Abstract
Background
Cognitive frailty (CF), defined by the IANA/IAGG criteria as the coexistence of physical frailty and cognitive impairment without dementia, is an emerging geriatric syndrome associated with disability and adverse health outcomes.
Aims
This study aimed to estimate the prevalence of CF and develop a prediction model among hospitalized older adults in Iran.
Methods
A cross-sectional survey was conducted on 300 patients aged ≥ 60 years admitted to Shahid-Beheshti Hospital, Kashan, between July and September 2024. Data were collected using structured interviews incorporating the Fried Frailty Phenotype, the Clinical Dementia Rating Scale, and a researcher-developed tool assessing demographic, clinical, nutritional, psychosocial, and economic factors. Descriptive statistics and multivariable logistic regression (backward method) were applied.
Results
The mean age of participants was 71.8 ± 8.0 years, and 54.3% were male. The prevalence of CF and potentially CF was 22.7% and 23%, respectively. Protective factors included positive self-rated health (OR = 0.179), non-smoking (OR = 0.150), higher handgrip strength (OR = 0.798), good nutritional status (OR = 0.330), larger mid-upper arm and calf circumferences, and adequate appetite (OR = 0.107). Conversely, higher BMI (OR = 1.315), physical inactivity (OR = 5.966), diabetes (OR = 3.692), comorbidities (OR = 8.77), frequent physician visits (OR = 6.974), stressful life events (OR = 4.764), and poor economic status (OR = 2.724) were associated with increased risk. The model demonstrated strong predictive validity (Nagelkerke R²=0.678; Specificity = 0.921).
Conclusions
CF is prevalent among hospitalized older adults in Iran and is strongly influenced by modifiable factors. Early detection and multidomain interventions—targeting nutrition, physical activity, chronic disease control, and psychosocial well-being—may prevent CF and improve quality of life in this vulnerable group.
Keywords: Cognitive impairment, Hospitalization, Frailty, Older adults, Cross-sectional studies
Introduction
The global population is aging rapidly due to rising life expectancy and declining birth rates. According to the United Nations, the number of people aged 60 years and older is expected to rise from 1 billion in 2020 to 1.4 billion by 2030 [1]. Iran is experiencing a similar demographic transition; the 2016 national census reported that 9.27% of its population was aged over 60 years [2]. This demographic shift presents major challenges for healthcare systems, particularly as age-related conditions become more prevalent. Among older adults, frailty and cognitive impairment are two common geriatric conditions that compromise physical functioning and cognitive independence [3].
Frailty is a multidimensional and dynamic clinical condition resulting from diminished physiological reserves, multisystem dysregulation, and reduced homeostatic capacity, leading to increased vulnerability to internal and external stressors [4]. According to Fried et al., frailty is characterized by weakness, fatigue, reduced exercise tolerance, low energy, and unintentional weight loss [5]. Mild cognitive impairment (MCI), another prevalent condition in older adults, represents a transitional phase between normal cognition and dementia, marked by a mild decline in cognitive performance that does not substantially interfere with daily life [6]. When frailty and cognitive impairment coexist, the risk of adverse outcomes—including Alzheimer’s disease, disability, falls, hospitalization, and mortality—rises sharply compared with either condition alone [7]. Evidence from meta-analyses confirms that physical frailty often precedes cognitive decline and may accelerate the onset of dementia [8]. These two conditions are bidirectionally related and share overlapping biological mechanisms, including chronic inflammation, oxidative stress, and hormonal dysregulation [9, 10].
The concept of cognitive frailty (CF) was first introduced in 2013 by the International Academy on Nutrition and Aging (IANA) and the International Association of Gerontology and Geriatrics (IAGG) to describe older adults exhibiting both physical frailty and cognitive impairment (MCI, operationalized as CDR 0.5–1) in the absence of dementia [11, 12]. Since its introduction, CF has emerged as a central focus in geriatric research, reflecting efforts to clarify the interplay between physical and cognitive decline and to design early preventive strategies [13]. Notably, CF is considered a potentially reversible pre-dementia state, emphasizing the clinical value of early detection and intervention [12, 13].
Reported prevalence rates of CF vary widely due to differences in population characteristics, clinical settings, and diagnostic criteria [13–16]. Wongtrakulrung et al. (2020) found a 6.7% prevalence among outpatient older adults in Thailand, identifying age over 70 years and low educational attainment as significant risk factors [16]. In a longitudinal study conducted in Peru, Vargas-Torres-Young et al. (2022) reported a CF prevalence of 11.3% and a mortality rate of 9.9%, showing that sedentary behavior, weakness, and exhaustion combined with cognitive impairment significantly increased mortality risk [15]. Similarly, Holland (2024) emphasized that socioeconomic, sociocultural, and psychological determinants—such as structural inequality, educational level, and chronic stress—play major roles in the development and progression of CF [14]. Nader et al. (2023) reported that CF affects 1–4.4% of community-dwelling older adults and 10.7–22% of clinical populations [13]. A recent systematic review and meta-analysis reported a pooled prevalence of approximately 10%, with higher rates observed in clinical and hospitalized populations [17]. A review study attributed its pathogenesis to a multifactorial interaction of biological, metabolic, and psychosocial factors, including chronic inflammation, oxidative stress, hypothalamic–pituitary–adrenal axis dysfunction, hormonal and metabolic imbalances (particularly insulin resistance), and lifestyle-related risks such as depression, social isolation, and physical inactivity [13]. In Iran, however, research on CF remains limited. Ghanbarinia et al. (2024) reported a 12% prevalence of CF among community-dwelling older adult [18], while findings from the Bushehr Elderly Cohort Study demonstrated an association between frailty and cognitive impairment but did not assess CF as a distinct clinical syndrome [19].
Although CF has been increasingly recognized, most available evidence comes from community-based or outpatient samples in high-income countries. Hospitalized older adults—often presenting with acute illness and multimorbidity—remain understudied, especially in low- and middle-income regions. This study provides hospital-based evidence from a Middle Eastern setting, contributing to the international understanding of CF by examining contextual factors such as limited outpatient access, low-protein diets, and family-centered caregiving. Accordingly, the study aimed to determine the prevalence, associated factors, and predictors of CF among hospitalized older adults in Iran.
Methods
Study design
This cross-sectional study was conducted in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [20].
Setting
The study was conducted at Shahid Beheshti Hospital, a tertiary teaching and referral center affiliated with Kashan University of Medical Sciences. The hospital has more than 650 active beds and provides specialized services in internal medicine, general surgery, cardiology, and intensive care. It serves a wide catchment area including Kashan, Aran and Bidgol, Natanz, and parts of Isfahan Province. Participants were recruited from three wards: internal medicine, general surgery, and the coronary care unit (CCU).
Participants
A convenience sampling was used to recruit hospitalized older adults from the three study wards between July and September 2024, during both morning and evening shifts.
Eligible participants were those who met the following inclusion criteria: Iranian nationality; age ≥ 60 years; ability to communicate in Persian; hospitalization for at least 24–72 h; written informed consent; and hemodynamic stability with adequate mental status and cooperation to participate in structured assessments. Exclusion criteria included a diagnosis of dementia or severe cognitive impairment (Clinical Dementia Rating [CDR] ≥ 2), voluntary withdrawal prior to completion of the assessment, or incomplete data records. All eligible participants were fully informed about the study objectives, confidentiality procedures, and their right to withdraw at any time without consequence. Written informed consent was obtained before participation.
Data collection procedures
All data were collected by a trained investigator (ZH), a Master’s student in Medical-Surgical Nursing with one year of clinical experience. Before data collection, the investigator received structured training and completed a pilot study with three older adults to ensure clarity, feasibility, and consistency in data collection.
Data were collected through structured face-to-face interviews and supervised questionnaire administration, with the exception of the associated factor questionnaire. For literate participants, the associated factor questionnaire was self-administered; for illiterate participants, they were completed via face-to-face interviews by the investigator. All assessments were performed within 24 h of recruitment to minimize fatigue and ensure accuracy.
Data collection instruments
Fried frailty phenotype
Frailty was assessed using the FFP, which evaluates five criteria: unintentional weight loss, exhaustion, low physical activity, slowness, and weakness. Each criterion was measured using standardized procedures: unintentional weight loss of ≥ 4.5 kg during the past year (self-reported); exhaustion assessed by two items; low physical activity; (4) slowness measured by 4-meter gait speed adjusted for sex and height; and (5) weakness assessed by handgrip strength using a calibrated dynamometer, adjusted for sex and BMI. Participants were classified as non-frail (score = 0), pre-frail (score = 1–2), or frail (score ≥ 3). This questionnaire was designed by Fried et al. in 2001 [5]. The reliability and validity of this scale have been examined in different studies and confirmed [21, 22]. The Persian version of the FFP, validated by Tavan and Asadullahi (2021), demonstrated satisfactory psychometric properties, including internal consistency (Cronbach’s α = 0.79), test–retest reliability (r = 0.90), and strong construct validity (clarity index = 0.87; high goodness-of-fit index) [22].
Cognitive assessment
Cognitive function was evaluated using the CDR scale, a standardized and validated tool for assessing six cognitive and functional domains: memory, orientation, judgment and problem-solving, community affairs, home and hobbies, and personal care. Each domain is rated on a five-point scale (0 = no impairment to 3 = severe impairment), and a global CDR score is derived using a validated algorithm, with higher scores indicating greater cognitive decline. The CDR was originally developed by Morris in 1993, and the initial validation demonstrated strong psychometric performance, including inter-rater reliability above κ = 0.80, test–retest reliability ranging from ICC ≈ 0.85 to 0.95, and confirmed concurrent and discriminant validity in differentiating healthy individuals, mild cognitive impairment, and Alzheimer’s disease [23]. The Persian version of the CDR has shown strong psychometric validity and diagnostic accuracy among Iranian older adults (Cronbach’s α = 0.73; reliability coefficient = 0.89; positive predictive value = 0.88; AUC = 0.968; sensitivity = 1.00; specificity = 0.94) [24].
Cognitive frailty
CF was operationally defined as the coexistence of physical frailty (FFP ≥ 3) and MCI (CDR = 0.5–1) in the absence of dementia (CDR ≥ 2). Potential CF was defined as the coexistence of pre-frailty (FFP = 1–2) and MCI (CDR = 0.5–1), excluding all participants with CDR ≥ 2 [12].
Associated factors
Associated factors were assessed using a researcher-developed questionnaire designed after an extensive review of the literature [25–27]. The questionnaire covered six domains: demographic, physical, clinical, nutritional, psychosocial, and economic factors. Content and face validity were confirmed by a panel of 10 faculty experts, and revisions were made accordingly.
This questionnaire consisted of demographic factors: ward of admission, length of hospitalization, age, sex, education, marital status, ethnicity, living arrangement, and number of children, employment status, health insurance, smoking, and substance use; physical factors: self-rated health, physical activity, body mass index (BMI), mid-arm and calf circumference, daily medication use, and handgrip strength; clinical factors: type of chronic diseases, number of medications, hospitalizations in the past year, history of pressure ulcers, and number of medical visits; nutritional factors: appetite, meal frequency, intake of dairy, eggs, meat/fish, fluids, and eating method; psychosocial factors: social interactions (telephone/in-person), perceived stress, adverse life events, life satisfaction, sleep quality, use of sleep medications, loneliness, and experiences of abuse (emotional or physical); and economic factors: ability to meet basic expenses, access to transportation, home ownership, and satisfaction with housing.
For anthropometric measurements, weight and height were measured using calibrated digital scales, and BMI was calculated as weight (kg)/height (m²). Mid-upper arm and calf circumferences were measured at standard anatomical landmarks using a non-stretch tape. Handgrip strength was assessed using a calibrated Constant® dynamometer. All instruments were calibrated according to manufacturer specifications.
Sample size
Based on an estimated prevalence of 23% reported by Yamamoto et al. [28] and a 95% confidence level (α = 0.05), the minimum required sample size was calculated as 272. Considering a 10% attrition rate, the final target sample size was set at 300 participants.
Data analysis
Data were analyzed using SPSS version 26 (IBM Corp., Armonk, NY, USA). Prior to analysis, data completeness and normality were verified using skewness (± 2) and kurtosis (± 3) criteria. Descriptive statistics—including means and standard deviations for continuous variables and frequencies and percentages for categorical variables—were calculated. Group comparisons between participants with and without CF were performed using independent t-tests or ANOVA for continuous variables and Chi-square or Fisher’s exact tests for categorical variables.
To identify independent predictors of CF, multivariable logistic regression analysis (backward conditional method) was conducted. All variables from the associated factors questionnaire were entered into the model simultaneously. Model fit was assessed using the Hosmer–Lemeshow goodness-of-fit test and the Omnibus Test of Model Coefficients, while discriminative performance was evaluated using Receiver Operating Characteristic (ROC) curve analysis. Diagnostic performance indices—including sensitivity, specificity, positive and negative predictive values (PPV, NPV), likelihood ratios (+ LR, −LR), and Youden’s J index—were calculated to assess model accuracy.
Ethical considerations
The study protocol was approved by the Ethics Committee of Kashan University of Medical Sciences (Approval Code: IR.KAUMS.NUHEPM.REC.1403.007). Formal permission was obtained from Shahid Beheshti Hospital and the heads of participating wards. All participants were informed about the study purpose, voluntary nature of participation, and data confidentiality. Written informed consent was obtained from all participants. They were also informed of their right to withdraw at any time without consequences and were offered the opportunity to receive a summary of the study findings upon completion.
Findings
A total of 325 hospitalized older adults were screened, and data from 300 participants were analyzed (Fig. 1). The mean (SD) age of the participants was 71.1 (7.98) years, and 54.3% were male (Table 1). The overall prevalence of CF was 22.7% (n = 68), and an additional 23.0% (n = 69) were classified as potentially cognitively frail (Table 5).
Fig. 1.
Flow diagram of participant selection: 325 assessed, 25 excluded for dementia (CDR ≥2), 300 enrolled and analysed
Table 1.
Demographic factors of cognitive frailty in hospitalized older adults
| Demographic Factors | Total | Non-CF | CF | P value |
|---|---|---|---|---|
| Age, mean (SD) c, d | 71.79 (7.98) | 71.51 (7.51) | 72.75 (9.41) | 0.322 |
| 60–69 | 132 (44) | 105 (45.3) | 27 (39.7) | 0.274 |
| 70–79 | 109 (36.3) | 86 (37.1) | 23 (33.8) | |
| ≥80 | 59 (19.7) | 41 (17.7) | 18 (26.5) | |
| Gender b | < 0.001 a | |||
| Female | 137 (45.7) | 92 (39.7) | 45 (66.2) | |
| Male | 163 (54.3) | 140 (60.3) | 23 (33.8) | |
| Educational level c | 0.739 | |||
| Illiterate | 121 (40.3) | 92 (39.7) | 29 (42.6) | |
| Primary School | 145 (48.3) | 112 (48.3) | 39 (57.4) | |
| Secondary School | 34 (11.3) | 28 (12.1) | 6 (8.8) | |
| Marital status b | 0.302 | |||
| Single | 109 (36.3) | 82 (35.3) | 27 (39.7) | |
| Married | 191 (63.7) | 150 (64.7) | 41 (60.3) | |
| Ethnicity b | 0.425 | |||
| Fars | 292 (97.3) | 225 (97) | 67 (98.5) | |
| Turk | 8 (2.7) | 7 (3) | 1 (1.5) | |
| Living arrangement b | 0.103 | |||
| Alone | 73 (24.3) | 52 (22.4) | 21 (30.9) | |
| With others | 227 (75.7) | 180 (77.6) | 47 (69.1) | |
| Number of children, Mean (SD) d | 4.6 (2.05) | 4.47 (2.04) | 5.03 (2.02) | 0.048 a |
a Significant Variables
b Fisher’s Exact Test
c Chi-Square Test
d Independent T-Test
In the univariate analysis, participants with CF were more likely to be female (p < 0.001), to have higher body mass index (p = 0.04), lower handgrip strength (p < 0.001), and smaller mid-arm and calf circumferences (p < 0.001) (Tables 1 and 2). Both men and women with CF exhibited significantly reduced handgrip strength and calf circumference compared with those without CF (p < 0.001). Participants with CF reported poorer self-rated health (p < 0.001) and higher rates of diabetes (p = 0.007), polypharmacy (p = 0.002), and physician visits (p = 0.001) (Tables 3 and 4). Psychosocial and nutritional factors were significant as well: loneliness (p = 0.001), stressful life events (p = 0.017), and malnutrition risk (p < 0.001) were markedly more common in cognitively frail participants (Table 4).
Table 2.
Physical and nutritional status of cognitive frailty in hospitalized older adults
| Physical Status | Total | Non-CF | CF | P value |
|---|---|---|---|---|
| Handgrip strength, Mean (SD) d | 23.66 (13.63) | 26.47 (14.01) | 14.06 (5.64) | < 0.001 a |
| Female | 19.49 (9.83) | 22.17 (10.26) | 14.00 (5.94) | < 0.001 a |
| Male | 27.16 (15.32) | 29.29 (15.39) | 14.18 (5.12) | < 0.001 a |
| BMI, Mean (SD) d | 24.35 (3.66) | 23.91 (3.39) | 25.35 (3.44) | 0.04 a |
| Female | 24.09 (4.28) | 24.12 (3.77) | 24.04 (5.22) | 0.921 |
| Male | 23.79 (3.84) | 24.03 (3.8) | 22.37 (3.84) | 0.05 |
| Mid-arm circumference (cm), Mean (SD) d | 24.04 (5.55) | 24.75 (5.65) | 21.61 (4.43) | < 0.001 a |
| Female | 23.28 (4.92) | 23.72 (5.01) | 22.36 (4.65) | 0.13 |
| Male | 24.69 (5.97) | 25.43 (5.96) | 20.13 (3.62) | < 0.001 a |
| Calf circumference (cm), Mean (SD) d | 29.07 (5.70) | 30.07 (5.43) | 25.69 (5.30) | < 0.001 a |
| Female | 28.18 (5.00) | 29.14 (4.42) | 26.21 (5.56) | 0.001 a |
| Male | 29.83 (6.14) | 30.67 (5.94) | 24.67 (4.70) | < 0.001 a |
| Smoking b | 0.85 | |||
| Yes | 91 (30.3) | 71 (30.6) | 20 (29.4) | |
| No | 209 (69.7) | 161 (69.4) | 48 (70.6) | |
| Substance use b | 0.022 a | |||
| Yes | 237 (79) | 177 (76.3) | 60 (88.2) | |
| No | 63 (21) | 55 (23.7) | 8 (11.8) | |
| Weekly exercise b | < 0.001 a | |||
| Never | 255 (85) | 188 (81) | 67 (98.5) | |
| Yes | 45 (15) | 44 (19) | 1 (1.5) | |
| Type of activity b | 0.013 a | |||
| Independent | 281 (93.7) | 222 (95.7) | 59 (86.8) | |
| Dependent | 19 (6.3) | 10 (4.3) | 9 (13.2) | |
| Nutritional Status | Total | Non-CF | CF | p value |
| Change in Appetite b | 0.07 | |||
| Moderate decrease | 39 (13) | 26 (11.2) | 13 (19.1) | |
| No change | 261 (87) | 206 (88.8) | 55 (80.9) | |
| Daily Full Meal b | 0.16 | |||
| Two meals per day | 56 (18.7) | 40 (17.2) | 16 (23.5) | |
| Three meals per day | 244 (81.3) | 192 (82.8) | 52 (76.5) | |
| Consume ≥ 1 serving of dairy products/day b | 0.537 | |||
| No | 63 (21) | 49 (21.1) | 14 (20.6) | |
| Yes | 237 (79) | 183 (78.9) | 54 (79.4) | |
| Consume ≥ 2 servings of legumes or eggs/week b | 0.567 | |||
| No | 62 (20.7) | 48 (20.7) | 14 (20.6) | |
| Yes | 238 (79.3) | 184 (79.3) | 54 (79.4) | |
| Meat, fish, or poultry/daily b | 0.084 | |||
| No | 139 (46.3) | 102 (44) | 37 (54.4) | |
| Yes | 161 (53.7) | 130 (56) | 31 (45.6) | |
| Consume ≥ 2 servings of fruits and vegetables/day b | 0.419 | |||
| No | 92 (30.7) | 70 (30.2) | 22 (32.4) | |
| Yes | 208 (69.3) | 162 (69.8) | 46 (67.6) | |
| Fluid consumption/day c | 0.153 | |||
| Less than 3 cups | 41 (13.7) | 27 (11.6) | 14 (20.6) | |
| 3 to 5 cups | 122 (40.7) | 98 (42.2) | 24 (35.3) | |
| More than 5 cups | 137 (45.7) | 107 (46.1) | 30 (44.1) | |
| Mode of Feeding b | < 0.001 a | |||
| Requires assistance | 43 (14.3) | 15 (6.5) | 28 (41.2) | |
| Self-feeding without difficulty | 257 (85.7) | 217 (93.5) | 40 (58.8) | |
| Self-reported of Nutritional Status c | < 0.001 a | |||
| Malnutrition | 96 (32) | 57 (24.6) | 39 (57.4) | |
| At risk of malnutrition | 173 (57.7) | 145 (62.5) | 28 (41.2) | |
| Normal | 31 (10.3) | 30 (12.9) | 1 (1.5) | |
a Significant Variables
b Fisher’s Exact Test
c Chi-Square Test
d Independent T-Test
Table 3.
Clinical status of cognitive frailty in hospitalized older adults
| Clinical status | Total | Non-CF | CF | P value |
|---|---|---|---|---|
| Hospital ward c | 0.805 | |||
| Internal medicine | 152 (50.7) | 117 (50.4) | 35 (51.5) | |
| Surgery | 101 (33.7) | 80 (34.5) | 21 (30.9) | |
| CCU | 47 (15.7) | 35 (15.1) | 12 (17.6) | |
| Length of hospital stay, Mean (SD) d | 5.35 (4.95) | 5.42 (4.69) | 5.12 (3.42) | 0.751 |
| Number of daily medications, mean (SD) d | 5.62 (4.36) | 5.13 (4.18) | 6.97 (4.35) | 0.002 a |
| History of Hospitalizations in Past Year b | 0.028 a | |||
| Never | 134 (44.7) | 111 (47.8) | 23 (33.8) | |
| Once | 166 (55.3) | 121 (52.2) | 45 (66.2) | |
| Comorbidity b | < 0.001 a | |||
| Yes | 204 (68) | 145 (62.5) | 59 (86.8) | |
| No | 96 (32) | 87 (37.5) | 9 (13.2) | |
| Diabetes b | 0.007 a | |||
| Yes | 131 (43.7) | 92 (39.7) | 39 (57.4) | |
| No | 169 (56.3) | 140 (60.3) | 29 (42.6) | |
| Hypertension b | 0.044 a | |||
| Yes | 174 (58) | 128 (55.2) | 46 (67.6) | |
| No | 126 (42) | 104 (44.8) | 22 (32.4) | |
| Cardiovascular Diseases b | 0.412 | |||
| Yes | 83 (27.7) | 63 (27.2) | 20 (29.4) | |
| No | 217 (72.3) | 169 (72.8) | 48 (70.6) | |
| Asthma b | 0.039 a | |||
| Yes | 26 (8.7) | 24 (10.3) | 2 (2.9) | |
| No | 274 (91.3) | 208 (89.7) | 66 (97.1) | |
| Hypothyroidism b | 0.537 | |||
| Yes | 12 (4) | 9 (3.9) | 3 (4.4) | |
| No | 288 (96) | 223 (96.1) | 65 (95.6) | |
| Kidney Diseases b | < 0.001 a | |||
| Yes | 53 (17.7) | 30 (12.9) | 23 (33.8) | |
| No | 247 (82.3) | 202 (87.1) | 45 (66.2) | |
| Pulmonary Diseases b | 0.157 | |||
| Yes | 32 (10.7) | 22 (9.5) | 10 (14.7) | |
| No | 268 (89.3) | 210 (90.5) | 58 (85.3) | |
| Other Diseases b | 0.016 a | |||
| Yes | 127 (42.3) | 90 (38.8) | 37 (54.4) | |
| No | 173 (57.7) | 142 (61.2) | 31 (45.6) | |
| Falls in the Past Year b | 0.039 a | |||
| Never | 55 (18.3) | 37 (15.9) | 18 (26.5) | |
| Once | 245 (81.7) | 195 (84.1) | 50 (73.5) | |
| History of wound b | 0.557 | |||
| Yes | 106 (35.3) | 82 (35) | 24 (35) | |
| No | 194 (64.7) | 150 (65) | 44 (65) | |
| Physician Visit c | 0.001 a | |||
| Never | 41 (13.7) | 33 (14.2) | 8 (11.8) | |
| Once | 121 (40.3) | 80 (34.5) | 41 (60.3) | |
| Twice or more | 138 (46) | 119 (51.3) | 19 (27.9) | |
a Significant Variables
b Fisher’s Exact Test
c Chi-Square Test
d Independent T-Test
Table 4.
Psychosocial and economic status of cognitive frailty in hospitalized older adults
| Total | Non-CF | CF | P value | |
|---|---|---|---|---|
| Psychosocial status | ||||
| Feeling Stressed b | 0.017 a | |||
| Never | 95 (31.7) | 81 (34.9) | 14 (20.6) | |
| Yes | 205 (68.3) | 151 (65.1) | 54 (79.4) | |
| Feeling Lonely b | 0.001 a | |||
| Often | 90 (30) | 58 (25) | 32 (47.1) | |
| Sometimes | 210 (70) | 174 (75) | 36 (52.9) | |
| Emotional Abuse b | 0.023 a | |||
| Yes | 125 (41.7) | 89 (38.4) | 36 (52.9) | |
| No | 175 (58.3) | 143 (61.6) | 32 (47.1) | |
| Physical Abuse b | < 0.001 a | |||
| Yes | 22 (7.3) | 6 (2.6) | 16 (23.5) | |
| No | 278 (92.7) | 226 (97.4) | 52 (76.5) | |
| Negative Life Events b | 0.349 | |||
| Never | 154 (51.3) | 121 (52.2) | 33 (48.5) | |
| Yes | 146 (48.7) | 111 (47.8) | 35 (51.5) | |
| Life Review Satisfaction b | 0.115 | |||
| Low | 95 (31.7) | 78 (33.6) | 17 (25) | |
| High | 205 (68.3) | 154 (66.4) | 51 (75) | |
| Sleep Satisfaction b | 0.042 a | |||
| Low | 67 (22.3) | 46 (19.8) | 21 (30.9) | |
| Moderate to Low | 233 (77.7) | 186 (80.2) | 47 (69.1) | |
| Use of Sleep Medication b | 0.121 | |||
| Yes | 62 (20.7) | 44 (19) | 18 (26.5) | |
| No | 238 (79.3) | 188 (81) | 50 (73.5) | |
| Phone Contact with others b | 0.02 a | |||
| Never | 17 (5.7) | 9 (3.9) | 8 (11.8) | |
| Yes | 283 (94.3) | 223 (96.1) | 60 (88.2) | |
| Meeting with others b | 0.332 | |||
| Never | 21 (7) | 15 (6.5) | 6 (8.8) | |
| Yes | 279 (93) | 217 (93.5) | 62 (91.2) | |
| Self-rated health b | < 0.001 a | |||
| Poor | 49 (16.3) | 23 (9.9) | 26 (38.2) | |
| Good | 251 (83.7) | 209 (90.1) | 42 (61.8) | |
| Economic status | ||||
| Employment status b | 0.002 a | |||
| Employed | 70 (23.3) | 63 (27.2) | 7 (10.3) | |
| Unemployed | 230 (76.7) | 169 (72.8) | 61 (89.7) | |
| Economic Status b | 0.406 | |||
| Low | 156 (52) | 122 (52.6) | 34 (50) | |
| Moderate | 144 (48) | 110 (47.4) | 34 (50) | |
| Ability to Pay Basic Living Expenses b | 0.231 | |||
| Yes | 177 (59) | 140 (60.3) | 37 (54.4) | |
| No | 123 (41) | 92 (39.7) | 31 (45.6) | |
| Housing Status b | 0.345 | |||
| Owned | 262 (87.3) | 206 (88.8) | 56 (82.4) | |
| Rented | 38 (12.7) | 26 (11.2) | 12 (17.6) | |
| Satisfaction with Living Facilities b | 0.159 | |||
| Low | 60 (20) | 43 (18.5) | 17 (25) | |
| Moderate | 240 (80) | 189 (81.5) | 51 (75) | |
| Limited Access to Transportation b | 0.345 | |||
| Yes | 65 (21.7) | 52 (22.4) | 13 (19.1) | |
| No | 235 (78.3) | 180 (77.6) | 55 (80.9) | |
| Basic insurance b | 0.564 | |||
| Yes | 283 (94.3) | 219 (94.4) | 64 (94.1) | |
| No | 17 (5.7) | 13 (5.6) | 4 (5.9) | |
| Private insurance b | 0.054 | |||
| Yes | 171 (57) | 126 (54.3) | 45 (66.2) | |
| No | 129 (43) | 106 (45.7) | 23 (33.8) | |
a Significant Variables
b Fisher’s Exact Test
In the multivariable logistic regression model (Table 6), several factors remained significant predictors of CF. Protective factors included positive self-rated health (OR = 0.179, p = 0.005), non-smoking (OR = 0.150, p < 0.001), greater handgrip strength (OR = 0.798, p < 0.001), good nutritional status (OR = 0.330, p < 0.001), larger mid-upper arm circumference (OR = 0.878, p = 0.015), larger calf circumference (OR = 0.897, p = 0.029), comorbidity (OR = 8.77, p = 0.037), and adequate appetite (OR = 0.107, p = 0.004). Conversely, higher BMI (OR = 1.315, p < 0.001), physical inactivity (OR = 5.966, p = 0.029), diabetes (OR = 3.692, p = 0.008), frequent physician visits (OR = 6.974, p < 0.001), stressful life events (OR = 4.764, p = 0.009), and poor economic status (OR = 2.724, p = 0.035) were identified as independent risk factors.
Table 6.
Logistic regression results for related factors of cognitive frailty
| Step | Variable | Beta Coefficient | Standard Error | Wald | P-value | Odds Ratio |
|---|---|---|---|---|---|---|
| Step 0 | Intercept | − 1.227 | 0.138 | 2.79 | < 0.001 a | 0.293 |
| Step 38 | Self-rated health | − 1.723 | 0.616 | 7.826 | 0.005 a | 0.179 |
| BMI | 0.274 | 0.069 | 15.752 | < 0.001 a | 1.315 | |
| Physical inactivity | 1.786 | 0.815 | 4.797 | 0.029 a | 5.966 | |
| Non-smoker | − 1.9 | 0.518 | 13.475 | < 0.001 a | 0.15 | |
| Mid-upper arm circumference | − 0.13 | 0.053 | 5.939 | 0.015 a | 0.878 | |
| Calf circumference | − 0.109 | 0.05 | 4.741 | 0.029 a | 0.897 | |
| Handgrip strength | − 0.226 | 0.041 | 30.091 | < 0.001 a | 0.798 | |
| Nutritional status | − 1.11 | 0.21 | 27.947 | < 0.001 a | 0.33 | |
| Appetite reduction | − 2.239 | 0.786 | 8.11 | 0.004 a | 0.107 | |
| Comorbidity | 2.171 | 1.038 | 4.373 | 0.037 a | 8.77 | |
| Diabetes | 1.306 | 0.489 | 7.149 | 0.008 a | 3.692 | |
| Monthly physician visit | ||||||
| Never | Reference | 13.009 | 0.001 a | |||
| Once | 1.362 | 0.709 | 3.689 | 0.055 | 3.903 | |
| Twice or more | 1.942 | 0.539 | 12.986 | < 0.001 a | 6.974 | |
| History of falls | 0.915 | 0.555 | 2.717 | 0.099 | 2.498 | |
| Negative events in past year | 1.561 | 0.599 | 6.797 | 0.009 a | 4.764 | |
| Low economic status | 1.002 | 0.477 | 4.422 | 0.035 a | 2.724 | |
| Intercept | 10.537 | 2.648 | 15.833 | < 0.001 a | 37683.046 |
a Significant Variables
-2 Log Likelihood: 144.092
Cox & Snell R²: 0.446
Nagelkerke R²: 0.678
Hosmer–Lemeshow Test: 0.864
Omnibus Test: 0.001
Table 5.
Cognitive frailty status in hospitalized older adults
| Frailty Status | Normal Cognition (CDR < 0.5) | Mild Cognitive Impairment (0.5 ≤ CDR ≤ 1) |
|---|---|---|
| Robust, N (%) | 32 (10.67) | 11 (3.67) |
| Pre-frail, N (%) | 100 (33.33) | 69 (23) |
| Frail, N (%) | 20 (6.67) | 68 (22.67) |
The final regression model demonstrated excellent predictive performance. The Nagelkerke R² = 0.678 indicated that approximately 68% of the variance in CF was explained by the included variables, and the Hosmer–Lemeshow test was non-significant (p = 0.864), confirming good model calibration (Table 6). The ROC curve demonstrated outstanding discriminatory power, with an AUC = 0.909 (95% CI 0.858–0.960, p < 0.001) and an optimal cutoff determined by Youden’s J = 0.737 (Fig. 2). Diagnostic performance metrics (Table 7) confirmed the model’s high accuracy, with sensitivity = 0.817, specificity = 0.921, overall accuracy = 0.900, positive predictive value = 0.721, negative predictive value = 0.953, and diagnostic odds ratio = 51.8. Collectively, these findings highlight the multifactorial nature of CF in hospitalized older adults, emphasizing the combined influence of physical, nutritional, psychological, and socioeconomic determinants on its occurrence and prevention.
Fig. 2.
ROC curve for the final multivariable logistic regression model predicting cognitive frailty in hospitalized older adults; AUC = 0.909 (95% CI 0.858–0.960, p < 0.001); optimal cutoff chosen by Youden’s J = 0.737
Table 7.
Diagnostic performance metrics of the predictive model
| Model/ Condition | CF | Non-CF | Totals |
|---|---|---|---|
| Model Predicts Positive | a (TP) = 49 | b (FP) = 19 | 68 |
| Model Predicts Negative | c (FN) = 11 | d (TN) = 221 | 232 |
| Totals | 60 | 240 | 300 |
| Statistics | Value | 95% CI | |
| Low | High | ||
| Overall Fraction Correct | 0.9 | 0.86 | 0.93 |
| Mis-classification Rate | 0.1 | 0.07 | 0.14 |
| Sensitivity | 0.817 | 0.717 | 0.891 |
| Specificity | 0.921 | 0.896 | 0.939 |
| Prevalence (estimated from sample) | 0.2 | 0.159 | 0.249 |
| Positive Predictive Value | 0.721 | 0.632 | 0.786 |
| Negative Predictive Value | 0.953 | 0.927 | 0.972 |
| Positive Likelihood Ratio | 10.316 | 6.877 | 14.708 |
| Negative Likelihood Ratio | 0.199 | 0.116 | 0.316 |
| Diagnostic Odds Ratio | 51.813 | 21.736 | 126.782 |
| Error Odds Ratio | 0.383 | 0.294 | 0.527 |
| Youden’s J | 0.737 | 0.612 | 0.83 |
| Equitable Threat Score | 0.541 | 0.412 | 0.654 |
| Relative Improvement Over Chance (RIOC) | 0.763 | 0.64 | 0.885 |
Discussion
This study examined the prevalence, associated factors, and predictive determinants of CF among hospitalized older adults in Iran. The findings revealed that more than one-fifth of participants met the diagnostic criteria for CF, and nearly one-quarter exhibited potential CF, defined as the coexistence of cognitive impairment with pre-frailty. The prevalence observed in this study was somewhat higher than that reported in previous international research. Nader et al. (2023) found CF rates of 10.7–22% among community-dwelling literate adults with a mean age of 66 [16], while Fang et al. (2024) reported lower rates of 1.8% and 8.9% among surgical patients, likely due to their younger age and higher literacy levels [29]. Similarly, a systematic review by Liu et al. (2022) reported estimates ranging from 2.3% to 43.2%, emphasizing the influence of demographic and socioeconomic factors such as age, sex, education, and employment [30]. These discrepancies reflect the impact of population characteristics and healthcare context. In Iran, sociocultural and systemic factors likely contribute to the higher prevalence observed. Older adults in Iran typically consume low-protein, high-carbohydrate diets, with adequate intake of fruits, vegetables, and dairy products but frequent malnutrition [31]. Family-based caregiving predominates, and hospitalization often occurs at advanced disease stages, leading to greater comorbidity [32]. Limited outpatient access and economic constraints further delay treatment and continuity of care [33, 34]. Moreover, in acute care settings, illness, immobility, polypharmacy—particularly the use of anticholinergic or sedative medications—and disrupted sleep patterns can exacerbate both cognitive decline and physical frailty. Together, these factors help explain the relatively high rate of CF among hospitalized Iranian older adults and underscore the need for culturally tailored, multidomain prevention strategies.
The current study demonstrates that CF arises from a complex interaction of biological, psychological, and environmental factors. Previous research supports these findings. Coral-Pérez et al. (2023) identified low muscle strength and low physical activity as key predictors of CF among community-dwelling older adults in Spain [35]. Deng et al. (2023) studied older adults over 60 in Kazakhstan and identified factors such as reduced appetite, low albumin levels, smaller arm circumference, chronic conditions such as diabetes, and depression as contributors to an increased risk of CF. In contrast, physical and mental activities was associated with a decreased likelihood of CF [36]. Ghanbarnia et al. (2024) also found that advanced age, illness history, low literacy, depression, and an inactive lifestyle are risk factors [18]. Also, Lorenzo-López et al. (2025) showed that maintaining occupations with low cognitive demands, having limited social support, and living in regions with high levels of multiple deprivation significantly increased the likelihood of experiencing frailty and cognitive impairment [37]. Furthermore, emerging evidence suggests that deficiencies in micronutrients, chronic inflammation, and sleep disturbances may mediate the relationship between frailty and cognitive impairment [38–40]. Therefore, healthcare professionals and family members should promote daily participation in physical and cognitive activities as a means of improving physical performance, stimulating brain function, and reducing the risk of cognitive decline. This study revealed that hospitalized older adults who consulted a physician two or more times per month were significantly more likely to present with CF. Although frequent medical visits can reflect health-seeking behavior and proactive care in some contexts, in hospitalized older adults they more likely indicate underlying morbidity or dependence. However, previous research has not directly examined the link between healthcare utilization and CF, previous studies suggest that individuals with cognitive impairment or frailty require more frequent medical attention, reflecting their vulnerable health [41, 42]. From a clinical standpoint, elevated healthcare utilization should be regarded as a pragmatic warning signal that warrants systematic evaluation rather than as a marker of protection. Hospitalized older adults with recurrent consultations represent a particularly high-risk cohort who would benefit from regular CF screening, comprehensive geriatric assessment, medication review—including deprescribing where feasible—and integrated care coordination to address the root determinants of their frequent healthcare use.
The predictive model achieved high specificity and sensitivity, demonstrating excellent discriminatory ability (AUC = 0.909). These findings confirm the model’s clinical utility for identifying CF among hospitalized older adults. Additional metrics, including a diagnostic odds ratio of 51.8 and Youden’s J index of 0.737, confirmed its strong discriminatory power and clinical utility. While previous research has predominantly focused on community-dwelling older adults, the present findings align with those studies. Chen et al. (2022) reported that a model using instrumental activities of daily living (IADL), self-rated health, daytime mental state, number of chronic diseases, age, nutritional status, and physical exercise achieved an AUC of 0.920 in community-dwelling populations [43]. Similarly, Huang et al. (2024), drawing on data from the Chinese Longitudinal Healthy Longevity Survey, demonstrated that a model with six predictors—age, sex, residence, BMI, exercise, and physical disability—achieved AUCs of 0.830 and 0.840 in development and external validation cohorts, respectively [44]. Furthermore, a systematic review by Huang et al. identified age and depression as the most common predictors, with reported AUC values ranging from 0.71 to 0.97 [45]. These results indicate that the current model is both accurate and clinically valuable for detecting CF in hospitalized older adults. Its high specificity and good sensitivity suggest it is particularly effective at identifying individuals without CF, while maintaining robust detection of those with the condition. Future research should prioritize the development and external validation of models with enhanced predictive performance and methodological rigor.
Limitations
This study has several limitations. The cross-sectional design restricts the ability to establish causal relationships. Convenience sampling and reliance on self-reported data may limit generalizability and introduce response bias. Potential under diagnosis of MCI could have occurred due to exclusive use of the CDR scale without complementary neuropsychological testing. Finally, the prediction model lacks external validation and was derived from a single hospital setting, which restricts applicability to other healthcare contexts.
Conclusion
CF was identified as a relatively prevalent, multifactorial condition among hospitalized older adults in Iran. The findings showed significant associations between CF and multiple domains, including self-rated health, handgrip strength, anthropometric measurements, nutritional status, physical activity, diabetes mellitus, exposure to stressful life events, frequency of physician visits, and socioeconomic status. The predictive regression model demonstrated robust statistical performance, indicating that these multidimensional factors collectively contribute to the presence of CF in hospitalized older adults. Future longitudinal studies with larger and more diverse samples are needed to validate these findings and enhance generalizability.
Acknowledgements
the authors extend their sincere appreciation to all individuals who took part in this research.
Author contributions
all authors contributed substantially to the study’s conception, design, data acquisition, analysis, and interpretation. They were collectively involved in drafting, revising, and reviewing the manuscript, approved the final version for submission, and assume full responsibility for the content.
Funding
This study was supported by the Research and Technology Chancellor of Kashan University of Medical Sciences (Grant No. 402206).
Data availability
this manuscript does not report data generation or analysis.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval
this study received ethical approval from the research ethics committee of Kashan University of Medical Sciences, with the ethical code IR.KAUMS.NUHEPM.REC.1403.007.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
this manuscript does not report data generation or analysis.