Abstract
Background
Chronic pain is associated with frailty in older adults; however, the mechanisms underlying this relationship are not fully understood.
Aims
To examine whether sleep quality is involved in the longitudinal association between chronic pain and frailty progression in community-dwelling older adults.
Methods
This longitudinal study included 363 community-dwelling adults aged ≥ 65 years who participated in the 2018 and 2019 waves of the Tarumizu Study in Japan. Chronic pain was defined as self-reported lower back and/or knee pain lasting for at least 2 months. Sleep quality was assessed using a single-item questionnaire and categorized as good, medium, or poor. Frailty was assessed using the Kihon Checklist, and progression was defined as an increase ≥ 2 points over 1 year. Logistic regression and mediation analyses were conducted.
Results
Frailty progression was observed in 27.3% of participants. Chronic pain was significantly associated with frailty progression (odds ratio [OR]: 1.79; 95% confidence interval [CI]: 1.08–2.97), although this association was attenuated after adjustment for sleep quality. Poor sleep quality was independently associated with frailty progression (OR: 2.02; 95% CI: 1.04–3.91). Mediation analysis showed a significant indirect effect of chronic pain on frailty progression through sleep quality (β = 0.08; 95% CI: 0.001–0.20).
Discussion
Poor sleep quality may represent one potential pathway underlying the association between chronic pain and frailty progression in community-dwelling older adults.
Conclusions
Integrated assessment of pain and sleep problems may help identify older adults at greater risk of frailty progression.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1007/s40520-026-03439-8.
Keywords: Frailty, Sleep disturbance, Chronic pain, Mediation analysis
Introduction
Frailty is a common geriatric syndrome characterized by reduced physiological reserves and increased vulnerability to adverse health outcomes, including disability, hospitalization, and mortality [1]. Its prevalence increases with age and has become a major public health concern in aging populations worldwide, particularly in countries experiencing rapid demographic aging [2]. As frailty often leads to reduced quality of life and increased care needs [3, 4], understanding its risk factors is essential to healthy aging. Japan is one of the most rapidly aging countries in the world; hence, frailty has become an increasingly important public health issue among community-dwelling older adults. Previous studies have reported that the prevalence of frailty and prefrailty among Japanese community-dwelling older adults is approximately 7–9% and 40–48%, respectively [5, 6].
Multiple physiological, psychological, and behavioral factors have been implicated in frailty progression, including chronic diseases, cognitive impairment, depressive symptoms, low physical activity, and sleep disturbance [7]. Chronic pain is an important and potentially modifiable condition, and previous studies have shown that chronic pain is associated with subsequent frailty in older adults [8]. Chronic pain differs from acute pain in terms of duration and neurophysiological and psychosocial consequences, including central sensitization and heightened emotional distress [9]. According to the Japanese Clinical Practice Guidelines for Chronic Pain, chronic pain is often accompanied by insomnia, fear, and anxiety, which may contribute to functional disorders and disuse syndrome [9]. These conceptual frameworks suggest that sleep-related problems may represent an important psychosocial pathway linking chronic pain to subsequent functional vulnerability in older adults. Given the rapid aging of the Japanese population, understanding potentially modifiable factors linking chronic pain with frailty progression may have important implications for healthy aging and disability prevention in community settings.
Despite these observations, the mechanisms linking chronic pain and frailty progression remain incompletely understood. Chronic pain may contribute to frailty progression through several physiological and behavioral pathways, including reduced physical activity, psychological distress, and impaired sleep quality. Sleep disturbance and psychological symptoms are common clinical features among individuals with chronic pain and may contribute to reduced activity and functional decline [9]. Moreover, poor sleep quality may contribute to frailty progression through mechanisms such as chronic inflammation, hypothalamic–pituitary–adrenal axis dysregulation, depressive symptoms, and reduced daily activity [10, 11]. Therefore, sleep quality may represent a clinically relevant and potentially modifiable pathway linking chronic pain and frailty progression in older adults. A recent cross-sectional study of older Chinese patients with cancer reported that sleep quality may partially mediate the association between chronic pain and frailty [12], suggesting a potential pathway linking pain-related distress and frailty through sleep disturbances. However, their study focused exclusively on hospitalized patients with cancer, and a temporal relationship between the variables could not be established. To the best of our knowledge, no longitudinal study has examined whether sleep quality mediates the relationship between chronic pain and frailty progression in community-dwelling older adults. Clarifying this pathway may help identify modifiable targets for early intervention.
Therefore, the aim of this study was to examine the longitudinal association between chronic pain and frailty progression and investigate whether sleep quality mediates this relationship in a sample of community-dwelling adults aged ≥ 65 years.
Methods
Study design and participants
This study was based on data from the Tarumizu Study, a community-based health survey conducted in Tarumizu City, Kagoshima Prefecture, Japan: July–December 2018 (baseline) and June–December 2019 (follow-up). Among the 859 community-dwelling adults aged ≥ 65 years who participated at baseline, those with acute pain, neurological disorders, dementia, or long-term care dependency were excluded to reduce potential heterogeneity related to severe functional impairment and to focus on frailty progression among generally independent community-dwelling older adults. Specifically, exclusions were made for those who: reported lower back or knee pain lasting less than 2 months (n = 140); had a history of stroke (n = 36), Parkinson’s disease (n = 1), or dementia (n = 8); required support or long-term care (n = 16); and had missing data (n = 28). Consequently, 630 participants with complete baseline data were included. At follow-up, 267 participants were lost and five had missing data, resulting in a final analytic sample of 363 participants. The mean follow-up period was 337.0 ± 55.2 days (Fig. 1). This study was approved by the Ethics Committee on Epidemiological and Related Studies of Sakuragaoka Campus, Kagoshima University (approval number: 170351). All participants provided written informed consent prior to their participation in accordance with the principles of the Declaration of Helsinki.
Fig. 1.
Flowchart of participant selection process for longitudinal analysis of individuals
Chronic pain
Chronic pain was assessed at baseline using a self-reported questionnaire. Participants were asked whether they had experienced low back or knee pain lasting 2 months or more. Low back and knee pain were specifically assessed because they are common musculoskeletal pain conditions associated with mobility limitation and frailty in older adults [13, 14]. Those who answered “yes” to either or both questions were classified as having chronic pain, while those who reported “no” to both were classified as not having chronic pain. This duration threshold was adopted to distinguish persistent pain from acute pain and was consistent with the original study protocol and a previous related study [15].
Frailty progression
Frailty was assessed at baseline and follow-up using the Kihon Checklist (KCL), a 25-item self-administered questionnaire developed by the Japanese Ministry of Health, Labour and Welfare [16, 17]. The KCL covers seven domains: instrumental activities of daily living, physical function, nutrition, oral function, social engagement, cognitive function, and mood [16]. The total score is 0–25, with higher scores indicating greater frailty risk. The total score has been validated as a predictor of long-term care dependency and mortality among community-dwelling older populations [17]. Because the total KCL score reflects frailty-related deficits across multiple domains and correlates with frailty severity, changes in total KCL score were used to operationally assess worsening frailty-related vulnerability over time [18].
To operationally define a threshold for frailty progression, two complementary approaches were applied [19, 20]. (i) Anchor-based approach: self-rated health was used as the external anchor, assessed using a single-item question, “Would you normally consider yourself healthy?” with four response options, “very healthy,” “fairly healthy,” “fairly unhealthy,” and “very unhealthy” [21]. Mean baseline KCL scores were calculated for each response category among the 363 participants included in the longitudinal analysis, and a weighted average of the differences in mean KCL scores between adjacent response categories was computed [22]. This yielded an anchor-based estimate of 1.86 points. (ii) Distribution-based approach: the threshold was defined as 0.5 standard deviation (SD) of the baseline KCL total score, a criterion considered a conservative estimate of meaningful change in patient-reported outcomes [19]. Based on the baseline mean KCL score of 3.5 and SD of 2.8, the distribution-based estimate was 1.40 points.
Because anchor-based approaches are generally considered more clinically interpretable than distribution-based approaches [19], greater emphasis was placed on the anchor-based estimate. As the KCL is scored using integer values, the anchor-based value (1.86) was operationally rounded to 2 points for interpretability and consistency with prior frailty transition studies. Consistent with a previous frailty transition study [22], the larger and more conservative threshold was adopted as the operational threshold for frailty progression. This threshold should therefore be interpreted as an operational definition rather than a formally established KCL-specific minimally important difference (MID) for community-dwelling older adults. Participants whose KCL total score increased by 2 points or more at follow-up were classified as having frailty progression, while those who did not were classified as having no progression.
Sleep quality
Baseline sleep quality was assessed using a single-item question, “How was your sleep quality at night?” Participants selected one of four responses: “good,” “medium,” “modestly poor,” or “poor.” For the analysis, participants were categorized into three groups based on their responses: “good,” “medium,” and “poor,” with “modestly poor” and “poor” combined into the “poor” category [23]. The sleep quality item was developed with reference to the subjective sleep quality component of the Pittsburgh Sleep Quality Index (PSQI), which similarly employs a single item to assess overall subjective sleep quality [24]. The wording and response options were adapted to improve comprehension and feasibility among community-dwelling older adults participating in a large-scale survey. Notably, the subjective sleep quality component is among the PSQI components showing the strongest correlations with the PSQI global score, supporting its relevance as a core indicator of subjective sleep quality [24].
Other variables
Other baseline variables were assessed for potential confounding or descriptive factors, including age, sex, years of education, low physical activity, polypharmacy (defined as regular use of five or more medications) [25], presence of chronic diseases (hypertension, hyperlipidemia, osteoporosis, and diabetes mellitus), body mass index, appendicular skeletal muscle index (ASMI), current smoking status, depressive symptoms, and cognitive function. Specifically, physical activity was assessed using two self-reported questions: “Do you engage in moderate levels of physical exercise or sports aimed at health?” and “Do you engage in low levels of physical exercise aimed at health?” Participants who responded “No” to both questions were classified as having low physical activity [26]. ASMI was assessed using a multi-frequency bioelectrical impedance analyzer (InBody 470; InBody Japan, Tokyo, Japan) and calculated as appendicular skeletal muscle mass divided by height squared (kg/m²) [27]. Depressive symptoms were assessed using the 15-item Geriatric Depression Scale (GDS-15) [28], with a score of ≥ 6 indicating the presence of depressive symptoms [29]. Cognitive function was evaluated using the Mini-Cog, a brief screening tool that includes a three-word recall task and clock-drawing test [30, 31]. The total Mini-Cog score is 0–5, with lower scores indicating poorer cognitive function.
Statistical analysis
Descriptive statistics were calculated for all variables. Differences in baseline characteristics between participants with and without chronic pain were examined using t-tests for continuous variables and chi-squared tests for categorical variables. Baseline characteristics were also compared between participants included in the final analysis and those lost to follow-up. Logistic regression analysis was performed to examine associations between chronic pain, sleep quality, and frailty progression. Model 1 included chronic pain as the independent variable and frailty progression as the dependent variable and adjusted for baseline age, sex, low physical activity, polypharmacy (≥ 5 medications), osteoporosis, diabetes mellitus, ASMI, current smoking, depressive symptoms (GDS-15 score ≥ 6), and cognitive function (Mini-Cog score). Model 2 included all Model 1 variables and baseline sleep quality.
Mediation analysis was conducted using the PROCESS macro (Model 4) developed by Hayes to assess whether sleep quality mediated the relationship between chronic pain and frailty progression [32]. The direct and indirect effects of chronic pain on frailty progression via sleep quality were evaluated while controlling for Model 2 covariates. The indirect effect was evaluated using a bootstrap method with 5,000 re-samples to calculate 95% bias-corrected confidence intervals. Analyses were conducted using IBM SPSS Statistics (version 30.0; IBM Japan, Tokyo, Japan), and statistical significance was set at p < 0.05.
Results
Participant characteristics
Table 1 shows participant baseline characteristics by chronic pain status. Of the 363 participants, 153 (42.1%) reported chronic pain. Participants with chronic pain were significantly older (75.3 ± 6.3 vs. 72.8 ± 5.3 years, p < 0.001), more often female (71.2% vs. 58.1%, p = 0.010), and had higher KCL scores (4.5 ± 3.2 vs. 2.8 ± 2.3, p < 0.001). Sleep quality also differed (p = 0.011), with poor sleep more common in the chronic pain group (24.8% vs. 12.9%).
Table 1.
Participants’ baseline demographics
| Variables | Overall (n = 363) | Without chronic pain (n = 210) | With chronic pain (n = 153) | p-value |
|---|---|---|---|---|
| Age, years, mean ± SD | 73.8 ± 5.8 | 72.8 ± 5.3 | 75.3 ± 6.3 | < 0.001 |
| Female sex, n (%) | 231 (63.6) | 122 (58.1) | 109 (71.2) | 0.010 |
| Years of education, mean ± SD | 11.5 ± 2.3 | 11.7 ± 2.2 | 11.3 ± 2.3 | 0.068 |
| Low physical activity, n (%) | 45 (12.4) | 30 (14.3) | 15 (9.8) | 0.201 |
| Polypharmacy (≥ 5 medications), n (%) | 90 (24.8) | 43 (20.5) | 47 (30.7) | 0.026 |
| Chronic disease, n (%) | ||||
| Hypertension | 157 (43.3) | 84 (40.0) | 73 (47.7) | 0.143 |
| Hyperlipidemia | 100 (27.5) | 56 (26.7) | 44 (28.8) | 0.660 |
| Osteoporosis | 68 (18.7) | 33 (15.7) | 35 (22.9) | 0.084 |
| Diabetes mellitus | 50 (13.8) | 22 (10.5) | 28 (18.3) | 0.033 |
| Body mass index, kg/m2, mean ± SD | 23.2 ± 3.2 | 22.7 ± 2.7 | 23.8 ± 3.6 | 0.003 |
| ASMI, kg/m2, mean ± SD | 6.4 ± 0.9 | 6.4 ± 0.9 | 6.4 ± 0.9 | 0.742 |
| Current smoking, n (%) | 23 (6.3) | 18 (8.6) | 5 (3.3) | 0.041 |
| GDS-15 score ≥ 6, n (%) | 38 (10.5) | 16 (7.6) | 22 (14.4) | 0.038 |
| Mini-Cog score, mean ± SD | 3.6 ± 1.2 | 3.7 ± 1.2 | 3.4 ± 1.3 | 0.049 |
| KCL score, mean ± SD | 3.5 ± 2.8 | 2.8 ± 2.3 | 4.5 ± 3.2 | < 0.001 |
| Sleep quality, n (%) | 0.011 | |||
| Good | 173 (47.7) | 109 (51.9) | 64 (41.8) | |
| Medium | 125 (34.4) | 74 (35.2) | 51 (33.3) | |
| Poor | 65 (17.9) | 27 (12.9) | 38 (24.8) |
ASMI, appendicular skeletal muscle index; GDS-15, 15-item Geriatric Depression Scale; KCL, Kihon Checklist
The baseline characteristics of participants included in the final analysis and those lost to follow-up are shown in Supplementary Table S1. Participants lost to follow-up were older, had fewer years of education, and had higher baseline KCL scores than those included in the final analysis.
Frailty progression by pain status
Figure 2 shows frailty progression during the follow-up period stratified by chronic pain status. Overall, 99 (27.4%) participants experienced frailty progression. Rates were higher among those with chronic pain than among those without (34.0% vs. 22.4%, respectively; p = 0.014).
Fig. 2.
Frailty progression among participants by chronic pain status
Association between chronic pain, sleep quality, and frailty progression
Table 2 presents the logistic regression results. In Model 1, chronic pain was associated with increased odds of frailty progression after covariate adjustment (odds ratio [OR]: 1.79; 95% confidence interval [CI]: 1.08–2.97; p = 0.023). In Model 2, which included sleep quality, the association was no longer statistically significant (OR: 1.66; 95% CI: 0.99–2.78; p = 0.054). However, poor sleep quality was independently associated with frailty progression compared with good sleep quality (OR: 2.02; 95% CI: 1.04–3.91; p = 0.038).
Table 2.
Association between chronic pain, sleep quality, and frailty progression
| Independent variable | Model 1 | Model 2 | ||
|---|---|---|---|---|
| OR (95% CI) | P-value | OR (95% CI) | p-value | |
| Chronic pain (vs. without) | 1.79 (1.08–2.97) | 0.023 | 1.66 (0.99–2.78) | 0.054 |
| Sleep quality | ||||
| Good | Reference | |||
| Medium | 1.33 (0.77–2.30) | 0.300 | ||
| Poor | 2.02 (1.04–3.91) | 0.038 | ||
Adjusted for age, sex, low physical activity, polypharmacy (≥ 5 medications), osteoporosis, diabetes mellitus, appendicular skeletal muscle index, current smoking, GDS-15 score ≥ 6, and Mini-Cog score
OR, odds ratio; CI, confidence interval
Mediation analysis of sleep quality
Figure 3 illustrates the mediation results. The direct effect of chronic pain on frailty progression was not statistically significant (Β = 0.51; 95% CI: -0.005–1.02). However, the indirect effect through sleep quality was significant (Β = 0.08; 95% CI: 0.001–0.20), suggesting that poor sleep quality partially mediated the association between chronic pain and frailty progression. Although modest in magnitude, the indirect effect was statistically significant.
Fig. 3.
Mediation model of the association between chronic pain and frailty progression via sleep quality. All paths were adjusted for baseline age, sex, low physical activity, polypharmacy (≥ 5 medications), osteoporosis, diabetes mellitus, appendicular skeletal muscle index, current smoking, GDS-15 score ≥ 6, and Mini-Cog score
Discussion
This longitudinal study was conducted to investigate the association between chronic pain and frailty progression among community-dwelling older adults and to determine whether sleep quality mediated this relationship. In the initial model, chronic pain was significantly associated with increased frailty risk. However, this association was not significant after adjustment for sleep quality, which was independently associated with frailty progression. Furthermore, the mediation analysis indicated a modest but statistically significant indirect effect of chronic pain on frailty progression through sleep quality. These findings suggest that sleep quality may represent one potential pathway underlying the association between chronic pain and frailty progression in older adults.
Our findings align with those of previous studies that showed an association between chronic musculoskeletal pain and frailty in older adults [33, 34]. Chronic pain, particularly in the weight-bearing joints, such as the lower back and knees, is a significant contributor to functional decline and adverse health outcomes in later life. Blyth et al. highlighted that chronic musculoskeletal pain is associated with frailty and reduced physical activity, mobility limitations, depression, cognitive decline, falls, and poor sleep quality [33], all of which are key components of frailty phenotypes [35]. Our study builds on this evidence by examining the potential mechanisms underlying the association between pain and frailty. While previous studies suggest that psychological and behavioral factors, such as sleep disturbance, may mediate this association, few have formally tested this relationship using mediation models [36]. A cross-sectional study of hospitalized older adults with cancer revealed that sleep quality partially mediated the relationship between pain and frailty [12]. However, this study focused on hospitalized patients with cancer in whom pain arose primarily from tumor invasion and cancer treatment; moreover, it used a cross-sectional design. The present study extends these findings to a longitudinal community-based sample of older adults without cancer, suggesting that this mediating pathway is not limited to oncological settings.
Several mechanisms may explain the mediating role of sleep quality in the relationship between chronic pain and frailty progression. Chronic pain disrupts sleep architecture by increasing sleep latency, reducing deep sleep, and causing frequent nighttime awakenings [37, 38]. Poor sleep quality may contribute to physiological dysregulation through multiple pathways, including increased inflammatory activity, alterations in hypothalamic–pituitary–adrenal axis function, and impaired neuroendocrine signaling [39, 40]. Sleep disturbances can also exacerbate psychological symptoms such as depressive symptoms, anxiety, and fatigue, thereby leading to physical inactivity, reduced social engagement, and cognitive decline, all of which are core domains of frailty [40]. In the present study, poor sleep quality remained independently associated with frailty progression after adjustment for chronic pain, and the observed indirect effect indicates that sleep quality is likely one of several relevant pathways. Frailty progression is considered a multifactorial process influenced by numerous physiological, behavioral, and psychosocial factors [7]. Therefore, even relatively small indirect associations, such as the modest indirect effect observed in this study, may provide insight into potentially modifiable pathways. Furthermore, the bidirectional relationship between pain and sleep has been well-established [41]. Therefore, the observed associations in the present study may reflect complex reciprocal relationships among chronic pain, sleep disturbance, and frailty-related vulnerability rather than a unidirectional causal pathway.
These findings suggest that assessing sleep quality in older adults with chronic pain may help identify those at greater risk of frailty progression. Interventions targeting sleep, such as cognitive behavioral therapy for insomnia, which have shown efficacy in patients with chronic pain [42], may warrant evaluation as part of broader strategies for frailty prevention. However, these suggestions require confirmation in future intervention studies.
Several limitations should be considered when interpreting the findings of the present study. First, chronic pain and sleep quality were assessed using brief self-reported measures at baseline, which may not have fully captured pain intensity, pain characteristics, pain-site-specific differences, sleep-related symptoms, frequency, or the multidimensional nature of these conditions. In particular, sleep quality was assessed using a brief single-item measure developed with reference to the subjective sleep quality component of the PSQI, rather than a standardized multidimensional sleep assessment instrument. As both variables were measured at the same time point, their temporal sequence could not be determined, limiting causal interpretation of the mediation pathway. Second, although the study used a longitudinal design, the follow-up period was relatively short (approximately 1 year). Third, frailty progression was operationally defined based on changes in KCL scores rather than definitive transitions between established frailty categories. Although both anchor-based and distribution-based approaches were used to define the threshold for frailty progression, a formally validated KCL-specific MID for community-dwelling older adults remains unavailable. Therefore, the threshold used in this study should be interpreted as an operational estimate rather than a definitive clinical threshold. These factors preclude drawing definitive conclusions about causality. Fourth, while chronic pain was defined as lasting ≥ 2 months, variation in duration beyond this threshold (e.g., several months to years) was not assessed. Therefore, differences in chronic pain classification may have influenced the observed associations. Finally, the study sample was limited to community-dwelling older adults from a single rural city in Japan, and only those who completed the follow-up assessments were included. These factors may have introduced selection bias. Participants lost to follow-up tended to be older and had higher baseline KCL scores, which may have resulted in underestimation of frailty progression and limited generalizability to broader populations.
Conclusions
In this study, we found that the association between chronic pain and frailty progression in older adults may be partially explained by sleep quality. Hence, addressing chronic pain and associated sleep impairments may be important for comprehensive strategies aimed at preventing frailty progression. These findings highlight the potential value of integrated assessments and interventions targeting both pain and sleep problems in older adults for improving the progression of frailty.
Supplementary Material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank all participants of the Tarumizu Study and the officials of Tarumizu City for their valuable contributions to this research. The authors are also grateful to the staff at the Tarumizu City Health Division, particularly Shoichi Nagata, Hideyuki Niwata, Hitoshi Osako, Shinya Kuraoka, and Mayuka Maeda, for their contributions.
Author contributions
Daijo Shiratsuchi: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Hyuma Makizako: Methodology, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition. Shoma Akaida: Investigation, Data curation, Writing – review & editing. Mana Tateishi: Investigation, Data curation, Writing – review & editing. Yuto Kiuchi: Investigation, Data curation, Writing – review & editing. Ryota Kuratsu: Investigation, Data curation, Writing – review & editing. Takuro Kubozono: Investigation, Data curation, Writing – review & editing. Toshihiro Takenaka: Investigation, Data curation, Writing – review & editing. So Kuwahata: Investigation, Data curation, Writing – review & editing. Mitsuru Ohishi: Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration.
Funding
Open Access funding provided by Kagoshima University. This study was supported by a Health Labour Sciences Research Grant (24FA1005) from the Ministry of Health, Labour and Welfare of Japan.
Data availability
The data used in this study cannot be publicly shared due to privacy concerns and ethical restrictions. The data can be obtained from the corresponding authors upon reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval
This study was approved by the Ethics Committee on Epidemiological and Related Studies of Sakuragaoka Campus, Kagoshima University (approval no. 170351).
Informed consent
Informed consent was obtained from all participants prior to their involvement in this study. Written informed consent was also obtained from all participants for the publication of their data-related findings.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP (2019) Frailty: implications for clinical practice and public health. Lancet 394:1365–1375. 10.1016/s0140-6736(19)31786-6 [DOI] [PubMed] [Google Scholar]
- 2.O’Caoimh R, Sezgin D, O’Donovan MR et al (2021) Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. Age Ageing 50:96–104. 10.1093/ageing/afaa219 [DOI] [PubMed] [Google Scholar]
- 3.Crocker TF, Brown L, Clegg A et al (2019) Quality of life is substantially worse for community-dwelling older people living with frailty: systematic review and meta-analysis. Qual Life Res 28:2041–2056. 10.1007/s11136-019-02149-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Makizako H, Shimada H, Tsutsumimoto K et al (2021) Physical Frailty and Future Costs of Long-Term Care in Older Adults: Results from the NCGG-SGS. Gerontology 67:695–704. 10.1159/000514679 [DOI] [PubMed] [Google Scholar]
- 5.Kojima G, Iliffe S, Taniguchi Y, Shimada H, Rakugi H, Walters K (2017) Prevalence of frailty in Japan: A systematic review and meta-analysis. J Epidemiol 27:347–353. 10.1016/j.je.2016.09.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Murayama H, Kobayashi E, Okamoto S et al (2020) National prevalence of frailty in the older Japanese population: Findings from a nationally representative survey. Arch Gerontol Geriatr 91:104220. 10.1016/j.archger.2020.104220 [DOI] [PubMed] [Google Scholar]
- 7.Dent E, Hanlon P, Sim M et al (2023) Recent developments in frailty identification, management, risk factors and prevention: A narrative review of leading journals in geriatrics and gerontology. Ageing Res Rev 91:102082. 10.1016/j.arr.2023.102082 [DOI] [PubMed] [Google Scholar]
- 8.Lin T, Zhao Y, Xia X, Ge N, Yue J (2020) Association between frailty and chronic pain among older adults: a systematic review and meta-analysis. Eur Geriatr Med 11:945–959. 10.1007/s41999-020-00382-3 [DOI] [PubMed] [Google Scholar]
- 9.The Committee for Clinical Practice Guideline for the Management of Chronic Pain (2021) Clinical Practice Guidelines For the Management of Chronic Pain. Publication Department of Medical Books, Shinko Trading Co. Ltd.; Tokyo [Google Scholar]
- 10.Vaughan L, Corbin AL, Goveas JS (2015) Depression and frailty in later life: a systematic review. Clin Interv Aging 10:1947–1958. 10.2147/cia.S69632 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Garbarino S, Lanteri P, Bragazzi NL, Magnavita N, Scoditti E (2021) Role of sleep deprivation in immune-related disease risk and outcomes. Commun Biol 4:1304. 10.1038/s42003-021-02825-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhang Q, Yu M, Tang R et al (2023) A pathway model of chronic pain and frailty in older Chinese cancer patients: The mediating effect of sleep. Geriatr Nurs 50:215–221. 10.1016/j.gerinurse.2023.01.015 [DOI] [PubMed] [Google Scholar]
- 13.Nakai Y, Makizako H, Kiyama R et al (2019) Association between Chronic Pain and Physical Frailty in Community-Dwelling Older Adults. Int J Environ Res Public Health 16. 10.3390/ijerph16081330 [DOI] [PMC free article] [PubMed]
- 14.Cruz-Almeida Y, Rosso A, Marcum Z et al (2017) Associations of Musculoskeletal Pain With Mobility in Older Adults: Potential Cerebral Mechanisms. J Gerontol Biol Sci Med Sci 72:1270–1276. 10.1093/gerona/glx084 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.McGreevy K, Bottros MM, Raja SN (2011) Preventing Chronic Pain following Acute Pain: Risk Factors, Preventive Strategies, and their Efficacy. Eur J Pain Suppl 5:365–372. 10.1016/j.eujps.2011.08.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Arai H, Satake S (2015) English translation of the Kihon Checklist. Geriatr Gerontol Int 15:518–519. 10.1111/ggi.12397 [DOI] [PubMed] [Google Scholar]
- 17.Satake S, Shimokata H, Senda K, Kondo I, Toba K (2017) Validity of Total Kihon Checklist Score for Predicting the Incidence of 3-Year Dependency and Mortality in a Community-Dwelling Older Population. J Am Med Dir Assoc 18:552e551–552e552. 10.1016/j.jamda.2017.03.013 [DOI] [PubMed] [Google Scholar]
- 18.Satake S, Senda K, Hong YJ et al (2016) Validity of the Kihon Checklist for assessing frailty status. Geriatr Gerontol Int 16:709–715. 10.1111/ggi.12543 [DOI] [PubMed] [Google Scholar]
- 19.Revicki D, Hays RD, Cella D, Sloan J (2008) Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. J Clin Epidemiol 61:102–109. 10.1016/j.jclinepi.2007.03.012 [DOI] [PubMed] [Google Scholar]
- 20.King MT (2011) A point of minimal important difference (MID): a critique of terminology and methods. Expert Rev Pharmacoecon Outcomes Res 11:171–184. 10.1586/erp.11.9 [DOI] [PubMed] [Google Scholar]
- 21.Shiratsuchi D, Makizako H, Akaida S et al (2025) Combinations of intrinsic capacity and frailty and their associations with self-rated health in community-dwelling older adults. Aging Clin Exp Res 37:275. 10.1007/s40520-025-03189-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Thompson MQ, Jadczak AD, Tucker GR, Theou O, Visvanathan R (2022) Frailty change based on minimally important difference in nursing home residents: FIRST cohort study findings. Age Ageing 51. 10.1093/ageing/afac246 [DOI] [PMC free article] [PubMed]
- 23.Masumitsu T, Kubozono T, Miyata M et al (2022) Association of Sleep Duration and Cardio-Ankle Vascular Index in Community-Dwelling Older Adults. J Atheroscler Thromb. 10.5551/jat.63594 [DOI] [PMC free article] [PubMed]
- 24.Doi Y, Minowa M, Uchiyama M et al (2000) Psychometric assessment of subjective sleep quality using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J) in psychiatric disordered and control subjects. Psychiatry Res 97:165–172. 10.1016/s0165-1781(00)00232-8 [DOI] [PubMed] [Google Scholar]
- 25.Thorell K, Midlöv P, Fastbom J, Halling A (2020) Use of potentially inappropriate medication and polypharmacy in older adults: a repeated cross-sectional study. BMC Geriatr 20:73. 10.1186/s12877-020-1476-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Satake S, Arai H (2020) The revised Japanese version of the Cardiovascular Health Study criteria (revised J-CHS criteria). Geriatr Gerontol Int 20:992–993. 10.1111/ggi.14005 [DOI] [PubMed] [Google Scholar]
- 27.Shiratsuchi D, Taniguchi Y, Akaida S et al (2025) Influence of exercise and dietary habits on the association of alpha-actinin-3 gene polymorphisms with physical function and body composition in community-dwelling individuals aged 60 years and older. Geriatr Gerontol Int 25:173–181. 10.1111/ggi.15042 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Yesavage JA (1988) Geriatric Depression Scale. Psychopharmacol Bull 24:709–711 [PubMed] [Google Scholar]
- 29.Dennis M, Kadri A, Coffey J (2012) Depression in older people in the general hospital: a systematic review of screening instruments. Age Ageing 41:148–154. 10.1093/ageing/afr169 [DOI] [PubMed] [Google Scholar]
- 30.Borson S, Scanlan J, Brush M, Vitaliano P, Dokmak A (2000) The mini-cog: a cognitive ‘vital signs’ measure for dementia screening in multi-lingual elderly. Int J Geriatr Psychiatry 15:1021–1027. 10.1002/1099-1166(200011)15:11<1021::aid-gps234>3.0.co;2-6 [DOI] [PubMed] [Google Scholar]
- 31.Borson S, Scanlan JM, Chen P, Ganguli M (2003) The Mini-Cog as a screen for dementia: validation in a population-based sample. J Am Geriatr Soc 51:1451–1454. 10.1046/j.1532-5415.2003.51465.x [DOI] [PubMed] [Google Scholar]
- 32.Hayes AF (2022) Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. Guilford
- 33.Blyth FM, Noguchi N (2017) Chronic musculoskeletal pain and its impact on older people. Best Pract Res Clin Rheumatol 31:160–168. 10.1016/j.berh.2017.10.004 [DOI] [PubMed] [Google Scholar]
- 34.Yang S, Wang H, Tao Y et al (2025) Association of chronic pain with frailty and pre-frailty in older adults: A systematic review and meta-analysis. Arch Gerontol Geriatr 131:105784. 10.1016/j.archger.2025.105784 [DOI] [PubMed] [Google Scholar]
- 35.Fried LP (2016) Interventions for Human Frailty: Physical Activity as a Model. Cold Spring Harb Perspect Med 6. 10.1101/cshperspect.a025916 [DOI] [PMC free article] [PubMed]
- 36.de Souza ÂMN, Fernandes DPS, Castro IS, Gróla FG, Ribeiro AQ (2025) Sleep quality and duration and frailty in older adults: a systematic review. Front Public Health 13:1539849. 10.3389/fpubh.2025.1539849 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Nijs J, Mairesse O, Neu D et al (2018) Sleep Disturbances in Chronic Pain: Neurobiology, Assessment, and Treatment in Physical Therapist Practice. Phys Ther 98:325–335. 10.1093/ptj/pzy020 [DOI] [PubMed] [Google Scholar]
- 38.Duo L, Yu X, Hu R, Duan X, Zhou J, Wang K (2023) Sleep disorders in chronic pain and its neurochemical mechanisms: a narrative review. Front Psychiatry 14:1157790. 10.3389/fpsyt.2023.1157790 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Li MT, Robinson CL, Ruan QZ, Surapaneni S, Southerland W (2022) The Influence of Sleep Disturbance on Chronic Pain. Curr Pain Headache Rep 26:795–804. 10.1007/s11916-022-01074-2 [DOI] [PubMed] [Google Scholar]
- 40.Herrero Babiloni A, De Koninck BP, Beetz G, De Beaumont L, Martel MO, Lavigne GJ (2020) Sleep and pain: recent insights, mechanisms, and future directions in the investigation of this relationship. J Neural Transm (Vienna) 127:647–660. 10.1007/s00702-019-02067-z [DOI] [PubMed] [Google Scholar]
- 41.Haack M, Simpson N, Sethna N, Kaur S, Mullington J (2020) Sleep deficiency and chronic pain: potential underlying mechanisms and clinical implications. Neuropsychopharmacology 45:205–216. 10.1038/s41386-019-0439-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Selvanathan J, Pham C, Nagappa M et al (2021) Cognitive behavioral therapy for insomnia in patients with chronic pain - A systematic review and meta-analysis of randomized controlled trials. Sleep Med Rev 60:101460. 10.1016/j.smrv.2021.101460 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data used in this study cannot be publicly shared due to privacy concerns and ethical restrictions. The data can be obtained from the corresponding authors upon reasonable request.