Subjective social status and functional and mobility impairments among older adults: life satisfaction and depression as mediators and moderators
BMC Geriatrics volume 23, Article number: 685 (2023)
While functional and mobility impairments (FMIs) have garnered the attention of health researchers in low and middle-income countries (LMICs), including India, research has yet to explore whether and to what extent the perception of one’s social status is associated with FMIs. We fill this gap in the literature by examining (1) the association between subjective social status (SSS) and FMIs among older adults in India and (2) whether this association between SSS and FMIs is mediated and moderated by life satisfaction and depression.
Data come from the 2017-18 wave 1 of the Longitudinal Aging Study in India (LASI) with a sample of 31,464 older adults aged 60 years and above. FMIs were assessed using established scales on impairments in activities of daily living (ADLs), instrumental activities of daily living (IADLs), and mobility. SSS was assessed using the Macarthur scale. Life satisfaction was measured using responses to five statements gauging respondent’s overall satisfaction with life. Depression was calculated using the shortened version of the Composite International Diagnostic Interview (CIDI-SF). Multivariable regression was employed to examine the association between variables, and the interaction terms and Karlson-Holm-Breen (KHB) method were used separately to test the mediation and moderation effects.
39.11% of the sample had a low SSS, 8.26% were depressed, and 32.07% reported low life satisfaction. A total of 8.74%, 10.91%, and 8.45% of the study population reported at least one impairment in ADL, IADL, and mobility, respectively. Older adults in the higher SSS group were less likely to have ADL impairment (beta: -0.017, CI: -0.030, -0.0032) and mobility impairment (beta: -0.044, CI: -0.076, -0.013). Depression moderated the association between SSS and mobility impairment (p-value: 0.025), and life satisfaction moderated the association between SSS and ADL impairments (p-value: 0.041) and SSS and IADL impairments (p-value: 0.037). Depression mediated 20.28%, 31.88%, and 18.39% of the associations of SSS with ADL, IADL, and mobility impairments, respectively. Similarly, life satisfaction mediated 23.24%, 52.69%, and 27.22% of the associations of SSS with ADL, IADL, and mobility impairments.
That SSS is associated with FMIs among older Indians, even after considering their objective socioeconomic status (SES), suggests that the use of SSS is relevant to the study of health inequalities in India. The finding that life satisfaction and depression mediate and moderate this association is crucial in pinpointing those older Indians at risk of the functional and mobility-related repercussions of lower SSS.
Functional and mobility impairments (FMIs) are important health-related challenges in later life [1, 2]. FMIs, which are assessed in many ways, driven by the conceptual models of disablement, indicate one’s ability to function independently [3,4,5,6,7,8]. Given that they compromise independence, reduce social engagement [1, 9], and elevate the risk for falls , restricted access to medical services [10, 11], and premature mortality [12,13,14], FMIs constitute a major challenge to the health care systems worldwide [14, 15]. While high-income nations have had the time to acclimate to such challenges, they remain particularly daunting for low and middle-income countries (LMICs) like India that are being pummeled by the simultaneous increases in aging adults and chronic diseases [16,17,18] – both likely to increase the prevalence of FMIs.
A recent study reported that 44% of older adults in India live with functional impairments, as measured by impairments in one or more activities of daily living (ADLs), which include essential competencies for survival, such as personal hygiene, continence, dressing, bathing, ambulating or transferring; and instrumental activities of daily living (IADLs), which are essential for independent living and include managing communication with others, finances, medications, food preparation, and housekeeping [19,20,21,22]. Another study found 57% and 73% of older Indians to have high IADL and bodily impairment scores . ADL and IADL assessments are critical in determining if older adults can “age in place” or require formal care assistance, including skilled nursing. For instance, a reduced ADL capacity to move about may raise the risk of falling [8, 20, 24]. Similarly, impaired ability to carry out IADLs, like managing medications, adversely impacts health in older adults [25, 26]. Given this, identifying factors that render older adults susceptible to FMIs is critical to crafting interventions that protect them against physical decline. One factor, which remains overlooked within the Indian context, is subjective social status (SSS).
SSS is an individual’s perceived social status compared to others in their community . Individuals with higher SSS report better health than their lower-SSS peers [28,29,30]. SSS often predicts health above and beyond the objective indicators of socioeconomic status (SES), including education, occupation, income, wealth, and even caste [29,30,31,32]. This may be because SSS encapsulates the psychosocial aspects associated with SES, such as power, prestige, ties to the mainstream, society, perceived fairness, and status internalization [30, 33,34,35]. Such less obvious aspects related to an individual’s sense of self and identity are not necessarily captured by objective markers of SES that are restricted to static positions in one’s life [36,37,38]. For instance, occupation and income may not accurately reflect later life SES, given that many older adults, especially in LMICs like India, may be retired or financially dependent on their adult children [37, 38]. Likewise, for older Indian women with constrained opportunities for higher education and paid work , education and occupational ranking may not accurately represent their SES. As such, it is critical to gauge the association between SSS and FMIs in resource restrained countries.
Low SSS, which often is linked to perceived unfairness, can cause “psychological pain” and set into motion adverse physiological stress reactions (e.g., hypervigilance, increased respiration and heart rate, elevated blood pressure, sleep disturbances, cortisol secretions, and chronic inflammation) [35, 40, 41], and these amplified physiological responses over time can debilitate physical function and mobility. Low SSS may also engender a perceived lack of power to change one’s life circumstances, which could prompt risky health behaviors [42, 43] and prevent health-promoting actions , which can shape functional health. The study of stress, no doubt, identifies SSS as a powerful social stressor affecting later life functionality [31, 44, 45].
That said, the stress process framework highlights the centrality of individual characteristics as potential mediators and moderators of the association between stressors and distress . Two such characteristics are life satisfaction and depression. While high life satisfaction, which encompasses feelings of autonomy, openness, optimism, and adaptation [47,48,49], is a resource for maintaining functional health and mobility, low life satisfaction, which is associated with higher levels of physiological stress, psychosocial, and behavioral problems , may increase the risk for FMIs. While high levels of depression result in internalizing behaviors of self-blame, denial, rumination, withdrawal, and self-neglect , which can increase the risk of FMIs, individuals with lower depression may remain at relatively minimal risk of FMIs. Both high life satisfaction [49, 52] and less depression  involve preemptive coping, which increases stress resistance that may be tied to lower SSS. Older adults who are more satisfied with life may adapt better to life circumstances and problem-solve challenges attached to material hardships underlying SSS. Similarly, those less depressed may cognitively reinterpret challenges [54, 55] related to low SES as more manageable, concentrating on those aspects of their SES they believe to be less intractable.
In sum, the present study hypothesizes that older adults with lower SSS are more likely to report more FMIs. We also hypothesize that both life satisfaction and depression mediate this association. Statistically, this implies that the SSS coefficients should attenuate once we adjust for life satisfaction and depression. Lastly, we posit that life satisfaction and depression also moderate the association between SSS and FMIs, such that this association is less pronounced among older adults with higher life satisfaction and lower depression than their peers who report lower life satisfaction and higher levels of depression. Figure 1summarizes the concept of our study.
The present study utilizes the individual-level data from the first wave of the Longitudinal Aging Study in India (LASI) conducted during 2017-18. The LASI is a country-representative longitudinal survey of more than 72,000 adults aged 45 years and over across all states and union territories of the country that provides vital information on the social, physical, psychological, and cognitive health of the Indian aging population. The LASI survey was conducted through a partnership of the International Institute for Population Sciences (IIPS), Harvard T. H. Chan School of Public Health (HSPH), and the University of Southern California (USC). In the LASI wave 1, the sample selection is based on a multistage stratified cluster sample design, including a three-stage sampling design in rural areas and a four-stage sampling design in urban areas. The details of the sampling design, survey instruments, and data collection procedures are provided elsewhere [16, 56]. The present study is conducted on the eligible respondents aged 60 years and above. Thus, the total sample size for the present study was 31,464 (15,098 men and 16,366 women) older adults aged 60 years and above.
The description of the study variables and our rationale for the selected covariates are provided.
below in Table 1.
We presented the results from descriptive statistics and bivariate analysis (cross-tabulation) and p-values from Chi-Square tests, indicating the statistical significance of the differences in outcome variables across the selected background variables. Multivariable logistic regression analysis was performed to determine the association between the outcome variables (ADL/IADL/mobility impairments) and SSS, depression, and life satisfaction. Further, interaction analyses of SSS, depression and life satisfaction on outcome variables were conducted, and margin plots were presented. As a post-hoc analysis, interaction terms and the Karlson–Holm–Breen (KHB) method were used separately to examine the effect modification and percent mediation by depression and life satisfaction in the association between SSS and outcome variables. The estimates were presented as adjusted beta coefficients with a 95% confidence interval. Statistical models were adjusted for the selected predictor variables, including age, gender, schooling, marital status, living arrangement, work status, multimorbidity, community involvement, household consumption quintiles, religion, caste, place of residence, and region of the country. Individual weights were applied during the analysis to account for the cluster sampling and to provide the population-level estimates. The statistical analysis was performed using Stata 15.1.
Table 2 presents the sample characteristics. A proportion of 39.1% of the sample had a low SSS, whereas only 7.3% had a high SSS in this study. 8.3% of the sample was depressed, and 32.1% reported low life satisfaction. More than 10% of the sample was aged 80 years or older and a significant proportion (56.4%) had no formal education. 36% of the sample was widowed, and 26.5% reported never having worked more than three months during their lifetime.
Table 3 presents the prevalence of ADL/IADL and mobility impairments among the sample population. A total of 8.7%, 10.9%, and 8.5% of the study population reported at least one impairment in ADL, IADL, and mobility, respectively, whereas 8%, 25.5%, and 56.2% of older adults reported more than two impairments in ADL, IADL and mobility, respectively. Those with a high SSS (4.2%, 18.2%, and 49.6%) or high life satisfaction (5.6%, 20.5%, and 52%) had a lower prevalence of ADL, IADL and mobility impairments. Alternatively, those who were depressed had a higher prevalence of ADL, IADL, and mobility impairments (18.7%, 43%, and 74.4%).
Table 4 presents the estimates from the multivariable analysis. Older adults in the higher SSS group were less likely to have ADL impairment (beta: -0.017, CI: -0.030, -0.0032) and mobility impairment (beta: -0.044, CI: -0.076, -0.013). Older adults who were depressed were more likely to have ADL (beta: 0.071, CI: 0.056, 0.086), IADL (beta: 0.12, CI: 0.096, 0.14), and mobility impairments (beta: 0.14, CI: 0.11, 0.16). Alternatively, those who report a higher level of life satisfaction were less likely to have any ADL (beta: -0.012, CI: -0.015, -0.0079), IADL (beta: -0.014, CI: -0.022, -0.0056) and mobility impairments (beta: -0.019, CI: -0.026, -0.011) in this study.
Figures 2, 3, 4, 5, 6 and 7 present the margin plots of the interaction effects of depression and life satisfaction on the associations of SSS with ADL, IADL, and mobility impairments. As can be seen from the plots, depression moderated the association between SSS and mobility impairment (p-value: 0.025). However, it did not moderate the association between SSS and ADL impairment (p-value: 0.137) and SSS and IADL impairment (p-value:0.690). Alternatively, life satisfaction moderated the association between SSS and ADL impairment (p-value: 0.041) and SSS and IADL impairment (p-value: 0.037). However, it did not moderate the association between SSS and mobility impairment (p-value: 0.446).
Table 5 presents the estimates from the mediation analysis. Depression mediated 20.3%, 31.9%, and 18.4% of the associations of SSS with ADL, IADL, and mobility impairments, respectively. Similarly, life satisfaction mediated 23.2%, 52.7%, and 27.2% of the associations of SSS with ADL, IADL, and mobility impairments, respectively, in this study.
The present study focused on the association between SSS and FMIs and examined the extent to which this association is mediated and moderated by life satisfaction and depression. Our data support our hypothesis that older Indians who report low SSS also report higher ADL and mobility impairments, and their peers with higher SSS are less likely to report ADL and mobility deficits. SSS remains consequential for measures of functional and mobility outcomes even after accounting for objective SES. This underscores that objective and subjective measures of SES are not interchangeable. Moreover, the association between SSS and FMIs is mediated and moderated by life satisfaction and depression.
That SSS is relevant for FMIs matches findings in past literature on SSS and physical health [27, 35, 41, 42, 44, 45]. Our study contributes to this literature by exploring the linkage between SSS and FMIs among aging adults in India, an otherwise underexamined LMIC, in this body of work. Examining the relevance of SSS for health in different aging populations across different countries is important, given that research points out group-level differences in self-appraised social standing. For example, studies on SSS in the US have suggested that the positive association between low SSS and poor health is less pronounced among Black Americans relative to their non-Hispanic White counterparts [45, 78, 79], and this probably is because individuals from diverse racial, ethnic, and cultural backgrounds may draw on diverse sources for their social status [45, 80, 81]. For example, compared to their non-Hispanic White peers, Black Americans may base their social status on racial identity, group solidarity, and intergenerational harmony . An additional advantage to a study of this type is that examining the health significance of SSS for older adults in a country with different physical, social, family, and financial contexts helps clarify elements of the aging experience that are likely universal and others which may manifest out of broader macrosocial conditions specific to one country or culture, rather than aging in and of itself.
Another way in which our study contributes to the existing literature is that our findings underscore the dual role that life satisfaction and depression play in both mediating and moderating the association between SSS and FMIs. Specifically, the mediating influence of these two factors is evident in our study because low SSS is associated with lower levels of life satisfaction and higher levels of depression —and this, in turn, is found linked to both functional and mobility impairments. Adverse health outcomes like FMIs do not always directly result from low SSS. Rather, the psychosocial stress -- as manifested in reduced life satisfaction and higher levels of depression -- of being socioeconomically disadvantaged is what produces adverse health outcomes . This finding is important because higher life satisfaction and lower depression are associated with greater social engagement, health-promoting behaviors (e.g., regular exercise, healthier nutrition, use of preventive health care), reduced risky behaviors (e.g., smoking, drinking, substance use), and enhanced biologic function (e.g., less hypertension, low inflammation, low levels of cortisol) [50, 52, 82]. Social engagement and better health behaviors, in turn, are critical to sustaining functional health and mobility [82,83,84].
In contrast to mediation, the moderation analyses reveal that the association between SSS and FMIs differs among older adults based on life satisfaction and depression. Older adults who are more satisfied with their lives experience fewer negative health consequences of SSS. Moreover, those with lower levels of depression may be psychologically better equipped to withstand the otherwise negative repercussions of SSS. Older adults with higher life satisfaction and lower depression may be more likely to appraise their lower social status as challenging rather than threatening, to be optimistic and hopeful about their financial situation, and to make favorable social comparisons. It also is possible that those who are relatively more satisfied and less depressed may increase their engagement in domains where SES may be less important, such as family relationships, friendships, and volunteering.
These findings provide evidence that perceived social standing is a means through which SES is linked to physical health. This finding implies that researchers, providers, and practitioners should redouble efforts to understand how people “feel” relative to others in their social circle instead of merely focusing on the objective markers of SES. Moreover, findings here compel us to consider that if higher SSS is indeed associated with higher life satisfaction and lower depression, then applying specific cognitive-behavioral therapies to minimize a person’s sense of social inferiority may be crucial for sustaining physical health. Most interventions directly tackle health deficits (e.g., increasing access to health care and the use of pharmacologics), and some aim to reduce the stigma attached to lower SES. While such interventions are invaluable, they need to be supplemented by initiatives and efforts to reduce the stress that may accrue due to lower SSS.
Limitations and future directions
The findings of this study must be considered within the context of important limitations. First, as the project is cross-sectional, we cannot make any predictive claims. A more definitive statement about the relationships among SSS, life satisfaction, depression, and FMIs may be reached by using the forthcoming waves of LASI. Second, although life satisfaction and depression are consequential in explaining the link between SSS and FMIs, neither fully explains it. Therefore, it would be worthwhile to identify other mediators of this association. For example, SSS may either undermine social relationships or motivate individuals to invest more time and effort into them, and social relationships, and in turn, social support, are powerful predictors of physical health . Stable social relationships and social support may mediate the link between SSS and FMIs; the implications of SSS for FMIs may also be differentially distributed based on the availability, quality, stability, and type of social resources. Third, the possibility of reverse causality lingers, given that FMIs, depression, and life satisfaction, each separately and interactively, could negatively shape perceptions of social status. Fourth, primary variables of interest, including depression, in our study are self-reported, which may overstate the relationship between SSS and FMIs due to their shared variance. Notwithstanding these limitations, our study is among the first in India to evaluate the impact of both objective SES and SSS on later life FMIs. Moreover, we do so by engaging a sizeable sample of a nationally representative aging population.
In conclusion, low SSS is associated with FMIs among older Indians. This association persisted even after accounting for objective SES and other conceptually relevant sociodemographic factors. However, the relationship between SSS and FMIs is mediated and moderated by individual characteristics of life satisfaction and depression. This knowledge may give providers and practitioners additional information to identify older adults most susceptible to FMIs.
The study uses secondary data which is available at the Gateway to Global Aging Data (https://g2aging.org/).
Musich S, Wang SS, Ruiz J, et al. The impact of mobility limitations on health outcomes among older adults. Geriatr Nur (Lond). 2018;39:162–9.
Cummings SR, Studenski S, Ferrucci L. A diagnosis of dismobility–giving mobility clinical visibility: a mobility Working Group recommendation. JAMA. 2014;311:2061–2.
Karvonen-Gutierrez CA, Strotmeyer ES. The urgent need for disability studies among midlife adults. Womens Midlife Health. 2020;6:8.
Spector WD, Fleishman JA. Combining activities of daily living with instrumental activities of daily living to measure functional disability. J Gerontol B Psychol Sci Soc Sci. 1998;53:46–57.
Verbrugge LM, Jette AM. The disablement process. Soc Sci Med 1982. 1994;38:1–14.
Studies of Illness in the Aged. : The index of ADL: a standardized measure of biological and psychosocial function | JAMA | JAMA Network, https://jamanetwork.com/journals/jama/article-abstract/666768 (accessed 13 March 2023).
Lawton MP, Brody EM. Assessment of Older people: Self-Maintaining and Instrumental Activities of Daily Living.
Nagarkar A, Kashikar Y. Predictors of functional disability with focus on activities of daily living: a community based follow-up study in older adults in India. Arch Gerontol Geriatr. 2017;69:151–5.
Rosso AL, Taylor JA, Tabb LP, et al. Mobility, disability, and social engagement in older adults. J Aging Health. 2013;25:617–37.
Lagu T, Hannon NS, Rothberg MB, et al. Access to subspecialty care for patients with mobility impairment: a survey. Ann Intern Med. 2013;158:441–6.
Hoffman JM, Shumway-Cook A, Yorkston KM, et al. Association of mobility limitations with health care satisfaction and use of preventive care: a survey of Medicare beneficiaries. Arch Phys Med Rehabil. 2007;88:583–8.
Hennessy S, Kurichi JE, Pan Q, et al. Disability stage is an Independent risk factor for mortality in Medicare beneficiaries aged 65 years and older. PM R. 2015;7:1215–25.
Melzer D, Lan T-Y, Guralnik JM. The predictive validity for mortality of the index of mobility-related limitation–results from the EPESE study. Age Ageing. 2003;32:619–25.
Hardy SE, Kang Y, Studenski SA, et al. Ability to walk 1/4 mile predicts subsequent disability, mortality, and health care costs. J Gen Intern Med. 2011;26:130–5.
Freiberger E, Sieber CC, Kob R. Mobility in Older Community-Dwelling persons: a narrative review. Front Physiol. 2020;11. https://doi.org/10.3389/fphys.2020.00881. (accessed 6 March 2023).
Bloom DE, Sekher TV, Lee J. Longitudinal aging study in India (LASI): new data resources for addressing aging in India. Nat Aging. 2021;1:1070–2.
Organization WH. World Report on Ageing and Health. World Health Organization; 2015.
MEDBOX | Caring for Our Elders. : Early Responses India Ageing Report – 2017, https://www.medbox.org/document/caring-for-our-elders-early-responses-india-ageing-report-2017#GO (accessed 13 March 2023).
Malik MA. Functional disability among older adults in India; a gender perspective. PLoS ONE. 2022;17:e0273659.
Edemekong PF, Bomgaars DL, Sukumaran S et al. Activities of Daily Living. In: StatPearls. Treasure Island (FL): StatPearls Publishing, http://www.ncbi.nlm.nih.gov/books/NBK470404/ (2023, accessed 23 June 2023).
Gao J, Gao Q, Huo L, et al. Impaired activity of Daily Living Status of the older adults and its influencing factors: a cross-sectional study. Int J Environ Res Public Health. 2022;19:15607.
Katz S. Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J Am Geriatr Soc. 1983;31:721–7.
Patel R, Srivastava S, Kumar P, et al. Socio-economic inequality in functional disability and impairments with focus on instrumental activity of daily living: a study on older adults in India. BMC Public Health. 2021;21:1541.
Wong MM, Pang PF. Factors Associated with Falls in Psychogeriatric inpatients and comparison of two fall risk Assessment Tools. East Asian Arch Psychiatry off J Hong Kong Coll Psychiatr Dong Ya Jing Shen Ke Xue Zhi Xianggang Jing Shen Ke Yi Xue Yuan Qi Kan. 2019;29:10–4.
Yang J, Zhang Y, Shen S, et al. Instrumental activities of daily living trajectories and risk of mild cognitive impairment among Chinese older adults: results of the Chinese longitudinal healthy longevity survey, 2002–2018. Front Public Health. 2023;11. https://doi.org/10.3389/fpubh.2023.1165753. accessed 23 June 2023.
Saintrain MV, de Saintrain L, de Oliveira Branco SV. Dependence in instrumental activities of daily living and its implications for older adults’ oral health. PLoS ONE. 2021;16:e0249275.
Adler NE, Epel ES, Castellazzo G, et al. Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy white women. Health Psychol off J Div Health Psychol Am Psychol Assoc. 2000;19:586–92.
Euteneuer F. Subjective social status and health. Curr Opin Psychiatry. 2014;27:337–43.
Zell E, Strickhouser JE, Krizan Z. Subjective social status and health: a meta-analysis of community and society ladders. Health Psychol off J Div Health Psychol Am Psychol Assoc. 2018;37:979–87.
Singh-Manoux A, Marmot MG, Adler NE. Does subjective social status predict health and change in health status better than objective status? Psychosom Med. 2005;67:855–61.
Cundiff JM, Matthews KA. Is subjective social status a unique correlate of physical health? A meta-analysis. Health Psychol off J Div Health Psychol Am Psychol Assoc. 2017;36:1109–25.
Srivastava S, Muhammad T. Socioeconomic vulnerability and frailty among community-dwelling older adults: cross-sectional findings from longitudinal aging study in India, 2017–18. BMC Geriatr. 2022;22:201.
Anderson C, Kraus MW, Galinsky AD, et al. The local-ladder effect: social status and subjective well-being. Psychol Sci. 2012;23:764–71.
Miyakawa M, Magnusson Hanson LL, Theorell T, et al. Subjective social status: its determinants and association with health in the Swedish working population (the SLOSH study). Eur J Public Health. 2012;22:593–7.
Demakakos P, Nazroo J, Breeze E, et al. Socioeconomic status and health: the role of subjective social status. Soc Sci Med 1982. 2008;67:330–40.
Wilkinson RG. Health, hierarchy, and social anxiety. Ann N Y Acad Sci. 1999;896:48–63.
Chan A, Ofstedal M, Hermalin A. Changes in subjective and objective measures of Economic Well-Being and their interrelationship among the Elderly in Singapore and Taiwan. Soc Indic Res Int Interdiscip J Qual–Life Meas. 2002;57:263–300.
Goldman N, Cornman JC, Chang M-C. Measuring subjective social status: a case study of older Taiwanese. J Cross-Cult Gerontol. 2006;21:71–89.
Singh N. Higher Education for Women in India—Choices and Challenges.
Cohen S, Alper CM, Doyle WJ, et al. Objective and subjective socioeconomic status and susceptibility to the Common Cold. Health Psychol off J Div Health Psychol Am Psychol Assoc. 2008;27:268–74.
Rahal D, Chiang JJ, Bower JE, et al. Subjective social status and stress responsivity in late adolescence. Stress Amst Neth. 2020;23:50–9.
D’Hooge L, Achterberg P, Reeskens T. Mind over matter. The impact of subjective social status on health outcomes and health behaviors. PLoS ONE. 2018;13:e0202489.
Rahal D, Chiang JJ, Huynh VW, et al. Low subjective social status is associated with daily selection of fewer healthy foods and more high-fat/high sugar foods. Appetite. 2023;180:106338.
O’Leary D, Uysal A, Rehkopf DH, et al. Subjective social status and physical health: the role of negative affect and reappraisal. Soc Sci Med. 2021;291:114272.
Tang KL, Rashid R, Godley J, et al. Association between subjective social status and Cardiovascular Disease and cardiovascular risk factors: a systematic review and meta-analysis. BMJ Open. 2016;6:e010137.
Pearlin LI, Menaghan EG, Lieberman MA, et al. The stress process. J Health Soc Behav. 1981;22:337–56.
Yang C, Xia M, Han M, et al. Social Support and Resilience as mediators between stress and life satisfaction among people with Substance Use Disorder in China. Front Psychiatry. 2018;9:436.
Sagone E, Caroli MED. A Correlational Study on Dispositional Resilience, Psychological Well-being, and coping strategies in University students. Am J Educ Res. 2014;2:463–71.
Almeida D, Monteiro D, Rodrigues F. Satisfaction with life: mediating role in the relationship between depressive symptoms and coping mechanisms. Healthcare. 2021;9:787.
KIM ES, DELANEY SW. Life satisfaction and subsequent physical, behavioral, and Psychosocial Health in older adults. Milbank Q. 2021;99:209–39.
Kalin NH. The critical relationship between anxiety and depression. Am J Psychiatry. 2020;177:365–7.
Gori A, Topino E, Di Fabio A. The protective role of life satisfaction, coping strategies and defense mechanisms on perceived stress due to COVID-19 emergency: a chained mediation model. PLoS ONE. 2020;15:e0242402.
Lundberg J, Kristenson M. Is subjective status influenced by psychosocial factors? Soc Indic Res. 2008;89:375–90.
Porter AC, Zelkowitz RL, Gist DC, et al. Self-evaluation and depressive symptoms: a latent variable analysis of Self-esteem, Shame-proneness, and self-criticism. J Psychopathol Behav Assess. 2019;41:257–70.
Philippi CL, Koenigs M. The neuropsychology of self-reflection in psychiatric Illness. J Psychiatr Res. 2014;54:55–63.
International Institute for Population Sciences (IIPS) N MoHFW, Harvard TH. Chan School of Public Health (HSPH) and the University of Southern California (USC). Longitudinal Ageing Study in India (LASI) Wave 1, 2017-18, India Report. Mumbai., https://www.iipsindia.ac.in/lasi/ (2020).
Adler NE, Epel ES, Castellazzo G, et al. Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy, white women. Health Psychol. 2000;19:586.
Hooker ED, Campos B, Hoffman L, et al. Is receiving social support costly for those higher in subjective socioeconomic status? Int J Behav Med. 2020;27:325–36.
Suchday S, Chhabra R, Wylie-Rosett J, et al. Subjective and objective measures of socioeconomic status: predictors of cardiovascular risk in college students in Mumbai, India. Ethn Dis. 2008;18:2–235.
Muhammad T, Sekher TV, Srivastava S. Association of objective and subjective socioeconomic markers with cognitive impairment among older adults: cross-sectional evidence from a developing country. BMJ Open. 2022;12:e052501.
Walters EE, Kessler RC, Nelson CB et al. Scoring the World Health Organization’s composite international diagnostic interview short form (CIDI-SF). Geneva World Health Organ.
Muhammad T. Life course rural/urban place of residence, depressive symptoms and cognitive impairment among older adults: findings from the longitudinal aging study in India. BMC Psychiatry. 2023;23:1–13.
Trainor K, Mallett J, Rushe T. Age related differences in mental health scale scores and depression diagnosis: adult responses to the CIDI-SF and MHI-5. J Affect Disord. 2013;151:639–45.
Kessler RC, Üstün BB, The World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO). Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research. Epub ahead of print 2004. https://doi.org/10.1002/mpr.168.
Haro JM, Arbabzadeh-Bouchez S, Brugha TS, et al. Concordance of the Composite International Diagnostic interview Version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health Surveys. Int J Methods Psychiatr Res. 2008;17(Suppl 1):78–S82.
Muhammad T, Skariah AE, Kumar M, et al. Socioeconomic and health-related inequalities in major depressive symptoms among older adults: a Wagstaff’s decomposition analysis of data from the LASI baseline survey, 2017–2018. BMJ Open. 2022;12:e054730.
Hu P, Wang S, Lee J. Socioeconomic gradients of Cardiovascular Risk factors in China and India: results from the China Health and Retirement Longitudinal Study and Longitudinal Aging Study in India. Int J Public Health. 2017;62:763–73.
International Institute for Population Sciences (IIPS)., NPHCE, MoHFW HTHCS of PH (HSPH) and the U of SC (USC). Longitudinal Ageing Study in India (LASI) Wave 1, 2017-18, India Report. Mumbai., 2020.
Sharma P, Maurya P, Muhammad T. Number of chronic conditions and associated functional limitations among older adults: cross-sectional findings from the longitudinal aging study in India. BMC Geriatr. 2021;21:1–12.
Muhammad T, Kumar P, Srivastava S. How socioeconomic status, social capital and functional independence are associated with subjective wellbeing among older Indian adults? A structural equation modeling analysis. BMC Public Health. 2022;22:1836.
Zacharias A, Vakulabharanam V. Caste Stratification and Wealth Inequality in India. World Dev. Epub ahead of print 2011. https://doi.org/10.1016/j.worlddev.2011.04.026.
Chauhan S, Kumar S, Bharti R, et al. Prevalence and determinants of activity of daily living and instrumental activity of daily living among elderly in India. BMC Geriatr. 2022;22:64.
Udofia EA, Yawson AE, Aduful KA, et al. Residential characteristics as correlates of occupants’ health in the greater Accra region, Ghana. BMC Public Health. 2014;14:244.
Tobiasz-Adamczyk B, Zawisza K. Urban-rural differences in social capital in relation to self-rated health and subjective well-being in older residents of six regions in Poland. Ann Agric Environ Med AAEM. 2017;24:162–70.
Muhammad T, Srivastava S, Hossain B, et al. Decomposing rural–urban differences in successful aging among older Indian adults. Sci Rep. 2022;12:1–14.
Saha A, Rahaman M, Mandal B, et al. Rural urban differences in self-rated health among older adults: examining the role of marital status and living arrangements. BMC Public Health. 2022;22:2175.
Patel P, Muhammad T, Sahoo H. Morbidity status and changes in difficulty in activities of daily living among older adults in India: a panel data analysis. PLoS ONE. 2022;17:e0269388.
Adler N, Singh-Manoux A, Schwartz J, et al. Social status and health: a comparison of British civil servants in Whitehall-II with European- and african-americans in CARDIA. Soc Sci Med 1982. 2008;66:1034–45.
Jm O, Ne A. M K, Objective and subjective assessments of socioeconomic status and their relationship to self-rated health in an ethnically diverse sample of pregnant women. Health Psychol Off J Div Health Psychol Am Psychol Assoc; 19. Epub ahead of print November 2000. https://doi.org/10.1037//0278-6188.8.131.523.
Rosenfield S. Triple jeopardy? Mental health at the intersection of gender, race, and class. Soc Sci Med 1982. 2012;74:1791–801.
Wolff LS, Acevedo-Garcia D, Subramanian SV, et al. Subjective Social Status, a New measure in Health disparities Research: do Race/Ethnicity and Choice of Referent Group Matter? J Health Psychol. 2010;15:560–74.
Aguilar-Latorre A, Serrano-Ripoll MJ, Oliván-Blázquez B et al. Associations Between Severity of Depression, Lifestyle Patterns, and Personal Factors Related to Health Behavior: Secondary Data Analysis From a Randomized Controlled Trial. Front Psychol; 13, https://www.frontiersin.org/articles/https://doi.org/10.3389/fpsyg.2022.856139 (2022, accessed 7 March 2023).
Ford ES, Zhao G, Tsai J, et al. Low-risk lifestyle behaviors and all-cause mortality: findings from the National Health and Nutrition Examination Survey III Mortality Study. Am J Public Health. 2011;101:1922–9.
Zaninotto P, Head J, Steptoe A. Behavioural risk factors and healthy life expectancy: evidence from two longitudinal studies of ageing in England and the US. Sci Rep. 2020;10:6955.
Umberson D, Montez JK. Social Relationships and Health: a flashpoint for Health Policy. J Health Soc Behav. 2010;51:54–S66.
The Longitudinal Aging Study in India Project is funded by the Ministry of Health and Family Welfare, Government of India, the National Institute on Aging (R01 AG042778, R01 AG030153), and United Nations Population Fund, India.
Manacy Pai’s time on this research is supported by The Research Council of Kent State University.
Ethics approval and consent to participate
Ethics approval was obtained from the Central Ethics Committee on Human Research (CECHR) under the Indian Council of Medical Research (ICMR) and the Institutional Review Boards of collaborating organizations including International Institute for Population Sciences (IIPS), Mumbai and the Ministry of Health and Family Welfare, Government of India. And all methods were carried out in accordance with the relevant guidelines and regulations of ICMR.
The survey agencies that conducted the field survey for the data collection have collected prior informed consent (signed and oral) for both the interviews and biomarker tests from the eligible respondents in accordance with Human Subjects Protection.
In case of illiterate older people, informed consent was obtained from their legal guardians for the study participation.
Consent for publication
Conflict of interest
The authors declare that there is no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Pai, M., Muhammad, T. Subjective social status and functional and mobility impairments among older adults: life satisfaction and depression as mediators and moderators. BMC Geriatr 23, 685 (2023). https://doi.org/10.1186/s12877-023-04380-5