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Abstract

Background:

The interplay of aging, diabetes, and homebound status introduces challenges that can substantially diminish overall well-being. Understanding the factors that influence subjective well-being in homebound older adults with diabetes is crucial for developing strategies that enhance their health and longevity. We aimed to identify independent correlates of well-being in community-dwelling homebound older adults aged 65 and older with diabetes.

Methods:

Using data from the National Health and Aging Trends Study, we analyzed responses from non-institutionalized older adults (≥65 years) with diabetes who were homebound (inability to leave home independently or without significant assistance). Subjective well-being was assessed via an 11-item well-being scale. Linear regression identified predisposing, enabling, and need factors associated with well-being.

Results:

In our weighted population (9.7 million) of community-dwelling older adults with diabetes in 2017, 26% were homebound. Non-Hispanic Black (p = .004) and Hispanic (p = .04) homebound older adults reported significantly higher subjective well-being than non-Hispanic Whites. Greater neighborhood social cohesion was positively associated with well-being (p < .001), while depression (p = .04) and anxiety (p < .001) were negatively associated.

Conclusion:

Enhancing neighborhood social cohesion, strengthening social support, and expanding access to mental health care may improve the well-being of homebound older adults with diabetes.

Keywords

homebound mental health older adults well-being diabetes

Introduction

Diabetes is a prevalent chronic condition among older adults. According to the Centers for Disease Prevention and Control [CDC] (2021), nearly 25% of US adults aged 65 and older have diabetes compared to 14.5% of those aged 45 to 64 (CDC, 2021). The prevalence of diabetes in adults 65 and older is projected to reach 28% by 2030 (Lin et al., 2018), alongside a projected significant increase in the US aging population (Vespa, 2018). Older adults with diabetes experience higher rates of functional disability, comorbidities, and greater risks of cognitive impairment, depression, frailty, diabetes-related complications, and injurious falls compared to older adults without diabetes (American Diabetes Association Professional Practice Committee, 2024).

The extensive complications and impairments associated with diabetes may lead older adults to become homebound. Being homebound is defined as an individual’s inability to leave home alone or without significant assistance or aid due to an illness or injury (Center for Medicare and Medicaid Services, 2018; Ornstein et al., 2015). About 30 to 40% of homebound older adults have diabetes (Musich et al., 2015; Qiu et al., 2006). Notably, Negron-Blanco et al. (2016) reported that older adults with diabetes have 2.3 times the odds of being homebound compared to older adults without diabetes, even after adjusting for disability, pain, and depressive symptoms. Being homebound is associated with higher rates of co-morbidities, poorer health status, reduced functional status, lower income, depression, social isolation, anxiety, greater healthcare utilization, and higher risk of premature death (Cohen-Mansfield et al., 2012; Musich et al., 2015; Oseroff et al., 2023; Soones et al., 2017). The interplay of aging, diabetes, and homebound status introduces complex challenges that can substantially diminish individuals’ overall well-being and perceived quality of life.

Subjective well-being refers to a self-evaluation of happiness, sense of purpose, meaning, and overall life satisfaction (Steptoe et al., 2013). Prior studies have shown a relationship between physical health and psychological well-being (Davidson et al., 2010; Feller et al., 2013; Wikman et al., 2011), with some studies showing that higher subjective well-being is associated with reduced risks of cardiovascular diseases, cognitive decline, and mortality (Cohen et al., 2016; Davidson et al., 2010; Feller et al., 2013; Steptoe et al., 2013; Wikman et al., 2011). Further, Liu et al. (2023) found that individuals with high subjective well-being scores tend to excel in all quality-of-life components.

Andersen’s Behavioral Model of Health Services Use (1995) offers a valuable framework for understanding the various factors that influence subjective well-being. According to the model, health-related outcomes are shaped by three domains: predisposing characteristics (e.g., age, gender, race/ethnicity, education), enabling resources (e.g., insurance status, social support, family and community resources), and need factors (e.g., comorbidities, functional limitations, perceived health status; Andersen, 1995; Babitsch et al., 2012). These domains may align closely with determinants of subjective well-being, as individual demographics, access to supportive resources, and perceived health needs can significantly influence how older adults evaluate their lives. Notably, the model emphasizes mutable enabling and needs factors, which can be targeted through individual, community, and policy-level interventions to enhance well-being.

Factors that predict subjective well-being in the general population include marital status, income, sex, employment status, social support, household structure, environmental factors, and age (Feng & Zheng, 2024; Mathentamo et al., 2024; Steptoe et al., 2015). Moreover, neighborhood factors such as social cohesiveness, a sense of trust, belonging, and willingness to participate for the common good of a group (Miller et al., 2020) have been associated with mental well-being (Miao et al., 2019; Williams et al., 2020). Past studies examining the relationship between age and subjective well-being have reported a U-shaped association, where subjective well-being declines at middle age and later increases with age (Blanchflower & Oswald, 2008). However, the above finding slightly differs from that of Frijters and Beatton, who found that subjective well-being increases around age 60, followed by a decline after age 75 (Frijters & Beatton, 2012).

Existing literature offers minimal insight into the subjective well-being of homebound older adults with diabetes. Aging, complicated by diabetes and being homebound, is associated with significant physical and social limitations; therefore, it is crucial to support individuals in this situation, enabling them to live well within these constraints. Understanding the factors associated with subjective well-being among homebound older adults with diabetes is a crucial first step toward identifying modifiable factors that influence subjective well-being in this population and guiding targeted interventions to promote well-being.

We aim to bridge the above knowledge gap by examining the independent correlates of subjective well-being among community-dwelling homebound older adults (aged 65 and older) with diabetes, using the 2017 National Health and Aging Trends Study (NHATS) dataset. We hypothesize that predisposing characteristics, enabling resources, and need factors are significantly associated with subjective well-being among US homebound older adults living with diabetes.

Methods

Study Sample

This cross-sectional analysis uses data from the National Health and Aging Trends Study (NHATS). NHATS has a nationally representative sample of Medicare beneficiaries aged 65 and older (Kasper & Freedman, 2014). NHATS, funded by the National Institute on Aging, aims to promote research that reduces disability, maximizes health, independent functioning, and quality of life in older adults. NHATS data are collected primarily through in-person interviews of sample persons or their proxies. The initial sample was collected in 2011 (round 1) and replenished in 2015 (round 5) and 2022/2023 (round 12); follow-up interviews are conducted yearly. This study was conducted using the 2017 (round 7) data, with an overall unweighted response rate of 93% (DeMatteis et al., 2021). The 2017 NHATS data were used because they included Dried Blood Spot data, which measured HbA1c, a key variable for assessing glycemic control in individuals with diabetes.

Participants’ diabetes status was determined by self-report. All non-institutionalized community-dwelling older adults who reported being told by their provider that they had diabetes were included. Those living in nursing homes and other residential facilities (e.g., assisted living facilities) were excluded. Those who did not have a measure of subjective well-being, our outcome of interest, were also excluded. As the NHATS dataset is publicly available and fully de-identified, this study was deemed exempt from review by the local Institutional Review Board (study ID 25.250).

Homebound Status

We categorized participants as homebound using the classification developed by Ornstein et al. (2015). Responses to the following four questions were used to determine participants’ homebound status: How often did you go out last month? Did anyone ever help you? How often do you go outside by yourself? How much difficulty did you have leaving the house by yourself?

Participants who never left home in the past month (completely homebound), rarely went out (once a week or less) (mostly homebound), and those who never go out by themselves or had difficulty going outside by themselves (semi-homebound) were grouped as homebound, while the remaining participants, who went out frequently, did not need help or have difficulty leaving home alone in the past month were grouped as not homebound, consistent with other studies (Musich et al., 2015; Ornstein et al., 2015).

Measures

Independent Variables

The selection of independent variables was informed by Andersen’s healthcare utilization model (Andersen, 1995). According to this model, predisposing, enabling, and need factors influence health-related outcomes. Our predisposing variables included self-reported age range (65–74, 75–84, and 85 years and above), gender (male or female), and race/ethnicity, classified as non-Hispanic White, non-Hispanic Black, Hispanic, and others (American Indian, Asian, Native Hawaiian, Pacific Islander, and non-Hispanic). We included marital status (categorized into two; not married/separated, divorced, widowed/ never married, or married/living with a partner), supplemental security income, Medicaid insurance use, number of social networks (number of individuals with whom the respondent discusses important matters), living arrangement (whether the respondent lives alone or with others), neighborhood social cohesion (respondents’ perception of how connected, supportive and trustworthy their neighbors are) and the number of help with activities of daily living (ADL Help) (feeding, bathing, dressing, toileting) as enabling factors. Our needs factors included self-reports of the number of comorbidities (heart attack, heart disease, hypertension, arthritis, osteoporosis, lung disease, stroke, dementia, cancer), depression, and anxiety.

Depression: Depression was measured by the Patient Health Questionnaire 2 (PHQ-2), which is a valid and reliable instrument for measuring depression (Kroenke et al., 2003). Depression was assessed by response to two questions: (1) In the last month, how often have you had little interest or pleasure in doing things? (2) felt down, depressed, or helpless? Answers range from 0 to 6; 0 = not at all, 1 = several days, more than half the days = 3, and nearly every day = 4. A score of ≥3 was noted as depression.

Anxiety: This was measured using Generalized Anxiety Disorder 2 (GAD-2), which is a valid and reliable instrument for measuring anxiety (Plummer et al., 2016). Anxiety was determined by response to the following two questions: (1) In the last month, how often have you felt nervous, anxious, or on edge? (2) been unable to stop or control worrying? Responses were rated 0 for not at all, 1 for several days, 2 for more than half the days, and 3 for nearly every day. Total responses ≥ 3 were noted as anxiety.

Neighborhood social cohesion: This was measured using responses to three questions: (1) people in my community know each other well; (2) people are willing to help each other; 3) people can be trusted. The answers were graded on a three-point scale: (1) do not agree, (2) agree a little, (3) agree a lot, with a possible score of 3–9, with higher scores indicating greater cohesion. The neighborhood social cohesion questions have been validated in previous studies for use in older adults (Cagney et al., 2009; Latham & Clarke, 2018).

Hemoglobin A1c (HbA1c): Glycemic control was assessed using Hemoglobin A1c (HbA1c) levels, which were obtained from dried blood spot (DBS) samples collected from participants. Only cognitively intact participants who could provide informed consent were eligible to provide DBS samples. DBS samples were processed to determine the percentage of glycosylated hemoglobin (%HbA1c). The HbA1c measurements were performed using the Variant II Hemoglobin Testing System, an automated ion-exchange high-performance liquid chromatography platform (Kasper et al., 2019). We categorized HbA1c results into two groups: <8% (good glycemic control) and ≥8% (poor glycemic control) to align with the American Diabetes Association’s recommendation of an HbA1c target of <8% in older adults with complex or intermediate health status (American Diabetes Association Professional Practice Committee, 2024). Note, due to a substantial proportion of missingness, this variable was excluded from models.

Subjective Well-Being

The outcome variable is subjective well-being, which was measured using responses to 11-item well-being questions in the NHATS dataset (Kim et al., 2016). Four questions measured the frequency of positive and negative emotions (during the last month, how often did you feel cheerful, bored, full of life, or upset?). The above questions used a five-point scale, where one indicated “every day,” 2 indicated “most days,” 3 indicated “some days,” 4 indicated “rarely,” and 5 indicated “never.” The remaining seven questions rated participants’ evaluation of their lives. Questions include: Tell me whether you agree or not, my life has meaning and purpose; I feel confident and good about myself; I gave up trying to improve my life a long time ago; I like my living situation very much; other people determine most of what I can and cannot do; when I really want to do something, I usually find a way to do it; I have an easy time adjusting to change. A three-point Likert scale, with options 1 (agree a lot), 2 (agree a little), and 3 (agree not at all), was used to score the above. Seven items were reverse-coded so that higher scores on the subjective well-being scale indicated higher levels of well-being (Kim et al., 2016).

Statistical Analysis

The survey methods package in SAS 9.4 (Cary, NC; PROC SURVEYFREQ, PROC SURVEYMEANS, PROC SURVEYREG) was used for the analyses, which accounts for NHATS analytic survey weights to adjust for differential non-response based on individual variables, for example, race and age (Montaquila et al., 2012). Continuous variables were presented as weighted means and standard errors, and categorical variables were presented as frequencies and weighted percentages. Collinearity was checked using the variance inflation factor, and no issues were identified. To examine factors associated with well-being, we conducted linear regression models that adjusted for predisposing, enabling, and need factors. The analysis was first performed on the overall sample, and the effect of being homebound was explicitly tested. Next, stratified subgroup analyses were performed by homebound status to identify the predisposing, enabling, and need factors associated with subjective well-being by homebound status. A p-value criterion of .05 was used to determine significance in all analyses. Of the initial 1,534 respondents, 18% were excluded due to missing data on key variables, including subjective well-being scores, neighborhood social cohesion, and the number of people in the social network. The final analytic sample consisted of 1259 participants (see Tables 2 and 3).

Results

Table 1 shows the demographic characteristics of our participants. In our weighted population (9.7 million) of US non-institutionalized community-dwelling older adults (65 years and older) with self-reported diagnosis of diabetes in 2017, 26% were homebound (Table 1). Homebound older adults with diabetes differed significantly from those who are not homebound in terms of subjective well-being scores, age, gender, race, marital status, receipt of Supplemental Security Income, Medicaid insurance coverage, number of comorbid conditions, rates of depression, anxiety, and neighborhood social cohesion scores. Specifically, 43% of homebound older adults were aged 65 to 74, and 19% were 85 years and older. Most homebound older adults with diabetes were female (61%), and 33% had Medicaid. The ethnic/racial composition of homebound older adults with diabetes compared to those who are not homebound was as follows: Non-Hispanic Whites (58% vs. 74%), non-Hispanic Blacks (15% vs. 12%), and Hispanics (21% vs. 10%). Thirty-five percent of homebound older adults with diabetes reported depression, while 28% reported anxiety, compared to 10% and 8%, respectively, in those who are not homebound.

Table 1.

Sample Characteristics of the United States Overall and Homebound Community-Dwelling Older Adults (65 Years and Above) with Diabetes, NHATS 2017.

Variables	Overall, N (%)	Not homebound, N (%)	Homebound,N (%)	p-value
Sample N	1,534	1,055 (68.91%)	476 (31.09%)
Sample N, weighted	9,749,937	7,177,670 (73.62%)	2,572,267 (26.38%)
Subjective wellbeing (range = 11–41), mean ± SD	33.98 ± 0.15	34.68 ± 0.15	31.79 ± 0.40	<.001
Predisposing factors
Age (years)				<.001
65–74	486 (50%)	380 (53%)	106 (43)
75–84	734 (39%)	514 (39%)	218 (38)
85+	314 (11%)	161 (8%)	152 (19)
Gender				<.001
Male	708 (50%)	536 (55%)	170 (39)
Female	826 (50%)	519 (45%)	306 (61)
Race				.0012
NH White	843 (70%)	627 (74%)	215 (58)
NH Black	477 (13%)	300 (12%)	175 (15)
Hispanic	144 (13%)	83 (10%)	61 (21%)
Others	45 (5%)	29 (4%)	16 (6%)
Enabling factors
Marital status				<.001
Unmarried*	826(46%)	522 (43%)	301 (55%)
Married or living with a partner	708 (54%)	533 (57%)	175 (45%)
Public assistance
Receiving supplemental security income	141(8%)	71 (6%)	69 (16%)	<.001
Medicaid	310 (17%)	158 (12%)	151 (33%)	<.001
Number of individuals in the social network				.51
0–1	587 (39%)	416 (40%)	169 (38%)
≥2	879 (61%)	624 (60%)	254 (62%)
Living arrangement				.60
Lives with others	1,097(74%)	744 (74%)	351 (76%)
Alone	437 (26%)	311 (26%)	125 (24%)
Neighborhood social cohesion	7.08 ± 0.07	7.24 ± 0.06	6.63 ± 0.16	<.001
Number of ADL help, range (0–4)	0.33 ± 0.03	0.12 ± 0.02	0.91 ± 0.08	<.001
Needs factors
Number of comorbid conditions (Range 0–6)	2.22 ± 0.04	2.05 ± 0.05	2.68 ± 0.07	<.001
Depression	192 (16%)	110 (10%)	159 (35%)	<.001
Anxiety	192 (13%)	92 (8%)	122 (28%)	<.001
HbA1c				.57
<8%	1,085(89%)	781 (89%)	303 (87%)
≥8%	140(11%)	99 (11%)	41 (13%)

Unmarried includes separated, divorced, widowed, or never married.

Bolded figures represent results that are statistically significant at p < .05.

Table 2 presents an unadjusted analysis of factors associated with subjective well-being among community-dwelling, homebound older adults with diabetes. As shown in the table, non-Hispanic Black homebound older adults with diabetes have significantly higher well-being scores compared to their non-Hispanic White peers (p = .03). Depression (p < .001) and anxiety (p < .001) were negatively associated with subjective well-being, whereas neighborhood social cohesion (p < .001) was positively associated with it.

Table 2.

Unadjusted Associations Between Selected Factors and Subjective Well-Being Among Community-Dwelling Homebound Older Adults With Diabetes (n = 349).

Variables	B	Standard error	p-value
Predisposing factors
Age (years)
65–74 (Ref)			.15
75–84	1.33	0.76	.09
85+	0.19	0.83	.82
Gender (ref: female)	−0.64	0.68	.35
Race (ref: non-Hispanic White)			.08
Non-Hispanic-Black	1.41	0.62	.03
Hispanic	0.06	1.07	.96
Others	1.48	0.77	.06
Enabling factors
Marital status
Married or living with a partner (ref: unmarried*)	0.08	0.56	.89
Public assistance
Receiving supplemental security income (ref: no)	0.18	1.41	.89
Medicaid (ref: no)	−0.35	0.79	.66
Number of individuals in the social network ≥ 2 (Ref: 0–1)	0.00	0.58	.99
Living arrangement, lives with others (ref: alone)	0.41	0.78	.60
Neighborhood social cohesion	0.87	0.18	<.001
Number of ADL help (range: 0–4)	−0.48	0.29	.09
Needs factors
Number of comorbid conditions (range: 0–6)	0.05	0.44	.91
Depression (ref: no)	−2.99	0.83	.001
Anxiety (ref: no)	−4.29	0.96	<.001

Bolded figures represent results that are statistically significant at p < .05.

Table 3 presents adjusted linear regression for factors associated with subjective well-being. Among homebound older adults living with diabetes, there was no significant difference in well-being scores across age groups or between males and females. A significant difference in well-being scores was observed across racial groups, with non-Hispanic Black (p = .004) and Hispanic homebound older adults (p = .04) reporting significantly higher well-being scores than their non-Hispanic White counterparts. Additionally, greater neighborhood social cohesion was significantly associated with higher well-being scores (p < .001). Conversely, homebound older adults with diabetes who reported having anxiety (p < .001) or depression (p = .04) had significantly lower well-being scores compared to those with no anxiety or depression.

Table 3.

Adjusted Associations Between Selected Factors and Subjective Well-Being Among Homebound Older Adults With Diabetes (n = 349).

Parameters	B	Standard error	p-value
Intercept	27.03	1.95	<.001
Predisposing factors
Age (years)
65–74 (Ref)			.39
75–84	0.65	0.63	.31
85+	−0.18	0.66	.79
Gender
Male (ref: female)	−0.29	0.61	.63
Race (ref: non-Hispanic White)			.02
Non-Hispanic-Black	2.04	0.68	.004
Hispanic	2.00	0.96	.04
Others	0.74	0.99	.46
Enabling factors
Marital status
Married or living with a partner (ref: unmarried)	−0.44	0.71	.54
Public assistance
Receiving supplemental security income (ref: no)	−0.13	1.39	.93
Medicaid (ref: no)	−0.64	0.74	.39
Number of individuals in the social network ≥ 2 (Ref: 0–1)	−0.48	0.53	.38
Living arrangement
Lives with others (ref: alone)	0.61	0.81	.46
Neighborhood social cohesion	0.85	0.16	<.001
Number of ADL help (range: 0–4)	−0.40	0.29	.18
Needs factors
Number of comorbid conditions (range: 0–6)	0.11	0.34	.74
Depression	−1.39	0.65	.04
Anxiety	−3.51	0.88	<.001

Bolded figures represent results that are statistically significant at p < .05.

Discussion

Among non-institutionalized, community-dwelling older adults with diabetes, 26% were classified as homebound, including both completely and semi-homebound individuals. These homebound individuals reported significantly lower subjective well-being scores compared to their non-homebound counterparts. Interestingly, within the homebound population, non-Hispanic Black and Hispanic older adults with diabetes exhibited significantly higher well-being scores than non-Hispanic White individuals. Moreover, greater neighborhood social cohesion was positively associated with higher subjective well-being, suggesting the importance of supportive community environments. In contrast, the presence of anxiety and depression was linked to lower well-being scores, underscoring the detrimental impact of mental health conditions on perceived well-being in this population.

Our finding that more than a quarter of older adults with diabetes were homebound has important clinical and social implications. Managing diabetes is inherently complex and significantly more challenging for individuals who are functionally impaired, presenting a profound challenge for affected individuals, their families, and the broader healthcare and social support systems (Bustillos & Sharkey, 2020; Scarton et al., 2014). Moreover, the multiple comorbidities and diabetes related complications associated with homebound older adults with diabetes necessitate coordinated, multidisciplinary care that is often unavailable to them due to their inability to access office-based primary and specialist care (Ida et al., 2020; Musich et al., 2015; Oseroff et al., 2023). Home-based primary care (HBPC) is associated with reduced hospitalization rates, improved life satisfaction, and enhanced quality of life in homebound older adults (Stall et al., 2014; Zimbroff et al., 2021). However, prior studies report that only 12% of homebound older adults have access to HBPC (Ornstein et al., 2015). There is a need to expand access to this valuable resource to ensure that this population gets the care they need. Moreover, the dual burden of being homebound and having diabetes intensifies physical, emotional, and logistical strains, underscoring the urgent need for innovative, integrated care models specifically designed to meet the unique needs of older adults with diabetes who are homebound.

We found that homebound older adults with diabetes had significantly lower well-being scores compared to those who were not homebound. Prior studies have reported poor physical and mental health among the homebound older adult population (Qiu et al., 2010), and that individuals with diabetes have poorer mental health compared to those without diabetes (The Lancet Diabetes and Endocrinology, 2024). The poorer subjective well-being among homebound older adults with diabetes compared to those who are not homebound may be attributed to a higher number of co-morbidities, depression, anxiety, impaired ADL, and reported lower neighborhood social cohesion scores among homebound older adults with diabetes in this study.

In this nationally representative US sample, non-Hispanic Black and Hispanic homebound older adults with diabetes had higher subjective well-being scores than their non-Hispanic White peers. Like this study, prior research has shown racial differences in subjective well-being among the US population, with older studies reporting lower levels of subjective well-being among racial and ethnic minority groups compared to non-Hispanic White individuals (Barger et al., 2009; Hughes & Thomas, 1998; Stevenson & Wolfers, 2012; Thomas & Hughes, 1986). However, more recent studies report similar or higher levels of happiness and life satisfaction among Black and Hispanic older adults (Tang et al., 2019; Wadsworth & Pendergast, 2021). These findings may be attributed to racial differences in social support, although social support was not measured in this study. Prior studies have shown that ethnic minority families provide more emotional and instrumental support to their sick relatives in the context of chronic illness (Peyrot et al., 2015; Pharr et al., 2014). For instance, in a study examining ethnic group differences in family support for individuals with diabetes in the US, Peyrot et al. (2015) found that patients with diabetes and their family members who were racial minorities rated family support as more frequent and more helpful than did White non-Hispanic patients and their family members. Similarly, Tang et al. (2019) reported that social support contributed to the highest percentage of variance in subjective well-being among both White and Black participants.

Further, we found that greater neighborhood cohesion was positively and significantly associated with subjective well-being. This finding aligns with that of other studies (Williams et al., 2020; Zhang et al., 2020). For instance, Zhang et al. (2020) found that among Chinese older adults in Hawaii, neighborhood social cohesion is positively and significantly associated with higher levels of life satisfaction. Conversely, older adults living in neighborhoods lacking social cohesiveness experienced lower well-being (Cramm & Nieboer, 2015). Additionally, social cohesion is inversely correlated with depression in older adults (Chen et al., 2015; Miao et al., 2019).

Depression and anxiety were negatively associated with well-being in this study. Prior studies have also found negative associations between anxiety, depression, and subjective well-being (Li et al., 2023; Malone & Wachholtz, 2018). For instance, Li et al. (2023) found that higher levels of subjective well-being significantly reduced perceived depression among Chinese participants. Given the high rates of depression and anxiety in homebound older adults with diabetes, as seen in this study and others (Ida et al., 2020; Negrón-Blanco et al., 2016; Oseroff et al., 2023; Qiu et al., 2010), there is a need to explore different strategies to address anxiety and depression in this population to enhance their sense of well-being. Programs that promote social engagement and connectedness, such as social and group-based activities (Chen et al., 2015; Miao et al., 2019; O’Rourke et al., 2018), may enhance feelings of well-being in homebound older adults with diabetes.

Overall, this study suggests that social and family support, as well as promoting neighborhood social cohesion, may improve subjective well-being in community-dwelling, homebound older adults with diabetes. We recommend that healthcare providers adopt a comprehensive approach that integrates the medical and psychosocial needs of homebound older adults to enhance their overall well-being. In addition, studies are needed to develop and test interventions that will enhance psychosocial well-being in homebound older adults with diabetes.

This study is not without limitations; its cross-sectional design makes it challenging to infer causality. Also, data were collected through self-reports, which are prone to bias. Additionally, while the NHATS dataset provides a valuable framework for identifying homebound status, its definition, based on the ability and frequency of leaving the home within a 1-month period, may not fully capture the distinction between temporary and persistent homebound states. This limitation should be taken into account when interpreting prevalence estimates. Grouping individuals who were completely homebound and those who were semi-homebound may have obscured differences between the two subgroups. Furthermore, subjective well-being was not collected via proxy report, therefore limiting our ability to examine social well-being among older adults with advanced dementia or other illnesses or disabilities that limit their ability to independently participate in an interview. This study involves an analysis of pre-collected data, which limits us to only the variables available in the dataset.

Conclusion

Among community-dwelling, homebound older adults with diabetes, greater neighborhood social cohesiveness was found to have a positive influence on subjective well-being. Non-Hispanic Black and Hispanic homebound older adults with diabetes have higher well-being scores than their non-Hispanic White counterparts. Conversely, anxiety and depression were negatively associated with subjective well-being. Implementing interventions that enhance neighborhood social cohesion and social support and widening access to services that promote mental health may be particularly beneficial in improving subjective well-being among homebound older adults with diabetes.

Footnotes

Acknowledgements

NHATS is sponsored by the National Institute on Aging (NIA grant number U01AG032947) and was conducted by Johns Hopkins University.

ORCID iD

Sandra Iregbu

Ethical Considerations

As the NHATS dataset is publicly available and fully de-identified, this study was deemed exempt from review by the Institutional Review Board at the University of Wisconsin–Milwaukee, study ID 25.250.

Informed Consent Statements

NHATS participants provided informed consent during the initial data collection, which included agreeing to the use of their information in future research.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Data Availability Statement

This study used the National Health and Aging Trends Survey data, which is publicly available at

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Factors Associated With Subjective Wellbeing in Community-Dwelling Homebound Older Adults With Diabetes in the US

Abstract

Background:

Methods:

Results:

Conclusion:

Keywords

Introduction

Methods

Study Sample

Homebound Status

Measures

Independent Variables

Subjective Well-Being

Statistical Analysis

Results

Discussion

Conclusion

Footnotes

Acknowledgements

ORCID iD

Ethical Considerations

Informed Consent Statements

Funding

Declaration of Conflicting Interests

Data Availability Statement

References