Effectiveness and usability of care robots in supporting older adults living with frailty: A systematic review

Abstract

Objective

Frailty is one of the most problematic expressions of aging and causes older adults to require more assistance in daily life. Care robots have been proposed as a potential solution to bridge the widening gap between the demand and supply of healthcare services. This review aims to evaluate the effectiveness and usability of care robots in supporting older adults living with frailty.

Methods

A systematic review was conducted, including peer-reviewed articles published in English from January 2000 to April 2025. A quality assessment was performed on the included studies, and the results were summarized narratively.

Results

Of the nine studies reviewed, five examined the impact of care robots on physical frailty. Three of these studies reported positive outcomes, while two found no significant effects on physical frailty. The impact of care robots on psychological frailty was assessed in four studies, but no significant effects were found. Regarding usability, the perceived usability of care robots among frail older adults was generally low, primarily due to technical limitations. Usability also varied depending on the specific contexts in which the robots were deployed.

Conclusions

Care robots have the potential to improve physical frailty in older adults, although their impact on psychological and social frailty remains limited. Usability challenges persist, underscoring the need for further refinement in robot design, user experience, and contextual factors to enhance their effectiveness and usability for this population.

Keywords

Care robots effectiveness usability frailty older adults

Introduction

Population aging, which is affecting many developed and developing countries, brings several challenges, one of which is frailty.¹ It has been estimated that 12% of older adults aged 60 years and above in high-income countries and 17.4% in low- and middle-income countries experience frailty.^2,3 Frailty is marked by increased vulnerability and reduced physiological reserves, leading to a higher risk of disability and a greater need for support.⁴ It is a multidimensional condition influenced by physical, psychological, and social factors.^5,6 Physical frailty is evidenced by impairments in physical capacity, such as muscle weakness, slow gait speed, and other physiological indicators.⁷ Psychological frailty refers to a heightened mental vulnerability and diminished psychological resilience, encompassing cognitive decline, emotional instability, and fatigue-related symptoms.⁸ Social frailty is characterized by a lack of essential social and general resources, deficits in social behaviors, and reduced self-management skills needed to meet social needs.⁹ Because older adults with frailty can experience significant declines in their physical and mental functions, they are at higher risk for adverse health outcomes—such as falls, reduced mobility, hospitalization, institutionalization, and early mortality—compared to their nonfrail counterparts.¹⁰ Even mild physical ailments or psychological issues can lead to serious consequences,¹¹ such as fatigue, unintentional weight loss, reduced physical activity, and loss of muscle mass.¹² Therefore, because frailty affects both physical and mental well-being, it has both physical and social effects for older adults⁵; for example, in the social dimension, frailty can result in reduced social interactions, limited social contact, and challenges in fulfilling social roles.¹³ Given the increased risk of adverse outcomes for frail older adults, healthcare systems and society need to better understand how to effectively manage frailty.

Frailty increases an older adult's need for daily living assistance.¹⁴ While informal caregivers play a vital role in providing care, it is unrealistic to expect them to take full responsibility.¹⁵ In Europe, approximately 80% of all care across age groups is given by informal caregivers.¹⁶ In specific age brackets, 7% of individuals aged 35–49 and 9% of those aged 50–64 stated that they engage in daily care for older adults.¹⁷ Hence, there is a necessity to alleviate the burden on informal caregivers while fulfilling the care requirements of older adults. Robotic technologies, such as care robots, have emerged as a potential solution to bridge the widening gap between the demand and supply of healthcare services.^18–20 Three main robot types have been assessed for older adults: assistive robots, monitoring robots, and companion robots.²¹ Assistive robots assist with daily activities, such as the Care-O-bot^®3, which is a mobile robot that can safely pass objects to its user.²² Monitoring robots, however, use sensors to track physical health indicators, such as weight, sleeping patterns, blood pressure, and daily movement. One example is Dori, a caregiver-monitoring robot that has an ethical sensing system that detects and recognizes human movements.²³ Companion robots are designed to enhance psychological well-being by providing companionship and social interaction.²⁴ Several studies on one companion robot, PARO, a robot seal, found that it reduced agitation and depression in older adults with dementia.^25,26

Although care robots cannot replace the touch and emotional connections provided by informal human caregivers,²⁷ they can supplement their efforts and contribute to a more comprehensive care approach for older adults with frailty.²⁸ Recent research has investigated the effectiveness of care robots for frail older adults. For example, Ozaki et al.²⁹ and Hashimoto et al.³⁰ conducted studies examining the usefulness of an exercise assist robot called balance exercise assistance robot (BEAR) in training frail older adults and found that BEAR could improve gait speed. However, Pollak et al.'s³¹ randomized controlled trial to examine the effectiveness of companion robotic pets in reducing social and physical frailty found no significant differences between participants with a robotic pet and those in the control group. Overall, although care robots have garnered growing academic and social interest, their effectiveness in addressing the frailty concerns of older adults remains uncertain. While a previous study examined the use of care robots in aged care environments,³² care robots being able to assist frail older adults is yet to be thoroughly explored. To bridge this research gap, this review investigates the effectiveness and usability of care robots in supporting older adults living with frailty.

Material and methods

This systematic review followed the conduct and reporting of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (see Supplementary Material 1) and was registered by the first and second authors in the International Prospective Register of Systematic Reviews database under the identification number [CRD42024544233].

Search strategy

We performed a systematic search of seven electronic databases: Embase, MEDLINE, ProQuest, PubMed, Web of Science, CINAHL, and Scopus. The specific search queries: (“care robot” OR robot* OR “social robot” OR “service robot” “robot* technol*” OR “assist* robot*” OR “monitor* robot*” OR “companion robot*”); were combined with terms to determine the outcome and population of interest: (frailty OR frail*) AND (“older adults” OR “older people” OR elder* OR senior* OR silver OR geriatric). The asterisk (*) was used as a truncation symbol to substitute any potential part of a word during the searches. Detailed search strategies are shown in Supplementary Material 2.

Inclusion and exclusion criteria

The inclusion criteria were as follows: empirical studies with any design and research method that reported on care robots or other robotic technologies and frail older adults; included participants aged 60 years and above who have experienced or used care robots; published between 1 January 2000 and 10 April 2025; peer-reviewed journal articles written in English. Eligible studies targeted an older population identified as frail using any validated frailty measurement instrument, including but not limited to the Fried Frailty Phenotype, Clinical Frailty Scale, Tilburg Frailty Indicator, Edmonton Frailty Scale, Groningen Frailty Indicator, Hospital Frailty Risk Score, FRAIL scale, and electronic Frailty Index. Studies that lacked a clear diagnostic criterion for frailty but reported the population as frail—such as older adults with physical or cognitive impairments—were also included if the overall context aligned with the aims of our review.

We excluded non-English and nonempirical studies, commentaries, letters to the editor, case series, animal studies, conference papers that featured only abstracts, and articles sourced from books. Grey literature was excluded from this review to ensure methodological consistency and maintain the credibility and quality of the included studies. Grey literature may rely on secondary sources that may be inaccurately represented or cited,³³ which can limit our ability to adequately assess methodological rigor and extract comparable data.

Data extraction and analysis

The first and second authors initially screened a subset of studies together to ensure consistent application of the inclusion and exclusion criteria. After reaching consensus and establishing a standardized screening approach, they independently reviewed the remaining studies. For each included study, the following information was extracted: author(s), publication year, study design, research setting, sample characteristics, study aim, outcomes assessed, and type of care robot used. The reference management software EndNote was used in this review to import, organize, and deduplicate the references retrieved from the database searches.

A narrative synthesis approach was employed to analyze the extracted data. Key characteristics of each study—including study design, location, sample characteristics, types of care robots used, frailty diagnostic criteria, assessed outcomes, and main findings—were systematically summarized. Based on the objectives of this review, the following thematic categories were established: (1) the effectiveness of care robots in addressing physical, cognitive, or social frailty among older adults, and (2) usability, as reported through validated usability scales and observational data. Patterns within the extracted data were further organized into subcategories. The first two authors independently identified these subcategories and verified them through collaborative discussions with the broader research team. Given the substantial heterogeneity in data sources, study designs, and measurement tools, a meta-analysis of the extracted data was not conducted.

Quality appraisal

The evidence quality of the included studies was assessed using the mixed-methods appraisal tool (MMAT).³⁴ The tool is designed to appraise the quality of empirical studies across qualitative research, randomized controlled trials, nonrandomized studies, quantitative descriptive studies, and mixed-methods studies.³⁵ Research has demonstrated that the MMAT offers a reliable and efficient approach for assessing diverse study types, particularly in reviews dealing with complex interventions and heterogeneous methodologies.^36,37 Beyond its initial development and validation, the MMAT has been increasingly applied in recent systematic reviews in health and social sciences to ensure a standardized and consistent evaluation of study quality.^38–40 All MMAT questions can be answered with “yes,” “no,” or “don’t know.” As calculating an overall score from the ratings for each criterion is discouraged by the MMAT developer, we provided more detail of the ratings for each criterion to assess the quality of the included studies. Similar to the process of screening studies, the first two authors performed independent quality appraisals, with divergence being resolved through discussion and in consultation with the third reviewer if needed.

Results

Study identification

A total of 649 articles (115 articles from Embase, 108 articles from MEDLINE, 102 articles from ProQuest, 102 articles from PubMed, 146 articles from Web of Science, 27 articles from CINAHL, and 49 articles from Scopus) were initially identified. After removing 343 duplicate records, 306 articles were extracted for independent title and abstract screening by two researchers. Eligibility uncertainty was addressed through consensus meetings with the third author. Thirty articles were screened for a full-text evaluation, and the 276 articles that did not meet the eligibility criteria were excluded. Finally, ten articles were included for the analysis and synthesis. Figure 1 shows the study identification and selection process.

Figure 1.

Flow diagram of the selection process.

Study characteristics

Table 1 gives a summary of the included studies. Of these, eight were quantitative; five involved randomized control trials,^{29,31,41–43} three involved one-group, pretest–posttest studies.^30,44,45 One study used a mixed-methods approach that employed both qualitative and quantitative analyses.⁴⁶ Among the nine included studies, two studies were conducted in Japan,^29,30 two in the Netherlands,^42,46 and two in Italy.^44,45 One study was conducted in Germany,⁴¹ one in the United States,³¹ and one in Spain.⁴³ The majority of participants in the included studies (n = 6) resided in residential or nursing facilities^{30,31,41–43,46}; however, in one study, the participants were living at home or in the community.^29,45 One study recruited two distinct participant groups: those living at home and those residing in residential homes.⁴⁴

Table 1.

Summary of included studies.

Author, year	Study design	Location	Sample characteristics	Diagnostic criteria of frailty	Aim	Key outcome	Key results	Type of care robot
Hashimoto et al., 2022³⁰	Quantitative, one-group, pretest–posttest study	Japan	N = 15, frail older adults aged 65+ who had been hospitalized for worsened cardiovascular disease (CVD)	Fried Frailty Index derived from Cardiovascular Health Study	To examine whether adding robot-supported balance exercises (BEAR) to cardiac rehabilitation improves the ability to balance in older adults with cardiovascular disease	Balance ability (i.e., gait speed, limb function, the strength of knee extension, postural stability, peak oxygen uptake), depression (Geriatric Depression Scale)	Balance Exercise Assist Robot (BEAR) improved balance (gait speed, limb function Timed Up and Go time, knee extension), but not depression in frail older adults with CVD	Assistive robot
Werner et al., 2018⁴¹	Quantitative, randomized controlled trial	Germany	N = 42 (with cognitive impariment:20; without cognitive impariment:22), frail older adults aged 65+ (M = 82.5 ± 8.7) living in a geriatric hospital and nursing homes	With moderate motor impairment & with mild-to-moderate cognitive impairment	To determine whether robotic rollators (RRs) improve navigation within a real-life environment in the intended user group of frail older adults with and without cognitive impairment currently using a rollator in daily life	Navigation performance (i.e., success rate, completion and stopping time, number of stops, walking distance, and gait speed when completing the navigating path), depression (Geriatric Depression Scale)	RRs were effective in improving navigation performance within a real-life environment but showed no significant impact on motor performance or depression.	Assistive robot
Pérez-Rodríguez et al., 202043	Quantitative, randomized controlled trial	Spain	N = 42 (intervention:22; control:20), frail older patients aged 75+ living in Getafe University Hospital	Have difficulties in mobility	To evaluate the usability, acceptance and user experience of the FriWalk (robotic walker) from the patients and clinical professionals’ perspectives	Usability (System Usability Scale)	The usability of the FriWalk is not yet above average, it is still usable with the help of caregivers	Assistive robot
Ozaki et al., 201729	Quantitative, randomized controlled trial	Japan	N = 10, community-dwelling frail older adults aged 65+	Fried Frailty Index derived from Cardiovascular Health Study	To examine the effect of postural strategy training using a balance exercise assist robot (BEAR) as compared with conventional balance training for frail older adults	Balance ability (e.g., gait stability, postural stability, muscle strength, grip strength)	Postural strategy training with the BEAR is effective in improving balance ability and muscle strength in frail older adults	Assistive robot
Pollak et al., 202231	Quantitative, randomized controlled trial	USA	N = 219 (intervention:107; control: 113), older adults aged 65+ living in a community hospital	Suffered from social frailty (Questionnaire to Define Social Frailty Status, QDSFS) or physical frailty (FRAIL questionnaire)	To investigate the effect of a robotic pet on social and physical frailty	Physical frailty (FRAIL questionnaire),social frailty (Questionnaire to Define Social Frailty Status, QDSFS),and depression (Geriatric Depression Scale)	No difference in physical, social frailty and depression in participants with a robotic pet compared to the control group	Companion robot
Schoone et al., 201142	Quantitative, randomized controlled trial	Netherlands	N = 24 (intervention:10; control:14), frail older adults aged 65+ who arrived at the nursing and rehabilitation center after a stay of approximately one week at the hospital	Impaired motor function of upper limb	To assess effects and costs of ACRE (ACtive REhabilitation) robotic device training for frail elderly patients	Motor abilities (The Action Research Arm Test and the Fugl-Meyer assessment for the arm), and depression (Geriatric Depression Scale)	No significant effects of the ACRE training on the studied outcomes were found	Assistive robot
Fiorini et al., 2022a44	Quantitative, one-group, pretest–posttest study	Italy	N = 23 frail older adults aged 65 (living at home: 6; living at residential home: 10)	Supported by domiciliary assistance, who were at risk of isolation because of COVID-19 restrictions	To measure the usability and acceptability of a telepresence robot	Usability (Ad-Hoc Questionnaire for Acceptability and Usability)	After using the robot, older adults living at home perceived a higher level of enjoyment, while those living at the residential home perceived a reduced level of enjoyment.	Multifunctional (Monitor & companion) robot
Fiorini et al., 2022b45	Quantitative, one-group, pretest–posttest study	Italy	N = 20, frail older adults aged 60+, living at home	Be already beneficiary of home assistant care	To investigate the impact of the field trials on the user experience and the acceptance/usability of a telepresence robot	Usability (Ad-Hoc Questionnaire for Acceptability and Usability)	Participants perceived the Double robot as usable and enjoyable after using the robot	Multifunctional (Monitor & companion) robot
Olde Keizer et al., 201947	Mixed method, cross-sectional study & interview	Netherlands	N = 20, frail older adults aged 70+ (M = 78.5 ± 7.1) living in an organization for elderly care	With cognitive or physical impairments	To uncover the usability and user experience issues that the socially assistive robot (the NAO humanoid robot) design needs to overcome to be an effective and well-accepted means among older adults for identifying, monitoring, and addressing frailty	Usability (System Usability Scale)	Participants perceived NAO as useful and enjoyable for health monitoring and training, but no significant difference in perceived usefulness and enjoyment was found between NAO and a PC tablet (as reference technology)	Multifunctional (assistive, monitor & companion) robot

In terms of the screening of frailty among participants, two studies employed clinical assessments using the Fried Frailty Index, derived from the Cardiovascular Health Study, to identify physical frailty.^29,30 One study utilized the Questionnaire to Define Social Frailty Status (QDSFS) to assess social frailty, alongside the FRAIL questionnaire to evaluate physical frailty.³¹ The remaining six studies reported participants as frail; however, they did not employ a standardized clinical tool to assess or define frailty of the participants.^41–45,46 Regarding the types of robots used in the included studies, five studies examined assistive robots designed to support older adults in performing daily activities.^{29,30,41–43} Additionally, three studies focused on multifunctional robots that combined at least two of the following functions: assistance with daily tasks, health monitoring, and companionship.^44,45,46

The quality appraisal results are shown in Table 2. Of the nine included studies, three met all the MMAT methodological quality criteria^29,30,46; two nonrandomized studies did not fully meet the assessment criteria due to uncertainties regarding the representativeness of the target population and the identification of potential confounding factors.^44,45 Additionally, four randomized controlled trials failed to meet all criteria because of uncertainties related to blinded outcome assessments.^31,41–43 Although not all included studies demonstrated high methodological quality, they were retained because they satisfied all predefined inclusion criteria and provided valuable information relevant to the aims of our review, particularly concerning the use of care robots among frail older adults.

Table 2.

Quality assessment of included studies.

Study design	Study	Screening questions		Methodological quality criteria
Quantitative non-randomized		Are there clear research questions?	Do the collected data address the research questions?	Are the participants representative of the target population?	Are measurements appropriate regarding both the outcome and intervention (or exposure)?	Are there complete outcome data?	Are the confounders accounted for in the design and analysis?	During the study period, was the intervention administered (or exposure occurred) as intended?
	Hashimoto et al.³⁰	Y	Y	Y	Y	Y	Y	Y
	Fiorini et al.⁴⁴	Y	Y	C	Y	Y	C	Y
	Fiorini et al.⁴⁵	Y	Y	C	Y	Y	C	Y
Quantitative randomized controlled trials				Is randomization appropriately performed?	Are the groups comparable at baseline?	Are there complete outcome data?	Are outcome assessors blinded to the intervention provided?	Did the participants adhere to the assigned intervention?
	Werner et al.⁴¹	Y	Y	Y	Y	Y	C	Y
	Pollak et al.³¹	Y	Y	Y	Y	Y	C	Y
	Schoone et al.⁴²	Y	Y	Y	Y	Y	C	Y
	Ozaki et al.²⁹	Y	Y	Y	Y	Y	Y	Y
	Pérez-Rodríguez et al.⁴³	Y	Y	Y	Y	Y	C	Y
Mixed methods				Is there an adequate rationale for using a mixed methods design to address the research question?	Are the different components of the study effectively integrated to answer the research question?	Are the outputs of the integration of qualitative and quantitative components adequately interpreted?	Are divergences and inconsistencies between quantitative and qualitative results adequately addressed?	Do the different components of the study adhere to the quality criteria of each tradition of the methods involved?
Mixed methods	Olde Keizer et al.⁴⁶	Y	Y	Y	Y	Y	Y	Y

Y: yes; N: no; C: can’t tell.

Effectiveness of care robots in supporting older adults living with frailty

Impact of care robots on physical frailty

Among the nine included studies, five studies discussed the impact of care robots on physical frailty among older adults using randomized controlled trials or quasi-trials.^{29–31,41,42} Of these, three studies indicated that care robots were effective in managing physical frailty.^29,30,41 In detail, compared to the control groups that received either no robotic training or conventional training, two studies by Hashimoto et al.³⁰ and Ozaki et al.²⁹ reported significant improvements in balance ability in the intervention groups that received training with the BEAR.

In the study of Hashimoto et al.,³⁰ the intervention group showed notable improvements in balance ability measures, including gait speed (increase of 0.17 m/s), timed up-and-go performance (reduction of 1.66 s), and knee extension strength (increase of 11.7%). All changes had p-values ≤ 0.001, indicating strong statistical significance. Similarly, Ozaki et al.²⁹ reported that a 3.2-s improvement in tandem gait speed (p = 0.012), a 2.8-cm increase in the Functional Reach Test (p = 0.002), a 0.7-s reduction in the Timed Up-and-Go Test (p = 0.023), and an increase in lower extremity muscle strength by 18–62 newtons (p = 0.001–0.030). While these outcomes were statistically significant, the magnitude of improvement in some measures—such as the Timed Up-and-Go Test—may fall below thresholds typically considered clinically meaningful.⁴⁷ Additionally, Werner et al.⁴¹ reported improved navigation performance in frail older adults using a robotic rollator (MOBOT). Compared to the unassisted condition, participants in the MOBOT-assisted navigation group demonstrated a significant reduction in stopping time (p = 0.016, reduced by 22.7 s) and walking distance (p = 0.014, reduced by 13.9 m).

However, two studies have not found a significant impact of care robots on physical frailty. Specifically, Schoone et al.⁴² reported a nonsignificant improvement in motor function among frail participants, with a 4-s reduction in the Action Research Arm Test following training with the ACRE robotic device. Moreover, Pollak et al.³¹ conducted a 30-day randomized controlled trial to examine the effect of a companion robot on physical frailty, as measured by the FRAIL questionnaire—which assesses fatigue, resistance, aerobic capacity, illnesses, and weight loss.⁴⁸ indicated no significant differences in physical frailty between older adults who interacted with a robotic pet and those who received standard post-discharge care.

Impact of care robots on psychological and social frailty

Although there is currently no universally accepted definition of psychological frailty, depression is commonly used as a proxy or operational indicator of this construct.^49–51 Among the nine studies included in this review, four assessed psychological frailty using the Geriatric Depression Scale, a widely validated instrument for measuring depressive symptoms in older adults. In all four studies, the use of care robots did not result in statistically significant improvements in depression scores among frail older participants.^30,31,41,42

Among the nine included studies, only the study conducted by Pollak et al.³¹ explicitly addressed the social dimension of frailty. This study employed the QDSFS, a validated tool that evaluates the degree of social isolation in older adults by assessing key aspects such as daily social activity, perceived social roles, and the quality of social relationships.⁵² Despite the intervention involving a robotic pet designed to provide companionship, the study found no statistically significant improvement in social frailty scores among participants in the intervention group compared to those in the control group.

Usability of care robots in supporting older adults living with frailty

Across the four studies that evaluated the usability of care robots, two employed the System Usability Scale (SUS),^43,46 while the other two used an Ad-Hoc Questionnaire for Acceptability and Usability.^44,45 The SUS consists of 10 items, each rated on a 5-point Likert scale ranging from “Strongly Disagree” to “Strongly Agree.” The total score of the scale ranged from 0 to 100, with a higher score indicating better usability⁵³; a score of 68 is generally considered the benchmark for average usability.⁵⁴ In the study of Pérez-Rodríguez et al.,⁴³ frail participants reported an average SUS score of 52.86—below the average threshold—after using a robotic walker. Participants also noted several technical issues that impacted usability, such as forgetting to charge the device or the system entering hibernation mode unexpectedly. Similarly, the study by Olde Keizer et al.⁴⁶ assessed the usability of NAO, a multifunctional humanoid robot, and reported a SUS score of 60.50, also below the average benchmark. Although participants viewed NAO as usable for tasks such as health monitoring and training, the study found no significant difference in usability compared to a standard PC tablet used as a reference technology.

The remaining two studies by Fiorini et al. assessed the usability of care robots using an Ad-Hoc Questionnaire for Acceptability and Usability, which evaluates four key dimensions: anxiety, enjoyment, trust, and perceived ease of use.⁵⁵ In one study,⁴⁵ which focused on frail older adults living at home, significant improvements were reported in the subdomains of enjoyment and perceived ease of use after using a telepresence robot (Double Robot), supporting the robot's usability in home settings. Another study by Fiorini et al.⁴⁴ assessed changes in enjoyment and anxiety before and after using the same robot. The findings revealed that older adults living at home experienced increased enjoyment and reduced anxiety, whereas those in residential care homes reported decreased enjoyment and increased anxiety following interactions with the robot.

Discussion

This review explored the role of care robots in supporting older adults living with frailty, focusing on both their effectiveness and usability. A total of nine studies met the inclusion criteria and were synthesized in this comprehensive analysis. Of these, five studies evaluated the effectiveness of care robots in addressing various dimensions of frailty—including physical, psychological, and social aspects—while the remaining four studies examined the perceived usability of these technologies from the perspective of frail older users.

Regarding the impact of care robots on physical frailty, the review identified three studies reporting significant improvements in physical frailty outcomes among older adults using care robots, particularly in balance ability and navigation performance.^29,30,41 These findings support the potential role of care robots like the BEAR in enhancing physical function in frail older adults. Despite these promising results, the review also identified two studies that reported no significant effects of robotic interventions on physical frailty outcomes.^31,42 This inconsistency highlights an important complexity in evaluating the effectiveness of care robots—namely, the heterogeneity of the older adult populations involved in these studies. In particular, differences in baseline frailty status may play a critical role in determining intervention efficacy. Previous research has demonstrated that care robots can effectively enhance physical functions among nonfrail older adults,^56,57 suggesting that the degree of frailty might mediate the impact of care robot interventions. Frail older adults often experience more pronounced physiological impairments,⁵⁸ which may limit their capacity to engage fully with robotic systems or derive comparable benefits. Thus, the variability in study outcomes could stem from differences in participants’ frailty levels. For example, the study by Pollak et al.³¹ recruited hospitalized older adults, a population likely to be more severely frail and experiencing acute or complex medical issues. In such contexts, the effectiveness of robotic interventions may be constrained by the participants’ limited mobility or cognitive function. In contrast, the study by Ozaki et al.²⁹ focused on community-dwelling frail older adults, who, while still experiencing frailty, may possess greater functional reserves and are more likely to benefit from structured robotic exercise programs.

For the dimensions of psychological and social frailty, this review found no significant impact of care robots. These findings highlight important limitations in the current application of care robots in addressing the emotional and relational aspects of frailty. Unlike physical frailty, which often involves tangible and measurable impairments such as balance or gait speed, psychological and social frailty encompasses more complex, subjective experiences such as loneliness, mood disturbances, and loss of perceived social roles.⁴⁹ Robots designed primarily for functional assistance may not adequately address these nuanced emotional needs.⁵⁹ For example, companion robots such as robotic pets or humanoids may simulate social presence⁶⁰ but might lack the genuine relational reciprocity necessary to reduce psychological or social frailty meaningfully. Although previous studies have demonstrated the effectiveness of companion pet robots in alleviating depression and other mood-related symptoms, these studies primarily focused on older adults with dementia and targeted dementia-related psychiatric symptoms, such as agitation.^61,62 Therefore, their findings may not be generalizable to frail older adults without other impairment. Moreover, it is possible that the duration and intensity of the care robot interventions in the included studies were insufficient to yield psychological or social benefits. Prior research has shown that longer-term use—spanning several weeks to months—can enhance the effectiveness of robotic interventions, with increased exposure often correlating with more positive outcomes.^63,64 This suggests that extended interaction may be necessary for care robots to foster meaningful emotional or social change. Emotional and social well-being often requires sustained engagement, personalized interaction, and trust-building—elements that may be underdeveloped in current robotic platforms. To improve the potential impact of care robots in these domains, future research should explore the development of more socially responsive and emotionally intelligent robotic systems. Additionally, studies should consider longer intervention periods, include more diverse frail populations, and evaluate outcomes using sensitive, multidimensional measures of psychological and social frailty.

In terms of usability, two studies reported that care robots were rated below the average usability benchmark following utilization by frail older adults.^43,46 One plausible explanation for this finding is that frail older individuals often require more personalized and comprehensive support when engaging with assistive technologies. This support may include individually tailored prescriptions, hands-on training sessions, and consistent follow-up care. These additional requirements highlight the unique challenges that frail older adults may encounter when interacting with robotic systems.⁶⁵ Moreover, our review indicated that technical issues associated with the operation of care robots can further hinder their usability for this population. Difficulties such as system errors, interface complexity, or limited responsiveness may exacerbate feelings of frustration or reduce user confidence, particularly among individuals with limited technological familiarity.⁴⁶ In general, frail older adults found their experiences with care robots to be novel and relatively unfamiliar.¹⁸ While the frail older adult acknowledged the usability and potential benefits of care robots, the robots’ care and assistance fell short of their expectations. Notably, these shortcomings were frequently attributed to technical limitations encountered during use.⁴³

Our findings further underscore that the usability of care robots among frail older adults varies significantly depending on the specific contexts in which these technologies are deployed. Notably, the environment—whether a private home or a residential care facility—appears to influence how older adults perceive and interact with care robots. For example, Vandemeulebroucke et al.⁶⁶ found that general older adults had positive views regarding care robots, but Fiorini et al.^44,45 identified a more nuanced dynamic: frail older adults living at home tended to rate the usability of care robots more favorably than their counterparts in residential care settings. This discrepancy suggests that the domestic setting may foster a more receptive attitude toward robotic assistance, possibly due to greater autonomy, familiarity with the environment, or a stronger desire to maintain independence. Conversely, older adults residing in care facilities may exhibit more resistance to adopting robotic technologies. One explanation is that they are often in more socially isolated conditions and may have reduced motivation or openness to engage with new technologies.⁴⁴ Furthermore, these individuals typically receive continuous service-oriented care from human caregivers. As a result, they may perceive robotic care as a less personal or inferior alternative, thereby diminishing its perceived value.⁶⁷ Given that older adults in residential facilities reported higher levels of anxiety and lower enjoyment following interactions with care robots,⁴⁴ the successful integration of care robots in residential facilities may require targeted interventions. These could include personalized onboarding processes, staff-mediated introductions to the technology, and ongoing support aimed at building trust and comfort.

Limitations and future directions

This review focused on frail older adults aged 60 years and above—an often-underrepresented group in technology-focused research. Given the significant physical, psychological, and social challenges associated with frailty, exploring the role of care robots in supporting this population offers valuable insights. While the review yielded novel findings regarding the effectiveness and usability of care robots, several limitations should be acknowledged.

First, generalizing the results across studies was challenging due to variability in study quality, sample characteristics, research designs, and types of care robots used. Second, the relatively small number of included articles retrieved from the seven databases may limit the generalizability and comprehensiveness of our findings. Although we carefully selected widely used and reputable databases to ensure the quality and relevance of the literature, the restricted number of sources may have led to the omission of relevant studies published elsewhere. Future research should consider broadening the range of databases searched and adopting a more expansive search strategy to capture a wider and more representative body of evidence.

Third, although the review covered literature from 2000 to 2023, the earliest eligible studies were published in 2011, suggesting that research in this area is still emerging. While the number of studies in this area remains limited, there is evidence of a growing body of research that future systematic reviews may be able to capture more comprehensively. Notably, none of the included studies employed longitudinal designs, which restricts our ability to assess the long-term impact of care robots on physical, psychological, and social dimensions of frailty. Given the chronic and progressive nature of frailty, longitudinal studies are essential for understanding the real-world effectiveness and potential unintended consequences of robotic interventions over time. Additionally, this review focused exclusively on older adults already classified as frail, thereby excluding studies on the use of care robots for frailty prevention. Given the promise of preventive approaches, particularly when integrated with assistive technologies. Therefore, future studies should prioritize longitudinal methodologies and consider the preventive potential of care robots to better understand their role in supporting aging populations across the frailty spectrum.

Moreover, we limited the search to English-language publications due to resource constraints, including the lack of capacity for translation and concerns about maintaining consistency and quality during data extraction and analysis across multiple languages. We acknowledge that this approach may have resulted in the exclusion of relevant studies published in languages other than English; therefore, future reviews should consider conducting multilingual searches to capture a more inclusive range of studies. Furthermore, while we took careful steps during screening and data extraction to identify studies addressing frail populations, the specificity of our search strategy—primarily focused on older adults with frailty assessed by validated measurement instruments—may have limited the comprehensiveness of our review. Studies involving older adults with conditions associated with frailty, but without an explicit diagnosis of frailty, may have been inadvertently excluded. Future reviews may consider employing a broader set of search terms related to frailty-associated conditions to enhance the comprehensiveness of the evidence base.

Implications

Although care robots can provide valuable support for older adults and address aging challenges,⁶⁸ their abilities to assist in managing frailty should be considered when implementing care robot interventions. The review findings have significant implications for future applied gerontology research. Specifically, while previous studies found that care robots could enhance balance and navigation for frail older adults,^29,30,41 some did not observe any significant support effects.^42,69 These inconsistent findings highlight the importance of identifying, compiling, and prioritizing the functional needs of frail older populations while designing care robots. Because frail older adults may encounter more daily activity challenges than their healthier counterparts,⁷⁰ prioritizing user-centered designs (UCD) is crucial while developing care robots. Such designs need to accurately define user requirements, create realistic use scenarios, and ensure alignment with user needs, perceptions, emotions, and rights. Particularly, the user criteria should be focused on basic and instrumental daily living activities, cognitive, and social support and motorization, in addition to privacy, safety, and adaptability.⁷¹

Moreover, frail older adults in aged care facilities find care robots less usable than home-based older adults.^44,45 This disparity reveals the diverse experiences of care robot users and highlights the need to involve health care professionals and caregivers while deciding to deploy care robots in aged care facilities. Because caregiving is multifaceted, the various human perception skills required are challenging for robotic systems.72 Therefore, to ensure the effective use of care robots and enhance the quality of care provided to frail older adults, health professionals and caregivers must utilize their caregiving expertise and skills to aid this vulnerable population in adapting to this technology.

Conclusion

While care robots are often promoted as a solution to address the growing imbalance between the demand for and supply of healthcare services, their effectiveness and usability in supporting older adults living with frailty remain uncertain. Our review included nine studies, and the findings indicated that care robots show potential in managing physical frailty but were largely ineffective in addressing psychological and social aspects of frailty. Moreover, the perceived usability of care robots among frail older adults was generally low, often due to technical limitations. Usability also varied depending on the specific contexts in which the robots were deployed. These findings highlight the critical need to consider the perspectives, unique needs, and living environments of frail older adults when designing and implementing care robot interventions. A UCD approach is essential to ensure that such technologies are both accessible and acceptable to this vulnerable population.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076251370058 - Supplemental material for Effectiveness and usability of care robots in supporting older adults living with frailty: A systematic review

Supplemental material, sj-docx-1-dhj-10.1177_20552076251370058 for Effectiveness and usability of care robots in supporting older adults living with frailty: A systematic review by Run-Ping Che, Yong-Xin Ruan, Naonori Kodate, Yiwen Shi, Xiaoting Liu, Sarah Donnelly, Sayuri Suwa, Wenwei Yu, Dexia Kong and Mei-Chun Cheung in DIGITAL HEALTH

Supplemental Material

sj-docx-2-dhj-10.1177_20552076251370058 - Supplemental material for Effectiveness and usability of care robots in supporting older adults living with frailty: A systematic review

Supplemental material, sj-docx-2-dhj-10.1177_20552076251370058 for Effectiveness and usability of care robots in supporting older adults living with frailty: A systematic review by Run-Ping Che, Yong-Xin Ruan, Naonori Kodate, Yiwen Shi, Xiaoting Liu, Sarah Donnelly, Sayuri Suwa, Wenwei Yu, Dexia Kong and Mei-Chun Cheung in DIGITAL HEALTH

Footnotes

Acknowledgements

The authors would like to acknowledge the editing assistance from the company Enago ().

ORCID iDs

Run-Ping Che

Yong-Xin Ruan

Naonori Kodate

Yiwen Shi

Xiaoting Liu

Sayuri Suwa

Wenwei Yu

Mei-Chun Cheung

Contributorship

R-PC contributed to conceptualization, methodology, software, data curation, and writing—original draft preparation; Y-XR contributed to methodology, software; and data curation; NK, YS, XL, and SD contributed to writing—review & editing and supervision; SS contributed to writing—review & editing; WY contributed to writing—review & editing; DK contributed to writing—review & editing; M-CC contributed to conceptualization, writing—review & editing, funding acquisition, and supervision.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This work was supported by the International Research Collaboration Fund granted by the Department of Social Work, The Chinese University of Hong Kong (Grant number: 19231108), and National Natural Science Foundation Project, China (Grant number: 72474194).

Supplemental material

Supplemental material for this article is available online.

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