**Prevention and Management of Frailty**

Editors

**Haewon Byeon Jaewon Nah**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Haewon Byeon Inje University Korea

Jaewon Nah Honam University Korea

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *International Journal of Environmental Research and Public Health* (ISSN 1660-4601) (available at: https: //www.mdpi.com/journal/ijerph/special issues/Prevet Frailty).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-5371-9 (Hbk) ISBN 978-3-0365-5372-6 (PDF)**

© 2022 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.

## **Contents**

#### **Haewon Byeon**

Meta-Analysis on the Effects of Transcranial Direct Current Stimulation on Naming of Elderly with Primary Progressive Aphasia Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 1095, doi:10.3390/ijerph17031095 . . . **1**

#### **Eduarda Oliosi, Federico Guede-Fern´andez and Ana Londral**

Machine Learning Approaches for the Frailty Screening: A Narrative Review Reprinted from: *Int. J. Environ. Res. Public Health* **2022**, *19*, 8825, doi:10.3390/ijerph19148825 . . . **11**

#### **Haewon Byeon**

Is the Random Forest Algorithm Suitable for Predicting Parkinson's Disease with Mild Cognitive Impairment out of Parkinson's Disease with Normal Cognition? Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 2594, doi:10.3390/ijerph17072594 . . . **23**

#### **Hayoung Shim, Miji Kim and Chang Won Won**

Motoric Cognitive Risk Syndrome Using Three-Item Recall Test and Its Associations with Fall-Related Outcomes: The Korean Frailty and Aging Cohort Study Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 3364, doi:10.3390/ijerph17103364 . . . **37**

#### **Daiki Watanabe, Tsukasa Yoshida, Keiichi Yokoyama, Yasuko Yoshinaka, Yuya Watanabe, Takeshi Kikutani, Mitsuyoshi Yoshida, Yosuke Yamada, Misaka Kimura and Kyoto-Kameoka Study Group**

Association between Mixing Ability of Masticatory Functions Measured Using Color-Changing Chewing Gum and Frailty among Japanese Older Adults: The Kyoto–Kameoka Study Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 4555, doi:10.3390/ijerph17124555 . . **53**

#### **Suah Kang, Miji Kim and Chang Won Won**

Spousal Concordance of Physical Frailty in Older Korean Couples Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 4574, doi:10.3390/ijerph17124574 . . . **67**

#### **Tatsuya Hirase, Hyuma Makizako, Yoshiro Okubo, Stephen R. Lord, Minoru Okita, Yuki Nakai, Toshihiro Takenaka, Takuro Kubozono and Mitsuru Ohishi**

Falls in Community-Dwelling Older Adults with Lower Back or Knee Pain Are Associated with Cognitive and Emotional Factors

Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 4960, doi:10.3390/ijerph17144960 . . **77**

#### **Jos ´e M. Tom ´as, Trinidad Sentandreu-Ma ˜n ´o and Irene Fern´andez**

Frailty Status Typologies in Spanish Older Population: Associations with Successful Aging Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 6772, doi:10.3390/ijerph17186772 . . . **85**

#### **Wioletta Dziubek, Weronika Pawlaczyk, Małgorzata Stefa ´nska, Joanna Walig ´ora, Maria Bujnowska-Fedak and Joanna Kowalska**

Evaluation of Psychophysical Factors in Individuals with Frailty Syndrome Following a 3-Month Controlled Physical Activity Program

Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 7804, doi:10.3390/ijerph17217804 . . . **97**

#### **Heeeun Jung, Miji Kim, Yunhwan Lee and Chang Won Won**

Prevalence of Physical Frailty and Its Multidimensional Risk Factors in Korean Community-Dwelling Older Adults: Findings from Korean Frailty and Aging Cohort Study Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 7883, doi:10.3390/ijerph17217883 . . **111**

#### **Magdalena Sacha, Jerzy Sacha and Katarzyna Wieczorowska-Tobis**

Determinants of Multidimensional and Physical Frailty and Their Individual Components: Interactions between Frailty Deficits Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 8656, doi:10.3390/ijerph17228656 . . **131**

#### **An-Chen Shih, Lee-Hwa Chen, Chin-Chueh Tsai and Jau-Yuan Chen**

Correlation between Sleep Quality and Frailty Status among Middle-Aged and Older Taiwanese People: A Community-Based, Cross-Sectional Study Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 9457, doi:10.3390/ijerph17249457 . . **153**

#### **Ana Filipa Cardoso, Elzbieta Bobrowicz-Campos, Lu´ısa Teixeira-Santos, Daniela Cardoso, Filipa Couto and Jo ˜ao Ap ´ostolo**

Validation and Screening Capacity of the European Portuguese Version of the SUNFRAIL Tool for Community-Dwelling Older Adults Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 1394, doi:10.3390/ijerph18041394 . . . **165**

#### **Fereshteh Mehrabi and Fran¸cois B´eland**

Frailty as a Moderator of the Relationship between Social Isolation and Health Outcomes in Community-Dwelling Older Adults Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 1675, doi:10.3390/ijerph18041675 . . . **175**

#### **Haewon Byeon**

Predicting the Severity of Parkinson's Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 2551, doi:10.3390/ijerph18052551 . . . **193**

#### **Haewon Byeon**

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 7625, doi:10.3390/ijerph18147625 . . . **203**

#### **Marcin Mikos, Tomasz Banas, Aleksandra Czerw, Bartłomiej Banas, Łukasz Strzepek and Mateusz Curyło**

Hospital Inpatient Falls across Clinical Departments Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 8167, doi:10.3390/ijerph18158167 . . . **219**

#### **Atsushi Nakamura, Michio Maruta, Hyuma Makizako, Masaaki Miyata, Hironori Miyata, Gwanghee Han, Yuriko Ikeda, Suguru Shimokihara, Keiichiro Tokuda, Takuro Kubozono, Mitsuru Ohishi and Takayuki Tabira**

Meaningful Activities and Psychosomatic Functions in Japanese Older Adults after Driving Cessation

Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 13270, doi:10.3390/ijerph182413270 . **229**

#### **Antonina Kaczorowska, Katarzyna Szwamel, Małgorzata Fortuna, Agata Mroczek, Ewelina Lepsy and Aleksandra Katan**

Assessment of Physical Fitness and Risk Factors for the Occurrence of the Frailty Syndrome among Social Welfare Homes' Residents over 60 Years of Age in Poland Reprinted from: *Int. J. Environ. Res. Public Health* **2022**, *19*, 7449, doi:10.3390/ijerph19127449 . . . **243**

#### **Ziyi Wang, Deyu Meng, Shichun He, Hongzhi Guo, Zhibo Tian, Meiqi Wei, Guang Yang and Ziheng Wang**

The Effectiveness of a Hybrid Exercise Program on the Physical Fitness of Frail Elderly Reprinted from: *Int. J. Environ. Res. Public Health* **2022**, *19*, 11063, doi:10.3390/ijerph191711063 . **261**

International Journal of *Environmental Research and Public Health*

## *Review* **Meta-Analysis on the E**ff**ects of Transcranial Direct Current Stimulation on Naming of Elderly with Primary Progressive Aphasia**

#### **Haewon Byeon**

Department of Speech Language Pathology, School of Public Health, Honam University, 417, Eodeung-daero, Gwangsan-gu, Gwangju 62399, Korea<EMAIL_ADDRESS>Tel.: +82-10-7404-6969

Received: 20 January 2020; Accepted: 7 February 2020; Published: 9 February 2020

**Abstract:** *Purpose*: This study aimed to conduct a qualitative evaluation by synthesizing previous studies on the effect of transcranial direct current stimulation (tDCS) on primary progressive aphasia (PPA)'s naming ability and prove the effects of tDCS mediation on PPA naming using meta-analysis. *Methods*: This study searched literature published from January 2000 to July 2019 using four academic databases (i.e., PubMed, Web of Science, MEDLINE, and Cochrane Library). The final seven publications were systematically evaluated and meta-analysis was conducted for two papers. The effect size was estimated by a standard mean difference (SMD) using Hedge's g, and the significance of effect size was confirmed using the 95% confidence interval. *Results*: The results of seven previous studies' quality assessments ranged from 15 to 26, which were rated above adequate. The results of the meta-analysis showed that the effect size was 0.82 (95% CI: 0.16–1.47), which was a significant 'large effect'. *Conclusions*: This meta-analysis proved that tDCS intervention significantly improved the naming performance of PPA. Future studies must confirm the effects of tDCS on naming intervention by using meta-analysis including many RCT studies.

**Keywords:** brain stimulation; dementia; meta-analysis; naming; primary progressive aphasia; qualitative evaluation

#### **1. Introduction**

Naming is widely used as a representative screening test for determining communication disorders around the world. Naming is divided into confrontation naming and generative naming [1]. The confrontation naming requires the complex coordination of visual stimuli, object recognition, linguistic system, vocabulary system, and phonological production system and it is affected by the function of the temporal lobe [1]. If the brain area associated with word recall is damaged, the confrontation naming function will be compromised [2,3].

On the other hand, generative naming is an evaluation that produces words in a specific category during a given time [4]. It is composed of semantic fluency, which expresses words associated with a presented category (e.g., animal) voluntarily, and phonemic fluency, which speaks given phonemes (e.g., words beginning with 'k') voluntarily [5]. Generation naming, unlike confrontation naming requiring the role of the temporal lobe, is affected by the frontal lobe's executive function, which searches for information and yields words using presented clues [6]. In particular, confrontation naming drew attention as an indicator for detecting neurolingual disorders as soon as possible and measuring the recovery of them because naming is the communication problem that commonly remains until the last recovery stage for patients with fluent aphasia and those with nonfluent aphasia [7]. It has been used as the most representative test for determining the communication problems of patients with neurolingual disorders [7].

Recent studies have reported that naming is one of the most prominent language problems due to dementia [8,9]. Although the degeneration of naming abilities occurs at a different stage depending on dementia types [10], it is a common language deficit of most dementia patients and naming issues are observed from the incipient stage [11]. Particularly, primary progressive aphasia (PPA), a type of dementia, is a neurological dysphasia associated with temporal lobe atrophy and it is different from other dementia types (e.g., Alzheimer's disease) in the aspect that a language defect occurs ahead of a cognitive ability defect [12]. PPA draws attention because of language disorders such as naming, advance gradually, unlike the aphasia, a neurogenic language disorder. In other words, PPA gradually loses naming abilities such as verbal fluency while maintaining other communication abilities such as articulation ability. Therefore, naming is an important indicator in identifying and intervening PPA in the early stage and many researchers have been interested in this topic due to this reason [13].

 It has many advantages: It is inexpensive compared to rTMS, is portable because it is light, and does not require a specific posture in the course of treatment [15].

Many studies have proved the effectiveness of tDCS since 2010, and meta-studies are actively conducted in recent years to establish the basis of tDCS [16–18]. In the early stages of development, tDCS was used mainly in the fields of exercise rehabilitation and mental health (e.g., schizophrenia and depression) [17]. However, the use of tDCS tended to increase in recent years as a tool for the linguistic mediation of patients with a neurological impairment such as aphasia and dementia [18]. However, since the research trends to date are mainly limited to the fields of exercise rehabilitation and mental health, more studies are needed to prove the effectiveness of tDCS on dementia.

Up to date, the effects of tDCS on cognition and linguistic abilities are still controversial [19] and, above all, no common implications have been drawn to improve the language ability of PPA. Therefore, it is needed to prove the therapeutic effect of PPA scientifically. This study aimed to conduct a qualitative evaluation by synthesizing previous studies on the effect of tDCS mediation on PPA's naming ability and prove the effects of tDCS mediation on PPA naming using meta-analysis.

#### **2. Methods**

This study carried out systematic analysis and meta-analysis in the process of research question selection, systematic literature search and selection, quality evaluation of literature, data extraction and coding, data analysis, and result report preparation.

#### *2.1. Literature Search*

This study searched literature published from January 2000 to July 2019 using four academic databases (i.e., PubMed, Web of Science, MEDLINE, and Cochrane Library). The search terms included 'Dementia', 'Primary progressive aphasia', 'Neurodegenerative diseases', 'Transcranial direct current stimulation', 'tDCS', 'Naming', 'Generative naming', 'Naming ability', 'Confrontational naming', 'Responsive naming', 'Semantic fluency', 'Verbal fluency', 'Phonemic fluency', 'Executive function', 'Cognitive rehabilitation', 'Cognitive training', 'Language recovery', and 'Language therapy'.

#### *2.2. Literature Selection*

The literature was selected based on the Patient–Intervention–Comparison–Outcome–Study design (PICOS) [20] of the PRISMA protocol. The selection and exclusion of the searched literature were conducted by three researchers independently. When there is a discrepancy in selection and exclusion, the three researchers discussed whether the publication should be included in or excluded from the systematic review or now. The inclusion criteria of this study were (1) studies conducted on PPA, (2) studies confirming the effects of tDCS, (3) experimental studies, and (4) studies published in

English. This study excluded qualitative studies, unpublished publications including dissertations, and articles published in other languages such as French, German, and Chinese.

This study found 132 publications in total. In the first step, 31 duplicated publications were excluded by comparing titles and abstracts. Moreover, 53 publications not related to the study topic were excluded. In the second step, the full texts of the remaining 48 publications were carefully examined and 41 publications were excluded. The excluded studies were non-experimental studies (n = 12), those without original full text (n = 3), those not evaluating dementia (n = 17), and those with inaccurate outcomes (n = 9). As a result, the final seven publications were systematically evaluated and meta-analysis was conducted for two papers, which we could extract representative values. The flow diagram of this study is shown in Figure 1.

**Figure 1.** The flow diagram of this study.

#### *2.3. Quality Assessment*

This study used "Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields [21]" for quality assessment. This evaluation tool measured scores using a three-point scale (Yes = 2, Partial = 1, No = 0, N/A) and summed the scores of 14 evaluation items. The total score was converted into a percentage value and divided into strong (>80%), good (70–80%), adequate (50–69%), and limited (<50%) to examine the overall quality of studies [22]. The quality assessment of studies was performed by two researchers independently. If there is a discrepancy in the quality assessment item of a specific study, the final score was determined by discussion.

#### *2.4. Meta-Analysis*

This study extracted the analysis data of the selected publications and conducted meta-analyses for publications that could be statistically integrated using R version 3.4.2 (Foundation for Statistical Computing, Vienna, Austria). The representative values used for the analysis were estimated by calculating the difference between the treatment group's mean and the control group's mean and the mean differences normalized by standard deviations. The mean differences normalized by standard deviations were calculated according to Equation (1).

$$\sqrt{\text{S1}\_{pve}^2 + \text{S1}\_{post}^2 - \left(2 \times \text{Corr} \times \text{S1}\_{pve} \times \text{S1}\_{post}\right)}\tag{1}$$

The effect size was estimated by a standard mean difference (SMD) using Hedge's g, and the significance of effect size was confirmed using the 95% confidence interval. The calculated effect size was interpreted as 'small effect' when it was smaller than 0.32, 'middle effect' when it was between 0.33 and 0.55, and 'big effect' when it was 0.56 or higher. Publication bias could not be estimated because target publications were less than 10.

#### **3. Results**

#### *3.1. Quality Assessment Results*

The quality assessment results of this study are presented in Table 1. The results of seven previous studies' quality assessments ranged from 15 to 26, which were rated above adequate. All seven studies systematically presented the 'objective of study', 'research design', and 'conclusion' suitable for each item. Six studies, except one study [23], described the procedure of random allocation in the methodology section. While conducting studies, three studies [24–26] blinded researchers and four studies [24,26–28] blinded subjects. Six studies [23–28], except for [29], described the measurement methods and evaluation tools in detail. However, only one study [25] conducted a power test before starting the experiment. Additionally, only two studies controlled confounding variables [24,28].

**Table 1.** Results of the publication's quality assessment.

#### *3.2. E*ff*ects of tDCS on Improving the Naming Ability for PPA*

The effects of tDCS on improving the naming ability for PPA were analyzed and the results are presented in Table 2. Ficek et al. (2018) [25] examined the combined effects of tDCS and speech therapy on 24 patients with PPA using letter accuracy. Their results showed that letter accuracy improved for the tDCS group and the placebo stimulation group but the improvement of the tDCS group was significantly larger. Hung et al. (2017) [23] evaluated the accuracy of naming by combining semantic feature training and tDCS intervention for patients with PPA and those with alzheimer's disease (AD). Hung et al. (2017) [23] tested the intervention effect by dividing the results of the compounded intervention into trained items and untrained items. It was found that the trained items had higher accuracy than the untrained items after tDCS intervention and the effect was maintained until the follow-up period. Tsapkini et al. (2014) [27] evaluated the compound effects of spelling intervention and tDCS for six patients with PPA. In the untrained spelling item, the group which received tDCS and spelling intervention maintained the improved ability longer than the group which received placebo stimulation and spelling intervention. Tsapkini et al. (2018) also examined the combined effects of tDCS and naming/spelling intervention on 36 patients with PPA and reported that the trained items of the tDCS group were significantly improved immediately after the intervention. The difference between the trained words and the untrained words increased for the tDCS group and the placebo stimulation group as time goes on.

TheffectsoftDCSnamingimprovementfor

*Int. J. Environ. Res. Public Health* **2020**, *17*, 1095

crossover n = 15)

#### *3.3. Meta-analysis for the E*ff*ects of tDCS Intervention on the Naming Performance of Patients with PPA*

SMD about the effects of tDCS intervention on naming performance was analyzed (Figure 2). The results showed that the effect size was 0.82 (95% CI: 0.16–1.47), which was a significant 'large effect'.

**Figure 2.** Effects of transcranial direct current stimulation (tDCS) intervention on patients with primary progressive aphasia (PPA's) naming performance.

#### **4. Discussion**

This study conducted systematic reviews and meta-analysis to establish the scientific basis regarding the effect of tDCS on PPA's naming ability based on literature published from January 2000 to May 2019. This study evaluated the quality of seven studies and found that, even though most of them were designed as RCT studies and blinded either researchers or subjects, only one study [25] conducted power analysis and only two studies controlled confounding variables [24,28]. Since the sample size bias has the possibility to distort the results of studies, it is recommended to carry out RCT studies that estimate sample size and control confounding variables before designing studies in the future.

This study conducted pre- and post-meta-analysis and found that tDCS intervention had a significant effect on improving PPA's naming ability. PPA is a degenerative disease that causes linguistic problems such as naming ahead of cognitive problems such as orientation and visuospatial abilities [12,25,30]. The problem of naming ability is clearly observed from the incipient stage [12]. PPA may be classified as speech logopenic progressive aphasia, semantic dementia, or progressive nonfluent aphasia [31]. Naming ability decreases in patients with PPA regardless of PPA types [32]. PPA shows the deficiency of linguistic ability primarily and tDCS may have a significant effect on the PPA's naming performance.

It is known that tDCS promotes and inhibits the spontaneous activity of the cranial nerve by stimulating with minute DC current through the scalp and making the DC reach the cerebral cortex [33]. In other words, tDCS stimulates the brain with a weak current below 2 mA to regulate the resting membrane potential voltage and induces changes in the spontaneous discharge rate of nerve cells and the activation of N-methyl-D-aspartic acid (NMDA) receptor [33]. However, how tDCS improves naming is not clearly known because the effectiveness of tDCS began to be evaluated in very recent years and there are no large-scale and long-term follow-up studies that evaluated the effects of tDCS on the improvement of naming [34]. Nevertheless, the results of this meta-analysis show that tDCS had a significant effect on improving PPA's naming performance suggested tDCS could be an effective language mediator of PPA. Long-term follow-up studies will be needed to identify the effects of tDCS fully.

The importance of this study was that this study established the scientific foundation to evaluate the effects of tDCS on the naming ability of PPA. The limitations of this study are as follows. First, although this study collected and analyzed literature through various academic databases, this study only evaluated publications written in English and excluded papers written in other languages such as French and Chinese. Second, this study could not conduct a bias test because meta-analysis only analyzed two studies and there was a limit in proving the results. If the sample is small, the variance and standard deviation of individual studies become relatively large, which affects the confidence interval of the overall effect size and increases type II error. However, it is believed that the bias due to

the small sample size was negligible because this study confirmed that the effects of tDCS intervention on PPA's naming performance were a significant 'big effect'. In the future, meta-analysis containing more samples is required.

#### **5. Conclusions**

This meta-analysis proved that tDCS intervention significantly improved the naming performance of PPA. However, the results should be generalized very carefully because the meta-analysis was conducted on only a few samples. Therefore, future studies must confirm the effects of tDCS on naming intervention by using meta-analysis using many RCT studies.

**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07041091, NRF-2019S1A5A8034211).

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**

© 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Review* **Machine Learning Approaches for the Frailty Screening: A Narrative Review**

**Eduarda Oliosi 1,2, Federico Guede-Fernández 1,2 and Ana Londral 1,3,\***

**Abstract:** Frailty characterizes a state of impairments that increases the risk of adverse health outcomes such as physical limitation, lower quality of life, and premature death. Frailty prevention, early screening, and management of potential existing conditions are essential and impact the elderly population positively and on society. Advanced machine learning (ML) processing methods are one of healthcare's fastest developing scientific and technical areas. Although research studies are being conducted in a controlled environment, their translation into the real world (clinical setting, which is often dynamic) is challenging. This paper presents a narrative review of the procedures for the frailty screening applied to the innovative tools, focusing on indicators and ML approaches. It results in six selected studies. Support vector machine was the most often used ML method. These methods apparently can identify several risk factors to predict pre-frail or frailty. Even so, there are some limitations (e.g., quality data), but they have enormous potential to detect frailty early.

**Citation:** Oliosi, E.;

Guede-Fernández, F.; Londral, A. Machine Learning Approaches for the Frailty Screening: A Narrative Review. *Int. J. Environ. Res. Public Health* **2022**, *19*, 8825. https:// doi.org/10.3390/ijerph19148825

Academic Editors: Haewon Byeon and Jaewon Nah

Received: 7 June 2022 Accepted: 19 July 2022 Published: 20 July 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Keywords:** frailty; indicators; screening; artificial intelligence; healthcare

#### **1. Introduction**

With the growing aging population worldwide, an important subject matter is "frailty" (or fragility) which is closely age-related [1]. Living longer can lead to a longer period of frailty with increased demand for care [2]. The proportion of the elderly is expected to be approximately 30% of the population by 2060, in Europe [3]. Moreover, aging expenditures are projected to increase by 1.5 percentage points of GDP, from 26.8% in 2013 to 28.3%, in 2060 [4]. Frailty is a broad term used to denote a complex clinical condition [5,6] that can be defined as a medical syndrome caused by multisystem dysregulation and contributors. In addition, it is characterized by loss of health reserves (e.g., physical fitness), reduced physiologic function, and impaired homeostasis, which increases an individual's vulnerability, resulting in risk for early dependency, morbidity, and/or death when exposed to stressors. In summary, the frailty syndrome involves the main domains: physical, psychological, social, cognitive, and environmental [6–11]. Moreover, frailty characterizes a state of impairments that increase the risk of negative health outcomes such as physical limitation, falls, fractures, disability, morbidity, dependence, hospitalization, institutionalization, lower quality of life, and premature death [4,12–15].

Barriers to implementing frailty screening in clinical settings still exist as a lack of consensus on the assessment tool best suited to each domain and undetermined costeffectiveness [16]. Moreover, it is imperative to note that the frailty assessment tools can provide different data regarding the incidence of frailty [17]. The identification of frailty might seem an ideal way to identify the elderly who need additional healthcare support services. In a recent review, Liotta and colleagues (2018), from a public health perspective, stressed that it is vital to identify factors that contribute to successful health and social care interventions and to the health systems' sustainability [18]. Nevertheless, there is a lack of substantial research evidence to support this strategy and to identify the most effective tools to detect frailty [10]. In addition, there is no consensus about the key components and assessment of frailty [19].

In a systematic review, Sutton and colleagues (2016) identified 38 multi-component frailty assessment tools where, surprisingly, only 5% (2/38) of the frailty assessment tools had evidence of reliability and validity that was within statistically significant parameters and of fair–excellent methodological quality: the Frailty Index—Comprehensive Geriatric Assessment and the Tilburg Frailty Indicator [20]. In addition, a score or set of criteria was used, developed, and validated to identify frailty. The most common frailty instruments used in research and clinical practice are the Fried frailty phenotype (FP), which is based on five items (slow walking speed, weak grip strength, low physical activity, unintended weight loss, and exhaustion), minimum of three of five criteria for classifying as frailty [21–23]. Nevertheless, there is insufficient evidence to determine the best tool for use in research and clinical practice [20].

According to an umbrella review, despite these broadly used conventional methods, few frailty measures seem to be valid, reliable, diagnostically accurate, and virtuous predictive abilities. Moreover, they reported that the Frailty Index (and gait speed) emerged as the most useful in routine care and community settings [24]. The traditional measurements of frailty have potential limitations and challenges: for example, single measures of physical performance (such as timed-get-up-and-go) or a set of physical features (such as FP) are clinically suitable and validated to predict poor outcomes in older adults. Nevertheless, they have shown low consistency, accuracy, reliability, and inter-rater understanding. Moreover, these measures require specialized equipment (e.g., dynamometer to grip strength), not always clinically viable (e.g., for patients with dementia), and also require a manual evaluation process (e.g., timed-get-up-and-go) that is subject to operator error due to the need for training beyond time to administer [25]. Furthermore, the prevalence of frailty varies across settings and adopted tests, making it difficult to scale to the population level [21,22,25,26]. In this view, an alternative is exploring approaches to screening frailty from routinely collected data (e.g., medical claims, prescriptions, administrative data, and individual records) [25].

The presented work is part of the Frailcare.AI project. Its primary objective is to develop intelligent tools that aim to improve pathways for the identification of fragility in senior citizens in the Portuguese population. This paper aims to review tools and clinical indicators for identifying early frailty and supply evidence for developing innovative tools and artificial intelligence (AI) technologies to support frailty care. This review provides recent evidence for the assessment and screening of frailty. It reviews the existing tools and clinical indicators for complex frailty, focusing on measures extracted from healthcare datasets. We seek to improve knowledge and application opportunities for machine learning (ML). This intelligent screening tool relies on an approach that includes ML methods.

#### *1.1. Background*

ML methods can adapt conventional frailty screening methods validated in previous studies. While AI is a subfield of computer science dedicated to providing computers with intelligent problem-solving capabilities, including planning, reasoning, perception, or learning (i.e., AI aims to mimic human intelligence and behavior through systems), ML, a subfield of AI, provides algorithms that build mathematical models based on sampled data. These models map input data to desired outputs. Inputs can be images and an arbitrary sequence of numerical or categorical data. The inputs are also known as features [27–30].

The AI resource includes advanced algorithms and methods that do not even process quantitative data. Consequently, comparing ML to traditional statistical methods

makes it coherent. Conventional statistical models focus on discovering interactions and confidence intervals between data points and outcomes; comparatively, ML approaches seek to reach high prediction accuracy, placing less emphasis on whether it is possible to interpret the model. Prediction is critical in ML to generate otherwise unavailable data. Moreover, ML is often better fitting for significant input variables (e.g., time series from biosignals), and the traditional analysis with statistical models is intended for data with tens of input columns [28].

#### 1.1.1. Decision Trees

Decision tree (DT) classification is broadly used for different classification tasks (for example, pattern recognition). DTs make their decisions from the root, all the way up to the branches. The DT approach essentially partitions the space into subspaces by computing the decision boundaries for each node, and it continues adding inputs to the tree nodes until no further improvement can be made to the prediction results. The leaf nodes in the decision tree are labeled according to the groups in the classification problem [31,32].

#### 1.1.2. K-Nearest Neighbours

K-nearest neighbor (KNN) is among the generally used classification approaches. Its algorithm does not create any model through learning strategies. Its training is based on sorting the class labels of the training dataset together with the feature vectors for each record. The accuracy of this model is comparable to more complicated classifiers [31,33].

#### 1.1.3. Support Vector Machine

Support vector machine (SVM) is a supervised classification algorithm; in supervised learning, the models are trained based on given examples, containing inputs and desired outputs provided by an expert (e.g., physical therapist). The SVM has been applied to many real-world classification problems because of its effectiveness, such as pattern recognition for text classification and bioinformatics systems. SVMs are robust to overfitting and have a prominent generalization capability, as well as being good at handling complex, nonlinear scenarios and tending not to overfit. Moreover, SVM is robust to bias and variance of data and results in accurate predictions for either binary or multiclass classifications. As such, SVM has been broadly used in health research, for example, to identify imaging biomarkers of neurological and psychiatric disease, cancer diagnosis, and early detection of Alzheimer's, among others [29,31–34].

#### 1.1.4. Artificial Neural Networks

As a brain's neurons, the artificial neural networks (ANN or NN) are a class of nonlinear statistical algorithms modeled, able to process information. Thus, this approach is defined by how the components of the network are linked and the weights of these connections. This learning process constructs derived parameters as linear combinations of the input parameters and then further models the outcome as a nonlinear constructions of these derived parameters. Although they are excellent at handling many inputs, they are rather computationally costly [32].

#### 1.1.5. Random Forest

The random forest (RF) consists of many decision trees that operate as an ensemble. Each tree provides a class prediction, and the prediction with the most votes turns into the overall model prediction. Therefore, this method is a random forest consisting of a set of individual decision trees; hence, individual errors of the trees are decreased. RF results in a good performance on imbalanced datasets while handling missing values well. These models are not substantially affected by outliers in data. Such decision trees are designed to have a low correlation to each other to encourage range among the trees. Moreover, RFs

use the rules of bootstrapping and aggregating to build trees based on several subsets of the training data using different subsets of features [21,35].

#### 1.1.6. Extreme Gradient Boosting

Extreme gradient boosting (XGBoost) is a supervised machine learning model. This method builds a robust model created on weaker models that are short decision trees. The XGBoost works on building a new weak model designed to predict the residual values between the ground truth and the robust model. These weak models are then added to the overall robust model. The predictions of the models are added simultaneously to make the final prediction. The main benefits are execution speed and model performance. These models use boosting, an ensemble method where each tree or model corrects errors made by earlier trees. XGBoost requires minimal feature engineering, allowing steps such as normalizations and scaling to be omitted, and outliers have little impact [21,35,36].

#### **2. Methods**

#### *Search Strategy and Data Extraction*

Studies were sought using general (Web of Science and Google Scholar) and healthcare (PubMed and The Lancet) databases. Two independent reviewers reviewed all the titles and abstracts in the first selection step. Three keywords were used without period restriction: Frailty screening, as this was the focus of this review, artificial intelligence (AI), and machine learning because ML was considered a subarea of AI. The study inclusion criteria were (i) it described frailty screening tools; (ii) the population was presented with pre-frail or frail conditions/concepts; (iii) studies about frailty indicators, validity studies, articles on frailty screening (frailty assessment, detection, or prediction), and contained significant determinants of frailty; (iv) or if they had a combination of all these criteria. The exclusion criteria were (i) frailty studies about intervention or prevalence; (ii) frailty screening through the inertial sensors; (iii) non-peer-reviewed and academic studies; (iv) all types of reviews (e.g., umbrella and systematic) or case reports or non-English language. There is no existing restriction to frailty screening assessment tools.

#### **3. Results**

The selection process produced six studies relevant to the aim of this review. Table 1 provides an overview of the selected studies in the frailty screening for ML methods. In general, all studies classified frailty with only one tool, such as the Rockwood Clinical Frailty Scale (CFS) [36], electronic Frailty Index (eFI) [31]; frailty phenotype (FP) [37]; electronic Frailty Score (eFS) [5]; and an exception that utilized a combination of tools [21], which included FRS-26-ICD (frailty drawn from ICD-10 Clinical Modification), ECI (The Elixhauser Comorbidity Index (ECI), high-risk medications (10 risk classification, Beers Criteria, 2019), sociodemographic characteristics, healthcare, and insurance utilization. Another exception used a set of predictors variables, including clinical and socioeconomic aspects, and six target variables (mortality, disability, urgent hospitalization, fracture, preventable hospitalization, and accessing the emergency department with red code) [38].

ML algorithms have been used to predict frailty-derived indicators based on healthrelated data. The eFI, which is based on the deficit accumulation approach, was predicted using several ML algorithms such as DT, KNN, and SVM [31]. They analyzed the data of 592 patients and the best performance was obtained with SVM, the accuracy was 93.5%, sensitivity 97.8%, and specificity 89.1%. The SVM algorithm requires 70 input variables and they remarked that SVM may prove less feasible in clinical scenarios where rule-based models, such as DT models, may be more interpretable to clinicians but the results in terms of accuracy are the poorest (42.4%) with DT models.

Aponte-Hao, in 2021, proposed to use ML algorithms to predict the CFS score based on two-year electronic medical records (EMR). The CFS ranges from one to nine, with one having the label of "very fit" and nine labeled "terminally ill" (the highest degree of frailty); the frailty was predicted using a dichotomized indicator into frail or not frail with a cut-off of five from the original physician-rated CFS score. After the removal of features with low variance or high correlation, they reduced the total number of features from 5466 to 75. They used DT, LR, SVM, NB, NN, KNN, RF, and XGBoost models, and the XGBoost was the model with the best results of the eight models which were developed; it achieved the highest sensitivity (78.14%) and specificity (74.41%), but the F1-score was not shown when they used the best threshold that was achieved by using the most optimal thresholds determined using ROC curves [36].

An ML-based tool for stratification of FP based on one-year hospital discharge data was developed and validated (Pogam 2022). They created a clinical knowledge-driven eFS calculated as the number of deficient organs/systems among 18 critical ones identified from the ICD-10 diagnoses coded in the year before FP assessment. In addition, for eFS development and internal validation, they linked individual records of the cohort database to inpatient discharge data for an 11-year period. The best-performing model for predicting the dichotomized FP was the LR model with four predictors: age and sex at FP assessment, time since last discharge, and the eFS. The eFS score was associated with all adverse health outcomes of interest (death, prolonged length of hospital stay, number of hospitalizations, and nursing home admission within 12 months after FP assessment). They also conducted an external validation which confirmed that the eFS was a significant predictor of the 13 adverse outcomes [5].

Six frailty conditions (mortality, urgent hospitalization, disability, fracture, and emergency admission) were predicted with ML models (Tarekegn 2020). These models were assessed with a dataset that contains 1,095,612 subjects and 64 variables (58 input and 6 output variables). They resolved the imbalanced nature of the data through a resampling process and they performed a comparative study between the different ML algorithms: ANN, genetic programming (GP), SVM, DT, and RF. The obtained results show that the prediction performance of ML models significantly varies from problem to problem in terms of different evaluation metrics. The mortality prediction outcome showed higher performance with ANN (F1-score 0.79) and SVM (F1-score 0.78) than predicting the other outcomes. On average, over the six problems, the DT classifier showed the lowest accuracy, while other models (GP, LR, RF, ANN, and SVM) performed better. All models showed lower accuracy in predicting an event of an emergency admission with a red code than predicting fracture and disability. In predicting urgent hospitalization, only SVM achieved better performance (F1-score 0.76) [38].

ML models were also developed for predicting 30-day unplanned readmissions for elderly patients by integrating variables such as frailty and comorbidities (Mohanty 2022). The models were developed with data from 68,152 patients, consisting of 18,840 readmissions and 109,741 non-readmissions and containing 458 variables that were used for the prediction of readmission. The ML models compared were RF, XGBoost, CatBoost, and logistic regression, and a stacking classifier CatBoost outperformed the other models with an AUROC of 79% and F1 score of 71%. They performed an in-depth study of the model explainability by assessing the feature importance by means of the SHAP methods [21].

Moreover, a deep learning approach was followed to classify pre-frail/frail vs. nonfrail older adults using heart rate response to physical activity [37]. They compared resting-state heart rate characteristics with heart rate monitoring without controlling for physical activities, the objective of the study. They assessed the performance of ML and deep learning models such as LSTM. The obtained results showed that LSTM outperformed other approaches. These results were obtained with a reduced sample size of 88 patients. This work shows that heart rate dynamics classification using LSTM deep learning models without any feature engineering may provide an accurate and objective marker for frailty screening [37].

16

GP: Genetic validation; IR Lc65+ CHUV: Individual Records Lc65+ cohort database to inpatient discharge data from Lausanne University Hospital (CHUV); KNN: K-nearest neighbors; Lasso-LR:

Lasso-penalized

Scales; PBC: Physical Behavior Checklist; RF: Random forest; SC: Stacking classifier; SVM: Support vector machine; VBC: Verbal Behavior Checklist; WC: Wandering checklist; XGBoost:

Extreme gradient boosting.

 logistic regression; LR: Logistic regression; LSTM: Long short-term memory; MLP: Multilayer perceptron; NB: Naive Bayes; PAS Score:

programming;

 HIU: Healthcare and insurance utilization; HR: Heart rate; HR-M: High-risk medications (Beers Criteria: 2019); German Modification;

 Int valid: Internal

Psychogeriatric

 Assessment

#### **4. Discussion**

This paper presents a literature review of screening tools and clinical indicators for identifying early frailty and provides evidence for developing innovative tools through the focus on artificial intelligence. To our knowledge, this is the first narrative review summarizing and discussing frailty and ML for frailty screening. However, previous related research has been published on the relevance of the role in osteoporosis of AI models to model the risk of fragility fracture [32]. The selected studies were delivered between 2020 and 2022.

As mentioned earlier, the condition of frailty involves many domains that are not always easily identified, as well as the differences between them (e.g., cognitive, and physical domains). There are various instruments and identification criteria, thus hindering an accurate evaluation. Therefore, approaches that encompass all (or most) of these domains become relevant since they seem to have relevance in the early identification of the frail condition. Thus, ML is a promising approach, supported by recent studies.

The main findings of this study are that older age, females, clinical conditions (such as arthritis, hypertension, osteoporosis, and diabetes), high use of healthcare utilization, and adverse health outcomes (such as fractures, prolonged length of hospital stay, and number of hospitalizations) were the most significant predictive variables for the screening outcomes in frail persons. Previous studies reported that frailty was the most important predictor of rehospitalization and the second most important predictor of mortality in patients with cardiovascular disease [39]. Not surprisingly, the sociodemographic questions revealed importance. According to other studies, sociodemographic variables, namely, age and gender, are significant features [16,40]. Furthermore, in another recent study, age was the most important variable in predicting 90-day mortality and the second-most important variable for 30-day mortality [41].

The unsupervised learning methods are often used to process large databases, such as EMRs or large patient cohorts. Then, they can also cluster patients (subdividing them into groups) and characterize outliers or other essential features. Online electronic diagnosis systems are increasingly used by the population and healthcare professionals to a lesser extent. Most symptom checkers are ruled-based systems based on simple (conventional methods) decision trees. Therefore, ML is increasingly applied to EMRs in various health fields because they contain large, heterogeneous datasets that can be used to train disease detection or classification approaches using the supervised learning method [29].

Regarding algorithms, SVM was the most often applied ML method for frailty screening [5,31,36,38]. SVMs are competent in finding the best possible separation of different categories by familiarizing the weights of polynomial functions. ML models are typically trained using EMR or national cohorts. However, they require challenges to be applied effectively to information: the quantity and quality of the data. For example, deep neural networks commonly require massive training sets. Therefore, poor-quality training data (e.g., missing values) from EMRs will reduce the model's overall quality [29]. Thus, ML has already shown clinically practical applications in frailty screening. It has the potential to support specialists in clinical and foster personalized health. Combined databases have the tremendous potential to provide sufficient data [29] because AI "feeds" on data. The more and better-quality data it accesses, the more it can excel at tasks. Some advanced algorithms need annotated data to ensure that those can learn. These annotated data depend on the health professionals. Relying on the algorithm utilized, they could require lots of annotated data. Thus, the dedicated contribution of "data annotators" is critical for the benefit of implementing AI in healthcare systems, as well as established standard methods to report data [28]. As is the case, Aponte Hao and colleagues include the RECORD Statement [42], promoting quality and transparency [36].

The accomplishment of AI and its place in clinical practice and healthcare depends on whether it can infiltrate the boundaries of an evidence-based approach, the lack of policies, and the lack of enthusiasm of health professionals to use it. On the other hand, the demand

for AI to be implemented into everyday health professions is increasing among researchers, policymakers, clinical professionals, patients, hospitals, and developers. Therefore, it is essential to an integrated and appropriate multidisciplinary approach [28].

Some limitations regarding the data quality that impact the results should be noted. Although it is a validated tool for screening frailty, the CFS could not be advantageous to AI, because of the dichotomization, which fails to capture the severity of diseases, i.e., a person classified with pre-frail could be classified in the same category as a person with severe frailty. Therefore, an alternative could be to evaluate CFS as a continuous variable, bringing the distribution underlying the distribution of classes closer to the distribution of classes, then generating decision limits for the transformation back to the ordinal CFS to evaluate performance [36]. As well as in the CFS, the FRS-26 also be prone to bias in the quality of data and incapacitation of capturing the severity of symptoms [21]. In addition, the eFP, which could not classify frailty adequately, is often thought to reduce frailty to physical deficiencies and ignore mental and cognitive health problems [5]. In another study, they also merged pre-frail and fragile groups into a single group due to the limited number of fragile participants, and the size of the data (time series) was transformed to improve quality [37]. Moreover, other potential limitations include extensive missing data and test data being relatively small [31].

The accuracy of AI-generated results is highly dependent on the quality of the input data. Whether frailty is identified via the ML methods, very-high-quality data must be utilized if identification is ultimately proven accurate [31,41]. Further, efforts devoted to increasing the quality of the input data, such as standardized codes rather than free text and regular attention to data cleansing, may substantially improve the accuracy of the result obtained. The limited availability of high-quality data for training correctly labeled in medical claims, lack of detailed physiologic information, and indicators of the severity of comorbidities are inconsistently assigned, leading to a training set with underprivileged reproducibility and no "ground truth" to learn associations [28].

The heterogeneity of models makes it difficult to understand how accurate these methods might be in clinical practice or how reproducible they are in various clinical environments. The successful application of AI within the healthcare sphere does not remove the requirement for maintaining the quality of databases; instead, it is dependent on such activities. Another limitation is the studies using the codes ICD-10. The codes do not fully capture disease severity and might also miss out on essential elements of frailty such as weakness, polypharmacy, and need for support in everyday living. In addition, the potential variation in documentation and coding of diagnoses could contribute to measurement error (e.g., routine diagnosis and documentation of conditions such as delirium vary between clinicians and/or hospitals) [26].

In the future, with rapidly advanced wearables and monitoring technologies, we suggest researching other available frailty indicators—for example, using biosignals for postural control, gait assessment, and home-based frailty assessment. These types of data are also easily adapted to AI. They could be practical and feasible, such as falls prevention, an essential issue for frailty screening [15,33,43–48].

#### **5. Conclusions**

This review explores the tools and clinical indicators for frailty assessment and screening, through AI-based innovative tools. These existing tools, and clinical indicators for complex frailty, focusing on measures extracted from healthcare datasets were reviewed. The typical "health-professionally dependent" approaches for frailty screening could be adapted for technology-based approaches, such as eFI. The potential of AI techniques was explored; according to our findings, these methods can be used to identify risk factors to predict pre-frail or frailty. Thus, they facilitate the process to find the best treatment strategies for a person as well as frailty screening at the public health level. We suggest that databases collected from different populations be shared for improving the AI-based models.

This narrative review described the complex condition of frailty involving multi factor and summarized the indicators and the tools that were most used in the recent literature, as well as the AI models and the accuracies—making it easier for the developer and clinical to infer important data/variables for screening frailty. This review aims not to compare methods but to investigate the evidence for frailty screening. It was possible to conclude that the potential for ML to focus on frailty is immense, and offers an overabundance of new opportunities [29].

**Author Contributions:** E.O. and F.G.-F. drafted the text and selected the articles to be revised. E.O., F.G.-F. and A.L. were involved in the concept of the review. A.L. critically revised and edited the final report. All authors have read and approved the final version of the manuscript.

**Funding:** This research was supported by Fundação para a Ciência e Tecnologia (FCT) under Frail.Care.AI project (DSAIPA/AI/0106/2019) and CardioFollow.AI project (DSAIPA/AI/0094/2020).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The authors would like to express thanks to Fundação para a Ciência e Tecnologia AI 4 COVID-19 Program for research support.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

International Journal of *Environmental Research and Public Health*

## *Article* **Is the Random Forest Algorithm Suitable for Predicting Parkinson's Disease with Mild Cognitive Impairment out of Parkinson's Disease with Normal Cognition?**

#### **Haewon Byeon**

Department of Speech Language Pathology, School of Public Health, Honam University, Gwangju 62399, Korea<EMAIL_ADDRESS>
Received: 6 March 2020; Accepted: 7 April 2020; Published: 10 April 2020

**Abstract:** Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson's disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions using the Parkinson's Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention) and to compare the prediction accuracy of our model with those of decision tree and multiple logistic regression models. We analyzed 96 subjects (PD-MCI = 45; Parkinson's disease with normal cognition (PD-NC) = 51 subjects). The prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity. Based on the random forest analysis, the major risk factors of PD-MCI were, in descending order of magnitude, Clinical Dementia Rating (CDR) sum of boxes, Untitled Parkinson's Disease Rating (UPDRS) motor score, the Korean Mini Mental State Examination (K-MMSE) total score, and the K- Korean Montreal Cognitive Assessment (K-MoCA) total score. The random forest method achieved a higher sensitivity than the decision tree model. Thus, it is advisable to develop a protocol to easily identify early stage PDD based on the PD-MCI prediction model developed in this study, in order to establish individualized monitoring to track high-risk groups.

**Keywords:** cognitive function; data mining; Parkinson's disease with mild cognitive impairment; random forest; neuropsychological test

#### **1. Introduction**

Over the past decade, the field of geriatrics has experienced emerging interest in Parkinson's disease with mild cognitive impairment (PD-MCI) [1–4]. The Sydney cohort study [5], the most highly representative epidemiology study on the subject, examined 136 patients diagnosed with Parkinson's disease (PD) over 20 years. The study reported that 84% of PD patients had cognitive impairment, and 50% of them progressed to PD dementia (PDD). Likewise, PD is often accompanied by cognitive dysfunction in addition to dyskinesia [2].

The mild cognitive impairment (MCI) stage is the earliest at which we can detect dementia [6]. Because it is possible to delay the progression of dementia when it is detected and treated in an early stage, identifying MCI is an important primary goal of dementia treatment [6]. PD-MCI is frequently found in patients with PD [7,8]. However, the sociodemographic and neuropsychological characteristics of PD-MCI are less well-known than those of MCI and vascular mild cognitive impairment (vascular-MCI) [7,8]. The distinctive neuropsychological characteristics found in early stage

PD-MCI are caused by executive function damage due to prefrontal hypofunction or malfunction [9]. However, it is difficult to distinguish PD-MCI from MCI or vascular-MCI, because they show similar symptoms [10]. Additionally, people with PD experience a slowly deteriorating cognitive deficit and impaired motor function, which can be mistaken for cognitive frailty as part of the normal aging process. As a result, it is difficult to diagnose early stage PD. MCI can be diagnosed based on interviewing, cognitive function evaluation via a standardized neuropsychological test, and brain imaging such as magnetic resonance imaging (MRI). It is possible to diagnose cerebrovascular diseases or to analyze brain atrophy using brain imaging. However, this is unsuitable for early PD diagnosis, because brain atrophy can be confirmed visually only at a very advanced stage. Therefore, neuropsychological testing that also tests cognitive function has been used as an effective screening test for diagnosing MCI [11].

Recent studies have pointed to the necessity of considering mental health, such as depression, while diagnosing MCI [12,13]. In particular, the development pattern and risk factors of cognitive impairment are known to vary according to race. Therefore, it is necessary to develop an MCI prediction model reflecting the characteristics of the neuropsychological indices and lifestyles of the elderly in South Korea; however, South Korea has less systematic epidemiological data on cognitive impairment in the elderly than other countries such as the United States and European countries. In South Korea, previous community-based epidemiological studies on PD have been conducted on patients living in a single city [14]. However, there has been no study to develop a prediction model based on a nationwide epidemiological survey. Moreover, most of the previous studies [15,16] evaluating the neuropsychological characteristics of patients with PD have used regression models. Regression models are effective in exploring the neuropsychological characteristics of individual risk factors but are limited in analyzing multiple risk factors simultaneously. It is also difficult to prioritize risk factors with regression models. Linear regression models in particular require several assumptions, including linearity, equal variance, and a normal distribution, but disease data have been known to violate these assumptions.

In recent years, the medical field has applied data mining to predict the risk of diseases and vulnerable groups [16,17]. Data mining is a type of big data analysis that examines the relationships and rules within a dataset to extract valuable information [18]. The health science field has traditionally used tree-based methods such as Classification and Regression Tree (CART) as data mining methods for disease prediction [19]. Decision trees carry the risk of overfitting, and the accuracy of decision trees can vary greatly depending on the training data (input variables). Random forests, a data mining method developed in 2001, were designed to overcome these limitations. Random forests generate multiple decision trees by conducting random sampling on the same dataset and combining them to predict the target variable. Therefore, the accuracy of random forests is higher than that of decision trees [20,21]. Moreover, random forests can be used to explore the relationship between explanatory variables and diseases when many (types of) explanatory variables are applied to a random forest model [22]. In addition, the prediction power of random forests outperforms the bagging model [22].

Several previous studies [23–25] have reported on Parkinson's dementia predictors using biomarkers such as cerebrospinal fluid (CSF) and electroencephalogram (EEG) data. However, we are unaware of any study that identifies the predictors of PD-MCI for patients with PD and normal cognition (PD-NC), taking into account sociodemographic factors, lifestyles, depression, and neuropsychological characteristics. The objectives of this study were to develop a random forest-based PD-MCI prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions by using the Parkinson's Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention), and to compare its prediction of accuracy with those of decision tree and multiple logistic regression models.

#### **2. Methods**

#### *2.1. Data Source*

This study was conducted using the Parkinson's Dementia Clinical Epidemiology Data obtained from the National Biobank of Korea, the Center for Disease Control and Prevention, the Republic of Korea (no. KBN-2019-005). We obtained the approval of the Research Ethics Review Board, the National Biobank of Korea (no. KBN-2019-005), and the data use approval of the Korea Centers for Disease Control and Prevention (no. KBN-2019-1327). The National Biobank of Korea was established in 2008 with the approval of the Ministry of Health and Welfare and is managed by the Korea Centers for Disease Control and Prevention for the emerging necessity of managing bio-data systematically at a national level. The ultimate goal of the National Biobank of Korea is to promote biomedical research and public health. Please refer to Lee et al. [26] for the specific activities of the National Biobank of Korea, including its quality control programs.

The Parkinson's Dementia Clinical Epidemiology Data used in this study were collected under the supervision of the Korea Centers for Disease Control and Prevention at 14 tertiary care organizations (university hospitals) from January to December 2015. Health surveys, including health behavior questions, were conducted using computer-assisted personal interviews. The data are composed of sociodemographic factors (e.g., gender), environmental factors (e.g., exposure to pesticides), health behaviors (e.g., smoking), disease history (e.g., hypertension), exercise characteristics related to PD (e.g., tremor), sleep behavior disorders (e.g., rapid eye movement (REM)), and neuropsychological characteristics (e.g., cognitive function). PD-MCI was diagnosed by neuropsychologists according to the criteria of the International Working Group on MCI [27].

#### *2.2. Subjects*

Observational studies frequently utilize secondary data and these studies are more likely to experience data imbalance while comparing patients and healthy subjects [28]. Propensity score matching (PSM) was used to minimize selection bias and resolve the imbalance of case-control [29]. This study found an imbalance between PD-NC and PD-MCI. In order to solve this issue, this study used PSM, balancing between populations using the nearest neighbor matching by controlling the age of the case-control group [30]. Moreover, this study excluded individuals (subjects) that did not match in both groups in common to ensure good data balance. Before matching, there were 274 subjects (PD-MCI = 223; PD-NC = 51), and, after conducting PSM, it was matched to 96 subjects (PD-MCI = 45, PD-NC = 51; Figure 1). This study finally analyzed 96 subjects.

**Figure 1.** Framework of study.

#### *2.3. Measurement*

The outcome variable is defined as the prevalence of PD-MCI classified by medical diagnosis. The explanatory variables included age (60–74 years old or ≥75 years old), gender (male or female), education (middle school graduate and below, or high school graduate and above), handedness (left hand, right hand, or both hands), family dementia history (yes or no), family PD history (yes or no), pack-years (non-smoking, 1–20, 21–40, 41–60, or ≥61 pack-years), coffee-drinking (yes or no), mean coffee intake per day (no, ≤1, 2–3, or ≥4 cups), coffee drinking period (no, ≤5, 6–9, or ≥10 years), pesticide exposure (never, currently not exposed but exposed previously, or currently exposed to pesticide), disease history (carbon monoxide poisoning, manganese poisoning, encephalitis, traumatic brain injury, stroke, alcoholism, diabetes, hypertension, hyperlipidemia, and/or atrial fibrillation), PD related motor signs (tremor, akinesia/bradykinesia, postural instability, and/or late motor complications), REM, sleep behavior disorders, neuropsychological characteristics such as those outlined in the Korean Mini Mental State Examination (K-MMSE) [31], Korean Montreal Cognitive Assessment (K-MoCA) [32], Geriatric Depression Score (GDS) [33], global Clinical Dementia Rating (CDR) score [34], Korean Instrumental Activities of Daily Living (K-IADL) score [35], Untitled Parkinson's Disease Rating (UPDRS) total score [36], UPDRS motor score [37], Hoehn and Yahr staging (H&Y staging) [38], and the Schwab and England Activities of Daily Living scale (Schwab and England ADL) [39]. These variables are defined in Table 1.

#### **Table 1.** Measurement and definition of variables.

Pack-years: Cumulative amount of smoking, based on one pack of smoking per day. For example, 30 pack-years means smoking one pack of cigarettes per day for 30 years or two packs of cigarettes per day for 15 years. CDR—Clinical Dementia Rating; K-IADL—Korean Instrumental Activities of Daily Living; UDPRS—Untitled Parkinson's Disease Rating; ADL—Schwab and England Activities of Daily Living scale.

#### *2.4. Development and Evaluation of Prediction Models*

The prediction model was developed using a random forest algorithm, and the results of the developed prediction model were compared with those of a decision tree based on multiple logistic regression and a classification and regression tree. The prediction accuracy of the model was calculated using the recognition rate.

Random forests are ensemble classifiers that randomly learn multiple decision trees. The random forest method consists of a training step that constructs several decision trees, and a test step that classifies or predicts an outcome variable based on an input vector. The ensemble form of random forest training data can be expressed as Forest F = {f*1*, ..., f*n*} (Figure 2). The distributions obtained from the decision trees of each forest were first averaged by T (the number of the decision trees) and then classification was conducted. The predictors of each sample were combined by using the mean for continuous target variables and the majority vote for categorical target variables.

$$L(p) = \frac{1}{T} \sum\_{t=1}^{T} P\_t(b \| I, p) \tag{1}$$

**Figure 2.** Ensemble classifiers that combines many single decision trees.

Random forest is similar to the bagging technique, because both approaches combine decision trees generated from multiple bootstrap samples using the majority vote principle in order to increase stability. However, they are different, because the former uses a few explanatory variables that were randomly selected from each bootstrap sample.

This study presented a partial dependence plot and variable importance to show the prediction power of the main explanatory variables. The variable importance indicates the effect of an explanatory variable on the accuracy of a model. Therefore, when an explanatory variable improves the performance of a model, the importance of the variable increases. A partial dependence plot shows the changes in response variables according to the continuous change of each explanatory variable. The contribution of a dependent variable to an independent variable is expressed as a function of a variable. The function of partial dependence is presented in Equation (2).

$$\left(\frac{p\_1(\mathbf{x}, \mathbf{x}\_{ic})}{p\_0(\mathbf{x}, \mathbf{x}\_{ic})}\right) \tag{2}$$

RF can be free from overfitting theoretically, and is not affected by noise or outliers much [20]. Moreover, it can generate high accuracy results by reducing generalization errors [20]. However, RF is more likely to have an elbow point, which means a steep drop in slope with more trees. Moreover, there is a higher probability that each tree will be more complex when an unimportant explanatory variable is selected. Therefore, this study improved the accuracy of the model by considering the number of mtry, the number of candidate explanatory variables, in advance.

The prediction performance of a model was validated while considering the overall accuracy, sensitivity, and specificity together. Sensitivity means the prediction accuracy of PD-MCI, while specificity indicates that of PD-NC. As the objective of this study was to develop a model that can predict PD-MCI, this study considered overall prediction accuracy and sensitivity as the most important factors for evaluating prediction performance. When the overall prediction accuracies and sensitivities of the two models were identical, their specificities were compared. This study first established a random forest model and then compared the results and the accuracies of models obtained from multiple logistic regression and CART. In this case, forward selection based on standard likelihood ratio tests was used to select variables in the multiple logistic regression analysis. All of the statistical analyses were conducted using the "RandomForest" package of R-version-3.6.1 (Foundation for Statistical Computing, Vienna, Austria).

#### **3. Results**

#### *3.1. General Characteristics of the Subjects*

The General characteristics of the subjects are presented in Table 2. Of the 96 subjects (after match), 47.9% were male, 52.1% were female, 38.5% had a high school or above level of education, 8.0% had a family history of PD, and 6.8% had a family history of Alzheimer's dementia. Additionally, 5.7%, 2.3%, 23.2%, and 40.0% of the subjects had a history of head injury (e.g., traumatic brain injury), stroke, diabetes, and hypertension, respectively.

**Table 2.** General characteristics of the subjects, *n* (%).

**Table 2.** *Cont*.

REM sleep behavior disorders—rapid eye movement sleep behavior disorders; PD-MCI—Parkinson's Disease with Mild Cognitive Impairment; PD-NC—Parkinson's Disease with Normal Cognition; K-MMSE—Korean Mini Mental State Examination; K-MoCA—Korean Montreal Cognitive Assessment; CDR—Clinical Dementia Rating; K-IADL—Korean Instrumental Activities of Daily Living; UPDRS—Untitled Parkinson's Disease Rating; H&Y staging—Hoehn and Yahr staging; Schwab and England ADL—Schwab and England Activities of Daily Living scale.

#### *3.2. Major Risk Factors of Random Forest-Based PD-MCI Prediction Model*

A PD-MCI prediction model was established using random forests, and the results are presented in Figure 3. Some of the random forest models estimated major risk factors using decreased in the GINI coefficient. The major risk factors of PD-MCI were, in descending order of magnitude, CDR sum of boxes, UPDRS motor score, the K-MMSE total score, and the K-MoCA total score. Among these factors, the UPDRS motor score was the most important predictor of PD-MCI. In contrast, the importance of atrial fibrillation and stroke was zero.

**Figure 3.** Variable importance in a random forest model (showing only the top 12 factors).

The partial dependence plot regarding the CDR sum of boxes, the most important variable in the predictive model, is presented in Figure 4. The results showed that, when other factors were constant, the risk of PD-MCI increased with a higher CDR sum of boxes (Figure 4).

**Figure 4.** Partial dependence plot (CDR sum of boxes).

#### *3.3. Comparison of the Accuracy of the Developed Prediction Models*

This study changed the mtry values (numbers), presenting the number of explanatory variables to be used in the decision tree constituting RF, from 5 to 15, and selected the value with the smallest error of Out-Of-Bag. The changes in the error of Out-Of-Bag are presented in Table 3. The optimal mtry to be applied in this study was 5, showing the lowest error rate (34.4%).

**Table 3.** Error of out-of-bag.

When ntree, the number of tree generation, and mtry were set as 500 and 5, respectively, the final RF model of this study had an overall accuracy of 65.6%, a sensitivity of 70.6%, and a specificity of 60.0% (Table 4). On the other hand, the overall accuracy of CART was calculated as 67.7%, higher than that of RF, but the sensitivity of it was the lowest (51.1%). In Figure 4, the black line indicates the changes in each error rate against 500 bootstrap samples. Figure 5 shows that the changes in error rate become relatively stable after the number of bootstrap samples exceeded 150.

**Table 4.** Comparison of accuracies developed prediction models, %.

NA—not available.

**Figure 5.** Out-of-bag error rate curve (random forest model). Black line—overall accuracy; red line—sensitivity; Green line—specificity.

#### **4. Discussion**

Diagnosing early stage PD-MCI is important in the health sciences, because it can delay the cognitive decline associated with PDD. Previous studies [22,40] have reported that the impairment of the executive function is a major cognitive feature of PDD. However, it is challenging to distinguish PD-MCI from PD-NC solely based on executive function. Therefore, we explored the major differential indicators of PD-MCI, taking into account sociodemographic variables, health habits, PD related motor and non-motor symptoms, cognitive tests, and neuropsychological tests. We developed a PD-MIC prediction model based on random forests, and confirmed that the CDR sum of boxes, UPDRS motor score, K-MMSE total score, and the K-MoCA total score were major predictors of PD-MCI. Among all of the neuropsychological screening tests, the CDR sum of boxes was the most important predictor for distinguishing PD-MCI from PD-NC. Therefore, when a neuropsychological test is performed to diagnose PD-MCI in patients with PD, the CDR (sum of boxes) scoring should be conducted first over other cognitive-language screening tests so as to achieve higher sensitivity.

Previous studies [41,42] examining the sociodemographic and emotional characteristics of PDD reported that depression is the main characteristic of PDD. For example, Aarsland et al. (2007) [41] evaluated 537 patients with PDD and observed that 58% of the patients had depression. However, in the present study, depression was not an important indicator for predicting PD-MCI. This might differ from previous studies [41,42], because previous studies compared healthy elderly individuals versus those with PD-MCI, while the present study only examined people with PD. In other words, depression is potentially not a major differential indicator in this study, because both PD and PD-MCI have high depression rates (31.3%). As only a few studies have tried to distinguish PD-MCI from PD-NC considering neuropsychological characteristics, health habits, and depression, more observation studies on PD-MCI are needed in order to verify the major predictors of PD-MCI.

Another meaningful finding of this study is that the sensitivity of random forests is higher than that of the decision tree model. These results agree with the results of previous studies predicting MCI [6] or cardiovascular disease in the elderly using random forests [43]. The prediction accuracy of random forests is higher than that of regression models or decision trees, because random forests are based on the bagging algorithm, which generates diverse decision trees using 500 bootstrap samples. As outliers can form decision tree nodes, the effects of the parameters that determine nodes are substantial, and, consequently, carry a risk of overfitting [44]. In contrast, random forests based on the bagging algorithm can prevent overfitting, because they reduce variance while maintaining tree bias. Moreover, random forests achieve a higher prediction accuracy than decision trees [45]. In addition, one advantage of random forests is their reduction of variance compared with the bagging model, which is achieved by decreasing the correlation between trees [43]. Random forests show a particularly better prediction accuracy than bagging models when there are many input variables [43]. Therefore, when selecting the key independent variables from a dataset containing many independent variables, such as the disease data used in this study, or developing prediction models on big data, random forests provide a higher accuracy than decision tree or multiple logistic regression models.

The merit of this study was the development of an MCI prediction model using examination data from a national survey. The limitations of this study are the following: (1) The number of study subjects was small. (2) The obsessive-compulsive symptoms commonly observed in patients with PD were not examined. (3) The prediction model did not include a biomarker, such as CFS. (4) This study adjusted the balance of the number of subjects between the groups by using age-matched PSM to solve the problem of unbalanced data. However, as a result of the PSM, a number of samples were excluded from the analysis, and the same size decreased. As a result, the overall accuracy, sensitivity, and specificity of the multiple logistic regression analysis were not calculated. Moreover, the age used for matching could not be used as an explanatory variable in the predictive model. Future studies will require more advanced techniques that can reduce the probability of overfitting to minimize imbalance, in addition to PSM. (5) Subjects taking PD medications (e.g., dopaminergics) were not

evaluated. As PD medication particularly affects the expression of cognitive and behavioral symptoms, future studies should consider whether or not a subject takes medication.

#### **5. Conclusions**

It is necessary to develop a protocol that can easily identify early stage PDD in order to establish individualized monitoring for tracking high-risk groups based on the PD-MCI prediction model developed in this study. Moreover, to further increase the prediction accuracy of the present method, a random forest model using weighted voting is warranted. In addition, the development of multi-modal data-based machine learning models that include biomarkers and brain imaging test indicators, as well as sociodemographic factors, health habits, and neuropsychiatric indicators, is needed.

**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07041091 and NRF-2019S1A5A8034211).

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**

© 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

International Journal of *Environmental Research and Public Health*

## **Motoric Cognitive Risk Syndrome Using Three-Item Recall Test and Its Associations with Fall-Related Outcomes: The Korean Frailty and Aging Cohort Study**

#### **Hayoung Shim 1, Miji Kim 2,\* and Chang Won Won 3,\***

Received: 19 April 2020; Accepted: 11 May 2020; Published: 12 May 2020

**Abstract:** Motoric cognitive risk (MCR) syndrome is originally defined as the presence of subjective cognitive complaints (SCCs) and slow gait (SG). MCR is well known to be useful for predicting adverse health outcomes, including falls and dementia. However, around four out of five older Korean adults reported SCCs, thereby, it may not be discriminative to define MCR in Korea. We adopted the three-item recall (3IR) test, instead of SCCs, to define MCR. This cross-sectional analysis included 2133 community-dwelling older adults aged 70–84 years, without dementia or any dependence in activities of daily living from the Korean Frailty and Aging Cohort Study. The newly attempted criteria of MCR using 3IR were met by 105 participants (4.9%). MCR using 3IR showed synergistic effects on fall-related outcomes, whereas the conventional definition of MCR using SCCs was not superior to SG only. MCR using 3IR was associated with falls (odds ratio [OR]: 1.92; 95% confidence interval (CI): 1.16–3.16), recurrent falls (OR: 2.19; 95% CI: 1.12–4.32), falls with injury (OR: 1.98; 95% CI: 1.22–3.22), falls with fracture (OR: 2.51; 95% CI: 1.09–5.79), fear of falling (OR: 3.00; 95% CI: 1.83–4.92), and low activities-specific balance confidence (OR: 3.13; 95% CI: 1.57–6.25). We found that MCR using 3IR could be useful in predicting fall-related outcomes in a cultural background reporting more SCCs, such as Korea.

**Keywords:** motoric cognitive risk syndrome; fall; gait speed; cognitive function; three-item recall; older adults

#### **1. Introduction**

Substantial links have been reported between cognition and gait, and the combination of these two factors has been conceptualized by motoric cognitive risk (MCR) syndrome [1,2]. Compared to each component alone, MCR has stronger predictive validity for adverse health outcomes, such as dementia, falls, disability, and death [3–5].

MCR was originally defined as the presence of subjective cognitive complaints (SCCs) and slow gait speed, without dementia and any dependence in activities of daily living [3]. Of these criteria, most studies have defined slow gait as below one standard deviation of the usual gait speed established in each cohort according to age and sex [6]. To the contrary, many previous studies flexibly adapted

various criterion of SCCs to suit their research environments, i.e., from standardized questionnaires regarding general cognitive performance, from one simple self-rating question regarding memory function, from cut-off scores used in several objective cognitive function tests, or from informant reports on subjects' cognitive problems [5,6]. The various criteria of SCCs in previous studies might have influenced the inconsistent results [6]. In particular, there were different results on the association between MCR and subtypes of dementia. Verghese et al., reported that MCR was strongly associated with vascular dementia (VaD), but not with Alzheimer's disease (AD), when identifying SCCs using comprehensive objective assessments [3]. By contrast, in some studies using self-rating questions on memory [7,8], MCR was associated with increasedrisk of AD [9].

MCR is expected to be useful for various settings because the assessments of each component, presence of SCCs and gait speed, are relatively convenient [9]. However, several studies have reported limitations of SCCs that may be influenced to a greater extent by depression, personality, or cultural differences, than actual cognitive performance [10–12]. Similarly, MCR, which includes SCCs among the criteria, was associated with anxio-depressive disorders, depression [13] and personality, particularly neuroticism [14]. Furthermore, a number of studies have reported disparities between SCCs and objective cognitive function, with robust evidence that depressive symptoms seemed to have the greatest influence on SCCs [12].

Moreover, several studies reported that older Korean adults may have a cultural tendency to report SCCs more frequently [15,16]. The prevalence of SCCs in community-dwelling older adults in Korea was 77.7% [16]. SCCs may not be discriminative in Korea. The tendency of high reporting of SCC in older Korean adults may stem from culturally based factors, such as high prevalence of depressive symptoms [15,17,18].

MCR was found to predict adverse health outcomes [4]. Particularly, MCR is well known to be associated with falls and their recurrence, or post-fall fractures [19–21]. Therefore, we aimed to explore another criterion for the cognitive aspects of MCR, based on fall-related outcomes. As most previous studies have identified SCCs in memory [22], we introduced three-item recall (3IR) test, instead of SCCs, for defining MCR. 3IR test is a simple and well validated objective memory test. We investigated the associations between MCR and comprehensive fall-related outcomes by comparing the new MCR using 3IR, and original MCR using SCCs.

#### **2. Materials and Methods**

#### *2.1. Study Population*

The Korean Frailty and Aging Cohort Study (KFACS) is an ongoing prospective cohort study to investigate the frailty status of older Korean adults [23]. The KFACS recruited 3014 community-dwelling older adults aged 70–84 years from 10 nationwide centers, including rural, suburban, and urban areas, in a 2-year comprehensive baseline survey. Among the participants of the KFACS, those who were dependent in any of the basic activities of daily living (ADL) (*n* = 69), self-reported a diagnosis of dementia (*n* = 11), or scored < 24 points in the Mini-Mental State Examination (MMSE; *n* = 641) [24], and had a history of Parkinson's disease (*n* = 1) or hemiplegia (*n* = 6), were excluded from the present study. In addition, those who had missing 3IR test scores (*n* = 48), a question about SCCs (*n* = 6), and fall-related outcomes (*n* = 51) and covariates (*n* = 48), were excluded. On the result, a total of 2133 older adults were selected for the present study (Figure 1). The Clinical Research Ethics Committee of Kyung Hee University Hospital approved the KFACS protocol (Institutional Review Board [IRB] number: 2015-12-103). The present study was exempt from the requirement for IRB approval by the Clinical Research Ethics Committee of the Kyung Hee University Medical Center (IRB No.: 2020–03–073).

**Figure 1.** Flow chart of the study population.

#### *2.2. Definitions of Motoric Cognitive Risk (MCR) Syndrome*

#### 2.2.1. Original MCR using Subjective Cognitive Complaints (SCCs)

A single question extracted from the Korean Version of Short Form Geriatric Depression Scale (SGDS-K) was used to ascertain SCCs: "Do you feel you have more problems with memory than most?" [25,26]. A positive response, "yes", to this question was defined as indicating the presence of SCCs [4,19,27]. Slow gait was defined as one standard deviation (SD) or below the age- and sex-specific mean values established in the KFACS [16]. The participants were asked to walk a 7 m distance, with initial acceleration and terminal deceleration sections of 1.5 m in their normal pace, and gait speed was calculated at the speed (m/s) of walking 4 m, the middle section of 7 m, using an automatic machine (Gaitspeedometer, Dyphi, Daejeon, Korea).

#### 2.2.2. New MCR using three-item recall (3IR)

The 3IR test of MMSE was administered to identify the deficits in memory function [28]. The examiner named three unrelated objects clearly and slowly, then asked the participant to name all three of them. The examiner repeated the words up to three times until the participant learned all of them, if possible. A few minutes later, the participant was asked to recall the three words as much as possible, without any hint. Scores were calculated from 0 (incorrect) to 1 (correct) for each item, where higher score indicates better ability. The sum of the 3IR tests was used to determine cognitive aspects of MCR. A score < 3 was considered to indicate deficits in memory recall [29]. Slow gait (SG) was still defined as one or more standard deviation (SDs) or below the age- and sex-specific mean values established in the KFACS [16].

#### *2.3. Definitions of Fall-Related Outcomes*

We collected the following six types of fall-related information using a standardized questionnaire: experience of a fall in the past 1 year, recurrent falls, falls with injury, falls with fracture, fear of falling, and low activities-specific balance confidence.

Experience of falls, recurrent falls, and falls with injury or fracture during the past 1 year were investigated. A fall was defined as an event that resulted in a person coming to rest unintentionally on the ground, not as a result of a major intrinsic event (such as stroke or syncope) or overwhelming hazard [30]. In addition, falls caused by acute medical events, such as sudden onset of paralysis and

epileptic seizure, or excess alcohol intake, were excluded [31]. Recurrent falls were defined as two or more falls in the past 12 months. Falls with injury were defined as those where the participants reported sprains, bruises, lacerations, and fractures after falls. When a participant reported fracture as a consequence of a fall, it was defined as fall with fracture.

Fear of falling (FoF) was assessed using a question presented with five response choices: "Are you usually afraid that you may fall?" If participants answered "considerably" or "very much," they were considered to have a FoF. Participants who responded "not at all," "a little," or "don't know" were considered to have no FoF [32].

Activities-specific balance confidence (ABC) was administered using a 16-item ABC scale in which participants rated their balance confidence when doing specific activities [33]. Scores ranged from 0 (no confidence) to 100 (complete confidence). A higher score indicated greater confidence, and the total score was calculated as the average of 16 items. Low ABC was defined as ABC scale score ≤ 58.13 [34].

#### *2.4. Measurements*

All participants were interviewed based on standardized surveys for collecting information, and were examined using health assessments. The trained investigators obtained sociodemographic and lifestyle information: education level, type of residence, living conditions, marital status and whether they receive social security aid, smoking status, alcohol intake, and physical activity level. Low physical activity was defined as < 494.64 kcal/week for men and < 283.50 kcal/week for women, using the International Physical Activity Questionnaire (IPAQ), values of which correspond to the lowest quintile (20%) of the total consumed energy established in a general population study of Korean older adults [35]. We inquired about the general health and medical history of each participant: body mass index (BMI), number of drugs taken daily, and medical conditions. Diseases were self-reported diagnoses by a physician, and comorbidities were defined as two or more of the following diseases: hypertension, diabetes mellitus, dyslipidemia, myocardial infarction, congestive heart failure, angina pectoris, cerebrovascular disease, peripheral vascular disease, osteoarthritis, rheumatoid arthritis, osteoporosis, asthma, and chronic obstructive pulmonary disease. Visual impairment was determined when the maximum value of left and right vision was < 0.3 [36]. Hearing impairment was identified as the minimum of the average value of left and right hearing exceeding 40 dB [37]. Instrumental activities of daily living (IADL) disability was determined when participants did not answer "completely independent" for one or more of the 10 activities using the Korea Instrumental Activities of Daily Living Scale (K-IADL) [38]. Nutritional status was determined using the Korean version of the short-from Mini-Nutritional Assessment (MNA-SF) [39]. Global cognitive function was assessed using the MMSE in the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (MMSE-KC) [40]. Depressive symptoms were assessed using the SGDS-K [41]. Participants were asked whether they perceived their health status as poor, fair, good, very good or excellent; "poor" or "fair" responses to the question were defined as fair/poor self-perceived health. Quality of life was evaluated using EuroQol five-demension scale (EQ-5D) [42]. Participants took the following physical function tests: handgrip strength, usual-pace gait speed, timed up and go (TUG) test [43], and short physical performance battery (SPPB) [44].

#### *2.5. Statistical Analyses*

Descriptive statistic analyses were conducted to compare the participants' characteristics according to MCR status. The Chi-square test or Fisher's exact tests was used for categorical variables, with adjusted standardized residuals as appropriate. In addition, after employing Levene's test to determine homogeneity of variances, one-way analysis of variance (ANOVA) with Bonferroni post hoc test, or Welch's ANOVA with Games–Howell post hoc tests, for continuous variables were conducted. Participants were divided into four groups for each definition: (1) MCR using SCCs: normal, SCCs only, SG only, and MCR using SCCs; (2) MCR using 3IR: normal, impaired 3IR only, SCCs only, SG only, MCR using 3IR. Multiple logistic regression analyses were performed to investigate the associations

between MCR status and fall-related factors. We adjusted confounding factors by dividing the models into four steps. First, we adjusted for the recruited center and sociodemographic factors in Model 1: the recruited center, age, sex, low educational level, residence area, living alone, without partner, and social security aid recipient. Second, we further adjusted for lifestyle-related factors in Model 2: current smoker, alcohol consumption (≥ 2 to 3 times/week) and low physical activity level. Third, further adjustments were conducted regarding general health and medical conditions in Model 3: BMI, number of drugs taken daily, number of diseases, urinary incontinence, visual impairment, hearing impairment, poor nutritional status, and IADL disability. In the final model, Model 4 adjusted for the same factors as Model 3, with the addition of psychological factors: depressive symptoms and fair/poor self-reported health. All analyses were performed using SPSS (ver. 25.0; IBM Corp., Armonk, NY, USA). In all analyses, two-sided *p* < 0.05 was taken to indicate statistical significance.

#### **3. Results**

#### *3.1. Descriptive Characteristics of the Study Population*

Characteristics of participants according to new MCR status using 3IR are shown in Table 1. Sociodemographic factors including sex, education level, residence area, living alone, without partner, and status of receiving social securities, were significantly different between groups (*p* < 0.05). However, the age of the participants was not significantly different between the groups. Participants with MCR using 3IR showed significantly lower physical activity level than the normal group (*p* < 0.001). Moreover, compared to the normal group, the MCR using 3IR group was taking more medicines per day, and had more diseases, especially diabetes (all *p* < 0.01). The disabilities on any of the IADL were significantly different among the groups (*p* = 0.01). With regard to psychological factors, the MMSE score was the highest in the normal group and the lowest in the MCR using 3IR group (*p* < 0.001). Individuals with SG only or MCR using 3IR were more likely to rate their health status as poor and to have depressive symptoms than other groups (all *p* < 0.001). In all physical function tests, including handgrip strength, usual gait speed, TUG and SPPB, new MCR using 3IR showed poorer performance compared to the normal or impaired 3IR group (*p* < 0.001). Meanwhile, characteristics of participants according to original MCR status using SCCs are shown in Table S1.

Of the total of 2133 older adults in this cohort study, 105 (4.9%) met the newly developed MCR criteria using 3IR, of whom 55.6% had impaired 3IR and 9.2% had SG (Figure 2). Of the participants, 134 (6.3%) had MCR defined conventionally using SCCs, and SCCs were present in 81.0% of the all participants. A total of 943 participants (44.2%) had both impaired 3IR and SCCs.

**Figure 2.** Venn diagram for the criteria and their overlaps.

The prevalence of fall-related outcomes according to MCR status using 3IR are presented in Figure 3. The MCR group using 3IR showed the highest prevalence of falls, recurrent falls, and falls with injury among the groups (all *p* < 0.0063). The prevalence of falls with fracture was lowest in the impaired 3IR only group, and highest in the MCR using 3IR group (all *p* < 0.0063). FoF and low ABC were significantly different among the groups (all *p* < 0.0063), with higher prevalence in the SG only or MCR using 3IR group.

**Figure 3.** The prevalence of fall-related outcomes according to MCR status using 3IR. The numbers and percentages of outcomes are presented as *n* (%). \* *p* < 0.0063.

#### *3.2. Associations of MCR using 3IR or MCR using SCCs with Fall-Related Outcomes*

Multiple logistic regression analyses were performed to investigate the associations between MCR and fall-related outcomes. Those for MCR using 3IR are shown in Table 2, and those of MCR using SCCs are presented in Table 3. Compared with the individual component of MCR using 3IR alone (i.e., impaired 3IR only and SG only), MCR using 3IR was only significantly associated with experience of falls in the past 1 year (OR: 1.92, 95% CI: 1.16–3.16), recurrent falls (OR: 2.19, 95% CI: 1.12–4.32), falls with injury (OR: 1.98, 95% CI: 1.22–3.22) and falls with fracture (OR: 2.51, 95% CI: 1.09–5.79) after considering all confounding factors. Moreover, the group with SG only (OR: 2.22, 95% CI: 1.31–3.75) and MCR using 3IR (OR: 3.00, 95% CI: 1.83–4.92) showed a significant association with FoF after adjusting for all confounders. With regard to low ABC, a significant association was observed in the groups with SG only (OR: 2.99, 95% CI: 1.47–6.12) and MCR using 3IR (OR: 3.13, 95% CI: 1.57–6.25) after adjustment for all confounding factors. MCR using 3IR showed synergistic effects on all fall-related outcomes (Table 2).

By contrast, MCR using SCCs was not associated with experience of falls, recurrent falls, falls with injury or falls with fracture (*p* > 0.05), while SG only was significantly associated with experience of falls (OR: 1.87, 95% CI: 1.01–3.45), recurrent falls (OR: 2.27, 95% CI: 1.04–4.96), and falls with injury (OR: 1.87, 95% CI: 1.02–3.42). In addition, both SG only (OR: 3.72, 95% CI: 1.86–7.43) and MCR using SCCs (OR: 2.04, 95% CI: 1.26–3.30) showed a significant association with FoF. Moreover, SG only (OR: 2.72, 95% CI: 1.24–6.00) and MCR using SCCs (OR: 2.75, 95% CI: 1.37–5.50) were associated with low ABC. However, the impact of SG only on most fall-related outcomes was stronger than that of MCR using SCCs.

Characteristicsoftheaccordingrisk(MCR)syndromethree-itemrecall

*Int. J. Environ. Res. Public Health* **2020**, *17*, 3364

**Table 1.** *Cont*. *Notes:*Assessment; GDSnormalgroupcomparisonbetween impaired 3IR only group and slow gait only group; comparison between impaired 3IR only group and MCR using 3IR group; comparison between slow gait only group and MCR using 3IR group. with Bonferroni post hoc test or Welch's ANOVA with Games-Howell post hoc test for continuous variables. *P* < 0.05 indicated in bold. \* Significance with *P* < 0.05 divided by the number of rows × columns, with post hoc test of chi-square tests. § Sample size. † Some missing data. ‡ *P*-values were calculated using the Chi-square test or Fisher's exact test for categorical variables and one-way ANOVA.

Associationsbetweenmotoricriskthree-itemrecallandthefall-related

*Int. J. Environ. Res. Public Health* **2020**, *17*, 3364

Model 4

 0.954 (0.751, 1.212)

 0.700

 2.218 (1.314, 3.746)

**0.003**

3.000 (1.830, 4.917)

<**0.001**

Model 3 Model 4

 0.609 (0.378, 0.979)

 0.790 (0.479, 1.304)

**0.041**

 0.356

 2.269 (1.038, 4.958)

2.411(1.113, 5.220)

**0.026** **0.040**

1.410 (0.672, 2.960)

1.214 (0.586, 2.513)

 0.601

 0.364

*Int. J. Environ. Res. Public Health* **2020**, *17*, 3364

**Table 3.** *Cont*. *Notes:* Model 1: Adjusted for recruited center and sociodemographic factors; age, sex, low education level, residence (urban/suburban/rural), living alone, without partner, receiving basic livelihood security and/or medical care aid. Model 2: Further adjustment on Model 1 for lifestyle-related factors; current smoker, alcohol consumption, low physical activity. Model 3: Further adjustment on Model 2 for general health and medical conditions; body mass index (underweight/normal/obese), number of drugs taken daily, number of diseases (self-reported doctor diagnosis of hypertension, diabetes mellitus, dyslipidemia, myocardial infarction, congestive heart failure, angina pectoris, cerebrovascular disease, peripheral vascular disease, osteoarthritis, rheumatoid arthritis, osteoporosis, asthma, and chronic obstructive pulmonary disease), urinary incontinence, visual impairment, hearing impairment, poor nutritional status, number of difficulties in instrumental activities of daily living (IADL). Model 4: Further adjustment on Model 4 for psychological factors; depressive symptoms, fair/poor self-reported health status. *P* < 0.05 indicated in bold.

#### **4. Discussion**

In this cohort study with 70–84 year-old community-dwelling older adults, the prevalence of the new MCR using 3IR was 4.9% (105/2133), and that of MCR using SCCs was 6.3% (134/2133), which is consistent with other previous studies [6]. The prevalence of MCR using SCCs in the present study was lower than that of our previous study (8.0%) [16]. Such a gap might be due to the different exclusion criteria of the studied populations, especially additional exclusion of dementia based on MMSE score (< 24). The overlap between the newly developed criteria, impaired 3IR, and the conventional criteria of SCCs was 44.2%, which simply assesses subjects' memory status, but differs in the method of identifying memory deficits. Our main finding is that MCR using 3IR was associated with all fall-related outcomes, including experience of falls, recurrent falls, falls with injury, falls with fracture, FoF, and low ABC, with synergistic effects of its components. By contrast, MCR using SCCs was not associated with falls, recurrent falls, and falls with injury or fracture, and SG only showed a stronger association with most fall-related outcomes.

The idea of a newly attempted MCR using 3IR was developed from our previous findings that SCCs, widely used to define MCR, had a considerably high prevalence rate (77.7%) in older Korean adults, and that the effect of SCCs on cognitive impairment was not additive to SG [16]. Elderly Koreans tend to report more SCCs [15] as well as depressive symptoms [17,45] compared to Western countries. This tendency may be influenced by cultural factors [46]. Several previous studies had shown that SCCs might be more closely associated with depressive symptoms than actual cognitive function [47–49]. Therefore, we intended to compensate for the limitations of SCCs for older Korean adults by establishing another criterion for the cognitive aspects of MCR.

We adopted the 3IR test of MMSE instead of SCCs for definition of MCR based on the following reasons. First, most previous studies identified SCCs using memory-related items [22]. Therefore, we intended to find another tool to represent the subjects' memory function. Second, the 3IR test is one of the components of MMSE, which has been widely used and validated in many clinical practices and community settings to screen for dementia [24]. Third, some studies have reported that the 3IR was one of the best discriminators among the subscales of MMSE for screening dementia [50]. Fourth, the 3IR test is simple and easy to conduct, which is in line with the usefulness of MCR in various clinical practice settings [9]. In addition, the 3IR test is also a part of the Mini-Cog test, which has high sensitivity and specificity for detecting AD and related dementia in community settings [51]. The Mini-Cog test may not be greatly affected by cultural differences [29]. In our study, the cut-off score to define impaired 3IR (< 3) was based on the first step of the Mini-Cog test, in which participants with a score less than 3 either required an additional step (score 1–2) or were diagnosed as demented (score 0) [29].

In the present study, we examined the associations of MCR using a 3IR test with various fall-related outcomes, and compared the results to those of original MCR using SCCs. Callisaya et al. reported that MCR was associated with increased risk of any falls and multiple falls (≥ 2 falls) based on five Western cohort studies, reporting stronger predictive capability of MCR than its individual components [19]. Similarly, in a study in New Zealand comparing the fall risks of Maori and non-M ¯ aori populations, ¯ MCR and its components were significantly associated with increased fall risks, with synergistic effects of the components in the non-Maori group. However, these associations were not observed in the M ¯ aori ¯ group [21]. In a French cohort study, subjects with MCR were at higher risk of having experienced falls, recurrent falls, and post-fall fractures, but not those with each component of MCR [20]. Our findings, in terms of the associations of MCR with fall-related outcomes, are consistent with these three previous studies. In our study, MCR using 3IR showed synergistic effects of individual components of MCR compared to MCR using SCCs.

By contrast, in the associations of MCR using SCCs and its components with fall-related outcomes, SG only had a stronger association than MCR using SCCs, and SCCs only showed protective associations with some fall-related outcomes. These protective associations of SCCs only with most fall-related outcomes, however, became nonsignificant after adjusting for psychological factors, including depressive symptoms and self-reported health status. This change may have been due to the influence of depression or self-rating questionnaire methods on SCCs. Several studies have reported associations between SCCs and depression or self-perceived health [48,52]. In addition, Carrasco et al. reported that the quantity or quality of memory complaints was an important issue for interpreting results, as the self-perceived state of health or mood may be the only factor influencing SCCs, particularly in subjects with fewer complaints [48], which may just be due to normal aging. Therefore, further studies are suggested to examine the effects of psychological factors to further investigate the concept of MCR.

Overall, the associations between MCR, regardless of its definition, and the fall-related outcomes showed specific features in this study. The pattern of the main results was different between each definition of MCR. MCR using 3IR showed a significant association with both fall history retrospectively collected, and self-evaluation of current fear or balance confidence, and SG only showed a significant association only with self-evaluated outcomes. In contrast to this pattern, MCR using SCCs showed a significant association with the self-rated outcomes, and did not show significance in the retrospective fall history. We suspected that these features might be related to the effects of psychological factors on both SCCs and MCR using SCCs [53].

This study had some limitations. First, the causal relationships between MCR, regardless of definition, and fall-related outcomes could not be determined because of the cross-sectional study design. Second, information on some fall-related outcomes was collected retrospectively, which may cause recall bias. Third, the subjects were relatively healthy because they were ambulatory older adults in the community. In addition, the participants were recruited in a research setting. Therefore, our findings may not be generalizable to other settings. Despite these limitations, our findings are important because we included a large, nationally representative sample of older Korean adults, and took into consideration various robust confounders.

#### **5. Conclusions**

MCR using 3IR was associated with fall-related outcomes, with synergistic effects of the individual components. However, SG only showed stronger effects on most fall-related outcomes than MCR using SCCs. SCCs was susceptible to psychological factors, such as depressive symptoms.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1660-4601/17/10/3364/s1, Table S1: Characteristics of participants according to MCR status using subjective cognitive complaints (SCCs).

**Author Contributions:** Conceptualization, C.W.W. and M.K.; methodology, C.W.W. and M.K.; formal analysis, H.S.; investigation and data curation, C.W.W., M.K. and H.S.; writing—original draft preparation, H.S.; writing—review and editing, C.W.W. and M.K.; supervision, C.W.W. and M.K.; project administration, H.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by a grant from the Korea Health Technology R&D Project through the Korean Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number: HI15C3153).

**Acknowledgments:** We would like to thank the study participants and the staff of the Korean Frailty and Aging Cohort Study for their cooperation in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

International Journal of *Environmental Research and Public Health*

## **Association between Mixing Ability of Masticatory Functions Measured Using Color-Changing Chewing Gum and Frailty among Japanese Older Adults: The Kyoto–Kameoka Study**

**Daiki Watanabe 1,2,\*, Tsukasa Yoshida 1,2,3,4, Keiichi Yokoyama 2,3, Yasuko Yoshinaka 5, Yuya Watanabe 1,3,6, Takeshi Kikutani 7, Mitsuyoshi Yoshida 8, Yosuke Yamada 1,2,3, Misaka Kimura 2,3,9,\* and Kyoto-Kameoka Study Group <sup>1</sup>**

Received: 26 May 2020; Accepted: 22 June 2020; Published: 24 June 2020

**Abstract:** The relationship between mixing ability of masticatory functions and frailty has not been well evaluated. This study investigated the prevalence of physical and comprehensive frailty and its association with mixing ability in 1106 older adults aged ≥65 years who underwent physical examination as part of the Japanese Kyoto–Kameoka Study. Mixing ability was assessed using color-changing chewing gum (1–5 points, 5 representing the best mixing ability). Participants were divided into four groups (5 points, 4 points, 3 points, and 1 or 2 points). The modified Japanese versions of the Cardiovascular Health Study (mJ-CHS) criteria and the validated Kihon Checklist (KCL) were used to assess physical and comprehensive frailty, respectively. Multivariate logistic regression was used to evaluate the association between frailty and mixing ability. The prevalence of physical and comprehensive frailty was 11.8% and 27.9%, respectively. After adjusting for confounders, the odds ratios of physical and comprehensive frailty comparing the highest to the lowest chewing gum score groups were 3.64 (95% confidence interval (CI): 1.62 to 8.18; *p* for trend = 0.001) and 2.09 (95% CI: 1.09 to 4.03; *p* for trend = 0.009), respectively. Mixing-ability tests involving chewing gum may be an indicator associated with both physical and comprehensive frailty.

**Keywords:** mixing ability; color-changing chewing gum; frailty; older adults; cross-sectional study

#### **1. Introduction**

Frailty is a condition in which multiple physiological systems decline in function owing to a loss of homeostasis in response to stress [1,2] and is considered a worldwide public health problem among older adults [3]. Two major concepts of frailty exist in the literature: the Fried phenotype (FP) model, which defines frailty based on five criteria (physical frailty) [1,4,5], and the health deficits model (e.g., the Rockwood Frailty Index), which defines frailty as a multidimensional clinical geriatric syndrome (comprehensive frailty) [2,4,6,7]. The Kihon Checklist (KCL) is considered a valid tool for assessment of comprehensive frailty as a multidimensional clinical geriatric syndrome and is widely used in Japan and other countries [4,8–10]. Overall, frailty is associated with increased risk of death [8,11,12], disability [8,13], and burden of disease using disability-adjusted life years [14] in older adults. Accordingly, there is a need to establish objective, simple tools for the evaluation of frailty, with concepts rooted in both conceptual models.

The prevalence of oral disease among older adults is high, and the number of people with poor masticatory functions increases with age [15]. Decline in masticatory functions limits food choices and reduces the enjoyment of eating, making it difficult to secure sufficient food intake to maintain physiological function, which, in turn, leads to malnutrition [16,17]. An association between poor masticatory functions and frailty is thus likely [18,19]. In fact, a number of previous studies have reported that masticatory functions are positively associated with grip strength [20,21] and gait speed [20], indicating that it could be used to assess physical function in older adults.

Previous epidemiological studies have investigated masticatory functions with self-reported measures, primarily questionnaire-based [22], which have also assessed the number of natural teeth of participants and denture use [23]. Objective methods of evaluating masticatory functions, likely to provide more accurate results, often require specialist technology and skill [17]. Recent studies have confirmed the validity of mixing-ability assessment using color-changing chewing gum in terms of the number of teeth [24], tongue motor function, tongue pressure [25], and chew count [26] and found that gum chewing can objectively measure masticatory performance without the requirement for specialist technology or skill. Previous studies have reported that poor oral health including a lower mixing ability is associated with physical [18,19,24,27] and comprehensive frailty [28,29]. However, those previous studies have focused only on frailty, and the association of mixing ability with the subdomains of each assessment tool for frailty has not been well evaluated. In particular, although one subdomain of the comprehensive frailty defined by KCL includes oral function, sensitivity analyses that examine the relationship between mixing ability and each domain of frailty are also needed. This study aimed to investigate the association between objective mixing ability of masticatory functions and physical and comprehensive frailty using two validated frailty assessment tools in a community-based cohort study of older adults. In addition, we also examined the association between mixing ability and the subdomains of each assessment tool for frailty. Although the term "frailty" appears simple, the definition of frailty varies between studies [30]. Therefore, the association between mixing ability and frailty defined using a multiple assessment tool should be evaluated because there are many such tools in existence, with greater heterogeneity in their classification and predictive abilities.

#### **2. Materials and Methods**

#### *2.1. Study Design and Population*

The Kyoto–Kameoka Study is a cohort study of older residents of Kameoka City, Kyoto Prefecture, aged ≥65 years, which conducted the Needs in the Sphere of Daily Life survey (baseline survey), including the KCL, on 13,294 residents on July 29, 2011 [31–35]. The Health and Nutrition Status Survey (additional survey) was conducted as a follow-up to the original surveys, and 8319 residents submitted valid responses. For our study, we randomly selected 10 from 21 areas that make up Kameoka City and sent postcards to 4831 residents inviting them to undergo a physical checkup [31]. Of those residents, 1379 participants underwent a physical checkup examination as part of the Kyoto–Kameoka Study during March or April 2012. Written informed consent was obtained from all the participants before data acquisition. Health-related information, including medical history, socioeconomic status, smoking status and alcohol consumption, and physical activity, was extracted from the baseline and additional surveys.

The participants of the present study were respondents for whom baseline data were available (*n* = 1379), excluding those with incomplete responses to the modified Japanese version of the Cardiovascular Health Study (mJ-CHS) criteria (*n* = 75) or the KCL (*n* = 124) and those with missing color-changing chewing gum data (*n* = 74). Ultimately, 1106 participants were included in this study.

This study was approved by the ethics review boards of the Kyoto Prefectural University of Medicine (No. RBMR-E-363), Kyoto University of Advanced Science (No. 20-1), and the National Institute of Health and Nutrition (No. NIHN187-3). The study was carried out in accordance with the principles of the Declaration of Helsinki.

#### *2.2. Frailty Definitions*

We assessed physical and comprehensive frailty according to the mJ-CHS criteria and the KCL [30]. The J-CHS criteria, based on the CHS, modified for validity in Japanese individuals [9], are based on the FP model and include five elements of shrinking, exhaustion, low activity, slowness, and weakness. We evaluated physical frailty according to the mJ-CHS criteria, which are based on the J-CHS criteria with one question substituted for a question evaluating low activity: (I) Have you lost 2–3 kg or more in weight over the past 6 months without trying? If a participant responded "Yes" to this question, this was defined as "weight loss." (II) Do you engage in sports, exercise, or keep-fit activities (other than walking?) If a participant responded "No" to this question, this was defined as "low activity." (III) During the past two weeks, have you felt tired for no reason? If a participant responded "Yes" to this question, this was defined as "exhaustion." (IV) If a participant's grip strength was <26 kg for men and <18 kg for women, this was defined as "weakness." (V) If a participant's normal gait speed was <1.0 m/s, this was defined as "slow gait speed." Individuals were defined as frailty if they met three out of five of the frailty reference criteria [9]. According to a prospective cohort study, FP-model-based physical frailty can also predict disability risk among older adults [36].

Grip strength was measured with a Smedley Hand Dynamometer (Grip-D TKK5101, Takei Scientific Instruments, Niigata, Japan). Measurements were taken twice for each hand, and the mean of the highest value for each hand was used. To evaluate each participant's habitual gait speed, they were instructed to walk a 10-m distance at a comfortable pace. The time taken to walk a 6-m distance, excluding the first 2 m after acceleration and the final 2 m before deceleration, was measured with a digital stopwatch. Gait speed was calculated as distance divided by walking time.

The KCL is a self-administered questionnaire consisting of 25 questions in seven subdomains, covering instrumental activities of daily living (IADL), physical function, nutritional status, oral function, social status, cognitive status, and depression, which provide a comprehensive assessment of frailty. The KCL's assessment principle thus resembles the health deficit model proposed by Rookwood et al. [6,7]. Every problem with activity or function received a point, and the higher the total score, the greater the difficulty in daily functioning, ranging from 0 (no frailty) to 25 (high frailty), with comprehensive frailty defined as a score of 7 or above [4,32–35]. The cutoff points of the KCL subdomains are the following: IADL disability, physical, nutrition, oral, social, cognitive, and depression defined as the IADL disability domain ≥10 points on 20 items including shopping; the physical inactivity domain ≥3 points on 5 items including walk continuously and history of fall; the malnutrition domain = 2 points on 2 items including physique; the oral dysfunction domain ≥2 points on 3 items including dry mouth and poor mastication; the socialization domain ≥1 point on 2 items including frequency of going out less; the memory domain ≥1 point on 3 items including memory loss; and the mood domain ≥2 points on 5 items including fulfilment and helpless [37]. In a prospective cohort study, KCL score was associated with increased risk of death or long-term care insurance certification within the next 3 years [8]. Additionally, we investigated the association between the subdomains evaluated using the mJ-CHS criteria and KCL [37] and mixing ability.

#### *2.3. Measurement of Mixing Ability*

Mixing ability was assessed using color-changing chewing gum (Xylitol Masticatory Performance Evaluating Gum, Lotte, Tokyo, Japan) [17,24–26,28,29,38,39]. This chewing gum contained 7 kcal energy, 2.3 g carbohydrate (0 g sugar), and 1.7 g xylitol (70 mm × 20 mm × 1 mm, 3.0 g). It contained ingredients that did not adhere to teeth or dentures as well as red, yellow, and blue dyes that change color when mixed with saliva and chewed. The red dyes are sensitive to pH changes, turning red under neutral or alkaline conditions. The unchewed gum contains citric acid, which maintains it in an acid environment (low pH) and is yellowish-green in color. When chewed and mixed with saliva, the internal pH of the gum changes from acidic to neutral or alkaline, turning it red. Therefore, a higher number of chews was associated with red colored gum, which indicated better mixing ability. This test principally evaluates the mixing ability of masticatory functions.

The study staff provided participants with detailed information on the chewing gum and explained the experimental protocol, instructing all participants to chew the gum as many times as possible in a 60-s period. They told participants to spit out the chewed gum into a piece of white tissue paper as soon as the measurement period was over and immediately compared it with the color chart on the packaging. If one of the study staff was unsure how to classify the color of the gum, they discussed it with other study staff before determining the color and assessing mixing ability. If the chewed gum contained more than one color (had not been thoroughly chewed), the study staff reduced the mixing ability score by 1 point. Mixing ability measured using color-changing chewing gum was graded in a range from 1 point (yellowish-green; the poorest mixing ability) to 5 points (red; the best mixing ability). Visual determination of chewing gum color was previously validated by comparison with chromaticity of colorimetry measurements [39].

#### *2.4. Statistical Analysis*