Validation of the Czech version of the Patterns of Activity Measure – Pain in patients with chronic low back pain

Overview

Objective: The aim of this study was to translate and validate the psychometric properties of the Patterns of Activity Measure –⁠ Pain in the Czech language. This tool assesses behavioral patterns related to physical activity in patients with chronic pain. As no validated instrument of this kind currently exists in the Czech context, the adaptation of the assessment tool represents an important contribution to diagnostic assessment and the individualization of therapeutic interventions in this population. Methods: A total of 151 patients (18–65 years) with chronic low back pain were included in the study. The instrument was linguistically adapted following international guidelines. Subsequently, exploratory and confirmatory factor analyses were conducted. Internal consistency was evaluated using Cronbach’s alpha, and convergent and concurrent validity were assessed through correlations with other validated instruments. Results: A three-factor structure of the tool (avoidance, overdoing, and activity pacing) was confirmed. The final confirmatory factor analysis model, after removing three items, showed an acceptable model fit. Internal consistency for the domains ranged from 0.89 to 0.94. Convergent and concurrent validity were supported by correlations with pain intensity, quality of life, psychological distress, and physical activity level. Conclusions: The Czech version of the Patterns of Activity Measure –⁠ Pain demonstrates good psychometric properties and can be considered as a reliable instrument for both clinical and research use in assessing activity-related behavioral patterns in patients with chronic low back pain.

Keywords:

Psychometrics – validation study – pain measurement – low back pain – activity pacing

This is an unauthorised machine translation into English made using the DeepL Translate Pro translator. The editors do not guarantee that the content of the article corresponds fully to the original language version.

 

Introduction

Chronic low back pain (CLBP) is one of the most common long-term painful conditions, representing a significant burden from both an individual and societal perspective. Globally, it ranks first among the causes of functional limitations [1]. It is estimated that by 2050, more than 800 million people worldwide will suffer from low back pain [2].

In individuals suffering from chronic pain, various patterns of behavior have been identified in response to pain that can significantly affect the course of the disease, functional abilities, and quality of life. These patterns include, in particular, avoidance of activity, excessive overloading, and so-called activity pacing [3].

Activity avoidance is a strategy in which the patient deliberately limits or completely omits activities associated with expected pain. Although this pattern may be motivated by an effort to prevent acute discomfort, long-term avoidance of physical activity has significant negative effects on the musculoskeletal and cardiovascular systems. Long-term application of this pattern contributes to "disuse syndrome," physical deconditioning, and increased sensitivity to pain. The result can be a spiral of chronic pain, loss of physical performance, disability, and impaired psychosocial adaptation. Avoidance of activity is empirically associated with higher levels of depressive symptoms, anxiety, and reduced quality of life [3–6].

Overdoing, on the other hand, is characterized by continuing an activity or work performance despite the presence of pain until the activity is completed. This behavioral pattern may be perceived as functional or productive in the short term, but in the long term it contributes to the persistence of pain, more frequent and more severe exacerbations, and increases the risk of functional overload [7].

Activity pacing is a potentially adaptive approach. It is defined as the targeted scheduling of physical activities into shorter periods with regular breaks, either based on pain perception or according to a predetermined schedule. This strategy is considered an effective tool for preventing overload and relapses while promoting increased tolerance to activities. It allows a balance to be achieved between activity and regeneration, which promotes functional recovery, pain stabilization, and a better quality of life. This approach is part of modern multimodal and cognitive-behavioral therapies for chronic pain [4,7].

In 2018, the prestigious journal The Lancet published a three-part series of scientific articles focused on low back pain, which addressed the definition, evidence-based treatment options, and directions for further research on this serious health problem. The series emphasized the importance of early intervention, recommending patient education, self-care support, and the use of physical and psychological interventions with an emphasis on an active approach to one's own health as the first-line treatment [8]. However, educational interventions aimed at promoting adaptive physical activity, especially a balanced lifestyle, should be preceded by a valid identification of individuals' existing movement patterns.

The Patterns of Activity Measure –⁠ Pain (POAM-P) assessment tool was developed for this purpose [9]. It is a standardized self-assessment questionnaire designed to identify and quantify three dominant patterns of physical activity in patients with chronic pain: avoidance of activities, overuse/excessive effort, and balanced style/modulation of activities. The POAM-P questionnaire has been adapted and validated into several language versions and has demonstrated good psychometric properties in different cultural contexts [5,10,11].

However, the Czech Republic still lacks a validated tool specifically designed to assess patterns of physical activity in patients with chronic pain. Although tools for measuring pain catastrophizing, such as the Pain Catastrophizing Scale (PCS), are available to assess avoidance behavior conditioned by fear of pain, these tools do not cover the spectrum of other relevant activity patterns.

The aim of this study was therefore to test the psychometric properties (especially construct and criterion validity) of the Czech version of the POAM-P questionnaire (POAM-P/CZ) and thus create a standardized tool suitable for use in clinical and research practice in the Czech environment.

 

Materials and methods

Sample

The research sample consisted of patients treated at the Pain Treatment Center of the Department of Anesthesiology, Resuscitation, and Intensive Care Medicine. The study included patients who visited the outpatient clinic between January 2024 and March 2025 and gave their informed consent to participate. The inclusion criteria were as follows: participants aged 18–65 years, pain lasting at least 3 months at the time of data collection, diagnosed pain in the lumbar and/or sacral region or lower back pain, and consent to participate in the research study. Exclusion criteria included severe visual or hearing impairments, other somatic or cognitive disorders preventing independent completion of the questionnaires, and cancer. When determining the sample size, general recommendations for validation studies of psychometric instruments were taken into account, where the minimum requirement is five times the number of questionnaire items [12]. Given the 30 items on the POAM-P scale, a target number of at least 150 respondents was set, with a reserve for possible losses during data collection. A total of 160 patients were approached, nine of whom withdrew from the study. The final size of the analyzed sample was thus 151 respondents.

 

Methods

The basic measurement tool was the Czech version of the POAM-P questionnaire (POAM-P/CZ) [9]. The tool contains 30 items rated on a five-point Likert scale (0–4), with ten items assigned to each of the three patterns. The resulting score is calculated separately for each domain, ranging from 0 to 40 points. The predominant activity pattern is determined by the domain with the highest score.

In the first phase of the study, the POAM-P/CZ tool underwent a process of cross-cultural adaptation in accordance with the methodology recommended by the Mapi Research Trust and the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) [13]. Subsequently, a questionnaire set was created, including sociodemographic data and other assessment tools.

 

Other assessment tools

Pain intensity was measured using a numerical rating scale (NRS, 0–10).

The mental state of patients was assessed using the Hospital Anxiety and Depression Scale (HADS), which consists of two subscales, HADS-A (anxiety) and HADS-D (depression), each containing seven items [14].

Quality of life was assessed using the short version of the World Health Organization's standardized questionnaire (WHOQOL-BREF) [15].

Physical activity was recorded using the short version of the International Physical Activity Questionnaire (IPAQ-7) [16].

 

Statistical evaluation method

Descriptive statistics were used to describe the distribution of values: median, interquartile range, mean, standard deviation, range, absolute and relative number.

Psychometric analysis of POAM-P was performed in the form of factor analysis with reliability assessment. In terms of reliability, the internal consistency of the tool was determined by calculating Cronbach's alpha coefficient. The threshold values for acceptable internal consistency of the test are established by convention in the literature; alpha values above 0.6 are acceptable, while values of 0.7 and above indicate a satisfactory level of internal consistency of the test [17].

The suitability of the data set for factor analysis was assessed using the Kaiser-Meyer-Olkin (KMO) method and Bartlett's test of sphericity, which should be significant. The KMO value can range from 0 to 1. The higher the value, the more suitable the data. The minimum limit value is 0.6, with values above 0.8 indicating very suitable data [18]. Next, the number of factors was determined. Factor retention was determined using eigenvalue analysis and parallel analysis. The eigenvalue was set to 1 as the standard (acceptance of as many factors as reached a value of 1 or more). Exploratory factor analysis (EFA) was performed using the maximum likelihood method and Promax rotation. Items were retained based on factor loadings (≥ 0.40) and communalities (≥ 0.30), taking into account the conceptual consistency of item loadings. Correlations between factors were estimated using Pearson's correlation coefficients.

Confirmatory factor analysis (CFA) was performed to test the original theoretical model and the factor structure derived from EFA. Model fit was evaluated using multiple indices, including chi-square (c ²), degrees of freedom (df), root mean square error of approximation (RMSEA) with 90% confidence intervals, standardized root mean square residual (SRMR), comparative fit index (CFI), Tucker-Lewis index (TLI), and Bayesian information criterion (BIC). CFI values above 0.90 indicate an acceptable model, and values above 0.95 indicate good model fit with the data. For RMSEA, the threshold for an acceptable model is 0.08 and for a good model is 0.06 [19].

The significance level was set at 0.05. Statistical analysis was performed using R software (version 4.4.1).

 

Results

The research sample consisted of 151 patients with chronic back pain aged 18–65 (median 54, IQR = 49–59). Of the total number, 46 were men (30.5%) and 105 were women (69.5%). Basic demographic variables were monitored: age, gender, level of education, marital status, and employment status (Table 1).

The average pain intensity reported over the last seven days was 6.09 points (SD = 1.51) on a numerical rating scale (NRS). The average lowest recorded value was 4.79 (SD = 1.91) and the highest was 7.43 (SD = 1.59) (Table 2). The average total quality of life score measured by the WHOQOL-BREF questionnaire was 2.56 (SD = 0.93), while the average health satisfaction score was 1.99 (SD = 0.64). The lowest domain score was recorded in the area of physical health (M = 8.20; SD = 2.20). Mental health assessed using the HADS scale revealed that 78 respondents (51.7%) had normal scores on the depression subscale, while 73 patients (48.3%) had scores corresponding to mild, moderate, or severe depression. On the anxiety subscale, 82 patients (54.3%) were assessed as normal, while 69 (45.7%) showed increased levels of anxiety (Table 2). The most common activity pattern identified by POAM-P was avoidance behavior, with an average score of 31.52 (SD = 7.44). The level of physical activity assessed by the IPAQ-7 questionnaire showed that most respondents (n = 76; 50.3%) had a moderate level of physical activity.

The adequacy of the data for factor analysis was confirmed by the KMO criterion value of 0.93, which indicates excellent data suitability. The KMO values for individual questionnaire items exceeded 0.80. Bartlett's test of sphericity was statistically significant (p < 0.001), further supporting the suitability of the data for factor analysis. EFA identified three factors with eigenvalues greater than 1 (12.93, 5.22, and 1.47), while the fourth factor had an eigenvalue of only 1.11 and showed a significant decrease compared to the third. Parallel analysis confirmed the three-factor solution as optimal, explaining a total of 61% of the variance (Factor 1 : 31%, Factor 2 : 18%, Factor 3 : 12%) (Table 3).

The factor structure showed the following distribution of items:

Factor 1 overload (mostly consistent): items 2, 4, 7, 10, 15, 18, 20, 23, 26

Factor 2 pace modulation (fully consistent): items 3, 5, 9, 12, 14, 17, 21, 24, 27, 29

Factor 3 avoidance (partially consistent): items 11, 16, 22, 25, 28

Significant correlations were observed between the factors: Factor 1 and Factor 3 correlated negatively (r = −0.72), while Factor 2 showed weaker correlations with both Factor 1 (r = −0.22) and Factor 3 (r = 0.40). This correlation structure, in conjunction with the POAM-P theoretical framework, provided sufficient justification for conducting a confirmatory CFA to verify the hypothetical three-factor model. CFA was first performed based on the of the original theoretical structure of POAM-P, testing Model 1, which included all items assigned to the three factors according to the original division. However, this model showed a low level of agreement with the data across multiple model fit indices. Even the addition of residual covariances did not lead to a significant improvement in model fit. Model 2, based on the results of exploratory factor analysis, was then tested. This model achieved better fit values, but the fit indices still indicated only partial adequacy. In the next step, Model 3 was specified, representing a modified version of Model 2. Three items (Nos. 7, 9, and 12) were removed from this model due to significant cross-loading, but two residual covariances (items 4 and 23, items 25 and 28) were included. The resulting final model showed an acceptable level of agreement according to standard fit indices, confirming its suitability for representing the factor structure of POAM-P in this sample (Table 4 and Figure 1).

The internal consistency of the Czech version of POAM-P/CZ was satisfactory. Cronbach's alpha coefficient for the individual subscales ranged from 0.89 (95% CI: 0.86–0.91) to 0.94 (95% CI: 0.93–0.95), and the total score reached 0.64 (95% CI: 0.60–0.72) (Table 5).

Convergent validity was supported by significant correlations between the POAM-P subscales and the level of physical activity assessed by IPAQ-7 (Table 6). Concurrent validity was demonstrated by correlations between individual POAM-P subscales and pain assessment results, WHOQOL-BREF, and HADS. The POAM-P avoidance subscale correlated strongly negatively with quality of life domains, while the overload subscale showed positive correlations. Pace modulation correlated negatively with the WHOQOL physical health domain and positively with the environment domain. The avoidance pattern also correlated strongly with pain intensity and negatively with the HADS subscales, while overuse correlated positively.

 

Discussion

The results of our research confirm that the Czech version of the POAM-P/CZ questionnaire exhibits a highly consistent three-factor structure that corresponds to the theoretical framework of the original instrument [9]. The three-factor structure (avoidance, overloading, pace modulation) was confirmed by both exploratory (EFA) and confirmatory (CFA) factor analysis, with the final modified model showing an adequate fit. These findings are consistent with the results of validations of language versions of POAM-P conducted in various countries [5,11,20]. There are also broader contexts in the literature confirming that POAM-P or similar instruments (e.g., Activity Patterns Scale –⁠ APS), which systematically differentiate maladaptive (avoidance, overloading) and adaptive behavior patterns, are associated with various psychosocial outcomes [21].

Cronbach's alpha for the subscales corresponded to or exceeded the values reported in foreign validations [5,20]. The correlations of the POAM-P/CZ subscales with the IPAQ 7, WHOQOL-BREF, and HADS scores confirm the expected relationships: avoidance is associated with lower physical activity, poorer quality of life, and higher distress, while overuse is associated with higher physical activity. This corresponds to analogous relationships from Turkish and Japanese studies [5,11].

The assessment of activity patterns allows for the targeted design of individualized interventions for patients with CLBP and contributes to overcoming the stereotypical approach, where in the vast majority of cases (up to 70%) only pain-free activities are recommended [22]. Each of the three identified behavioral patterns requires a specific therapeutic approach.

Patients with a predominantly avoidance pattern often experience significant fear of movement (kinesiophobia), which leads to a reduction in normal daily activities, physical deconditioning, and an increased risk of developing disability. In these cases, graded exposure therapy is recommended, in which patients are systematically and controllably exposed to the physical activities they fear in order to reduce maladaptive responses to pain [23,24]. At the same time, it is advisable to incorporate elements of cognitive-behavioral therapy, which will enable the modification of catastrophic thoughts related to pain. Patient education plays an important role within the bio-psycho-social model of pain and the concept of pain neuroscience education, which aims to change the understanding of pain from passive to active management [24]. Patients with an identified overloading behavior pattern show a tendency to repeatedly exceed their physical limits despite the presence of pain, which can lead to fluctuations in pain intensity and a cycle of overload and exhaustion. Interventions for these patients should focus on training energy management techniques, including activity planning, setting limits, and scheduling breaks [24]. Education on the importance of balancing activity and rest in order to maintain stable function and prevent overload is also important. Behavioral modification of daily activities and support for awareness of bodily signals using mindfulness techniques are recommended. These approaches lead to greater self-regulation and reduce the risk of pain becoming chronic as a result of repeated overload.

For patients who already exhibit a modulated, balanced pattern of behavior, it is crucial to maintain and reinforce this approach. These patients are able to plan activities realistically, adjust their pace, and incorporate rest. Interventions should focus on supporting this adaptive style through activity monitoring (e.g., activity diaries), deepening knowledge of self-regulation, and utilizing available self-regulation techniques. It is also appropriate to participate in rehabilitation groups where they can share coping strategies and strengthen their motivation to maintain an active approach in the long term.

Regardless of the prevailing pattern of behavior, it is essential that interventions be part of an interdisciplinary approach involving physical therapists, psychologists, pain management specialists, and other professionals. The treatment plan should always be individualized to the specific needs of the patient and should include elements of education, behavioral intervention, and self-management support.

Conclusion

The validation of the Czech version of POAM-P/CZ brings significant benefits to the Czech research and clinical environment. The results confirm that POAM-P/CZ has comparable psychometric properties to adaptations in other cultures –⁠ it exhibits a stable factor structure, high internal consistency, and demonstrable convergent and concurrent validity. Its application is recommended not only for research but also for the clinical identification of maladaptive behavior patterns in patients with chronic pain, for planning targeted behavioral interventions, and for optimizing treatment plans.

 

Study registration

The study was registered in the clinical trial registry prior to the enrollment of the first patient in the study at: www.clinicaltrials.gov (ID: NCT05922007).

 

Financial support

Supported by the FNO institutional support grant project "The impact of chronic pain and its treatment on quality of life, disability, and physical activity" 11/RVO-FNOs/2023. All rights under intellectual property protection regulations are reserved.

 

Ethical aspects

The work was carried out in accordance with the 1975 Helsinki Declaration and its revisions in 2004 and 2008. Approved by the FNO Ethics Committee on March 23, 2023 (No. 203/2023).

 

Conflict of interest

The authors declare that they have no conflict of interest in relation to the subject of the study.

 

		Median (IQR)/n (%)
Age (years)		54 (49; 59)
Age (years)	30–39	3 (2.0)
	40–49	40 (26.5)
	50–59	79 (52.3)
	60	29 (19.2)
Gender	Male	46 (30.5)
	female	105 (69.5)
Education	elementary	10
	Secondary	136 (90.1)
	university	5 (3.3)
Marital status	single	2 (1.3)
	Married	80 (53.0)
	Living with partner	25 (16.6)
	divorced	42 (27.8)
	Widowed	2 (1.3)
Employment	full-time	10 (6.6)
	Part-time	44 (29.1)
	full disability pension	97 (64.3)

 

Table 1. Basic demographic characteristics (n = 151 patients).

% –⁠ relative frequency; IQR –⁠ interquartile range; n –⁠ absolute frequency

 

		Median (IQR)	Mean (SD)	n (%)
NRS	current pain	6 (5; 7)	6.06 (1.64)
	average pain	6 (5; 7)	6.09 (1.51)
	Least pain	5 (3; 6)	4.79 (1.91)
	strongest pain	8 (6; 8)	7.43 (1.59)
WHOQOL-BREF	Quality of life	2 (2; 3)	2.56 (0.93)
	health satisfaction	2 (2; 2)	1.99 (0.64)
	Physical health	8.6 (6.9; 9.1)	8.20 (2.20)
	Mental health	13.3 (12.0; 14.7)	13.33 (2.39)
	social relationships	14.7 (13.3; 16.0)	14.37 (2.60)
	living conditions	13.5 (12.5; 15.0)	13.74 (2.31)
HADS –⁠ Depression	score	7 (5; 10)	7.72 (3.78)
	Normal			78 (51.7)
	low			37 (24.5)
	medium			29 (19.2)
	high			7 (4.6)
HADS –⁠ Anxiety	score	7 (5; 10)	7.49 (3.91)
	Normal			82 (54.3)
	low			37 (24.5)
	medium			23 (15.2)
	high			9 (6.0)
IPAQ	score	832 (396; 1575)	1379 (1528)
	low			60 (39.7)
	medium			76 (50.3)
	high			15 (10.0)
POAM-P	avoidance	34 (27; 37)	31.52 (7.44)
	tempo modulation	29 (24; 35)	28.99 (6.57)
	overloading	13 (10; 20)	14.95 (7.35)

 

 .

% –⁠ relative frequency; HADS –⁠ Hospital Anxiety and Depression Scale; IQR –⁠ interquartile range; IPAQ –⁠ International Physical Activity Questionnaire; n –⁠ absolute frequency; NRS –⁠ Numeric Rating Scale; POAM-P –⁠ Patterns Of Activity Measure-Pain; SD –⁠ standard deviation; WHOQOL-BREF –⁠ World Health Organization Quality of Life questionnaire –⁠ short version

 

Item number	Wording of POAM-P items	Factor 1	Factor 2	Factor 3	Communalities
1	When the pain starts to get worse, I stop what I am doing.	–0.79	0.12	–0.06	0.62
2	When I do something, I don't stop until I finish it.	0.71	–0.06	–0.04	0.56
3	When I do an activity, I alternate between working and taking breaks.	–0.04	0.63	–0.02	0.40
4	When I have a good day in terms of pain, I add extra tasks.	0.88	0.26	0.19	0.57
5	When I start an activity, I think about how to divide it into smaller parts.	0.09	0.74	–0.09	0
6	There are many activities that I avoid because they make my pain worse.	–0.41	0.08	0	0.53
7	I use my good days, in terms of pain, to get more things done.	0.86	0.43	0.12	0.67
8	When the pain starts to get worse, I know it's time to stop what I'm doing.	–0.71	0.13	0	0.69
9	I perform my activities at a slow and calm pace.	–0.46	0.45	–0.02	0.48
10	I continue what I am doing until the pain becomes so severe that I have to stop.	0.76	–0.08	–0.02	0.64
11	I avoid activities that I know make my pain worse.	–0.35	0	0.58	0.75
12	When I am doing something, I stop after a while and then return to it later and continue working.	0.42	0.62	–0.05	0.45
13	Most days, the pain prevents me from doing almost anything.	–0.58	–0.04	0	0.38
14	When I do something, I do it more slowly and work at a calm pace.	–0.29	0.51	0.03	0.44
15	Once I start an activity, I continue until I finish it.	0.85	–0.17	0	0.74
16	I limit my activities to those that I know will not worsen my pain.	–0.48	0.01	0.44	0.73
17	When I perform a task, I divide it into smaller parts and do one part at a time.	0.07	0.79	0	0.63
18	I ignore the pain and continue what I am doing as long as possible.	0.64	–0.19	–0.16	0.69
19	Most days, I spend more time resting than being active because of pain.	–0.72	–0.05	–0.04	0
20	I continue doing what I am doing until I can no longer bear the pain.	0.72	–0.15	–0.04	0.62
21	Instead of doing an activity all at once, I do it in small parts.	–0.09	0.74	0.01	0.59
22	I do not start an activity if I know it will worsen my pain.	–0.19	–0.17	0.79	0.79
23	On days when the pain is less severe, I do something extra.	0.73	0.39	–0.10	0.65
24	When I do something, I do it all at once.	–0.14	0.69	0	0.54
25	If I know that something will make my pain worse, I don't do it anymore.	–0.18	–0.08	0.84	0.90
26	When I do something, I do it all at once.	0.77	–0.22	–0.01	0.72
27	Instead of doing the whole activity, I divide it into small parts and do one part at a time.	–0.01	0.82	0	0.69
28	I have limited my activities by not doing those th that worsen my pain.	–0.19	–0.06	0.82	0.90
29	When I do something, I work for a while, take a break, and then go back to work.	–0.02	0.86	–0.11	0.67
30	Some days I do a lot, other days I do little.	–0.26	0.12	0.16	0.20

Table 3. Exploratory factor analysis of POAM-P.

Factor loadings and communalities for POAM-P items Patterns of Activity Measure-Pain.

POAM-P –⁠ Patterns Of Activity Measure-Pain

 

	Model 1	Model 2	Model 3
χ2  (df)	1195.849 (402)	733.343 (249)	349.877 (184)
P	< 0.001	< 0.001	< 0.001
χ2 /df	2.975	2.945	1.902
RMSEA (90% CI)	0.117 (0.109; 0.125)	0.117 (0.107; 0.126)	0.080 (0.067; 0.093)
SRMR	0.141	0.149	0.096
CFI	0.777	0.827	0.930
TLI	0.759	0.808	0.920
BIC	9648.088	7691.293	6608.195

Table 4. Model testing (CFA): (1) Model based on the original structure of the subscales; (2) Factor structure according to EFA results; (3) Refined model 2 with correlated residuals and item reduction.

χ² –⁠ chi-square; BIC –⁠ Bayesian information criterion; CFI –⁠ comparative fit index; df –⁠ degrees of freedom; P –⁠ p-value for chi-square test; RMSEA –⁠ root mean square error of approximation; RMSEA (90% CI) –⁠ 90% confidence interval for RMSEA; SRMR –⁠ standardized root mean squared residual; TLI –⁠ Tucker-Lewis Index

 

	Cronbach's α (95% CI)	Pace modulation	Overloading
Avoidance	0.94 (0.93; 0.95)	0.21 (0.09; 0.32)***	–0.65 (–0.71; –0.58)***
Pace modulation	0.90 (0.87; 0.92)	–	–0.10 (–0.22; 0.01)
overloading	0.89 (0.86; 0.91)	–	–
whole	0.64 (0.60; 0.72)	–	–

Table 5. Cronbach's alpha coefficient for each subscale and the entire POAM-P instrument and inter-scale correlations. 

Kendall's correlation coefficient, ***p < 0.001; **p < 0.01; *p < 0.05, 95% CI

CI –⁠ confidence interval

 

	POAM-P
	Avoidance	Pace modulation	Overloading
Pain intensity	0.14 (0.03; 0.24)*	–0.02 (–0.13; 0.09)	–0.07 (–0.17; 0.03)
impact of pain on life	0.20 (0.08; 0.31)***	0.26 (0.16; 0.36)***	–0.09 (–0.20; 0.01)
WHOQOL-BREF
Quality of life	–0.26 (–0.37; –0.15)***	0.04 (–0.07; 0.15)	0.19 (0.07; 0.30)**
health satisfaction	–0.30 (–0.38; –0.20)***	–0.08 (–0.18; 0.02)	0.22 (0.13; 0.31)***
Physical health	–0.34 (–0.44; –0.24)***	–0.19 (–0.30; –0.07)**	0.20 (0.09; 0.31)***
Mental health	–0.26 (–0.37; –0.16)***	–0.06 (–0.16; 0.04)	0.19 (0.09; 0.30)***
social relationships	–0.11 (–0.22; 0.00)	0.07 (–0.05; 0.18)	0.04 (–0.07; 0.16)
living conditions	–0.17 (–0.28; –0.06)**	0.21 (0.11; 0.31)***	0.18 (0.07; 0.29)**
HADS
depression	0.34 (0.23; 0.45)***	–0.06 (–0.16; 0.05)	–0.34 (–0.46; –0.23)***
anxiety	0.23 (0.12; 0.34)***	0.15 (0.04; 0.26)**	–0.13 (–0.24; –0.02)*
IPAQ
score	–0.32 (–0.42; –0.23)***	–0.02 (–0.13; 0.10)	0.36 (0.26; 0.46)***

Table 6. Convergent and concurrent validity (POAM-P and pain subscales, WHOQOL-BREF, HADS, and IPAQ .

Kendall's correlation coefficient, ***p < 0.001; **p < 0.01; *p < 0.05, 95% CI

CI –⁠ confidence interval; HADS –⁠ Hospital Anxiety and Depression Scale; IPAQ –⁠ International Physical Activity Questionnaire; POAM-P –⁠ Patterns of Activity Measure-Pain; WHOQOL-BREF –⁠ World Health Organization Quality of Life –⁠ short version