(2024) 22:313
Chen et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03534-5

BMC Medicine

RESEARCH ARTICLE

Open Access

The associations of chronic pain and 24-h
movement behaviors with incident mental
disorders: evidence from a large-scale cohort
study
Jiade Chen1†, Xuanbi Fang1,2†, Fan Zhang1†, Jiaxin Shen1, Yuanhang Liu1, Peng Xu3,4, Rongrong Ye1,
Qingguang Zhong1, Guanren Chen1, Zhehao Wang1, Shentong Chen1, Lixia Li3*, Ziqiang Lin1* andYanhui Gao1,5*

Abstract
Background Chronic pain was associated with a higher risk of mental disorders (e.g., depression and anxiety). However, the role of 24-h movement behaviors in the association remains unclear.
Methods A total of 72,800 participants with accelerometer data and free of mental disorders from the UK Biobank wereanalyzed. The compositional mediation model and isotemporal substitution model were used to explore the associations
between chronic pain, 24-h movement behaviors, and the incidence of overall mental disorders, depression, and anxiety.
Results With a median follow-up of 13.36 years, participants with chronic pain had a higher rate of incident overallmental disorders (hazard ratio (HR): 1.281, 95% confidence interval (CI): 1.219 to 1.344), anxiety (HR: 1.391, 95% CI:
1.280 to 1.536), and depression (HR: 1.703, 95% CI: 1.551 to 1.871). Increased sedentary behavior (SB) and reduced
moderate-to-vigorous physical activity (MVPA) caused by chronic pain both increased the risk of mental disorders.
Twenty-four-hour movement behaviors explained the relationship between chronic pain and overall mental disorders, depression, and anxiety by 10.77%, 5.70%, and 6.86%, respectively. Interaction effects were found between MVPA
and chronic pain when predicting the incidence of depression and between MVPA, sleep (SLP), and chronic pain
when predicting the incidence of mental disorders. People with chronic pain would recommend at least 0.5 h per day
of MVPA and 7 h per day of SLP and restricting SB below 11.5 h per day.
Conclusions Twenty-four-hour movement behaviors played a significant mediating role in the association
between chronic pain and mental disorders. Individuals with chronic pain should engage in more MVPA, less sedentary behavior, and have 7-h sleep per day.
Keywords Physical activity, Chronic pain, Depression, Anxiety, Mental health, UK Biobank†

Jiade Chen, Xuanbi Fang and Fan Zhang contributed equally to this work.

*Correspondence:
Lixia Li
llx19@163.com
Ziqiang Lin
Linziqiang0314@jnu.edu.cn
Yanhui Gao
gao_yanhui@163.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
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Chen et al. BMC Medicine(2024) 22:313

Background
Chronic pain is defined as the pain that persists or recurs
for more than 3 months [1], including explainable symptoms, such as headaches, stomachs, and facial pains, or
idiopathic sources with an unknown origin for the illness,such as widespread generalized pain. As a major causeof disability, chronic pain can lead to a series of secondary issues, resulting in decreased physical function, poor
quality of life, and increased psychological distress [2].
The prevalence of chronic pain ranges from 18 to 20%
globally [3–5], and up to 50% of adults in the UK were
suffering from chronic pain [6]. Chronic pain may alterpatients’ pain tolerance [7] and cognitive and emotional
function [8]. Accumulating evidence showed that chronic
pain and pain-related symptoms are premorbid risk factors for various psychological comorbidities [9, 10]. The
mechanisms between chronic pain and mental disorders
are complex. Many studies have explained this from amolecular biology perspective, but whether individuals’
daily movement behaviors affect the association remains
unclear [11].
A recent meta-analysis showed that, among people
who suffered from chronic pain, resulted pain catastrophizing, fear of pain, and pain vigilance may leadto pain-related negative affect, anxiety, and depression[12]. According to the fear-avoidance model, chronic
pain and related negative affect can result in avoidance
behavior and reducing the intensity of activities [13]. It
was suggested that driven by the increased tendency tominimize discomfort or decreased confidence in coping with pain [14], avoidance behavior triggered bychronic pain [15, 16] would inevitably change patient’s
24-h movement behaviors [17]. As a result, existing literature has found that patients with chronic pain weremore likely to report reduced level of physical activity
and sleep deficiency [18, 19]. In addition, lower levelsof physical activity, inadequate sleep, and prolonged
inactivity are also associated with higher risk of various
mental disorders [20–22]. However, most studies only
considered the role of a single component of movement behavior in the relationship between chronic
pain and mental disorder [23, 24]. According to theActivity-Balance Model and Time-Use Epidemiology
Theory [25, 26], 24-h movement behaviors (i.e., including moderate-to-vigorous physical activity (MVPA),
light physical activity (LPA), sedentary behavior (SB),and sleep (SLP)) have an interconnected nature whichshould be considered as a whole when investigating it
in health outcomes. Therefore, to provide a more comprehensive understanding about the mediation effectof 24-h movement behaviors, the current study has
adopted compositional data analysis. In addition, it has

Page 2 of 13

remained unclear about the optimal balance points fordifferent movement behaviors among the patients withchronic pain. At present, many countries and regions,such as Canada and New Zealand, have developed
physical activity guidelines for adults or adolescentsbased on 24-h movement behaviors [27, 28]. The World
Health Organization (WHO) suggested adults should
undertake ≥ 150-min moderate or ≥ 75-min vigorous
physical activity per week [29]. However, evidence froma randomized controlled trial (RCT) showed that thisguideline may not be applied to the population withchronic pain symptoms (such as fibromyalgia) [30]. In
addition, unlike self-reported data, which is prone torecall bias and social desirability bias [31], 24-h movement behaviors measured by accelerometer are an
objective measure to improve the accuracy and reliability of the findings [32, 33].
The current study based on the UK Biobank population aimed to (i) explore the association between
chronic pain, 24-h movement behaviors, and incident
mental disorders; (ii) address the role of 24-h movement behaviors in the relationship between chronic
pain and mental disorders; (iii) determine the dose–
response relationship of 24-h movement behaviors on
mental disorders among individuals without chronic
pain and deliver a recommendation of movement
behaviors allocation across 24 h for people with chronicpain.

Methods
Study design and population

The UK Biobank (application # 90162), a nationwide
prospective cohort study, was used to conduct the current analysis. Over 500,000 participants aged 40–69
years were included in the cohort study between 2006
and 2010, providing first-record information on genetics, demographics, health-related characteristics, physical activity, etc. From 2013 to 2015, a total of 236,519
participants were randomly invited to wear accelerometers continuously on their dominant hand over a 7-day
period. At the end, data containing participants’ 24-h
movement behaviors was received from a total of 103,660
participants. Based on these participants, the exclusion
criteria were as follows: (1) the participants who withdrew (n = 10); (2) The participants had insufficient duration of equipment (less than 72 hours or no data during
each hour of the 24-hour cycle (n = 6991); (3) the participants whose records are with missed covariate information at baseline (n = 11,016); and (4) the participants
who had mental disorders at baseline (n = 581). After thescreening, a total of 72,800 participants were included in
the study (Fig. S1 in Additional file 2).

Chen et al. BMC Medicine(2024) 22:313

Assessment of chronic pain

In the UK Biobank, data on chronic pain was ascertained
from a touchscreen questionnaire at the baseline assessment between 2006 and 2010. Participants were asked tochoose one or more conditions (i.e., headache, facial pain,neck or shoulder pain, back pain, stomach or abdominal
pain, hip pain, knee pain) that interfered with usual activities based on the situation of the past month. Participants who reported any site of pain were asked whether
their pain lasted for more than 3 months. Therefore,
chronic pain is defined as having pain that lasts longer
than 3 months in one or more sites, consistent with thedefinition of the International Association for the Studyof Pain (IASP) [34]. Those who answered pain all over the
body would be excluded as evidence showed that pain allover the body may differ from one who selected sevenindividual sites of pain [35, 36].
Assessment of 24‑h movement behaviors

From 2013 to 2015, 24-h movement behaviors were
measured via a wrist-worn Axivity AX3 triaxial accelerometer over a 7-day period. The data was captured at
100 Hz with a dynamic range of ± 8 g and then further
underwent calibration to local gravity, noise and gravity removal, and non-wear detection by the UK Biobank
accelerometer expert working group to form a dataset
for analysis. Twenty-four-hour movement behavior was
divided into four components across the 1440 min of theday, namely, MVPA, LPA, SB, and SLP. Each component
represents time spent in the activity relatively, and different activities are independent of and associated witheach other in a 24-h period. According to previous studies [35, 37–40], acceleration intensity distribution wasused to define 24-h movement behaviors by calculating
the proportion of time spent on different activities except
sleep hours. Sleeping time was evaluated by an itemof touchscreen questionnaire “About how many hours
sleep do you get in every 24 h? (please include naps)”.
Responses < 1 or > 23 were rejected automatically. Therefore, SB, LPA, and MVPA were defined as ≤ 30 milli-g(minus sleep duration), > 30 milli-g and < 125 milli-g,
and ≥ 125 milli-g, respectively.
Ascertainment of outcomes

The occurrence of diseases was extracted from the firstoccurrences of health-related outcomes based on theInternational Statistical Classification of Diseases andRelated Health Problems 10th Revision (ICD-10) coding.
The main outcome was overall mental disorders (ICD-10
code: F00-F99); see Table S1 in Additional file 2. Depression (ICD-10 code: F32, F33, F34, F38, F39) and anxiety(ICD-10 code: F40, F41) were also analyzed as secondary

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outcomes. The ascertainment of these outcomes was
based on the collation of primary care data, hospital
inpatient data, death register records, and self-reported
data. Each participant will be followed from the date of
the first assessment until the date of death, occurrence ofany study outcome, or the last known follow-up date asof 10 August 2022, depending on whichever came first.
Covariates

Age, gender, ethnicity, education level, BMI, current
smoking and alcohol use, history of cancer, and historyof pain-related diseases were included as covariates. Ageand gender were obtained from the central registry at
recruitment. Ethnicity, education level, current smoking, and alcohol use were self-reported through a touch
screen questionnaire at baseline. BMI was calculatedusing height and weight and divided into triple categorical variables (< 18.5, 18.5 ~ 25.0, > 25.0). History of cancer
was ascertained from cancer registries (NHS Digital andPublic Health England for England and Wales; NHS Central Register for Scotland), defined as an ICD-10 code of
C00-C99. Pain-related diseases (ICD-10 code: L98, M05M06, M13, M16-M19, M45, M46, M79, M86, M60, G72)
were extracted from the first occurrence of health-related
outcomes [41].
Statistical analysis

Categorical variables were presented as frequency(percentage). Continuous variables were presented asmean ± standard deviation (SD), except for 24-h movement behaviors, which were described by geometricmean component time. For better interpretation andless spurious correlation of 24-h movement behaviors,
our study used compositional data analysis (CoDA) proposed by Aitchison et al. [42] to interpret 24-h data by
isometric log-ratio (ilr) transformations of the simplex[43]. The compositional mediation model, proposed by
Ziqiang Lin et al. [44], was used to calculate the estimatesand mediation proportion by the mediators (time spentin each movement behavior in 24 h) for the association
between chronic pain and each outcome. Compared tousing single movement behavior as a mediator, the compositional mediation model considers all types of movement behaviors over a 24-h period. Our study extends
this approach by combining the Cox proportional hazard models (Additional file 2). The proportional hazard
assumption was tested by Schoenfeld residuals before fitting compositional Cox proportional hazard models, andwe found no significant deviation from the assumption.
To explore the dose–response relationship of 24-h
movement behaviors on outcomes, we used the compositional Cox isotemporal substitution model (ISM) to
analyze the effect of each component in 24-h movement

Chen et al. BMC Medicine(2024) 22:313

behaviors on outcomes in chronic pain versus painfree populations separately (Methodology supplementin Additional file 2) [45]. In each group, the geometricmeans of the four components were set as the reference
values (HR = 1). The modification effect was evaluatedby creating the interaction term between chronic pain
and 24-h movement behaviors.
To find practical recommendations for individuals
with chronic pain, a time-use combination was conducted following the methodology in Dorothea Dumuid
et al. [46]. Firstly, based on the compositional Cox ISM
mentioned previously, using 10 min as the amount ofvariation between the different components, the combinations of practicable physical activity times wereascertained in the chronic pain population mentioned
above (truncated at ± 2 SD of their univariate distributions). Then, the compositional Cox regression models
in the first step were used to determine the mapping
relationships between time-use combination patterns,
and the three outcomes predicted scores in the chronic
pain population. Third, the score of overall mental disorders, depression, and anxiety were ranked with thebottom 25%, 50%, and 75% of predictions corresponding to a range of time-use combinations as regions for‘high risk’, ‘median risk’, and ‘low risk’.
In addition, subgroup analysis was conducted toinvestigate whether age, gender, ethnicity, smoking, anddrinking modified the relationship between chronic
pain and outcomes and the mediation proportion of
24-h movement behaviors in different subgroups. We
evaluated the potential modification effect by testing
additive interaction and the mediation proportion of
24-h movement behaviors.
Several sensitivity analyses were performed in ourstudy to test the robustness of our main results. First,
we excluded participants with prevalent diabetes, CVD,
cancer, chronic bronchitis, emphysema, or chronic
obstructive pulmonary disease, because these diseases may be associated with chronic pain and affectindividuals’ mental health. Second, we excluded casesthat occurred within the first 2 years of follow-up to
reduce potential reverse causation. Third, we used theclassification of the patient health questionnaire-9(PHQ-9) and generalized anxiety disorder-7 (GAD-7)
questionnaire to replace incident depression and anxiety. Fourth, we used the E-value methodology [47] toexamine the influence of potential confounders andbiases. Fifth, we repeat our analysis by regarding thePatient Health Questionnaire (PHQ)-4 scales as longterm mental health outcomes predictors. All statistical analyses were performed using the SAS 9.4 and R
software 4.3.0 (R Development Core Team, Vienna,
Austria).

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Results
Population characteristics

The study included a total of 72,800 participants, with
26,989 (37.07%) of them reporting suffering chronic pain.
Among all participants, the mean age was 55.8 ± 7.9 years
old, with 55.3% being female. Descriptive results of the
population are presented in Table 1. Over two thirds(70.6%) of the participants were overweight, most
(95.1%) were currently using alcohol, and 60.3% had
never smoked. Forty-eight percent of them obtained
an educational level of college or university above, and64.9% were employed. Adults experiencing chronic pain
were more likely to be female, overweight, smokers, currently non-drinkers, and retirees. The incidence of overallmental disorders, depression, and anxiety among participants with chronic pain were 8.10, 2.77, and 2.51 per1000 person-years, respectively, which were higher thanthat in the population without chronic pain (5.88, 1.44,
1.66 per 1000 person-years, respectively). The geometricmeans and proportion of each movement behavior were
presented (Fig. 1).
Compositional mediation analysis of 24‑h movement
behaviors in the association between chronic pain
and the risk of mental disorders

After adjusting for the covariates, compositional mediation Cox model analysis was conducted (Table 2 andFig. 2). The hazard ratio (HR) for the incidence of overall mental disorders, depression, and anxiety among
participants with chronic pain compared to those without chronic pain was 1.281 (95% CI: 1.219 to 1.344),
1.703 (95% CI: 1.551 to 1.871), and 1.391 (95% CI:
1.280 to 1.536) (Table 2). Chronic pain was associatedwith decreased MVPA, LPA, and SLP and increased
SB (β: − 0.065 (95% CI: − 0.072 to − 0.057), − 0.006(95% CI: − 0.010 to − 0.003), − 0.011 (95% CI: − 0.013
to − 0.009), and 0.014 (95% CI: 0.011 to 0.016)) (Fig. 2).
The effects of MVPA and SB on overall mental disorders, depression, and anxiety were found, while there
was no significant relationship between SLP and all outcomes. With MVPA and SB as mediators, there were significant indirect effects on depression, with an adjusted
HR of 1.023 (95% CI: 1.023 to 1.030) and 1.006 (95%
CI: 1.003 to 1.009). It means that decreased MVPA andincreased caused by chronic pain were associated withhigher risk of incident depression. Total indirect effectsof 24-h movement behaviors were found in the relationship between chronic pain and all outcomes, accountingfor 5.70%, 6.86%, and 10.77% of the total effect for overall mental disorders, depression, and anxiety, respectively(Table 2). Similar results were found in anxiety and overall mental disorders. Subgroup analyses showed that theChen et al. BMC Medicine(2024) 22:313

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Table 1 Characteristics of UK Biobank participants by pain status
Variables

Overall (N = 72,800)

No pain (N = 45,811)

Chronic pain
(N = 26,989)

p valueAge (years)

55.8 ± 7.9

55.7 ± 7.9

55.9 ± 7.8

0.001

Female40,246 (55.3)

24,348 (53.1)

15,898 (58.9)

Male32,554 (44.7)

21,463 (46.9)

11,091 (41.1)

Others5785 (7.9)

3629 (7.9)

2156 (8.0)

White

67,015 (92.1)

42,182 (92.1)

24,833 (92.0)

29,641 (40.7)

19,754 (43.1)

9887 (36.6)

Sex (%)< 0.001

Ethnicity (%)

0.748

BMI (%)
18.5 ~ 25.0

< 0.001< 18.5422 (0.6)287 (0.6)

135 (0.5)> 25.042,737 (58.7)

31,424 (56.3)

16,967 (62.9)

43,901 (60.3)

28,259 (61.7)

15,642 (58.0)

Smoking (%)
Never< 0.001

Previous

25,028 (34.4)

15,192 (33.2)

9836 (36.4)

Current

3871 (5.3)

2360 (5.2)

1511 (5.6)

Drinking (%)< 0.001

Never

1941 (2.7)

1143 (2.5)

798 (3.0)

Previous

1612 (2.2)

864 (1.9)748 (2.8)

Current

69,247 (95.1)

43,804 (95.6)

25,443 (94.3)

34,946 (48.0)

23,064 (50.3)

11,882 (44.0)

Education (%)
College or university< 0.001

A/AS levels

10,491 (14.4)6553 (14.3)

3938 (14.6)

O/GCSEs

16,090 (22.1)

9593 (20.9)

6497 (24.1)

Others11,273 (15.5)

6601 (14.4)

4672 (17.3)

No60,421 (83.0)

45,346 (86.8)

23,907 (72.9)

Yes

12,379 (17.0)6915 (13.2)

8903 (27.1)

No68,477 (94.1)

49,164 (94.1)

30,863 (94.1)

Yes4323 (5.9)

3097 (5.9)

1947 (5.9)

Pain-related diseases (%)< 0.001

Cancer (%)

0.762

Outcome, no. (event rate, per 1000 person-years)
Incident mental disorder6518 (7.01)

3601 (5.88)

2917 (8.10)

Incident depression

1877 (1.95)

879 (1.44)

998 (2.77)

Incident anxiety

1919 (1.99)

1015 (1.66)

904 (2.51)

associations between chronic pain and depression was
greater in females (p for additive interaction: 0.008) andoverweight participants (p for additive interaction: 0.001)(Fig. S2 in Additional file 2). The association between
chronic pain and overall mental disorders was also
greater in females (p for additive interaction: 0.005) (Fig.
S4 in Additional file 2). In addition, the mediation proportion of 24-h movement behaviors in depression andoverall mental disorders were greater among the people aged 65 and above, with 19.16% and 32.87% respectively, compared to 3.37% and 7.33% in younger people.
It means that elderly people with chronic pain may be

more susceptible to effects of 24-h movement behaviors(Appendix Figs. S2 and S4 in Additional file 2).
Dose–response relationship between chronic pain, 24‑hmovement behaviors, and mental disorders

When predicting depression, similar trends were foundin the effects of LPA and SB in both participants with andwithout chronic pain (p for interaction: 0.842 and 0.571
respectively) (Fig. 3 (3.1 B and C)). Increased MVPA was
negatively associated with incident depression among
both participants with and without chronic pain, while
the effect was weaker in the chronic pain group (p forChen et al. BMC Medicine(2024) 22:313

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Fig. 1 Geometric mean of 24-h movement behaviors. A Participants with no chronic pain. B Participants with chronic pain. MVPA,
moderate-to-vigorous physical activity; LPA, light physical activity; SB, sedentary behavior; SLP, sleep

interaction: 0.028) (Fig. 3 (3.1 A)). Changes in SLP had no
significant effect in the group without chronic pain. Nevertheless, increase in SLP showed greater benefits for thegroup with chronic pain. If time in SLP increases from
420 to 480 min/day, the risk of overall mental disorderwould be decreased by up to 10%. Similar results were
found for depression and anxiety (Figs. 3 (3.2) and Fig. 3(3.3)). Notably, the effect of the interaction between SLP
and chronic pain on overall mental disorders was significant (p for interaction: 0.025) (Fig. 3 (3.1 D)), suggesting that among individuals with chronic pain, increased
SLP can reduce the risk of overall mental disorders more
effectively.
Twenty‑four‑hour time use combinations associatedwith the highest risk of incident overall mental disorders
depression and anxiety in the chronic pain population

In the chronic pain population, the ilrs contain four components associated with overall mental disorders, depression, and anxiety (all p < 0.001). The analyses produced
similar time-use combinations (MVPA < 0.5 h per day,
SLP < 7 h per day, SB > 11.5 h per/day), associated withthe highest risk of overall mental disorders, depression,and anxiety (Table 3).
Sensitivity analysis

The robustness of our findings was confirmed by thesensitivity analyses using alternative parameters. The
sensitivity analysis results showed no significant change

except when using the scores of depression and anxiety
scales as outcomes in the mediation analysis of chronic
pain and sleep duration (Figs. S5-11 and Tables S2-9 in
Additional file 2). A decrease in SLP increased the risk ofincident depression (adjusted odds ratio (OR): 1.011, 95%
CI: 1.008 to 1.016) and anxiety (adjusted OR: 1.010, 95%
CI: 1.006 to 1.014).

Discussion
Previous studies have reported that chronic pain was
associated with an increased risk of mental disorders [9,10]. A meta-analysis suggested patients with chronic pain
are more likely to develop psychological problems suchas depression, anxiety, and phobias [48]. Meanwhile, sufficient daily physical activity and decreased SB help lower
the risk of mental diseases [49, 50]. Based on previous
theories and research [25, 27, 28, 51], it is necessary to
explore how people with chronic pain can prevent mental diseases through the proper allocation of 24-h movement behaviors. However, little research has examined
the mediation role of 24-h movement behaviors in therelationship between chronic pain and mental disorders.
By analyzing the UK Biobank (median follow-up time:
13.36 years), we found that the durations of MVPA andSB mediated the association between chronic pain andmental disorders.
In the current study, individuals with chronic pain
showed a higher risk of overall mental disorder, depression, and anxiety by 28%, 70%, and 39%, respectively. This

Chen et al. BMC Medicine(2024) 22:313

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Table 2 The hazard ratios of direct, indirect, and total effects
of chronic pain on outcomes mediated by 24-h movement
behaviors
Outcome

EffectAdjusted HR

Proportion

Overall mental disorderTotal effect1.281 (1.219, 1.344)

100.00%

Direct effect

1.247 (1.188, 1.310)

89.23%
10.77%

Indirect effect1.027 (1.022, 1.031)

MVPA

1.025 (1.020, 1.030)

LPA

0.998 (0.997, 1.000)

SB

1.003 (1.002, 1.005)

SLP

1.001 (0.999, 1.003)

Anxiety
Total effect

1.391 (1.280, 1.536)

100.00%

Direct effect1.360 (1.250, 1.502)

93.14%
6.86%

Indirect effect1.023 (1.015, 1.030)

MVPA

1.018 (1.011, 1.025)

LPA

0.998 (0.997, 1.000)

SB

1.003 (1.001, 1.007)

SLP

1.003 (1.000, 1.006)

Depression
Total effect1.704 (1.551, 1.871)

100.00%

Direct effect1.653 (1.505, 1.813)

94.30%
5.70%

Indirect effect1.031 (1.023, 1.039)

MVPA

1.023 (1.017, 1.030)

LPA

1.000 (0.999, 1.002)

SB

1.006 (1.003, 1.009)

SLP

1.001 (0.998, 1.004)

Model adjusted for age, gender, ethnicity, BMI, smoking status, drinking status,
education, cancer, and pain-related diseases
MVPA, moderate-to-vigorous physical activity; LPA, light physical activity; SB,sedentary behavior; SLP, sleep

increased risk may be due to reduced physical activity
levels caused by chronic pain. Indeed, our results showedthat people with chronic pain have more time in SB
and less in LPA, MVPA, and SLP, whereas decreases in
MVPA and increases in SB lead to a greater risk of mental disorders. A semi-structured interview guide showedthat most patients with chronic pain see SB as a means
to adapt to everyday life [52]. Previous studies also suggested that physical activity could irritate the pain sites[53–55], and patients may be discouraged from engaging in moderate or vigorous physical activities, believing
they should take additional rest to relieve the pain. Nevertheless, systematic reviews of RCTs and prospective
cohort studies have indicated that active engagement inphysical activity would lower the risk of mental disorders [56, 57]. This may be because appropriate physicalactivity can activate the endocannabinoid system andrelease endorphins, resulting in pain relief and happiness [58, 59]. Furthermore, people with chronic pain
may experience feelings of inferiority that increase therisk of mental disorders, but exercise may buffer against
this risk by improving social interaction, self-esteem, andself-efficacy [60, 61]. Consistent with the evidence mentioned above, our study found that although the effectof MVPA in the chronic pain population was lower thanthat in the pain-free population, the relationship between
MVPA and mental disorders in the chronic pain population was not distorted. Several studies have also provided evidence for this perspective that physical activities
among individuals with chronic pain. Under planned andsupervised conditions, physical activity has been shownto bring benefits—including but not limited to pain relief,
reduced disability scores, improved social functioning, and enhanced mental health [62–64]. Therefore, we
believe that patients with chronic pain should appropriately increase MVPA based on the existing activity routine. Evidence from randomized controlled trials (RCTs)
indicated that moderate-to-high intensity physical activities, such as neuromuscular exercise [65], inverted bicycle riding [66], yoga [67], and dancing [68], are effective
in reducing symptoms of depression or lowering perceived stress in individuals suffering from chronic pain.
We strongly recommend that patients with chronic pain
tailor their exercise regimen based on their needs andphysical conditions. Based on our findings mentioned
above, patients with chronic pain should increase their
MVPA and reduce SB in daily life. However, there is alack of literature on how individuals with chronic pain
should allocate the 24 h. Consequently, we utilized a 24-htime use combination based on the isotemporal substitution model to develop a guideline. A combination of daily
engagement in MVPA > 0.5 h, SLP > 7 h, and SB < 11.5 h
can significantly reduce the incident mental disorders.
Compared with clinical therapeutic interventions such as
taking opioids and minimally invasive procedures, physical activity is a more cost-effective approach with fewerside effects. Therefore, chronic pain patients should
engage in physical activity, particularly MVPA, properly.
Although other study reported increased sleep timein patients with chronic pain [44], our study showeddecreased sleep time in this population. Extensive studies suggested chronic pain and sleep disturbance interact
in a complex bidirectional way [19, 69]. Chronic pain wasreported to be associated with more extended insomnia[70], difficulties in falling asleep, excessive awakenings,
early awakenings, and malaise [71]. Meanwhile, sleep
deficiency affects various systems of nociceptive processing, leading to a vicious cycle [19]. Consistent with theseChen et al. BMC Medicine(2024) 22:313

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Fig. 2 The mediating effect of chronic pain to mental orders mediated by 24-h movement behaviors. Model adjusted for age, gender, ethnicity,
BMI, smoking status, drinking status, education, cancer, and pain-related diseases. MVPA, moderate-to-vigorous physical activity; LPA, light physicalactivity; SB, sedentary behavior

findings, we found that an interaction between chronic
pain and sleep time when affecting mental disorder. Onlyin the chronic pain population did we observe a significant dose–response relationship between sleep time on

all outcomes, suggesting that the effects of SLP differedin the group with and without chronic pain. In summary,compared with pain-free populations, adequate sleep
time brought greater benefits and reduced the risk ofChen et al. BMC Medicine(2024) 22:313

Fig. 3 Dose–response relationship (with 95% CI) between 24-h
movement behaviors and mental disorders. A MVPA vs residualcomponent. B SB vs residual component. C LPA vs residualcomponent. D SLP vs residual component. Models adjusted for age,gender, ethnicity, BMI, smoking status, drinking status, education,
cancer, and pain-related diseases. MVPA, moderate-to-vigorous
physical activity; LPA, light physical activity; SB, sedentary behavior;
SLP, sleep

Page 9 of 13

morbidity in chronic pain populations. Therefore, complementary therapies, such as mindfulness and relaxationthat can improved sleep quality and relieved pain [72],should be considered and integrated in regimens.
To improve the stability of the results, we also replaced
the original outcomes with scale-measured symptoms ofdepression and anxiety in the sensitivity analyses. In contrast to the primary result, this sensitivity analysis showedthat SLP mediated the association between chronic pain
and symptoms of depression and anxiety. This is consistent with the findings of Roya et al. [73]. Possible reasons
were (1) the scale was only a rapid screening tool to identify individuals with symptoms of depression or anxiety,(2) the causal relationship between the mediator and theoutcome was not considered.
This study has several strengths. To our knowledge, thisis the first study using the compositional data analysis
approach to address the role of 24-h movement behaviors in mental disorders related to chronic pain. Second, alarge prospective cohort study of the UK Biobank with along follow-up period was used, allowing us to obtain sufficient cases. Third, accelerometers were used for measuring physical activity, which can avoid self-reported bias.
Last, the dose–response relationships between physicalactivity and mental health disorders were explored in thegroup with chronic pain, and detailed guidelines on how
to allocate daily time to various activities were developed
accordingly.
The study has also some limitations. First, this is anobservational study, and causal relationships cannot
be inferred from it. In future studies, it is necessary to
determine whether increased physical activity, reduced
sedentary behavior, and sufficient sleep can reduce therisk of chronic pain leading to mental disorders. Second,
chronic pain status was only evaluated at baseline, but
this could change over time. Third, we did not identify
the effects of chronic pain at different or multiple sites.
Fourth, we did not identify the effects of chronic pain
on specific types of physical activity and mental disorders due to the lack of corresponding information. Fifth,
though we used the E-value methodology to describe theimpact of potential confounding factors, the bias fromself-selection and unmeasured lifestyle factors remains apossibility. we cannot completely dismiss the likelihoodthat these factors may reverse the associations observed.
Last, the accelerometer measures only the intensity ofphysical activity but cannot differentiate the specifictypes of activity, and there is also a controversy regarding the classification among MVPA, LPA, SB, and SLP.
In light of the results of this study, these aspects deserve
further study.

Chen et al. BMC Medicine(2024) 22:313

Page 10 of 13

Table 3 Twenty-four-hour time use combinations with the highest risk of incident mental diseases
Overall mental disorder
Geometric mean (p25p75)

DepressionpGeometric mean (p25p75)

< 0.001

Anxietyp

Geometric mean (p25p75)

< 0.001p< 0.001

High risk
MVPA

22 (10, 30)

22 (10, 30)

25 (10, 40)

SLP

418 (360, 470)

411 (350, 460)

396 (340, 440)SB

689 (620, 750)

705 (640, 760)694 (630, 750)

LPA

311 (260, 370)

301 (250, 360)

325 (280, 380)

Median risk
MVPA

36 (20, 60)

36 (20, 60)

40 (20, 70)

SLP

418 (360, 470)

413 (350, 460)

402 (340, 450)SB

679 (610, 750)694 (630, 750)

680 (610, 740)

LPA

307 (250, 370)

297 (240, 360)

319 (270, 380)

Low risk
MVPA

50 (30, 80)

50 (30, 90)

52 (30, 90)

SLP

419 (360, 470)

415 (350, 680)

413 (350, 460)SB

667 (590, 730)

680 (610, 740)

667 (590, 730)

LPA

304 (250, 370)

295 (240, 360)

308 (250, 370)

p-value of ilrs was calculated by Wald test. Model adjusted for age, gender, ethnicity, BMI, smoking status, drinking status, education, cancer and pain-related diseases
MVPA, moderate-to-vigorous physical activity; LPA, light physical activity; SB, sedentary behavior; SLP, sleep

Conclusion
This study explored the role of 24-h movement behaviors in chronic pain and mental disorders, particularly
how the changes related to chronic pain on 24-h movement behaviors would affect the incidence of mental disorders. Meanwhile, we further provided evidence-based
recommendations for how people with chronic pain
should allocate 24-h activity time. We believe that evenpatients with chronic pain should engage in appropriate moderate-to-vigorous physical activity and sleep
duration should be extended while time in sedentary
behavior should be reduced. Randomized controlled trials based on our recommendations are needed to determine whether such a time-use combination can preventthe development of mental disorders in individuals withchronic pain.
Abbreviations
CI	Confidence interval
CoDA	Compositional data analysis
GAD-7	Generalized anxiety disorder-7HR	Hazard ratio
ICD-10	International Statistical Classification of Diseases and Related Health
Problems 10th Revisionilr	Isometric log-ratio
ISAP	International Association for the Study of Pain
ISM	Isotemporal substitution model
LPA	Light-intensity physical activity
MVPA	Moderate-to-vigorous physical activity
OR	Odds ratio
PHQ-4	Patient health questionnaire-4
PHQ-9	Patient health questionnaire-9

RCT​	Randomized controlled trial
SB	Sedentary behavior
SD	Standard deviation
SLP	Sleep

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03534-5.
Additional file 1. STROBE Statement—Checklist of items that should be
included in reports of cohort studies.
Additional file 2. Methodology supplement. Table S1. Specific diseases
included in overall mental disorders. Table S2. The hazard ratios of direct,
indirect and total effects of chronic pain on outcomes mediated by 24-h
movement behaviors (excluding common chronic diseases). Table S3.
24-h time use combinations with the highest risk of incident mental
diseases (excluding common chronic diseases). Table S4. The hazard ratiosof direct, indirect and total effects of chronic pain on outcomes mediatedby 24-h movement behaviors (excluding outcome patients in the first two
years). Table S5. 24-h time use combinations with the highest risk of incident mental diseases (excluding outcome patients in the first two years).
Table S6. The hazard ratios of direct and indirect effects of chronic painon outcomes mediated by 24-h movement behaviors (using PHQ-9 andGAD-7 as proxies for depression and anxiety). Table S7. 24-h time use combinations with the highest risk of incident mental diseases (using PHQ-9
and GAD-7 as proxies for depression and anxiety). Table S8. the effect ofcovariates and E-values on all outcomes. Table S9. The hazard ratios of
direct, indirect and total effects of chronic pain on outcomes mediated by
24-h movement behaviors (adding PHQ-4 as the predictors). Fig. S1. Study
population flowchart. Fig. S2. Subgroup analysis for depression. Fig. S3.
Subgroup analysis for anxiety. Fig. S4. Subgroup analysis for overall mental
disorders. Fig. S5. The mediating effect of chronic pain to overall mental
disorders, depression, and anxiety mediated by 24-h movement behaviors(excluding common chronic diseases). Fig. S6. Dose–response relationship (with 95% CI) between 24-h movement behaviors and all outcomesChen et al. BMC Medicine(2024) 22:313(excluding common chronic diseases). Fig. S7. The mediating effect ofchronic pain to overall mental disorders, depression, and anxiety mediatedby 24-h movement behaviors (excluding outcome patients in the first two
years). Fig. S8. Dose–response relationship (with 95% CI) between 24-h
movement behaviors and all outcomes (excluding outcome patients inthe first two years). Fig. S9. The mediating effect of chronic pain to depression and anxiety mediated by 24-h movement behaviors (using PHQ-9
and GAD-7 as proxies for depression and anxiety). Fig. S10. Dose–response
relationship (with 95% CI) between 24-h movement behaviors and all
outcomes (using PHQ-9 and GAD-7 as proxies for depression and anxiety).
Fig. S11. The mediating effect of chronic pain to depression and anxiety
mediated by 24-h movement behaviors (adding PHQ-4 as the predictors).
Acknowledgements
We express our sincere gratitude to all participants in the UK Biobank as wellas to everyone who contributed to the establishment of the UK Biobank study.
This research has been conducted using the UK Biobank Resource under
Application #90162.
Authors’ contributions
JC, YG, and ZL conceived the study and designed the statistical analyses. JC
did the statistical analyses. JC, XF, and FZ prepared the draft of the manuscript
under the supervision of YG, ZL, and LL. JC, XF, FZ, YL, PX, RY, QZ, JS, GC, ZW, SC,
YG, ZL, and LL substantively revised the manuscript. All authors contributed
to the interpretation of data. The corresponding authors attest that all listed
authors meet authorship criteria and that no others meeting the criteria havebeen omitted. All authors read and approved the final manuscript.
Funding
This work is supported by the Guangdong Province Graduate Demonstration
Course “Biostatistics II” and the Fundamental Research Funds for the Central
Universities (grant number: 11623328). The funding had no role in study
design and data analyses.
Availability of data and materials
Data are available on application to the UK Biobank (www.​ukbio​bank.​ac.​uk/).

Declarations
Ethics approval and consent to participate
All methods concerning human participants in our study were conductedin accordance with the ethical standards laid out in the 1964 Declaration of
Helsinki and its subsequent amendments. The UK Biobank obtained ethicalapproval from the North West Multicentre Research Ethics Committee (RECreference for UK Biobank 21/NW/0157). All participants signed a written
informed consent form at recruitment. We have adhered to the STROBE statement (see Additional file 1).
Consent to publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Public Health and Preventive Medicine, School of Medicine,
Jinan University, Guangzhou, Guangdong, China. 2 Department of Infectious
Diseases and Public Health, Jockey Club College of Veterinary Medicine
and Life Sciences, City University of Hong Kong, Hong Kong SAR, China.
3
School of Public Health, Guangdong Pharmaceutical University, Guangzhou,
Guangdong, China. 4 The First Affiliated Hospital of Bengbu Medical University,
Bengbu, Anhui, China. 5 Key Laboratory of Viral Pathogenesis & Infection
Prevention and Control (Jinan University), Ministry of Education, Guangzhou,
China.
Received: 16 January 2024 Accepted: 17 July 2024

Page 11 of 13

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