How Are Homebound Older Adults Identified? Definitions,Approaches,and Challenges

Abstract

Homebound status increasingly emerges as a critical social and health concern among older adults. Recognized as a “hard-to-reach” group, there is a need to better understand how to define and identify homebound older adults. Using the Arksey and O’Malley framework (2005), we searched eight databases with a librarian-guided strategy. Two authors independently screened titles, abstracts, and full texts, and extracted data based on set criteria. We included 19 studies that define homebound status through factors such as the duration and frequency of confinement; consequences of physical, social, or cognitive impairments; and those needing assistance. Researchers identify homebound individuals through health records, outpatient clinics, reports from family, neighbors, or community agencies like Meals on Wheels. Challenges include over-reliance on convenience and snowball sampling, as well as time constraints that limit the ability of researchers and clinicians to systematically identify those missing clinical appointments. A framework is needed to guide a formal identification.

Keywords

homebound older adults identification

What This Paper Adds

• Provides strategies used to identify homebound older adults globally

• Highlights the practical challenges in identifying homebound individuals, emphasizing the need for a standardized framework.

Applications of Study Findings

• Findings can inform clinicians, researchers, and policymakers in developing a framework to consistently identify homebound older adults.

• Supports improved targeting of services and interventions (e.g., home care, community programs, and outreach) for this “hard-to-reach” population.

Introduction

The World Health Organisation forecasts a global demographic shift in which more than 20% of the world’s population will surpass the age of 65 by 2050 (Rudnicka et al., 2020). Concurrently with this projected increase in the proportion of older adults, there will be increased age-related conditions, such as reduced muscle mass leading to mobility limitations, or social factors, such as social isolation leading to a higher risk of prolonged confinement indoors (Schirghuber & Schrems, 2021a). Evidence from a systematic review indicates that factors such as climate change and mobility limitations among older adults worldwide contribute to prolonged periods of home confinement, a term coined as homebound (Engberg et al., 2001).

Homebound status has been defined differently across the literature. Collectively, the definitions of homebound status have been broadly grouped into two conceptual categories: (1) confinement, which refers to limited ability to leave the home and/or insufficient resources or assistance to do so (Engberg et al., 2001; Ko & Noh, 2021); and (2) infrequent going outside the house, which reflects reduced out-of-home activity shaped by physical, social, psychological, and environmental factors (Umegaki et al., 2015). For example, Lee et al. (2022) defined homebound status as individuals who consistently stay home for a certain period (such as a week or month) without leaving home for specific purposes such as shopping, leisure walking, medical appointments, community engagements, or attending work and social activities. Furthermore, other studies have defined homebound status as situations where individuals have limited out-of-home visits, such as once a month, week, or cannot leave their home independently (Soones et al., 2017; Xiang et al., 2020). Other studies have described homebound status as individuals facing mobility limitations and chronic diseases that make it difficult to leave their home (Schirghuber & Schrems, 2021b; Zhao et al., 2019).

Discrepancies and inconsistencies in the definition of homebound status have led to varied prevalence rates globally. In countries such as Japan, the United States, Israel, Spain, France, the United Kingdom, and China reported prevalence estimates ranged from 3.5% to 39.8% (Lee et al., 2022). Ko and Noh (2021), in a review of 47 studies, reported a prevalence of 5.6% when evaluating the frequency of leaving the home and a rate of 19.6% when considering functional disability as a reason for staying at home. Similar trajectories of prevalence have been documented in Canada, specifically within the long-term care system, with a prevalence of 30% in the span of 4–12 months period (Lapointe-Shaw et al., 2022). Therefore, one of the aims of our review is to reconceptualize the definition of homebound status and provide additional guidance or a framework to develop a more comprehensive understanding of this population.

Reviews on homebound status among older adults exist, most focusing on multidimensional factors affecting homebound older adults (Lee et al., 2022), social issues, such as loneliness (Ezeokonkwo et al., 2021), and the use of technological interventions to reduce social isolation among older adults who are homebound (Tan et al., 2024). Other reviews have focused on outcomes of home-based primary care programs (Sabetfakhri, 2023) and access to non-primary healthcare services (Ezeokonkwo et al., 2021; Tan et al., 2024) in the homebound population. However, despite this rich body of literature, none have specifically addressed the challenges of identifying homebound individuals or the methods used to do so. This gap is significant because the inability to systematically identify homebound older adults hampers efforts to address their needs, making it a critical social issue with far-reaching health and social consequences. Without a clear understanding of how to properly identify this population, efforts to mitigate their risks and improve their quality of life remain limited.

We conducted a preliminary search of OVID MEDLINE® and the Open Science Framework, a free, open-source platform for the preregistration of studies, including scoping reviews. We found no evidence of existing systematic or in-progress scoping reviews that address how to identify homebound older adults or the challenges associated with their identification. We chose a scoping review over other reviews, such as a systematic review, to identify the types of available evidence and the key features of strategies employed globally for identifying/recruiting homebound older adults. Our review aims to reconceptualize how homebound status is defined within the aging population and to describe the methods used to identify homebound older adults, including the challenges associated with this process.

Methods

This scoping review was guided by the methodological framework proposed by Arksey & O’Malley (2005) and was reported following the Preferred Reporting Items for Systematic Review and Meta-Analyses extension for scoping reviews (PRISMA-Scr) (Tricco et al., 2018). The five stages included: (1) identifying the research question, (2) identifying the relevant studies, (3) selecting studies, (4) charting the data, and (5) summarizing and reporting the findings (Arksey & O’Malley, 2005). The study protocol was preregistered at Open Science Framework (Kumapayi et al., 2024).

Stage 1—Identifying the Research Question

This review is guided by three research questions:

(a) How has homebound status been defined in the literature concerning the aging population?

(b) What methods are used to identify homebound older adults?

Stage 2—Identifying Relevant Studies and Study Selection

The search strategy was developed in consultation with a social science librarian. We structured our search strategy using Medical Subject Headings (MeSH) and keywords, following the framework of Population (older adults), Concept (homebound), and Context (identification) (Peters et al., 2020). To ensure a comprehensive search, we employed two complementary approaches: a conceptual approach and an objective approach (Hausner et al., 2016). The conceptual approach involved using relevant concepts, including keywords, synonyms, and MeSH terms when available, to maximize sensitivity (Hausner et al., 2016). The objective approach involves using seed articles—studies that should appear in the initial search results if their keywords are included in the search strategy (Wang et al., 2022). This method is often employed to test and ensure the sensitivity of the search strategy (Hausner et al., 2016). For the conceptual approach, we systematically reviewed prior literature, including seminal systematic reviews by Lee et al. (2022) and Ko & Noh (2021), and adapted their terminology related to older adults and homebound populations. Since the term “identification” was not explicitly included in our initial search strategy, we employed objective approach by identifying seed articles such as Mickler et al. (2021) and Cheng et al. (2022), extracting and integrating their key keywords, particularly those related to identification, such as “recruitment,” to enhance the sensitivity and comprehensiveness of our search strategy. This approach enabled us to conduct a preliminary search, during which both Mickler et al. (2021) and Cheng et al. (2022) were successfully retrieved in our initial results, thereby confirming the sensitivity and effectiveness of our search strategy. Detailed search strategies are presented in Table 1. Keywords and terms were combined using OR within each concept, and the resulting concepts were then combined with AND to generate the final search strategy, which was exported to Covidence for screening. These strategies were applied across 11 databases, including OVID MEDLINE ®, APA PsycINFO, EBM Review (Cochrane Central Register of Controlled Trials, April 2024), EBM Reviews (Cochrane Database of Systematic Reviews), CINAHL, AGELINE, Sociological Abstracts, Social Service Abstracts, and Web of Science. No restrictions were placed on language or publication year to maximize comprehensiveness.

Table 1.

Search Strategy

Population (Older adults)	(Aged OR Ag*ing (MESH) OR elder OR senior OR retiree OR older OR geriatric OR mature OR gerontology)
Concepts (homebound)	(Homebound persons (MESH) OR home-bound OR housebound OR confinement OR mobility OR residence OR immobile)
Context (identification)	(Process OR identify OR identification OR pick OR pinpoint OR find OR locate OR recognize OR spot OR discover)

We combined each concept with “AND”.

Stage 3 - Selecting Studies

All citations were exported into Covidence©, a platform designed to streamline systematic review processes by enhancing efficiency and maintaining transparency. Covidence facilitates the removal of duplicate citations, ensuring a clean dataset for study selection. The study selection process was conducted in two stages: initial screening of titles and abstracts, followed by full-text review. Each screening stage was done with predefined inclusion and exclusion criteria in Box 1. Each stage was pilot tested to ensure every individual understood the inclusion and exclusion criteria. Four authors piloted the first 50 articles of the title and abstract screening. We calculated the inter-reliability with a Kappa of 0.70, indicating moderate agreement (DeVellis, 2005). As a result of the agreement, title/abstract screening and full-text screenings were done in pairs; any disagreement was resolved by a third reviewer or discussion in a research meeting every week.

Box 1. Inclusion and Exclusion Criteria

An article is included if

a. It provided detailed descriptions of recruitment strategies that would enable us to understand how homebound older adults can be identified.

b. The mean age of the study population was 60 years and above, residing in the community in their home, assisted living, senior housing, retirement home, or any home without 24-hour medical care.

c. It was a peer-reviewed publication, including qualitative, quantitative, mixed-methods studies, or other forms such as co-creation or opinion papers. We included opinion papers because, in this emerging area of research, insights from expert perspectives can offer valuable context and help explain why this topic is rarely studied.

d. It was published in the English language

Articles were excluded if the

a. Participant resides in a nursing home or long-term care facility with 24-hour medical care, or those admitted to the hospital.

There was no year restriction during the database searches (i.e., each database was searched from inception till September 2025), and in selecting the included articles provided they meet the criteria.

Stage 4—Charting the Data and Synthesis

Data extraction was conducted in Covidence© with a modified data extraction Covidence template to ensure we captured information that would enable us to answer our research questions. The Covidence template has a place holder for author name(s), year of publication, the country where the study was conducted, study aims/research questions/hypothesis, the study settings, type of study (qualitative, quantitative, and mixed methods), study design, sampling methods, and participants characteristics (e.g., number of participants, mean age of the participants, sex/gender, and race/ethnicity). We modified by including a definition of homebound and duration, older adults with defined conditions, methods of recruitment (e.g., phone, mail, clinic patients, voluntary, and organization), assessment methods, data analysis, findings, author recommendations, study limitations, and conclusion. Data extraction was piloted to ensure accuracy and to understand the interpretation each extractor gives to the information presented in the article. Despite the pilot testing, data were extracted in pairs, and a third extractor served as a confirmer in case the two extractors did not extract the same information, and all other mismatched extracted information was discussed in weekly research meetings.

Data Synthesis was conducted by the first and last authors of the review. We used frequency count, percentages, and ranges where applicable to describe metadata of the included studies, including study location, design, recruitment strategies, and setting, participant characteristics such as sample size, age, gender/sex, ethnicity, and race. To conceptualize how homebound status is defined in the literature among older adults, we extracted all definitions from the selected articles and entered them into a spreadsheet. Two authors independently reviewed each definition and classified them based on their perceptions. The authors then met to discuss their classifications, merging and refining their wording to develop a comprehensive framework that captures all relevant definition categories. For example, one author initially grouped definitions into two categories: “confinement to their home” and “infrequent outings” and another author grouped them into two groups—psycho-social and physical limitations and difficulty coping at home. Following their initial classifications, the authors convened to conduct further analysis, resulting in the identification of three overarching categories that encapsulate how homebound status has been conceptualized in the literature: (1) duration and frequency of confinement; (2) consequences of physical, social, and cognitive impairments; and (3) those needing assistance. A similar approach was employed to describe the strategies and challenges associated with identifying homebound older adults. Three authors independently reviewed the recruitment sections of the included articles line by line to develop a distinct category for each identification approach and noted the challenges for each identification approach. The authors then convened to discuss their individual categories, merging or refining them to create more comprehensive headings. These categories were subsequently reviewed and further refined during a research meeting involving additional authors, with subsequent revisions and modifications made by those unable to attend the meeting.

Results

Of the 24518 citations retrieved from databases and websites, 3,945 duplicates were removed. After screening titles and abstracts, 20532 articles were excluded, leaving 39 for full-text review. Ultimately, 19 articles were included in the review (see Figure 1).

Characteristics of the Articles Included

Of the 19 included articles, 14 were conducted in the USA, with the remaining from the UK (Stewart & McVittie, 2011), Japan (Ida et al., 2020; Yamagata et al., 2020), South Korea (Kim & Jung, 2022), and Israel (Levin & Levin, 2016). The reported mean age ranged from 65 years (Choi et al., 2012) to 85 years (Lin et al., 2025). See Table 2 for study characteristics presented in the review.

Table 2.

Study Characteristics

Authors name, year country	Study design	Sample size (% of female)	Race/Ethnicity	Homebound status condition	Homebound definition	Method of recruiting homebound	Homebound status information source	Challenges reported
Cheng et al. (2022) USA	Qualitative description	18 (72.2%)	9 African American, 8 White, and 1 Asian	N/A	• Homebound status was defined as being unable to leave one’s home without assistance or being confined to the home for a majority (4 days) a week	• Community agencies (JABA and Mom’s Meals)	• Convenience sampling from clinical and community programs that serve homebound.	•Convenience sampling and snowball sampling can create disadvantages. Recruitment challenges as there was not much diversity in the participants.
Cheng et al. (2022) USA						•Snowballing methods	• Participants also referred eligible individuals to the study by providing contact information
Choi et al. (2012) USA	Randomized controlled trials	66 (78.8%)	Non-Hispanic White (53%), African American (39.4%) and Hispanic (7.6%)	Psychological limitations	N/A	• Community agencies (Meals on Wheels)	• Case managers at a Meals on Wheels program and other agencies serving homebound older adults in central Texas referred to the project’s potential subjects.	N/A
Choi et al. (2012) USA						• Snowballing methods
Crawford Shearer et al. (2010) USA	Qualitative description	59 (71.19%)	White, non-Hispanic	Physical limitations	• Homebound is defined as individuals who are unable to leave their home for reason of illness or incapacitating disability	• Community agencies (spans >10 yrs)	• Endorsement from community leaders, characterized by partnership with a local community action agency that had spanned more than 10 years.	• Reliance on home-delivered meal drivers for initial contact may lead to inconsistent outreach.
Crawford Shearer et al. (2010) USA								• Occasional lapses in distributing study flyers to new clients by meal drivers.
Cudjoe et al. (2022)USA	Retrospective observational (longitudinal)	7788 (56.3%)	White and Black	Social limitations	• Defined homebound as either needing help to leave home (assistive devices or assistance from others) or never leaving home in the last month	• Dataset (NHATS)	• NHATS (The National Health and Aging Trends Study), a nationally longitudinal cohort of US population Medicare beneficiaries (65 and above)	• Small sample size. Insufficient recruitment of black older adults and even smaller number of individuals classified as Hispanic or others.
Cudjoe et al. (2022)USA								• Measures required self-reporting; hence, findings were subject to recall bias.
Gadbois et al. (2022) USA	Interpretive description	21 (62%)	White (57%), Black (29%), Native American (10%), Hispanic (5%)	Physical limitations	• Homebound is conceptualized as having difficulty leaving their homes independently	• Community agencies (home-delivered program) using the distribution of flyers	• Talking tech intervention was embedded within and delivered by a home-delivered meal program. Cross organization collaboration between a university, parent organization (local home delivered meal provider), well connected (virtual senior provider)	• Limited financial resources, lack of accessible technology support, and technology illiteracy posed significant barriers to adoption among older adults.
Gadbois et al. (2022) USA						• Dataset (annual client survey)	• Intervention participants were recruited from local home-delivered meal program client lists.	• Recruitment methods were initially limited, relying heavily on meal delivery drivers’ recommendations, which yielded a slow enrollment rate.
							• Additionally, program staff used responses from an annual client survey	• A challenge in the identification process was that recruitment was managed by program staff who performed these tasks alongside their regular full-time duties.
Ida et al. (2020) Japan	Cross sectional study	558 (42.65%)	Japanese participants	Diabetes	• Homebound status is assessed using the question, “how often do you go out”. Individuals who respond with “less than once a day” are classified as homebound	• Datasets (survey)	• Participants were recruited from an outpatient clinic of Ise Red Cross Hospital in Ise City, Mie Prefecture, Japan.	• The low frequency of living alone and the small sample size may have contributed to an insignificant association between living alone and social isolation/being homebound compared with the previous community studies.
Ida et al. (2020) Japan						• Health condition	• Having an eligibility criteria of patients with diabetes who visited the outpatient clinic
Jiménez et al. (2021) USA	Case study	21(N/A)	N/A	Social limitations	N/A	• Community agencies: Meals on Wheels Rhode Island (MOWRI).	• Homebound recruited from their home-based delivered meal program (including driver referral, program flyers, email outreach to clients self-identified in an annual client survey.	• Staff time and organizational resources needed to implement the program were greater than anticipated.
						• Data sets (annual client survey)		• Data sharing among project partners was inhibited by system and/or organisation privacy requirements and sharing restrictions.
								• Volunteer and participant recruitment were the most challenging component of program implementation. Time commitment to the program was a challenge.
Jupiter (2003) USA	Cross sectional study	41 (65.86%)	N/A	Physical limitations, Auditory limitations	N/A	• Community agencies (run by VNS health @ New York)	• Elderly individuals receiving home health care under Medicare	• Denial of hearing loss among the elderly
						• Homebased care	• Elderly individuals in the study chosen from the caseload of the three nurses working in VNS health	• Difficulty in evaluating the degree of hearing loss or handicap due to varying conditions and environments.
								• Insidious onset of hearing loss leading to unawareness of impairment.
								• Other physical disabilities taking precedence over hearing loss.
								• Limited social interactions reducing awareness of communication difficulties.
Lee et al. (2021) USA	Embedded (mixed method)	15 (80%)	Non-Hispanic White non-Hispanic Black Hispanics	Physical limitations	N/A	• Community agencies (partner organization)	• Staff from a partner organization referred homebound waiting to be matched with a social companion and existing caregiver support programs	• Participants’ hesitation in responding to unknown recruitment calls.

Levin and Levin (2016) Isreal	Cross sectional study	1006 (67.5%)	Israeli	Physical limitations	Homebound individuals are defined as inpatients who are confined to their homes and are treated by the community medicine system in general health services	• Outpatient clinic	• Older adults treated at Klalit Health centre, Haifa district and western Galiee, associated with community clinics.	• N/A
Liu et al. (2022) USA	Embedded (mixed method)	63 (72%)	Non-Hispanic White non-Hispanic Black hispanic or Latino more than 70% were racial minorities	Physical, psychological, and social limitations	• Defined homebound as individuals who cannot freely leave their homes and require help in doing so due to medical conditions or mobility-affecting impairments	• Community agency	• Mixed method approach, qualitative data from five clients receiving services from the Wellness Together program designed by university settlement (a community agency in NYC).	• Funding issues to support the program.
Liu et al. (2022) USA						• Datasets (database system)	• Quantitative data from agency database systems including client profiles gathered when they enrolled and service documents	• Shrinking government revenue and private funding resources made staff members dedicate extra time to cooperating with partner agencies on resource coordination, referrals and working with social work schools to arrange interns’ schedules, which generated extra work that appeared to increase staff burden.
Lin et al. (2025)	Mixed method feasibility study	85 (76.9%)	White (76.9%), Black (15.4%), and Asian (7.7)	11 comorbid diagnoses, most common are hypertension, hyperlipidemia, dementia and cognitive heart failure	N/A	• Outpatient clinics/Homebased care	• Recruited through home based primary care program for older adults through house calls with team of nurse practitioners, nurse case coordinator, health specialist, pharmacist and two geriatricians.	• Small sample size makes it hard to generalize the results.
Mickler et al. (2021) USA	Qualitative description	30 (73%)	Homebound: 7 Black/African American, 4 White, 2 Latinx/Native American caregivers: 4 Black/African American, 9 White, 4 Latinx/Native American	Physical limitations	N/A	• Outpatient clinics/Homebased care	• Participants from HBMC (home-based medical care) practices were recruited	• Homebound older adults included reflects a relatively long-lived group, given that the annual mortality rate in HBMC practices is approximately 20%. Thus, our participants may have been in HBMC longer than average, which may have impacted their perceptions of care.
Mickler et al. (2021) USA							• Clinical staff from HBMC identified eligible homebound patients and caregivers.
Park et al. (2025)	Quantitative, secondary data analysis	653 (73.5%)	Non-white (39.4%)	Chronic conditions	• Homebound was defined as “in the last month, how often did you leave your building/home to go outside”. Homebound if they left their home some days but reported substantial difficulty doing so, rarely or never went out independently, or were unable to leave due to lack of assistance	• Datasets (NHATES, 2019–2020) and COVID-19 survey	NHATS (The National Health and Aging Trends Study), a nationally longitudinal cohort of US population Medicare beneficiaries (65 and above)	• N/A
Simonsick et al. (1998) USA	Longitudinal (prospective)	1002 (100%)	African American women in total of 284 and White consisted of 718 persons.	Physical limitations	• Participants are considered homebound if they responded to “no” to question of “during the typical week, weather permitting do you leave your home or if they responded yes but reported the frequency of leaving their home in a typical week as “less than once a week”	• Datasets: WHAS (Women Health and Aging Study)	• WHAS a prospective cohort study of the causes and course of physical disability in older women. WHAS identified participants through an in-home screening interview.	N/A
Stewart and McVittie (2011) United Kingdom	Interpretative phenomenological analysis	8 (87.5%)	Scottish (European) background.	Physical limitations	N/A	• Health conditions (house-bound older adults who have experienced multiple falls)	• Letters sent to potential participants, identified by community physiotherapist, discharged from their service within 6 weeks.	N/A
Stewart and McVittie (2011) United Kingdom						• Snowballing method
Xiang et al. (2020) USA	Longitudinal	7609 (56.6%)	White non- Hispanic: 80.6% Black: 8.1% Hispanic: 6.7% Others: 4.6%	Social, physical, and psychological limitations	• Participants were classified as homebound if they replied that they “never” or rarely (once a week or less) left home/building to go outside during the last month.	• Datasets: NHATES (National Health and Aging Trends Study)	• The National Health and Aging Trends Study (NHATS) is a nationally representative, longitudinal study of persons aged 65 and older and enrolled in Medicare.	N/A
Yamagata et al. (2020) Japan	Retrospective observational (longitudinal)	4,859 (51%)	Japanese	Social, physical, and psychological limitations	• Elderly individuals going out less than once a week. The authors seem to be following this definition of confinement.	• Datasets/survey (Kyoto-Kameoka study)	• Kyoto-Kameoka study- a large prospective cohort study in Japan	N/A
Yamagata et al. (2020) Japan							• Health status was checked with subjective health perception, presence of treated disease, joint pain, incontinence, basic checklist for care prevention
You-Ri Kim & Hye-Sun Jung (2020) South Korea	Cross sectional study	6444(N/A)	Korean	Social, physical, and psychological limitations	• Homeboundness is defined as going out once or few times a week or a decreased frequency of going out compared to the preceding year	• Datasets	• Survey data from participants who consented to receive and registered for home care service	N/A
You-Ri Kim & Hye-Sun Jung (2020) South Korea						• Home-based care	• Study data were collected by home-visiting nurses through surveys at the home of participants in the home care service program at 42 public health centers in Gyeonggi province SK

# - hard to reach described as groups such as single young unemployed males, women who are the victims of domestic violence, black and Hispanic people (Freimuth & Mettger, 1990), HIV positive people (Messeri et al., 1995), drug users, prostitutes, people who exhibit deviant behavior (drug and alcohol abuse), people with low socio-economic status (Freimuth & Mettger, 1990) and native Americans (Burhansstipanov & Krebs, 2005) have all been seen to be hard to reach.

Study Design

Study designs were quantitative (n = 11), qualitative (n = 4), mixed methods (n = 3), and a case study (Crawford Shearer et al., 2010). The quantitative studies comprised eight cross-sectional and three longitudinal designs. The qualitative studies included three descriptive and one phenomenological, while the three mixed-method studies employed a convergent parallel design. Sample sizes in qualitative studies ranged from 8 (Stewart & McVittie, 2011) to 59 (Crawford Shearer et al., 2010), whereas quantitative and mixed-method studies ranged from 15 (Lee et al., 2021) to 7788 (Cudjoe et al., 2022).

Study Settings

Most articles (n = 14) recruited participants from their homes, while others (n = 5) recruited from settings including community (Liu et al., 2022), retirement housing, and assisted living across various environments (Mickler et al., 2021; Park et al., 2025; Stewart & McVittie, 2011), with some studies not reporting the setting (Choi et al., 2012). Study settings varied: urban (n = 10), suburban (Gadbois et al., 2022; Kim & Jung, 2022; Yamagata et al., 2020), rural (Jiménez et al., 2021; Jupiter, 2003), across the three settings (Cheng et al., 2022; Mickler et al., 2021), and two did not state their study setting (Crawford Shearer et al., 2010; Cudjoe et al., 2022).

Study Population

Eighteen studies included older adults with diverse health conditions, such as physical impairments (Cheng et al., 2022; Crawford Shearer et al., 2010; Lee et al., 2021; Levin & Levin, 2016; Liu et al., 2022), cognitive, and psychological issues including depressive symptoms (Cheng et al., 2022; Choi et al., 2012; Gadbois et al., 2022; Kim & Jung, 2022; Mickler et al., 2021; Xiang et al., 2020) and dementia (Lee et al., 2021; Simonsick et al., 1998; Xiang et al., 2020). Additional conditions encompassed heart disease (Lin et al., 2025; Mickler et al., 2021), diabetes (Ida et al., 2020; Mickler et al., 2021), lung disease (Mickler et al., 2021), vision impairment (Mickler et al., 2021), hearing problems (Jupiter, 2003; Mickler et al., 2021), bowel incontinence (Mickler et al., 2021; Yamagata et al., 2020), hypertension, and hyperlipidemia (Lin et al., 2025). Four articles reported participants experiencing social isolation (Cudjoe et al., 2022; Gadbois et al., 2022; Jiménez et al., 2021; Liu et al., 2022).

Among the nineteen studies, one featured exclusively female participant (Simonsick et al., 1998), fifteen had a higher proportion of females than males (Cheng et al., 2022; Choi et al., 2012; Crawford Shearer et al., 2010; Cudjoe et al., 2022; Gadbois et al., 2022; Jupiter, 2003; Lee et al., 2021; Levin & Levin, 2016; Lin et al., 2025; Liu et al., 2022; Mickler et al., 2021; Park et al., 2025; Stewart & McVittie, 2011; Xiang et al., 2020; Yamagata et al., 2020), one had more males (Ida et al., 2020), and two did not report sex distribution (Jiménez et al., 2021; Kim & Jung, 2022). Racially, eight studies predominantly included Non-Hispanic White participants (Choi et al., 2012; Gadbois et al., 2022; Lee et al., 2021; Lin et al., 2025; Liu et al., 2022; Park et al., 2025; Simonsick et al., 1998; Xiang et al., 2020), or only Non-Hispanic Whites (Crawford Shearer et al., 2010), with three having more Black participants (Cheng et al., 2022; Cudjoe et al., 2022; Mickler et al., 2021). Participants from diverse ethnic backgrounds were recruited in five studies, including Japanese, Scottish, Israeli, and Korean populations (Ida et al., 2020; Kim & Jung, 2022; Levin & Levin, 2016; Stewart & McVittie, 2011; Yamagata et al., 2020), whereas two studies did not specify race or ethnicity (Jiménez et al., 2021; Jupiter, 2003).

Definition of Homebound (what)

Homebound status was categorized into three groups: (a) duration and frequency (eight articles—Cheng et al., 2022; Cudjoe et al., 2022; Ida et al., 2020; Kim & Jung, 2022; Mickler et al., 2021; Park et al., 2025; Simonsick et al., 1998; Xiang et al., 2020), (b) consequences of physical, social, and cognitive impairment (two articles (Crawford Shearer et al., 2010 and Levin & Levin, 2016), and (c) those needing assistance (four articles—(Cudjoe et al., 2022; Gadbois et al., 2022; Liu et al., 2022; and Park et al., 2025). Seven articles explicitly defined homebound status (Choi et al., 2012; Jiménez et al., 2021; Jupiter, 2003; Lee et al., 2021; Lin et al., 2025; Stewart & McVittie, 2011; and Yamagata et al., 2020). See Table 3 for how each study defined homebound status.

Table 3.

Definitions of Homebound status

Category	Definitions (authors and country)
Duration and frequency	As being unable to leave one’s home without assistance or being confined to the home for a majority (≥4 days) of the week (Cheng et al., 2022; USA)
	Homebound status is assessed using the question, “How often do you go out”. Individuals who respond with “less than once a day” are classified as homebound (Ida et al., 2020; Japan)
	Homebound is defined as participants who responded “no” to the question, “During a typical week, weather permitting, do you leave your home?” or those who responded “yes” but reported leaving their home less than once a week (Simonsick et al., 1998; USA)
	Homebound is defined as participants who reported that they “never” or rarely (once a week or less) left their home or building to go outside during the last month (Xiang et al., 2020; USA)
	Homeboundness is defined as going out once or few times a week or a decreased frequency of going out compared to the preceding year (Kim & Jung, 2022; South Korea)
	Homebound is defined as individuals who never or rarely leave their homes (Mickler et al., 2021; USA)
	Homebound defined as never leaving home in the last month (Cudjoe et al., 2022; USA)
	Homebound was defined as “In the last month, how often did you leave your building/home to go outside”. Homebound if they left their home some days but reported substantial difficulty doing so, rarely or never went out independently, or were unable to leave due to lack of assistance (Park et al., 2025; USA)
Consequences of physical, social or cognitive impairment	Homebound individuals are defined as inpatients who are confined to their homes and are treated by the community medicine system in general health services (Levin & Levin, 2016; Israel)
Consequences of physical, social or cognitive impairment	Homebound is defined as individuals who are unable to leave their home for reason of illness or incapacitating disability (Crawford Shearer et al., 2010; USA)
Those needing assistance	Defined homebound as either needing help (assistive devices or assistance from others) to leave home (Cudjoe et al., 2022; USA)
	Defined homebound as individuals who cannot freely leave their homes and require help in doing so due to medical conditions or mobility-affecting impairments (Liu et al., 2022; USA)
	Homebound is conceptualized as having difficulty leaving their homes independently (Gadbois et al., 2022; USA)
	Homebound was defined as “In the last month, how often did you leave your building/home to go outside.” Homebound if they left their home some days but reported substantial difficulty doing so, rarely or never went out independently, or were unable to leave due to lack of assistance (Park et al., 2025; USA)

Identification Process (How)

Our analysis found that homebound older adults were identified through: (a) community agencies (eight studies—Cheng et al., 2022; Choi et al., 2012; Crawford Shearer et al., 2010; Gadbois et al., 2022; Jiménez et al., 2021; Jupiter, 2003; Lee et al., 2021; and Liu et al., 2022); (b) outpatient clinics (five studies—(Jupiter, 2003; Kim & Jung, 2022; Levin & Levin, 2016; Lin et al., 2025; and Mickler et al., 2021); (c) snowball sampling techniques (three studies—Cheng et al., 2022; Choi et al., 2012; Stewart & McVittie, 2011); (d) assessment of health conditions (two studies—Ida et al., 2020; Stewart & McVittie, 2011); and use of dataset (ten studies (Cudjoe et al., 2022; Gadbois et al., 2022; Ida et al., 2020; Jiménez et al., 2021; Kim & Jung, 2022; Liu et al., 2022; Park et al., 2025; Simonsick et al., 1998; Xiang et al., 2020; Yamagata et al., 2020). It is important to note that some studies utilized more than one of these strategies.

Community Agencies

Identification through community agencies was one of the most prevalent methods for recognizing homebound older adults. These agencies were primarily private nonprofit organizations in the United States that offer services such as Meals on Wheels (Cheng et al., 2022; Choi et al., 2012; Crawford Shearer et al., 2010; Gadbois et al., 2022; Jiménez et al., 2021; Liu et al., 2022) or Medicaid-funded visiting nursing programs (Jupiter, 2003; Lee et al., 2021). These organizations, for example, Meals on Wheels delivers nutritious meals tailored to older adults’ needs, requiring recipients to be aged 65 or older, disabled, needing assistance with grocery shopping, and enrolled in a Medicaid Advantage plan. These organizations often inform healthcare professionals or researchers about older adults who are homebound.

Outpatient Clinics and Home Care Visits

Outpatient clinics and home care visits were common methods for recruiting homebound older adults. In Israel (Levin & Levin, 2016), older adults visiting hospitals were asked to self-identify as homebound during consultations, procedures, or treatments. In the USA, studies (Jupiter, 2003; Lin et al., 2025; Mickler et al., 2021) reported identifying homebound individuals through home care visits in which physicians or healthcare providers visit patients’ homes to provide social or healthcare services. One study conducted in South Korea (Kim & Jung, 2022) recruited homebound older adults through home-visiting nurses who provided care as part of a home care service program.

Snowballing Method

Snowballing method, defined as a recruitment technique where existing study participants refer or recruit other eligible individuals, was one of the approaches used to identify homebound older adults. Some studies reported that older adults were referred by peers who also identified as homebound (Cheng et al., 2022), while healthcare professionals providing care observed and reported the likelihood of certain individuals being homebound (Cheng et al., 2022; Choi et al., 2012; Stewart & McVittie, 2011).

Use of Dataset

The use of large, often longitudinal datasets, including the National Health and Aging Study (Cudjoe et al., 2022; Park et al., 2025; Xiang et al., 2020), Kyoto-Kameoka study (Yamagata et al., 2020), and the Women’s Health and Aging Study (Xiang et al., 2020), was common for identifying homebound older adults. These datasets facilitated population-level analyses to derive indicators and predictors of homebound status. Cudjoe et al. (2022) utilized a representative sample of 7,788 Medicare beneficiaries aged 65+ from 2011 to 2019, assessing the odds of becoming homebound based on social isolation, and found that socially isolated older adults were more likely to become homebound both at baseline and over time. Similarly, Xiang et al. (2020) utilized a representative sample of 7,607 Medicare beneficiaries aged 65+ from 2011 to 2017, examining the trajectories of homebound status and shaping risk factors. They identified three trajectories: never, onset, and chronic. Most participants remained non-homebound (never, 65.5%); a smaller group was persistently homebound (chronic, 8.3%); and the onset group (26.2%) showed a rapid rise in homebound risk over 7 years. Compared with the never group, a higher risk of onset/chronic status was associated with older age, Hispanic ethnicity, social isolation, past/current smoking, IADL limitations, probable dementia, and use of a walker or wheelchair. Park et al. (2025) utilized public-use data files from the NHATES main surveys for 2019–2020, supplemented with the 2020 COVID-19 survey, with a sample of 3961, to highlight that providing information and communication technology (ICT) is not sufficient to reduce e-health disparities among homebound older adults.

Simonsick et al. (1998) utilized the Women’s Health and Aging study, a prospective cohort study consisting of 1002 moderately to severely disabled community-dwelling Medicare beneficiaries women age 65 +, assessing association of disability and social interaction, and found that older age, lower levels of education attainment, driver at home, hearing difficulties, incontinence and African American race were related to home confinement and low social contact. Lastly, Yamagata et al. (2020) utilized the Kyoto-Kameoka study, a prospective cohort study of 6,696 community-dwelling adults aged 65 years and older in Japan, to examine 2-year changes in confinement and related factors. Among 375 participants, most (86.9%) remained in the non-confinement group. Low social role was associated with a higher likelihood of becoming confined, whereas improvements were observed among those with no medical conditions and higher intellectual activity. In addition to large datasets such as NHATS and WHAS, several studies (Gadbois et al., 2022; Ida et al., 2020; Jiménez et al., 2021; Kim & Jung, 2022; Liu et al., 2022) used annual client surveys containing client-profile information as a supplementary approach to identify homebound older adults enrolled in programs or services.

Health Conditions

Findings from 2 studies were mapped based on health conditions, as they all involved recruitment of homebound older adults with various limitations (Ida et al., 2020; Stewart & McVittie, 2011). Stewart and McVittie (2011) recruited participants through a community physiotherapy service, targeting individuals recently discharged after experiencing multiple falls due to physical disability. In contrast, Ida et al. (2020) recruited homebound older adults diagnosed with diabetes, including classifications of type 1, type 2, and prediabetes.

Challenges in Identifying Homebound Older Adults

Sampling and recruitment challenges were prevalent in studies included in this review. Cheng et al. (2022) noted that their reliance on convenience and snowball sampling methods resulted in limited participant diversity, potentially undermining the generalizability of their findings. This limitation may stem from the nature of snowball sampling, which relies on participant referrals. Such referrals often lead to the recruitment of individuals with similar characteristics, thus introducing sampling bias (Cheng et al., 2022). Some studies also depended on meal delivery services for recruitment, which created inconsistent outreach and delays in the distribution of recruitment flyers to potential participants, ultimately slowing enrollment rates (Liu et al., 2022; Gadbois et al., 2022). Additionally, programs requiring meal delivery drivers, such as Meals-on-Wheels, demanded increased staff time and resources (Liu et al., 2022; Mickler et al., 2021). Also, it is important to realize that there were no specific studies on the identification of homebound older adults; however, the included articles reported the recruitment process in their methods section.

Discussion

To the best of the authors’ knowledge, this is the first review to explore existing literature on the identification process of homebound older adults globally. This review specifically explored the “what” (definitions associated with homebound status), the “how” (identification processes employed), and the challenges associated with the recruitment process.

We noted a pattern in the homebound status definition that varies significantly across different healthcare systems. In studies conducted in the United States, the definition is primarily centered on the duration of an individual’s indoor confinement, as well as any physical incapacities that hinder their ability to leave home and necessitate care (Musich et al., 2015). This aligns closely with the Medicare Home Care Benefit Criteria, which emphasizes that the homebound older adults must be under the care of a physician, need skilled services, and have physical limitations that prevent exit from their home (Ornstein et al., 2015). Conversely, research from countries with universal healthcare, such as Japan, Israel, and South Korea, expands the definition of homebound status to encompass not only physical abilities but also social, environmental and cognitive factors that reduces the frequencies a person leave their home (Ida et al., 2020; Kim & Jung, 2022; Levin & Levin, 2016; Yamagata et al., 2020). These studies suggest that an individual may be classified as homebound even if they possess the physical capability to leave their home, indicating a more nuanced understanding of the concept that accounts for psychological and social influences. This disparity in definitions underscores the varying approaches to addressing homebound needs across different healthcare systems. Such variations may serve as potential reasons for the challenges in identifying individuals who qualify as homebound. Moreover, the absence of a standardized definition presents significant hurdles in accurately understanding the relationship between these definitions and the recruitment processes for homebound older adults. Therefore, researchers, clinicians, older adults at risk of becoming homebound, and their families must engage in collaborative discussions. Through this exchange, stakeholders can work towards establishing a cohesive global definition or set of criteria for homebound status, which is essential for addressing the needs of an aging population worldwide.

An objective of the review was to describe the “how” or process of identifying homebound older adults. However, to date, there is a lack of empirical studies in this area. Consequently, we shifted our focus to examining how existing studies have recruited participants identified as homebound. Among the strategies employed, endorsements from community-based leaders, organizations, and the use of datasets emerged as the most prominent method for recruiting homebound older adults. This is largely due to the established trust and rapport that these leaders and organizations have within their communities, which can significantly enhance their outreach to individuals with homebound status (Spencer et al., 2018). These organizations are typically well-aware of the specific needs and preferences of residents, allowing them to craft tailored messages that resonate with the homebound audience, thereby driving collective action (Kaholokula, 2024). These findings underscore the wealth of knowledge that community-based organizations and leaders possess regarding the identification of homebound status, making them the ideal first point of contact. The absence of empirical evidence outlining the process for identifying homebound status highlights the necessity for such studies, with involvement of community-based organizations and leaders serving as a promising starting point.

Themes of over-reliance on meal delivery drivers and potential recall bias among participants emerged as potential challenges when recruiting “hard to reach” populations. These findings may reflect the labor-intensive and time-consuming nature of recruiting homebound older adults, as meal delivery drivers spend considerable time traveling between visits and distributing flyers (Cohen-Mansfield et al., 2010; Ritchie & Dennis, 1999). Additionally, meal delivery drivers often need to take a proactive approach to build rapport and ensure that homebound individuals feel comfortable allowing them into their homes; this process can be stressful (Cohen-Mansfield et al., 2012). As such, meal delivery drivers can become overwhelmed with the tasks of both delivering meals and intentionally identifying those who are homebound. There is an ethical concern regarding the use of meal delivery drivers as champions of identifying homebound individuals, as it may be seen as a breach of privacy. We emphasize the importance of ensuring that meal delivery drivers understand these ethical considerations; drivers can be trained to obtain informed consent from older adults whom they perceive as homebound. Further research is needed to determine how the use of meal delivery drivers would be beneficial in identifying homebound older adults.

Study Limitations

While our review has expanded the evidence on the phenomenon of homebound among older adults, there are some limitations. First, although we ensured that all eligible studies that passed the inclusion criteria were included in the review, we could still have omitted some articles as a result of search engines utilized that might not include articles from regions like Africa. Also, by limiting the inclusion criteria to only homebound older adults residing at home, we may have excluded articles that combine homebound older adults residing at home and long-term care. Lastly, overlap among identification methods across studies may have increased the risk of misclassification. To mitigate this, we assigned each article to a recruitment category (e.g., endorsement from community agencies) based on the recruitment details reported in the manuscript and a content analysis conducted by a senior researcher. Although some interpretive judgment was unavoidable given variation in reporting and the use of multiple approaches within some studies, we believe our categorizations are consistent with the available evidence. Readers are nevertheless encouraged to consider this when interpreting the review findings.

Conclusion

We identified multiple factors for each category of what, how, and challenges of identifying homebound older adults (60 and above) globally. The review showed the disparity in definitions of “homebound” across healthcare systems, with some countries adopting broader criteria that allow for more inclusive identification. Factors contributing to the identification of homebound older adults were categorized into five domains: health conditions, endorsements from community agencies, snowballing methods involving family members or caregivers, referrals from outpatient clinics, and datasets. These variations in definitions create significant barriers to accurately identifying homebound individuals, ultimately hindering the delivery of appropriate services and support. Therefore, establishing a consensus definition of homebound status is essential for ensuring consistency in identifying and addressing the needs of this population.

Supplemental Material

Supplemental material - How Are Homebound Older Adults Identified? Definitions, Approaches, and Challenges—A Scoping Review

Supplemental material for How Are Homebound Older Adults Identified? Definitions, Approaches, and Challenges—A Scoping Review by Peace Kumapayi, Blessing Ugochi Ojembe, Amanda Grenier, Ethan Chan, Prince Onyekere, Benita Ugwu, Ravjyot Ughra, and Michael E Kalu in Journal of Applied Gerontology.

Footnotes

Acknowledgments

We want to acknowledge Emerging Research and Professionals in Aging-African Network (ERPANN) for their support and for allowing us to involve some of their members in this research.

ORCID iDs

Peace Kumapayi

Michael E. Kalu

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project is supported by Social Science Humanities Research Council (SSHRC) Insight Development Grant (430-2024-0110); SSHRC Undergraduate Student Research Award; and York University Black Seed Grant.

Declaration of Conflicting Interests

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

Supplemental Material

Supplemental material for this article is available online.

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How Are Homebound Older Adults Identified? Definitions,Approaches,and Challenges—A Scoping Review

Abstract

Keywords

What This Paper Adds

Applications of Study Findings

Introduction

Methods

Stage 1—Identifying the Research Question

Stage 2—Identifying Relevant Studies and Study Selection

Stage 3 - Selecting Studies

Box 1. Inclusion and Exclusion Criteria

Stage 4—Charting the Data and Synthesis

Results

Characteristics of the Articles Included

Study Design

Study Settings

Study Population

Definition of Homebound (what)

Identification Process (How)

Community Agencies

Outpatient Clinics and Home Care Visits

Snowballing Method

Use of Dataset

Health Conditions

Challenges in Identifying Homebound Older Adults

Discussion

Study Limitations

Conclusion

Supplemental Material

Supplemental material - How Are Homebound Older Adults Identified? Definitions, Approaches, and Challenges—A Scoping Review

Footnotes

Acknowledgments

ORCID iDs

Funding

Declaration of Conflicting Interests

Supplemental Material

References