Applied Science, Faculty of
Graduate and Postdoctoral Studies
Non UBC
Biomedical Engineering, School of
DSpace
International Journal of Environmental Research and Public Health 22 (5): 713 (2025)
University of British Columbia. Innovation in Dementia and Aging Lab
Hung, Lillian
Wong, Joey Oi Yee
Ren, Haopu (Lily)
Zhao, Yong
Fu, Jason Jincheng
Mann, Jim
Li, Lun
Telepresence robots can enhance social connection and support person-centered care in long-term care (LTC) homes. This study evaluates their impact in facilitating virtual visits between family caregivers and older residents in Canadian LTC homes. Telepresence robots were placed in residents’ rooms, allowing virtual visits at mutual convenience. A total of 18 residents and 17 family caregivers participated. Quantitative assessments included the Zarit Burden Interview, the De Jong Gierveld Loneliness Scale, and the Quality of Life in Alzheimer’s Disease scale, while qualitative data were collected through interviews, field notes, and observations. Repeated ANOVA showed that using telepresence robots significantly reduced caregiver burden (p = 0.008), improved residents’ quality of life (p = 0.028), and decreased resident loneliness (p = 0.038). Older caregivers experienced the greatest burden reduction, with scores dropping from a mean of 25.0 at baseline to 16.1 at two months. Qualitative findings provided further context, revealing that residents felt more connected, close, and engaged, while families found the robots facilitated continuity of care, complemented in-person visits, reduced stress and guilt, and provided reassurance. These findings suggest that telepresence robots can enhance the well-being of both residents and caregivers in LTC homes, though future research should explore their long-term impact and technological limitations.
Academic Editor: GiovanniLuca MasalaReceived: 19 March 2025Revised: 17 April 2025Accepted: 28 April 2025Published: 1 May 2025Citation: Hung, L.; Wong, J.O.Y.; Ren,H.; Zhao, Y.; Fu, J.J.; Mann, J.; Li, L. TheImpact of Telepresence Robots onFamily Caregivers and Residents inLong-Term Care. Int. J. Environ. Res.Public Health 2025, 22, 713. https://doi.org/10.3390/ijerph22050713Copyright: © 2025 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).ArticleThe Impact of Telepresence Robots on Family Caregivers andResidents in Long-Term CareLillian Hung 1,2,* , Joey Oi Yee Wong 1,2, Haopu (Lily) Ren 1,3 , Yong Zhao 1 , Jason Jincheng Fu 1,4, Jim Mann 1and Lun Li 51 IDEA Lab, School of Nursing, The University of British Columbia, Vancouver, BC V6T 2B5, Canada2 School of Nursing, The University of British Columbia, Vancouver, BC V6T 2B5, Canada3 School of Interdisciplinary Studies, The University of British Columbia, Vancouver, BC V6T 2B5, Canada4 School of Biomedical Engineering, The University of British Columbia, Vancouver, BC V6T 2B5, Canada5 Department of Social Work, MacEwan University, Edmonton, AB T5H 0K9, Canada* Correspondence: lillian.hung@ubc.caAbstract: Telepresence robots can enhance social connection and support person-centeredcare in long-term care (LTC) homes. This study evaluates their impact in facilitating virtualvisits between family caregivers and older residents in Canadian LTC homes. Telepresencerobots were placed in residents’ rooms, allowing virtual visits at mutual convenience.A total of 18 residents and 17 family caregivers participated. Quantitative assessmentsincluded the Zarit Burden Interview, the De Jong Gierveld Loneliness Scale, and theQuality of Life in Alzheimer’s Disease scale, while qualitative data were collected throughinterviews, field notes, and observations. Repeated ANOVA showed that using telepresencerobots significantly reduced caregiver burden (p = 0.008), improved residents’ quality oflife (p = 0.028), and decreased resident loneliness (p = 0.038). Older caregivers experiencedthe greatest burden reduction, with scores dropping from a mean of 25.0 at baseline to 16.1at two months. Qualitative findings provided further context, revealing that residents feltmore connected, close, and engaged, while families found the robots facilitated continuityof care, complemented in-person visits, reduced stress and guilt, and provided reassurance.These findings suggest that telepresence robots can enhance the well-being of both residentsand caregivers in LTC homes, though future research should explore their long-term impactand technological limitations.Keywords: telepresence robots; long-term care; quality of life; loneliness; caregiver burden1. IntroductionLoneliness in long-term care (LTC) remains an enduring and multifaceted challenge,intensified by the COVID-19 pandemic [1]. For residents in LTC settings, loneliness mayextend beyond mere physical isolation, encompassing social disconnection and feelingsof abandonment [2,3]. These factors collectively contribute to adverse health outcomes,including cognitive decline, depression, and reduced quality of life. Emerging evidencesuggests that older men may be particularly vulnerable to loneliness in LTC settings. Forexample, Dahlberg et al. [4] and Wright et al. [5] described that older men, especially thosewho have lost social supports such as spouses, report higher levels of loneliness comparedto women. This heightened vulnerability can be attributed to a confluence of factors,including the erosion of traditional social roles following retirement, a comparatively nar-rower social network, and societal expectations that discourage emotional expressivenessInt. J. Environ. Res. Public Health 2025, 22, 713 https://doi.org/10.3390/ijerph22050713Int. J. Environ. Res. Public Health 2025, 22, 713 2 of 20among men [6,7]. These issues may be further exacerbated by limited opportunities forspontaneous social engagement and meaningful interactions.Family caregivers often experience substantial burdens during the transition of lovedones into LTC homes [8]. The reconfiguration of caregiving roles, from primary caregiverto a more peripheral yet emotionally invested visitor, faces unique challenges, for example,the feelings of guilt about placing their loved one into LTC, the loss of lifelong company athome, and the disrupted sense of purpose as a caregiver [9,10]. Such a caregiver burden notonly undermines the well-being of family members but also impacts residents’ quality oflife. Strong family relationships are associated with enhanced emotional well-being, greatersocial connectedness, and improved overall quality of life among LTC residents [11].In response to these dual challenges—resident loneliness and caregiver burden—technology-driven interventions offer promising solutions to facilitate meaningful socialconnections. In this context, we have explored a more accessible option for virtual videocalls by using telepresence robots [12]. These robotic systems allow for immersive, remote-controlled virtual visits, enabling easy, personalized interactions. The telepresence robotadopted in our study consists of a tablet (the head), a pole (the body), and wheels (thebase). The tablet consists of a screen, a camera, and speakers for conversations; the poleallows adjustments of the robot’s height to facilitate communication; the wheels allow therobot to move in all directions (see Figure 1). The telepresence robot is fully controlled anddriven by the callers, i.e., family members. With a device with a camera and microphone(e.g., smartphones, tablets, and laptops) and an Internet connection, the family memberscan call in via the telepresence robot. The call recipients (i.e., residents) do not need to pressany buttons to receive the calls. However, there is an “end call” function on the screen forthem to discontinue the call if necessary. Although all allow real-time communications,telepresence robots in LTC have benefits compared to traditional approaches, such astelephone calls or video conferencing via handheld devices. These traditional approachesoften fall short due to technological barriers, sensory impairments, and the reliance on stafffor operational assistance [13,14]. As family members can drive the telepresence robotstoward the resident and start the calls, residents are not required to recognize the ringtone,look for or hold a device, or press the right button to pick up calls [15]. Unlike regularphone or video calls, these hand-free telepresence robots do not require staff to be presentand assist during the calls. Family members can interact more and even perform exerciseswith residents. Without staff presence, privacy can be better maintained during virtualcalls. Family members can call in at a more flexible frequency and for a longer time [13,14].Recent advancements in telehealth and AI-driven assistive technologies have intro-duced diverse tools to support the social and emotional well-being of older adults inLTC environments. Innovations such as socially assistive robots, AI chatbots, and remotemonitoring systems have shown promise in alleviating loneliness, enhancing caregiverengagement, and improving quality of life. For instance, socially assistive robots like PAROand LOVOT have demonstrated measurable reductions in loneliness and depression amongLTC residents by promoting meaningful engagement and companionship [16,17]. Similarly,AI-powered chatbots and voice assistants are being piloted to provide older adults withon-demand emotional support and routine social interaction, with early evidence suggest-ing improvements in perceived connectedness and mood [18]. A recent systematic reviewalso highlighted the role of smart home and remote monitoring technologies in reducingcaregiver burden and fostering more timely, person-centered responses in LTC and demen-tia care [19]. Even though the technological design of telepresence robots may no longerbe novel, their application in real-world LTC settings using a longitudinal mixed-methodsapproach remains limited.Int. J. Environ. Res. Public Health 2025, 22, 713 3 of 20Int. J. Environ. Res. Public Health 2025, 22, x FOR PEER REVIEW 3 of 22 Figure 1. A telepresence robot used in our study. Recent advancements in telehealth and AI-driven assistive technologies have intro-duced diverse tools to support the social and emotional well-being of older adults in LTC environments. Innovations such as socially assistive robots, AI chatbots, and remote mon-itoring systems have shown promise in alleviating loneliness, enhancing caregiver en-gagement, and improving quality of life. For instance, socially assistive robots like PARO and LOVOT have demonstrated measurable reductions in loneliness and depression among LTC residents by promoting meaningful engagement and companionship [16,17]. Similarly, AI-powered chatbots and voice assistants are being piloted to provide older adults with on-demand emotional support and routine social interaction, with early evi-dence suggesting improvements in perceived connectedness and mood [18]. A recent sys-tematic review also highlighted the role of smart home and remote monitoring technolo-gies in reducing caregiver burden and fostering more timely, person-centered responses in LTC and dementia care [19]. Even though the technological design of telepresence ro-bots may no longer be novel, their application in real-world LTC settings using a longitu-dinal mixed-methods approach remains limited. Previous studies have demonstrated that telepresence robots help maintain social bonds, enhance residents’ sense of being valued and remembered, and support person-centered care [15,20]. From the residents’ perspective, telepresence robots nurture emo-tional closeness and meaningful engagement, while family members experience reduced stress, alleviated feelings of guilt, and reassurance regarding their loved one’s well-being. While emerging qualitative evidence highlights the positive impact of telepresence robots, several concerns and barriers to implementation have also been noted [21,22]. Care facility staff have expressed concerns regarding potential resident confusion, privacy is-sues, and their own lack of skills and additional workload associated with managing the robots. Thus, systematic longitudinal assessments are needed to understand the long-term implications of using telepresence robots in LTC settings. This study addresses that gap by systematically examining the impact of telepresence robots on loneliness and quality of life among LTC residents, as well as the caregiver burden of their family caregivers. By Figure 1. A telepresence robot used in our study.Previous studies have demonstrated that telepresence robots help maintain socialbonds, enhance residents’ sense of being valued and remembered, and support person-centered care [15,20]. From the residents’ perspective, telepresence robots nurture emotionalcloseness and meaningful engagement, while family members experience reduced stress,alleviated feelings of guilt, and reassurance regarding their loved one’s well-being.While emerging qualitative evidence highlights the positive impact of telepresencerobots, several concerns and barriers to implementation have also been noted [21,22].Care facility staff have expressed concerns regarding potential resident confusion, privacyissues, and their own lack of skills and additional workload associated with managing therobots. Thus, systematic longitudinal assessments are needed to understand the long-termimplications of using telepresence robots in LTC settings. This study addresses that gapby systematically examining the impact of telepresence robots on loneliness and qualityof life among LTC residents, as well as the caregiver burden of their family caregivers. Byintegrating quantitative measures with rich qualitative insights, we aim to understand thebroader implications of technology-driven interventions for improving the emotional andsocial well-being of older adults in LTC and their family caregivers. Our research questionsare the following:1. What are the experiences f res dents an f mili s in using telepresence obot ?2. Does the telepresence robot reduce loneliness, improve the quality of life of residentsin LTC homes, and red ce the burden for family caregivers?2. Methods2.1. DesignWe adopted a mixed-methods design, a convergent parallel mixed-methods approachunder which quantitative and qualitative data were collected simultaneously and analyzedseparately and independently [23] (p. 280) [24] (p. 519). Quantitative methods are ad-vantageous in showing what relationships are established among variables; in contrast,qualitative methods are powerful in showing how a d why these relationships develop [24]Int. J. Environ. Res. Public Health 2025, 22, 713 4 of 20(p. 519). With quantitative and qualitative data complementing each other, the approachallows us to draw on the strengths of both methods for a more comprehensive understand-ing of our research focus, which is the impact of using telepresence robots on the emotionaland social well-being of older adults in LTC and their family caregivers [25] (p. 318). Theapproach also enables us to compare findings from both datasets to gain better insights intothe breadth and depth of individual experiences with telepresence robots and contextualfactors [26]. Figure 2 shows the convergent parallel mixed-methods approach used inthis study [27] (p. 218). Refer to Table S1 for the COREQ checklist and Table S2 for theGRAMMS checklist.Int. J. Environ. Res. Public Health 2025, 22, x FOR PEER REVIEW 4 of 22 integrating quantitative measures with rich qualitative insights, we aim to understand the broader implications of technology-driven interventions for improving the emotional and social well-being of older adults in LTC and their family caregivers. Our research ques-tions are the following: 1. What are the experiences of residents and families in using telepresence robots? 2. Does the telepresence robot reduce loneliness, improve the quality of life of residents in LTC homes, and reduce the burden for family caregivers? 2. Methods 2.1. Design We adopted a mixed-methods design, a convergent parallel mixed-methods ap-proach under which quantitative and qualitative data were collected simultaneously and analyzed separately and independently [23] (p. 280) [24] (p. 519). Quantitative methods are advantageous in showing what relationships are established among variables; in con-trast, qualitative methods are powerful in showing how and why these relationships de-velop [24] (p. 519). W h quantitative and quali tive data complementing ac other, the approach allows us to draw o e strengths of both methods for a mor compreh nsive understanding of our re earch focus, which is the impact of usi g telepresence robots on the em t onal and social well-being of older adults in LTC and their family caregivers [25] (p. 318). The approach als enables us to compare findings from both dataset to gain better insig ts into the breadth and depth of i dividual expe iences with eleprese ce ro-bots and contextual factors [26]. Figure 2 shows the convergent parallel mixed-methods approach used in this study [27] (p. 218). Refer to Table S1 for the COREQ checklist and Table S2 for t GRAMMS checklist. Figure 2. Convergent parallel mixed-methods approach used in this study. 2.2. Data Collection 2.2.1. Research Instruments We used three survey instruments for the quantitative study: Zarit Burden Interview, DeJong Gierveld Loneliness Scale, and Quality of Life—Alzheimer’s Disease (QOL-AD) scale. Firstly, the Zarit Burden Interview (12 items) [28] was used to measure caregiver burdens. The questionnaire has questions such as “How often do you feel that because of the time you spend with your relative, you do not have enough time for yourself?” and “How often do you feel angry when you are around your relative?”. Participants could choose from “never”, “rarely”, “sometimes”, “quite frequently”, or “nearly always” (scored from 0 to 4, respectively) to answer. The higher the total score, the greater the burden on the family caregiver. Secondly, the 6-item DeJong Gierveld Loneliness Scale [29] was used to measure loneliness, with 3 items corresponding to emotional loneliness Figure 2. Convergent parallel mixed-methods approach used in this study.2.2. Data Collection2.2.1. Research InstrumentsWe used three survey instruments for the quantitative study: Zarit Burden Interview,DeJong Gierveld Loneliness Scale, and Quality of Life—Alzheimer’s Disease (QOL-AD)scale. Firstly, the Zarit Burden Interview (12 items) [28] was used to measure caregiverburdens. The questionnaire has questions such as “How often do you feel that because ofthe time you spend with your relative, you do not have enough time for yourself?” and“How often do you feel angry when you are around your relative?”. Participants couldchoose from “never”, “rarely”, “sometimes”, “quite frequently”, or “nearly always” (scoredfrom 0 to 4, respectively) to answer. The higher the total score, the greater the burdenon the family caregiver. Secondly, the 6-item DeJong Gierveld Loneliness Scale [29] wasused to measure loneliness, with 3 items corresponding to emotional loneliness and socialloneliness each. Participants could choose from “Yes”, “More or less”, and “No” for ananswer that best described their feelings. Items for emotional loneliness were negativelyworded (e.g., I experience a general sense of emptiness), and answers of “Yes” or “More orless” were scored three or two and “No” scored one. Questions for social loneliness werepositively worded (e.g., There are many people I can trust completely), and the scoring wasreversed. A higher score represents greater loneliness. Lastly, the QOL-AD scale was usedto measure participants’ quality of life [30]. The QOL-AD contains 13 items related to agroup of life domains, including physical health, relationships, activities, and capabilities toperform some daily tasks. Participants could answer each question by choosing an answerfrom “poor”, “fair”, and “good” to “excellent”, with “poor” assigned 1 point and excellentassigned 4 points. A higher total score means a higher quality of life.For qualitative data collection, we conducted semi-structured interviews with ourparticipants as we asked them questions on the abovementioned scales. With questionssuch as “Can you tell me more about your experience of using the robot?” and “Canyou share some stories with us?” semi-structured interviews allowed us to follow upInt. J. Environ. Res. Public Health 2025, 22, 713 5 of 20on experiences raised by our participants and gain rich, in-depth information about ourstudy focus [31] (p. 36). We also observed them during the data collection and took fieldnotes to document our real-time observations and reflections, adding contextual depth toour data [32]. These qualitative methods complemented quantitative data to enrich theevidence on our research topic and enhanced the overall quality of our quantitative andqualitative data collected [33].2.2.2. SettingThis study is part of a larger, three-year project that explores the experiences ofresidents, family caregivers, and staff in implementing telepresence robots in LTC settingsfor older people with dementia. We placed 20 robots in five LTC homes in an urban areaof British Columbia, Canada. See Table 1 for detailed information about our partneredcare sites.Table 1. Detailed information on partnered care sites: types of rooms, staff–resident ratio,and funding.Pseudonymof LTC SiteTypes of RoomsAvailable Staff–Resident Ratio FundingJasmineResidenceIndividual andShared RoomsDay: 1 staff member to 6 residentsPubliclyfundedEvening: 1 staff member to20 residentsRoseResidenceIndividual andShared RoomsDay: 1 staff member to 6 residentsPubliclyfundedEvening: 1 staff member to8 residentsNight: 1 staff member to25 residentsZinniaResidence Individual RoomDay: 3 nurses and 4 care aides(7 staff members in total to19 residents)PubliclyfundedEvening: 3 nurses and 3 care aides(6 staff members in total to19 residents)Night: 2 nurses and 2 care aides(4 staff members to 19 residents)TulipResidence Individual RoomDay: 1 nurse and 2 to 3 care aidesfor 16 to 17 residentsNon-profitEvening: 1 nurse and 2 to 3 careaides for 16 to 17 residentsNight: 1 staff member (nurse orcare aid) per care neighbourhood(16–27 residents)SunflowerResidence Individual RoomLTC: 1 resident care partner for5–6 residentsPrivatelyfunded,for-profit2.2.3. Participant Recruitment and SamplingWe recruited participants with purposive sampling. The research team collaboratedwith staff champions from the recreation and rehabilitation departments of each care site tointroduce the telepresence robot to residents and family caregivers and explain the study.Participants were intentionally recruited to reflect diverse racial and ethnocultural back-Int. J. Environ. Res. Public Health 2025, 22, 713 6 of 20grounds, genders (male and female), and various age groups. We purposefully includedresidents with different mobility impairments requiring assistive devices like wheelchairsor walkers, as well as those living with various forms and stages of dementia. We had noparticipant drop-off during the two-month intervention.This pre–post design, utilizing each participant as their own control, enhances sta-tistical power even with a smaller sample size. The Partial Eta-Squared (η2p) values forcaregiver burden (0.318), quality of life (0.191), and loneliness (0.203) in this study showlarge effect sizes, indicating substantial within-subject changes due to the intervention.Using G*Power (version 3.1) with set parameters (α = 0.05, 1 − β = 0.80, and 3 mea-surements), we determined that the recommended sample size was 9, based on Cohen’s fcalculated from the Partial Eta-Squared (η2p) values provided above. Given these strongeffect sizes, the sample size of 35 (18 residents and 17 family caregivers) provided adequatepower to detect meaningful changes.2.2.4. TimelineData collection for this paper was performed between December 2022 and September2023, which was the third phase of the larger project. Data collection for each participantlasted eight weeks (see Figure 3). For residents, we collected data on their quality of lifeand loneliness at three points in time: before they started using the robot (T0), four weeksafter using the robot (T1), and eight weeks after using the robot (T2). Similarly, we collecteddata on the caregiving burden from family caregivers at T0, T1, and T2. Notably, everyparticipant’s T0 starts at a different time, as they started using the robot at a different time.Int. J. Environ. Res. Public Health 2025, 22, x FOR PEER REVIEW 7 of 22 Figure 3. Timeline for data collection for an individual participant. We interviewed all residents in person as they preferred this approach. We scheduled data collection at a time and location at their convenience, often when they felt most en-ergetic in the day and in their individual room at the care site. For family caregivers, we interviewed some of them in person during their visits to their loved ones in the LTC home. Interviews with other family caregivers were conducted remotely over Zoom or phone. Each interview lasted between 20 and 60 minutes. Data collected were recorded and transcribed verbatim. Data collection ceased when the research team agreed that the data were sufficient to answer our research questions. 2.2.5. The Research Team Our team consists of multidisciplinary researchers and trainees. The Principal Inves-tigator (PI), LH, is a professor in the School of Nursing at the University of British Colum-bia. LL is a professor in the School of Social Work at MacEwan University. The PI oversees the project and trains undergraduate and graduate trainees. JW, YZ, and LR are PhD stu-dents in nursing, medical science, and occupational therapy. JF is an undergraduate stu-dent in biomedical engineering. The team also involves patient partners living with de-mentia and family caregivers who contribute lived experience expertise. Weekly research meetings allowed the team to discuss findings, critically challenge assumptions, and deepen our understanding of the research topic from different perspectives. JW, LR, and JF collected quantitative and qualitative data simultaneously. LL and YZ performed quan-titative data analysis, while JW and LR led qualitative analysis. 2.3. Data Analysis 2.3.1. Quantitative Data Analysis Statistical analyses were conducted using SPSS (version 29.0.2.0), with a significance level set at 0.05 for all two-tailed tests. First, descriptive analyses were performed to ex-amine gender, age group, ethnicity, and family relationships, reporting counts and per-centages to provide a comprehensive view of participants’ demographic characteristics. Descriptive statistics were then used to calculate the mean and standard deviation for the three scales (caregiver burden, quality of life, and loneliness) across three time points. A repeated-measures analysis of variance (ANOVA) was conducted to assess changes in each scale’s scores over time within the entire sample. If the assumption of sphericity was violated, a Greenhouse–Geisser correction was applied. When a significant effect was detected, Bonferroni post hoc tests were performed to determine which time points significantly differed. This analysis was then repeated for key sociodemographic subgroups, including binary gender (male and female), ethnicity (Asian and white), and age groups (for the caregiver burden scale only: younger than 60 years vs. 60 years and Figure 3. Timeline for data collection for an individual participant.We interviewed all residents in person as they preferred this approach. We scheduleddata collection at a time and location at their convenience, often when they felt mostenergetic in the day and in their individual room at the care site. For family caregivers,we interviewed some of them in person during their visits to their loved ones in the LTChome. Interviews with other family caregivers were conducted remotely over Zoom orphone. Each interview lasted between 20 and 60 min. Data collected were recorded andtranscribed verbatim. Data collection ceased when the research team agreed that the datawere sufficient to answer our research questions.2.2.5. The Research TeamOur team consists of multidisciplinary researchers and trainees. The Principal Investi-gator (PI), LH, is a professor in the School of Nursing at the University of British Columbia.LL is a professor in the School of Social Work at MacEwan University. The PI oversees theproject and trains undergraduate and graduate trainees. JW, YZ, and LR are PhD studentsin nursing, medical science, and occupational therapy. JF is an u dergraduate student inInt. J. Environ. Res. Public Health 2025, 22, 713 7 of 20biomedical engineering. The team also involves patient partners living with dementia andfamily caregivers who contribute lived experience expertise. Weekly research meetingsallowed the team to discuss findings, critically challenge assumptions, and deepen ourunderstanding of the research topic from different perspectives. JW, LR, and JF collectedquantitative and qualitative data simultaneously. LL and YZ performed quantitative dataanalysis, while JW and LR led qualitative analysis.2.3. Data Analysis2.3.1. Quantitative Data AnalysisStatistical analyses were conducted using SPSS (version 29.0.2.0), with a significancelevel set at 0.05 for all two-tailed tests. First, descriptive analyses were performed toexamine gender, age group, ethnicity, and family relationships, reporting counts andpercentages to provide a comprehensive view of participants’ demographic characteristics.Descriptive statistics were then used to calculate the mean and standard deviation for thethree scales (caregiver burden, quality of life, and loneliness) across three time points.A repeated-measures analysis of variance (ANOVA) was conducted to assess changesin each scale’s scores over time within the entire sample. If the assumption of sphericitywas violated, a Greenhouse–Geisser correction was applied. When a significant effectwas detected, Bonferroni post hoc tests were performed to determine which time pointssignificantly differed. This analysis was then repeated for key sociodemographic subgroups,including binary gender (male and female), ethnicity (Asian and white), and age groups (forthe caregiver burden scale only: younger than 60 years vs. 60 years and older). Cronbach’salpha values indicated acceptable to high internal consistency for the scales. For thecaregiver burden scale, reliability was 0.932 at Time 1, 0.895 at Time 2, and 0.870 at Time 3.For the loneliness scale, Cronbach’s alpha was 0.745 at Time 1, 0.643 at Time 2, and 0.654 atTime 3. The quality-of-life scale showed reliability values of 0.853 at Time 1, 0.804 at Time 2,and 0.911 at Time 3.2.3.2. Qualitative Data AnalysisWe followed the reflexive thematic analysis process proposed by Braun and Clark [34,35].Step 1: LH trained the research team in mixed data analysis. Step 2: JW and LR familiarizedthemselves with the qualitative data. Step 3: JW and LR coded the qualitative data manuallyand developed initial themes (see Table 2). Step 4: In the research meeting, JW and LRpresented themes, and the research team discussed and reviewed the themes. Step 5: Informedby the team discussion, the two trainees, JW and LR, refined the themes. Step 6: The teamapproved the updated themes and agreed on the final draft of the manuscript.Table 2. Examples of coding.Quotes Codes ThemesThe robot helps with relieving the pressure ofdriving and commuting. I have two youngkids, and it’s hard having the time to drive tohim [the resident] with two hours of travellingback and forth. It had been a hard time makingtime for myself and my kids. . . and it’s hard tosee my dad. The robot visits help save us timefor commuting.• Saves travel time• Enabling self-carefor familycaregiversReduces Stress andGuiltShe [My granddaughter] will ask me where Ifeel pain. I feel happy to talk to her through therobot.• Engages residentsin the conversation• Brings happinessProvidesMeaningfulEngagementInt. J. Environ. Res. Public Health 2025, 22, 713 8 of 202.4. Data IntegrationWe compared, merged, and interpreted quantitative and qualitative data to identifypoints of alignment and differences in findings [36]. Then, we developed narrativesto explain how our quantitative and qualitative data complemented each other in thediscussion, providing richer context and deeper insight.2.5. RigourIn our regular research meetings, we consistently involved patient partners, familycaregivers, and clinicians to remain reflexive about our positions and assumptions aboutour research activities and participants. We involved patient partners in the pilot testingsurvey and interview questions and reframed them accordingly to make these questionsunderstandable for participants, especially residents with dementia. We adopted multipledata collection methods, including surveys, interviews, field notes, and observations, togain a more comprehensive understanding of the research topic. In addition, LH trainedthe trainees using different methods to collect data from participants respectfully and sothe data collection was conversational and accessible.2.6. EthicsThis study was approved by the Behavioral Research Ethics Board of the Universityof British Columbia on 28 September 2022. All our participants gave the research teaminformed consent before participating in this study. Pseudonyms were used for participantsand LTC homes to protect confidentiality.3. ResultsThis study included 35 participants, comprising 18 residents and 17 family members(see Table 3). Among the residents, males accounted for 55.6%, while females made up44.4%. The majority of the residents were aged 81 years or older (66.7%), followed by thoseaged 71–80 years (27.8%) and a smaller proportion aged 61–70 years (5.6%). In terms ofethnicity, 61.1% of residents identified as Asian, while 38.9% were white.Table 3. Demographic characteristics of participants (n = 35).Residents FamiliesGenderMale 10 (55.6%) Male 3 (17.6%)Female 8 (44.4%) Female 14 (82.3%)Age group61–70 years 1 (5.6%) 21–40 years 2 (11.8%)71–80 years 5 (27.8%) 31–50 years 4 (23.5%)81- years 12 (66.7%) 51–60 years 4 (23.5%)61–70 years 5 (29.4%)71–80 years 2 (11.8%)EthnicityWhite 7 (38.9%) White 7 (41.2%)Asian 11 (61.1%) Asian 10 (58.8%)RelationshipsDaughter 11 (64.7%)Son 3 (17.6%)Spouse 1 (5.9%)Granddaughter 1 (5.9%)Friend 1 (5.9%)Total 18 Total 17Int. J. Environ. Res. Public Health 2025, 22, 713 9 of 20Among the family members, the gender distribution was predominantly female(82.3%), with males making up 17.6%. Family members’ ages varied, with the largestproportion in the 61–70 age group (29.4%), followed by 51–60 years (23.5%) and 31–50 years(23.5%). Smaller proportions were in the 71–80 (11.8%) and 21–40 (11.8%) age groups.Ethnically, 58.8% of family members identified as Asian, while 41.2% were white. Mostof the caregivers were daughters to residents (64.7%), followed by sons and other roles(spouse, granddaughter, friend).3.1. Quantitative ResultsThe results in Table 4 show significant changes in family caregiver burden, residents’quality of life, and loneliness over three time points: baseline (T0), 1 month after using arobot (T1), and 2 months after using a robot (T2). For family caregiver burden, the meandecreased from 21.5 ± 11.09 at T0 to 16.5 ± 7.92 at T1 and further to 15.8 ± 7.68 at T2.ANOVA revealed a significant overall difference (F(2) = 7.464, p = 0.008, partial η2 = 0.318),indicating a large effect size. Post hoc comparisons showed significant reductions betweenT0 and T1 (p = 0.011) and T0 and T2 (p = 0.043). Subgroup analysis (Figure 4) revealed nostatistical differences at T0 between subgroups based on gender, age, or ethnicity, althoughdifferences in mean values were observed. The highest burden score of 40 was reportedby a spouse, followed by daughters with a mean of 22.1 and sons with a mean of 23.7,compared to a granddaughter with 8 and a friend with 2. Male caregiver burden wasalso notably higher at 23.7 than in female caregivers at 21.0 (Figure 4). Female caregivers(n = 14) showed similar levels and trends to the total group, with a baseline score of 21.0,which decreased to 16.0 at 1 month and 15.2 at 2 months (p = 0.025). Caregivers over 60reported a higher caregiver burden at all three time points compared to those under 60,although the differences were not statistically significant. The older adult subgroup beganwith the highest burden score of 25.0 at baseline, which decreased to 18.9 at 1 month and16.1 at 2 months (p = 0.01). These findings suggest a significant reduction in caregiverburden over time across all groups, with the greatest benefit observed in the older adultcaregiver subgroup.Table 4. Changes in caregiver burden, quality of life, and loneliness over time.TimePointsMean ±SDANOVA F(df),p-ValuePost Hoc Comparison(p-Value)CaregiverBurdenT0 21.5 ± 11.09 F(2) = 7.464,p = 0.008 **T0 vs. T1 (p = 0.011) *T1 16.5 ± 7.92 T0 vs. T2 (p = 0.043) *T2 15.8 ± 7.68 T1 vs. T2 (p = 1.000)Quality ofLifeT0 29.9 ± 6.93 F(2) = 4.024,p = 0.027 *T0 vs. T1 (p = 0.109)T1 31.6 ± 6.55 T0 vs. T2 (p = 0.124)T2 32.2 ± 7.62 T1 vs. T2 (p = 1.000)LonelinessT0 11.1 ± 3.43 F(2) = 4.084,p = 0.026 *T0 vs. T1 (p = 0.120)T1 9.9 ± 2.99 T0 vs. T2 (p = 0.085)T2 9.8 ± 2.73 T1 vs. T2 (p = 1.000)Note: ANOVA p-values indicate overall differences across time points. Post hoc comparisons were performedusing Bonferroni correction. T0 = Baseline, T1 = 1-Month Follow-Up; T2 = 2-Month Follow-Up. * p < 0.05,** p < 0.01.Int. J. Environ. Res. Public Health 2025, 22, 713 10 of 20Int. J. Environ. Res. Public Health 2025, 22, x FOR PEER REVIEW 11 of 22 Table 4. Changes in caregiver burden, quality of life, and loneliness over time. Time Points Mean ± SD ANOVA F(df), p-Value Post Hoc Comparison (p-Value) Caregiver Burden T0 21.5 ± 11.09 F(2) = 7.464, p = 0.008 ** T0 vs. T1 (p = 0.011) * T1 16.5 ± 7.92 T0 vs. T2 (p = 0.043) * T2 15.8 ± 7.68 T1 vs. T2 (p = 1.000) Quality of Life T0 29.9 ± 6.93 F(2) = 4.024, p = 0.027 * T0 vs. T1 (p = 0.109) T1 31.6 ± 6.55 T0 vs. T2 (p = 0.124) T2 32.2 ± 7.62 T1 vs. T2 (p = 1.000) Loneliness T0 11.1 ± 3.43 F(2) = 4.084, p = 0.026 * T0 vs. T1 (p = 0.120) T1 9.9 ± 2.99 T0 vs. T2 (p = 0.085) T2 9.8 ± 2.73 T1 vs. T2 (p = 1.000) Note: ANOVA p-values indicate overall differences across time points. Post hoc comparisons were performed using Bonferroni correction. T0 = Baseline, T1 = 1-Month Follow-Up; T2 = 2-Month Follow-Up. * p < 0.05, ** p < 0.01. Figure 4. Changes in caregiver burden across demographic subgroups over time. This figure illus-trates mean caregiver burden scores at baseline (T0), 1 month (T1), and 2 months (T2) across sub-groups based on gender, ethnicity, and age group. * p < 0.05, ** p < 0.01 (statistical significance de-termined by ANOVA with post hoc test). Regarding quality of life, the mean increased from 29.9 ± 6.93 at T0 to 31.6 ± 6.55 at T1 and 32.2 ± 7.62 at T2. Although ANOVA showed a significant overall difference (F(2) = 4.024, p = 0.027, partial η2 = 0.191), post hoc comparisons did not reveal significant differ-ences between any pair of time points (Table 4). Subgroup analysis (Figure 5) indicated similar trends across different demographic groups without statistically significant differ-ences, regardless of gender or ethnicity. Male residents and white residents consistently reported higher quality of life scores than female and Asian counterparts without statisti-cally significant differences. Fig re Changes in caregiver burden across demographic subgroups over time. This figureillustrates me n caregive burden scores t bas line (T0), 1 month (T1), and 2 months (T2) acrosssubgroups based on gender, ethnicity, and age group. * p < 0.05, ** p < 0.01 (statistical significancedetermined by ANOVA with post hoc test).Regarding quality of life, the mean increased from 29.9 ± 6.93 at T0 to 31.6 ± 6.55at T1 and 32.2 ± 7.62 at T2. Although ANOVA showed a significant overall difference(F(2) = 4.024, p = 0.027, partial η2 = 0.191), post hoc comparisons did not reveal significantdifferences between any pair of time points (Table 4). Subgroup analysis (Figure 5) indi-cated similar trends across different demographic groups without statistically significantdifferences, regardless of gender or ethnicity. Male residents and white residents consis-tently reported higher quality of life scores than female and Asian counterparts withoutstatistically significant differences.For resident loneliness scores, the mean decreased from 11.1 ± 3.43 at T0 to 9.9 ± 2.99at T1 and slightly to 9.8 ± 2.73 at T2 (F(2) = 4.084, p = 0.026, partial η2 = 0.203), with nofurther significant pairwise differences found between any time points (Table 4). Subgroupanalysis (Figure 6) revealed a similar pattern across all groups. Specifically, within theethnic subgroups, Asian residents experienced a greater decrease in loneliness, with scoresdropping from 12.5 at T0 to 10.7 at T1, followed by a slight increase to 11.0 at T2 (p = 0.030).In contrast, the white subgroup did not report a reduction in loneliness at T1 but showed asignificant decrease at T2, which differed from the trend observed in other subgroups. Nosignificant differences in loneliness scores were found between male and female subgroups.These findings suggest that while loneliness decreased over time for all groups, the changesvaried by ethnicity, with white residents showing a delayed improvement in loneliness.Int. J. Environ. Res. Public Health 2025, 22, 713 11 of 20Int. J. Environ. Res. Public Health 2025, 22, x FOR PEER REVIEW 12 of 22 Figure 5. Changes in quality of life across demographic subgroups over time. This figure illustrates mean quality of life scores at baseline (T0), 1 month (T1), and 2 months (T2) across subgroups based on gender and ethnicity. * p < 0.05 (statistical significance determined by ANOVA). For resident loneliness scores, the mean decreased from 11.1 ± 3.43 at T0 to 9.9 ± 2.99 at T1 and slightly to 9.8 ± 2.73 at T2 (F(2) = 4.084, p = 0.026, partial η2 = 0.203), with no further significant pairwise differences found between any time points (Table 4). Subgroup anal-ysis (Figure 6) revealed a similar pattern across all groups. Specifically, within the ethnic subgroups, Asian residents experienced a greater decrease in loneliness, with scores drop-ping from 12.5 at T0 to 10.7 at T1, followed by a slight increase to 11.0 at T2 (p = 0.030). In contrast, the white subgroup did not report a reduction in loneliness at T1 but showed a significant decrease at T2, which differed from the trend observed in other subgroups. No significant differences in loneliness scores were found between male and female sub-groups. These findings suggest that while loneliness decreased over time for all groups, the changes varied by ethnicity, with white residents showing a delayed improvement in loneliness. Overall, a reduction in caregiver burden and an improvement in quality of life and loneliness were reported during the implementation of the telepresence robot. Most of these improvements occurred within the first month after initiation and were sustained through the second month. Figure 5. Changes in quality of life across demographic subgroups over time. This figure illustratesmean quality of life scores at baseline (T0), 1 month (T1), and 2 months (T2) across subgroups basedon gender and ethnicity. * p < 0.05 (statistical significance determined by ANOVA).Int. J. Environ. Res. Public Health 2025, 22, x FOR PEER REVIEW 13 of 22 Figure 6. Changes in loneliness across demographic subgroups over time. This figure illustrates mean loneliness scores at baseline (T0), 1 month (T1), and 2 months (T2) across subgroups based on gender and ethnicity. * p < 0.05 (statistical significance determined by ANOVA). 3.2. Qualitative Results The analysis of the qualitative data generated three themes on the impact of telepresence robots on residents and family caregivers (refer to Table 5 for a summary of themes). Table 5. Summary of themes. Impact on Residents Impact on Family Caregivers • Promotes connections and nurtures closeness • Provides meaningful engagement • Acknowledges robots’ limitations Reduces stress and guilt Enables continuity in care and comple-ments in-person visits Offers reassurance on residents’ quality of life 3.2.1. Impact on Residents Promotes Connections and Nurtures Closeness With their function for video calls, telepresence robots connect residents with their families and friends, spanning multiple generations. Family caregivers described sharing everyday experiences such as artwork, music, and family events. One family caregiver who lived far from the LTC home shared how she showed her artwork and played ukulele for her dad through the telepresence robot. She said, “He can get a feel of what is going on in my life” (Candy, daughter of resident, white, Sunflower Residence). Residents expressed joy and closeness when engaging with the robot. A resident shared, “I have four grandchil-dren. Two of them will call in through the robot… Usually, we will talk for a long time” (Winky, a resident, Asian, Tulip Residence). A family caregiver shared family moments through the robot with her father. She expressed, “When I show him the Christmas tree and different Figure 6. hanges in loneliness across de ographic subgroups over ti e. This figure illustratesmean loneliness scores at baseline (T0), 1 month (T1), and 2 months (T2) across subgroups based ongender and ethnicity. * p < 0.05 (statistical significance determined by ANOVA).Int. J. Environ. Res. Public Health 2025, 22, 713 12 of 20Overall, a reduction in caregiver burden and an improvement in quality of life andloneliness were reported during the implementation of the telepresence robot. Most ofthese improvements occurred within the first month after initiation and were sustainedthrough the second month.3.2. Qualitative ResultsThe analysis of the qualitative data generated three themes on the impact of telepres-ence robots on residents and family caregivers (refer to Table 5 for a summary of themes).Table 5. Summary of themes.Impact on Residents Impact on Family Caregivers• Promotes connections and nurturescloseness• Provides meaningful engagement• Acknowledges robots’ limitations• Reduces stress and guilt• Enables continuity in care andcomplements in-person visits• Offers reassurance on residents’quality of life3.2.1. Impact on ResidentsPromotes Connections and Nurtures ClosenessWith their function for video calls, telepresence robots connect residents with theirfamilies and friends, spanning multiple generations. Family caregivers described sharingeveryday experiences such as artwork, music, and family events. One family caregiverwho lived far from the LTC home shared how she showed her artwork and played ukulelefor her dad through the telepresence robot. She said, “He can get a feel of what is going on inmy life” (Candy, daughter of resident, white, Sunflower Residence). Residents expressed joyand closeness when engaging with the robot. A resident shared, “I have four grandchildren.Two of them will call in through the robot. . . Usually, we will talk for a long time” (Winky, aresident, Asian, Tulip Residence). A family caregiver shared family moments through therobot with her father. She expressed, “When I show him the Christmas tree and different thingsgoing on in my life, he is happy. One time my sister showed my dad her daughter’s ballet dance, hewas so happy” (Cecilia, daughter of a resident, Asian, Tulip Residence).Residents also preferred visual interactions offered by robots compared to traditionalphone calls. For example, a resident stated, “The robot allows me to see the face of my daughter.Yes, I feel somehow happy seeing my daughter. There are differences between the phone and the robotbecause I can see her” (Cherry, a resident, Asian, Tulip Residence).Provides Meaningful EngagementTelepresence robots offered residents opportunities for meaningful interactions andconversations, significantly reducing feelings of isolation. A resident expressed, “Thereare no people that I can chat with [in the LTC home]. On the robot, my granddaughter will askme where I feel pain. I feel happy to talk to her through the robot” (Winky, a resident, Asian,Tulip Residence). A daughter shared, “With more frequent calls through the robot, my dad canrecall our conversations. He can chat and talk forever. It feels like an in-person visit to him” (Flora,daughter of a resident, white, Rose Residence). One caregiver described how frequent robotinteractions enhanced a resident’s “sense of self and self-worth”. Another emphasized howvisual interaction helped adapt communication strategies effectively. A family membershared, “The robot has helped in such a way that I can see her [the resident] when I’m askingher a question. I can see if she’s struggling with the answer, and then that means that I need torephrase the question or talk about something different” (Shereen, daughter of a resident, white,Sunflower Residence).Int. J. Environ. Res. Public Health 2025, 22, 713 13 of 20Acknowledges Robots’ LimitationsA few family members voiced the limitations of telepresence robots. As the familycaregivers are the main controllers of the telepresence robots, the residents cannot initiatecalls. The residents’ role is more passive in the call via telepresence robots. Therefore, someresidents recognized the lack of control over the robot. A resident remarked, “If I don’thave the robot, that’s no big deal to me because I didn’t have this robot in my whole life. I can usethe phone to call my family” (Paul, a resident, Asian, Tulip Residence). Another residentcommented, “The robot will move towards me when I am sitting. I don’t know how to use therobot. I feel annoyed with myself. I feel helpless. I feel useless. I have so much pain in my legs.No one can help me, not even the robot” (Cherry, a resident, Asian, Tulip Residence). Therewere also periods when the telepresence robots had technical issues (e.g., mobility issues,inability to adjust the height) and needed to be sent for repairs. A family member sharedan experience: “When Kirby [name of the telepresence robot] had a tech issue, we [the resident andherself] joked about Kirby having a glitch and needing some downtime or vitamins. We had a goodlaugh” (Lily, wife of a resident, white, Jasmine Residence).3.2.2. Impact on Family CaregiversReduces Stress and GuiltFamily caregivers reported that the robot reduced stress and guilt associated withchallenges in visiting LTC homes, especially during adverse weather and personal illness.Telepresence robots provided a practical alternative, easing caregiving burdens. Onecaregiver shared relief over less commuting, stating, “With the snow, I am not able to see her[the resident] for a while. I think about her sitting in the nursing home by herself ” (Catherine,friend of a resident, white, Jasmine Residence). Another family caregiver said, “I felt bad notseeing him [the resident] every day as he was expecting me. After I had been sick, it was becomingmore challenging to maintain my visits” (Lily, wife of a resident, white, Jasmine Residence). Aresident’s son said, “The key benefit of the robot is time-saving for commute. The robot helps me toshow up and be there more with my father” (Jeffrey, son of a resident, Asian, Tulip Residence).Another described, “The robot helps with relieving the pressure of driving. I have two youngkids, and it’s hard having the time to drive to him [the resident] with two hours of travel” (Janice,daughter of a resident, white, Zinnia Residence). Another family caregiver also sharedhow the telepresence robot supported her “visits to her mum” when she was sick. She said,“I was sick three weeks ago. I had a fever. Thank goodness I have the robot. I can visit my mom”(Ruby, daughter of a resident, Asian, Tulip Residence).Enables Continuity in Caregiver’s Role and Complements In-Person VisitsTransferring to LTC homes can be a challenging transition period for residents andfamily caregivers. Many caregivers in our study described how telepresence robots supportthe continuation of their caregiving roles and their involvement in the care of residents inLTC homes.A daughter of a resident illustrated this continuity: “The robot helps with the feeling thatI am still looking after Mom, seeing what she’s doing, and making sure that she knows that I amaround. I can go on the robot and see her. And I know her routine. At least to say, ‘Hello mum’”(Ruby, daughter of a resident, Asian, Tulip Residence).A resident’s son also shared how he supported staff in the resident’s care. He said,“Before, when the staff was helping my dad, he would get very scared because he didn’tknow who they were. With the telepresence robot, I can call in and tell him who they [thestaff] are, and he feels better. He knows that his family is around, and calms down” (Jeffrey,son of a resident, Asian, Tulip Residence).Int. J. Environ. Res. Public Health 2025, 22, 713 14 of 20Another caregiver shared how telepresence robots allowed advocacy for their lovedones in LTC during the COVID-19 pandemic: “The robot was really good when she [the resident]was confined because of the COVID outbreak. . . She was confused and distressed. After I got off[logged out] from the robot, I asked the staff to talk to her. It was helpful as I could not go into thenursing home” (Catherine, friend of a resident, white, Jasmine Residence).Notably, most family caregivers emphasized that despite the presence of telepresencerobots, they were still eager to visit their loved ones and have a personal touch. Onecaregiver said, “Even though I use the robot, I still come three times a week to visit the care home.The robot doesn’t replace that time” (Ruby, daughter of a resident, Asian, Tulip Residence).Another caregiver echoed the preference of his mother, “The robot is a great addition. [But] formy mom, she would prefer an in-person visit” (Louis, son of a resident, Asian, Rose Residence).Offers Reassurance on Residents’ Quality of LifeThe telepresence robots helped reassure family caregivers about enhancing residents’quality of life in LTC. One family caregiver shared, “The staff told me that [the resident] iscalmer and not as lonely, more settled” (Lily, wife of a resident, white, Jasmine Residence).Another family caregiver described, “The robot really helps with his spirits. . . It’s obviouslybetter” (Nancy, daughter of a resident, Asian, Jasmine Residence).Knowing the benefits of the telepresence robots on residents had a positive impact onthe caregivers. One of them smiled and said, “I think it helps mum. . . She is quite happy whenshe sees me, even though I am on the robot. Knowing that she is at ease is good for me too. I feelhappier knowing that Mum is happy seeing me. I can tell that she is happy from her expression. Sheis not very verbal. For mum, she will be more grounded if she sees me more” (Ruby, daughter of aresident, Asian, Tulip Residence).4. Discussion and ImplicationsThis study examined the impact of a telepresence robot intervention on the familycaregiver burden, residents’ quality of life, and loneliness. The findings demonstrated asignificant reduction in caregiver burden, particularly during the first month of implemen-tation, indicating immediate relief through improved communication, reduced logisticalchallenges, and increased reassurance for caregivers. Our qualitative insights reinforcedthe quantitative results, revealing reduced caregiver stress and guilt as caregivers providedemotional support and reduced residents’ anxiety using the telepresence robots. Thesefindings align with Marvardi et al. [37], who highlighted the caregiver burden as primarilyinfluenced by residents’ behavioral disturbances and disability, alongside caregiver anxietyand depression [37]. Their findings align with our qualitative themes, which highlightthe reduction in caregiver burden when caregivers can help decrease residents’ anxiety,calm residents down, and provide emotional support for residents by calling in with thetelepresence robot. Importantly, while caregivers in our study emphasized that telepresencerobots would not replace in-person visits, these robots offered additional benefits, such asreducing the travel time, which reduced caregivers’ stress and guilt.Previous research identified the caregiver burden as closely related to residents’ neu-ropsychiatric symptoms and caregivers’ competence, with emotional burdens exacerbatedby residents’ high affiliation and caregiver gender [38]. Our findings partially supportedthese conclusions. The highest burden scores in our study were reported by one individualspouse and older caregivers’ subgroup, aligning with earlier findings by Rinaldi et al. [39],in which older caregivers experienced stress levels three times higher than younger groups,and children or spouses reported burden-distress five to six times higher than other rela-tives. Although our study did not find statistically significant differences based on residents’gender or caregiver affiliation due to the limited sample size, the highest burden score wasInt. J. Environ. Res. Public Health 2025, 22, 713 15 of 20reported by a spouse, followed by daughters and sons, compared to a granddaughter anda friend, which aligns with the above two research studies [38,39]. Notably, male caregiversreported higher initial burden levels than female caregivers, which is also consistent withprior research [39]. Older caregivers, who initially reported the highest burden, demon-strated the greatest reduction in burden following the intervention. This trend suggeststhat older caregivers, often facing higher stress and physical strain, may benefit most frominterventions that ease perceived caregiving demands. An older adult spouse in our studyexpressed that hospitalization was once a barrier for her to visit the LTC home, which ledto the feeling of guilt. Her physical decline since then also impacted the frequency withwhich she could visit the LTC home. While gender, age, and ethnicity did not significantlyaffect how caregivers reflected on their burden at the baseline, female caregivers and thoseover 60 appeared particularly receptive to technological support. This contributes newinsights by showing that communication technology, like telepresence robots, plays a rolein reducing caregiver burden alongside previously identified determinants like residents’neuropsychiatric symptoms and caregivers’ competence. Our qualitative findings furthersupport this conclusion, highlighting that relief from guilt and stress, reassurance fromLTC homes, and mediation in residents’ care planning—with the support and availabilityof telepresence robots as a communication technology—all contribute to alleviating thecaregiver burden.A gradual improvement in residents’ quality of life and reduction in loneliness wasobserved over time in our study, with the most significant changes within the first month.Due to the limited sample size, post hoc comparisons did not reveal statistically significantdifferences between time points. Another possible explanation is that the stringency of themultiple-comparisons correction renders the pairwise post hoc tests more conservative, re-sulting in p-values slightly above the 0.05 threshold. One research study reported lonelinessas closely associated with the quality of life in older adults [40], which aligned with ourfindings. Although one recent scoping review reported few studies focusing on the impactof assistive technology on the loneliness of residents with dementia [41], one study in thisreview showed promising results of assistive technologies—not telepresence robots—whichcould reduce loneliness in people with dementia in LTC [42]. Telepresence robots werereported in one review of their potential to enhance social connection, which contributes toa reduction in loneliness [43]. Our qualitative interviews also echoed this potential throughthe experiences of using the telepresence robot shared by residents and family caregivers.For example, telepresence robots brought these individuals a sense of closeness, connectedresidents with their children, grandchildren, and even great-grandchildren, and allowedthe sharing of instant life story updates between family caregivers and residents. Besidesloneliness, the changes in quality of life were possibly influenced by additional factors suchas resident’s baseline health conditions, social engagement, and individual preferencesfor virtual communication [44,45]. The qualitative data also reported and acknowledgedthe limitations of the telepresence robots’ impact on residents’ physical health and energylevels despite the other benefits they brought to residents’ and caregivers’ lives. Interest-ingly, male and white residents reported higher quality of life scores than their female andnon-white counterparts, which may reveal cultural or gender-related variations in howresidents perceive and engage with telepresence technology, requiring further investigationinto individualized interventions tailored to different demographic groups.The integration of quantitative and qualitative findings in this study offers a more com-prehensive understanding of how telepresence robots influence both measurable outcomesand lived experiences. Quantitatively, reductions in caregiver burden and improvementsin resident’ quality of life and loneliness were statistically significant or trending towardsignificance, particularly within the first month of intervention. These shifts were furtherInt. J. Environ. Res. Public Health 2025, 22, 713 16 of 20contextualized and enriched by qualitative data, which illustrated how caregivers experi-enced immediate emotional relief, reduced guilt, and enhanced connection-mechanismsthat help explain the numerical changes observed. Similarly, residents’ improved qualityof life and reduced loneliness were reflected in their narratives about feeling more socialconnected and emotionally supported. These converging findings underscore that therobots’ impact was not merely technological but relational, affecting emotional well-being,family dynamics, and care continuity. By triangulating quantitative measures with in-depthqualitative narratives, the mixed study design effectively addresses the research question.In terms of practice implications, this study suggests that telepresence robots canoffer a valuable tool for supporting family caregivers, particularly older caregivers, whoreported the highest initial burden. The sustained decrease in burden over time indicatesthat continuous use of telepresence robots may offer long-term benefits. Implementingthese robots could assist caregivers who face emotional stress and logistical challenges.Given the positive outcomes for both residents and caregivers, policymakers and LTCadministrators can consider integrating such technologies to enhance the well-being ofresidents and their families. This study also offers insights into the target populationsthat may best benefit from the use of telepresence robots. With limited resources, LTCadministrators can explore how these technologies can be best used in their organizations,such as shared use of the robots or for residents who have older adult spouses or children.Furthermore, while the robots facilitate improved social connections, they should be usedas a supportive, complementary communication tool rather than replacing the need forin-person visits.In terms of research implications, further studies should examine the long-term sus-tainability of the effects observed in this study regarding the reduction in caregiver burden,residents’ level of loneliness, and the improvement in residents’ quality of life. Given thepotential differences observed in subgroups, future research should include larger samplesizes and more rigorous study designs to better identify and target the most likely beneficia-ries. Also, future studies can investigate the cost-effectiveness of implementing such robotsand the staff’s perspectives (those from LTC homes and from different social and culturalcontexts). Additionally, since the quality of life may be influenced by multiple factors, suchas ethnocultural backgrounds and gender, further exploration of the relationship betweentelepresence robots and these determinants is warranted to understand their broader im-pact in detail. Given the significant impact of telepresence robots on the older adult group,researchers could explore the potential challenges of using these technologies (e.g., thetechnical challenges) and strategies to best support older caregivers in using these robots.Researchers can also further explore the support of telepresence robots for intergenerationalbonding between residents and their grandchildren, as well as ethical considerations fromresidents, family caregivers, and staff with different levels of technological literacy in areasincluding data privacy and residents’ autonomy.Strengths and LimitationsThis study utilized a mixed-methods approach, which combines both qualitative andquantitative data to provide a more comprehensive understanding of the research problem.Our research team is composed of a diverse group of multidisciplinary researchers andtrainees with expertise in nursing, medicine, occupational therapy, biomedical engineering,and social work, contributing to a more comprehensive investigation. Additionally, theteam includes patient partners with dementia and family caregivers, who contributed theirlived experience to this study. This engagement helps to ensure that our interventions andfindings are relevant, practical, and sensitive to the needs of the participants.Int. J. Environ. Res. Public Health 2025, 22, 713 17 of 20Our study has the following limitations. This pre–post design relied on each partici-pant serving as their own control, which can increase statistical power with smaller samplesizes. However, the absence of a control group means that it is difficult to definitivelyattribute changes in outcomes solely to the intervention, as other confounding factorsmay have contributed to the observed improvement. For instance, reductions in caregiverburden and resident loneliness could partly reflect increased attention from the researchteam, growing familiarity with the technology, or natural fluctuations in caregiver–residentdynamics over time. For the quantitative study part, our sample size was constrainedby the number of robots available. We have 20 robots in total. Further, our study wasconducted in five different LTC homes based in urban areas of West Canada. The uniquecontext may not be transferrable to LTC homes in rural areas or in other countries. Weacknowledge that external validity remains limited, particularly given the sample size, thespecific context of participating LTC homes, and the demographic characteristics of oursample. Another limitation is the lack of variations in staff engagement and support anddetailed tracking of telepresence robot use frequency across sites (only qualitative dataprovided), which might have influenced the success of robot implementation and residents’experience; future research should include usage metrics to better contextualize the impact.Lastly, since finalizing this paper, our team has found out that the company that developedthe telepresence robots used in our project has dissolved. Nevertheless, the findings aresignificant and important enough to highlight the positive outcomes benefiting residents inLTC. The findings can provide insights to support efforts at adopting or developing othersimilar products to be used in the long term and facilitate the socialization of residents,their families, and friends in LTC homes. Future research should consider randomizedcontrolled trials with larger and more diverse samples across rural and international set-tings to strengthen generalizability. Additionally, comparative studies exploring differentmodels of telepresence technology and their implementation under varying conditions ofstaff support and funding mechanisms will be essential to identifying sustainable solutions.5. ConclusionsThis two-month telepresence robot intervention demonstrated the potential to reducethe family caregiver burden, enhance quality of life, and lessen loneliness in residents livingwith dementia in LTC homes. From the residents’ perspectives, the telepresence robotfosters a sense of connection, nurtures closeness, and provides meaningful engagement.From the families’ perspective, telepresence robots enable continuity in care, complementin-person visits, reduce stress and guilt, and offer comfort through reassurance of residents’well-being, with older caregivers as the greatest beneficiaries. This study offers insightsfor policymakers and LTC administrators in planning and considering the adoption ofsimilar technologies. Future research could explore the long-term impacts on the well-being of residents and their families, the impact of different factors on residents’ andfamily care partners’ perceptions of telepresence robots, how to better support olderadult caregivers in using telepresence robots, and the robots’ potential in supportingintergenerational connections.Supplementary Materials: The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ijerph22050713/s1, Table S1: COREQ checklist [46]; Table S2: GRAMMSchecklist [47]; S3: Dataset.Int. J. Environ. Res. Public Health 2025, 22, 713 18 of 20Author Contributions: Conceptualization, L.H. and J.M.; methodology, L.H.; formal analysis, L.H.,J.O.Y.W., H.R., L.L. and Y.Z.; data curation, J.O.Y.W. and Y.Z.; writing—original draft preparation,J.O.Y.W., H.R., L.L., J.J.F. and Y.Z.; writing—review and editing, L.H. and J.M.; supervision, L.H. andL.L.; project administration, J.O.Y.W. and H.R.; funding acquisition, L.H. All authors have read andagreed to the published version of the manuscript.Funding: This research was funded by the Vancouver Foundation Participatory Action Re-search (PAR), grant number GR019971, Vancouver General Hospital Foundation, and RichmondHospital Foundation.Institutional Review Board Statement: This study was conducted in accordance with the Declarationof Helsinki and approved by the Behavioral Research Ethics Board of the University of BritishColumbia (H22-02402).Informed Consent Statement: Written informed consent and assent were obtained from all subjectsinvolved in this study. Informed consent and assent have been obtained from residents and familypartners to publish this paper.Data Availability Statement: The original contributions presented in this study are included in thearticle/Supplementary Materials. Further inquiries can be directed to the corresponding author.Acknowledgments: We extend our heartfelt gratitude to the residents, families, and staff of thelong-term care homes for their invaluable contributions to this study.Conflicts of Interest: The authors declare no conflicts of interest.AbbreviationsThe following abbreviations are used in this manuscript:ANOVA Analysis of varianceLTC Long-term carePI Principal investigatorQOL-AD Quality of Life—Alzheimer’s DiseaseReferences1. White, E.M.; Wetle, T.F.; Reddy, A.; Baier, R.R. Front-Line Nursing Home Staff Experiences During the COVID-19 Pandemic.J. Am. Med. Dir. Assoc. 2021, 22, 199–203. [CrossRef] [PubMed]2. Drageset, J.; Kirkevold, M.; Espehaug, B. Loneliness and Social Support among Nursing Home Residents without CognitiveImpairment: A Questionnaire Survey. Int. J. Nurs. Stud. 2011, 48, 611–619. [CrossRef] [PubMed]3. Paque, K.; Bastiaens, H.; Van Bogaert, P.; Dilles, T. Living in a Nursing Home: A Phenomenological Study Exploring Residents’Loneliness and Other Feelings. Scand. J. Caring Sci. 2018, 32, 1477–1484. [CrossRef] [PubMed]4. Dahlberg, L.; Andersson, L.; McKee, K.J.; Lennartsson, C. Predictors of Loneliness among Older Women and Men in Sweden: ANational Longitudinal Study. Aging Ment. Health 2015, 19, 409–417. [CrossRef]5. Wright, M.R.; Hammersmith, A.M.; Brown, S.L.; Lin, I.-F. The Roles of Marital Dissolution and Subsequent Repartnering onLoneliness in Later Life. J. Gerontol. Ser. B 2020, 75, 1796–1807. [CrossRef]6. de Boise, S.; Hearn, J. Are Men Getting More Emotional? Critical Sociological Perspectives on Men, Masculinities and Emotions.Sociol. Rev. 2017, 65, 779–796. [CrossRef]7. Kemperman, A.; van den Berg, P.; Weijs-Perrée, M.; Uijtdewillegen, K. Loneliness of Older Adults: Social Network and the LivingEnvironment. Int. J. Environ. Res. Public Health 2019, 16, 406. [CrossRef]8. Mueller, A.; Beach, S.R.; Bowers, B.J.; Fields, B. Relationship between Health Care Interactions and Care Partner Burden. Fam.Syst. Health 2022, 40, 225–231. [CrossRef]9. Fowler, J.; Blundell, B.; Morrisby, C.; Hendrick, A. ‘You’ve Got No Support’: The Experiences of Older Male Caregivers WhenTheir Partner Enters Residential Care. J. Gerontol. Soc. Work. 2022, 65, 529–544. [CrossRef]10. Crawford, K.; Digby, R.; Bloomer, M.; Tan, H.; Williams, A. Transitioning from Caregiver to Visitor in a Long-Term Care Facility:The Experience of Caregivers of People with Dementia. Aging Ment. Health 2015, 19, 739–746. [CrossRef]11. Street, D.; Burge, S.; Quadagno, J.; Barrett, A. The Salience of Social Relationships for Resident Well-Being in Assisted Living.J. Gerontol. Ser. B 2007, 62, S129–S134. [CrossRef] [PubMed]Int. J. Environ. Res. Public Health 2025, 22, 713 19 of 2012. Double Robotics. Double 3. The Future of Work Is Hybrid. Available online: https://www.doublerobotics.com (accessed on 10March 2025).13. Zamir, S.; Hennessy, C.H.; Taylor, A.H.; Jones, R.B. Video-Calls to Reduce Loneliness and Social Isolation within Care Environ-ments for Older People: An Implementation Study Using Collaborative Action Research. BMC Geriatr. 2018, 18, 62. [CrossRef][PubMed]14. Chu, C.H.; Yee, A.; Stamatopoulos, V. Poor and Lost Connections: Essential Family Caregivers’ Experiences Using Technologywith Family Living in Long-Term Care Homes during COVID-19. J. Appl. Gerontol. 2022, 41, 1547–1556. [CrossRef] [PubMed]15. Hung, L.; Hu, G.; Wong, J.; Ren, H.; Ahmed, N.; Hussein, A.; Young, E.; Berndt, A.; Mann, J.; Corepal, R.; et al. TelepresenceRobots in Long-Term Care Settings in British Columbia During the COVID-19 Pandemic: Analyzing the Experiences of Residentsand Family Members. Gerontol. Geriatr. Med. 2023, 9, 233372142311662. [CrossRef]16. Yen, H.-Y.; Huang, C.W.; Chiu, H.-L.; Jin, G. The Effect of Social Robots on Depression and Loneliness for Older Residents inLong-Term Care Facilities: A Meta-Analysis of Randomized Controlled Trials. J. Am. Med. Dir. Assoc. 2024, 25, 104979. [CrossRef]17. Hung, L.; Zhao, Y.; Alfares, H.; Shafiekhani, P. Ethical Considerations in the Use of Social Robots for Supporting Mental Healthand Wellbeing in Older Adults in Long-Term Care. Front. Robot. AI 2025, 12. [CrossRef]18. Yang, Y.; Wang, C.; Xiang, X.; An, R. AI Applications to Reduce Loneliness Among Older Adults: A Systematic Review ofEffectiveness and Technologies. Healthcare 2025, 13, 446. [CrossRef]19. Albarqi, M.N. Exploring the Effectiveness of Technology-Assisted Interventions for Promoting Independence in Elderly Patients:A Systematic Review. Healthcare 2024, 12, 2105. [CrossRef]20. Hu, G.; Wong, J.; Ren, L.H.; Kleiss, S.; Berndt, A.; Wong, L.; Hussein, A.; Ahmed, N.; Mann, J.; Hung, L. Care Partner Experiencewith Telepresence Robots in Long-Term Care during COVID-19 Pandemic. Digit. Health 2025, 11, 20552076251319820. [CrossRef]21. Niemelä, M.; van Aerschot, L.; Tammela, A.; Aaltonen, I.; Lammi, H. Towards Ethical Guidelines of Using Telepresence Robots inResidential Care. Int. J. Soc. Robot. 2021, 13, 431–439. [CrossRef]22. Moyle, W.; Jones, C.; Cooke, M.; O’Dwyer, S.; Sung, B.; Drummond, S. Connecting the Person with Dementia and Family: AFeasibility Study of a Telepresence Robot. BMC Geriatr. 2014, 14, 7. [CrossRef] [PubMed]23. Teddlie, C.; Tashakkori, A. Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Socialand Behavioral Sciences; Sage: Los Angeles, CA, USA, 2009.24. Hitchcock, J.H.; Onwuegbuzie, A.J. The Routledge Handbook for Advancing Integration in Mixed Methods Research; Routledge: London,UK, 2022; ISBN 9780429432828.25. Ivankova, N.V.; Creswell, J.W.; Plano Clark, V.L. Foundations and Approaches to Mixed Methods Research. In First Steps inResearch; Van Schaik Publishers: Pretoria, South Africa, 2016; pp. 305–336.26. Shorten, A.; Smith, J. Mixed Methods Research: Expanding the Evidence Base. Evid.-Based Nurs. 2017, 20, 74–75. [CrossRef][PubMed]27. Creswell, J.W.; Creswell, J.D. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th ed.; SAGE Publications:Newbury Park, CA, USA, 2018.28. Bédard, M.; Molloy, D.W.; Squire, L.; Dubois, S.; Lever, J.A.; O’Donnell, M. The Zarit Burden Interview. Gerontologist 2001, 41,652–657. [CrossRef] [PubMed]29. De Jong Gierveld, J.; Van Tilburg, T. The De Jong Gierveld Short Scales for Emotional and Social Loneliness: Tested on Data from7 Countries in the UN Generations and Gender Surveys. Eur. J. Ageing 2010, 7, 121–130. [CrossRef]30. Logsdon, R.G.; Gibbons, L.E.; McCurry, S.M.; Teri, L. Assessing Quality of Life in Older Adults With Cognitive Impairment.Biopsychosoc. Sci. Med. 2002, 64, 510–519. [CrossRef]31. Olson, K. Essentials of Qualitative Interviewing; Left Coast Press: Walnut Creek, CA, USA, 2011.32. Phillippi, J.; Lauderdale, J. A Guide to Field Notes for Qualitative Research: Context and Conversation. Qual. Health Res. 2018, 28,381–388. [CrossRef]33. Els, R.C.; Meyer, H.W. The Influence of Leaders’ Attitudes and Commitment to Quality Management of Training on OrganisationalExcellence: A Mixed-Methods Study. Hum. Resour. Dev. Int. 2025, 28, 79–109. [CrossRef]34. Braun, V.; Clarke, V. Reflecting on Reflexive Thematic Analysis. Qual. Res. Sport Exerc. Health 2019, 11, 589–597. [CrossRef]35. Braun, V.; Clarke, V. Using Thematic Analysis in Psychology. Qual. Res. Psychol. 2006, 3, 77–101. [CrossRef]36. Fetters, M.D.; Curry, L.A.; Creswell, J.W. Achieving Integration in Mixed Methods Designs—Principles and Practices. Health Serv.Res. 2013, 48, 2134–2156. [CrossRef]37. Marvardi, M.; Mattioli, P.; Spazzafumo, L.; Mastriforti, R.; Rinaldi, P.; Polidori, M.C.; Cherubini, A.; Quartesan, R.; Bartorelli, L.;Bonaiuto, S.; et al. The Caregiver Burden Inventory in Evaluating the Burden of Caregivers of Elderly Demented Patients: Resultsfrom a Multicenter Study. Aging Clin. Exp. Res. 2005, 17, 46–53. [CrossRef] [PubMed]38. van der Lee, J.; Bakker, T.J.E.M.; Duivenvoorden, H.J.; Dröes, R.-M. Do Determinants of Burden and Emotional Distress inDementia Caregivers Change over Time? Aging Ment. Health 2017, 21, 232–240. [CrossRef] [PubMed]Int. J. Environ. Res. Public Health 2025, 22, 713 20 of 2039. Rinaldi, P.; Spazzafumo, L.; Mastriforti, R.; Mattioli, P.; Marvardi, M.; Polidori, M.C.; Cherubini, A.; Abate, G.; Bartorelli, L.;Bonaiuto, S.; et al. Predictors of High Level of Burden and Distress in Caregivers of Demented Patients: Results of an ItalianMulticenter Study. Int. J. Geriatr. Psychiatry 2005, 20, 168–174. [CrossRef] [PubMed]40. Vespa, A.; Spatuzzi, R.; Fabbietti, P.; Di Rosa, M.; Bonfigli, A.R.; Corsonello, A.; Gattafoni, P.; Giulietti, M.V. Association betweenSense of Loneliness and Quality of Life in Older Adults with Multimorbidity. Int. J. Environ. Res. Public Health 2023, 20, 2615.[CrossRef]41. Budak, K.B.; Atefi, G.; Hoel, V.; Laporte Uribe, F.; Meiland, F.; Teupen, S.; Felding, S.A.; Roes, M. Can Technology ImpactLoneliness in Dementia? A Scoping Review on the Role of Assistive Technologies in Delivering Psychosocial Interventions inLong-Term Care. Disabil. Rehabil. Assist. Technol. 2023, 18, 1107–1119. [CrossRef]42. Robinson, H.; MacDonald, B.; Kerse, N.; Broadbent, E. The Psychosocial Effects of a Companion Robot: A Randomized ControlledTrial. J. Am. Med. Dir. Assoc. 2013, 14, 661–667. [CrossRef]43. Moyle, W.; Arnautovska, U.; Ownsworth, T.; Jones, C. Potential of Telepresence Robots to Enhance Social Connectedness in OlderAdults with Dementia: An Integrative Review of Feasibility. Int. Psychogeriatr. 2017, 29, 1951–1964. [CrossRef]44. Beridze, G.; Ayala, A.; Ribeiro, O.; Fernández-Mayoralas, G.; Rodríguez-Blázquez, C.; Rodríguez-Rodríguez, V.; Rojo-Pérez, F.;Forjaz, M.J.; Calderón-Larrañaga, A. Are Loneliness and Social Isolation Associated with Quality of Life in Older Adults? Insightsfrom Northern and Southern Europe. Int. J. Environ. Res. Public Health 2020, 17, 8637. [CrossRef]45. Boehlen, F.H.; Maatouk, I.; Friederich, H.-C.; Schoettker, B.; Brenner, H.; Wild, B. Loneliness as a Gender-Specific Predictor ofPhysical and Mental Health-Related Quality of Life in Older Adults. Qual. Life Res. 2022, 31, 2023–2033. [CrossRef]46. Tong, A.; Sainsbury, P.; Craig, J. Consolidated Criteria for Reporting Qualitative Research (COREQ): A 32-Item Checklist forInterviews and Focus Groups. Int. J. Qual. Health Care 2007, 19, 349–357. [CrossRef]47. O’cathain, A.; Murphy, E.; Nicholl, J. The Quality of Mixed Methods Studies in Health Services Research. J. Health Serv. Res. Policy2008, 13, 92–98. [CrossRef]Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individualauthor(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury topeople or property resulting from any ideas, methods, instructions or products referred to in the content.
Article
eng
Reviewed
Vancouver : University of British Columbia Library
Multidisciplinary Digital Publishing Institute
CC BY 4.0
Faculty
Researcher
telepresence robots
long-term care
quality of life
loneliness
caregiver burden
The Impact of Telepresence Robots on Family Caregivers and Residents in Long-Term Care
Text