Virtual Reality Experience Intervention May Reduce Responsive Behaviors in Nursing Home Residents with Dementia: A Case Series

Abstract

Background:

People with advanced dementia often exhibit responsive behaviors such as apathy, depression, agitation, aggression, and psychosis. Non-pharmacological approaches (e.g., listening to music, watching television, doing arts and crafts) are now considered as a first-line strategy to manage responsive behaviors in clinical practice due to the potential risks associated with the antipsychotic medications. To date, no evaluations of immersive non-head mounted virtual reality (VR) experience as a non-pharmacologic approach for people with advanced dementia living in nursing homes have been reported.

Objective:

To evaluate the feasibility (acceptance and safety) of VR experience.

Methods:

A single site case series (nonrandomized and unblinded) with a convenience sample (N = 24; age = 85.8±8.6 years; Cognitive Performance Scale score = 3.4±0.6) measuring depression and agitation before and after the intervention. The intervention was a 30-min long research coordinator– facilitated VR experience for two weeks (10 sessions).

Results:

The intervention was feasible (attrition rate = 0% ; adverse events = 0). A reduction in depression and in agitation was observed after the intervention. However, we suggest extreme caution in interpreting this result considering the study design and small sample size.

Conclusion:

This study provides the basis for conducting a randomized controlled trial to evaluate the effect of VR experience on responsive behaviors in nursing homes. Since our intervention uses a smart remote-controlled projector without a headset, infectious exposure can be avoided following the COVID-19 pandemic-induced physical distancing policy in care homes.

Keywords

Dementia nursing home responsive behaviors virtual reality experience intervention

INTRODUCTION

Alzheimer’s disease alone accounts for 60%–70% of all types of dementia [1]. Responsive behaviors, also known as behavioral and psychological symptoms of dementia, are common in people with dementia that may include but are not limited to symptoms such as apathy, depression, agitation, aggression, sleep disorders, and psychosis [2, 3]. Around 90% of people with dementia exhibit at least one responsive behavior over the course of their condition [4]. The prevalence of responsive behaviors among the Canadian nursing home residents are as high as 87% reflecting the association of dementia severity with responsive behaviors and with nursing home entry [5]. The nursing homes and family caregivers are the largest contributors to direct cost associated with responsive behavior in Canada [6].

The most common type of responsive behaviors in people with advanced (moderate to severe) dementia include apathy (∼80%), depression (∼62%), anxiety (∼60%), and aggression (42%–85%) [7]. Apathy is characterized by a lack of emotional responsiveness [8], while depression includes loss of energy/interest, change in appetite, impaired concentration, insomnia, sadness, anhedonia, suicidal ideation, self-blame, weight change, sexual disinterest, and hypersomnia [9]. Typically, agitation is referred to as inappropriate verbal, vocal, or motor activity that cannot be otherwise explained [10]. Aggression can be explained as deliberate, overt, and harmful acts toward another person, object, organism, or oneself [11]. Responsive behaviors can harm the individual exhibiting such behaviors, their caregivers, and people cohabitating with them in terms of physical injuries, emotional trauma, and even death [12 –14]. Individuals caring for people with dementia are at higher risk of being physically injured or emotionally abused as responsive behaviors manifest more during direct care such as toileting, bathing, mouth care, feeding, and dressing [15, 16].

The American Geriatrics Society strongly cautions against using pharmacotherapy for responsive behavior management because of the increased risk for cognitive decline, falls, and serious adverse effects leading to increased mortality [17]. The Canadian best practice guideline on dementia based on current evidence recommended non-pharmacological approaches as a first-line strategy for responsive behavior management [18]. Activities such as listening to music, watching television, singing, dancing, reading, painting, drawing, cooking, knitting, talking, listening to others, playing with a pet, playing video games, and virtual reality exposure are being used as non-pharmacological interventions in clinical settings [19 –23].

The innovative virtual reality (VR) experience uses internet-based multimedia technology to reduce the sense of loneliness, to enhance the sense of identity, to monitor daily activities for safety, to engage in self-paced exercise program, and to reduce responsive behaviors without leaving the comfort and safety of home [24 –30]. Unlike other available non-pharmacological interventions, the VR experience is self-paced, flexible, and safe [31, 32], possibly cost-effective [33], customizable [24–30 , 33–35], and does not need a specialist or a technical person to conduct the therapy [21].

Unlike pharmacologic interventions, non-pharma-cologic interventions are not required by provincial or federal regulations to prove their effectiveness before clinical implementation [36] and thus researchers have little motivation to support clinical trials into their efficacy or effectiveness. However, premature dissemination of non-pharmacologic interventions exposes vulnerable older adults with dementia to the potential risks of ineffective therapies and exposes a health care system limited by constrained resources to potentially wasteful expenditures. These considerations highlight the importance of a carefully designed study. Since effect of non-head mounted VR experience reducing responsive behaviors in nursing home residents has not been explored, we wanted to evaluate its feasibility useful for clinical research.

MATERIALS AND METHODS

Materials

We used a smart remote-controlled projector (MK Player 360©) providing interactive and immersive customized three-dimensional visual and auditory experiences as VR experience intervention. The technology was developed by a Barcelona based company in 2017 to be used in [34]. The intervention was provided with MK Player 360© projector and BroomX© software installed in a smartphone. The projector was set either in a predetermined intervention area or in the participants’ own room depending on their mobility status. The projector was easy to carry, to set up, to disinfect, and to fit in tight spaces. We could also ensure minimum human contact as the intervention did not need a headset.

The projector provided an immersive experience by controlling the distance of the image, light, and sound covering the user’s field of vision (180° horizontal view x 120° vertical view) with full high-definition resolution [34]. The software allowed customized interactive visual and auditory experiences (See Fig. 1: Technical information on Broomx©) [34].

Fig. 1

Technical information on Broomx©. Reprinted from “MKplayer360” by Broomx Technologies, 2019, Retrieved from http://www.broomx.com/mkplayer360.php Copyright 2019 by Broomx Technologies. Reprinted with permission.

We approached the family members of the nursing home residents to learn the residents’ preferred leisure activities, objects of attention, preferences for musical instruments, genres of music, images of nature, and urban scenes at the beginning of the study. Our study partner, Crosswater Digital Media [https://crosswater.net/], created a customized multimedia library of 360° video scenes and music based on family members’ responses for this study (https://www.youtube.com/watch?v=houqV_zi0bo). For instance, the library item, Farm, was created for participants who used to be farmers or grew up on a farm. The library items used for the study were: Cherry Blossom (an afternoon stroll in the park with blooming cherry flowers set to soft classical music), Farm (morning walk in a farmyard with cows and chicken set to animal and bird sound), Truck Driving (day time simulated driving in the country roads), Symphony (a concert playing classical music), London, UK (aerial view of city streets and iconic building in London, UK set to soft classical music), Bavarian Alps (a morning stroll in the alpine meadow set to bird sound), Fishing (simulated fishing in the river set to water sound), Dolphin Swim Club (simulated underwater diving set to water sound), and Ireland (aerial view of city streets and iconic building in Ireland set to soft classical music). Different areas were captured within the canopy of a library item. For an example, Cherry Blossoms included a bride and groom posing for pictures, children climbing or frolicking in the grass, people walking their dogs, joggers passing by so that the viewer engages with a variety of park life experiences as if they were actually in the park again.

Study design and participants

The design of the study was a single site case ser-ies (before-after intervention study). The participants were nursing home residents aged ≥65 years with documented moderate to severe dementia (a Minimum Data Set- Cognitive Performance Scale (MDS-CPS) [35] score = 3–5) exhibiting at least one responsive behavior within the past four weeks. Ontario nursing homes are mandated to evaluate the residents’ cognitive status regularly (at least once a year) with MDS-CPS score [37]. Usually a registered nurse/clinician complete the MDS-CPS score of the nursing home residents [37]. The MDS-CPS agrees with the Mini Mental State Examination (MMSE) [38] in the identification of cognitive impairment (sensitivity = 0.94, specificity = 0.94) [39]. Therefore, selecting CPS score indicating the severity of dementia was practical for the study. The MDS-CPS combines information from the Resident Assessment Instrument-Minimum Data Set 2.0 [40] on memory, decision-making skills, communication and eating [41]. Residents experiencing no difficulties in these four areas score 0 while residents having severe memory problems and are unable to make daily decisions or feed themselves or are comatose score 6 on the scale [41]. A CPS score 0 indicates no dementia, whereas a score of 1–2 indicates mild, a score of 3 indicates moderate, and a score of 4–6 indicates severe dementia [41]. We excluded those diagnosed with epilepsy, those who were legally blind, at the end of life, and unable to communicate in English for practicality of the intervention implementation.

Recruitment

The study site was a for profit 192 beds nursing home situated in a mid-size city in southwestern Ontario, Canada. Clinical staff members of the site reviewed medical charts of their residents to identify the eligible participants for the study. Once identified, the nursing home administrative staff spoke with the residents and their family members face-to-face or by phone to learn if they would agree to be contacted for possible study participation. As we anticipated an ethical dilemma in obtaining valid informed consent from the nursing home residents based on their cognitive status [42], we approached the family members instead to consent in writing on behalf of the study participants. The recruitment period was six weeks (26 November 2018 to 4 January 2019).

Intervention

We provided the intervention for 30 min, five days a week (Monday to Friday) for two weeks based on similar studies with VR experience intervention in people with dementia [21 , 43–46]. The timing of the intervention was customized for each participant to avoid their usual lunch time, visiting hours, and nap time. However, the two-week intervention schedule was fixed for the convenience of the project. If a participant missed the intervention for the day for any reason, there was no makeup session on a different day.

At the beginning of the intervention period, we carried the MK Player360© projector to the participants’ own rooms. We plugged the electrical cord from the projector into a working electrical outlet, kept the projector in a vertical position, and used the nursing home’s Wi-Fi connection to pair the device with the study smartphone. We covered television with a bedsheet, brought down any wall hangings, and closed windows and door curtains for a clearer visualization inside a participant’s room. Later on, we assembled the projector in a pre-determined area inside the nursing home before starting the intervention sessions to accommodate participants’ discomfort and practicality of time management.

We placed a recliner at the corner of the intervention area where participants could sit and enjoy their VR experience. The projector was placed behind the recliner to avoid visual distraction. A Research Assistant (RA) sat beside the participant during the entire intervention session, watching the participant, playing the library items, and stopping the intervention if needed. Each item was 20 min long. The RA played the same customized library item repeatedly if the participant did not express opinions or when the participant requested to experience the particular item. There was no limit to the frequency or variety of the played library items. For safety concerns, the RA was instructed to stop the intervention if any participant exhibited responsive behaviors during the intervention. The RA offered assistance to the participants with postural balance in case they wanted to get up from the recliner or leave the intervention area, thus reduced the risk of falls. The nursing home caregivers also were on call during the sessions in case of any unforeseen adverse events.

Outcome measures

The feasibility of the intervention was measured with 1) acceptance and 2) safety. The acceptance was measured with a) recruitment rate (the proportion of consenting participants to actually approached participants over the six-week recruitment period), b) adherence rate (the proportion of participants attending 10 intervention sessions to the number of participants allocated to the intervention), c) attrition rate (proportion of participants lost to follow-up), d) tolerance to the intervention (measured with i) the proportion of participants who were able to experience at least 80% (8/10) of the planned sessions, ii) the mean length of a session experienced, iii) health care resources used in terms of caregiver staff time and new prescription of psychotropic drugs, and iv) before-after intervention change in Euro-Qol 5-Dimension scale (EQ-5D) [47]). The safety was measured with the number of adverse events during the intervention period. An adverse event was operationalized as any event leading to emergency transfer, hospitalization, death, a persistent or significant incapacity or substantial disruption of the participants’ ability to conduct the activities of daily living following the Code of Federal Regulations [48].

We considered declaring the intervention feasible if the attrition rate was <10%, the mean length of a session experienced was 20 min, and no adverse events based on contemporary studies with similar population [44 –46].

We also collected the before-after (baseline and at the end of the intervention period) change of depression and agitation with Cornell Scale for Depression in Dementia (CSDD) [49] and Cohen-Mansfield Agitation Inventory (CMAI) scale [50] as secondary outcome. Responsive behaviors are usually measured with subjective psychometric tools, originally developed to rate feelings or opinions or attitudes [51]. The 19-item CSDD detects depression in dementia from interviews with a caregiver and is feasible for those with advanced dementia living in nursing homes [49, 51]. Each item is rated for severity based on symptoms occurring during the week before the interview on a scale of 0–2 where 0 indicates no symptoms, and 2 indicates severe symptoms [49]. The interrater reliability (k = 0.67) and internal consistency (α= 0.84) of CSDD is high [49]. The association between CSDD and Research Diagnostic Criteria Depression [49], a scale measuring similar construct, also is strong (r = 0.83, p < 0.001) [52]. The 29-item CMAI assesses the agitation based on physical and verbal aggression [10] and is applicable for those living in nursing homes [51]. Each component is rated on a 7-point scale of frequency, ranging from never manifesting such behavior (1 point) to manifesting such behavior several times an hour (7 points) [50]. The scale is reliable (test-retest reliability coefficient = 0.830; p < 0.001) [53] and valid (i.e., strong association with a similar scale, Agitated Behaviour in Dementia, r = 0.62; p < 0.001) [54]. We hypothesized that the change in CSDD and CMAI score will be small (Effect Size [ES] = 0.21–0.49 [55]) based on literature [56, 57].

The nursing home caregiver staffs (Personal Support Workers and Registered Practical Nurses) completed CSDD and CMAI scales as they provide care for the residents 24/7. One of us trained the caregiver staffs on how to complete the scales before starting this study. One of the researchers blinded to participants’ dementia status completed the CSDD/CMAI scoring.

We explored the association between the before-after intervention change in CSDD/CMAI and a generic health perception scale (Global Rating of Change [GRC]) [58] with Pearson’s r (rho) [59]. The GRC scale measures an individual’s perspective (in this case, nursing home caregiver staff’s perspective) on their (in this case, the nursing home resident participants) health condition (depression and agitation) [58]. The scale quantifies the change (from a small, unimportant change to a great deal of change) using scores 0 to 7 (0 = no change, +1 to +7 = a perceived improvement in condition, and –1 to –7 = a perceived deterioration in condition) [58]. We classified GRC as a lot worse (GRC = –6, –7), moderately worse (GRC = –4, –5), minimally worse (GRC = –1, –2, –3), stable (GRC = 0), minimally better (GRC = 1, 2, 3), moderately better (GRC = 4, 5), and a lot better (GRC = 6, 7). We expected that a GRC rating of 0 would be associated with little to no change in the CSDD/CMAI scale. Our a priori hypothesis for the correlation between CSDD/CMAI score change and GRC was weak to moderate.

Statistical methods

Descriptive statistics was used to report feasibility of the intervention. We reported the before-after intervention change of responsive behaviors with ES (a ratio of mean change scores [δx = x2- x1] to the standard deviation [SD] of the baseline scores [55]) based on our assumption of the participants being homogenous at the baseline and might exhibit a change by approximately the same amount over the study period [59]. Moreover, we assumed that reporting the change with ES may aid future researchers to determine the sample size [55] and to conduct a meta-analysis [60]. The association between the before-after change and GRC with Pearson’s correlation was explored with SPSS© version 26.

RESULTS

The recruitment rate was 38% (95% CI, 28% to 50%), the adherence rate was 21% (95% CI, 7% to 42%), and the attrition was 0% (95% CI, 0% to 14%). We presented the number of eligible participants who were approached, screened, signed/withdrew informed consent, received/not received the intervention, lost to follow up/discontinued, and analyzed, describing the reasons for each one in Fig. 2 (The flow chart for participant enrollment, allocation, follow-up, and analysis). We presented the participant characteristics in Table 1. At least 18 out of 24 participants were able to complete 80% of the planned sessions; the mean length of a session experienced was 22.2 (95% CI, 23.5 to 20.9) min. The reasons for not completing the 30-min session include 1) falling asleep during the VR session (n = 22), 2) being distracted and leaving the intervention area (n = 1), 3) feeling uncomfortable being accompanied by a family member (n = 1). We had to shorten the length of individual VR library experiences for 5 participants (e.g., Bavarian Alps reminded a participant of World War II memories, Dolphin Swim club frightened a participant of being drowned, Aerial view of different cities reminded 3 participants of their late spouses).

Fig. 2

The flow chart for participant enrollment, allocation, follow-up, and analysis. SDM, family members/substitute decision makers.

Table 1

The demographic and clinical characteristics of the participants

Characteristics of participants
Number of participants	24
Age (mean, standard deviation [SD])	85.8±8.6
Sex (n, %)	Female (18, 75%)
CPS score (mean, SD)	3.4±0.6
Dementia types (clinical diagnosis on patient chart) and stages of dementia:
Alzheimer’s disease (n, %) with moderate dementia	3, 12.5%
Unspecified dementia (n, %) with moderate to severe dementia	21, 87.5%
Comorbidities:
Depression (n, %)	21, 87.5%
Diabetes mellitus (n, %)	3, 12.5%
Stroke (n, %)	2, 8.3%
Concussion (n, %)	1, 4.2%

The most frequently selected library items were Cherry Blossom (127 times), Farm (61 times), Truck driving (34 times), and Symphony (32 times).

No adverse events were observed during the intervention period. None of the participants needed caregiver staff time outside of their routine care and no new psychotropic drug was prescribed to the participants during the intervention period. However, the dose of prescribed psychotropic drugs was reduced for eight out of 24 participants after the intervention ended. The frequencies and proportions by EQ-5D indicated no detectable change in participants’ health condition from baseline to end of the intervention period (Table 2). The EQ-5D index value following van Hout and colleagues’ [61] work (https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/valuation-standard-value-sets/crosswalk-index-value-calculator/) also indicated no detectable change (mean EQ-5D±SD at baseline = 0.4±0.2 and at week-2 = 0.4±0.3). The feasibility indicator scores aligned with our assumption.

Table 2

Distribution of Euro-Qol 5-Dimention scale dimension responses at baseline and week 2

Euro-Qol 5-Dimention scale (EQ-5D) Baseline profile	EQ-5D Baseline index value	EQ-5D Week-2 profile	EQ-5D Week-2 index value
22432	0.6	12232	0.7
55355	–0.1	54334	0.2
55435	0.1	55532	0.1
35322	0.4	53432	0.3
35543	0.3	44453	0.3
15411	0.5	13222	0.7
11121	0.7	11112	0.9
44532	0.4	54221	0.3
23423	0.6	22322	0.7
55511	0.2	55522	0.1
55513	0.2	55332	0.2
15521	0.4	15121	0.5
14411	0.7	25421	0.4
55543	0.1	55544	0.0
24532	0.4	24332	0.6
34433	0.5	24423	0.5
12421	0.7	12121	0.8
25534	0.3	24433	0.5
45523	0.3	55544	0.0
54333	0.2	55432	0.2
23311	0.8	12124	0.6
23244	0.4	24332	0.6
24222	0.6	14322	0.7
25431	0.4	23332	0.6
Mean index value±standard deviation	0.4±0.2	0.4±0.3

We also observed a statistically insignificant change in before-after CSDD (ES = 0.4) and CMAI (ES = 0.2) scores (Table 3) and a weak association between GRC and CSDD/CMAI before-after change score confirming our a priori hypothesis (Table 4).

Table 3

Mean scores, pre-post score difference, sensitivity to change, and size of response of Cornell Scale for Depression in Dementia and Cohen-Mansfield Agitation Inventory scores

Scales	Baseline (mean, SD)	Week 2 (mean, SD)	Pre-post difference (mean, SD)	Sensitivity to change (ES)	Size of response
CSDD	9.7±5.3	7.5±4.1	2.1±4.9	0.4	Small
Mood	3.0±1.8	2.5±1.8	0.5±1.8	0.3	Small
Behavior	2.4±1.6	1.7±1.2	0.7±1.9	0.4	Small
Physical signs	1.8±1.3	1.4±1.2	0.5±1.4	0.4	Small
Cyclic function	1.8±1.7	1.3±1.3	0.5±1.7	0.3	Small
Ideation	0.6±0.8	0.7±1.0	0.1±1.1	0.1	Trivial
CMAI	53.3±19.1	48.8±17.2	4.8±15.2	0.2	Small, (Clinically meaningful)
Physical aggressive	18.8±10.7	16.4±6.7	2.3±7.7	0.2	Small
Physical non-aggressive	18.3±6.3	16±5.8	2.3±6.5	0.4	Small
Verbal aggressive	6.4±4.3	5.9±3.1	0.5±3.2	0.1	Trivial
Verbal non-aggressive	10.2±6.3	10.7±7.1	0.5±5.7	0.1	Trivial

SD, standard deviation; CSDD, Cornell Scale for Depression in Dementia; CMAI, Cohen-Mansfield Agitation Inventory; ES, effect size. ES is trivial if it is less than 0.20, small if it is between 0.21–0.49, moderate if it is between 0.51–0.79, and large if it is greater than 0.80 [53]. We considered the change to be clinically meaningful when ES of CMAI score was ≥0.2 [67].

Table 4

Association between Cornell Scale for Depression in Dementia/ Cohen-Mansfield Agitation Inventory score changes and Global Rating of Change after the intervention

CSDD domain before-after change N = 24	GRC-depression^*Pearson correlation	CMAI domain before-after change N = 24	GRC-agitation^**Pearson correlation
Total score	–0.16	Total score	0.06
Mood-related signs	–0.09	Physical aggressive	0.08
Behavioral disturbance	–0.25	Physical non-aggressive	0.21
Physical signs	–0.08	Verbal aggressive	0.06
Cyclic functions	0.07	Verbal non-aggressive	–0.22
Ideational disturbance	–0.18

SD, standard deviation; CSDD, Cornell Scale for Depression in Dementia; CMAI, Cohen-Mansfield Agitation Inventory; GRC, Global Rating of Change. ^*One outlier with GRC-depression score (+7) is not included in the table. One outlier for GRC-agitation score (–4) is not included in the table.

DISCUSSION

Since this is a single-arm study with no control group, the observed changes should be interpreted with extreme caution. No specialization was required for setting up and conducting the intervention and could ensure minimum human contact. As for feasibility, we declare our novel intervention to be feasible in nursing home residents with dementia exhibiting responsive behaviors based on safety (adverse events = 0) and acceptance (attrition 0%, mean length of experience = 22.2 min). However, the EQ-5D domain-specific responses exhibited an overall tendency toward remaining the same probably because the intervention was too short to evoke any changes in everyday life. The association between CSDD/CMAI and the GRC was weak and statistically insignificant. However, the CSDD scale showed a negative association suggesting reduction of depression symptoms while the CMAI scale showed mostly positive associations suggesting worsening agitation, except in verbal aggressive and non-aggressive domain. Therefore, we are unsure how to interpret this result and suggest extreme caution. We also noticed that our population were mostly stable when assessed for depression (both GRC and CSDD scores are 0; n = 16) at the baseline in comparison to agitation (both GRC and CMAI scores are 0; n = 10). A comparison between a stable control group and a depressed/agitated group might have yielded a clearer association between CSDD/CMAI and the GRC.

A recent literature review on similar intervention (3D customized VR 360 degree video) with similar population (aging nursing home residents with cognitive impairment) also reported VR experience intervention to be feasible [62]. However, the review only included studies that used head mounted VR experience [62]. Another recent study using head mounted VR experience observed a statistically insignificant improvement of apathy, especially phonemic verbal fluency in nursing home residents with cognitive impairment [63]. Unlike us, they observed dizziness and discomfort, eye strain, and nausea in six out of 17 participants [63]. Similar to us, VR experience intervention in nursing home residents with severe cognitive impairment was provided one-to one in a recent study and observed a significant reduction of apathy (Z = –2.818, p = 0.005) [64]. However, they did not report how long each VR session was or how long a participant experienced VR in each session [64]. A recent study with head mounted VR experience intervention reported a high adherence rate (78%) in nursing home residents with severe cognitive impairment (Montreal Cognitive Assessment = 7.2±5.3) [65]. However, they provided only four 10 min long intervention [65]. Therefore, we are unable to compare their result with ours.

Limitations

Our recruitment rate was low. We assume that having a short (six weeks) recruitment phase during the holiday season (Christmas and New Year) and being dependent on reaching the family members either during their occasional visit to the nursing home or over the phone only during weekdays from 9 to 5 pm have contributed to this low recruitment rate. For example, 38 out of 81 family members of eligible participants could not be reached during the recruitment period. We realized while conducting the study that legally blind individuals might also appreciate the intervention through auditory sensation, and participants need not be capable of communicating in English to appreciate the intervention. Therefore, we believe our exclusion criteria (legally blind individuals and non-English speakers) have also contributed to our low recruitment rate.

Our adherence rate was also low. We assume a short (two-weeks) and rigid (no makeup session) intervention period have contributed to the low adherence rate. For example, five participants missed intervention sessions due to the conflicting family visit schedule. A longer (four weeks) and flexible (seven days a week) intervention period might have improved the adherence rate.

Overall, our study was biased in inherent to the study design. For example, the absence of a control group impaired our ability to report whether changes in outcome reflected the intervention or simply the ups and downs associated with dementia progression. We also suspect that presence of RA during the entire one-on-one VR session might have influenced CSDD/CMAI score for better simply due to added human interaction and attention. Therefore, we caution against interpreting the change in responsive behaviors with VR experience intervention.

Implication

As the COVID-19 pandemic induced infection prevention policies in nursing homes has escalated responsive behaviors among the nursing home residents [66], our intervention promises a novel approach to reduce responsive behaviors in nursing home residents as the intervention can be implemented following infection prevention policies such as physical distancing, disinfection routine, and social isolation.

Footnotes

ACKNOWLEDGMENTS

The authors would like to acknowledge Primacare Living Solutions Inc.™ for allowing access to study protocol and data. The study was ethically approved by the ADVARRA Canada Ethics board. The protocol number of this study is Pro00030688. The protocol can be accessed on https://www.cirbi.net with permission from the sponsor (Primacare Living Solutions Inc.™). Data and materials can be accessed on with permission from the sponsor (Primacare Living Solutions Inc.™), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of Primacare Living Solutions Inc.™. The study was funded by a nonprofit external grant from the Centre for Aging and Brain Health Innovation as part of the Industry Innovation Partnership Program (I2P2) to the partners Primacare Living Solutions Inc.™ and Crosswater Digital Media, LLC. The funders supplied intervention materials (hardware and software) and paid the research staff for their time; the partner, Primacare Living Solutions Inc.™, provided space for the intervention, access to residents’ medical charts, and their staff in communicating, transferring, accompanying the participants, and completing the outcome measures; and the other partner, Crosswater Digital Media, LLC., created library items for this study.

Authors’ disclosures available online ().

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