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COVID-19 forced the implementation of restrictive measures in Spain, such as lockdown, home confinement, social distancing, and isolation. It is necessary to study whether limited access to basic services and decreased family and social support could have deleterious effects on cognition, quality of life, and mental health in vulnerable older people.
This study aims to explore the impact of the COVID-19 outbreak on cognition in older adults with mild cognitive impairment or dementia as the main outcome and the quality of life, perceived health status, and depression as secondary outcomes and to analyze the association of living alone and a change in living arrangements with those outcomes and other variables related with the use of technology and health services. Likewise, this study aims to analyze the association of high and low technophilia with those variables, to explore the access and use of health care and social support services, and, finally, to explore the informative-, cognitive-, entertainment-, and socialization-related uses of information and communications technologies (ICTs) during the COVID-19 outbreak.
This cohort study was conducted in Málaga (Spain). In total, 151 participants with mild cognitive impairment or mild dementia, from the SMART4MD (n=75, 49.7%) and TV-AssistDem (n=76, 50.3%) randomized clinical trials, were interviewed by telephone between May 11 and June 26, 2020. All participants had undergone 1-3 assessments (in 6-month intervals) on cognition, quality of life, and mood prior to the COVID-19 breakout.
The outbreak did not significantly impact the cognition, quality of life, and mood of our study population when making comparisons with baseline assessments prior to the outbreak. Perceived stress was reported as moderate during the outbreak. After correction for multiple comparisons, living alone, a change in living arrangements, and technophilia were not associated with negative mental health outcomes. However, being alone was nominally associated with self-perceived fear and depression, and higher technophilia with better quality of life, less boredom, perceived stress and depression, and also less calmness. Overall, health care and social support service access and utilization were high. The most used ICTs during the COVID-19 outbreak were the television for informative, cognitive, and entertainment-related uses and the smartphone for socialization.
Our findings show that the first months of the outbreak did not significantly impact the cognition, quality of life, perceived health status, and depression of our study population when making comparisons with baseline assessments prior to the outbreak. Living alone and low technophilia require further research to establish whether they are risk factors of mental health problems during lockdowns in vulnerable populations. Moreover, although ICTs have proven to be useful for informative-, cognitive-, entertainment-, and socialization-related uses during the pandemic, more evidence is needed to support these interventions.
ClinicalTrials.gov NCT04385797; https://clinicaltrials.gov/ct2/show/NCT04385797
RR2-10.2196/26431
COVID-19 was declared a worldwide pandemic by the World Health Organization on March 11, 2020 [
In Spain, the government decided to declare a national state of alarm, implementing restrictive measures from March 15 until June 21, 2020. The measures included lockdown, home confinement, social distancing, and isolation (activities were limited to basic needs, such as buying food or medication, attending health care centers and financial institutions); closure of schools and nonessential activities; ban of all internal travels except for essential ones; and border closure [
The elderly population is 1 of the groups most socially vulnerable to this disease. Age alone is by far the most significant factor for death due to COVID-19 [
Recent data suggest that in addition to old age and medical comorbidities (eg, hypertension, diabetes, obesity), dementia is associated with an increased risk of having severe COVID‐19 and related mortality [
Loneliness and social isolation often coexist and are all too common in older adults. Loneliness refers to the subjective state of feeling alone, separated, or apart from others. Social isolation, in contrast, is defined as the objective physical separation from other people, such as living alone, in which one has few social relationships or there is a low frequency of interaction with others [
Considering the latter definition, we can understand that the COVID-19 pandemic has increased the social isolation of older adults as restrictive measures have enforced staying at home, distancing, and shutting down all nonessential activities. This has meant that people have been forced to minimize their social interactions to avoid the spread of the virus, leaving those who live alone completely isolated. Social isolation has been identified as a health risk factor as it reduces well-being and is associated with higher prevalence of depression [
During quarantine, factors such as boredom and a lack of activities play an important role. They can contribute to depression [
In the “information age,” information and communications technologies (ICTs) have emerged for combating loneliness and social isolation [
The aims of this study were (1) to explore the impact of the COVID-19 outbreak on cognition in community-dwelling older adults with MCI/MD as the main outcome and the quality of life, perceived health status, and depression as secondary outcomes; (2) to analyze the differences between individuals living alone and living with others regarding mental health, and other variables related with the use of technology and health services during the COVID-19 outbreak and, likewise, to explore the effect of a change in living arrangements on cognition, quality of life, perceived health status, and depression; (3) to analyze the differences between individuals with high and low technophilia regarding mental health and other variables related with use of technology and health services during the COVID-19 outbreak; (4) to explore the access and use of health care and social support services during the COVID-19 outbreak; and finally (5) to explore the informative-, cognitive-, entertainment-, and socialization-related uses of ICTs during the COVID-19 outbreak.
This cohort study was conducted in the Spanish region of Málaga (Andalucía) and approved by the North-East Malaga Ethics Committee (1078-N-20). Interviews were telephone-administered to guarantee the safest means to communicate during the COVID-19 pandemic. Researchers contacted participants by telephone, explained the study in detail, answered any questions that arose, and obtained consent from those willing to participate in the study [
The study was approved by the North-East Malaga Ethics Committee (1078-N-20). Participants provided written consent before taking part.
This study was registered in ClinicalTrials.gov (NCT04385797).
Participants were identified from the Support, Monitoring and Reminder Technology for Mild Dementia (SMART4MD; NCT03325699) [
Researchers from the Biomedical Research Institute of Malaga contacted 210 potential respondents from the SMART4MD (n=111, 52.9%) and TV-AssistDem (n=99, 47.1%) RCTs by telephone. In total, 151 participants, SMART4MD (n=75, 49.7%) and TV-AssistDem (n=76, 50.3%), agreed to participate. However, for 8 (5.3%) of them, it was not possible to assess the main variable (cognition) and the secondaries variables (quality of life, perceived health status, and depression), because their abilities to answer the questionnaires were compromised during the time of assessment.
Participants were eligible for inclusion when the following criteria applied: participating in the SMART4MD and TV-AssistDem RCTs and agreeing to participate by giving consent. Eligibility criteria of the aforementioned RCTs were age>55 years or >60 years, perception of memory problems for at least 6 months, score of 20-28 or 23-27 points in the Mini-Mental State Exam (MMSE), independently living, having an informal caregiver, and taking care of their medical prescription. Patients with a score above 11 on the Geriatric Depression Scale (GDS), a terminal illness, or specific cognitive or physical conditions that would reduce their ability to use a tablet or a television were excluded.
Participants were contacted by telephone by 5 health care professionals (2 neuropsychologists, 1 clinical psychologist, 1 psychologist, and 1 psychiatric and mental health clinical nurse specialist). Researchers had previously established relationships with participants during both RCTs. Quantitative and qualitative strategies were used to create an unstandardized ad hoc telephone-based survey in order to gather as much information as soon as possible. The exceptional situation did not allow us to test the instrument prior to its implementation by phone. To minimize the interference of this situation in the results, validated phone versions tests were used.
The survey was a useful tool for guiding the interviewers and gathering information simultaneously in a homogenous way. A model of the questionnaire used is attached in Annex 1 in
Researchers interviewed the participants between May 11 and June 26, 2020. The variables of sociodemographic data (age, sex, and living arrangements), health perception-management (change in living arrangements due to lockdown, presence of COVID-19 symptoms, frequency of access to COVID-19 information), sleep-rest patterns, types of ICTs (smartphone, tablet, television, laptop), and their uses (informative, cognitive, entertainment, and socialization) were collected from the participants unless their abilities to answer such a long interview were compromised, in which case the caregivers were interviewed on their behalf. The questionnaires that evaluated the main variables (cognition, quality of life, depression, perceived stress, and technophilia) were answered by the participants.
The mean time from the start of the lockdown and home confinement measures to the interview was 70.36 days (SD 12.40, range 52-102).
The primary outcome variable was cognition. During the assessment prior to the COVID-19 outbreak (T0), the MMSE [
During the COVID-19 outbreak (T1), the validated telephone version of the MMSE had to be used to maintain health and safety measures. This phone version has a maximum score of 22 because it cannot cover all sections [
Although the full version of the MMSE was used in the T0 assessment, for data analysis, the scoring was based on the 22 items of the phone version.
The health-related quality of life (HRQoL) of the participants was measured in both assessments using the total score of the Quality of Life-Alzheimer’s Disease Scale (QoL-AD) [
The European Quality of Life 5 Dimensions 3 Levels (EuroQoL-5D-3L) [
The EuroQoL-5D-3L consists of 5 questions along with a visual analog scale (VAS). The VAS records the patient’s self-rated health on a vertical scale, where the endpoints are “the best health you can imagine” and “the worst health you can imagine.” Due to the impossibility of the patients to see the VAS during the T1 assessment, they were asked to rate their health status. Only the VAS-perceived health status assessment was used for this study, combined with the Qol-AD.
The short form of the GDS was used during the T0 assessment [
During the COVID-19 lockdown (T1), the telephone version of the GDS [
To measure older people’s attitudes and enthusiasm toward technologies, the Instrument for Measuring Older People’s Attitudes Toward Technology (TechPH) was used during the T1 assessment [
The Perceived Stress Scale (PSS) [
Other variables were sociodemographic data, including age, sex, and living arrangements; health perception-management (ie, change in living arrangements due to lockdown, presence of COVID-19 symptoms, frequency of access to COVID-19 information); coping-stress tolerance (ie, self-perceived mental health and well-being and mood); sleep-rest patterns (ie, self-perceived alterations in usual sleep patterns); and types of ICTs (smartphone, tablet, television, laptop) and their uses (informative, cognitive, entertainment, and socialization).
The survey data followed Gordon’s Functional Health Patterns (
The flow of participants is shown schematically with counts in a participant flow diagram (
Participant flow diagram. SMART4MD: Support, Monitoring and Reminder Technology for Mild Dementia; TV-AssistDem: TV-based ASSistive Integrated Service to supporT European adults living with mild DEMentia or mild cognitive impairment.
The change in means in the main outcome (cognition) and in the secondary outcomes (quality of life, perceived health status, and depression) were analyzed with respect to the last assessment of the RCTs (SMART4MD and TV-AssistDem) using the repeated measure
For the analysis of groups based on living arrangements (living alone vs living with others), the
For the analysis using groups based on the score in technophilia (based on the median of the TechPH index as the cut-off point), the
To establish the Bonferroni correction for multiple comparisons (regarding living arrangements and technophilia groups), the number of independent tests was estimated with principal component analysis. Of the 37 variables analyzed, the first 35 components explained >99% (99.3%) of the variance. Thus, we considered the values significant with α<.0014.
To analyze the assumptions of all linear regression models, the Ramsey RESET linearity test, the Breusch-Pagan homoscedasticity test, and the Shapiro-Wilk normality test of the model residuals were used (see Annex 2 in
The R version 4.0.4 program was used for all statistical analysis [
Of the 210 potential respondents (n=111 [52.9%] from SMART4MD and n=99 [47.1%] from TV-AssistDem), a total of 165 (78.6%) of 210 respondents was successfully reached, of which 151 (91.5%) agreed to participate (
The mean time between the last assessment of the RCTs (T0) and the interview during the lockdown (T1) was 199.33 days (SD 52.43, range 67-395), and the mean duration of the calls was 50.14 minutes (SD 16.40).
The mean age of the sample was 74.31 years (SD 6.48), 97 (64.2%) of 151 participants were women, 36 (23.8%) lived alone, and 80 (53.3%) had high attraction to technology (high technophilia). The COVID-19 outbreak forced 22 (14.6%) of 151 participants to change their living arrangements (
Sample sociodemographic characteristics and differences between living alone and living with others, and high technophilia and low technophilia.
Characteristics | Total participants (N=151) | Living alone (n=36) | Living with others (n=115) | Statistics | High technophilia (n=80) | Low technophilia (n=65) | Statistics | |||
Age (years), mean (SD) | 74.31 (6.48) | 76.31 (5.38) | 73.69 (6.69) | .03 | 73.69 (6.33) | 74.74 (6.63) | .34 | |||
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Male | 54 (35.8) | 4 (11.1) | 50 (43.5) | χ21=12.50 | <.001 | 31 (63.3) | 18 (36.7) | χ21=1.96 | .16 |
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Female | 97 (64.2) | 32 (88.9) | 65 (56.5) | χ21=12.50 | <.001 | 49 (51.0) | 47 (49.0) | χ21=1.96 | .16 |
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Yes | 22 (14.6) | 6 (16.7) | 16 (13.9) | χ21=0.17 | .68 | 11 (13.8) | 10 (15.4) | χ21=0.08 | .78 |
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No | 129 (85.4) | 30 (83.3) | 99 (86.9) | χ21=0.17 | .68 | 69 (86.3) | 55 (84.6) | χ21=0.08 | .78 |
Regarding the differences between the period before and during the outbreak, there were no differences in the main outcome: cognition. After correction for multiple comparisons, there were no statistically significant differences in the quality of life, perceived health status, or depression between the 2 periods (
Differences in cognition, quality of life, perceived health status, and depression prior to and during the COVID-19 outbreak.
Outcomes | Before the COVID-19 outbreak | During the COVID-19 outbreak | Statistics | |||||
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Cognition (MMSEa), median (IQR) | 19 (17-20) | 19 (17-21) | Z=–0.798 | .43 | |||
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QoL-ADc, mean (SD) | 35.97 (4.74) | 36.25 (5.44) | .43 | ||||
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Perceived health status (EuroQoL-5D-3Ld thermometer), median (IQR) | 70 (50-80) | 70 (60-85) | Z=–1.94 | .05 | |||
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Depression (GDSe), median (IQR) | 3 (1-5) | 2 (1-4) | Z=–0.01 | .99 |
aMMSE: Mini-Mental State Exam.
bSignificant results with
cQoL-AD: Quality of Life-Alzheimer's Disease Scale.
dEuroQoL-5D-3L: European Quality of Life 5 Dimensions 3 Levels.
eGDS: Geriatric Depression Scale.
Regarding social isolation (living alone and living with others), after Bonferroni correction, there was no significant association between the variables of the study (
The change in living arrangements was not associated with cognition (unadjusted model: B=–0.21,
Health perception-management, coping-stress tolerance, and sleep-rest functional health patterns during the COVID-19 outbreak and differences between living alone and with others.
Overall health status | Total participants (N=151) | Living alone (n=36) | Living with others (n=115) | Statistics | Odds ratio (OR)/Ba | |||
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No symptoms | 147 (97.4) | 35 (97.2) | 112 (97.4) | χ24=4.13 | .13 | —b | — |
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Symptoms without test | 3 (2.0) | 0 | 3 (2.6) | χ24=4.13 | .13 | — | — |
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Symptoms and positive test | 1 (0.7) | 1 (2.8) | 0 | χ24=4.13 | .13 | — | — |
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Hospitalized | 0 | 0 | 0 | χ24=4.13 | .13 | — | — |
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Intensive care unit (ICU) inpatient | 0 | 0 | 0 | χ24=4.13 | .13 | — | — |
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Deceased | 0 | 0 | 0 | χ24=4.13 | .13 | — | — |
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Well | 108 (71.5) | 23 (63.9) | 85 (73.9) | W1=1.27 | .26 | 1.64 | .27 |
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Calm | 64 (42.7) | 14 (38.9) | 50 (43.9) | W1=1.20 | .27 | 1.47 | .37 |
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Sad | 49 (32.7) | 15 (41.7) | 34 (29.8) | W1=0.05 | .83 | .91 | .83 |
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Worried | 69 (46.0) | 20 (55.6) | 49 (43.0) | W1=0.68 | .41 | .65 | .31 |
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Afraid | 34 (22.7) | 13 (36.1) | 21 (18.4) | W1=4.27 | .04 | .37 | .04 |
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Anxious | 33 (22.0) | 9 (25.0) | 24 (21.1) | W1=0.31 | .58 | .71 | .49 |
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Bored | 28 (18.7) | 9 (25.0) | 19 (16.7) | W1=1.84 | .18 | .46 | .14 |
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Maintained | 117 (77.5) | 29 (80.6) | 88 (76.5) | W1=0.12 | .73 | OR=1.07 | .90 |
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Altered | 34 (22.5) | 7 (19.3) | 27 (23.5) | W1=0.12 | .73 | OR=1.07 | .90 |
Cognition (MMSEc), median (IQR) | 19 (17-21) | 19 (17-21) | 19 (17-20.75) | Z=–0.57 | .57 | Bd=0.30 | .52 | |
Quality of life (QoL-ADe, mean (SD) | 36.25 (5.44) | 35.03 (4.39) | 36.66 (5.70) | .13 | Bd=–1.88 | .02 | ||
Perceived health status (EuroQoL-5D-3Lf thermometer), median (IQR) | 70 (60-85) | 75 (60-100) | 70 (60-80) | Z=–1.51 | .13 | Bd=6.67 | .12 | |
Depression (GDSg), median (IQR) | 2(1-4) | 3 (2-5) | 2 (1-4) | Z=–2.10 | .04 | Bd=0.83 | .06 | |
Perceived stress (PSSh), mean (SD) | 19.5 (8.64) | 20.44 (7.96) | 19.19 (8.87) | .45 | Bi=0.08 | .43 |
aMultivariate models (logistic or lineal) with living arrangements (living alone and living with others ) as the independent variable and gender, age, and technophilia (high technophilia and low technophilia) as covariates. More information about linear regression models is shown in Annex 2 in
bNot applicable.
cMMSE: Mini-Mental State Exam.
dRobust linear regression.
eQoL-AD: Quality of Life-Alzheimer's Disease Scale.
fEuroQoL-5D-3L: European Quality of Life 5 Dimensions 3 Levels.
gGDS: Geriatric Depression Scale.
hPSS: Perceived Stress Scale.
iAs the residuals of the model were not normal, we transformed the dependent variable in its logarithmic form.
After correction for multiple comparisons, there was no significant association between technophilia and the variables of the study. Only some variables reached nominal significant associations: self-perceived boredom (high technophilia 10.1% vs 27.7%; χ2=7.44;
Health perception-management, coping-stress tolerance, and sleep-rest functional health patterns during the COVID-19 outbreak and differences between the high- and low-technophilia groups.
Overall health status | Total participants (N=151) | High technophilia (n=80) | Low technophilia (n=65) | Statistics | Odds ratio (OR)/Ba | |||
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No symptoms | 147 (97.4) | 78 (97.5) | 63 (96.9) | χ22=1.39 | .50 | —b | — |
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Symptoms without test | 3 (2.0) | 2 (2.5) | 1 (1.5) | χ22=1.39 | .50 | — | — |
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Symptoms and positive test | 1 (0.7) | 0 | 1 (1.5) | χ22=1.39 | .50 | — | — |
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Hospitalized | 0 | 0 | 0 | χ22=1.39 | .50 | — | — |
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Intensive care unit (ICU) inpatient | 0 | 0 | 0 | χ22=1.39 | .50 | — | — |
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Deceased | 0 | 0 | 0 | χ22=1.39 | .50 | — | — |
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Well | 108 (71.5) | 54 (76.9) | 50 (67.5) | χ21=1.57 | .21 | 1.64 | .20 |
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Calm | 64 (42.7) | 25 (31.6) | 34 (52.3) | χ21=6.30 | .01 | 2.23 | .02 |
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Sad | 49 (32.7) | 22 (27.8) | 25 (38.5) | χ21=1.83 | .18 | 1.41 | .36 |
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Worried | 69 (46.0) | 31 (39.2) | 35 (53.8) | χ21=3.06 | .08 | 1.75 | .11 |
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Afraid | 34 (22.7) | 17 (21.5) | 16 (24.6) | χ21=0.194 | .66 | 1.21 | .65 |
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Anxious | 33 (22.0) | 13 (16.5) | 18 (27.7) | χ21=2.67 | .10 | 2.01 | .10 |
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Bored | 28 (18.7) | 8 (10.1) | 18 (27.7) | χ21=7.44 | .01 | 3.69 | .01 |
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Maintained | 117 (77.5) | 66 (82.5) | 47 (72.3) | χ21=2.17 | .14 | 1.92 | .11 |
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Altered | 34 (22.5) | 14 (17.5) | 18 (27.7) | χ21=2.17 | .14 | 1.92 | .11 |
Cognition (MMSEc), median (IQR) | 19 (17-21) | 19 (17-21) | 18 (16.25-21) | Z=–1.13 | .26 | Bd=0.30 | .52 | |
Quality of life (QoL-ADe), mean (SD) | 36.25 (5.44) | 37.33 (5.48) | 35.33 (4.90) | t139=2.24 | .03 | Bd=1.64 | .03 | |
Perceived health status (EuroQoL-5D-3Lf thermometer), median (IQR) | 70 (60-85) | 80 (60-90) | 70 (60-80) | .10 | Bd=6.44 | .04 | ||
Depression (GDSg), median (IQR) | 2(1-4) | 2 (1-4) | 3 (1-5) | Z=–2.16 | .03 | Bd=–0.83 | .03 | |
Perceived stress (PSSh), mean (SD) | 19.5 (8.64) | 18.1 (8.77) | 21.23 (8.21) | .03 | Bi=–0.19 | .02 |
aMultivariate models (logistic or lineal) with technophilia (high and low) as the independent variable and gender, age, and living arrangements (living alone and living with others) as covariates. More information about linear regression models is shown in Annex 2 in
bNot applicable.
cMMSE: Mini-Mental State Exam.
dRobust linear regression.
eQoL-AD: Quality of Life-Alzheimer's Disease Scale.
fEuroQoL-5D-3L: European Quality of Life 5 Dimensions 3 Levels.
gGDS: Geriatric Depression Scale.
hPSS: Perceived Stress Scale.
iAs the residuals of the model were not normal, we transformed the dependent variable in its logarithmic form.
Of 148 participants, 39 (26.4%) reported accessing extreme and 32 (21.6%) reported accessing too much COVID-19 information. The most frequent ICT used to access COVID-19 information was mainly the television (134/147, 91.2%), and most participants were also informed through family and friends (120/148, 81.1%). Furthermore, only 46 (30.7%) of 150 participants did not contact health or social services (
Health care and social support service access and utilization and informative-related uses of ICTsa during the COVID-19 outbreak and differences between living alone and with others, and high and low technophilia.
Characteristic | Total participants (N=151) | Living alone (n=36) | Living with others (n=115) | Chi-square ( |
High technophilia (n=80) | Low technophilia (n=65) | Chi-square ( |
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None | 3 (2.0) | 1 (2.9) | 2 (1.8) | 2.42 (4) | .66 | 0 | 1 (1.6) | 4.21 (4) | .38 | |||||||||
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Too little | 33 (22.3) | 8 (23.5) | 25 (21.9) | 2.42 (4) | .66 | 22 (27.5) | 10 (15.9) | 4.21 (4) | .38 | |||||||||
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Moderate | 41 (27.7) | 12 (35.3) | 29 (25.4) | 2.42 (4) | .66 | 20 (25.0) | 19 (30.2) | 4.21 (4) | .38 | |||||||||
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Too much | 32 (21.6) | 7 (20.6) | 25 (21.9) | 2.42 (4) | .66 | 16 (20.0) | 16 (25.4) | 4.21 (4) | .38 | |||||||||
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Extreme | 39 (26.4) | 6 (17.6) | 33 (28.9) | 2.42 (4) | .66 | 22 (27.5) | 17 (27.0) | 4.21 (4) | .38 | |||||||||
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Family and friends | 120 (81.1) | 29 (85.3) | 91 (79.8) | 0.51 (1) | .48 | 64 (80.0) | 55 (87.3) | 1.35 (1) | .25 | |||||||||
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Television | 134 (91.2) | 32 (94.1) | 102 (90.3) | 0.48 (1) | .49 | 70 (88.6) | 61 (96.8) | 3.31 (1) | .07 | |||||||||
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Smartphone | 56 (38.1) | 13 (37.1) | 43 (38.4) | 0.02 (1) | .89 | 34 (42.5) | 22 (35.5) | 0.72 (1) | .40 | |||||||||
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Tablet | 12 (8.2) | 1 (2.9) | 11 (9.8) | 1.73 (1) | .19 | 10 (12.7) | 2 (3.2) | 4.08 (1) | .04 | |||||||||
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Laptop | 10 (6.8) | 1 (2.9) | 9 (8.0) | 1.06 (1) | .30 | 6 (7.7) | 4 (6.3) | 0.10 (1) | .76 | |||||||||
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Newspaper | 12 (8.2) | 0 | 12 (10.6) | 3.93 (1) | .05 | 7 (8.9) | 5 (7.9) | 0.04 (1) | .84 | |||||||||
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Digital media | 71 (49.0) | 16 (47.1) | 55 (49.5) | 0.07 (1) | .80 | 41 (52.6) | 30 (48.4) | 0.24 (1) | .62 | |||||||||
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Radio | 37 (24.5) | 10 (29.4) | 27 (24.1) | 0.39 (1) | .53 | 18 (22.8) | 19 (30.6) | 1.11 (1) | .29 | |||||||||
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None | 46 (30.7) | 10 (27.8) | 36 (31.6) | 0.19(1) | .67 | 21 (26.3) | 21 (32.8) | 0.74 (1) | .39 | |||||||||
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Health services | 88 (58.3) | 17 (47.2) | 71 (61.7) | 2.38 (1) | .12 | 46 (57.5) | 38 (58.5) | 0.01 (1) | .91 | |||||||||
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COVID-19 services | 5 (3.3) | 1 (2.8) | 4 (3.5) | 0.42 (1) | .84 | 4 (5.0) | 1 (1.5) | 1.29 (1) | .26 | |||||||||
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Emergency services | 10 (6.6) | 3 (8.3) | 7 (6.1) | 0.22 (1) | .64 | 5 (6.3) | 5 (7.7) | 0.12 (1) | .73 | |||||||||
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Social service nongovernment organization (NGO) | 5 (3.3) | 3 (8.3) | 2 (1.8) | 3.68 (1) | .06 | 4 (5.0) | 1 (1.6) | 1.25 (1) | .26 |
aICT: information and communications technology.
Although most of the participants (46/151, 30.7%) preferred paper-based memory exercises, the most frequent ICT used for cognition was the television (16/151, 10.7%). The most used ICTs for entertainment were the television (138/151, 92%), followed by the smartphone (60/151, 40%), and for socialization, the smartphone (75/151, 50.3%). Detailed information is given in
Cognitive-, entertainment-, and socialization-related uses of ICTsa during the COVID-19 outbreak and differences between living alone and living with others, and high and low technophilia.
Activity category | Total participants (N=151) | Living alone (n=36) | Living with others (n=115) | Chi-square ( |
High technophilia (n=80) | Low technophilia (n=65) | Chi-square ( |
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|
Paper | 46 (30.7) | 14 (40.0) | 32 (27.8) | 7.52 (5) | .19 | 23 (28.7) | 21 (32.8) | 5.44 (5) | .36 |
|
Smartphone | 3 (2.0) | 0 | 3 (2.6) | 7.52 (5) | .19 | 3 (3.8) | 0 | 5.44 (5) | .36 |
|
Tablet | 7 (4.7) | 1 (2.9) | 6 (5.2) | 7.52 (5) | .19 | 3 (3.8) | 4 (6.3) | 5.44 (5) | .36 |
|
Laptop | 1 (0.7) | 1 (2.9) | 0 | 7.52 (5) | .19 | 0 | 1 (1.6) | 5.44 (5) | .36 |
|
Television | 16 (10.7) | 5 (14.3) | 11 (9.6) | 7.52 (5) | .19 | 11 (13.8) | 5 (7.8) | 5.44 (5) | .36 |
|
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|
Smartphone | 60 (40.0) | 13 (37.1) | 47 (40.9) | 0.16 (1) | .69 | 37 (46.3) | 23 (35.9) | 1.56 (1) | .21 |
|
Tablet | 18 (12.0) | 1 (2.9) | 17 (14.8) | 3.61 (1) | .07 | 11 (13.8) | 7 (10.9) | 0.26 (1) | .61 |
|
Laptop | 20 (13.3) | 3 (8.6) | 17 (14.8) | 0.90 (1) | .41 | 12 (15.0) | 8 (12.5) | 0.19 (1) | .67 |
|
Television | 138 (92.0) | 31 (88.6) | 107 (93.0) | 0.73 (1) | .39 | 74 (92.5) | 59 (92.2) | 0.01 (1) | .94 |
|
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|
Home visits | 87 (58.8) | 29 (82.9) | 58 (51.3) | 10.97 (1) | <.001 | 46 (57.5) | 40 (63.5) | 0.53 (1) | .47 |
|
Smartphone | 75 (50.3) | 16 (45.7) | 59 (51.8) | 0.39 (1) | .53 | 47 (58.8) | 27 (42.2) | 3.90 (1) | .05 |
|
Tablet | 10 (6.8) | 0 | 10 (8.8) | 3.32 (1) | .12 | 7 (8.9) | 3 (4.7) | 0.95 (1) | .33 |
|
Laptop | 5 (3.4) | 0 | 5 (4.4) | 1.59 (1) | .59 | 3 (3.8) | 2 (3.1) | 0.04 (1) | .84 |
|
Television | 6 (4.0) | 0 | 6 (5.3) | 1.91 (1) | .34 | 5 (6.3) | 1 (1.6) | 1.96 (1) | .23 |
aICT: information and communications technology.
This cohort study was conducted to understand the impact of restrictive measures in community-dwelling older adults with MCI and MD during the first COVID-19 outbreak.
Our findings show that the first months of the outbreak did not significantly impact the cognition, quality of life, perceived health status, and depression of our study population when making comparisons with baseline assessments prior to the outbreak. Change in living arrangements had no influence on these variables either. Living alone and technophilia were not associated with mental health–related variables after correction for multiple comparisons. However, being alone was nominally associated with self-perceived fear and depression, and higher technophilia with better quality of life, less boredom, perceived stress, and depression but also less calmness. Overall, health care and social support service access and utilization were high. The most used ICTs during the COVID-19 outbreak were the television for informative-, cognitive-, and entertainment-related uses and the smartphone for socialization.
To the best of our knowledge, few studies have addressed the consequences of the COVID-19 outbreak on the cognition of the elderly, and the use of technologies during this ongoing societal change.
Several studies have shown that quarantine measures have changed the behaviors and lifestyle of older people with cognitive decline [
Regarding the lack of differences in the quality of life and perceived health status before and during the outbreak in people with MCI/MD, a similar conclusion was reached by another cohort study in a similar population in Spain using the EuroQoL-5D-3L [
Regarding mental health, longitudinal studies have established that it is has been affected by the pandemic [
Another factor to consider was whether living alone during COVID-19 confinement was associated with a higher prevalence of depression. Although several studies have found a significant association between depression and living alone during the pandemic [
Regarding technophilia, our study did not find an association between a better attitude toward technology and better mental health. In line with these results, a multicenter study conducted in Norway, the United Kingdom, the United States, and Australia also found no change in loneliness and the quality of life in adults over 70 years who used ICTs to maintain social contact during the COVID outbreak [
A main limitation of this study was changing the interview administration from face-to-face before the outbreak to telephonic during the outbreak. Interviews were performed by the same professionals in both cases to reduce this possible bias, the measures were rescaled accordingly, and the validated phone versions of the tests were used. In addition, the interview had a mean duration of 50.14 minutes, which could cause fatigue in this population and alter their performance.
Another limitation is that the sample came from 2 RCTs and the participants who agreed to participate in the RCTs may have special characteristics that make them not representative of the general population. Furthermore, the sample was from only 1 center in Andalusia. However, it is a larger sample than in other studies carried out in Spain to date with this type of population.
Moreover, 15 caregivers answered on behalf of patients whose ability to answer for themselves was compromised. Although they did not respond to the questionnaires that evaluated the main variables, their answers may have interfered with the results.
The Bonferroni correction for multiple comparisons is conservative and could have increased type II errors.
Some studies have pointed out that the effects of changes during the lockdown may be temporary compared to long-lasting ones. Therefore, future effects will need to be explored as it is possible that once the lockdown is over, many people may not return to their “normal routine” as before the pandemic and will continue to avoid face-to-face activities, especially those regarding social and physical activity due to the fear of the contagion [
During the COVID-19 outbreak, governments’ restrictive measures demonstrated being effective in viral spread prevention. Although these restrictions have had negative effects on health and well-being and have changed lifestyles worldwide, our study showed how a presumably vulnerable population has shown more resilience to restrictive measures than expected. The people with MCI/MD did not experience a significant decline in cognition, quality of life, perceived health status, or depression during the period of the COVID-19 outbreak. The study also showed that being alone and a negative attitude toward technology are not associated with worse mental health after correcting for multiple comparisons. In addition, the data were collected over a short period, and further research is needed to explore whether maintaining restrictive measures for longer influences a worsening of cognitive abilities, quality of life, perceived health status, or depression and which factors increase the risk of poor mental health in this population. They reported overall well-being, maintained sleep quality, and presented moderate perceived stress. This could be related to the fact that our sample continued participating in daily activities, which plays a crucial role in enhancing and maintaining cognition [
Model of the questionnaire used.
European Quality of Life 5 Dimensions 3 Levels
Geriatric Depression Scale
health-related quality of life
information and communications technology
mild cognitive impairment
mild dementia
Mini-Mental State Examination
Perceived Stress Scale
Quality of Life-Alzheimer’s Disease Scale
randomized clinical trial
Support, Monitoring and Reminder Technology for Mild Dementia
Instrument for Measuring Older People’s Attitudes Toward Technology
TV-Based Assistive Integrated Service to Support European Adults Living with Dementia
visual analog scale
The authorship of this manuscript follows the International Committee of Medical Journal Editors standards. EDP, JMGC, AVN, CGSL, FMC, and JGP made substantial contributions to the conception and design of the work; EDP, JMGC, GGP, AVN, EVM, MG, MQ, and PBF acquired the data; JGP analyzed and interpreted the data; and EDP, JMGC, and JGP drafted the work. All authors revised it critically for important intellectual content and gave final approval of the version to be published.
None declared.