The Strong Correlation Between Pain Interference and Abnormal Nutritional Status in Older Adults With Chronic Musculoskeletal Pain

Abstract

Objectives:

Musculoskeletal pain, one of the most common issues faced by older adults, has multidimensional effects including an increased risk of malnutrition. Therefore, this study aimed to investigate the association between pain interference and nutritional status in older adults with chronic musculoskeletal pain.

Methods:

In this cross-sectional study, data were collected from older adults (age: >60 years) using the brief pain inventory and mini nutritional assessment questionnaire. The correlation between pain interference, pain severity, and nutritional status was assessed using the chi-square test and Spearman’s rank correlation. Multiple logistic regression analysis was used to analyze the variables associated with abnormal nutrition status.

Results:

Overall 241 older adults were recruited in the study. The median (IQR) age of the participants was 70 (11) years, pain severity subscale was 4.2 (1.8), and pain interference subscale was 3.3 (3.1). Abnormal nutritional status was positively correlated with pain interference (Odds ratio [OR]: 1.26; 95% confidence interval [CI]: 1.08-1.48; P = .004), pain severity (OR: 1.25; 95% CI: 1.02-1.53; P = .034), age (OR 1.06; 95% CI: 1.01-1.11, P = .011), and hypertension (OR = 2.17; 95% CI: 1.11-4.26; P = .024).

Conclusions:

This study reports a strong correlation between pain interference and nutritional status. Therefore, pain interference can be a useful pain assessment tool to indicate risk of abnormal nutritional status in older adults. In addition, related factors, including age, underweight, and hypertension, were associated with a higher risk of malnutrition.

Keywords

musculoskeletal pain pain interference pain severity malnutrition older adult

Highlights

This study assessed the effect of pain interference on nutritional status.

The study participants were older adults with chronic musculoskeletal disorders.

Strong correlation between pain interference and nutritional status was observed.

Age, underweight, and hypertension were associated with nutritional status.

The association was observed even in older adults with mild-to-moderate pain.

Introduction

The global population is rapidly aging with the older population growing at the rate of 11% to 22% worldwide and likely to increase from 1 billion to 1.4 billion by 2030.¹ The associated degenerative changes affecting different aspects of their life can increase the burden on public health. Moreover, 80% of older adults have been reported to live in low-to middle-income countries. Therefore, health of the older population has become a major concern and challenge for health care systems.²

Musculoskeletal pain is one of the most common health problems in older adults, with a prevalence of 30% to 60%.³ This pain has been proposed to be caused by body deterioration, decreased bone density, and reduced muscle strength^3,4 Musculoskeletal pain limits movement and interferes with the daily activities of older adults. Thus, the effect of this deterioration is not limited to pain. Pain interference is an instrument used to assess functional interference caused by pain, and represents the effects of pain on daily activity restriction.^5,6 Previous study have found a correlation between pain intensity and pain interference in moderate-to-severe pain⁷ Moreover, previous studies have reported the association between pain interference and physical function, fatigue, depression, stress, and fear avoidance behavior.^7-9 Accordingly, pain interference has been suggested for pain measurement and management in clinical practice^5-7 Moreover, chronic musculoskeletal disorders are primarily treated by relieving pain intensity, which is not enough for multidisciplinary treatment of pain.¹⁰ However, other factors need to be considered by healthcare providers including mental health, perception, environment, social, and nutritional status.

According to the World Health Organization (WHO), malnutrition is a state of imbalance between the nutrient demand and supply of the body. They are divided into 3 categories: undernutrition, micronutrient-related malnutrition, and overweight. Saunders (2010)¹¹ proposed that malnutrition affects the function and recovery of several internal organs. This effect starts with a decrease in cardiac muscle mass, followed by decrease in cardiac output, and ultimately organ damage, increasing the likelihood of morbidity and mortality.¹¹ Previous studies have reported that multiple factors are associated with geriatric malnutrition, including increased age,^12-14 female sex,^12,14,15 sedentary and decreased occupational physical activity,¹⁶ comorbidities in the older population, especially diabetes mellitus, cardiovascular disease,¹⁴ gastrointestinal disease, limited activities of daily living,¹⁷ and chronic pain.^4,18 Notably, pain severity including that caused by musculoskeletal pain, is reported to be significantly associated with decreased appetite and dietary intake and increased malnutrition risk by 30% in older adults.⁴ Moreover, being overweight causes excessive joint loading.^19-21 While, undernutrition leads to a loss of muscle and bone mass and absorption capacity.²² Thus, nutritional status is proposed to be affected by musculoskeletal pain.³

Therefore, the primary objective of this study was to investigate the correlation between pain interference and nutritional status in older adults with chronic musculoskeletal pain and the secondary objective was to compare the magnitude of pain severity and interference and their correlation with nutritional status to prevent or reduce the risk of abnormal nutritional status using proper pain assessment in older adults.

Materials and Methods

Participants

This cross-sectional study included 241 older adults (aged ≥60 years) who visited rehabilitation, orthopedic, or physical therapy outpatient clinics at Songklanagarind Hospital, Prince of Songkla University, Thailand from September 15, 2022, to October 5, 2022. Inclusion criteria were older adults with chronic musculoskeletal pain, defined as a persistent pain for ≥3 months, and suffering from ongoing pain during the past 30 days. All participants were able to communicate in Thai. Exclusion criteria were a history of cerebrovascular disease, bedridden status, wheelchair use in daily life, symptoms of major depressive disorder, and active cancer. The population size was calculated using 2 independent proportion methods with the N4Study application. The α error probability was set to .05. All variables used in the sample size formula represent determinants including musculoskeletal disorder, age, female sex, and multiple comorbidities that related to abnormal nutritional status in older adults. This study was approved by the Human Research Ethics Committee of the Faculty of medicine, Prince of Songkla University (REC 65-325-9-1). Eligible participants were enrolled in the study after obtaining their informed consent.

Data Collection

The data were collected using face-to-face interviews. All participant information was collected using a standard questionnaire. The first part of the questionnaire included general information, including sex, age, weight, height, body mass index (BMI), area of musculoskeletal pain, and underlying diseases. The second part was the Thai version of the brief pain inventory short form (BPI), and the third part was the Thai version of mini nutritional assessment (MNA). Flow of the study is presented in Figure 1.

Figure 1.

Flow diagram of the study.

Outcomes

Pain interference

The BPI is divided into 2 subscales: pain severity and pain interference. This study used the BPI (pain interference subscale) to assess 7 different functional interferences, general activity, mood, walking ability, normal work, relation with other people, sleep, and enjoyment of life. The score ranges from 0 to 10, where 0 indicates no interference, and 10 indicates complete interference. The scores for all aspects were calculated as mean. The validity and reliability of the pain interference subscale in the BPI was reported with a Cronbach’s alpha value of .88 to .93.²³

Pain severity

Pain severity was assessed using the numeric pain rating scale (NPRS) and BPI (Pain severity subscale). Both scales are scored in the same way. The pain severity score ranges from 0 to 10, with 0 representing no pain and 10 representing the worst pain imaginable. The NPRS was used to rate their resting pain. After that, the participants were asked about the worst, average, and least pain they experienced in the past 24 h, and current pain using the BPI. The mean value was then used to signify the pain severity based on the BPI. The validity and reliability of the pain severity subscale in the BPI was reported with a Cronbach’s alpha value of .89 to .94.²³

Nutritional status

Nutritional status was evaluated using the full form of MNA questionnaire, divided into 2 parts: screening for malnutrition (14 points) and assessment of nutritional status (16 points). The MNA also included measurements of the mid-arm and calf circumference. Total points were summarized and categorized into 3 different categories: normal (24-30 points), risk of malnutrition (17-23.5 points), and undernutrition (<17 points). The sensitivity, specificity, and predictive values were reported as 96%, 98%, and 97%, respectively.^24,25

Data Analysis

Data management was performed using R studio Version 3.3.0 (Public Benefit Corporation, USA, 2009). The medians, interquartile ranges, means, and percentages were calculated for descriptive analyses. Differences between pain interference, pain severity, and associated factors among nutritional status groups were evaluated using Wilcoxon Rank Sum Test, Chi-square test, Fisher’s exact test, and t-test. The correlation between pain severity, pain interference, and MNA score was analyzed using Spearman’s rank correlation coefficient. Multiple logistic regression analysis was used to analyze the association between other variables and malnutrition. The level of significance was set at P < .05.

Results

A total of 392 patients at the outpatient clinics were screened for eligibility, 241 matched the inclusion criteria, and 151 were excluded from the study based on the exclusion criteria. Overall, 67.6% (n = 163) of the participants were female. The overall median (IQR) age group was 70 (65, 76) years. Of these, hypertension and dyslipidemia were the most prevalent comorbidities. Among all the participants, 57.3% (n = 138) had pain in one location. Moreover, 43.2% (n = 104) of the participants experienced pain in their lower extremities. The mean (SD) of the following items were reported as having the highest pain interference score: walking (3.50 [4.95]), general activity (3.00 [4.25]), and normal work (which includes both work outside the home and housework) (3.00 [4.24]), respectively.

This study found 2.5% (n = 6) of the participants were underweight (BMI < 18.5 kg/m²), which could be classified with the MNA score, and found the prevalence of undernutrition was 1.7% (n = 4), and the risk of malnutrition was 30.3% (n = 73). Because of the small number of “undernutrition” and “risk of malnutrition” responses, they were merged into a single “abnormal nutritional status” response for analysis. The median (IQR) of pain interference subscale was 3.3 (1.9, 5.0). The mean (SD) pain severity subscale was 4.2 (1.8) and the median (IQR) of the drug efficiency scale was 7 (7.0, 39.0) (Table 1).

Table 1.

Participant Characteristics.

		Nutritional status
Characteristics	Overall	Abnormal (n = 77)	Normal (n = 164)	P-value
Age, median (IQR)	70 (11)	72 (10)	68.5 (11)	<.001^a
Sex, n (%)				.247^b
Male	78 (32.4)	21 (27.3)	57 (34.8)
Female	163 (67.6)	56 (72.7)	107 (65.2)
BMI (kg/m²), n (%)				<.001^c
Underweight (<18.5 kg/m²)	6 (2.5)	5 (6.5)	1 (0.6)
Normal range (18.5-22.9 kg/m²)	83 (34.4)	38 (49.4)	45 (27.4)
Obese (>22.9 kg/m²)	152 (63.1)	34 (44.2)	118 (72)
Underlying disease, n (%)
DM	49 (20.3)	19 (24.7)	30 (18.3)	.251^b
HT	119 (49.4)	45 (58.4)	74 (45.1)	.054^b
DLP	116 (48.1)	39 (50.6)	77 (47)	.592^b
CKD	6 (2.5)	3 (3.9)	3 (1.8)	.387^c
CAD	13 (5.4)	4 (5.2)	9 (5.5)	1.000^c
GI disease	12 (5)	4 (5.2)	8 (4.9)	1.000^c
Area of pain, n (%)				.524^b
Upper extremities	49 (20.3)	13 (16.9)	36 (22)
Lower extremities	104 (43.2)	31 (40.3)	73 (44.5)
Lower back	76 (31.5)	29 (37.7)	47 (28.7)
Neck	12 (5)	4 (5.2)	8 (4.9)
Number of pain locations, n (%)				.396^b
1	138 (57.3)	38 (49.4)	100 (61)
2	25 (10.4)	10 (13)	15 (9.1)
3	29 (12)	11 (14.3)	18 (11)
> 3	49 (20.3)	18 (23.4)	31 (18.9)
Pain score (NPRS), n (%)				.050^b
Mild	140 (58.1)	36 (46.8)	104 (63.4)
Moderate	81 (33.6)	33 (42.9)	48 (29.3)
Severe	20 (8.3)	8 (10.4)	12 (7.3)
Pain severity, mean (±SD)	4.2 ± 1.8	4.6 ± 1.8	4 ± 1.7	.011^d
Pain interference, median (IQR)	3.3 (3.1)	4.1 (3.4)	2.9 (2.9)	<.001^a
Treatment efficiency, median (IQR)	7 (3)	5 (4)	7 (3)	.145^a

Abbreviations: BMI, Body Mass Index; CA, Cancer; CAD, Coronary artery disease; CKD, Chronic kidney disease; DLP, Dyslipidemia; DM, Diabetes mellitus; GI, Gastrointestinal; HT, Hypertension.

Statistical significance in the Wilcoxon Rank Sum Test.

Statistical significance in the Chi-square test.

Statistical significance in the Fisher’s exact test.

Statistical significance in the t-test.

The Spearman’s rank correlation revealed a stronger correlation between pain interference and MNA score (ρ = −.27, P < .001) than between pain severity and MNA score (ρ = −.19, P = .0026) in this study (Table 2).

Table 2.

Spearman’s Rank Correlation of Pain Severity, Interference, and MNA Score.

	Pain severity	Pain interference	MNA score
Pain severity		—	—
Pain interference	ρ = .430	—	—
Pain interference	P < .001	—	—
MNA score	ρ = −.190	ρ = —.270	—
	P = .003	P < .001

In Table 3, the multivariate analysis revealed that age, BMI, pain severity and pain interference were significantly associated with the MNA score. The pain interference subscale was significantly correlated with the MNA score (OR: 1.28; 95% CI: 1.08-1.53; P = .005) and pain severity (OR: 1.3; 95% CI: 1.04-1.63; P = .023).

Table 3.

Multiple Logistic Regression Predicting Nutritional Status Between Abnormal and Normal Nutritional Status.

Variables	Crude OR(95% CI)	CrudeP value	AdjustedOR (95% CI)	P (Wald’s test)	P (LR-test)
Age	1.08 (1.04, 1.13)	<.001	1.07 (1.02, 1.13)	0.009	.008*
Sex	0.7 (0.39, 1.28)	.248	0.76 (0.35, 1.69)	0.509	.507
BMI (Underweight as reference)					<.001*
Normal	0.17 (0.02, 1.51)	.111	0.16 (0.01, 1.75)	.132
Obese	0.06 (0.01, 0.51)	.010	0.03 (0, 0.38)	.006
Pain severity	1.22 (1.04, 1.43)	.013	1.30 (1.04, 1.63)	.023	.020*
Treatment efficiency	0.93 (0.86, 1.02)	.138	0.98 (0.88, 1.1)	.747	.747
Pain interference	1.24 (1.09, 1.41)	<.001	1.28 (1.08, 1.53)	.005	.004*
Area of pain (Upper extremities as reference)					.568
Lower extremities	1.18 (0.55, 2.52)	.676	1.55 (0.59, 4.06)	.378
Lower back	1.71 (0.78, 3.75)	.181	1.16 (0.42, 3.19)	.775
Neck	1.38 (0.36, 5.38)	.638	2.86 (0.54, 15.1)	.217
Number of pain locations(1 as reference)					.391
2	1.75 (0.73, 4.24)	.212	2.57 (0.82, 8.11)	.106
3	1.61 (0.7, 3.72)	.266	0.92 (0.32, 2.70)	.886
More than 3	1.53 (0.77, 3.05)	.229	1.49 (0.58, 3.81)	.407
Underlying disease
DM	1.46 (0.76, 2.81)	.253	1.02 (0.42, 2.52)	.958	.958
HT	1.71 (0.99, 2.96)	.055	2.24 (1.03, 4.87)	.042	.040*
DLP	1.16 (0.67, 1.99)	.592	1.76 (0.82, 3.77)	.145	.142
CKD	2.18 (0.43, 11.04)	.348	2.57 (0.28, 23.66)	.404	.413
CAD	0.94 (0.28, 3.17)	.925	0.89 (0.17, 4.62)	.894	.894
GI disease	1.07 (0.31, 3.66)	.916	2.12 (0.47, 9.51)	.325	.338

Abbreviations: BMI, Body Mass Index; CAD, coronary artery disease; CKD, chronic kidney disease; DLP, dyslipidemia; DM, diabetes mellitus; GI, gastrointestinal; HT, hypertension; OR, Odds ratio.

Log-likelihood = −112.0453, No. of observations = 241, AIC value = 270.0905.

significance at P < .05.

However, variables such as sex, number of pain locations, area of pain, and treatment efficiency were not significantly associated with the MNA score (P > .05).

Table 4 presents the results of the multiple logistic regression model after identifying potential confounding variables. Age, BMI, pain severity, and pain interference were significantly associated with nutritional status.

Table 4.

Multiple Logistic Regression with Potential Confounders Predicting Nutritional Status Between Abnormal and Normal Nutritional Status.

Variables	Crude OR(95% CI)	CrudeP value	Adjusted OR(95% CI)	P (Wald’s test)	P (LR-test)
Age	1.08 (1.04, 1.13)	<.001	1.06 (1.01, 1.11)	.011	.010*
Sex	0.7 (0.39, 1.28)	.248	0.69 (0.34, 1.42)	.317	.313
BMI (Underweight as ref.)					<.001*
Normal	0.17 (0.02, 1.51)	.111	0.20 (0.02, 2.04)	0.175
Obese	0.06 (0.01, 0.51)	.010	0.05 (0.01, 0.53)	.012
Pain severity	1.22 (1.04, 1.43)	.013	1.25 (1.02, 1.53)	.034	.031*
Pain interference	1.24 (1.09, 1.41)	<.001	1.26 (1.08, 1.48)	.004	.003*
Underlying disease
HT	1.71 (0.99, 2.96)	.055	2.17 (1.11, 4.26)	.024	.022*

Abbreviations: BMI, Body Mass Index; HT, Hypertension; OR, Odds ratio.

Log-likelihood = −116.7861; No. of observations = 241; AIC value = 251.5723.

significance at P < .05.

Discussion

This is the first study to examine the correlation between pain interference and nutritional status in older adults with chronic musculoskeletal pain. The current study revealed that pain interference was significantly and positively correlated with nutritional status (MNA score) and had a stronger correlation than pain severity. Additionally, factors that were significantly associated with nutritional status were age, BMI, and hypertension.

The prevalence of abnormal nutritional status in this study was 32% (almost one-third of participants). Previous studies have reported that the prevalence of malnutrition by MNA ranges from 3% to 30% in older adults.^26,27 Therefore, older adults with musculoskeletal pain are likely to have a slightly higher prevalence of abnormal nutritional status than those without pain.

Pain interference has been considered a useful self-rating scale for measuring pain-related activity limitations in previous studies.^8,9,28 The current study reports that pain interference and pain severity have a significant positive correlation with nutritional status, and pain interference was correlated with pain severity (ρ = .43, P < .001) in older adults with musculoskeletal pain. This finding corresponds with previous studies that reported a positive correlation between pain severity and pain interference in moderate-to-severe trauma and cancer pain.^29-31 Moreover, previous studies have suggested a link between moderate to severe pain and obesity,⁷ which corresponds with the current study, that found most of the participants were classified as obese by BMI. Obesity is indicated as a significant factor in musculoskeletal pain because of the increasing load on joint structures.^3,32 In contrast, undernutrition, especially of proteins, can lead to a loss of skeletal muscle functions and bone integrity, as well as bone absorption capacity.^3,33 Moreover, the progressive severity of malnutrition has been proposed as a risk factor for osteoporosis and sarcopenia, which can reduce the quality of life in older adults.^34,35

Although most of the participants in this study reported mild to moderate pain, the findings revealed a strong correlation between pain and risk of malnutrition. Thus, this study findings supported that musculoskeletal pain may induce abnormal nutritional status even mild to moderate pain by inducing activity limitation, emotional disturbance, and a lower quality of life.³⁶ Therefore, the early detection of nutritional status in this patient population is important.

The underlying mechanism of the association between pain interference and abnormal nutritional status involves higher secretion of cortisol induced by pain and stress, resulting in nutrient malabsorption and nutrient deficiency.³⁷ Furthermore, sleep deprivation in patients with musculoskeletal pain can decrease appetite and food intake³⁸ due to gastrointestinal disturbances including dyspepsia, abdominal bloating, nausea, vomiting, and irregular bowel habits.³⁹

In the current study, most of the participants reported their pain in the lower extremities (43.2%) and lower back (31.5%), which can affect the ability to walk, which is presented with the highest pain interference score. Previous studies reported the possibility that reducing gait performance can increase malnutrition risk because decreased walking ability is related to long term loss of lean muscle mass, muscle strength, physical functions, and an abnormal BMI.^40-42 Therefore, the older adults with difficulty walking could be vulnerable, which can affect their independence in daily activities. Moreover, in this study, the participants complained that pain sometimes severely interferes with their functions, leading to limited mobility, and contributing to lesser food and dietary consumption. Therefore, evaluation of pain interference should be used in medical practice in addition to musculoskeletal pain management in older adults as this facilitates a holistic approach for better treatment outcomes.

The current study also reports that age was significantly associated with abnormal nutritional status (OR 95% CI: 1.07, 1.02-1.12; P = .002). This result was comparable to that of previous studies,^12-14 reporting an association between age and higher prevalence of malnutrition. This could be associated with the physiological and psychological changes in older adults.² These findings could be beneficial for health care providers, especially geriatric nutrition specialists, in providing proper nutrients for their patients.

For BMI, the odds ratio of obese participants and abnormal nutritional status in this study were 0.3% lower than that of patients who were underweight (Adjusted OR = 0.03 [0, 0.038]; P = .006). This could be due to some patients with an abnormal nutritional status already suffering from wasting and stunning.⁴ Previous studies have reported a significantly higher prevalence of malnutrition in women.^12,14,15 On the contrary, the results of the current study showed that sex was not significantly associated with abnormal nutritional status (Adjusted OR [95% CI] 0.82 [0.41, 1.63]; P = .574). However, the proportion of female participants in the current study was twice as that of the male population, which causes ineffective representation of the actual population. Further research is needed to assess if the female sex is more vulnerable to malnutrition. A study in 2018 demonstrated that the physiological causes of malnutrition in female sex included dietary patterns, lower physical activities, lower nutrient requirements, and endocrine factors.⁴³ Therefore, to improve nutritional status, various aspects of determinants should be considered, especially in the female population.

The current study found that hypertension is associated with abnormal nutritional status in older adults. Sun et al (2017) reported that 52.4% of patients with hypertension had mild malnutrition and 27.1% had moderate-to-severe malnutrition.⁴⁴ A study by Yang et al (2022) also reported a high prevalence of malnutrition among hypertensive patients.⁴⁵ The present study found that participants with hypertension had a higher risk of abnormal nutritional status than those with other underlying diseases (OR = 2.17; 95% CI: 1.11-4.26; P = .024), correlating with results of previous studies.

In this study, the lower extremities and lower back were the most common areas of pain, with prevalence rates of 43.2% to 31.5%, respectively. The area of pain was not significantly associated with abnormal nutritional status in older adults. However, a systematic review has reported that chronic lower back pain reduces muscle strength and limits activity levels.⁴⁶ Limited physical activity increased body weight and simultaneously increased BMI, which was significantly and negatively associated with the MNA score, as observed in this study. The same result was found in the association of the number of pain locations and drug efficiency with nutritional status in older adults. Although the number of pain areas was not statistically significant, suffering from pain tended to increase the risk of abnormal nutritional status. Although the use of pain relievers, particularly NSAIDs, was proposed as a related factor leading to gastrointestinal complications such as gastric and duodenal ulcers⁴⁷ which may induce malnourished.⁴⁸ However, there has been no previous investigation into this notice. According to the current study, no relationship was observed between drug efficiency and nutritional status. Nevertheless, effective pain management is still a factor that clinicians should pay attention to because there is significant potential for improving the quality of life.⁴⁹ Adequate pain control to minimize chronic pain also plays a role in encouraging patients to cooperate with treatment plans and enhance their nutritional status because preventing malnutrition may have positive impacts and decrease the economic burden of treating it in older persons.

The current study had several limitations. First, it included an excessive number of female participants. Second, the different education backgrounds of each participant also affected their understanding and evaluation of the questions on the BPI. Finally, this study was conducted in a few clinical settings, making it difficult to generalize the findings to other clinical settings.

According to this study, an abnormal nutritional status in older adults with chronic musculoskeletal pain is correlated with pain interference. This study supports the use of pain interference evaluation in addition to pain severity assessment in clinical settings by healthcare providers, particularly in underweight patients and those with hypertension, to prevent abnormal nutritional status, which can affect patient well-being and increase the risk of malnutrition.

Conclusions

This study revealed that 32% of older adults with chronic musculoskeletal pain have an abnormal nutritional status. The related factors, including age, underweight, and hypertension, were associated with a higher abnormal nutritional status. This study demonstrated that pain interference is strongly correlated with the MNA score, which represents nutritional status. Therefore, it could be a useful pain assessment tool to indicate an increased risk of malnutrition in older adults.

Footnotes

Acknowledgements

All the patients who volunteered to participate in the study are gratefully acknowledged by the authors.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Narucha Komolsuradej

Siwaluk Srikrajang

Khanin Iamthanaporn

References

World Health Organization. Aging and health. 2022. Accessed February 10, 2023. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health.

Jingjit

Komolsuradej

Factors influencing the quality of life among sub-urban older adults in southern part of Thailand. J Public Hlth Dev. 2022;18(3):5-15.

Costa

ABP

Machado

LAC

Dias

JMD

, et al. Nutritional risk is associated with chronic musculoskeletal pain in community-dwelling older persons: the PAINEL study. J Nutr Gerontol Geriatr. 2016;35:43-51.

Welsh

Yang

Makris

UE.

Musculoskeletal pain in older adults: a clinical review. Med Clin. 2020;104:855-872.

Bauer

Hödl

Eglseer

Association between malnutrition risk and pain in older hospital patients. Scand J Caring Sci. 2021;35:945-951.

Thomas

Peat

Harris

Wilkie

Croft

PR.

The prevalence of pain and pain interference in a general population of older adults: cross-sectional findings from the North Staffordshire Osteoarthritis Project (NorStOP). Pain. 2004; 110(1-2):361-368.

Eslami

Katz

White

, et al. Pain intensity and pain interference in older adults: role of gender, obesity, and high-sensitivity C-reactive protein. Gerontology. 2017;63(1):3-12.

Farrar

JT.

Pain interference on quality of life is not just about physical function. Pain Med. 2019;20(2):211-212.

Karayannis

Sturgeon

Chih-Kao

Cooley

Mackey

SC.

Pain interference and physical function demonstrate poor longitudinal association in people living with pain: a PROMIS investigation. Pain. 2017;158(6):1063-1068.

10.

Miettinen

Kautiainen

Mäntyselkä

Linton

Kalso

Pain interference type and level guide the assessment process in chronic pain: categorizing pain patients entering tertiary pain treatment with the brief pain inventory. PLoS One. 2019;14(8):e0221437.

11.

Saunders

Smith

Malnutrition: causes and consequences. Clin Med. 2010;10(6):624-627.

12.

Alzahrani

El Sayed

Alshamrani

SM.

Prevalence and factors associated with geriatric malnutrition in an outpatient clinic of a teaching hospital in Jeddah, Saudi Arabia. Ann Saudi Med. 2016;36(5):346-351.

13.

Bakhtiari

Pourali

Omidvar

Nutrition assessment and geriatric associated conditions among community dwelling Iranian elderly people. BMC Geriatr. 2020;20(1):278.

14.

Saintrain

MVL

Sandrin

RLESP

Bezerra

Lima

AOP

Nobre

Braga

DRA

. Nutritional assessment of older adults with diabetes mellitus. Diabetes Res Clin Pract. 2019; 155:107819.

15.

Ghosh

Dasgupta

Paul

Sembiah

Biswas

Mallick

Screening for malnutrition among the elderly with MNA Scale: a clinic based study in a rural area of West Bengal. Int J Contemp Med. 2017;4:1978-1982.

16.

Abdu

Yimamu

Kahsay

AA.

Predictors of malnutrition among older adults aged above 65 years in eastern Ethiopia: neglected public health concern. BMC Geriatr. 2020;20(1):497.

17.

Cohen

Lofgren

Greene

Delmonico

Greaney

ML.

Relationship between diet quality, physical activity, and health-related quality of life in older adults: findings from 2007-2014 National Health and Nutrition Examination Survey. J Nutr Health Aging. 2018;22(9):1072-1079.

18.

Elma Yilmaz

Deliens

, et al. Do nutritional factors interact with chronic musculoskeletal pain? A systematic review. J Clin Med. 2020;9(3).

19.

Butterworth

Landorf

Smith

Menz

HB.

The association between body mass index and musculoskeletal foot disorders: a systematic review. Obes Rev. 2012;13(7):630-642.

20.

Messier

Gutekunst

Davis

DeVita

Weight loss reduces knee-joint loads in overweight and obese older adults with knee osteoarthritis. Arthritis Rheum. 2005;52(7):2026-2032.

21.

Powell

Teichtahl

Wluka

Cicuttini

FM.

Obesity: a preventable risk factor for large joint osteoarthritis which may act through biomechanical factors. Br J Sports Med. 2005; 39(1):4-5.

22.

Hao

Guo

Anorexia, undernutrition, weight loss, sarcopenia, and cachexia of aging. Eur Rev Aging Phys Activity. 2012;9(2):119-127.

23.

Chaudakshetrin

Validation of the Thai version of brief pain inventory (BPI-T) in cancer patients. J Med Assoc Thai. 2009;92(1):34-40.

24.

Vellas

Guigoz

Garry

, et al. The Mini Nutritional Assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition. 1999;15(2):116-122.

25.

Krishnamoorthy

Vijayageetha

Kumar

Rajaa

Rehman

Prevalence of malnutrition and its associated factors among elderly population in rural Puducherry using mini-nutritional assessment questionnaire. J Fam Med Prim Care. 2018;7(6):1429-1433.

26.

Crichton

Craven

Mackay

Marx

de van der Schueren

Marshall

A systematic review, meta-analysis, and meta-regression of the prevalence of protein-energy malnutrition: associations with geographical region and sex. Age Ageing. 2019;48(1):38-48.

27.

Leij-Halfwerk

Verwijs

van Houdt

, et al. Prevalence of protein-energy malnutrition risk in European older adults in community, residential, and hospital settings, according to 22 malnutrition screening tools validated for use in adults ≥65 years: a systematic review and meta-analysis. Maturitas. 2019;126:80-89.

28.

Judge

Clasey

Crofford

Segerstrom

SC.

Optimism and pain interference in aging women. Ann Behav Med. 2020; 54(3):202-212.

29.

Prastika

Kitrungrote

Damkliang

Pain intensity and pain interference among trauma patients: a literature review. Belitung Nurs J. 2016;2(6):131-139.

30.

Chang

SF.

Frailty is a major related factor for at risk of malnutrition in community-dwelling older adults. J Nurs Scholarsh. 2017;49(1):63-72.

31.

Boggero

Geiger

Segerstrom

Carlson

CR.

Pain intensity moderates the relationship between age and pain interference in chronic orofacial pain patients. Exp Aging Res. 2015;41(4):463-474.

32.

Onyemaechi

Anyanwu

Obikili

Onwuasoigwe

Nwankwo

OE.

Impact of overweight and obesity on the musculoskeletal system using lumbosacral angles. Patient Prefer Adherence. 2016;10:291-296.

33.

Conzade

Phu

Vogrin

, et al. Changes in nutritional status and musculoskeletal health in a geriatric post-fall care plan setting. Nutrients. 2019;11(7):1551.

34.

Verstraeten

LMG

van Wijngaarden

Pacifico

Reijnierse

Meskers

CGM

Maier

. Association between malnutrition and stages of sarcopenia in geriatric rehabilitation inpatients: RESORT. Clin Nutr. 2021;40(6):4090-4096.

35.

Siddique

O’Donoghue

Casey

Walsh

JB.

Malnutrition in the elderly and its effects on bone health—a review. Clin Nutr ESPEN. 2017;21:31-39.

36.

Roux

Guillemin

Boini

, et al. Impact of musculoskeletal disorders on quality of life: an inception cohort study. Ann Rheum Dis. 2005;64(4):606-611.

37.

Konturek

Brzozowski

Konturek

SJ.

Stress and the gut: pathophysiology, clinical consequences, diagnostic approach, and treatment options. J Physiol Pharmacol. 2011;62(6):591-599.

38.

Radcliffe

Whitney

Fagnant

, et al. Severe sleep restriction suppresses appetite independent of effects on appetite regulating hormones in healthy young men without obesity. Physiol Behav. 2021;237:113438.

39.

Vitetta

Coulson

Linnane

Butt

The gastrointestinal microbiome and musculoskeletal diseases: a beneficial role for probiotics and prebiotics. Pathogens. 2013;2(4):606-626.

40.

Lengelé

Bruyère

Beaudart

Reginster

Locquet

Impact of malnutrition status on muscle parameter changes over a 5-Year follow-up of community-dwelling older adults from the sarcoPhAge cohort. Nutrients. 2021;13(2):407.

41.

Misu

Asai

Doi

, et al. Association between gait abnormality and malnutrition in a community-dwelling elderly population. Geriatr Gerontol Int. 2017;17(8):1155-1160.

42.

Chiu

Tsai

Wang

JY.

Combined effect of body mass index and physical activity on the decline in walking ability amongst older Taiwanese. Healthcare. 2014;33:637-648.

43.

Christian

Smith

ER.

Adolescent undernutrition: Global Burden, physiology, and nutritional risks. Ann Nutr Metab. 2018;72(4):316-328.

44.

Sun

Luo

Zhao

Controlling Nutritional Status (CONUT) score as a predictor of all-cause mortality in elderly hypertensive patients: a prospective follow-up study. BMJ Open. 2017;7(9):e015649.

45.

Yang

Z-W

Wei

X-B

Chen

J-Y

DQ.

Prevalence and prognostic significance of malnutrition in hypertensive patients in a community setting. Front Nutr. 2022;9:822376.

46.

Sadler

Cassidy

Peterson

Spink

Chuter

Gluteus medius muscle function in people with and without low back pain: a systematic review. BMC Musculoskelet Disord. 2019; 20(1):463-517.

47.

Bindu

Mazumder

Bandyopadhyay

Non-steroidal anti-inflammatory drugs (NSAIDs) and organ damage: a current perspective. Biochem Pharmacol. 2020;180:114147.

48.

Lim

EJ.

Education and imaging. Gastrointestinal: Ibuprofen-induced duodenal stricture resulting in malnutrition. J Gastroenterol Hepatol. 2010;25(7):1330.

49.

Katz

The impact of pain management on quality of life. J Pain Symptom Manag. 2002;24(1 Suppl):S38-S47.