item version of QUALIDEM to assess momentary quality of life which was repeatedly administered using
a tablet device after baseline. In all, 150 people with dementia from 10 long-term facilities participated. Momentary quality
of life and comprehensive quality of life, age, gender, activities of daily living (Barthel Index), Functional assessment stag-
ing (FAST), and Geriatric Depression (GDS) have been assessed.
Results Comprehensive and momentary quality of life showed good internal consistency with Cronbach’s alpha of .86
and .88 to .93, respectively. For multiple associations of momentary quality of life with the comprehensive quality of life,
momentary quality of life was significantly related to comprehensive quality of life (B = .14, CI .08/.20) and GDS (B = − .13,
CI − .19/− .06). More specifically, the comprehensive QUALIDEM subscales ‘positive affect’, ‘negative affect’, ‘restlessness’,
and ‘social relationships’ showed significant positive associations with momentary quality of life (p < .001).
Conclusion We found that momentary quality of life, reliably assessed by tablet, was associated with comprehensive measures
of quality of life and depressive symptoms in people with dementia. Broader use of tablet-based assessments within frequent
QoL measurements may enhance time management of nursing staff and may improve the care quality and communication
between staff and people with dementia.
Keywords Caregiving· Well-being· Nursing home· Quality of life· Alzheimer’s disease· Touchscreen tablet
Introduction
As there is not yet a curative treatment for dementia, a major
goal of caring for people with dementia (PwD) is the main-
tenance of quality of life (QoL). In the USA [1, 2] as well as
in Germany [3], about half of the older adults aged 65 and
more living in nursing homes are diagnosed with dementia,
which is 19-fold higher than the prevalence of dementia in
individuals over 65 living in the community [3].
Even though there is a consensus about the importance
of QoL as a goal of care in PwD, there is still a debate about
theory, assessment, and factors associated with QoL in PwD
[4]. However, so far only a few assessment tools are based
on theory; most were proxy rating compared to self-rating
and conceptualized QoL as general health related or domain
specific [5]. Therefore, reliable instruments to assess QoL
Electronic supplementary material The online version of this
article (https ://doi.org/10.1007/s1113 6-020-02418 -4) contains
supplementary material, which is available to authorized users.
* Stefan Junge
stefan.junge@charite.de
1 Institute ofMedical Sociology andRehabilitation Science,
Charité – Universitätsmedizin, Charitéplatz 1, 10117Berlin,
Germany
2 Quality andUsability Lab, Technical University ofBerlin,
Ernst-Reuter-Platz 7, 10587Berlin, Germany
1722 Quality of Life Research (2020) 29:1721–1730
1 3
are necessary. In a recent systematic review of QoL in PwD,
three factors that are positively associated with a better
QoL were having close relationships, social engagement,
and functional abilities. [6]. Focusing on the psychosocial
domains of QoL in PwD, the QoL assessment tool QUALI-
DEM showed the best acceptability interviewing PwD and
their proxies. It was considered as acceptable in the long and
short forms profiling PwD with mild-to-very severe stages
of dementia living in nursing homes [4, 7–9].
Although many established QoL instruments allow a
comprehensive assessment of QoL in PwD, momentary
variations in QoL across time may be uncaptured [10]. Eco-
logical Momentary Assessment (EMA) showed accuracy,
minimization of retrospective bias, and revealing dynamic
processes as compared to more traditional comprehensive
QoL assessments across studies [11, 12]. Having a positive
mood and being in social interaction assessed on a momen-
tary level, i.e., assessed on a daily basis, has been related
to a higher level of QoL. These findings were assessed on
a comprehensive-level QoL (i.e., by the QoL-AD) in the
Maastricht Electronic Daily Life Observation (MEDLO)
study [13] as well as in other intervention studies in PwD
[14]. These studies provided first hints about the association
of momentary and comprehensive assessment of QoL.
New technologies may help to assess QoL in PwD due to
the frequent QoL measurements across time. Previous stud-
ies suggest the feasible use of technology-based and more
specifically touchscreen-based assessments for elderly peo-
ple, PwD, and other people with neurodegenerative disorders
[15–17]. Other studies investigated the use of smartphones
to measure the momentary QoL in PwD and people with
cognitive impairment and identified a good acceptability,
feasibility, and accuracy as well [18–21]. However, further
research is needed that examines the relation of momentary
QoL with comprehensively assessed QoL in PwD.
Aims ofthestudy
The aim of our study was to validate a brief version of the
QUALIDEM that would be suitable for momentary assess-
ment by analyzing the association of momentary (assessed at
several time points) and comprehensive QoL in PwD living
in nursing homes. We investigated factors that were associ-
ated in the eight-item and the 37-item version of QUALI-
DEM at baseline measurements. Inspecting correlations of
those two scales may help us to enhance our knowledge on
the mechanisms of QoL over time and may be helpful for
the nursing staff to assess QoL in the future. In the first step,
we aimed at testing the momentary and time-lagged reli-
ability of a momentary assessment of QoL. Furthermore, we
hypothesized a positive association of momentary QoL with
comprehensive QoL. Additionally, we hypothesized that the
relationship between momentary and comprehensive QoL
exists when adjusting for age, gender, cognitive status, func-
tional status, and depressive symptoms as well as temporal
trend and between-facility variation.
Methods
Study design
The PflegeTab (engl. CareTab) study aimed to develop and
evaluate a tablet-based psychosocial intervention tailored
to the needs of PwD. A tablet application was developed
and combined with innovative care concepts in order to
enable a flexible, patient-centered care approach. The app
was tested in ten nursing homes in Berlin over an 8-week
intervention period, in which participating residents received
activation sessions 3 times per week for up to 30min. The
full 37-item version of the QUALIDEM questionnaire was
used for the assessment of comprehensive QoL before and
after the intervention period. In the present analyses in this
article, we only regarded the individual measurements that
were administered before the intervention (at baseline). As
our main focus lay on momentary and comprehensive QoL,
momentary QoL was measured via tablet during the inter-
vention period before and after each activation session (For
the present analyses, before session assessments were used
only; Table1).
Participants andprocedure
The planned sample size of the PflegeTab study was
N = 240 PwD across eight nursing homes [22]. The sam-
ple size calculation was based on the main study, which
included a randomized trial design, where the sample size
referred to a medium-large effect size of Cohen’s d = 0.5
(α = 0.05; 1 − β = 0.80; 20% dropout rate; between clus-
ter variance ICC = 0.005; G*Power 3.1) between the two
arms where we primarily used the assessments at baseline
(see ISRCTN98947160). Although the number of par-
ticipating care facilities was increased to ten during the
planning phase, the target sample size could not be fully
achieved. Participants were included if they were nursing
home residents and had a medical diagnosis of dementia
(International Statistical Classification of Diseases and
Related Health Problems—ICD-10: F00-F03), including
Alzheimer’s disease, vascular dementia, and unspecified
dementia [23]. Participants were excluded if other seri-
ous chronic psychiatric diagnosis were given F10-29,
exceptions: F10.1; F17.1; F17.2; F32.2; and F32.3 may
be included. An admission to the nursing home less than
4weeks beforehand was also a criterion for exclusion. A
total of 203 people (eligible nursing home residents or
1723Quality of Life Research (2020) 29:1721–1730
1 3
their conservators) were contacted and 163 PwD took part
in the PflegeTab study. For the current analyses, 13 of
the participants could not be included because the base-
line momentary QoL and momentary QoL could not be
imputed. As QUALIDEM is a proxy-rated assessment tool,
all participants were assessed by the nursing staff working
in the nursing homes (Fig.1).
Measures
Comprehensive QoL was assessed with a 37-item version
of QUALIDEM suitable for PwD with mild-to-severe
dementia using proxy rating [24]. Out of 40 existing items,
Item 9: ‘Does not want to eat’, item 30: ‘Likes to lie down
in bed’, and item 15: ‘Enjoys meals’ was not represented
Table 1 Sample characteristics
SD standard deviation, GDS Geriatric Depression Scale; FAST functional assessment staging; ADL activity
of daily living; QoL quality of life
Scale Mean (SD) Empirical range Items NCronbach’s α
Age, years 84.9 (7.1) 53–100 1 150 –
Women, % 75 1 112 –
Depressive symptoms, GDS 3.9 (2.9) 0–15 15 111 .68
Functional status, Barthel Index/ADL 54.1 (26.3) 0–95 10 149 .89
Dementia stage, FAST 9.0 (1.9) 4–16 16 149 .75
Baseline momentary QoL 5.4 (1.2) 1.6–7.0 8 150 .89
Baseline comprehensive QoL (sum score) 83.3 (14.9) 43–115 37 149 .86
QUALIDEM subscales
Care relationship 15.5 (4.6) 1–21 7 148 .84
Positive affect 13.1 (3.7) 1–18 6 148 .86
Negative affect 6.4 (2.2) 0–9 3 148 .77
Restlessness 5.6 (2.6) 0–9 3 149 .63
Positive self-image 6.9 (2.0) 1–9 3 148 .49
Social relationship 11.5 (3.8) 1–18 6 149 .74
Social isolation 6.8 (1.9) 1–9 3 149 .42
Feeling at home 9.1 (2.6) 2–12 4 143 .60
Having something to do 2.2 (1.6) 0–6 2 149 .20
Fig. 1 a Displays the individual mean scores of momentary quality of
life across sessions. b Shows the predicted individual mean scores of
momentary quality of life across sessions estimated by a multivariate
model with fixed effect (linear) of session number, random intercept,
and slope
1724 Quality of Life Research (2020) 29:1721–1730
1 3
because we excluded the subscale J in our study; thus, 37
items remained. In order to test the main hypothesis, a sum
score was created across all 37 items at baseline. Further,
we studied the determinants of QoL according to the lit-
erature [25–27] to establish nine subscales: care relation-
ship (7 items), positive affect (6 items), negative affect (3
items), restlessness (3 items), positive self-image (3 items),
social relationship (6 items), social isolation (3 items), feel-
ing at home (4 items), and having something to do (2 items)
(Table). These subscales are sum scores across the respec-
tive items, which were presented in a Likert format ranging
from never (0), rarely (1), and sometimes (2) to frequently
(3). Across all 37 items, a principal component analyses
(PCA) extracted ten components with an Eigenvalue over
1 although the visual evaluation of the Scree plot suggested
four components only. The varimax-rotated PCA solution
accounted for 67.2% of the overall variance. Next to the sub-
scales, we created a composite sum score across all 37 items,
which has been used extensively in the literature as well
[25–30]. Thus, in our analyses, we look into both the com-
prehensive sum score (Table2) as well as subscale results
(Table3) and its relation with momentary QoL.
Momentary QoL was assessed with an eight-item version
from the 37-item long version of QUALIDEM. All eight
items had been executed on tablet computers at repeated
time points. Aiming to use a brief scale that was considered
as suitable for the tablet-based momentary QoL assessment
in real life nursing home environment by nurses, we chose
eight items: restlessness [item 19 of the QUALIDEM], mood
[item 10], anxiousness [item 6], body language [item 22],
communication [item 12], happiness [item 05], sadness [item
11] and sociability [item 34] belonging to the four subscales:
‘positive affect’, ‘negative affect’, ‘restlessness’, and ‘social
relationships’ of the full 37-item version of QUALIDEM
(i.e., subscale B, subscale C, subscale D, and subscale F)
[8, 31]. As part of a workshop, the study team was selecting
the items based on the following considerations: Two items
from four subscales of the German QUALIDEM version
were used for the short version. In addition, some scales
were not suitable for nursing home residents with severe
stages of dementia. The item length played a minor role and
was relevant only when selecting the two items from a scale.
In that case, shorter items were preferred, since we assumed
that all items belonging to the same scale were assumed
equivalent in content and therefore largely interchangeable.
Table 2 Univariate and multiple
associations with momentary
quality of life
B z-standardized B coefficient that can be interpreted as standardized beta coefficient, 95% CI lower and
upper limit 95% confidence interval, P-value level of significance(Significant values are shown in bold),
AR diagonal random variance of session of measurements between individuals, AR rho residual correlation
between sessions of measurement, QoL quality of life, GDS Geriatric Depression Scale, FAST Functional
assessmentstaging. N = 148 across models (two cases could not be imputed)
Univariate model Multiple model
B95% CI P-value B95% CI P-value
Comprehensive QoL .17 .11 .23 < .001 .14 .08 .20 < .001
Session, linear .05 − .01 .11 .123 .03 − .02 .09 .264
Age − .06 − .12 .01 .081 − .06 − .13 .00 .054
Gender, women .01 − .05 .07 .723 .03 − .03 .09 .321
Barthel Index − .006 − .07 .06 .859 − .02 − .11 .06 .602
GDS score − .16 − .22 − .10 < .001 − .13 − .19 − .06 < .001
FAST score .04 − .03 .10 .240 .04 − .04 .13 .358
Random effects
AR diagonal .90 .84 .96 < .001 .88 .82 .95 < .001
AR rho .39 .35 .43 < .001 .37 .33 .41 < .001
Variance between facilities .08 .03 .22 .062 .09 .03 .25 .058
Table 3 Multiple associations of momentary quality of life with com-
prehensive QUALIDEM subscales
B z-standardized B coefficient that can be interpreted as standardized
beta coefficient. P-value level of significance (Significant values are
shown in bold). Coefficients of each subscale were derived from a
separate model, which have been adjusted for age, gender, time, GDS,
Barthel Index, and FAST as described in the analysis section. N = 148
across models (two cases could not be imputed)
QUALIDEM subscale B95%CI P-value
Care relationship .02 − .04 .08 .535
Positive affect .17 .11 .23 < .001
Negative affect .13 .07 .19 < .001
Restlessness .07 .01 .14 .023
Positive self-image − .01 − .07 .05 .836
Social relationships .16 .09 .22 < .001
Social isolation .07 .01 .14 .027
Feeling at home .04 − .02 .11 .212
Having something to do .03 − .04 .09 .441
1725Quality of Life Research (2020) 29:1721–1730
1 3
Dementia stage was measured with the Functional assess-
ment staging (FAST) which is scored from 1 to 16 for 7
consecutive stages and 9 substages [32]. As PwD progress
in severity, the numerical value of the FAST increased, with
stage 4 corresponding to mild dementia, stage 5 to moderate,
stage 6 to moderately severe, and stage 7 to severe demen-
tia. According to Arons etal. (2017), the QUALIDEM is
applicable in PwD with all stages of dementia severity [7].
Functional status was measured with the Barthel Index
[33]. Barthel Index is scored from 0 to 100, served as a
covariate and activities of daily living (ADL) related to self-
care activities such as bathing, dressing, grooming, and other
activities. Barthel Index ranged from 0 to 95.
Depressive symptoms were measured with GDS-SF-15
(Geriatric Depression Scale– 15 items Short Form which
served as a covariate [34]. Total score ranged from 0–15.
A score of 5 or more indicated probable depression. GDS
ranged from 0 to 15 points.
Session (time trend) refers to the measurement session
where the eight-item QUALIDEM was administered. Ses-
sion variable started from 0 (at baseline; first session, 1,
2, 3,…). Mostly, there were three sessions a week and the
median total number of sessions per resident was 8 sessions.
Using the session variable, the timing of the momentary
assessment can be modeled.
Additional covariates were ID (i.e., unique identifier of
facility), age, and gender of the residents.
Statistical analysis
The univariate and multivariate associations of momentary
and comprehensive QoL were estimated using mixed mod-
eling which takes the nested data structure of measurement
occasions in individuals in nursing homes into account.
Momentary QoL (level-1) was regressed on momentary
session (time trend; level-1) and comprehensive variables,
i.e., comprehensive QoL, age, gender, functional and cogni-
tive status, and depressive symptoms (all level-2). The ID
of the nursing home of each person with dementia was used
as a clustering variable (level-3). Intercepts were allowed
to vary across individuals and facilities (intercept only
model; variance components). Furthermore, an autoregres-
sive covariance matrix was assumed for the time variables
to account for autocorrelation (based on inspection of fit
indices [AIC; Akaike’s Information Criterion [35], where
smaller values indicate better fit: AICautoregressive = 6272;
AICunstructured = 6544, without reaching convergence;
AICidentity = 6617; AICvariance components = 6622] and theoreti-
cal plausibility). Prior to the estimation of the multivariate
models, we inspected the trends graphically as well as com-
pared the AIC values. Based on the inspection, we decided
for a fixed slope random intercept model (AIC = 6272)
as quadratic and cubic fixed and random effects did not
provide improvement in the fit of the model with the data
(AIC = 6278 to 6307). All variables have been standardized
allowing the coefficients to vary between − 1 and 1 and,
thus, can be interpreted as beta coefficients. All data analy-
ses were conducted with MIXED procedures in SPSS (IBM
SPSS Statistics for Windows, Version 25.0, released 2017.
Armonk, NY: IBM Corp.) using the Restricted Maximum
Likelihood estimator (REML), which accommodates for
unbalanced datasets, i.e., missing cases in repeated meas-
ure designs [35]. Scale missing values were imputed using
Expectation–Maximization algorithm for Barthel Index
(n = 1), FAST (n = 1), GDS (n = 38), and comprehensive
QoL (n = 1). Models with and without imputation did not
differ in the direction or magnitude of the main findings.
For the evaluation of the internal consistency, at each
session (time point), Cronbach’s alpha was used (Kuder-
Richardson Reliability Coefficients KR20 in the case of
binary items). Internal consistency was considered as
excellent if Cronbach’s alpha was α ≥ .90 or higher, as
good if α ≥ .80, as acceptable if α ≥ .70 and as question-
able to unacceptable if α ≥ .60 [36]. For test–retest reli-
ability, lagged momentary QoL variables (lag 1, lag 2)
were calculated and regressed on momentary QoL in a
model that accounted for the nested structure as described
above (except for using a variance components covariance
structure to capture autocorrelation merely within the coef-
ficients). According to Dichter etal. (2011), test–retest
reliability can be seen as the response stability over time,
which was operationalized as the regression coefficient
of the same test across subsequent time points [37, 38].
Thereby, lag 1 refers to the regression of the value of
momentary QoL in one session onto the value of momen-
tary QoL in the prior session. Accordingly, lag 2 refers
to the regression of the value of momentary QoL in one
session onto the value assessed two sessions before. In
the tested models, momentary QoL was compared with
each lagged momentary QoL for the nested data structure
(Supplementary TableS2).
Results
The sample compromised of 150 PwD from 10 long-term
facilities with a mean age of 84.9 (SD 7.1) years (Table1).
The majority (75%; n = 112) were women. Mean GDS score
was 3.9 (SD 2.9) and the mean Barthel Index score was 54.1
(SD 26.3). Concerning the dementia stage, the mean FAST
score was 9.0 (SD = 1.9) at baseline, which indicates mild
cognitive impairment. The mean Comprehensive QoL was
83.3 (SD 14.9), whereas the mean momentary QoL at base-
line was 5.4 (SD 1.2). Ranging from 1 to 29 measurement
1726 Quality of Life Research (2020) 29:1721–1730
1 3
sessions the mean number of sessions (time trend) was 8.6
(SD 6.3).
Internal consistency
We found that the internal consistency of the comprehensive
sum scale of the QUALIDEM was good with a Cronbach’s
alpha of α = .86. Likewise, the internal consistency of the
eight-item momentary QoL was excellent as indicated by
Cronbach’s alpha with a range from α = .88 to α = .93, indi-
cating decent reliability at each time point (Supplementary
TableS1). Regarding unidimensionality of the momentary
QoL scale, a PCA extracted one factor with an Eigenvalue
over 1 and the evaluation of the Scree plot suggested one
factor as well. The PCA solution accounted for 62.6% (sums
of squared loadings) of the overall variance. Regarding
test–retest reliability, momentary QoL was associated with
each lagged momentary QoL (i.e., lag 1) with B = .40 (CI
.36/.44, p < .001) when accounting for the nested data struc-
ture (Supplementary TableS2). Additionally, the lagged
association of momentary QoL lagged across two sessions
of assessment (i.e., lag 2) was B = .36 (CI .31/.40, p < .001).
Finally, in a model where both (consecutive lag 1 and lag 2
time points momentary QoL) were entered, lag 1 association
was B = .32 (CI .27/.36, p < .001) and lag 2 was B = .23 (CI
.19/.27, p < .001; Supplementary TableS2).
Univariate associations
Regarding univariate associations, momentary QoL
was not significantly related to session of measurement
(B = .05, CI − .01/.11, p = .123), age (B = − .06, CI − .12/.1,
p = .081), gender (B = .01, CI − .05/.07, p = .72), Barthel
Index (B = − .006, CI − .07/.06, p = .86), and FAST score
(B = .04, CI − .03/.10, p < .240; Table2). However, we did
find a significant positive association of momentary QoL
with comprehensive QoL (B = .17, CI .11/.23, p < .001) and
a significant negative association of momentary QoL with
GDS (B = − .16, CI − .22/− .10, p < .001).
Regarding the random effects in the univariate model,
where momentary QoL was regressed on comprehensive
QoL, the random variance of session of measurements
between PwD (autoregression, diagonal) was significant
in the univariate model (B = .90, CI .84/.96, p < .001), as
well in the multiple model (B = .90, CI .82/.95, p < .001;
Table2). Furthermore, the residual correlation between two
sessions of measurement was significant in the univariate
model (autoregression, rho; B = .39, CI .35/.43, p < .001), as
well as in the multiple model (B = .37, CI .33/.41, p < .001),
indicating that a higher-than-average rating in one session
is associated with a higher-than-average rating on the con-
secutive session. However, the random variance between
facilities was neither significant in the univariate model
(B = .08, CI .03/.22, p = .062), nor in the multiple model
(B = .09 CI .03/.25, p = .058).
Multiple associations
For the multiple associations of momentary QoL with the
comprehensive QoL, momentary QoL was not significantly
related to session of measurement (B = .03, CI − .02/.09,
p < .264); Barthel Index (B = − .02, CI − .11/.06, p < .602);
FAST (B = .04, CI − .04/.13, p < .358); age (B = − .06,
CI − .13/.00, p = .054); and gender (B = .03 CI − .03/.09,
p < .321). However, the momentary QoL was significantly
positively related to comprehensive measured QoL at base-
line (B = .14, CI .08/.20, p < .001) and significantly nega-
tively related to GDS (B = − .13, CI − .19/− .06, p < .001).
Finally, concerning associations of QUALIDEM sub-
scales with comprehensive QoL, positive (B = .17, CI
.11/.23) and negative affect (B = .13, CI .07/.19), restlessness
(B = .07, CI .01/.14) ,as well as social relationships (B = .16,
CI .09/.22) and isolation (B = .07, CI .01/.14) were signifi-
cantly related (all p > .05; see Table3).
Discussion
Validating a short form of QUALIDEM for purposes of in
the moment assessment of QoL and changes in QoL over
time, we hypothesized a positive relation between momen-
tary and comprehensive QoL with and without adjusting
for covariates. While internal consistency of the momentary
QoL was demonstrated, we found univariate and multiple
associations of momentary and comprehensive QoL sug-
gesting QoL can be assessed validly. In the multiple case,
momentary QoL was significantly negatively related with
GDS. Multiple associations of momentary QoL with com-
prehensive QUALIDEM subscales indicated positive and
negative affect and restlessness, and social relationships and
social isolation were significantly positively associated with
momentary QoL, whereas the others were not.
In our multiple analysis, we found that GDS was substan-
tially and negatively related with momentary QoL. This find-
ing was in line with majority of the research that investigated
mood, depressive symptoms, affective status, and happiness
[39–41]. Further, most studies showed comparable results
investigating multidimensional associations of the QUALI-
DEM subscales [25–30]. Moreover, in our study, we did
not find significant outcomes regarding gender, age, FAST,
and Barthel Index, which contradicts findings from the lit-
erature [42, 43]. Thus, more research is needed to examine
the association of momentary QoL and other indicators of
health and functioning.
1727Quality of Life Research (2020) 29:1721–1730
1 3
Furthermore, we found subscales for positive and nega-
tive affect, restlessness, and social relationships that were
significantly related with momentary QoL. This is largely
in line with findings from the MEDLO study, showing that
being in a positive mood and engaging in social interac-
tions at state-level assessment was related to a higher QoL
assessed on an individual level in PwD in nursing homes
[13, 44]. Further studies showed that social engagement is
essential for PwD and related with increased levels of QoL
[40, 45]. Beerens etal. (2018) suggested focusing on the
type of social interactions and rating the quality of interac-
tion [44]. The subscales positive self-image, care relation-
ship, and having something to do were not significantly asso-
ciated with momentary and comprehensive QoL. This is in
line with the literature because in people with very severe
dementia the domains such as positive self-image, having
something to do, and feeling at home cannot be assessed
[46].
Nordheim etal. (2015) showed in their pilot study that
the use of tablet computers by PwD living in nursing homes
improved the contact to family members and the nursing
staff. Additionally, the well-being of residents had been
improved [47]. Additionally, technology may be assistive
for PwD and can also be used as a telecare service in a home
care setting. Furthermore, a combination of several tech-
nologies should be investigated regarding momentary and
comprehensive QoL and other covariates (GDS, Barthel
Index and FAST) of PwD living in nursing homes [48].
Dementia-specific assessment of QoL is multidimen-
sional and depends on the individual environment of PwD.
Adaption influences the rating of QoL of PwD living in
nursing homes and are described in the adaptive coping
model [31]. Ettema etal. (2007) described the life domains
of QUALIDEM that were chosen by consensus [41]. Law-
ton etal. (1991) considered the well-being of elderly peo-
ple as the main outcome which is affected by the person-
environment system of PwD [49]. Due to this importance
of variables that affect QoL, we took different variables
into account. Momentary and comprehensive QoL were
examined. Nursing home of every PwD had been used as
a clustering variable to detect effects between the ten nurs-
ing homes. QoL instruments such as QUALIDEM had not
yet been tested completely to investigate psychometrical
variables.
Strengths andlimitations
Strengths of our study include the ecological design with a
large number of observations across time and an innovative
and relevant research topic, which may inform future QoL
assessment in research and practice. In our study, limitations
include the moderate number of long-term care facilities
that may have covered the potential between-facility effects,
which should be tested in future studies. Additionally, we
were not able to measure other scales such as FAST or Bar-
thel Index at momentary level; thus, time-lagged associa-
tions across scales at momentary level could not be detected
in the present study design. Another limitation concerns the
exclusion of important aspects of QoL, such as social isola-
tion. The subscale ‘social isolation’ was represented in our
sum score of the 37-item version of QUALIDEM with the
item 16: ‘Is rejected by other residents’, item 20: ‘Openly
rejects contact with others’, and item 32: ‘Calls out’. Previ-
ous studies of reliability and validity between the 18-item
version and the 37-item version of QUALIDEM showed that
the subscales ‘having something to do’ and ‘social isola-
tion’ were weakly or not scalable, according to the Loev-
ingers coefficient of homogeneity and scalability [9, 38].
However, the subscale ‘social isolation’ was associated with
our momentary QoL scale; thus, the short version may at
least cover some aspects of social isolation. This might be
due to the fact that communication, sociability, and sad-
ness, which likely cover some aspects of social isolation
and loneliness, were included in our short version. Nonethe-
less, future studies should inspect the further role of ‘social
isolation’ and ‘social relationships’ in the eight-item version
of QUALIDEM.
Implications forresearch andpractice
Mobile technologies may help to monitor patients with mild
and severe dementia or other clinical situations and can be
a low-cost option to support caregivers of PwD in a non-
clinical setting. Although intervention apps for PwD may be
time intensive, it might save time, if the technology would be
established and integrated in the activities of daily life [50].
Our eight-item Version QUALIDEM which represents the
domains of QoL had been examined as a reliable and valid
tool to assess QoL in people with mild and severe dementia
living in nursing homes. It may be used by clinical staff in
regular bases to assess and diagnose residents of nursing
homes every day [51].
Nevertheless, more longitudinal studies are needed to
determine if more factors are related to a change in QoL
over time. This information could be important for the devel-
opment of interventions that aim to improve QoL and for
diagnosing and daily assessment of QoL of PwD living in
nursing homes [13]. Other tools for the assessment of QoL
may be compared and evaluated with our method as well.
Furthermore, more touchscreen interventions should be
conducted to compare those with our method and evaluate
QoL including variables such as the QUALIDEM subscales.
Moreover, studies using technology-based assessment tools
1728 Quality of Life Research (2020) 29:1721–1730
1 3
outline the advantages such as accuracy, efficiency, accept-
ability, and feasibility [18–21].
A positive association of momentary QoL and compre-
hensive QoL indicates that a touchscreen-based assess-
ment instrument could be used to measure mood and social
engagement of PwD [15].
According to other subscales such as positive self-image
and care relationship, we found no association with momen-
tary QoL, which is likely to be related to the selection of the
eight state items from subscales that were not related with
self-image and care relationship. However, both subscales
were unlikely to vary substantially across short periods
of time, which may be a further explanation for the non-
significant relationship. Nonetheless, future studies should
inspect the relationship between perceived care relationship
and momentary QoL, being uncovered by our findings. Stud-
ies demonstrated that mood of PwD was correlated with
factors such as unfulfilled needs or environmental factors
[13]. Thus, investigating activity categories in future stud-
ies is needed.
The negative association of GDS and momentary QoL is
important, as depression is a common comorbid disorder of
dementia affecting between 23 and 54% of PwD, which is
substantially higher compared to the general population [52,
53]. Thus further studies are necessary to find more relation-
ships between the use of technology-based interventions and
depression in PwD in nursing homes [17].
Robertson etal. showed that staff who were more dis-
tressed rated QoL of PwD lower than those raters being less
distressed [54]. Further studies are necessary to find effects
of raters on the variables that we are targeting for. Broader
use of tablet-based assessments may improve the time man-
agement of nursing staff. This conclusion is in line with the
study by Muller etal. that considered tablet-based assess-
ment of dementia and mild cognitive disorders as an efficient
assessment tool to diagnose dementia faster [55]. Using the
short eight-item version of QUALIDEM for momentary
assessment of QoL in our study showed good reliability;
therefore, we suggest the broader implementation of the
short eight-item version of QUALIDEM in further studies
or clinical settings. The gain of data may improve the care
quality and communication between staff and PwD. Future
studies may investigate the use of tablet-based interventions
in nursing home environments or other clinical settings, to
evaluate QoL not only in PwD, but in other geriatric patient
groups as well. In that case, disease-specific instruments
should be applied [19, 51, 56, 57].
Conclusions
We found that momentary QoL was associated with com-
prehensive QoL as well as depressive symptoms in PwD liv-
ing in nursing homes. The use of tablet-based assessments,
especially the short eight-item version of QUALIDEM may
enhance our knowledge on the mechanisms of QoL over
time and may improve assessment by nursing staff and ulti-
mately QoL in PwD.
Acknowledgements Open Access funding provided by Projekt DEAL.
This research was supported by the German National Association of
Statutory Health Insurance Funds (GKV-Spitzenverband Grant Number
0001). There is no financial relationship between the authors and the
sponsors.
Author contributions SJ, PG, and JN made substantial contributions to
the concept of the manuscript. JN, PG, JLOS, JNVA, SM, AK, and JN
conducted the study. SJ and PG analyzed the data, and all authors were
involved in reviewing the data. SJ conducted review of the literature
and wrote the first draft of the manuscript. SJ, PG, JLOS, JNVA, SM,
AK, and JN reviewed the manuscript. All authors revised the current
manuscript for submission. All authors read and approved the final
manuscript.
Compliance with ethical standards
Conflict of interest All authors declare that there are no conflicts of
interest.
Ethical approval This study was approved by the local ethics commit-
tee of the Charité (number EA1/013/16).
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.
References
1. Moyle, W., Mcallister, M., Venturato, L., & Adams, T. (2007).
Quality of life and dementia: The voice of the person with demen-
tia. Dementia, 6(2), 175–191.
2. Magaziner, J., German, P., Zimmerman, S. I., Hebel, J. R., Bur-
ton, L., Gruber-Baldini, A. L., etal. (2000). The prevalence of
dementia in a statewide sample of new nursing home admissions
aged 65 and older: Diagnosis by expert panel. Epidemiology of
Dementia in Nursing Homes Research Group. The Gerontologist,
40(6), 663–672.
1729Quality of Life Research (2020) 29:1721–1730
1 3
3. Hoffmann, F., Kaduszkiewicz, H., Glaeske, G., van den Bussche,
H., & Koller, D. (2014). Prevalence of dementia in nursing home
and community-dwelling older adults in Germany. Aging Clinical
and Experimental Research, 26(5), 555–559.
4. Graske, J., Verbeek, H., Gellert, P., Fischer, T., Kuhlmey, A., &
Wolf-Ostermann, K. (2014). How to measure quality of life in
shared-housing arrangements? A comparison of dementia-specific
instruments. Quality of Life Research: An International Journal
of Quality of Life Aspects of Treatment, Care and Rehabilitation,
23(2), 549–559.
5. Bowling, A., Rowe, G., Adams, S., Sands, P., Samsi, K., Crane,
M., etal. (2015). Quality of life in dementia: A systematically
conducted narrative review of dementia-specific measurement
scales. Aging & Mental Health, 19(1), 13–31.
6. Martyr, A., Nelis, S. M., Quinn, C., Wu, Y. T., Lamont, R. A.,
Henderson, C., etal. (2018). Living well with dementia: A sys-
tematic review and correlational meta-analysis of factors associ-
ated with quality of life, well-being and life satisfaction in people
with dementia. Psychological Medicine, 48(13), 2130–2139.
7. Arons, A. M. M., Wetzels, R. B., Zwijsen, S., Verbeek, H., van de
Ven, G., Ettema, T. P., etal. (2017). Structural validity and inter-
nal consistency of the Qualidem in people with severe dementia.
International Psychogeriatrics, 30, 49–59.
8. Dichter, M. N., Dortmann, O., Halek, M., Meyer, G., Holle, D.,
Nordheim, J., etal. (2013). Scalability and internal consistency of
the German version of the dementia-specific quality of life instru-
ment QUALIDEM in nursing homes - a secondary data analysis.
Health and Quality of Life Outcomes, 11, 91.
9. Bouman, A. I., Ettema, T. P., Wetzels, R. B., van Beek, A. P.,
de Lange, J., & Droes, R. M. (2011). Evaluation of Qualidem:
a dementia-specific quality of life instrument for persons with
dementia in residential settings; scalability and reliability of sub-
scales in four Dutch field surveys. International Journal of Geri-
atric Psychiatry, 26(7), 711–722.
10. Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological
momentary assessment. Annual Review of Clinical Psychology,
4, 1–32.
11. Ebner-Priemer, U. W., & Trull, T. J. (2009). Ecological momen-
tary assessment of mood disorders and mood dysregulation. Psy-
chological Assessment, 21(4), 463–475.
12. Cain, A. E., Depp, C. A., & Jeste, D. V. (2009). Ecological
momentary assessment in aging research: a critical review. Jour-
nal of Psychiatric Research, 43(11), 987–996.
13. Beerens, H. C., de Boer, B., Zwakhalen, S. M. G., Tan, F. E.
S., Ruwaard, D., Hamers, J. P. H., etal. (2016). The association
between aspects of daily life and quality of life of people with
dementia living in long-term care facilities: a momentary assess-
ment study. International Psychogeriatrics, 28(8), 1323–1331.
14. Schall, A., Tesky, V. A., Adams, A.-K., & Pantel, J. (2017). Art
museum-based intervention to promote emotional well-being and
improve quality of life in people with dementia: The ARTEMIS
project. Dementia, 17(6), 728–743.
15. Robert, P., König, A., Amieva, H., Andrieu, S., Bremond, F., Bull-
ock, R., etal. (2014). Recommendations for the use of Serious
Games in people with Alzheimer’s Disease, related disorders and
frailty. Frontiers in Aging Neuroscience, 6, 54.
16. Manera, V., Ben-Sadoun, G., Aalbers, T., Agopyan, H., Askenazy,
F., Benoit, M., etal. (2017). Recommendations for the use of seri-
ous games in neurodegenerative disorders: 2016 Delphi panel.
Frontiers in Psychology, 8, 1243.
17. Tyack, C., & Camic, P. M. (2017). Touchscreen interventions and
the well-being of people with dementia and caregivers: A system-
atic review. International Psychogeriatrics, 29(8), 1261–1280.
18. Liddle, J., Wishink, A., Springfield, L., Gustafsson, L., Ireland, D.,
& Silburn, P. (2017). Can smartphones measure momentary qual-
ity of life and participation? A proof of concept using experience
sampling surveys with university students. Australian Occupa-
tional Therapy Journal, 64(4), 294–304.
19. Ramsey, A. T., Wetherell, J. L., Depp, C., Dixon, D., & Lenze, E.
(2016). Feasibility and acceptability of smartphone assessment in
older adults with cognitive and emotional difficulties. Journal of
Technology in Human Services, 34(2), 209–223.
20. Wu, Y.-H., Vidal, J.-S., de Rotrou, J., Sikkes, S. A. M., Rigaud,
A.-S., & Plichart, M. (2017). Can a tablet-based cancellation test
identify cognitive impairment in older adults? PLoS ONE, 12(7),
e0181809.
21. Huang, Y. P., Singh, A., Chen, S., Sun, F. J., Huang, C. R., & Liu,
S. I. (2017). Validity of a novel touch screen tablet-based assess-
ment for mild cognitive impairment and probable AD in older
adults. Assessment.
22. Chenot, J.-F. (2009). Cluster-randomisierte Studien: eine
wichtige Methode in der allgemeinmedizinischen Forschung
(p. 103). Zeitschrift für Evidenz: Fortbildung und Qualität im
Gesundheitswesen.
23. Palm, R., Junger, S., Reuther, S., Schwab, C. G., Dichter, M. N.,
Holle, B., etal. (2016). People with dementia in nursing home
research: A methodological review of the definition and identifica-
tion of the study population. BMC Geriatrics, 16, 78.
24. Ettema, T. P., Dröes, R.-M., de Lange, J., Mellenbergh, G. J.,
& Ribbe, M. W. (2007). QUALIDEM: development and evalua-
tion of a dementia specific quality of life instrument––validation.
International Journal of Geriatric Psychiatry, 22(5), 424–430.
25. Appelhof, B., Bakker, C., Van Duinen-van den Ijssel, J. C. L.,
Zwijsen, S. A., Smalbrugge, M., Verhey, F. R. J., etal. (2017).
The determinants of quality of life of nursing home residents
with young-onset dementia and the differences between demen-
tia subtypes. Dementia and Geriatric Cognitive Disorders,
43(5–6), 320–329.
26. Palm, R., Trutschel, D., Sorg, C. G. G., Dichter, M. N., Haastert,
B., & Holle, B. (2019). Quality of life in people with severe
dementia and its association with the environment in nurs-
ing homes: An observational study. The Gerontologist, 59(4),
665–674.
27. Wetzels, R. B., Zuidema, S. U., de Jonghe, J. F., Verhey, F. R.,
& Koopmans, R. T. (2010). Determinants of quality of life in
nursing home residents with dementia. Dementia and Geriatric
Cognitive Disorders, 29(3), 189–197.
28. Husebø, B. S., Ballard, C., Aarsland, D., Selbaek, G., Slettebo,
D. D., Gulla, C., etal. (2019). The effect of a multicomponent
intervention on quality of life in residents of nursing homes: A
randomized controlled trial (COSMOS). Journal of the Ameri-
can Medical Directors Association, 20(3), 330–339.
29. van Dam, P. H., Achterberg, W. P., Gussekloo, J., Husebo, B. S.,
& Caljouw, M. A. A. (2018). Quality of life and paracetamol in
advanced dementia (Q-PID): protocol of a randomised double-
blind placebo-controlled crossover trial. BMC Geriatrics, 18(1),
279.
30. van de Ven-Vakhteeva, J., Bor, H., Wetzels, R. B., Koopmans, R.
T. C. M., & Zuidema, S. U. (2013). The impact of antipsychotics
and neuropsychiatric symptoms on the quality of life of people
with dementia living in nursing homes. International Journal
of Geriatric Psychiatry, 28(5), 530–538.
31. Ettema, T. P., Dröes, R.-M., de Lange, J., Mellenbergh, G. J., &
Ribbe, M. W. (2007). QUALIDEM: Development and evalua-
tion of a dementia specific quality of life instrument. Scalabil-
ity, reliability and internal structure. International Journal of
Geriatric Psychiatry, 22(6), 549–556.
32. Reisberg, B. (1988). Functional assessment staging (FAST).
Psychopharmacology bulletin., 24(4), 653–659.
33. Mahoney, R. (1965). Barthel index (BI). Surya Shah, PhD,
OTD, MEd, OTR, FAOTA, Professor Occupational Therapy and
1730 Quality of Life Research (2020) 29:1721–1730
1 3
Neurology, Visiting Professor Neurorehabilitation, University
of Tennessee Health Sciences Center., 930, 1.
34. Sheikh JI, Yesavage JA. Geriatric Depression Scale (GDS):
Recent evidence and development of a shorter version. Clinical
Gerontologist: The Journal of Aging and Mental Health. 1986.
35. Bruce Schaalje G. Linear mixed models, a practical guide using
statistical software. Brady T. West, Kathleen B. Welch, Andrzej
T. Galecki, Chapman & Hall/CRC, Boca Raton, FL, 2007. No.
of pages: xix +353. Price: $71.96. ISBN: 1-58488-480-0. Sta-
tistics in Medicine. 2008;27(15):2979–80.
36. George, D., & Mallery, P. (2003). SPSS for windows step-by-
step: A simple guide and reference, 14.0 update (7th Edition).
37. Wise, K. L., Kelly, B. J., Knudsen, M. L., Macalena, J. A.
(2019). Reliability studies and surveys. In V. Musahl, J. Karls-
son, M. T. Hirschmann, O. R. Ayeni, R. G. Marx, J. L. Koh,
etal. (Eds.), Basic methods handbook for clinical orthopaedic
research: A practical guide and case based research approach
(pp. 343–358). Berlin, Heidelberg: Springer.
38. Dichter, M., Bartholomeyczik, S., Nordheim, J., Achterberg,
W., & Halek, M. (2011). Validity, reliability, and feasibility
of a quality of life questionnaire for people with dementia.
Zeitschrift fur Gerontologie und Geriatrie, 44(6), 405–410.
39. Banerjee, S., Samsi, K., Petrie, C. D., Alvir, J., Treglia, M.,
Schwam, E. M., etal. (2008). What do we know about quality
of life in dementia? A review of the emerging evidence on the
predictive and explanatory value of disease specific measures
of health related quality of life in people with dementia. Inter-
national Journal of Geriatric Psychiatry, 24(1), 15–24.
40. Cahill, S., & Diaz-Ponce, A. M. (2011). ’I hate having nobody
here. I’d like to know where they all are’: Can qualitative research
detect differences in quality of life among nursing home residents
with different levels of cognitive impairment? Aging & Mental
Health, 15(5), 562–572.
41. Ettema, T. P., Droes, R. M., de Lange, J., Ooms, M. E., Mellen-
bergh, G. J., & Ribbe, M. W. (2005). The concept of quality of
life in dementia in the different stages of the disease. International
Psychogeriatrics, 17(3), 353–370.
42. Sclan, S. G., & Reisberg, B. (1992). Functional assessment staging
(FAST) in Alzheimer’s disease: Reliability, validity, and ordinal-
ity. International Psychogeriatrics, 4(3), 55–69.
43. Gellert, P., Hausler, A., Suhr, R., Gholami, M., Rapp, M., Kuh-
lmey, A., etal. (2018). Testing the stress-buffering hypothesis of
social support in couples coping with early-stage dementia. PLoS
ONE, 13(1), e0189849.
44. Beerens, H. C., Zwakhalen, S. M. G., Verbeek, H., Tan, E. S.
Jolani, S., Downs, M., etal. (2018). The relation between mood,
activity, and interaction in long-term dementia care. Aging &
Mental Health, 22(1), 26–32.
45. Moyle, W., Borbasi, S., Wallis, M., Olorenshaw, R., & Gracia, N.
(2011). Acute care management of older people with dementia:
A qualitative perspective. Journal of Clinical Nursing, 20(3–4),
420–428.
46. Dichter, M. N., Schwab, C. G. G., Meyer, G., Bartholomeyczik,
S., Dortmann, O., & Halek, M. (2014). Measuring the quality of
life in mild to very severe dementia: Testing the inter-rater and
intra-rater reliability of the German version of the QUALIDEM.
International Psychogeriatrics, 26(5), 825–836.
47. Nordheim, J., Hamm, S., Kuhlmey, A., & Suhr, R. (2015). Tab-
let computers and their benefits for nursing home residents with
dementia: Results of a qualitative pilot study. Zeitschrift fur Ger-
ontologie und Geriatrie, 48(6), 543–549.
48. Ienca, M., Jotterand, F., Elger, B., Caon, M., Pappagallo, A. S.,
Kressig, R. W., etal. (2017). Intelligent assistive technology for
Alzheimer’s disease and other dementias: A systematic review.
Journal of Alzheimer’s Disease: JAD, 56(4), 1301–1340.
49. Lawton, M. P. (1991). A multidimensional view of quality of life
in frail elders. The concept and measurement of quality of life in
the frail elderly (pp. 3–27). San Diego, CA: Academic Press.
50. Brown, E. L., Ruggiano, N., Li, J., Clarke, P. J., Kay, E. S., &
Hristidis, V. (2017). Smartphone-based health technologies for
dementia care: Opportunities, challenges, and current practices.
Journal of Applied Gerontology, 38(1), 73–91.
51. Possin, K. L., Moskowitz, T., Erlhoff, S. J., Rogers, K. M., John-
son, E. T., Steele, N. Z. R., etal. (2018). The brain health assess-
ment for detecting and diagnosing neurocognitive disorders. Jour-
nal of the American Geriatrics Society, 66(1), 150–156.
52. Gutzmann, H., & Qazi, A. (2015). Depression associated with
dementia. Zeitschrift fur Gerontologie und Geriatrie., 48(4),
305–311.
53. Giebel, C., Sutcliffe, C., Verbeek, H., Zabalegui, A., Soto, M.,
Hallberg, I. R., etal. (2016). Depressive symptomatology and
associated factors in dementia in Europe: Home care versus long-
term care. International Psychogeriatrics, 28(4), 621–630.
54. Robertson, S., Cooper, C., Hoe, J., Hamilton, O., Stringer, A., &
Livingston, G. (2017). Proxy rated quality of life of care home
residents with dementia: A systematic review. International Psy-
chogeriatrics, 29(4), 569–581.
55. Muller, S., Preische, O., Heymann, P., Elbing, U., & Laske, C.
(2017). Diagnostic Value of a Tablet-based drawing task for dis-
crimination of patients in the early course of Alzheimer’s disease
from healthy individuals. Journal of Alzheimer’s Disease : JAD,
55(4), 1463–1469.
56. Rullier, L., Atzeni, T., Husky, M., Bouisson, J., Dartigues, J. F.,
Swendsen, J., etal. (2014). Daily life functioning of community-
dwelling elderly couples: An investigation of the feasibility and
validity of Ecological Momentary Assessment. International
Journal of Methods in Psychiatric Research, 23(2), 208–216.
57. Scanlon, L., O’Shea, E., O’Caoimh, R., & Timmons, S. (2016).
Usability and validity of a battery of computerised cognitive
screening tests for detecting cognitive impairment. Gerontology,
62(2), 247–252.
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
