Abstract
Aim
We explored the performance of demographic characteristics, physiological state, cognitive function, sensory function, and biomarkers when used as predictors of frailty for patients with schizophrenia.
Design
A cross‐sectional study design was adopted.
Methods
Demographic data and data on physiological state, cognitive function, sensory function, biochemical indices, and frailty status of patients with schizophrenia were collected. The data were analysed using descriptive statistics, a chi‐square test, one‐factor analysis of variance, and logistic regression.
Results
The results revealed that frailty was prevalent among patients with lower educational attainment, longer hospital stay, higher skeletal muscle mass, higher basal metabolic rate, lower cognitive function, the use of tranquillisers and sleeping pills, and the use of assistive equipment as well as having fallen in the past year. In addition, cognitive function (p < 0.05), use of a wheelchair (p < 0.05), and use of an assistive walker (p < 0.001) were used as predictors of frailty condition of patients with schizophrenia.
Patient Contribution
Patients with schizophrenia have higher risk of having complications than patients with other chronic illnesses. Therefore, medical staff should regularly assess the levels of frailty risk to help patients with schizophrenia.
Keywords: cognitive function, frailty, physiological state, schizophrenia, sensory function
1. INTRODUCTION
Schizophrenia is a brain disorder that affects an individual's cognition, emotional state, behaviour, and movement. Common symptoms include false beliefs, unclear or confused thinking, delusions, hallucinations, decreased social activity and emotional expression, and lack of motivation (Trémeau, 2006). Schizophrenia is a chronic mental illness with a lifetime prevalence of 1%. More than 20 million people worldwide have schizophrenia (World Health Organization [WHO], 2020). A previous study found that patients with schizophrenia have a higher risk of premature death and that their life expectancy is 10 to 20 years shorter than average (Tang et al., 2021). In addition, the physiological condition of psychiatric patients gradually deteriorates with age. A previous comparative study on older adults' inpatients with mental illness found that 53% of 120 patients had signs of frailty, of whom 49% died within 5 years; this finding indicates that frailty is a major predictor of mortality (Benraad et al., 2020). Regarding the health status of older inpatients with acute mental illness, Benraad et al. (2020) reported that walking speed slows and frailty guilty increases with age; thus, comorbidities and walking speed are predictors for patient discharge.
Older adults typically experience frailty. Clinical symptoms include weight loss, decreased activity and appetite, loss of muscle and bone mass, gait and balance disorders, and fatigue; in addition, frailty may include cognitive dysfunction, a syndrome that can trigger physiological disorders and susceptibility to complications (Chu et al., 2021). The National Health Research Institutes (2022) conducted data analysis using HALST (Healthy Aging Longitudinal Study in Taiwan) and determined that the comorbidities of frailty in older adults (aged 65 years or above) include hypertension, diabetes, and cardiovascular disease. In addition, the prevalence of comorbidities in older adults with frailty was higher than that in healthy and prefrail older adults (National Health Research Institutes, 2022). Tsai, Chang, et al. (2018); Tsai, Lee, et al. (2018) conducted a study on 561 patients with schizophrenia regarding the risk of fall; the results revealed that 10.2% of patients met the criteria for frailty status; the prevalence of frailty in patients aged 65 years or older was 28.1%, and low physical activity was a factor for increased incidences of falls.
Biochemical for intrinsic capacity and frailty are nucleic acid‐based, protein‐based, and metabolic‐based. Frailty is significantly associated with biomarkers (Morley, 2020). Patients with schizophrenia may have elevated level of certain adverse biochemical markers due to their long‐term use of antipsychotics (Kraguljac et al., 2021). In addition, patients with schizophrenia might represent poor metabolic indices due to long‐term use of antipsychotics. Meanwhile, they are susceptible to developing unhealthy habits, and their risk for other chronic conditions increases overtime. Furthermore, malnutrition, psychotropic medication, and chronic disease play a critical role in the debilitation and deterioration of patients with schizophrenia (Tsai, Chang, et al., 2018; Tsai, Lee, et al., 2018; Wang et al., 2022). However, the number of studies on patients with schizophrenia and the biochemical factors influencing their frailty status is limited. Therefore, additional studies are required to provide a reference for potential medical interventions.
1.1. Aim
We explored the performance of demographic characteristics, physiological state, cognitive function, sensory function, and biomarkers when used as predictors of frailty for patients with schizophrenia.
2. METHODS
2.1. Participants
This cross‐sectional study used purposive sampling to collect the data on adults aged ≥55 years diagnosed with schizophrenia in a psychiatric institution. The study period was between November 2019 and April 2020.
2.1.1. Schizophrenia
Schizophrenia was defined as follows: According to the DSM‐5, a schizophrenia diagnosis requires the presence of at least two of the five major symptoms, including delusions, hallucinations, disorganized or incoherent speaking, disorganized or unusual movements, and negative symptoms.
2.1.2. Inclusion criteria
The inclusion criteria were as follows: aged ≥55 years, with schizophrenia, with stable condition, with Mandarin or Taiwanese speaking skills, and with signed written informed consent.
2.1.3. Exclusion criteria
The exclusion criteria were as follows: having a limb coordination that did not meet the test requirements; having severe visual and hearing impairments; and having psychiatric symptoms that bothered and affected the patient.
2.2. Research tools
2.2.1. Participant characteristics
We collected data on patient characteristics, such as sex, age, educational attainment, history of chronic disease, history of falls, length of hospital stay, type of disease, and type of medication.
2.2.2. Physiological state
Physiological data included Body Mass Index (BMI), automatically calculated using a BW‐1116MH (Nagata). We conducted a bioelectrical impedance analysis (BIA) for body composition analysis using InBody370S. A previous cross‐sectional study on 232 young men, which used BIA based on the index of muscle loss, found that the sensitivity and specificity of Inbody were 73% and 95.9%, respectively (Alkahtani, 2017). In addition, in the Mini Nutritional Assessment (MNA), the maximum score of the scale is 30 and scores ranging from 17 (not inclusive) to 23.5 represent a present risk of malnutrition and scores of 24 (inclusive) and higher represent good nutrition. At a geriatric outpatient clinic, the frailty statuses of 1003 older adults were screened using MNA. The Cronbach's α value for the MNA was 0.730. The retest reliability correlation coefficient was 0.776; the test sensitivity was 45.7%–71.2%; and the test specificity was 78.3%–92.8% (Soysal et al., 2019).
2.2.3. Cognitive function
The Mini–Mental State Examination (MMSE) developed by Folstein et al. (1975) includes items such as orientation, information registration, attention and calculation skills, short‐term memory, and language comprehension. Guo et al. (1988) translated the scale into Chinese. At present, the scale is the most common screening tool used in clinical epidemiological studies on cognitive dysfunction and comprises 11 items. The total score ranges from 0 to 30; higher scores represent better cognitive function, and scores of or below 23 represent potential cognitive deficits, such as dementia and delusion. A previous study found that for the diagnosis of intellectual disability, the sensitivity of MMSE was 85% and its specificity was 82% (Karuza et al., 1997). The MMSE is affected by educational attainment, and several patients with dementia may have a score greater than 24, whereas other patients may have a score less than 24 due to low educational attainment, even when they do not have cognitive impairment (Anthony et al., 1982). The retest reliability was 0.56–0.98; the scoring validity was 0.82; and the Cronbach α value was 0.96 (Folstein et al., 1975). A Cronbach α value of 0.86 was reported in a study on older inpatients (Dai et al., 1999).
2.2.4. Sensory function
This study used the Snellen eye chart to collect data on patient sight and a diapason to test hearing. The presence or absence of teeth and the use of assistive equipment were noted.
2.2.5. Frailty
We used the Study of Osteoporotic Fractures Index (SOF index) of Ensrud et al. (2008) to measure frailty. The measurement method was as follows: (1) asking patients about any unintentional weight loss in the past year which was more than 3 kg or 5% of body weight; (2) asking patients to stand up from a chair 5 times without using their arms; and (3) asking patients “Do you feel full of vitality?” “Frailty” was indicated when two or more (included) criteria were met; “pre‐frailty” was indicated when one criterion was met; “no frailty” was indicated when no criterion was met. Lin et al. (2022) assessed the sensitivity, specificity, and accuracy of the SOF index for older patients with cancer. The results revealed that, compared with comprehensive geriatric assessment (CGA), the sensitivity, specificity, and accuracy of the SOF index were 89.0%, 81.1%, and 86.5%, respectively. This finding indicates that the SOF index can identify 89.0% of the frailty cases and 81.1% of no frailty cases. A comparative analysis on 471 older adults aged >65 years in Taiwan was conducted using the SOF index and Fried's frailty phenotype. The results showed that Fried's frailty phenotype was moderately correlated with the SOF index (p < 0.001); according to the area under the curve, the finding indicates that the SOF index was reliable regarding the predicted value of fall (p < 0.001) (Hu et al., 2018).
2.2.6. Data collection and statistical analysis
The quantitative data were analysed using SPSS Statistics 22. The questionnaires were distributed to patients. The statistics analysed were the frequency, percentage, average, standard deviation (SD), minimum, and maximum, and the methods used were the chi‐square test, one factor analysis of variance, and logistic regression analysis.
2.2.7. Ethics statement
The study was approved by the Research Ethics Committee of “REDACTED”. Patients signed a consent form after being fully informed of the purpose, methods, and period of the study to protect their privacy and rights. The information obtained was strictly confidential and was used only for academic purpose.
2.3. Sample size
The requisite minimum sample size was calculated using G* Power 3.1. The effect size = 0.15, α error probability = 0.05, and Power = 0.8 were substituted to obtain the required size (n = 183).
3. RESULTS
3.1. Patient characteristics
All patient characteristics are shown in Table 1. We included 183 patients with schizophrenia in this study. They were divided into “no frailty” (44; 24%), “pre‐frailty” (119; 65%), and “frailty” (20; 11%) groups. In total, 110 men (60.1%) and 73 women (39.9%) were enrolled with an average age of 63.48 years (SD = 6.00). Regarding educational attainment, 38.8% of patients (70) had a “primary school” education level. The average length of hospital stay was 5442.5 days (SD = 1397.8) and more than 15 years for the majority of patients (51.9%).
TABLE 1.
Attributes, physiological state, cognitive function, sensory function, and biochemical indices of patients with schizophrenia (N = 183).
| Category | Number | Percentage (%) | Range | Average | SD |
|---|---|---|---|---|---|
| Stage of frailty | |||||
| Normal | 44 | 24 | |||
| Prefrailty | 119 | 65 | |||
| Frailty | 20 | 11 | |||
| Sex | |||||
| Male | 110 | 60.1 | |||
| Female | 73 | 39.9 | |||
| Age | |||||
| 55–59 years | 51 | 27.9 | 55–87 | 63.48 | 6.00 |
| 60–64 years | 68 | 37.2 | |||
| 65–69 years | 34 | 18.6 | |||
| 70–74 years | 20 | 10.9 | |||
| 75 years or above | 10 | 5.5 | |||
| Educational attainment | |||||
| No education | 3 | 1.6 | |||
| Primary school | 70 | 38.3 | |||
| Junior high school | 63 | 34.4 | |||
| Senior high school | 33 | 18 | |||
| Junior college | 14 | 5.5 | |||
| University | 4 | 2.2 | |||
| Hospitalization duration | |||||
| 1–5 year(s) | 4 | 2.2 | 1177–9173 | 5442.5 | 1397.8 |
| 6–10 years | 17 | 9.3 | |||
| 11–15 years | 67 | 36.6 | |||
| >15 years | 95 | 51.9 | |||
| Physiological condition | |||||
| History of chronic disease | |||||
| Yes | 178 | 97.3 | |||
| No | 5 | 2.7 | |||
| Hypertension | 63 | 34.4 | |||
| Diabetes | 88 | 48.1 | |||
| Hyperlipidaemia | 32 | 17.5 | |||
| Cardiovascular disease | 56 | 30.6 | |||
| Kidney disease | 60 | 32.8 | |||
| Hepatitis or cirrhosis | 40 | 21.9 | |||
| Cancer | 12 | 6.6 | |||
| Others | 14 | 7.7 | |||
| History of falls | |||||
| Yes | 6 | 3.3 | |||
| No | 177 | 96.7 | |||
| Type of medicine | |||||
| Traditional antipsychotics | 56 | 30.6 | |||
| Second‐generation antipsychotics | 160 | 87.4 | |||
| Clozapine | 51 | 31.8 | |||
| Antidepressant | 12 | 6.6 | |||
| Mood stabilizers | 44 | 24 | |||
| Anxiolytics/tranquillisers and sleeping pills | 100 | 54.6 | |||
| BMI kg/m2 | |||||
| Underweight: <18.5 | 7 | 3.8 | |||
| Healthy weight range: 18.5–24 | 66 | 36.1 | |||
| Overweight: 24–27 | 52 | 28.4 | |||
| Obesity: >27 | 58 | 31.7 | |||
| Body composition analysis | |||||
| Weight of body fat | 163 | 4.60–57.4 | 22.1 | 8.3 | |
| Skeletal muscle mass | 163 | 15.2–36.3 | 23.8 | 4.5 | |
| Basal metabolic rate | 160 | 995–1775 | 1315.2 | 160.14 | |
| Waist‐to‐hip ratio | 160 | 0.69–1.11 | 0.9 | 0.07 | |
| Mini Nutritional Assessment | |||||
| Normal | 171 | 93.4 | |||
| Risk of malnutrition | 12 | 6.6 | |||
| Cognitive function | |||||
| Complete | 100 | 54.6 | 11–30 | 23.6 | 3.84 |
| Mild cognitive dysfunction | 71 | 38.8 | |||
| Severe cognitive dysfunction | 12 | 6.6 | |||
| Sensory function | |||||
| Sight (left eye) | 0.2–1.5 | 0.8 | 0.2 | ||
| Sight (right eye) | 0.1–2.0 | 0.8 | 0.3 | ||
| Cataract | 34 | 18.6 | |||
| Glaucoma | 6 | 3.3 | |||
| Hearing | |||||
| Normal | 172 | 94 | |||
| One ear cannot hear clearly | 3 | 1.6 | |||
| Neither ears can hear clearly | 1 | 0.5 | |||
| Hearing loss in one ear | 4 | 2.2 | |||
| Hearing loss in both ears | 3 | 1.6 | |||
| Dental status | |||||
| Missing teeth | 180 | 98.4 | |||
| False teeth | 15 | 8.2 | |||
| Assistive equipment | |||||
| Wheelchair | 7 | 3.8 | |||
| Walker | 8 | 4.4 | |||
| Biochemical indices | |||||
| Liver function (U/L) | 5–178 | 23 | 17.6 | ||
| Kidney function (mg/dL) | 0.6–4.5 | 1 | 0.34 | ||
| Blood glucose (mg/dL) | 70–214 | 100.17 | 21.58 | ||
| Total cholesterol (mg/dL) | 84–231 | 135.55 | 27.51 | ||
| High‐density lipoprotein | 24–88 | 45.55 | 12.30 | ||
| Low‐density lipoprotein | 26–191 | 71.33 | 24.22 | ||
| Triglycerides | 34–304 | 106.23 | 47.07 | ||
The majority of patients had diabetes (88; 48.1%), 3.3% of patients (6) fell “once a year,” and the majority of patients (160; 87.4%) took standard medication (i.e., second‐generation antipsychotics). Their BMI ranged from 17.00 to 43.70 kg/m2, the average BMI was 25.18 kg/m2 (SD = 4.03 kg/m2), and 66 (36.1%) were in the healthy weight range.
Regarding body composition (BIA), the average weight of body fat was 22.1 kg (SD = 8.3 kg), the average skeletal muscle mass was 23.84 kg (SD = 4.5 kg), the average basal metabolic rate was 1315.2 (SD = 160.14), and the average waist‐to‐hip ratio was 0.90 (SD = 0.07). According to the patients' MNA scores, 171 had good nutrition (93.4%), and 12 were at risk of malnutrition (6.6%). The cognitive function scores ranged from 11 to 30; the scores of the majority of patients (100 patients; 54.6%) ranged from 24 to 30, representing normal cognitive function.
In terms of sensory function, with regard to sight, that of the left eye ranged from 0.2 to 1.5 and that of the right eye ranged from 0.1 to 2.0; 34 (18.6%) patients had cataracts. Regarding hearing, those with normal hearing made up the majority (172; 94%). Regarding dental status, 180 had missing teeth (98.4%), and 15 had false teeth (8.2%). Regarding the use of assistive equipment, seven used a wheelchair (3.8%) and eight used a walker (4.4%).
Regarding biochemical indices, for liver function, the average value was 23 U/L (SD = 17.6 U/L); for kidney function, the average level of creatinine was 1 mg/dL (SD = 0.34 mg/dL); and for blood glucose, the average level of Glucose AC was 100.16 mg/dL (SD = 21.58 mg/dL). The average total cholesterol level was 135.55 mg/dL (SD = 27.51 mg/dL), and the average level of high‐density lipoproteins was 45.55 mg/dL (SD = 12.30 mg/dL). The average level of low‐density lipoproteins was 71.33 mg/dL (SD = 24.22 mg/dL). The average level of triglycerides was 106.23 mg/dL (SD = 47.07 mg/dL).
3.2. Correlation of patient characteristics and physiological state with the level of frailty
As shown in Table 2, educational attainment (χ 2 = 18.560, p < 0.001), length of hospital stay (χ 2 = 20.036, p < 0.001), history of falls in the past year (χ 2 = 20.084, p < 0.001), use of anxiolytics/tranquillisers and sleeping pills (χ 2 = 6.177, p < 0.001), and level of cognition (χ 2 = 16.281, p < 0.001) were statistically significant predictors of frailty. The use of assistive equipment was also a statistically significant predictor of frailty (χ 2 = 114.882, p < 0.001).
TABLE 2.
Correlations of the patients attributes, physiological state, cognitive function, and sensory function to level of frailty.
| Basic attributes | Stage of frailty | χ 2 | ||
|---|---|---|---|---|
| Normal: n (%) | Pre‐frailty: n (%) | Frailty: n (%) | ||
| Sex | ||||
| Male | 23 (20.9) | 78 (70.9) | 9 (8.2) | 4.498 |
| Female | 21 (28.8) | 41 (56.2) | 11 (15.1) | |
| Age group | ||||
| (1) 55–59 years | 14 (27.5) | 31 (60.8) | 6 (8.8) | 4.047 |
| (2) 60–64 years | 16 (23.5) | 46 (67.6) | 6 (8.8) | |
| (3) 65–69 years | 10 (29.4) | 21 (61.8) | 3 (8.8) | |
| (4) 70–74 years | 3 (15) | 14 (70) | 3 (15) | |
| (5) 75 years or above | 1 (10) | 7 (70) | 2 (20) | |
| Educational attainment | ||||
| (1) Primary school or under | 16 (21.9) | 43 (58.9) | 14 (19.2) | 18.560** |
| (2) Junior high school | 10 (15.9) | 49 (77.8) | 4 (6.3) | |
| (3) Senior high school | 14 (42.4) | 19 (57.6) | 0 (0) | |
| (4) Junior college or higher | 4 (28.6) | 8 (57.1) | 2 (14.3) | |
| Grouping by hospitalization duration | ||||
| (1) 1–5 year(s) | 4 (100) | 0 (0) | 0 (0) | 20.036** |
| (2) 6–10 years | 2 (11.8) | 13 (76.5) | 2 (11.8) | |
| (3) 11–15 years | 22 (32.8) | 40 (59.7) | 5 (7.5) | |
| (4) 15 years or longer | 15 (16.8) | 66 (69.5) | 13 (13.7) | |
| Physiological status | ||||
| History of chronic disease | ||||
| (1) Yes | 42 (95.5) | 117 (98.3) | 19 (95) | 1.427 |
| (2) No | 2 (4.5) | 2 (1.7) | 1 (5) | |
| Hypertension | ||||
| (1) Yes | 14 (31.8) | 44 (37) | 5 (25) | 1.262 |
| (2) No | 30 (68.2) | 75 (63) | 15 (75) | |
| Diabetes | ||||
| (1) Yes | 22 (50) | 55 (46.2) | 11 (55) | 0.164 |
| (2) No | 22 (50) | 64 (53.8) | 9 (45) | |
| Hyperlipidaemia | ||||
| (1) Yes | 11 (25) | 18 (15.1) | 3 (15) | 2.267 |
| (2) No | 33 (75) | 101 (84.9) | 17 (85) | |
| Cardiovascular disease | ||||
| (1) Yes | 14 (31.8) | 35 (29.4) | 7 (35) | 0.292 |
| (2) No | 30 (68.2) | 84 (70.6) | 13 (65) | |
| Kidney disease | ||||
| (1) Yes | 11 (25) | 40 (33.6) | 9 (45) | 2.601 |
| (2) No | 33 (75) | 79 (66.4) | 11 (55) | |
| Hepatitis or cirrhosis | ||||
| (1) Yes | 12 (27.3) | 26 (21.8) | 2 (10) | 2.402 |
| (2) No | 32 (72.7) | 93 (78.2) | 18 (90) | |
| Cancer | ||||
| (1) Yes | 1 (2.3) | 9 (7.6) | 2 (10) | 1.902 |
| (2) No | 43 (97.7) | 110 (92.4) | 18 (90) | |
| Others | ||||
| (1) Yes | 2 (4.5) | 9 (7.6) | 3 (15) | 2.131 |
| (2) No | 42 (95.5) | 110 (92.4) | 17 (85) | |
| History of falls in the past year | ||||
| (1) Yes | 0 (0) | 2 (1.7) | 4 (20) | |
| (2) No | 44 (100) | 117 (98.3) | 16 (80) | 20.084*** |
| Type of medicine | ||||
| Traditional antipsychotics | ||||
| (1) Yes | 12 (27.3) | 37 (31.1) | 7 (35) | 0.425 |
| (2) No | 32 (72.7) | 82 (68.9) | 13 (65) | |
| Second‐generation antipsychotics | ||||
| (1) Yes | 40 (90.9) | 105 (88.2) | 15 (75) | 3.367 |
| (2) No | 4 (9.1) | 14 (11.8) | 5 (25) | |
| Antidepressant | ||||
| (1) Yes | 1 (2.3) | 10 (8.4) | 1 (5) | 2.059 |
| (2) No | 43 (97.7) | 109 (91.6) | 19 (95) | |
| Mood stabilizer | ||||
| (1) Yes | 13 (29.5) | 23 (19.3) | 8 (40) | 4.967 |
| (2) No | 31 (70.5) | 96 (80.7) | 12 (60) | |
| Anxiolytics/tranquillisers and sleeping pills | ||||
| (1) Yes | 21 (47.7) | 63 (52.9) | 16 (80) | 6.177** |
| (2) No | 23 (52.3) | 56 (47.1) | 4 (20) | |
| BMI | ||||
| (1) Underweight <18.5 | 1 (2.3) | 5 (4.2) | 1 (5) | 4.206 |
| (2) Normal 18.5–24 | 21 (47.7) | 39 (32.8) | 6 (30) | |
| (3) Overweight 24–27 | 10 (22.7) | 37 (31.1) | 5 (25) | |
| (4) Obesity >27 | 12 (27.3) | 38 (31.9) | 8 (40) | |
| Mini Nutritional Assessment | ||||
| (1) Normal | 41 (93.2) | 113 (95) | 17 (85) | 2.777 |
| (2) Risk of malnutrition | 3 (6.8) | 6 (5) | 3 (15) | |
| Cognitive function | ||||
| (1) >24 points, complete function | 26 (59.1) | 70 (58.8) | 4 (20) | 16.281** |
| (2) 18–23 points; mild impairment | 18 (40.9) | 41 (34.5) | 12 (60) | |
| (3) 0–17 points; severe impairment | 0 (0) | 8 (6.7) | 4 (20) | |
| Sensory function | ||||
| Cataract | ||||
| (1) Yes | 7 (15.9) | 23 (19.3) | 4 (20) | 0.278 |
| (2) No | 37 (84.1) | 96 (80.7) | 16 (80) | |
| Glaucoma | ||||
| (1) Yes | 0 (0) | 5 (4.2) | 1 (5) | 1.998 |
| (2) No | 44 (100) | 114 (95.8) | 19 (95) | |
| Others | ||||
| (1) Yes | 1 (2.3) | 3 (2.5) | 0 (0) | 0.511 |
| (2) No | 43 (97.7) | 116 (97.5) | 20 (100) | |
| Dental status | ||||
| Missing teeth | ||||
| (1) Yes | 43 (97.7) | 117 (98.3) | 20 (100) | 0.444 |
| (2) No | 1 (2.3) | 2 (1.7) | 0 (0) | |
| False teeth | ||||
| (1) Yes | 7 (15.9) | 7 (5.9) | 1 (5) | 4.579 |
| (2) No | 37 (84.1) | 112 (94.1) | 19 (95) | |
| Hearing | ||||
| (1) Normal | 42 (95.5) | 111 (93.3) | 19 (95) | 5.967 |
| (2) One ear cannot hear clearly | 0 (0) | 3 (2.5) | 0 (0) | |
| (3) Neither ears can hear clearly | 0 (0) | 1 (0.8) | 0 (0) | |
| (4) Hearing loss in one ear | 2 (4.5) | 2 (1.7) | 0 (0) | |
| (5) Hearing loss in both ears | 0 (0) | 2 (1.7) | 1 (5) | |
| Use of assistive equipment | ||||
| (1) None | 44 (100) | 118 (99.2) | 6 (30) | 114.882*** |
| (2) Wheelchair | 0 (0) | 1 (0.8) | 6 (30) | |
| (3) Walker | 0 (0) | 0 (0) | 8 (40) | |
**p < 0.01, ***p < 0.001.
As indicated in Table 3, skeletal muscle mass was a statistically significant and negatively associated predictor of frailty (F = 5.256, p < 0.01). Basal metabolic rate was also a statistically significant and positively associated predictor of frailty (F = 4.971, p < 0.01). Cognitive function was also a statistically significant and positively associated predictor of frailty (F = 8.726, p < 0.01).
TABLE 3.
Analysis of correlations of the patients physiological state, cognitive function, sensory function, and biochemical indices to frailty.
| Item | Frailty | F | p | Post hoc comparison | ||
|---|---|---|---|---|---|---|
| ① Normal | ② Prefrailty | ③ Frailty | ||||
| N (M ± SD) | N (M ± SD) | N (M ± SD) | ||||
| Age | 44 (62.34 ± 4.68) | 119 (63.64 ± 6.28) | 20 (65.10 ± 6.70) | 1.571 | 0.211 | |
| Hospitalization (days) | 44 (5110.80 ± 1680.02) | 119 (5509.16 ± 1315.71) | 20 (5775.45 ± 1085.05) | 1.962 | 0.144 | |
| BMI index | 44 (24.66 ± 3.83) | 119 (25.12 ± 3.70) | 20 (26.65 ± 5.87) | 1.729 | 0.180 | |
| Physiological state | ||||||
| Weight of body fat | 38 (23.72 ± 9.35) | 105 (21.61 ± 7.85) | 20 (21.25 ± 9.01) | 0.995 | 0.372 | |
| Skeletal muscle percentage | 38 (22.59 ± 3.05) | 105 (23.79 ± 4.83) | 20 (26.48 ± 3.63) | 5.256** | 0.006 | ③>②>① |
| Basal metabolic rate | 38 (1275.29 ± 110.02) | 103 (1311.87 ± 173.08) | 19 (1412.84 ± 135.65) | 4.971** | 0.008 | ③>②>① |
| Waist‐to‐hip ratio | 38 (0.9 ± 0.07) | 103 (0.90 ± 0.06) | 19 (0.92 ± 0.08) | 1.064 | 0.347 | |
| Cognitive function | 44 (25.05 ± 3.25) | 119 (23.57 ± 3.96) | 20 (20.90 ± 2.81) | 8.726*** | <0.001 | ①>③>② |
| Sensory function | ||||||
| Sight (left eye) | 44 (0.85 ± 0.19) | 119 (0.83 ± 0.24) | 20 (0.73 ± 0.25) | 2.087 | 0.127 | |
| Sight (right eye) | 44 (0.89 ± 0.28) | 119 (0.84 ± 0.24) | 20 (0.77 ± 0.28) | 1.560 | 0.323 | |
| Biochemical indices | ||||||
| Liver function (U/L) | 44 (22.9 ± 17.17) | 119 (23.57 ± 18.74) | 20 (19.8 ± 10.25) | 0.391 | 0.667 | |
| Kidney function (mg/dL) | 44 (0.97 ± 0.22) | 119 (1.02 ± 0.38) | 20 (0.95 ± 0.21) | 0.583 | 0.585 | |
| Blood glucose (mg/dL) | 44 (100.7 ± 20.48) | 119 (101.11 ± 23.14) | 20 (93.4 ± 11.47) | 1.112 | 0.331 | |
| Total cholesterol (mg/dL) | 44 (135.39 ± 27.28) | 119 (136.14 ± 27.11) | 20 (132.35 ± 31.39) | 0.162 | 0.850 | |
| High‐density lipoprotein | 44 (45.98 ± 12.65) | 119 (45.5 ± 12.3) | 20 (44.9 ± 12.12) | 0.055 | 0.947 | |
| Low‐density lipoprotein | 44 (70.39 ± 22.45) | 119 (71.96 ± 25.05) | 20 (60.65 ± 24.02) | 0.120 | 0.887 | |
| Triglycerides | 44 (116.32 ± 53.89) | 119 (104.98 ± 46.10) | 20 (91.45 ± 31.18) | 2.062 | 0.130 | |
**p < 0.01, ***p < 0.001.
Cognitive function (F = 8.726, p < 0.001) in terms of MMSE score was a statistically significant predictor of frailty, with higher cognitive function scores (cognitive dysfunction was lower) and the presence of severe cognitive function corresponding to lower levels of frailty.
3.3. Predicting frailty in patients with schizophrenia through physiological state and health state
A logistic regression was used to examine demographic characteristics, physiological state, cognitive function, and sensory function when used as predictors of frailty. The results revealed that cognitive function (β = −0.108, p < 0.05), use of an assistive wheelchair (β = 3.502, p < 0.01), and use of a walker (β = 4.247, p < 0.001) could effectively predict frailty (Table 4).
TABLE 4.
Main predictors of frailty in the patients': attributes, physiological state, cognitive function, and sensory function.
| Independent variable | β value | Standard deviation | Wald | Significance | 95% confidence interval |
|---|---|---|---|---|---|
| Educational attainment | −0.098 | 0.182 | 0.290 | 0.590 | −0.454 to 0.258 |
| Hospitalization duration | 0.000 | 0.000 | 3.464 | 0.063 | 0.000 to 0.001 |
| Skeletal muscle percentage | 0.850 | 0.781 | 1.185 | 0.276 | −0.680 to 2.381 |
| Basal metabolic rate | −0.020 | 0.021 | 0.909 | 0.340 | −0.063 to 0.022 |
| History of falls in the past year | 0.435 | 1.272 | 0.117 | 0.732 | −2.058 to 2.928 |
| Anxiolytics/tranquillisers and sleeping pills | 0.204 | 0.364 | 0.313 | 0.576 | −0.510 to 0.918 |
| MMSE | −0.108 | 0.051 | 4.482 | <0.05* | −0.207 to −0.008 |
| Assistive device – wheelchair | 3.502 | 1.066 | 10.802 | <0.01** | 1.414 to 5.591 |
| Assistive device – walker | 4.247 | 0.974 | 19.005 | <0.001*** | 2.337 to 6.156 |
*p < 0.05, **p < 0.01, ***p < 0.001.
4. DISCUSSION
This study enrolled patients with schizophrenia aged ≥55 years in a psychiatric institution. The majority were men; the average age was 63.48 years; and the largest age group was 60–64 years. The majority of patients had no higher than primary school education. This finding is comparable to that of previous study on long‐term chronic psychiatric inpatients, where most participants lacked literacy skills or primary school education (Shafie et al., 2018). In the present study, the majority of participants were hospitalized for 15 years or longer. The majority of participants also had prefrailty, as indicated by answers to the item, “Do you feel full of vitality?” The prevalence of frailty was 11%; this finding is comparable to that of a survey by Chang et al. (2022) on frailty among older male adults in Taiwan (prevalence of frailty = 12.7%). Chou et al. (2019) studied the factors influencing frailty condition of 124 inpatients over 65 years old in a medical centre in the Southern Taiwan. The prevalence of frailty among the older inpatients was 88.7%, and the degree of frailty was positively correlated with age. These findings are consistent with those of this study. The screening scales are typically used internationally, including the SOF index, frailty phenotype of Cardiovascular Health Study (CHS), and Clinical Frailty Scale (CFS). Due to the differences in patient characteristics, the consistent use of such screening tools among scholars remains uncommon. However, previous studies on frailty in patients with mental diseases have found that frailty can be used as the predictor of the mortality of patients during hospitalization (Benraad et al., 2020; Stolz et al., 2020).
Regarding physiological state in this study, the history of falls in the previous year and taking anxiolytics/tranquillisers and sleeping pills were significantly correlated with the level of frailty. This finding is consistent with that of previous study on 582 chronic patients with schizophrenia in psychiatric institution, which indicated that frailty may be a risk factor for fall incidence (Tsai, Chang, & Wu, 2018; Tsai, Lee, et al., 2018). In addition, the finding of our study is consistent with that of an analysis on the frailty of older outpatients with chronic disease, which found a significant correlation between a history of falls in the past year and frailty (Kim et al., 2022). A previous study on cases of frailty in older psychiatric patients in nursing homes and the criteria for inappropriate prescription revealed that patients with frailty may be associated with the use of anxiolytics/tranquillisers and sleeping pills, antipsychotics, and anticholinergics (Maclagan et al., 2017; Muhlack et al., 2019). This finding is consistent with that of this study. In this study, regarding the patients' history of chronic disease, diabetes was most prevalent, followed by hypertension. This finding is consistent with that of previous study on physiological traits of patients with schizophrenia; that study found that the average fasting blood glucose of patients was higher than that of a healthy control group and reported that the proportion of patients with hyperglycaemia was higher (Kosmalski et al., 2022). Regarding cognitive status, cognitive status score and grouping by cognitive function were significantly correlated with frailty, indicating that cognitive function MMSE was significantly correlated with frailty. Previous integrated analyses of frailty and cognitive impairment have revealed that frailty was associated with the risk of cognitive dysfunction (Borges et al., 2019; Furtado et al., 2019). Furthermore, a previous study on frailty and health status among self‐financed older persons in long‐term care institutions showed that cognitive function was highly correlated with the level of frailty (Peng, 2016).
Mild or severe cognitive dysfunction accounted for 45.6% of the participants, which is consistent with that of Ong et al. (2016) involving 110 long‐term inpatients with schizophrenia and schizoaffective disorders in Singapore, where patients with earlier onsets and longer hospital stays tended to have lower MMSE scores. With regard to sight, most patients had cataracts. The medicine administered to psychiatric patients may induce various ocular disorders, including cataracts, glaucoma, and retinopathy; in addition, retinopathy was associated with high‐dose typical antipsychotic, and the occurrence was proportional to the total quantity of long‐term medication (Kumar et al., 2021). The use of assistive devices was significantly correlated with frailty; this finding is comparable to that of study conducted by Kosmalski et al. (2022) on older chronic inpatient frailty, which found that mobility barriers can serve as a predictor of frailty.
Dedeyne et al. (2017) reviewed the literature on the effects of diverse interventions on the cognitive state of older persons with frailty. The results revealed that, compared with single interventions, multifaceted interventions produced better outcomes for the improvement of physical function and muscle strength. Tsugawa et al. (2020) studied the cognitive status of older care home residents in relation to exercises; the results indicated that the exercise can effectively improve the cognitive function of care home residents in need of long‐term nursing. Currently, the number of studies on frailty in patients with schizophrenia is limited. According to the findings of this study, enhancing training of cognitive function for patients with schizophrenia, including patients requiring assistive equipment as subjects of intensive care, as well as providing diverse interventions of clinical nursing, occupational therapy, and psychological aspects, could reduce the incidence of frailty in patients with schizophrenia.
In addition, regarding SOF frailty indicators (Ensrud et al., 2008), two of the three measurement indicators were related to physiological indicators, including weight change and physical fitness assessment. However, only one indicator inquires about the status of self‐conscious mental vitality. Therefore, the application of SOF index in studies on patients with schizophrenia may be limited. Moon et al. (2022) noted that frailty may have implications for the identification of older persons at risk of deteriorating mental health. Therefore, future development of fragility assessment tools for patients with mental illness is recommended.
4.1. Limitations
Our study has several limitations. Because this study was cross‐sectional and quantitative and only involved patients in Taiwan, the results may not be generalizable outside of Taiwan and the study period.
5. CONCLUSION
The findings indicate that physiological state, cognitive function, and sensory function are correlated with the incidence of frailty in patients with schizophrenia. Clinicians caring for patients with schizophrenia should evaluate their cognitive function and use of assistive wheelchairs and walkers to minimize the incidence of frailty.
Patients with schizophrenia have a higher risk of having complications than patients with other chronic illnesses. Therefore, medical staff should regularly assess the levels of frailty risk, pay attention to changes in physiological states, maintain cognitive function, and reduce the use of assistive devices to help improve the bodily function of patients with schizophrenia.
AUTHOR CONTRIBUTIONS
SFC made substantial contributions to research conception. She also designed the draft of the research process and submitted the manuscript as corresponding author. HCT and LHW made substantial contributions to analysis and interpretation of data. SFC supported the study design to avoid the confounding factor. She had been involved in revising manuscript critically for important intellectual content. HCT, LHW, and SFC modified the manuscript format, discussed reviewer opinions, and clarified the professional name. All authors read and approved the final manuscript.
FUNDING INFORMATION
This study was founding by Cardinal Tien Hospital Grant CTH111AK‐NHS‐2232 and National Science and Technology Council Grant MOST 111‐2314‐B‐227‐003 in Taiwan.
CONFLICT OF INTEREST STATEMENT
The authors report no actual or potential conflicts of interest.
ACKNOWLEDGEMENTS
We would like to thank all the patients who participated this study.
Tsai, H.‐C. , Wu, L.‐H. , & Chang, S.‐F. (2023). Prediction of physiological state, cognition, sensory function, and biomarkers for frailty in patients aged 55 years or more with schizophrenia. Nursing Open, 10, 5044–5055. 10.1002/nop2.1741
Contributor Information
Li‐Hui Wu, Email: evoneno5@yahoo.com.tw.
Shu‐Fang Chang, Email: linda@ntunhs.edu.tw.
DATA AVAILABILITY STATEMENT
The data that support the findings of this research are available from the corresponding author upon reasonable request.
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Associated Data
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Data Availability Statement
The data that support the findings of this research are available from the corresponding author upon reasonable request.