Next Article in Journal
Impact of Physical Activity on Overall Survival and Liver Cirrhosis Incidence in Steatotic Liver Disease: Insights from a Large Cohort Study Using Inverse Probability of Treatment Weighting
Next Article in Special Issue
Malnutrition Diagnosis and Food Consumption in Subacute Post-Stroke Patients During Rehabilitation
Previous Article in Journal
Nutrition, Lipoproteins and Cardiovascular Diseases
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Relationship between Improvement in Physical Activity and Three Nutritional Assessment Indicators in Patients Admitted to a Convalescent Rehabilitation Ward

1
Department of Rehabilitation, Wakakusa-Tatsuma Rehabilitation Hospital, 1580 Ooaza Tatsuma, Daito 574-0012, Osaka, Japan
2
Department of Nutrition, Wakakusa-Tatsuma Rehabilitation Hospital, 1580 Ooaza Tatsuma, Daito 574-0012, Osaka, Japan
3
Department of Physical Therapy, Konan Women’s University, 6-2-23 Morikita-cho, Higashinada-ku, Kobe 658-0001, Hyogo, Japan
4
Department of Medicine, Wakakusa-Tatsuma Rehabilitation Hospital, 1580 Ooaza Tatsuma, Daito 574-0012, Osaka, Japan
*
Author to whom correspondence should be addressed.
Nutrients 2024, 16(15), 2531; https://doi.org/10.3390/nu16152531
Submission received: 11 May 2024 / Revised: 21 July 2024 / Accepted: 30 July 2024 / Published: 2 August 2024

Abstract

:
We investigated the relationship between three nutritional indicators, the Mini Nutritional Assessment-Short Form (MNA-SF), Geriatric Nutritional Risk Index (GNRI), and Controlling Nutrition Status (CONUT), and physical activity at discharge in patients admitted to convalescent rehabilitation wards. The study included 1601 patients (77 ± 12 years, male 46.2%) discharged from convalescent rehabilitation wards between April 2018 and September 2023. MNA-SF, GNRI, and CONUT scores were obtained on admission. Patients were divided into two groups according to their level of Functional Independence Measure (FIM) walk score at discharge. The walking group (n = 1181, FIM walk score ≥ 5, 76 ± 13 years, male 47.2%) was significantly younger than the wheelchair group (n = 420, 79 ± 12 years, FIM walk score < 5, male 43.8%) and had significantly higher MNA-SF (6.5 ± 2.5 vs. 4.7 ± 2.4) and GNRI (93.1 ± 12.4 vs. 86.7 ± 10.9) scores and significantly lower CONUT (3.1 ± 2.3 vs. 3.9 ± 2.3) scores than the wheelchair group (all p < 0.01). Multivariate logistic regression analysis showed that age, handgrip strength, Functional Oral Intake Scale, and MNA-SF score were independently associated with walking ability at discharge (all p < 0.01). In addition, MNA-SF scores were independently associated with Rehabilitation Effectiveness. These results suggest that nutritional status, particularly MNA-SF scores on admission, is associated with improvement of physical activity at discharge.

1. Introduction

Malnutrition is one of the most relevant conditions adversely affecting the health of the elderly [1], and the prevalence of malnutrition is high in convalescent rehabilitation wards with large elderly populations [2]. Indeed, Kaiser et al. reported that the prevalence of malnourished patients in rehabilitation settings was 50.5%, with 41.2% at risk of malnutrition; only 8.5% were classified as having a normal nutritional status [3]. In addition, malnutrition is associated with longer hospitalizations, higher risk of institutionalization, lower quality of life, and cognitive performance [4,5,6,7]. Patients with malnutrition exhibit poorer performance in activities of daily living (ADL) at admission [8] and poorer recovery of ADL during hospitalization than patients without malnutrition [9]. Thus, malnutrition is a serious condition for patients admitted to a convalescent rehabilitation ward, and one that must be overcome.
Given the negative impact of malnutrition on rehabilitation outcomes, the ability of nutritional indicators to serve as predictors of rehabilitation outcomes has been investigated [10,11,12]. For instance, it was recently reported that three nutritional indicators, the Mini-Nutritional Assessment Short Form (MNA-SF) [13], Geriatric Nutritional Risk Index (GNRI) [14], and Controlling Nutritional Status (CONUT) [15], are good predictors of improved scores on ADL indicators, such as the Functional Independent Measure (FIM) or Barthel Index (BI), in patients admitted to convalescent rehabilitation wards. In addition, we previously reported that nutritional indicators are predictive of rehabilitation effectiveness (REs), which is an indicator of ADL improvement [16,17]. Those studies illustrate the need for a practical nutritional assessment tool capable of predicting functional outcomes with high accuracy. However, there are few studies that have compared nutritional screening tools for prediction of rehabilitation outcomes in patients admitted to convalescent rehabilitation wards.
The aim of this study, therefore, was to determine the nutritional screening tool that optimally associates with rehabilitation outcome among the MNA-SF, GNRI and CONUT in patients admitted to convalescent rehabilitation wards.

2. Materials and Methods

2.1. Ethics

This study adhered to the Declaration of Helsinki and was approved by the Wakakusa-Tatsuma Rehabilitation Hospital Ethics Committee (approval number: 19100761). The requirement for informed consent was waived because this study was based on a retrospective analysis of routinely collected data. Furthermore, information about the study was disclosed on the bulletin board and hospital homepage, and patients were allowed to refuse use of their medical information through an opt-out procedure.

2.2. Participants and Setting

This retrospective cohort study was conducted in the convalescent rehabilitation wards at Wakakusa-Tatsuma Rehabilitation Hospital in Osaka, Japan. Enrolled in the study were patients admitted to the convalescent rehabilitation wards between April 2018 and September 2023 and subsequently discharged. During the research period, 2516 consecutive patients were admitted. Among those, patients who had a spinal cord injury (n = 54), required emergency transfer (n = 368), died (n = 33), required assistance with ADL before admission (n = 402), or whose data were missing (n = 58) were excluded. Ultimately, 1601 patients were studied (Figure 1). In the convalescent rehabilitation ward in Japan, public medical insurance covers individual rehabilitation for patients provided by physical therapists, occupational therapists, and speech–language–hearing therapists for a maximum of nine units per day (1 unit = 20 min), 7 days per week. The time allocation for each type of rehabilitation can be tailored according to the needs of the individual patient. In this study, patients underwent a rehabilitation program including conventional physical therapy, occupational therapy, and speech–language–hearing therapy which was performed 6 to 8 units per day according to the patient’s condition until discharge. Physical therapists performed active-assisted and active mobilizations, exercises for muscle-strength recovery, postural passages and transfers, sitting-and-standing training, motor-coordination and balance training, and walking training. Patients who were considered to benefit from robot-assisted gait training underwent walking training using the one-leg assisted-gait robot, called Welwalk WW-2000 (Toyota Motor Corporation, Aichi, Japan). Occupational therapists assessed the patient’s home environment and performed ADL training appropriate for the living environment. Speech–language–hearing therapists performed voice training, dysarthria training, cognitive training, and dysphagia rehabilitation.
Basic information, including age, sex, height, weight, body mass index (BMI), reason for admission (e.g., stroke, musculoskeletal disease, or hospitalization-associated disability), comorbidities, onset-to-admission interval, and medications at admission were collected from medical records. Clinical data such as results from physical examinations, swallowing function tests, blood tests and ADL measurements were also collected. The physical examinations included handgrip strength and quadriceps strength. The handgrip strength of the dominant hand (or, in the case of hemiparesis, the non-paralyzed hand) was measured using a Takei TKK 5401 digital dynamometer (Takei Scientific Instruments Co., Ltd., Tokyo, Japan), with the greatest of three measurements recorded. The quadriceps strength of the dominant leg (or, in the case of hemiparesis, the non-paralyzed leg) was measured using a hand-held dynamometer (Sakai Medical Co., Ltd., Tokyo, Japan). For measurement of quadriceps strength, the participant was seated on a plinth, with their back resting against a firm support, thighs fully supported, knees flexed to 90° and the lower legs hanging freely. The pad of a hand-held dynamometer was positioned at 80% of the tibial length, to resist maximal isometric force of the quadriceps. The participants were asked to push against the dynamometer as hard as possible for 3 s. Swallowing function was evaluated using the Functional Oral Intake Scale (FOIS) [18]. Blood tests were performed to evaluate serum albumin, C-reactive protein (CRP), creatinine, estimated glomerular rate (eGFR), and hemoglobin. B-type natriuretic peptide (BNP) was also measured by commercial immunoassay. ADL was evaluated using the BI and FIM instrument. The BI measures ten functions that are important for independent living: eating, dressing, transferring, grooming, bathing, toileting, walking, stair climbing, bowel control, and bladder care. BI scores ranged from 0 to 100 points, with higher BI scores indicating lower dependency [19]. The FIM consists of a motor domain with 13 sub-items and a cognitive domain with 5 sub-items. Each item is scored on a scale of 1 to 7 points. The total FIM-motor and FIM-cognitive scores range from 13 to 91 and 5 to 35 points, respectively. The total FIM scores ranged from 18 (reflecting full dependence) to 126 (reflecting complete independence) [20].

2.3. Assessment of the Nutritional Indicators

Each patient’s nutritional status was assessed based on MNA-SF, GNRI and CONUT scores. These nutritional indicators are useful nutritional screening tools that have been validated in several studies of patients admitted to convalescent rehabilitation wards [13,14,15,16,17,21]. The MNA-SF is a simple nutritional screening tool consisting of six questionnaire items (appetite, weight loss, mobility, recent illness/stress, dementia/depression and BMI). MNA-SF scores range from 0 to 14 points, and patients with a score of 12 or less are defined as being at nutritional risk [22]. GNRI scores were calculated from the patients’ BMI and serum albumin concentrations using the following formula: GNRI = (14.89 × serum albumin [g/dL]) + (41.7 × [actual bodyweight/ideal bodyweight]. Ideal body weight was defined as a BMI of 22.0 kg/m2. A GNRI < 92 was defined as moderate or severe malnutrition risk, while a GNRI score > 92 was defined as low or no malnutrition risk [23]. CONUT scores were calculated based on serum albumin levels, total peripheral lymphocyte counts, and total cholesterol levels. CONUT scores ranged from 0 to 12 points, and patients with a score of 2 or more were defined as being at nutritional risk [24]. We also collected patients’ energy and protein intakes on admission and discharge. Nutritional indicators were collected or evaluated at the time of admission by a registered dietitian at our hospital. After admission, the registered dietitian evaluated the patient’s nutritional indicators at least once a month, and appropriate nutrition was provided by the registered dietitian in consultation with the patient’s physician, based on the patient’s weight change and the intensity of rehabilitation exercise, and the results were recorded in the patient’s medical record.

2.4. Rehabilitation Outcome

The primary rehabilitation outcome was the proportion of patients who were able to walk without assistance at discharge. In this study, a walk FIM score of 5 or higher was defined as the walking group. The detailed scoring criteria for the FIM walk domain indicate that a score of 6 or higher is the ability to walk at least 50 m independently. If a patient has difficulty walking more than 50 m but is able to walk between 15 m and 49 m, the walk FIM score is scored as 5. A walk FIM score of 5 or higher indicates that the patient can walk without assistance, so in a previous report examining the walking patterns of stroke patients, subjects were selected based on a walk FIM score of 5 or higher [25]. We divided the subjects into two groups according to their walking status at discharge: the walking group and the wheelchair group (Figure 1).
As a secondary outcome, we assessed REs, which was calculated using the FIM instrument and the following formula: (FIM at discharge/FIM at admission)/(126 − FIM at admission) × 100%. By expressing REs as a percentage reflecting the proportion of potential improvement actually achieved during rehabilitation, it was corrected for a ceiling effect [26].

2.5. Statistical Analysis

Parametric continuous data are presented as the mean ± standard deviation, and non-parametric data as the median (interquartile range 25–75 percentile). Normality was confirmed by the Shapiro–Wilk test. Differences between the two groups defined based on walking ability at discharge were analyzed using Student’s t-test or the Mann–Whitney U-test. Differences in REs were evaluated using analysis of covariance (ANCOVA) adjusted for age and gender. Categorical data were expressed as incidences and percentages, and comparisons were made using the chi-square test.
Univariate and multivariate logistic regression analyses were used to determine the association between MNA-SF, GNRI and CONUT scores and the walking status at discharge. In addition, the relationship between each nutritional indicator of nutritional status at admission and walking status at discharge were assessed using receiver operating (ROC) curves. Differences in diagnostic performance were compared based on the area under the ROC curve (AUC). We performed univariate linear regression analysis with REs as a dependent variable. We also performed multiple regression analysis with REs as a dependent variable using factors that showed a significant (p < 0.05) correlation with REs in univariate linear regression analysis as independent variables. Multicollinearity between factors was assessed using the variance inflation factor.
Values of p < 0.05 were considered statistically significant. Statistical analyses were performed using SPSS version 28.0 (IBM, Armonk, NY, USA).

3. Results

Among the 1601 patients studied, the reasons for their hospitalization were stroke in 643 patients, musculoskeletal disease in 635 patients, and hospital-associated disability in 323 patients; 1181 were classified into the walking group and 402 were classified into the wheelchair group. The numbers and percentages of subjects in the independent walking group who were in rehabilitation due to stroke, musculoskeletal disease, and hospital-associated disability were 424 (35.9%), 520 (44.0%) and 237 (20.1%), respectively.
The demographic and clinical baseline characteristics of each group are shown in Table 1. The walking group was significantly younger and had higher BMIs than the wheelchair group; however, there was no significant difference with respect to gender. Regarding comorbidities, the rate of dyslipidemia was significantly higher in the walking group; however, there were no differences in the rates of hypertension, diabetes and atrial fibrillation. Handgrip strength, quadriceps strength and FOIS were all significantly higher in the walking than wheelchair group. ADL parameters at admission, including BI score, motor FIM, cognitive FIM or total FIM score were also significantly higher in the walking than in the wheelchair group. The laboratory data showed that serum albumin and hemoglobin were significantly higher and plasma B-type natriuretic peptide was significantly lower in the walking than in the wheelchair group.
Among the walking group, nutritional status indicated by MNA-SF and GNRI scores were significantly higher and CONUT scores were significantly lower compared to the wheelchair group. Energy intake was also higher in the walking than in the wheelchair group.
Rehabilitation outcomes at discharge among the 1601 subjects are presented in Table 2. Handgrip strength, quadriceps strength, FOIS and ADL parameters were all significantly higher in the walking than in the wheelchair group. REs and rate of discharge to home were also significantly higher in the walking than in the wheelchair group. Length of hospitalization was significantly lower in the walking than in the wheelchair group. In addition, the walking group had significantly higher MNA-SF and GNRI scores and significantly lower CONUT scores than the wheelchair group, with Cohen’s d effect sizes of 0.69, 0.60 and 0.50, respectively (all p < 0.01).
Rehabilitation outcomes at discharge for each disease are presented in Table 3. For all diseases, the muscle strength index, FOIS, ADL parameters, and rate of discharge to home were significantly higher in the walking than in the wheelchair group. Length of hospitalization was significantly shorter in the walking than in the wheelchair group for stroke and musculoskeletal diseases, but there was no difference for hospital-associated disability. Among stroke patients, the walking group had significantly higher MNA-SF and GNRI scores and significantly lower CONUT scores than the wheelchair group, with Cohen’s d effect sizes of 0.81, 0.54 and 0.27, respectively (all p < 0.01). Likewise, for patients with musculoskeletal diseases, the walking group had significantly higher MNA-SF and GNRI scores and significantly lower CONUT scores than the wheelchair group, with Cohen’s d effect sizes of 0.51, 0.60 and 0.47, respectively (all p < 0.01). And for patients with hospital-associated disability, the walking group also had significantly higher MNA-SF and GNRI scores and significantly lower CONUT scores than the wheelchair group, with Cohen’s d effect sizes of 0.66, 0.49 and 0.31, respectively (all p < 0.01).
The results of multivariate logistic regression analyses with walking without assistance at discharge as a dependent variable and each nutritional index as an independent variable are shown in Table 4. In the analysis of all 1601 patients, univariate logistic regression showed that MMA-SF, GNRI and CONUT scores as well as age, handgrip strength, quadriceps strength, FOIS, hemoglobin, energy intake and BNP were all significantly associated with walking without assistance at discharge (p < 0.001). However, the multivariate logistic regression analysis adjusted for confounders revealed that MNA-SF and GNRI scores were significantly associated with walking without assistance at discharge, but CONUT scores were not. In the analysis of each condition, MNA-SF was independently and significantly associated with walking without assistance at discharge, even after adjusting for confounding factors. Multivariate logistic regression analysis with walking without assistance at discharge as the dependent variable using factors that were significant in the univariate logistic regression analysis as independent variables showed that age, handgrip strength, FOIS, and MNA-SF score were all independently associated with walking without assistance at discharge (all p < 0.01).
We also performed ROC analyses to assess walking without assistance at discharge. For each index, the AUC for walking without assistance at discharge was as follows: for the MNA-SF, 0.698 (95% CI: 0.669–0.727); for the GNRI, 0.651 (95% CI: 0.622–0.681); and for the CONUT score, 0.603 (95% CI: 0.572–0.634).
Figure 2 shows the distribution of MNA-SF scores at admission among the patients and the rate of walking without assistance at discharge for each MNA-SF score. Based on the MNA-SF classification, 1096 (68.5%) patients were classified as malnourished, 495 (30.9%) were classified as being at risk of malnutrition, and 10 (0.6%) were classified as well-nourished. The rate of walking without assistance at discharge increased with increases in the MNA-SF scores.
Table 5 shows the results of the multivariate linear regression analysis with REs as the dependent variable and MNA-SF, GNRI and CONUT scores as independent variables. After adjustment for age, sex and other confounding factors, MNA-SF was a significant predictor of REs for patients with stroke or musculoskeletal disease. GNRI scores were significantly associated with REs only for patients with musculoskeletal disease, and CONUT scores were not associated with REs. Multivariate linear regression analysis with REs as the dependent variable using MNA-SF and factors that were significant in the univariate linear regression analysis showed that age, FOIS, handgrip strength, quadriceps strength, and MNA-SF were all independently associated with REs (all p < 0.01).

4. Discussion

In the present study, we showed that MNA-SF scores were associated with walking without assistance at discharge as a functional outcome across all diseases among patients admitted to convalescent rehabilitation wards, even after adjusting for confounding factors. GNRI and CONUT scores also showed significant associations with walking without assistance at discharge in univariate analyses, but not in a multivariate analysis. In addition, as a secondary outcome, MNA-SF scores were independently associated with REs in patients with stroke and musculoskeletal disease. These results suggest that of the three nutritional indicators tested (MNA-SF, GNRI and CONUT), MNA-SF scores were the best nutritional assessment tool for predicting improvement in physical activity including walking ability and ADL at discharge in patients admitted to convalescent rehabilitation wards.
Many of the patients admitted to convalescent rehabilitation wards are malnourished at admission [2]. In this study, the prevalence of malnutrition assessed with the MNA-SF was 68.5%, which is similar to findings reported previously [2]. MNA-SF scores are used to evaluate elderly patients worldwide, in part because the scoring consists of six items that include functional as well as psychological and cognitive parameters. Additionally, the MNA-SF includes an item that asks about declining food intake and weight loss during the previous 3 months. As a result, a strength point of the MNA-SF is that it can be used to evaluate changes in nutritional indicators over a 3-month period. GNIR and CONUT scores do not give that kind of information.
Patients are admitted to convalescent rehabilitation wards only after completing acute treatment at a hospital. It is therefore possible for weight loss to occur during a patient’s acute hospitalization. Consistent with that idea, Paquereau et al. reported that patients often experience a reduction in body weight of approximately 3 kg during the acute phase of their treatment [27]. Unfortunately, that loss of body weight is reportedly an important feature of malnutrition that can adversely affect a patient’s ability to return to performing ADL [13]. Indeed, BMI at admission is significantly associated with motor FIM gain in stroke patients [28]. Thus, body weight loss prior to admission to a convalescent rehabilitation ward likely influences the progress toward performance of ADL during subsequent rehabilitation. In addition, rehabilitation outcomes of patients with diseases such as stroke, hip fracture, and cardiovascular disease are often negatively affected by cognitive impairment [29,30,31]. All of these factors are addressed by the MNA-SF, making it useful for accurately predicting functional outcomes in elderly patients admitted to convalescent rehabilitation wards [16,17,32,33].
By contrast, our multivariate logistic analysis showed that the GNRI and CONUT were not significantly associated with walking without assistance at discharge. The primary reason is likely the inclusion of serum albumin values in the GNRI and CONUT scores. Serum albumin has traditionally been considered a useful biochemical marker for nutrition assessment [34,35]. However, recent studies have shown that serum albumin reflects inflammation rather than nutritional status or protein–energy malnutrition [36,37]. Both acute and chronic illnesses are characterized by inflammation, and various inflammatory cytokines inhibit the synthesis of albumin, resulting in lower serum albumin concentrations [38]. In addition, inflammation leads to a redistribution of serum proteins due to an increase in capillary permeability and promotes resting energy expenditure, leading to increased protein and energy requirements. Consequently, there is an association between inflammation and serum albumin, but not between malnutrition and serum albumin. By including serum albumin levels, GNRI and CONUT scores may be useful for assessing risk of inflammation-related diseases, but they are not suitable for assessing nutrition per se. In recent years, walking robots have been introduced into rehabilitation aimed at regaining walking ability, and their effectiveness has been reported [39,40]. In addition, because elderly patients may experience age-related intestinal malabsorption [41], robotic rehabilitation based on nutritional assessment may contribute to walking independence at discharge in patients admitted to convalescent rehabilitation wards.
This study has three limitations. First, because it is a retrospective observational study, the causal relationship between baseline malnutrition and outcomes is unclear. Second, it was conducted at a single hospital, so the generalizability of the results may be limited. Multicenter prospective cohort studies will be needed to overcome the limitations of this study. Third, patients were provided with standard rehabilitation treatment in the convalescent rehabilitation wards, but we were unable to mention the details of the type of rehabilitation. We were also unable to collect detailed information about the patients’ social backgrounds. In the future, it will necessary to verify the impact of more detailed rehabilitation factors, including type of rehabilitation provided to patients, contents of rehabilitation programs and patients’ social backgrounds on rehabilitation outcome such as physical activity and discharge to home. Nonetheless, a strength of this study is that we were able to analyze over 1600 consecutive inpatients. From our findings with these patients, we believe that assessing MNA-SF on admission may be useful for predicting improvement in physical activity at discharge in patients admitted to convalescent rehabilitation wards.

5. Conclusions

Our findings indicate that of the three nutritional indicators tested (MNA-SF, GNRI and CONUT), MNA-SF scores were the best nutritional assessment tool for predicting improvement in physical activity at discharge in patients admitted to convalescent rehabilitation wards after hospitalization.

Author Contributions

Conceptualization, Y.T., C.H., S.S., M.M. and T.N.; data curation, Y.T., S.S. and T.N.; formal analysis, Y.T., C.H. and T.N.; writing—original draft preparation and writing—review and editing, Y.T. and T.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The Institutional Review Board of the hospital where the study was conducted (approval number: 19100761, Date of Approval: 7 October 2019) approved this study.

Informed Consent Statement

An opt-out procedure allowed participants to withdraw from the study at any time. Written informed consent could not be obtained due to limitations resulting from the retrospective study design.

Data Availability Statement

The data are not publicly available, owing to opt-out restrictions. Data sharing is not applicable.

Acknowledgments

The authors highly appreciate the Wakakusa-Tatsuma Rehabilitation Hospital for their cooperation in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Norman, K.; Pichard, C.; Lochs, H.; Pirlich, M. Prognostic impact of disease-related malnutrition. Clin. Nutr. 2008, 27, 5–15. [Google Scholar] [CrossRef] [PubMed]
  2. Wakabayashi, H.; Sakuma, K. Rehabilitation nutrition for sarcopenia with disability: A combination of both rehabilitation and nutrition care management. J. Cachexia Sarcopenia Muscle 2014, 5, 269–277. [Google Scholar] [CrossRef] [PubMed]
  3. Kaiser, M.J.; Bauer, J.M.; Rämsch, C.; Uter, W.; Guigoz, Y.; Cederholm, T.; Thomas, D.R.; Anthony, P.S.; Charlton, K.E.; Maggio, M.; et al. Frequency of malnutrition in older adults: A multinational perspective using the mini nutritional assessment. J. Am. Geriatr. Soc. 2010, 58, 1734–1738. [Google Scholar] [CrossRef] [PubMed]
  4. Marshall, S.; Bauer, J.; Isenring, E. The consequences of malnutrition following discharge from rehabilitation to the community: A systematic review of current evidence in older adults. J. Hum. Nutr. Diet. 2014, 27, 133–141. [Google Scholar] [CrossRef]
  5. Kruizenga, H.; van Keeken, S.; Weijs, P.; Bastiaanse, L.; Beijer, S.; Huisman-de Waal, G.; Jager-Wittenaar, H.; Jonkers-Schuitema, C.; Klos, M.; Remijnse-Meester, W.; et al. Undernutrition screening survey in 564,063 patients: Patients with a positive undernutrition screening score stay in hospital 1.4 d longer. Am. J. Clin. Nutr. 2016, 103, 1026–1032. [Google Scholar] [CrossRef] [PubMed]
  6. Inaba, M.; Okuno, S.; Ohno, Y. Importance of Considering Malnutrition and Sarcopenia in Order to Improve the QOL of Elderly Hemodialysis Patients in Japan in the Era of 100-Year Life. Nutrients 2021, 13, 2377. [Google Scholar] [CrossRef] [PubMed]
  7. Giovannini, S.; Iacovelli, C.; Loreti, C.; Lama, E.; Morciano, N.; Frisullo, G.; Biscotti, L.; Padua, L.; Castelli, L. The role of nutritional supplement on post-stroke fatigue: A pilot randomized controlled trial. J. Nutr. Health Aging 2024, 28, 100256. [Google Scholar] [CrossRef] [PubMed]
  8. Wojzischke, J.; van Wijngaarden, J.; van den Berg, C.; Cetinyurek-Yavuz, A.; Diekmann, R.; Luiking, Y.; Bauer, J. Nutritional status and functionality in geriatric rehabilitation patients: A systematic review and meta-analysis. Eur. Geriatr. Med. 2020, 11, 195–207. [Google Scholar] [CrossRef] [PubMed]
  9. Sanchez-Rodriguez, D.; Marco, E.; Annweiler, C.; Ronquillo-Moreno, N.; Tortosa, A.; Vázquez-Ibar, O.; Escalada, F.; Duran, X.; Muniesa, J.M. Malnutrition in postacute geriatric care: Basic ESPEN diagnosis and etiology based diagnoses analyzed by length of stay, in-hospital mortality, and functional rehabilitation indexes. Arch. Gerontol. Geriatr. 2017, 73, 169–176. [Google Scholar] [CrossRef]
  10. Inoue, T.; Misu, S.; Tanaka, T.; Sakamoto, H.; Iwata, K.; Chuman, Y.; Ono, R. Pre-fracture nutritional status is predictive of functional status at discharge during the acute phase with hip fracture patients: A multicenter prospective cohort study. Clin. Nutr. 2017, 36, 1320–1325. [Google Scholar] [CrossRef]
  11. Irisawa, H.; Mizushima, T. Correlation of Body Composition and Nutritional Status with Functional Recovery in Stroke Rehabilitation Patients. Nutrients 2020, 12, 1923. [Google Scholar] [CrossRef] [PubMed]
  12. Kobayashi, D.; Yoshimura, Y.; Mori, T.; Hashizume, E. Usefulness of the GLIM criteria to predict recovery of activities of daily living in older adults with post-acute stroke. J. Stroke Cerebrovasc. Dis. 2023, 32, 107345. [Google Scholar] [CrossRef] [PubMed]
  13. Nishioka, S.; Wakabayashi, H.; Momosaki, R. Nutritional status changes and activities of daily living after hip fracture in convalescent rehabilitation units: A retrospective observational cohort study from the Japan rehabilitation nutrition database. J. Acad. Nutr. Diet. 2018, 118, 1270–1276. [Google Scholar] [CrossRef] [PubMed]
  14. Sato, M.; Ido, Y.; Yoshimura, Y.; Mutai, H. Relationship of malnutrition during hospitalization with functional recovery and postdischarge destination in elderly stroke patients. J. Stroke Cerebrovasc. Dis. 2019, 28, 1866–1872. [Google Scholar] [CrossRef] [PubMed]
  15. Kishimoto, H.; Yozu, A.; Kohno, Y.; Oose, H. Nutritional improvement is associated with better functional outcome in stroke rehabilitation: A cross-sectional study using controlling nutritional status. J. Rehabil. Med. 2020, 52, jrm00029. [Google Scholar] [CrossRef] [PubMed]
  16. Tamamura, Y.; Matsuura, M.; Shiba, S.; Nishikimi, T. Effect of heart failure and malnutrition, alone and in combination, on rehabilitation effectiveness in patients with hip fracture. Clin. Nutr. ESPEN 2021, 44, 356–366. [Google Scholar] [CrossRef] [PubMed]
  17. Tamamura, Y.; Matsuura, M.; Shiba, S.; Nishikimi, T. Effect of comorbid heart failure assessed by plasma B-type natriuretic peptide level on the activities of daily living in patients with hospitalization-associated disability after aspiration pneumonia. Eur. Geriatr. Med. 2024, 15, 67–72. [Google Scholar] [CrossRef] [PubMed]
  18. Crary, M.A.; Mann, G.D.; Groher, M.E. Initial psychometric assessment of a functional oral intake scale for dysphagia in stroke patients. Arch. Phys. Med. Rehabil. 2005, 86, 1516–1520. [Google Scholar] [CrossRef] [PubMed]
  19. Mahoney, F.I.; Barthel, D. Functional evaluation: The Barthel Index. Md. Med. J. 1965, 14, 56–61. [Google Scholar]
  20. Ottenbacher, K.J.; Hsu, Y.; Granger, C.V.; Fiedler, R.C. The reliability of the functional independence measure: A quantitative review. Arch. Phys. Med. Rehabil. 1996, 77, 1226–1232. [Google Scholar] [CrossRef]
  21. Tamamura, Y.; Matsuura, M.; Shiba, S.; Nishikimi, T. Heart failure assessed based on plasma B-type natriuretic peptide (BNP) levels negatively impacts activity of daily living in patients with hip fracture. PLoS ONE 2020, 15, e0237387. [Google Scholar] [CrossRef] [PubMed]
  22. Rubenstein, L.Z.; Harker, J.O.; Salvà, A.; Guigoz, Y.; Vellas, B. Screening for undernutrition in geriatric practice: Developing the short-form mini-nutritional assessment (MNA-SF). J. Gerontol. A Biol. Sci. Med. Sci. 2001, 56, M366–M372. [Google Scholar] [CrossRef]
  23. Bouillanne, O.; Morineau, G.; Dupont, C.; Coulombel, I.; Vincent, J.P.; Nicolis, I.; Benazeth, S.; Cynober, L.; Aussel, C. Geriatric nutritional risk index: A new index for evaluating at-risk elderly medical patients. Am. J. Clin. Nutr. 2005, 82, 777–783. [Google Scholar] [CrossRef] [PubMed]
  24. de Ulíbarri, J.I.; González-Madroño, A.; de Villar, N.G.; González, P.; González, B.; Mancha, A.; Rodríguez, F.; Fernández, G. CONUT: A tool for controlling nutritional status. First validation in a hospital population. Nutr. Hosp. 2005, 20, 38–45. [Google Scholar]
  25. Ioue, S.; Mori, N.; Tsujikawa, M.; Ishii, R.; Suzuki, K.; Kondo, K.; Kawakami, M. Determinants of Step-through Gait Pattern Acquisition in Subacute Stroke Patients. Prog. Rehabil. Med. 2022, 7, 20220035. [Google Scholar] [CrossRef]
  26. Koh, G.C.; Chen, C.H.; Petrella, R.; Thind, A. Rehabilitation impact indices and their independent predictors: A systematic review. BMJ Open 2013, 3, e003483. [Google Scholar] [CrossRef]
  27. Paquereau, J.; Allart, E.; Romon, M.; Rousseaux, M. The long-term nutritional status in stroke patients and its predictive factors. J. Stroke Cerebrovasc. Dis. 2014, 23, 1628–1633. [Google Scholar] [CrossRef]
  28. Kokura, Y.; Wakabayashi, H.; Nishioka, S.; Maeda, K. Nutritional intake is associated with activities of daily living and complications in older inpatients with stroke. Geriatr. Gerontol. Int. 2018, 18, 1334–1339. [Google Scholar] [CrossRef]
  29. Kishimoto, H.; Nemoto, Y.; Maezawa, T.; Takahashi, K.; Koseki, K.; Ishibashi, K.; Tanamachi, H.; Kobayashi, N.; Kohno, Y. Weight Change during the Early Phase of Convalescent Rehabilitation after Stroke as a Predictor of Functional Recovery: A Retrospective Cohort Study. Nutrients 2022, 14, 264. [Google Scholar] [CrossRef] [PubMed]
  30. Liu, H.; Lou, V.W.Q. Functional recovery of older stroke patients discharged from hospital to home: The effects of cognitive status and different levels of therapy intensity. J. Clin. Nurs. 2019, 28, 47–55. [Google Scholar] [CrossRef]
  31. Yoshitaka, T.; Shimaoka, Y.; Yamanaka, I.; Tanida, A.; Tanimoto, J.; Toda, N.; Akimori, T.; Hamawaki, J. Cognitive Impairment as the Principal Factor Correlated with the Activities of Daily Living Following Hip Fracture in Elderly People. Prog. Rehabil. Med. 2022, 7, 20220026. [Google Scholar] [CrossRef] [PubMed]
  32. Ishihara, K.; Izawa, K.P.; Kitamura, M.; Shimogai, T.; Kanejima, Y.; Morisawa, T.; Shimizu, I. Influence of mild cognitive impairment on activities of daily living in patients with cardiovascular disease. Heart Vessel. 2019, 34, 1944–1951. [Google Scholar] [CrossRef] [PubMed]
  33. Nishioka, S.; Wakabayashi, H.; Nishioka, E.; Yoshida, T.; Mori, N.; Watanabe, R. Nutritional Improvement Correlates with Recovery of Activities of Daily Living among Malnourished Elderly Stroke Patients in the Convalescent Stage: A Cross-Sectional Study. J. Acad. Nutr. Diet. 2016, 116, 837–843. [Google Scholar] [CrossRef] [PubMed]
  34. Fujitaka, Y.; Tanaka, N.; Nakadai, H.; Sato, R.; Watanabe, M.; Kageyama, N.; Okamoto, T. Differences in FIM improvement rate stratified by nutritional status and age in stroke patients in kaifukuki (convalescent) rehabilitation ward. Jpn. J. Compr. Rehabil. Sci. 2018, 8, 98–103. [Google Scholar] [CrossRef]
  35. Blackburn, G.A.; Bistrian, B.R.; Maini, B.S.; Schlamm, H.T.; Smith, M.F. Nutritional and metabolic assessment of the hospitalized patient. JPEN J. Parenter. Ent Nutr. 1977, 1, 11–21. [Google Scholar] [CrossRef]
  36. Seltzer, M.H.; Bastidas, J.A.; Cooper, D.M.; Engler, P.; Slocum, B.; Fletcher, H.S. Instant nutritional assessment. JPEN J. Parenter. Enter. Nutr. 1979, 3, 157–159. [Google Scholar] [CrossRef]
  37. Evans, D.C.; Corkins, M.R.; Malone, A.; Miller, S.; Mogensen, K.M.; Guenter, P.; Jensen, G.L.; ASPEN Malnutrition Committee. The Use of Visceral Proteins as Nutrition Markers: An ASPEN Position Paper. Nutr. Clin. Pract. 2021, 36, 22–28. [Google Scholar] [CrossRef]
  38. Chojkier, M. Inhibition of albumin synthesis in chronic diseases: Molecular mechanisms. J. Clin. Gastroenterol. 2005, 39 (Suppl. S2), S143–S146. [Google Scholar] [CrossRef]
  39. Calafiore, D.; Negrini, F.; Tottoli, N.; Ferraro, F.; Ozyemisci-Taskiran, O.; de Sire, A. Efficacy of robotic exoskeleton for gait rehabilitation in patients with subacute stroke: A systematic review. Eur. J. Phys. Rehabil. Med. 2022, 58, 1–8. [Google Scholar] [CrossRef]
  40. Kato, H.; Watanabe, H.; Koike, A.; Wu, L.; Hayashi, K.; Konno, H.; Machino, T.; Nishi, I.; Sato, A.; Kawamoto, H.; et al. Effects of Cardiac Rehabilitation with Lumbar-Type Hybrid Assistive Limb on Muscle Strength in Patients with Chronic Heart Failure—A Randomized Controlled Trial. Circ. J. 2021, 86, 60–67. [Google Scholar] [CrossRef]
  41. Holt, P.R. Intestinal malabsorption in the elderly. Dig. Dis. 2007, 25, 144–150. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Flow chart of this study.
Figure 1. Flow chart of this study.
Nutrients 16 02531 g001
Figure 2. Distribution of patients based on MNA-SF scores at admission and the percentage capable of walking at discharge for each MNA-SF score.
Figure 2. Distribution of patients based on MNA-SF scores at admission and the percentage capable of walking at discharge for each MNA-SF score.
Nutrients 16 02531 g002
Table 1. Baseline demographic and clinical characteristics of the walking and the wheelchair groups.
Table 1. Baseline demographic and clinical characteristics of the walking and the wheelchair groups.
TotalWalking GroupWheelchair Group p Value
n16011181420
Age, year77 ± 1276 ± 1379 ± 12<0.001 *
Male, n (%)739 (46.2)555 (47.0)184 (43.8)0.261
Height, m1.6 ± 0.11.6 ± 0.11.5 ± 0.10.067 *
Weight, kg51.3 ± 11.952.3 ± 12.048.5 ± 11.1<0.001 *
Body mass index, kg/m221.1 ± 4.021.5 ± 4.020.3 ± 3.8<0.001 *
Reason for admission, n (%)
 Stroke643 (40.2)424 (35.9)219 (52.1)<0.001
 Musculoskeletal disease635 (39.7)520 (44.0)115 (27.4)<0.001
 Hospitalization-associated disability323 (20.2)237 (20.1)86 (20.5)0.858
Comorbidity, n (%)
 Hypertension997 (62.3)737 (62.4)259 (61.7)0.259
 Diabetes368 (23.0)274 (23.2)94 (22.4)0.732
 Dyslipidemia346 (21.6)273 (23.1)72 (17.1)0.010
 Atrial fibrillation194 (12.1)133 (11.3)61 (14.5)0.078
Time from onset, day 29 (21–42)28 (20–40)31 (22–46)0.063 **
Handgrip strength, kg17.0 ± 10.018.1 ± 10.312.7 ± 6.6<0.001 *
Quadriceps strength, kg13.3 ± 7.313.8 ± 7.410.7 ± 6.6<0.001 *
FOIS6 ± 26 ± 14 ± 2<0.001 *
Barthel Index, score45 (15–65)50 (35–70)10 (5–30)<0.001 **
FIM, score
 Motor32 (20–45)38 (25–48)16 (13–23)<0.001 **
 Cognitive20 (14–25)23 (18–27)13 (8–17)<0.001 **
 Total52 (36–69)60 (45–74)31 (22–39)<0.001 **
Nutritional Index at admission
 MNA-SF6.1 ± 2.66.5 ± 2.54.7 ± 2.4<0.001 *
 GNRI91.4 ± 12.493.1 ± 12.486.7 ± 10.9<0.001 *
 CONUT score3.3 ± 2.33.1 ± 2.33.9 ± 2.3<0.001 *
 Energy intake, kcal1405 ± 4061453 ± 3941270 ± 407<0.001 *
 Protein intake, g56.7 ± 15.758.4 ± 15.151.9 ± 16.4<0.001 *
Medication, n5.2 ± 3.05.2 ± 3.15.1 ± 2.80.402 *
BNP, pg/mL37 (17–86)35 (16–80)43 (18–100)<0.001 **
Albumin, g/dL3.5 ± 0.53.5 ± 0.53.2 ± 0.5<0.001 *
CRP, mg/dL0.9 ± 1.80.8 ± 1.71.1 ± 2.00.002 *
Creatinine, mg/dL0.9 ± 0.40.9 ± 0.40.8 ± 0.40.043 *
eGFR, mg/min63.8 ± 19.063.6 ± 18.764.3 ± 20.10.002 *
Hemoglobin, g/dL12.0 ± 1.812.1 ± 1.811.7 ± 1.8<0.001 *
Data are presented as mean ± SD for parametric continuous data, median (IQT) for non-parametric data, and n (%) for categorical data. * student’s t test. ** Mann–Whitney U test. chi-square test. BNP: B-type natriuretic peptide, CONUT: Controlling Nutritional Status, CRP: C-reactive protein, eGFR: estimated glomerular filtration rate, FIM: Functional Independence Measure, FOIS: Functional Oral Intake Scale, GNRI: Geriatric Nutritional Risk Index, MNA-SF: Mini Nutritional Assessment-Short Form.
Table 2. Rehabilitation outcomes of the Walking and Wheelchair groups.
Table 2. Rehabilitation outcomes of the Walking and Wheelchair groups.
TotalWalking GroupWheelchair Group p Value
n16011181420
Handgrip strength, kg17.6 ± 8.418.8 ± 8.412.9 ± 6.9<0.001 *
Quadriceps strength, kg15.7 ± 7.816.6 ± 7.911.4 ± 5.9<0.001 *
FOIS6 ± 27 ± 14 ± 2<0.001 *
Barthel Index, score85 (55–100)90 (85–100)35 (15–55)<0.001 **
FIM, score
 Motor73 (51–83)79 (70–86)31 (19–44)<0.001 **
 Cognitive25 (19–31)28 (23–33)16 (12–21)<0.001 **
 Total98 (71–114)107 (95–117)50 (32–64)<0.001 **
Nutritional Index at discharge
 MNA-SF9.4 ± 2.89.9 ± 2.68.1 ± 2.8<0.001 *
 GNRI89.9 ± 12.392.0 ± 11.884.9 ± 12.1<0.001 *
 CONUT3.1 ± 2.72.7 ± 2.23.9 ± 2.6<0.001 *
 Energy intake, kcal1582 ± 3541622 ± 3341469 ± 382<0.001 *
 Protein intake, g63.7 ± 14.465.2 ± 13.159.5 ± 16.8<0.001 *
Handgrip strength gain, kg0.7 (0.0–2.7)0.9 (0.0–2.8)0.5 (0.0–2.4)0.624 **
Quadriceps strength gain, kg1.8 (0.0–4.5)1.9 (0.2–4.7)0.8 (0.0–0.3)<0.001 **
Barthel Index gain, score30 (15–45)35 (20–50)20 (5–35)<0.001 **
FIM gain, score
 Motor34 (19–43)39 (30–46)12 (4–21)<0.001 **
 Cognitive4 (1–7)5 (1–8)2 (1–5)<0.001 **
 Total38 (22–50)43 (33–53)14 (6–25)<0.001 **
Rehabilitation effectiveness, %55.9 ± 38.368.9 ± 35.519.2 ± 15.2<0.001 §
Length of hospital stay, day86 (64–117)85 (60–93)90 (81–165)<0.001 **
Discharge to home, n (%)1033 (64.5)876 (74.2)156 (37.1)<0.001
Data are presented as mean ± SD for parametric continuous data, median (IQT) for non-parametric data, and n (%) for categorical data. * student’s t test. ** Mann–Whitney U test. chi-square test. § analysis of covariance adjusted for age and gender. CONUT: Controlling Nutritional Status, FIM: Functional Independence Measure, FOIS: Functional Oral Intake Scale, GNRI: Geriatric Nutritional Risk Index, MNA-SF: Mini Nutritional Assessment-Short Form.
Table 3. Rehabilitation outcomes for stroke, musculoskeletal diseases, and hospital-associated disability.
Table 3. Rehabilitation outcomes for stroke, musculoskeletal diseases, and hospital-associated disability.
TotalWalking GroupWheelchair Group p Value
Stroke
  n643424219
  Handgrip strength at admission, kg18.4 ± 12.520.0 ± 13.313.4 ± 7.4<0.001 *
  Quadriceps strength at admission, kg14.3 ± 7.915.0 ± 7.810.9 ± 7.2<0.001 *
  FOIS at admission5 ± 26 ± 24 ± 2<0.001 *
  Barthel Index at admission, score38 (10–60)50 (30–70)10 (0–20)<0.001 **
  FIM at admission, score
   Motor26 (15–42)36 (23–47)14 (13–19)<0.001 **
   Cognitive18 (11–23)21 (16–25)10 (7–16)<0.001 **
   Total44 (28–64)58 (42–71)25 (21–34)<0.001 **
  Nutritional Index at admission
   MNA-SF5.7 ± 2.66.4 ± 2.54.4 ± 2.3<0.001 *
   GNRI92.6 ± 12.594.8 ± 12.588.2 ± 11.4<0.001 *
   CONUT3.2 ± 2.33.0 ± 2.23.7 ± 2.3<0.001 *
   Energy intake, kcal1435 ± 4171522 ± 4061265 ± 388<0.001 *
   Protein intake, g57.7 ± 15.760.7 ± 15.051.9 ± 15.6<0.001 *
  Handgrip strength at discharge, kg19.0 ± 9.121.0 ± 9.013.7 ± 3.7<0.001 *
  Quadriceps strength at discharge, kg16.8 ± 8.518.3 ± 8.511.2 ± 5.6<0.001 *
  FOIS at discharge6 ± 27 ± 14 ± 2<0.001 *
  Barthel Index at discharge, score85 (45–100)95 (85–100)30 (10–50)<0.001 **
  FIM at discharge, score
   Motor70 (37–84)80 (70–87)29 (17–41)<0.001 **
   Cognitive24 (17–30)28 (23–32)15 (10–20)<0.001 **
   Total94 (55–112)108 (95–117)44 (31–59)<0.001 **
  Nutritional Index at discharge
   MNA-SF9.2 ± 2.910.0 ± 2.67.8 ± 2.8<0.001 *
   GNRI91.6 ± 12.294.9 ± 10.086.5 ± 13.5<0.001 *
   CONUT2.8 ± 2.22.4 ± 1.93.4 ± 2.5<0.001 *
   Energy intake, kcal1269 ± 3771704 ± 3371482 ± 408<0.001 *
   Protein intake, g65.2 ± 15.267.9 ± 12.860.0 ± 17.9<0.001 *
  Handgrip strength gain, kg1.1 (0.0–3.3)1.2 (0.0–3.6)1.0 (0.0–3.2)0.791 **
  Quadriceps strength gain, kg2.0 (0.2–5.0)2.3 (0.4–5.2)1.0 (0.0–4.0)0.022 **
  Barthel Index gain, score30 (15–45)35 (20–50)18 (5–30)<0.001 **
  FIM gain, score
   Motor33 (15–44)41 (31–49)11 (3–20)<0.001 **
   Cognitive5 (2–9)6 (3–10)3 (1–6)<0.001 **
   Total38 (19–52)46 (35–57)15 (5–26)<0.001 **
  Rehabilitation effectiveness, %51.8 ± 30.369.0 ± 20.118.5 ± 15.5<0.001 §
  Length of hospital stay, day141 (89–176)129 (76–173)161 (114–178)<0.001 **
  Discharge to home, n (%)361 (56.1)299 (70.5)62 (28.3)<0.001
Musculoskeletal disease,
  n635520115
  Handgrip strength at admission, kg16.5 ± 8.317.2 ± 8.412.3 ± 6.0<0.001 *
  Quadriceps strength at admission, kg12.7 ± 6.913.0 ± 6.911.3 ± 6.40.063 *
  FOIS at admission6 ± 16 ± 15 ± 2<0.001 *
  Barthel Index at admission, score50 (30–70)55 (40–70)20 (5–38)<0.001 **
  FIM at admission, score
   Motor36 (24–48)40 (29–49)22 (15–27)<0.001 **
   Cognitive23 (17–28)25 (20–28)15 (11–19)<0.001 **
   Total59 (42–75)64 (50–78)38 (27–45)<0.001 **
  Nutritional Index at admission
   MNA-SF6.7 ± 2.57.0 ± 2.55.7 ± 2.4<0.001 *
   GNRI92.6 ± 11.693.8 ± 11.787.0 ± 9.2<0.001 *
   CONUT3.1 ± 2.12.9 ± 2.13.9 ± 2.2<0.001 *
   Energy intake, kcal1390 ± 3791406 ± 3711316 ± 4080.031 *
   Protein intake, g56.2 ± 14.956.9 ± 14.753.1 ± 15.90.019 *
  Handgrip strength at discharge, kg16.5 ± 7.917.4 ± 8.012.8 ± 5.7<0.001 *
  Quadriceps strength at discharge, kg15.1 ± 7.315.6 ± 7.312.0 ± 6.5<0.001 *
  FOIS at discharge6 ± 17 ± 15 ± 2<0.001 *
  Barthel Index at discharge, score90 (70–100)95 (85–100)50 (23–60)<0.001 **
  FIM at discharge, score
   Motor76 (62–84)79 (70–80)40 (27–52)<0.001 **
   Cognitive27 (21–33)29 (24–34)17 (13–22)<0.001 **
   Total103 (83–116)107 (95–118)57 (42–72)<0.001 **
  Nutritional Index at discharge
   MNA-SF9.8 ± 2.510.0 ± 2.59.0 ± 2.5<0.001 *
   GNRI89.9 ± 12.290.9 ± 12.585.4 ± 9.7<0.001 *
   CONUT3.1 ± 2.42.9 ± 2.34.0 ± 2.60.005 *
   Energy intake, kcal1535 ± 3031546 ± 3031481 ± 3010.037 *
   Protein intake, g62.4 ± 12.363.1 ± 12.159.6 ± 12.8<0.001 *
  Handgrip strength gain, kg0.5 (0.0–2.2)0.5 (0.0–2.3)0.3 (0.0–1.6)0.254 **
  Quadriceps strength gain, kg1.6 (0.0–3.9)1.6 (0.1–4.1)1.1 (0.0–2.1)0.005 **
  Barthel Index gain, score30 (20–45)35 (20–45)20 (5–25)<0.001 **
  FIM gain, score
   Motor34 (23–43)37 (29–44)15 (7–25)<0.001 **
   Cognitive3 (1–6)3 (1–7)2 (1–5)<0.001 **
   Total38 (26–47)41 (31–50)18 (9–30)<0.001 **
  Rehabilitation effectiveness, %61.6 ± 48.070.3 ± 48.322.2 ± 15.5<0.001 §
  Length of hospital stay, day78 (56–87)75 (53–86)85 (71–89)<0.001 **
  Discharge to home, n (%)475 (74.8)411 (79.0)64 (55.7)<0.001
Hospitalization-associated disability,
  n32323786
  Handgrip strength at admission, kg15.7 ± 7.316.7 ± 7.311.9 ± 5.8<0.001 *
  Quadriceps strength at admission, kg12.6 ± 7.113.3 ± 7.39.6 ± 5.5<0.001 *
  FOIS at admission5 ± 26 ± 24 ± 2<0.001 *
  Barthel Index at admission, score40 (20–60)50 (35–65)13 (5–30)<0.001 **
  FIM at admission, score
   Motor28 (19–41)35 (24–45)17 (13–24)<0.001 **
   Cognitive20 (15–25)22 (17–27)15 (9–21)<0.001 **
   Total49 (37–60)56 (44–71)35 (25–43)<0.001 **
  Nutritional Index at admission
   MNA-SF5.5 ± 2.45.9 ± 2.44.3 ± 2.2<0.001 *
   GNRI86.7 ± 12.588.3 ± 12.882.4 ± 10.5<0.001 *
   CONUT4.0 ± 2.63.8 ± 2.64.6 ± 2.40.009 *
   Energy intake, kcal1375 ± 4291433 ± 4061218 ± 450<0.001 *
   Protein intake, g55.6 ± 16.954.7 ± 15.750.4 ± 19.00.003 *
  Handgrip strength at discharge, kg16.8 ± 7.517.9 ± 7.212.7 ± 7.2<0.001 *
  Quadriceps strength at discharge, kg14.7 ± 7.315.6 ± 7.411.1 ± 5.7<0.001 *
  FOIS at discharge6 ± 26 ± 14 ± 2<0.001 *
  Barthel Index at discharge, score85 (55–95)90 (80–100)35 (10–55)<0.001 **
  FIM at discharge, score
   Motor71 (47–81)77 (67–84)31 (17–41)<0.001 **
   Cognitive25 (19–31)28 (23–32)18 (14–24)<0.001 **
   Total97 (68–110)103 (91–114)51 (32–64)<0.001 **
  Nutritional Index at discharge
   MNA-SF9.0 ± 2.89.5 ± 2.77.6 ± 2.7<0.001 *
   GNRI87.1 ± 12.289.8 ± 12.181.0 ± 10.0<0.001 *
   CONUT3.6 ± 2.53.0 ± 2.24.8 ± 2.6<0.001 *
   Energy intake, kcal1583 ± 3861641 ± 3601422 ± 412<0.001 *
   Protein intake, g63.2 ± 16.365.0 ± 15.058.2 ± 18.50.003 *
  Handgrip strength gain, kg1.0 (0.0–2.5)1.1 (0.0–3.0)0.3 (0.0–2.1)0.086 **
  Quadriceps strength gain, kg1.4 (0.0–4.7)1.6 (0.1–4.8)0.8 (0.0–2.4)0.049 **
  Barthel Index gain, score35 (15–45)40 (25–50)15 (5–34)<0.001 **
  FIM gain, score
   Motor34 (19–44)39 (31–47)10 (3–18)<0.001 **
   Cognitive3 (1–7)4 (1–8)2 (1–6)0.006 **
   Total38 (21–50)43 (34–54)14 (6–22)<0.001 **
  Rehabilitation effectiveness, %52.9 ± 28.765.8 ± 20.517.1 ± 13.5<0.001 §
  Length of hospital stay, day85 (62–89)85 (62–88)86 (71–90)0.702 **
  Discharge to home, n (%)197 (61.0)167 (70.5)30 (34.9)<0.001
Data are presented as mean ± SD for parametric continuous data, median (IQT) for non-parametric data, and n (%) for categorical data. * student’s t test. ** Mann–Whitney U test. chi-square test. § analysis of covariance adjusted for age and gender. CONUT: Controlling Nutritional Status, FIM: Functional Independence Measure, GNRI: Geriatric Nutritional Risk Index, MNA-SF: Mini Nutritional Assessment-Short Form.
Table 4. Univariate and multivariate logistic regression analyses of the association between each nutritional screening tool and walking without assistance at discharge.
Table 4. Univariate and multivariate logistic regression analyses of the association between each nutritional screening tool and walking without assistance at discharge.
Crude ModelAdjusted Model 1Adjusted Model 2
OR95% CIp ValueOR95% CIp ValueOR95% CIp Value
Overall
 MNA-SF1.3361.272–1.404<0.00011.9611.961–1.399<0.00011.1851.185–1.292<0.0001
 GNRI1.0451.035–1.056<0.00011.0431.032–1.054<0.00011.0211.001–1.0420.038
 CONUT0.8860.826–0.908<0.00010.8820.839–0.927<0.00010.9410.857–1.0330.199
Stroke
 MNA-SF1.3861.286–1.494<0.00011.3661.266–1.474<0.00011.1631.011–1.3380.035
 GNRI1.0461.031–1.601<0.00011.0391.023–1.055<0.00010.9890.959–1.0200.484
 CONUT0.8870.827–0.9530.0010.9290.861–1.0010.0541.1550.998–1.3370.053
Musculoskeletal disease
 MNA-SF1.2221.126–1.327<0.00011.1881.091–1.293<0.00011.1821.037–1.3470.012
 GNRI1.0531.034–1.073<0.00011.0471.027–1.068<0.00011.0270.999–1.0560.057
 CONUT0.8130.742–0.891<0.00010.8360.758–0.921<0.00010.9250.809–1.0570.251
Hospital-associated disability
 MNA-SF1.4741.291–1.684<0.00011.5011.305–1.726<0.00011.3181.053–1.6500.016
 GNRI1.0441.021–1.068<0.00011.0421.018–1.0670.0011.0300.998–1.0730.163
 CONUT0.8960.813–0.9980.027 *0.9110.823–1.0090.0730.8510.719–1.0090.063
CONUT: Controlling Nutritional Status, GNRI: Geriatric Nutritional Risk Index, MNA-SF: Mini Nutritional Assessment-Short Form. Adjusted Model 1: adjusted for age and sex. Adjusted Model 2: adjusted for age and sex as well as variables with a p-value of <0.05 in the univariate analyses (handgrip strength, quadriceps strength, Functional Oral Intake Scale, energy intake, B-type natriuretic peptide, and hemoglobin). * Body mass index was excluded because it is a component of the GNRI and MNA-SF.
Table 5. Univariate and multivariate liner regression analyses of the association between each nutritional screening tool and rehabilitation effectiveness.
Table 5. Univariate and multivariate liner regression analyses of the association between each nutritional screening tool and rehabilitation effectiveness.
Crude ModelAdjusted Model 1Adjusted Model 2
βB95% CIp ValueβB95% CIp ValueβB95% CIp Value
Overall
 MNA-SF0.2570.0380.031–0.045<0.00010.2390.0350.028–0.042<0.00010.1280.0120.006–0.019<0.0001
 GNRI0.2310.0070.006–0.009<0.00010.1980.0060.005–0.009<0.00010.0910.0020.001–0.0030.004
 CONUT−0.14−0.023−0.031–−0.015<0.0001−0.105−0.017−0.026–−0.009<0.0001−0.058−0.006−0.013–0.0010.052
Stroke
 MNA-SF0.4040.0460.038–0.054<0.00010.3650.0420.034–0.050<0.00010.1550.0160.005–0.0260.004
 GNRI0.3510.0080.007–0.010<0.00010.2920.0070.005–0.009<0.00010.0980.0020.001–0.0040.067
 CONUT−0.22−0.029−0.039–−0.019<0.0001−0.155−0.021−0.031–−0.010<0.0001−0.013−0.002−0.013–0.0100.792
Musculoskeletal disease
 MNA-SF0.1360.0260.011–0.041<0.00010.0980.0190.004–0.0330.0140.1080.010.001–0.0190.026
 GNRI0.1740.0070.004–0.010<0.00010.1330.0060.002–0.0090.0010.1140.0020.001–0.0040.02
 CONUT−0.086−0.019−0.037–−0.020.031−0.05−0.011−0.029–0.0070.218−0.067−0.007−0.017–0.0030.149
Hospital-associated disability
 MNA-SF0.320.0390.026–0.052<0.00010.2960.0360.024–0.049<0.00010.0820.0090.006–0.0240.253
 GNRI0.1960.0040.002–0.007<0.00010.1490.0030.001–0.0060.008−0.036−0.0010.003–0.0020.587
 CONUT−0.12−0.014−0.026–−0.0010.031−0.073−0.008−0.021–0.0040.194−0.029−0.003−0.015–0.0090.634
CONUT: Controlling Nutritional Status, GNRI: Geriatric Nutritional Risk Index, MNA-SF: Mini Nutritional Assessment-Short Form. Adjusted Model 1: adjusted for age and sex. Adjusted Model 2: adjusted for age and sex as well as variables with a p-value of <0.05 in the univariate analyses (handgrip strength, quadriceps strength, Functional Oral Intake Scale, and energy intake).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tamamura, Y.; Hachiuma, C.; Matsuura, M.; Shiba, S.; Nishikimi, T. Relationship between Improvement in Physical Activity and Three Nutritional Assessment Indicators in Patients Admitted to a Convalescent Rehabilitation Ward. Nutrients 2024, 16, 2531. https://doi.org/10.3390/nu16152531

AMA Style

Tamamura Y, Hachiuma C, Matsuura M, Shiba S, Nishikimi T. Relationship between Improvement in Physical Activity and Three Nutritional Assessment Indicators in Patients Admitted to a Convalescent Rehabilitation Ward. Nutrients. 2024; 16(15):2531. https://doi.org/10.3390/nu16152531

Chicago/Turabian Style

Tamamura, Yusuke, Chihiro Hachiuma, Michiko Matsuura, Sumiko Shiba, and Toshio Nishikimi. 2024. "Relationship between Improvement in Physical Activity and Three Nutritional Assessment Indicators in Patients Admitted to a Convalescent Rehabilitation Ward" Nutrients 16, no. 15: 2531. https://doi.org/10.3390/nu16152531

APA Style

Tamamura, Y., Hachiuma, C., Matsuura, M., Shiba, S., & Nishikimi, T. (2024). Relationship between Improvement in Physical Activity and Three Nutritional Assessment Indicators in Patients Admitted to a Convalescent Rehabilitation Ward. Nutrients, 16(15), 2531. https://doi.org/10.3390/nu16152531

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop