Abstract

This study aims to enhance the existing literature on the sense of belonging by exploring its impact on health-related quality of life among older adults in New York City. It also examines how this sense of belonging varies across different demographic characteristics and health conditions. We conducted a cross-sectional study using the Sense of Belonging Instrument-Psychological State (SOBI-P) and Health-related Quality of Life-4 (HRQOL-4) to collect online data from 378 participants, all aged 60 and above, residing in the five boroughs of New York City. Our findings indicate that males have a lower sense of belonging, while Black/African Americans scored significantly higher than those in the 'other' category. Additionally, individuals with hearing and visual impairments scored significantly lower on the sense of belonging compared to those with other health conditions. The study also indicates that a stronger sense of belonging is linked to better perceived health. Conversely, it found that a lower sense of belonging is associated with more days of poor physical and mental health, which in turn are connected to fewer days of engaging in usual activities. Recommendations to enhance the sense of belonging among older adults are provided.

1. Introduction

Humans harness the safety and security of our social circles not merely to survive, but also to thrive and achieve well-being ¹ However, in later life, many individuals experience a diminution in the aggregate magnitude of their social network ^{2, 3}, the periodicity of their social interactions ⁴ and the count of individuals from whom they procure emotional and social support ⁵. This is because individuals aged 50 and above have a higher propensity to encounter many risk factors that may contribute to or intensify feelings of social isolation or loneliness ⁶. This could be attributed to the bereavement of their spouse and friends, solitary living conditions, relinquishment of societal roles, limited mobility and cognitive capabilities, chronic diseases, worsening vision and hearing, and cessation of favorite activities, all of which contribute to a reduction in their ability to sustain social interaction ^{2, 7, 8}. According to the report from the National Academies of Sciences, Engineering and Medicine ⁶, more than one-third of Americans aged 45 and above express feelings of loneliness, and approximately a quarter of adults aged 65 and over are deemed to be socially estranged. Older adults who experience social isolation are at increased risk of experiencing loneliness ⁸. The Gallup National Health and Wellbeing Index ⁹ showed that individuals living in large urban areas are more likely to report experiencing significant loneliness, with a rate that is notably higher (20%) than those in rural areas (12%).

Loneliness is postulated to emanate from a human need to belong and humans inherently strive to build and maintain a minimum quantity of social connections, indicating a natural inclination towards fostering a sense of belonging ¹⁰. While loneliness and a sense of belonging can often be used interchangeably, a sense of belonging is characterized as the feeling that one is an integral part of a social system or environment ¹¹. In other words, by accentuating the subjective and emotional experience, the experienced sense of belonging is firmly within the individual’s appraisal of their own fit and valued involvement among others ¹¹. Research has found that a sense of belonging is crucial for older adults, as the process of aging often leads to increased social isolation and loneliness, which can have significant effects on one’s health ^{12, 13}. A high sense of belonging has been linked to positive health outcomes such as better physical and mental health, longer lifespan, and quicker recovery from illness ¹⁴. For example, a study by Kitchen and his colleagues ¹⁵ found that a stronger connection to one’s local community correlates with better physical and mental health, lower stress, stronger social support, and increased physically activity. This sense of community is more pronounced among women residing in rural settings and those who are financially comfortable. Choenarom and his colleagues ¹⁶ emphasized the psychological facets of sense of belonging and found that there is a negative correlation between a sense of belonging and both stress and depression. In other words, as sense of belonging increases, levels of stress and depression decrease.

The psychological experience of sense of belonging, such as valued involvement and fit, has been found to impact overall health among the aging population, particularly a reduction in anxiety about one’s fit within their community. However, only 32% of American adults feel that they belong nationally, meaning that 68% feel excluded and that they do not fit in ¹⁷. In New York City, 20% of older adults above the age of 45 years felt a sense of psychological distress in 2022, and psychological distress was lower among older adults that had strong social ties and support within their community ¹⁸. Several research studies have shown that limited contact from family and friends negatively impacts sense of belonging among older adults, resulting in higher levels of stress and anxiety ^{19, 20, 21}. Disruptions in daily routines and social contact within the community also negatively impacts quality of life in older adults ^{22, 23, 24}. It has also been found that mental health is negatively impacted by increased depression and loneliness among older adults ^{21, 25}. These negative health outcomes can sometimes compound the overall quality of life among older adults, which may result in lower life expectancy.

Defining quality of life is challenging due to its multifaceted nature involving various parameters such as economic stability, health-related factors, and environmental conditions. Particularly, health-related quality of life demotes an individual’s self-perceived health status, reflecting their subjective perceptions, satisfaction, and the importance they attribute to various health domains, including physical, mental and social aspects ^{26, 27, 28, 29, 30}. It transcends simple metrics like life expectancy or absence of disease, emphasizing instead on the capacity to respond to factors in physical and social environments to maintain a healthy life ³¹. A crucial aspect of caring for older adults is to sustain or enhance their quality of life. Hence, comprehending how various social environments, such as a sense of belonging, affect health-related quality of life is a pivotal agenda for an aging society.

The health outcomes of sense of belonging in older adults have been well documented, but these studies often focus on social ostracism, loneliness, social acceptability, community integration, and/or place attachment, rather than their psychological experiences with others. Moreover, while one study has made efforts to explore the different levels of loneliness among older ethnic minority people through qualitative study ³², there remains a substantial gap in research on sense of belongingness among older adults with different demographic characteristics and health conditions, particularly for those residing in metropolitan areas. Existing literature has reached a consensus that a strong sense of belonging has been linked to positive health outcomes. However, the crucial research question that requires comprehensive examination across various populations is how a sense of belongingness relates to individual’s health-related quality of life, which extends beyond just the absence of illness or infirmity. Hence, this study aims to address the above gaps in the literature review by examining sense of belonging and its impact on health-related quality of life. To accomplish the study aim, five research questions were formulated: (1) how does a sense of belonging affect self-rate health? (2) how does a sense of belonging affect physical health? (3) how does a sense of belonging affect mental health? (4) how does a sense of belonging affect activity limitation? and (5) are there any differences in the sense of belongingness among older adults with different demographic characteristics and health conditions in New York City?

2. Materials and Methods

In this cross-sectional study, we collected data from 378 participants using an online survey that was distributed through Qualtrics. The participants, all aged 60 years and above, were residents of the five boroughs of New York City. The survey included demographic questions to better understand the diverse population of New York City and employed two survey instruments: the Sense of Belonging Instrument (SOBI) – Psychological State subscale (SOBI-P), and the Health-Related Quality of Life scale (HRQOL-4).

This instrument is an eighteen-item subscale (negatively worded) of the sense of belonging instrument, developed by Hagerty and Patusky ³³ to measure the psychological perception and experience of belonging (i.e., valued involvement and fit). Respondents rated the items on a 4-point Likert scale, ranging from 1 (strongly disagree) to 4 (strongly agree). Examples of items from the SOBI-P are: “If I died tomorrow, very few people would come to my funeral” (valued involvement); and “I wonder if there is any place on earth where I really fit in” (fit).

This instrument is a set of 4 questions, developed by the Centers for Disease Control and Prevention ³⁴ to measure self-perceived health status, including self-rated health, physically unhealthy days, mentally unhealthy days, and activity limitation days. The single-item self-rated general health question from the HRQOL-4 (Q1) is an ordinal variable that asks, “Would you say that in general your health is,” with five response levels scored from 1, indicating “excellent” health, to 5, indicating “poor” health. The remaining three items from the HRQOL-4 (Q2, Q3, and Q4) were analyzed as continuous variables to measure health over the past 30 days. These items included the number of physically unhealthy days (“Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?”), the number of mentally unhealthy days (“Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”), and the number of days with activity limitation (“During the past 30 days, approximately how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation”).

Body Mass Index (BMI): Participants BMIs were calculated by their mass (kg) divided by their height (m)². BMI was categorized as underweight (below 18.5), normal (18.5-24.9), overweight (25.0-29.5), and obese (30 and above).

Health Conditions: Participants were asked to identify all the health conditions that have at the time they answered the survey. The number of health conditions were tallied for each participant and categorized as 0-1, 2-3, and 4 or more health conditions.

Participant characteristics were summarized using cell counts and percentages for categorical variables; N, mean, and standard deviation (SD) were used to summarize numeric variables. The participant characteristics included demographics (age, gender, race/ethnicity, household income, education, BMI category, and number health conditions), psychological sense of belonging index (SOBI-P), and health-related QoL variables HRQOL-4. The HRQOL-4 variables included the questions: 'in general your health is', 'number days in the month your physical health was not good', 'number days in the month your mental health was not good', and 'number days in the month poor physical or mental health kept you from your usual activities'.

SOBI-P was analyzed using an ANCOVA model that included all demographic variables as predictors; the statistical tests of each demographic variable (overall F test and pairwise comparisons to a reference category) were adjusted for the other demographics. SOBI-P was further analyzed, using the 2-sample t-test as a function of the reported presence or absence of 15 individual health conditions. Each condition was analyzed separately, and due to the small number reported for many conditions, no attempt was made to adjust for covariates.

The Likert-type general health variable 'in general your health is' was analyzed as a function of demographics similarly to SOBI-P using a proportional odds regression model for ordinal outcomes. The distribution of the 'number days' health-related QoL variables is heavily concentrated at zero days, at a rate beyond that which can be analyzed using a count model like Poisson or negative binomial. Consequently, these variables were analyzed using a zero-inflated negative binomial (ZINB) model using the same covariate approach as that used for ‘in general your health is’. The ZINB models the overabundance of zeros via a mixture of a 'zero' component (logistic regression model) and a count component (negative binomial model with log link) ³⁵. 95% confidence intervals and p-values associated with ZINB model-based estimates of the mean and the proportion of zeros were based on parametric bootstrap sampling ³⁶.

The general health variable was further analyzed using a proportional odds model that included SOBI-P as the key predictor, while adjusting for demographics by including them as covariates. The ‘number days’ variables were analyzed similarly as a function of SOBI-P using the ZINB model. As this study was exploratory in nature, no adjustment for multiple comparisons was planned or implemented. No imputations of missing data were used; only observed data were analyzed. P-values < 0.05 were considered statistically significant. All analyses were conducted using R version 4.3.3 (R Core Team, 2024). The proportional odds regression model and the ZINB model were fit using the VGAM package (version 1.1-10) ³⁷ and the pscl package (version 1.5.9), respectively. The regression models underlying summaries in selected tables and figures are detailed in the Supplemental Tables.

3. Results

Table 1 shows that 378 individuals aged 60 and older, and all but one residing in New York City, participated in the study. However, 20 respondents had missing age data, and one individual reported living in Washington DC. The age distribution was as follows: 47.5% participants were aged 60-65, 41.3% were aged 66-75, and 11.2% were over 75. Among the respondents, 57.9% were female and 42.1% were male. In terms of ethnicity, 48.5% identified as white, 27.6% as Hispanic, 15.6% as African American, and 8.2% as other. The distribution across New York City boroughs was Manhattan (25.2%), Brooklyn (24.1%), Queens (24.4%), Bronx (18.8%), and Staten Island (7.6%). Annual household income varied, with the largest group (34.4%) reporting incomes between $35,000 and $74,999. Additionally, 78.8% of respondents had a college degree, 64.1% were overweight or obese, and 66.2% had two or more health conditions. The participants had an average sense of belonging score of 55.5 out of 72, with scores ranging from 19 to 72. Regarding general health, 6.9% rated their health as excellent, 27.0% as very good, 41.8% as good, 18.5% as fair, and 5.8% as poor. Over the past 30 days, participants reported an average of 6.8 days of poor physical health and 4.4 days of poor mental health. Additionally, they experienced an average of 4.0 days where poor physical or mental health prevented them from engaging in usual activities like self-care, work, or recreation.

Table 2a summarizes the ANCOVA analysis of Sense of Belonging (SOBI-P) versus demographic variables. This table reports a statistically significant association with gender (p=0.014), where females had a higher SOBI-P mean score of 56.7, compared to 54.3 for males. Within race, a statistically significant pairwise comparison was observed for Black/African American versus ‘Other’ (p=0.045), where the SOBI-P mean score for Black/African American was 57.8, compared to a lower mean score of 53.1 for ‘Other’. However, compared to Black/African Americans, there were no statistically significance differences versus Hispanic or White. None of the other demographics showed a statistically significant association with SOBI-P. No statistically significant associations were observed for age, household income, education, BMI category, and number health conditions.

Table 2b summarizes the t-test analysis of SOBI-P versus individual health conditions. Attention was restricted to health conditions for which the number of participants reporting the condition was 10 or more. This table shows a statistically significant association with hearing impairment (p=0.043) and visual impairment/eye health issues (p<0.001). For each of these conditions, a lower mean score for SOBI-P was observed for those reporting the condition: 51.9 versus 55.9 for hearing impairment, and 50.3 versus 56.3 for visual impairment/eye health issues.

Table 3a presents the proportional odds regression analysis of the self-reported general health status versus the demographic variables. Participants who earned a graduate degree were more likely to report favorable general health compared to those who earned a high school degree or less (p=0.006); the percentage reporting very good or excellent health among those with a graduate degree was 46.2%, compared to 24.5% for those with a high school degree or less. Individuals who were obese reported less favorable general health compared to those who were underweight or normal weight (p=0.022); the percentage reporting very good or excellent health among obese participants was 24.8%, compared to 38.2% for the underweight/normal weight participants. Statistically significant comparisons (p<0.001) to the lowest health conditions category (0-1 conditions) were observed for the 2-3 and 4+ conditions categories; the percentage reporting very good or excellent health was 53.3%, 26.9%, and 16.8% for the 0-1, 2-3, and 4+ categories, respectively. No statistically significant associations were observed for age, gender, race, and household income. Therefore, in this sample, general health status was positively associated with higher educational attainment, while negatively associated with increased number of health conditions and BMI.

Table 3b presents the zero-inflated regression analysis of the number of days within a month that their physical health was not good versus the demographic variables. The only demographics with a statistically significant association with this 'number days' variable were number of health conditions and race. Participants who had four or more health conditions had a statistically significantly higher mean score (p<0.001) of days when their physical health was not good (mean=10.7), compared to those who had at most one health condition (mean=4.5). Table 3b also shows that the model-based percentage with zero days decreased from 65.0% for those with 0-1 conditions to 26.1% for those with 4+ conditions. White participants had a statistically significantly higher mean score (p=0.036) of days when their physical health was not good (mean=7.7) compared to Black/African American participants (mean=4.4); the model-based percentage with zero days was 44.1% for White participants compared to 64.1% for Black/African American participants.

Table 3c summarizes the zero-inflated regression analysis of the number of days within a month that their mental health was not good versus the demographic variables. Although none of the pairwise comparisons of means to the lowest age category was statistically significant, the overall test of the association between age and number days their mental health was not good was statistically significant (p=0.004), driven by the increasing trend in the model-based percentage of zero days: 46.9%, 57.2%, and 83.3% for the 60-65, 66-75, and 76+ age categories, respectively. Similarly, none of the pairwise comparisons of means to the lowest number health conditions category was statistically significant, but the overall test of the association with number health conditions was statistically significant (p=0.001), driven primarily by a lower percentage of zero days in the highest category: 67.6%, 60.1%, and 34.0% for 0-1, 2-3, and 4+ conditions, respectively. White participants had a statistically significantly higher mean score (p<0.001) of days when their mental health was not good (mean=5.7) compared to Black/African American participants (mean=2.7); the percentage with zero days was 44.6% for White participants compared to 66.4% for Black/African American participants. Hispanic participants also had a statistically significant higher mean score (p=0.024; mean=4.1) compared to Black/African American participants. Their percentage of zero days (57.0%) was also lower than that for Black/African American participants. No statistically significant associations were observed for gender, household income, education, and BMI category.

Table 3d presents the zero-inflated regression analysis of the number of days within a month that poor physical or mental health kept them from their usual activities, versus the demographic variables. Statistically significant associations with activity limitations were observed for race and number health conditions. White participants had a statistically significantly higher mean score (p=0.007) of activity limitation days (mean=4.8) compared to Black/African American participants (mean=2.6); the model-based percentage with zero days was 58.4% for White participants compared to 73.2% for Black/African American participants. Hispanic participants also had a statistically significant higher mean score (p=0.013; mean=4.3) compared to Black/African American participants; their percentage of zero days (54.2%) was also lower than that for Black/African American participants. Health conditions negatively impacted usual activities. Participants with four or more health conditions had a statistically significantly higher mean score (p<0.001) of activity limitation days (mean=6.8) compared to those with at most one (mean=2.6); the percentage of zero days was 36.9% for the 4+ category, compared to 74.6% for the 0-1 category.

Figure 1 summarizes the proportional odds analysis of the general health variable ‘in general your health is’ by SOBI-P. The regression coefficient for SOBI-P was positive and statistically significant (p=0.005), indicating that a greater sense of belonging is associated with more favorable answers to the general health question. Figure 2 reports the model-based percentages for 3 selected SOBI-P values: the 15th, 50th, and 85th percentiles; these percentiles were chosen to represent a lower, middle, and upper value for SOBI-P, respectively. The percentages in the very good/excellent categories were 25.5%, 30.7%, and 30.0% for the 15th, 50th, and 85th percentiles, respectively. Full details of the model are included in Supplemental Tables.

Figures 2a-c summarize the ZINB analyses of the health-related ‘number days’ variables, by SOBI-P. A statistically significant association (p<0.001) with SOBI-P was found for each ‘number days’ variable. As shown in Figure 2a, a greater sense of belonging is associated with less days their physical health was not good, and a greater percentage of zero days physical health was not good. Similar trends are evident for the ‘number days’ variables in Figures 2b and 2c. Full details of the ZINB models are included in Supplemental Tables.

4. Discussion

This present study examined if the sense of belonging influences the quality of life among older adults particularly, within New York City and how this sense of belonging varies across different demographics characteristics, and health conditions. We found that female participants scored higher on SOBI-P than male participants. Related studies conducted in the U.S. were scarce, however, Cornwell and Cagney ³⁸ investigated neighborhood social cohesion, social ties, and danger among aging adults, and similarly found that older women tend to have greater neighborhood cohesion and ties than older men, but also have a higher perception of danger within their neighborhoods. Levasseur and his colleagues ³⁹ also investigated an association between resilience, community belonging, and social participation among community dwelling older adults in Canada, with 2,560 individuals identifying as female and 2,100 individuals identifying as male, finding that community belonging was also stronger among female participants. Our findings may ensure that older men may experience a lower sense of belonging. To address this issue effectively, there is a need for empirical studies to investigate the underlying reasons for these gender differences.

When considering differences in sense of belonging by race, the current study found that Black/African Americans scored significantly higher than the ‘other’ category, however, there were no other significant differences between the Black/African Americans, Hispanic, or White participants. In contrast, Clark and his colleagues ⁴⁰ found that White community members reported higher sense of belonging than Black community members in a Wisconsin neighborhood; however, the ages of the participants among this difference were not identified. Although research investigating differences in sense of belonging among the aging population in the United States is limited, Gonyea and his colleagues ⁴¹ investigated neighborhood safety and sense of belonging among 216 older adults living in the U.S., with a sample made up of 50% that identified as Black and 45% that identified as Latino and found that a greater sense of belonging positively impacted depressive symptoms. Birditt and his colleagues ⁴² explored the differences in stress, life changes, and social ties among a range of ages in a predominantly White sample (74%) but found no significant racial differences when examining whether pandemic-related stress and social isolation predicted psychological well-being.

Our study also found the sense of belonging varies among individuals with different health conditions. Indeed, individuals with hearing impairments and visual impairments scored significantly lower on the SOBI-P compared to those with other health conditions. While previous literature lacks comprehensive research on the differences in health conditions, some studies have highlighted significant findings. Huang ⁴³ and Shukla ⁴⁴ discovered that older adults with hearing loss tend to experience increased loneliness and social isolation. Additionally, a study involving 456 middle-aged and elderly participants with visual impairments reveled that these individuals reported high levels of loneliness and low social support ⁴⁵. Indeed, researchers have discovered that a lack of sense of belonging in older adults can lead to feelings of loneliness ^{46, 47}. In the same vein, a possible explanation for our results is that health conditions, such as hearing and visual impairments, lead to more social isolation and loneliness. This increased isolation may contribute to a lower sense of belonging, particularly within these populations. Consequently, our findings highlight the importance of further research to better understand and address these disparities.

Additionally, the current study revealed a positive association between sense of belonging and quality of life, including four variables: general health, physical health, mental health, and physical and mental health kept from usual activities, and this association remained significant after adjustment for demographic variables. Our findings suggest that a stronger sense of belonging is linked to better perceived health. This aligns with previous research findings, which consistently shows a significant correlation between belongingness and health ^{15, 48, 49, 50}. Our findings confirm that fostering a strong sense of belonging for older adults in urban areas is crucial, as it significantly contributes to their overall health.

Our study also found that a sense of belongingness is strongly associated with physical health. It showed a greater sense of belonging is linked to fewer days of poor physical health and a higher percentage of days with good physical health. Indeed, previous studies have found that loneliness is linked to various physical health issues, including self-reported chronic diseases, high cholesterol, and diabetes throughout life ⁵¹. Additionally, loneliness significantly impacts physical health of older adults, being negatively associated with higher blood pressure, poorer sleep quality, and stress responses in the immune system ⁵². However, to our knowledge, no study had yet examined the association of a sense of belonging with physical health in the older adult population. Consequently, possible explanation for our findings is that a lower sense of belonging may result in increased loneliness, which in turn could be positively linked to poorer physical health.

It is well established that older adults are more inclined to sustain positive mental health if they feel a sense of belonging ^{53, 54}. Studies have shown that a low sense of belonging has been identified as a strong predictor of increased anxiety ⁵⁵, and depression ^{56, 57}, suicidal thoughts ⁵⁶, and a lack of sense of purpose ⁵⁸. Our findings parallel the extant literature by suggesting that a greater sense of belonging is associated to fewer days of experiencing poor mental health of older adults. According to the report from New York City Department of Health and Mental Hygiene (NYC DHMH), approximately 8% of NYC residents aged 65 and older encounter mental health challenges stemming from social isolation, financial insecurity, and limited access to mental health services ⁵⁹. Thus, emphasizing the importance of a sense of belonging by strengthening social support networks and creating a more supportive environment for older adults is crucial for preventing mental health issues.

Lastly, this study provides evidence that a greater sense of belonging is linked to fewer days where poor physical or mental health prevents older adults from engaging in their usual activities. The findings present novel insights into the relationship between a sense of belonging and their usual activities of older adults as it addresses a gap in the existing literature, with no prior studies exploring this specific connection. More importantly, the body of knowledge on sense of belonging has predominantly concentrated on the community aspect, where individuals feel part of a larger group. This sense of social attachment among individuals signifies their external affiliations and membership within the community ¹⁵. However, our study is more about the individual’s internal feelings of being valued and fitting in. Therefore, our research addresses an important gap in the literature by elucidating the significant impact of the psychological experience of belonging on the health of older adults in urban areas. Future studies should continue to investigate this relationship to develop targeted interventions that promote sense of belonging and improve the health of older adults.

There are several limitations to this research. To begin with, the online survey reflects a specific subset of older adults with varying levels of proficiency in using web-based information. Although the survey was designed to be completed on smartphones to accommodate the general older population, not all older adults are familiar with smartphones. Additionally, there may have been memory distortion among participants, especially older adults, who might have encountered difficulties in accurately recollecting past days of poor health, thereby potentially compromising the veracity of their responses. Another limitation is the study's cross-sectional design, where data were collected at a single point in time. Consequently, the exposure and outcome variables were analyzed concurrently, precluding the establishment of a cause-and-effect relationship. Lastly, our sample comprised of older adults from New York City, thus the findings cannot be extrapolated beyond this population.

Psychological sense of belonging is vitally important for aging, community-dwelling adults’ quality of life and mental and physical well-being. Torres ⁶⁰ suggested that there are several factors that influence sense of belonging in aging adults in relation to establishments throughout their communities and they are proximity (i.e., distance from home), cost and accessibility, physical design and layout that facilitates socialization, and surveillance (i.e., businesses that allow customers to stay as long as they want). While proximity to these establishments is an aspect that influences sense of belonging, proximity to family, friends, and, particularly, young people is another ⁶¹. Ratnayake ⁶¹ recommended strategies and interventions to further influence sense of belonging for aging adults such as education, advocacy, infrastructure and accessibility (i.e., the built environment), and financial support. One suggested approach was the introduction of the Intergenerational Service-Learning model, which was introduced as a student club at the University of Delaware to encourage college students to assist with daily responsibilities in supporting aging individuals with chronic disease ⁶¹. While this is one strategy in improving sense of belonging in community-dwelling, aging populations, there are some that include recreation and leisure opportunities.

Smith and his colleagues ⁶² suggested that accessible recreation and leisure participation decreases feelings of loneliness and solitude in aging adults, particularly among women. In their study, it was suggested several interventions to reduce loneliness and social isolation: leisure participation (physical activity, social leisure, and home-based leisure) and visits to community recreation and cultural facilities, including cost and ease of getting into ⁶². Inoue and his colleagues ⁶³ even suggested that support and morale fostered by attending local or regional sports games as team fans provided significant emotional support that influenced a positive sense of belonging. It was also suggested several approaches and strategies to improve sense of belonging among the aging population such as intergenerational programs ⁶¹, aging-friendly communities, community-based group physical and recreational activity, and using technology ⁶⁴.

The introduction of and access to technology significantly improves social connectedness for older adults living at home ⁶⁴. The use of mobile phones and access to the internet/email assists aging adults with maintaining communication with friends and family, accessing digital media, and educational resources. An emerging AI-driven tool (ElliQ) was designed to improve social relationships, improve health outcomes, moods, and reduce loneliness among older adults ⁶⁵. Access to technology for educational purposes is boundless and can help aging adults to not only learn more about how to improve their social connectedness and engagement, but other factors that influence their mental and physical health such as diet, sleep, physical activity, and stress ⁶⁶. The relationship between these factors and health-related outcomes such as cognitive decline are vital to address, and as Eubank and his colleagues ⁶⁶ suggested, requires additional educational interventions among aging, community-dwelling African Americans, Hispanic Americans, and other minority populations in the United States.

5. Conclusions

Sense of belonging in older adults influences their health-related quality of life. This research highlighted specific variables, particularly physical health, mental health, activity limitation, and overall wellbeing which are associated with sense of belonging. In addition, sense of belonging was positively associated with being female, and Black, while negatively associated with hearing loss, and visual impairment. Numerous interventions can be used to improve sense of belonging among older adults, such as recreation, leisure activities, using technology, and joining community-based support groups. While this list is not exhaustive, it provides actionable suggestions for older adults to feel connected to others. If older adults engage in some of these interventions, they may experience greater sense of belonging, which could positively improve their overall health and specific health-related quality of life.

ACKNOWLEDGEMENTS

The study was funded through the City University of New York, Lehman College Tax Levy, Dean's budget.

Authors have no competing interests.

Supplemental

Table 1: Proportional Odds Regression Model for ‘in general your health is’ by SOBI-P

Table 2: Zero-Inflated Negative Binomial Analysis of ‘Number Days Your Physical Health Was Not Good’ by SOBI-P

Table 3: Zero-Inflated Negative Binomial Analysis of ‘Number Days Your Mental Health Was Not Good’ by SOBI-P

Table 4: Zero-Inflated Negative Binomial Analysis of ‘Number Days Poor Physical or Mental Health Kept You From Usual Activities’ by SOBI-P

References