Introduction: Studies of community-dwelling older adults find subjective age affects health and functional outcomes. This study explored whether younger subjective age serves as a protective factor against hospital-associated physical, cognitive, and emotional decline, well-known consequences of hospitalization among the elderly. Methods: This study is a secondary data analysis of a subsample (N = 262; age: 77.5 ± 6.6 years) from the Hospitalization Process Effects on Mobility Outcomes and Recovery (HoPE-MOR) study. Psychological and physical subjective age, measured as participants’ reports on the degree to which they felt older or younger than their chronological age, was assessed at the time of hospital admission. Independence in activities of daily living, life-space mobility, cognitive function, and depressive symptoms were assessed at hospital admission and 1 month post-discharge. Results: The odds of decline in cognitive status, functional status, and community mobility and the exacerbation of depressive symptoms were significantly lower in those reporting younger vs. older psychological subjective age (odds ratio [OR] = 0.68, 95% CI = 0.46–0.98; OR = 0.59, 95% CI = 0.36–0.98; OR = 0.64, 95% CI = 0.44–0.93; OR = 0.64, 95% CI = 0.43–0.96, respectively). Findings were significant after controlling for demographic, functional, cognitive, emotional, chronic, and acute health predictors. Physical subjective age was not significantly related to post-hospitalization outcomes. Conclusion: Psychological subjective age can identify older adults at risk for poor hospitalization outcomes and should be considered for preventive interventions.

Hospital-associated psychological and functional decline is a common phenomenon among older adults, found by studies set in different countries, health care systems, and research cohorts [1‒3]. A recent meta-analysis found that the prevalence of functional decline in basic activities of daily living was 31% across 16 studies [1]. More specifically, limitations in community space mobility, cognitive decline, and depression are prevalent across studies: 18–53% [4, 5], 22–37.8% [6, 7], and 14–45.5% [8, 9], respectively. Given the high incidence of hospitalization among older adults [10] and the continuously stable impact of hospitalization on new disability rates and quality of life [11], it is imperative to identify older adults at risk and to determine possible buffering mechanisms and factors [12].

Numerous risk factors and processes associated with hospitalization-related functional decline have been identified, and a number of assessment tools have been developed and extensively utilized in this context. Common tools for early identification of patients at risk, such as ISAR, SHERPA, HARP, COMPRI [13], and many additional risk scales [14, 15], rely on lists of biomedical, biological, functional, and sociodemographic factors. The World Health Organization (WHO) recently suggested adopting a person-centered approach to the assessment and identification of patients’ risks and strengths, based on the concept of intrinsic capacity (IC) [16]. IC represents a compound of physical and psychological attributes on which individuals can draw upon at any point in their lives to cope and adapt to a change or challenge [17]. One of the distinctive attributes of IC is vitality representing factors reflecting psychological and physiological reserves or resilience [18]. Various measures can be used to capture this multidimensional concept; one of these is a person’s subjective age.

Subjective age is defined as the inner evaluation of one’s own feeling of being younger or older than one’s chronological age [19]. In simple terms, it is the answer to the question “How old do you feel?” [20]. Thus, it can be used to approximate individuals’ subjective assessment of their resources: feeling younger than one’s chronological age may reflect more than adequate coping resources available in terms of physical ability and psychosocial resources. Feeling physically and psychologically older than one’s actual age may reflect the opposite. Hence, it is plausible to refer to this factor as an indicator of an individual’s intrinsic resource level [21].

Evidence supports the potential importance of this concept. Subjective age has been associated with a range of health and functional outcomes in older adults [22, 23]. Most of the evidence comes from studies in community-dwelling older adults. Whether a person’s physical and psychological subjective age during acute hospitalization, at a time point characterized by a state of poor health, uncertainty, stress, and limited functioning within an alien environment, is similarly related to poor health and functional outcomes is yet to be studied.

The current study aimed to explore whether older adults’ subjective age at the time of hospital admission serves as a protective factor against hospital-associated physical, cognitive, and emotional decline. As some studies have found that physical and psychological subjective age have different effects on health-related outcomes [24], we explored their differential associations with older adults’ post-hospitalization outcomes.

Study Design and Participants

The study used a subsample of a larger multicentered prospective observational study, Hospitalization Process Effects on Mobility Outcomes and Recovery (HoPE-MOR), conducted in two hospitals in northern Israel between 2019 and 2021. The main study aimed to understand and evaluate the association between patients’ personal factors, hospitalization care processes, and in-hospital mobility. Hospitalized patients were followed prospectively from their admission to an internal medical unit to the end of their hospital stay and then at 1 month post-discharge. The original study included older adults (≥65) admitted for a nonsurgical condition. Exclusion criteria were acute stroke or coma, mechanical ventilation, admission to end-of-life care, cognitive impairments, or total dependency in mobility prior to admission. Out of 361 participants with full baseline data, 96 (26.6%) were excluded from the current investigation (15 had died; 41 could not be reached after 15 attempts; 26 refused to participate; 14 were excluded due to lack of follow-up data). The final study sample comprised 262 participants for whom subjective age data were collected at time of admission and who had reported at least one post-discharge outcome. No statistically significant differences were observed in physical or psychological subjective age (t(357.1) = 0.78, p = 0.55; t(357.1) = 0.60, p = 0.43) among participants included in and excluded from this data analysis and the original study sample.

Measures

Psychological subjective age and physical subjective age were assessed by two items referring to how old the individual felt physically and psychologically at time of admission to the study [25]. The items were rated on a scale ranging from 1 to 5 (1 = “feeling much younger than my age”; 5 = “feeling much older than my age”). Previous studies have supported the validity of this method of assessment [26].

Independence in activities of daily living was estimated using self-reports on 10 items from the Modified Barthel Index for Activities of Daily Living (BADL). A functional decline in the BADL was defined as a decrease of five or more points on the BADL [27] from admission stage to 1-month follow-up or expressing a loss of independence in one of the BADL criteria, such as walking [5]. Any improvement was transformed to zero to reflect lack of decline.

Life-space mobility was measured using the Aging Life-Space Assessment (LSA), an instrument to evaluate mobility. The LSA measures a person’s usual pattern of mobility during the month preceding the assessment. It permits assessment of the full range of mobility, ranging from mobility dependent on assistance from another person and limited to the room where the individual sleeps to daily independent travel out of the individual’s town [28]. A significant decline in life-space mobility was defined as a decrease of five or more points in LSA exceeding the 95% CI for the change in LSA associated with change in walking status [29]. In the intake interview, participants were asked about their life-space mobility in the month prior to hospitalization; their life-space mobility was reassessed 1 month after the end of hospitalization.

Cognitive function was assessed using the Telephone version of the Mini-Mental State Examination (MMSE-T). The MMSE-T is a brief measure of cognition assessing domains of orientation, attention, concentration, memory, and language. Scores range from 0 to 22 points, with lower scores indicating worse performance. The MMSE-T was administered after intake, at discharge, and 1 month post-discharge in a telephone follow-up interview. Cognitive decline was defined as a decrease of two points in the score [30].

Depressive and anxiety symptoms were assessed using seven questions for anxiety and seven for depressive symptoms from the Hospital Anxiety and Depression Scale (HADS) [31]. The Likert-type response scale for each item ranges from 0 to 3 (0 = “not at all”; 3 = “all the time”). Examples include “I feel as if I am slowed down” and “I can laugh and see the funny side of things” (reverse score). A minimal clinically important difference for the HADS is 1.7 points [32]. The reliability of the anxiety subscale was very good, α = 0.81. Reliability of the depression subscale was excellent, α = 0.90.

Covariates

Potential confounders included demographic data (age, gender, and years of education) collected at time of admission. Information about chronic health conditions, acute level at time of admission, and length of hospital stay was retrieved from medical records. Chronic illness burden was assessed using the Charlson Comorbidity index (CCI); the CCI has a weighted score for 19 health conditions, ranging from 1 to 6 [33]. Severity of acute illness was assessed using the National Early Warning Score (NEWS). Scores for the NEWS range from 0 to 20, with higher scores indicating higher risk [34]. Mobility ability at time of admission was assessed with the de Morton Mobility Index (DEMMI) [35]. Data on rehospitalization were collected 1 month post-discharge in a follow-up telephone interview.

Statistical Analysis

To assess the association between study variables and subjective age, we conducted a Pearson correlation test (for quantitative variables) and a t test for independent variables (for dichotomized variables). We performed a series of multivariate logistic regressions to analyze the relations of subjective and chronological age with study outcomes, including potential confounders. For this purpose, functional, cognitive, life-space mobility, and depressive symptom outcomes were defined as decline/exacerbation from premorbid condition to condition 1 month post-discharge. Any improvement was transformed to zero to reflect lack of decline. The decision to transform any improvement to zero in our study was made to maintain consistency in our data analysis and to ensure that our results accurately reflected the absence of decline. The original psychological and physical subjective age scores were recoded, so younger subjective age would represent a higher score, conceptualized as a protective factor.

Study participants were between the ages of 67 and 96 years (mean age: 77.45 ± standard deviation = 6.57 years), 42% female (n = 110), with an average 13.03 ± 4.01 (standard deviation) years of education. The majority of the participants were independent in their functioning at time of admission; 30.2% reported having depressive symptoms; 17.3% reported having anxiety symptoms. The average hospital stay was 5.62 days, and 17.6% (n = 46) were rehospitalized within 30 days after discharge. Table 1 provides a full description of the sample’s characteristics and main study variables. More than 55% (n = 145) of participants reported feeling psychologically younger than their actual age, but only 27.5% (n = 72) reported feeling physically younger. A correspondingly smaller proportion reported feeling older than their chronological age psychologically (8.8%); a larger percentage felt older physically (27.8%). Lower independence in functional and actual mobility ability, cognitive status at time of admission, community mobility prior to hospitalization, increased depressive and anxiety symptoms, and being female were associated with older physical subjective age. Older psychological subjective age was associated with fewer years of education, higher comorbidity burden, lower mobility ability and community mobility, and more severe depressive symptoms. Around 30% (n = 77) experienced clinically significant functional decline, 20% (n = 43) cognitive decline, 59% (n = 150) reduction in community mobility, and 42% (n = 99) exacerbation in depressive symptoms (see Fig. 1).

Table 1.

Sample description and association of study variables with subjective aging

Association with subjective age (Pearson r/t test)
N = 262psychologicalphysical
Gender: female, n (%), 1t test 110 (42.0) 0.39 2.01* 
Age, mean (SD), (67–96) 77.45 (6.57) −0.02 −0.02 
Marital status: married, n (%), αt test 154 (58.8) 0.08 1.61 
Place of living status, community living, n (%), αt test 246 (93.9) 0.44 0.62 
Education, mean (SD), (0–22), years 13.03 (4.01) −0.21** 0.09 
Functional status at time of admission (ADL), mean (SD), (18–100) 91.59 (16.31) −0.08 −0.20** 
Cognitive status (MMSE), mean (SD), (12–22) 19.08 (2.61) −0.10 −0.13* 
Comorbidity score, mean (SD), (0–10) (2.09) 2.68 0.12* 0.11 
Mobility ability at admission (DEMMI), mean (SD), (0–100) 53.8 (13.80) −0.15* −0.29** 
Community mobility (LSA) prior to hospitalization, mean (SD), (4–120) 57.87 (33.29) −0.13* −0.22** 
Severity of illness (NEWS), mean (SD), (0–9) 1.76 (1.86) 0.09 0.06 
Depressive symptoms (HADS), mean (SD), (0–20) 5.20 (4.31) 0.24** 0.34** 
Anxiety symptoms (HADS), mean (SD), (0–21) 4.23 (3.91) 0.09 0.25** 
Length of stay in hospital, mean (SD), (2–36) 5.62 (4.22) 0.08 0.06 
Readmission within 30 days, yes, n (%), αt test 46 (17.6) 0.28 0.81 
Psychological subjective age, mean (SD), (0–5) 2.31 (0.97) 0.56** 
Physical subjective age, mean (SD), (0–5) 2.92 (0.95) 
Association with subjective age (Pearson r/t test)
N = 262psychologicalphysical
Gender: female, n (%), 1t test 110 (42.0) 0.39 2.01* 
Age, mean (SD), (67–96) 77.45 (6.57) −0.02 −0.02 
Marital status: married, n (%), αt test 154 (58.8) 0.08 1.61 
Place of living status, community living, n (%), αt test 246 (93.9) 0.44 0.62 
Education, mean (SD), (0–22), years 13.03 (4.01) −0.21** 0.09 
Functional status at time of admission (ADL), mean (SD), (18–100) 91.59 (16.31) −0.08 −0.20** 
Cognitive status (MMSE), mean (SD), (12–22) 19.08 (2.61) −0.10 −0.13* 
Comorbidity score, mean (SD), (0–10) (2.09) 2.68 0.12* 0.11 
Mobility ability at admission (DEMMI), mean (SD), (0–100) 53.8 (13.80) −0.15* −0.29** 
Community mobility (LSA) prior to hospitalization, mean (SD), (4–120) 57.87 (33.29) −0.13* −0.22** 
Severity of illness (NEWS), mean (SD), (0–9) 1.76 (1.86) 0.09 0.06 
Depressive symptoms (HADS), mean (SD), (0–20) 5.20 (4.31) 0.24** 0.34** 
Anxiety symptoms (HADS), mean (SD), (0–21) 4.23 (3.91) 0.09 0.25** 
Length of stay in hospital, mean (SD), (2–36) 5.62 (4.22) 0.08 0.06 
Readmission within 30 days, yes, n (%), αt test 46 (17.6) 0.28 0.81 
Psychological subjective age, mean (SD), (0–5) 2.31 (0.97) 0.56** 
Physical subjective age, mean (SD), (0–5) 2.92 (0.95) 

SD, standard deviation; ADL, activities of daily living; MMSE, Mini-Mental State Examination; DEMMI, de Morton Mobility Index; LSA, Life-Space Assessment; NEWS, National Early Warning Score; HADS, Hospital Anxiety and Depression Scale.

1t test – (n = 260).

*p < 0.05; **p < 0.01.

Fig. 1.

One-month post-hospitalization outcomes. The percent of functional (ADL and community mobility), cognitive, and emotional (exacerbation of depressive symptoms) decline one month after discharge from the hospital compared with prior to hospitalization (community mobility) or at time of admission status (cognition, ADL, depressive symptoms). ADL, activities of daily living.

Fig. 1.

One-month post-hospitalization outcomes. The percent of functional (ADL and community mobility), cognitive, and emotional (exacerbation of depressive symptoms) decline one month after discharge from the hospital compared with prior to hospitalization (community mobility) or at time of admission status (cognition, ADL, depressive symptoms). ADL, activities of daily living.

Close modal

Multivariate logistic regression analysis revealed that psychological subjective age served as a protective factor for each of the study outcomes, controlling for cognitive, functional, and emotional function at time of admission; comorbidity burden; illness acuity; community mobility; years of education; length of hospital stay; rehospitalization, age; and gender. The odds of decline in cognitive status, functional status, community mobility, and the exacerbation of depressive symptoms were significantly lower with any point of increase (feeling older) in psychological subjective age (odds ratio [OR] = 0.68, 95% CI = 0.46–0.98; OR = 0.59, 95% CI = 0.36–0.98; OR = 0.64, 95% CI = 0.44–0.93; OR = 0.64, 95% CI = 0.43–0.96, respectively). Physical subjective age did not demonstrate a significant association with any of the outcomes. Chronological age and anxiety were the most consistent significant covariates (see Table 2).

Table 2.

Multivariate logistic regression analysis of subjective and chronological age relationship with study outcomes

ADL decline (N = 262), OR (95% CI)Cognitive decline (N = 211), OR (95% CI)Community mobility decline (N = 254), OR (95% CI)Depression exacerbation (N = 235), OR (95% CI)
Subjective psychological age 0.68 (0.46–0.98)* 0.59 (0.36–0.98)* 0.64 (0.44–0.93)** 0.64 (0.43–0.96)* 
Subjective physical age 1.04 (0.68–1.56) 1.57 (0.93–2.66) 0.98 (0.67–1.42) 1.16 (0.77–1.73) 
Age, years 1.07 (1.02–1.12)** 1.07 (1.00–1.14)* 1.07 (1.02–1.13)** 1.09 (1.03–1.15)** 
Gender, female 1.29 (0.66–2.50) 1.73 (0.75–3.99) 2.43 (1.29–1.13)** 1.41 (0.72–2.88) 
Education, years 0.82 (0.65–1.04) 0.73 (0.55–0.98)* 1.03 (0.83–1.29) 0.95 (0.88–1.04) 
Functional status at admission (ADL) 1.02 (0.99–1.04) 0.98 (0.96–1.02) 0.99 (0.96–1.01) 0.98 (0.86–1.12) 
Cognitive status (MMSE) 1.03 (0.90–1.18) 1.54 (1.22–1.94)** 0.99 (0.88–1.13) 1.06 (0.92–1.21) 
Comorbidity 1.19 (1.03–1.38)* 0.94 (0.77–1.14) 1.01 (0.88–1.16) 0.93 (0.78–1.07) 
Mobility ability at admission (DEMMI) 0.99 (0.96–1.02) 0.99 (0.96–1.03) 0.99 (0.97–1.02) 1.03 (0.92–1.06) 
Community mobility (LSA) 0.98 (0.97–0.99)** 1.01 (0.99–1.02) 1.01 (1.00–1.02)* 0.98 (0.97–0.99)** 
Severity of illness (NEWS) 1.00 (0.84–1.19) 0.84 (0.67–1.04) 1.06 (0.91–1.09) 0.98 (0.82–1.15) 
Depressive symptoms (HADS) 0.93 (0.84–1.02) 1.02 (0.90–1.15) 0.99 (0.91–1.09) 0.74 (0.66–0.84)** 
Anxiety symptoms (HADS) 1.14 (1.03–1.26)** 1.04 (0.92–1.18) 1.11 (1.00–1.23)* 1.18 (1.02–1.25)* 
Length of stay in hospital, days 1.11 (1.03–1.20)** 1.05 (0.96–1.15) 1.1 (1.02–1.20)* 0.97 (0.89–1.07) 
Readmission within 30 days (yes) 1.61 (0.74–0.3.51) 2.24 (0.86–5.84) 1.05 (0.50–2.21) 1.54 (0.66–3.58) 
ADL decline (N = 262), OR (95% CI)Cognitive decline (N = 211), OR (95% CI)Community mobility decline (N = 254), OR (95% CI)Depression exacerbation (N = 235), OR (95% CI)
Subjective psychological age 0.68 (0.46–0.98)* 0.59 (0.36–0.98)* 0.64 (0.44–0.93)** 0.64 (0.43–0.96)* 
Subjective physical age 1.04 (0.68–1.56) 1.57 (0.93–2.66) 0.98 (0.67–1.42) 1.16 (0.77–1.73) 
Age, years 1.07 (1.02–1.12)** 1.07 (1.00–1.14)* 1.07 (1.02–1.13)** 1.09 (1.03–1.15)** 
Gender, female 1.29 (0.66–2.50) 1.73 (0.75–3.99) 2.43 (1.29–1.13)** 1.41 (0.72–2.88) 
Education, years 0.82 (0.65–1.04) 0.73 (0.55–0.98)* 1.03 (0.83–1.29) 0.95 (0.88–1.04) 
Functional status at admission (ADL) 1.02 (0.99–1.04) 0.98 (0.96–1.02) 0.99 (0.96–1.01) 0.98 (0.86–1.12) 
Cognitive status (MMSE) 1.03 (0.90–1.18) 1.54 (1.22–1.94)** 0.99 (0.88–1.13) 1.06 (0.92–1.21) 
Comorbidity 1.19 (1.03–1.38)* 0.94 (0.77–1.14) 1.01 (0.88–1.16) 0.93 (0.78–1.07) 
Mobility ability at admission (DEMMI) 0.99 (0.96–1.02) 0.99 (0.96–1.03) 0.99 (0.97–1.02) 1.03 (0.92–1.06) 
Community mobility (LSA) 0.98 (0.97–0.99)** 1.01 (0.99–1.02) 1.01 (1.00–1.02)* 0.98 (0.97–0.99)** 
Severity of illness (NEWS) 1.00 (0.84–1.19) 0.84 (0.67–1.04) 1.06 (0.91–1.09) 0.98 (0.82–1.15) 
Depressive symptoms (HADS) 0.93 (0.84–1.02) 1.02 (0.90–1.15) 0.99 (0.91–1.09) 0.74 (0.66–0.84)** 
Anxiety symptoms (HADS) 1.14 (1.03–1.26)** 1.04 (0.92–1.18) 1.11 (1.00–1.23)* 1.18 (1.02–1.25)* 
Length of stay in hospital, days 1.11 (1.03–1.20)** 1.05 (0.96–1.15) 1.1 (1.02–1.20)* 0.97 (0.89–1.07) 
Readmission within 30 days (yes) 1.61 (0.74–0.3.51) 2.24 (0.86–5.84) 1.05 (0.50–2.21) 1.54 (0.66–3.58) 

OR, odds ratio; ADL, activities of daily living; MMSE, Mini-Mental State Examination; DEMMI, de Morton Mobility Index; LSA, Life-Space Assessment; NEWS, National Early Warning Score; HADS, Hospital Anxiety and Depression Scale.

*p < 0.05; **p < 0.01.

This is the first study showing that subjective age is a potential protective IC factor against negative hospitalization outcomes. Psychological subjective age seems to serve as a protective factor against cognitive, functional, and life-space mobility decline and against exacerbation of depressive symptoms 1 month after acute hospitalization. These findings add to the literature, which has mostly demonstrated the relationship between subjective age and long-term outcomes across the life course. The only previous study evaluating the relationship between subjective age and care outcomes was conducted in a subacute care setting, during rehabilitation for poststroke and fracture patients [36]. In that study, subjective age was associated with functional recovery during rehabilitation. Our study extends these findings by demonstrating the importance of subjective age within acute care units with relatively short length of stay on patient outcomes. Our findings suggest the robustness of the subjective age concept as a substantial IC factor, helping individuals withstand adversity. Moreover, its simultaneous effect on number of post-discharge outcome may point out on subjective age multidimensionality of influence [37].

Our study examined separately the predictive value of psychological and physical subjective age and found that only psychological subjective age had a significant impact, whereas physical subjective age did not. Unlike previous research that mainly focused on the composite score [37], combining both psychological and physical domains, or global felt age, our findings suggest that a more nuanced assessment of physical and psychological subjective age is essential. A prior community-based study found that physical subjective age was associated with mortality, whereas psychological subjective age was not [25]. The inconsistent results highlight the need for distinct evaluations of these dimensions rather than relying on composite scores, as advocated by Palgi et al. [38].

Our findings on the differentiated effects of physical and psychological subjective age should be understood within the context of the setting in which they were assessed. Older adults admitted to hospital care for acute illness or exacerbation of a chronic condition are most directly affected physically; therefore, it can be assumed that during hospitalization, their physical subjective age will be more negatively affected than their psychological subjective age. This assumption is supported by descriptive statistics from our study, indicating that over half the participants described themselves as psychologically younger than their age, whereas only about a quarter did so for their physical age. Similarly, more participants reported feeling physically older than their chronological age compared to reports of feeling psychologically older. Studies in non-acute settings found less discrepancy between reports of physical and psychological subjective age [25, 39].

The importance of measuring subjective age was previously criticized as having no unique contribution to the assessment of older adults’ physical and psychological status beyond other objective or widely used subjective health status measures, serving merely as a psychological biomarker [40]. We challenge this criticism. A strength of this study is that we controlled for numerous potential physical and psychological status confounders. Our results demonstrated that although basic function (especially mobility) and anxiety were strong predictors of all investigated outcomes, the relationship between subjective age and all outcomes was retained in their presence.

Limitations

Our study has some limitations. First, our outcomes rely on participants’ self-reports. However, we assessed change in outcomes rather than general reports of subjective global function and depressive symptoms, thus increasing the robustness of our findings. Second, we did not perform any manipulation of subjective aging. We therefore suggest that future research should examine how interventions to improve younger subjective age affect post-hospitalization outcomes. Nonetheless, as we used a prospective design, our findings suggest a directional relationship between subjective age and outcomes and, as such, represent a much-needed preliminary step prior to testing causality. Fourth, our results should be interpreted in light of the study context: it was part a larger study originally aimed at examining mobility care-related processes. Participants with significant mobility limitations at admission were not included in the larger study, and further assessments of in-hospital subjective aging should include more diverse populations. Finally, our sample was not balanced between individuals who reported a younger physical subjective age compared to their actual age, with more participants feeling physically older. This may limit our study’s ability to detect differences between physical subjective age and hospitalization outcomes. Samples analyzing a more diverse range of hospital patients, rather than primarily focusing on internal-medical conditions, may yield different results regarding the relationships between physical subjective age and these outcomes.

In-hospital subjective age is an important indicator of post-hospitalization outcomes and should be assessed at older adults’ admission to acute care settings. As subjective age has been shown to be prone to manipulation via self-perception enhancement interventions [41], our findings suggest hospitalization may serve as a turning point for improving subjective age and thus contribute to the minimization of negative hospitalization outcomes. This is another step toward more holistic approaches to care of older adults, considering the person as a whole and not simply focusing on the physical ailment.

The authors thank Elizabeth Thomson for editing the manuscript.

The study was conducted following the Declaration of Helsinki. The study was approved by the Helsinki Ethics Committee of the Carmel Medical Center (approval number 0166-18-CMC), the Helsinki Ethics Committee of Rambam Health Care Campus (approval number 0263-19-RMB), and the Haifa University Review Board (approval number 324/17). All participants provided written informed consent, and participation was voluntary and confidential.

The authors have no conflicts of interest to disclose.

This work was supported by the Israeli Science Foundation (Grant No. 1216/17). The funding authority was not involved in research design and preparation of the article.

A.Z., E.S., N.G.‐Y., and Y.P. conceptualized and designed the study. A.Z., E.S., and N.G.‐Y. obtained funding. K.S., A.Z., J.S., and A.R. collected and interpreted the data. A.Z., N.G.‐Y., and K.S. analyzed data and prepared and reviewed figures. A.Z. and N.G.‐Y. wrote the original draft. Y.P., E.S., K.S., J.S., and A.R. provided critical revisions of the manuscript. All the authors read and approved the final manuscript.

The data that support the findings of this study are openly available in the repository Zenodo at https://doi.org/10.5281/zenodo.7556394. Further inquiries can be directed to the corresponding author.

1.
Loyd
C
,
Markland
AD
,
Zhang
Y
,
Fowler
M
,
Harper
S
,
Wright
NC
, et al
.
Prevalence of hospital-associated disability in older adults: a meta-analysis
.
J Am Med Dir Assoc
.
2020
;
21
(
4
):
455
61.e5
. .
2.
de Foubert
M
,
Cummins
H
,
McCullagh
R
,
Brueton
V
,
Naughton
C
.
Systematic review of interventions targeting fundamental care to reduce hospital-associated decline in older patients
.
J Adv Nurs
.
2021
;
77
(
12
):
4661
78
. .
3.
Chen
Y
,
Almirall-Sánchez
A
,
Mockler
D
,
Adrion
E
,
Domínguez-Vivero
C
,
Romero-Ortuño
R
.
Hospital-associated deconditioning: not only physical, but also cognitive
.
Int J Geriatr Psychiatry
.
2022
;
37
(
3
). .
4.
Loyd
C
,
Beasley
TM
,
Miltner
RS
,
Clark
D
,
King
B
,
Brown
CJ
.
Trajectories of community mobility recovery after hospitalization in older adults
.
J Am Geriatr Soc
.
2018
;
66
(
7
):
1399
403
. .
5.
Brown
CJ
,
Kennedy
RE
,
Lo
AX
,
Williams
CP
,
Sawyer
P
.
Impact of emergency department visits and hospitalization on mobility among community-dwelling older adults
.
Am J Med
.
2016
;
129
(
10
):
1124.e9
1124.e15
. .
6.
Girard
TD
,
Self
WH
,
Edwards
KM
,
Grijalva
CG
,
Zhu
Y
,
Williams
DJ
, et al
.
Long-term cognitive impairment after hospitalization for community-acquired pneumonia: a prospective cohort study
.
J Gen Intern Med
.
2018
;
33
(
6
):
929
35
. .
7.
Tonkikh
O
,
Zisberg
A
,
Shadmi
E
.
Association between continuity of nursing care and older adults’ hospitalization outcomes: a retrospective observational study
.
J Nurs Manag
.
2020
;
28
(
5
):
1062
9
. .
8.
Brown
A
,
Peres
L
,
Brown
T
,
Haines
T
,
Stolwyk
R
.
A prospective investigation of factors associated with depressive symptoms in older adults’ post-hospitalisation
.
Int J Geriatr Psychiatry
.
2020
;
35
(
6
):
671
82
. .
9.
Ciro
CA
,
Ottenbacher
KJ
,
Graham
JE
,
Fisher
S
,
Berges
I
,
Ostir
GV
.
Patterns and correlates of depression in hospitalized older adults
.
Arch Gerontol Geriatr
.
2012
;
54
(
1
):
202
5
. .
10.
Gindi
R
.
Health, United States, 2019
.
Hyattsville, MD
.
2021
.
11.
Dharmarajan
K
,
Han
L
,
Gahbauer
EA
,
Leo-Summers
LS
,
Gill
TM
.
Disability and recovery after hospitalization for medical illness among community-living older persons: a prospective cohort study
.
J Am Geriatr Soc
.
2020
;
68
(
3
):
486
95
. .
12.
Adamowicz
DH
,
Lee
EE
.
Predicting and improving hospital outcomes for older adults
.
Int Psychogeriatr
.
2021
;
33
(
3
):
205
7
. .
13.
Hoogerduijn
JG
,
Schuurmans
MJ
,
Duijnstee
MSH
,
de Rooij
SE
,
Grypdonck
MFH
.
A systematic review of predictors and screening instruments to identify older hospitalized patients at risk for functional decline
.
J Clin Nurs
.
2007
;
16
(
1
):
46
57
. .
14.
Hoogerduijn
JG
,
Buurman
BM
,
Korevaar
JC
,
Grobbee
DE
,
de Rooij
SE
,
Schuurmans
MJ
.
The prediction of functional decline in older hospitalised patients
.
Age Ageing
.
2012
;
41
(
3
):
381
7
. .
15.
van Grootven
B
,
Jeuris
A
,
Jonckers
M
,
Devriendt
E
,
Dierckx de Casterlé
B
,
Dubois
C
, et al
.
Predicting hospitalisation-associated functional decline in older patients admitted to a cardiac care unit with cardiovascular disease: a prospective cohort study
.
BMC Geriatr
.
2020
;
20
(
1
):
112
. .
16.
Beard
JR
,
Officer
AM
,
Cassels
AK
.
The world report on ageing and health
.
2016
.
17.
Cesari
M
,
Araujo de Carvalho
I
,
Amuthavalli Thiyagarajan
J
,
Cooper
C
,
Martin
FC
,
Reginster
JY
, et al
.
Evidence for the domains supporting the construct of intrinsic capacity
.
J Gerontol A Biol Sci Med Sci
.
2018
;
73
(
12
):
1653
60
. .
18.
Chhetri
KJ
,
Harwood
HR
,
Ma
L
,
Michel
JP
,
Chan
P
.
Intrinsic capacity and healthy ageing
.
Age Ageing
.
2022
;
51
:
1
3
. .
19.
Shrira
A
,
Palgi
Y
,
Diehl
M
.
Advancing the field of subjective views of aging: an overview of recent achievements
.
2022
. p.
11
37
.
20.
Wurm
S
,
Diehl
M
,
Kornadt
AE
,
Westerhof
GJ
,
Wahl
HW
.
How do views on aging affect health outcomes in adulthood and late life? Explanations for an established connection
.
Dev Rev
.
2017
;
46
:
27
43
. .
21.
Spitzer
N
,
Segel-Karpas
D
,
Palgi
Y
.
Close social relationships and loneliness: the role of subjective age
.
Int Psychogeriatr
.
2022
;
34
(
7
):
651
5
. .
22.
Alonso Debreczeni
F
,
Bailey
PE
.
A systematic review and meta-analysis of subjective age and the association with cognition, subjective well-being, and depression
.
J Gerontol B Psychol Sci Soc Sci
.
2021
;
76
(
3
):
471
82
. .
23.
Stephan
Y
,
Sutin
AR
,
Terracciano
A
.
Subjective age and mortality in three longitudinal samples
.
Psychosom Med
.
2018
;
80
(
7
):
659
64
. .
24.
Ihira
H
,
Furuna
T
,
Mizumoto
A
,
Makino
K
,
Saitoh
S
,
Ohnishi
H
, et al
.
Subjective physical and cognitive age among community-dwelling older people aged 75 years and older: differences with chronological age and its associated factors
.
Aging Ment Health
.
2015
;
19
(
8
):
756
61
. .
25.
Uotinen
V
,
Rantanen
T
,
Suutama
T
.
Perceived age as a predictor of old age mortality: a 13-year prospective study
.
Age Ageing
.
2005
;
34
(
4
):
368
72
. .
26.
Avidor
S
,
Benyamini
Y
,
Solomon
Z
.
Subjective age and health in later life: the role of posttraumatic symptoms
.
J Gerontol B Psychol Sci Soc Sci
.
2016
;
71
(
3
):
415
24
. .
27.
Shah
S
,
Vanclay
F
,
Cooper
B
.
Improving the sensitivity of the Barthel Index for stroke rehabilitation
.
J Clin Epidemiol
.
1989
;
42
(
8
):
703
9
. .
28.
Peel
C
,
Sawyer Baker
P
,
Roth
DL
,
Brown
CJ
,
Brodner
EV
,
Allman
RM
.
Assessing mobility in older adults: the UAB study of aging life-space assessment
.
Phys Ther
.
2005
;
85
(
10
):
1008
119
.
29.
Kennedy
RE
,
Almutairi
M
,
Williams
CP
,
Sawyer
P
,
Allman
RM
,
Brown
CJ
.
Determination of the minimal important change in the life-space assessment
.
J Am Geriatr Soc
.
2019
;
67
(
3
):
565
9
. .
30.
Andrews
JS
,
Desai
U
,
Kirson
NY
,
Zichlin
ML
,
Ball
DE
,
Matthews
BR
.
Disease severity and minimal clinically important differences in clinical outcome assessments for Alzheimer’s disease clinical trials
.
Alzheimers Dement
.
2019
;
5
:
354
63
. .
31.
Zigmond
AS
,
Snaith
RP
.
The hospital anxiety and depression scale
.
Acta Psychiatr Scand
.
1983
;
67
(
6
):
361
70
. .
32.
Lemay
KR
,
Tulloch
HE
,
Pipe
AL
,
Reed
JL
.
Establishing the minimal clinically important difference for the hospital anxiety and depression scale in patients with cardiovascular disease
.
J Cardiopulm Rehabil Prev
.
2019
;
39
(
6
):
E6
11
. .
33.
Charlson
ME
,
Pompei
P
,
Ales
KL
,
MacKenzie
CR
.
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation
.
J Chronic Dis
.
1987
;
40
(
5
):
373
83
. .
34.
Smith
GB
,
Prytherch
DR
,
Meredith
P
,
Schmidt
PE
,
Featherstone
PI
.
The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death
.
Resuscitation
.
2013
;
84
(
4
):
465
70
. .
35.
de Morton
NA
,
Lane
K
.
Validity and reliability of the de Morton Mobility Index in the subacute hospital setting in a geriatric evaluation and management population
.
J Rehabil Med
.
2010
;
42
(
10
):
956
61
. .
36.
Kalir
DM
,
Shrira
A
,
Palgi
Y
,
Batz
C
,
Ben-Eliezer
A
,
Heyman
N
, et al
.
Feeling younger, rehabilitating better: reciprocal and mediating effects between subjective age and functional independence in osteoporotic fracture and stroke patients
.
Gerontology
.
2022
;
69
:
109
17
. .
37.
Kotter-Grühn
D
,
Kornadt
AE
,
Stephan
Y
.
Looking beyond chronological age: current knowledge and future directions in the study of subjective age
.
Gerontology
.
2016
;
62
(
1
):
86
93
. .
38.
Palgi
Y
,
Shrira
A
,
Neupert
SD
.
Views on aging and health: a multidimensional and multitemporal perspective
.
J Gerontol B Psychol Sci Soc Sci
.
2021
;
76
(
5
):
821
4
. .
39.
Barak
B
,
Schiffman
LG
.
Cognitive age: a nonchronological age variable
.
Adv Consum Res
.
1981
;
8
.
40.
Mitina
M
,
Young
S
,
Zhavoronkov
A
.
Psychological aging, depression, and well-being
.
Aging
.
2020
;
12
(
18
):
18765
77
. .
41.
Stephan
Y
,
Chalabaev
A
,
Kotter-Gruhn
D
,
Jaconelli
A
.
“Feeling younger, being stronger”: an experimental study of subjective age and physical functioning among older adults
.
J Gerontol B Psychol Sci Soc Sci
.
2013
;
68
:
1
7
. .