From Accelerometer Data to Activity Patterns in Chronic Pain: Methodical Reasoning is Key

1. Introduction

Chronic pain (CP), defined as pain persisting or recurring for at least three months [1], significantly impacts various aspects of daily life, including physical, mental, social, economic [2,3] and spiritual [4] domains. Individuals with CP often experience negative emotions, fatigue, depression, deconditioning, sleep dysfunctions and reduced self-efficacy [5]. These challenges can lead to limitations in expressing the self, work, leisure and family life and to social isolation [2,3]. For healthcare providers, CP presents a complex challenge due to the interplay of biological, social, and psychological factors. Often, there is no definitive intervention to resolve CP, leading healthcare interventions to focus on self-management and mitigating the negative consequences, such as limitations in activities, participation, and quality of life [6,7].

It is hypothesized that CP interferes with activity patterns (AP) within daily living [8,9,10,11,12,13]. AP have been defined as the temporal structure of physical activity and sedentary behavior accumulated over a specified period during waking hours [14]. Therefore, AP capture the succession of activity and rest, rather than cumulative measures like total activity per day. It is theorized that AP in individuals with CP differ from those in healthy populations, are influenced by coping styles and are associated with various health-related outcomes [9,15,16,17,18,19,20,21,22]. Several theoretical models have been proposed to characterize AP in CP, including the avoidance-endurance model [13].

Traditionally, AP have been assessed using self-report questionnaires. More recently, objective measurement using accelerometry, which is the measurement of acceleration forces with a small body-worn device to detect activity, inactivity and activity intensity, has gained attention [23,24,25]. However, consensus on optimal assessment methods remains lacking. Comparisons of questionnaires with accelerometer outcomes have yielded no or inconsistent associations [26,27,28]. This evokes uncertainty about the validity of questionnaire outcomes for measuring AP on the one hand, and the effects of methodical choices in accelerometry on the other hand [26,29,30,31,32]. Self-report instruments are inherently subjective, relying, amongst others, on retrospective recall, personal perceptions and emotional state, which limits their ability in capturing actual behavior. The validity of accelerometry is influenced by incidental user-related factors, such as variations in the wear angle of the sensor.

More importantly, validity of accelerometry is structurally affected by methodical choices, including variables in data processing, and outcome variable selection. For example, very short epoch lengths may introduce unnecessary noise, whereas overly long epoch lengths may obscure temporal detail. Moreover, accelerometer studies often rely on coarse metrics such as total time per day [33,34,35] which fail to capture the nuanced temporal structure of activity and rest.

To interpret findings from previous studies and relate them to methodical choices, detailed insight into these methods is needed. AP are constructs – abstract representations that require clear definitions and operationalization for empirical investigation. This process involves thoroughly defining the concept followed by its operationalization with measurable variable and outcome measures.

Although recent reviews have explored behavior-related activity parameters from accelerometry [36,37] and cumulative activity in CP populations [38], no review has specifically focused on AP parameters relevant to CP. Therefore, this scoping review aims to provide an overview of methods for extracting AP parameters relevant to CP care from accelerometer time series. The primary aim is to elucidate the process of methodical reasoning for extracting AP from accelerometer data in patients with CP. This will inform researchers on the usability and availability of methods to investigate AP in patients with CP, and on the underlying concepts and definitions. The secondary aim is to interpret and compare clinically relevant findings in light of methods employed.

The review is structured around the methodical reasoning process, encompassing: (1) selection of the AP-related concept, (2) its definition or specification (conceptualization), (3) the definition of variables and indicators that can be observed and measured (operationalization), and (4) the measurement properties and the methods for extracting the indicators from raw accelerometer data. The overview of conceptualization, operationalization and measurement properties enables qualitative assessment of content validity of the methods included.

This scoping review [39] includes studies employing triaxial accelerometers to assess AP in individuals with chronic primary musculoskeletal pain. This condition is defined as CP in the muscles, bones, joints, or tendons that is characterized by significant emotional distress or functional disability, that cannot be accounted for by another condition [40,41].

2. Materials and Methods

The research protocol has been published in the Open Science Framework (OSF; OSF | Accelerometry in chronic pain). This scoping review is conducted and reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews PRISMA-ScR guidelines [42]. A scoping review is the most appropriate method to fit our aim, as it allows us to provide an overview of the volume and the focus of available literature, examine how research has been conducted, identify key characteristics, and identify knowledge gaps [39].

2.1. Eligibility Criteria

According to the recommendations of JBI Manual of Evidence Synthesis (Chapter 11, Scoping Reviews) [43], population, concept and context of interest were defined. This review focusses on the population of adults with chronic primary musculoskeletal pain as a main condition. The concept of study is the investigation of AP with triaxial accelerometry. The context of study is physical activity in daily life. All analytical methods were included if AP were involved, with emphasis on the temporal structure of activity versus sedentary behavior.

Full-text publications were included with (1) populations over 18 years, (2) primary musculoskeletal CP, (3) AP as a primary outcome measure, (4) triaxial accelerometers and (5) at least five consecutive days of physical activity measurement. In line with the definition of primary CP, publications were excluded if the sample consisted of individuals with musculoskeletal CP resulting from identifiable underlying conditions. Primary CP was selected as the focus of this review because it emphasizes the interaction between pain and AP, rather than the interaction between physical impairments and AP, as seen in conditions such as M. Parkinson or severe osteoarthritis. With mixed samples, papers were included when at least 50% of the sample fulfilled the second inclusion criterium. When subgroups were investigated separately, only data pertaining to the subgroup that met the inclusion criteria were considered.

The minimum of five consecutive measurement days is based on the recommendations of Verbunt et al. (2012; [44]) for assessing physical activity in CP, as well as prior reliability studies in the general population [45], older adults [46], and individuals with rheumatoid arthritis [47]. Eligible publications included peer-reviewed articles, gray literature and dissertations, provided they were written in English, German, Dutch, or French.

2.2. Search Strategy

PubMed, Embase and Cinahl databases were searched from inception until November 2024. For gray literature the first ten pages of Google Scholar were scanned and dissertations were searched in ProQuest via PsycInfo. The search strategies were developed in cooperation with a specialized information specialist (TE). The search strings for PubMed, Embase, Cinahl, GoogleScholar and PsycInfo were composed by TE. The search strings are included in Appendix A. Search was restricted to title, abstract and MeSh terms and included CP, physical behavior, accelerometry and synonyms. Synonyms were based on keyword, MeSh terms (PubMed) and Emtree terms (Embase) of relevant publications. The PubMed, Embase and Cinahl searches were performed by TE. Dissertations were searched by AD in consultation with TE. Finally, references of the included articles were scanned for missed publications.

Six key references [48,49,50,51,52,53] were selected in advance and in the search results it was verified that the six pre-selected key publications were included.

2.3. Processing of Search Results and Selection

References found in PubMed were removed from the Embase and Cinahl results by subtracting PMID-numbers. The outcome was deduplicated in RefWorks Legacy via the close deduplication method and this was double-checked with SR-accelerator Deduplicator.

Selection on inclusion criteria was done in two steps, with (1) a screening phase and (2) a selection phase. Both phases were performed by AD and HW independently with all search results. Inclusions after both screening and selection were compared, discrepancies were discussed, and decisions were made. A third researcher (MV) could be consulted in case of persistent doubt.

For the screening phase title and abstract of all references were imported into Active Learning for Systematic Reviews (ASReview) Lab software [54]. With ASReview the screening phase was assisted with an AI-approach, namely Active Learning with different Machine Learning (ML)-algorithms. With this approach, the number of references that need to be manually labeled as relevant or irrelevant is reduced by approximately 90 percent, while maintaining or even improving reliability [55]. The active learning method (the ‘ASReview Pipeline’) is summarized in box 1 and is extensively elucidated by Van der Schoot et al. (2021) [54] and Boetje and Van der Schoot (2024) [55]. The 6 preselected key references were used to check the results of the ASReview-assisted screening phase.

For step 1, ‘adding prior knowledge for training’, AD and HW independently screened and labelled 100 references manually and compared, discussed and adjusted the selection. Steps 2 and 3 were performed by AD and HW independently as well. The resulting two shortlists of included references were compared, and discrepancies were discussed which resulted in a final shortlist. These papers were read full-text and accordance with all inclusion criteria led to final inclusion for this scoping review.

As a final step titles of all reference lists of the included papers were screened independently by HW and AD. The selection phase was similar as with the other databases.

2.4. Data Extraction

The study characteristics were extracted from the included articles by the researchers (AD and HW), along with the information necessary to understand the process of methodical reasoning for the quantification of AP. This process involved the successive steps of conceptualizing and operationalizing the concept, followed by selecting measurement properties and data processing.

To find evidence for the existence of concepts or associations with a concept, the concept must be clearly stated and quantifiably defined. The operational definition should include information on the variables (the properties or characteristics) of the concept and its indicators (the methods of quantifying the variables).

This process of conceptualization and operationalization was extracted from the included papers and consisted of four successive steps: (1) formulating the theoretical concept of the study related to AP within CP, (2) defining the concept as precisely as possible (conceptualization), (3) operationalizing the concept with concrete and specific variables, which are the properties and characteristics of the concept, and (4) identifying the concomitant indicators, which quantify variables. This process is clarified by an example in Figure 1. Additionally, measurement properties and data processing were summarized.

Lastly, based on this overview of conceptualization, operationalization and data processing, content validity of the included studies will be judged. Content validity is assumed the most important measurement property of a measurement instrument [56,57], and is defined as the degree to which the content of a measurement instrument reflects the intended outcome being measured [58]. When content validity is limited, the value of the conclusions drawn is doubtful.

Content validity encompasses three aspects: (1) relevance for the outcome, target population and context, (2) comprehensiveness, which reflects whether the method includes all necessary components and (3) comprehensibility, which is ‘the extent to which the content of an instrument is understood by the individuals involved in the measurement process, in a manner that aligns with the intention of the developers.’ [56] These measurement properties were not assessed within the included papers, but relevance, comprehensiveness and comprehensibility can be qualitatively assessed by the reader, based on information of conceptualization and operationalization.

3. Results

As illustrated in Figure 2, after deduplication, the search in PubMed, Cinahl and Embase yielded 11366 records. With ranking and labelling according to the ASReview pipeline, in total 2300 of 11366 references were screened and labelled by HW and AD (in step 1 100 by HW and 100 by AD, in step 2 2 times 1000, and in step 3 2 times 50). Forty-two references were labelled as relevant, which included all 6 key references. These 42 papers were read full text and inventoried on the inclusion criteria after which 14 papers were included. Ten publications were excluded because they were posters, meeting abstracts or commentaries instead of full text peer reviewed papers. 17 papers were excluded because they did not meet at least one of the inclusion criteria, of which 16 did not investigate AP according to the definition of ‘the temporal structure of physical activity and sedentary behavior accumulated over a specified time period during waking hours,’ and one had a measurement period of one day for investigating activity variability during the day. Of these 17 excluded papers one used a biaxial accelerometer instead of triaxial. In one paper [59] the number of accelerometer-axes was not specified, and, on inquiry, the author could not clarify the number of axes. Paraschiv et al. (2008) used a biaxial and a uniaxial accelerometer on different body locations and Paraschiv et al. (2012) used three biaxial accelerometers which was assumed to deliver at least the same dimensionality and order of detail as one triaxial accelerometer. Therefore, these two studies by Paraschiv et al. were included. One publication could not be found.

With Google Scholar, 14 papers were ranked as relevant of which 13 could be excluded by reading title and abstract. One publication was selected for full text reading and was excluded because it did not investigate AP. Screening of the titles of ProQuest results yielded 6 records for reading the abstract after which one dissertation was screened full text [60]. This dissertation was excluded based on the inclusion criteria. After scanning the reference lists of the 14 included papers 26 titles and abstracts were screened and all were excluded because they did not meet the inclusion criteria. One publication [61] that was not found in the databases was added from the personal archives, which yielded 15 papers for this scoping review.

3.1. Study Characteristics

The included studies are described in Table 1. Most studies had a cross-sectional set-up. Three studies had a longitudinal set up to investigate treatment outcomes [53], associations between changed behavior-type and disability and quality of life [50] or associations between changed activity level and changed pain intensity [62]. CP sample size ranged from 15 to 292. Some studies included a sample without pain [25,63,64] or with acute pain [62] for comparison. Most participants were recruited from multidisciplinary pain centers and hospital departments of rehabilitation. In two studies participants were recruited from the general population through their physician [65,66] and one study recruited participants with advertisement on a university campus [67]. In most studies women were predominant and age ranged from 20 to over 74 years (SD 8). In most studies the type of CP was not specified. We assumed that CP in tertiary multidisciplinary pain centers, as in Andrews et al. (2023, 2015 and 2014), Liszka-Hackzell et al. (2004), Paraschiv et al. (2012 and 2008) predominantly comprised primary musculoskeletal CP. In Fanning et al. (2024 and 2023) inclusion criteria were CP in at least two sites of neck, shoulder, back, hip or knee, without specification on the cause of pain. We assumed that this pain mainly comprised chronic primary musculoskeletal pain.

3.2. Concepts of Investigation, Definitions, Variables and Indicators

In most papers a quantifiable definition of the concept of study was available or could be derived from reasoning. As a next step, operational definitions should describe how the concept of study was measured and how the measurements were interpreted. Concepts, their definitions and the definition of variables and their indicators are summarized in Table 2.

Concepts and Definitions (Conceptualization)

The concepts used to investigate AP were diverse (Table 2), and the meaning of these concepts could be extracted from all papers. Definitions and specifications of these concepts are presented in Table 2. A distinction could be made between concepts related to behavioral patterns and those derived from physics.

Six papers [48,49,50,52,53,68] utilized existing models of behavioral patterns related to activity and rest as their research concept, analyzing accelerometer time series, sometimes combined with pain intensity time series.

The definition of overactivity in Andrews et al. (2014) was in line with the definition of persistence in Huijnen et al. 2011_1 and 2011_2. Andrews et al. (2015 and 2023) expanded this definition to include pain aggravation after prolonged sedentary periods. Huijnen et al., 2020 changed the definition of persistence to ‘doing too much, not respecting one’s physical limits and experiencing a rebound effect of over-activity’ Andrews et al. (2023) introduced the concept of pacing, defined as a lower frequency of overactivity.

Time series of pain were included in four of the six papers on persistence and overactivity due to the hypothesized relationship between pain and activity in these behavioral patterns. Andrews et al. (2014) and Huijnen et al. (2011_2) did not include pain levels, focusing on fluctuation values of physical activity intensities , daily uptime and mean activity (Huijnen et al. 2011_2).

The remaining nine papers used physics derived measures to capture temporal patterns or complexity: (1) fitted Fourier functions (Fanning et al., 2024), (2) the pattern of activity and rest accumulation (Fanning et al., 2023), (3) the causal relation between activity level and pain (Liszka-Hackzell et al., 2004, (4) the rhythmicity of rest and activity during the day with fitted cosine curves (Neikrug et al., 2017 and Sarwar et al. 2022), (5) the dynamics of human activity (Paraschiv et al. 2008), more clearly defined in Paraschiv et al. (2012) as the temporal and dynamical structure of human physical activity, and adopted in Paraschiv et al. (2016). The three Paraschiv-papers build on the method of constructing time series of activity type and intensity of walking derived from accelerometry as presented in Paraschiv et al. (2004). Lastly, Zheng et al. (2023) used the temporal organization of activity intensity levels combined with transitions between intensity levels to investigate AP.

Variables and Indicators (Operationalization)

In all the papers, observable and measurable variables for operationalizing the concept of study were defined, and descriptions of the procedures for quantifying these variables were included.

In the six papers that used behavioral concepts [48,49,50,52,53,68], variables were formulated to inventory the occurrence or nonoccurrence of the behavior. Due to the expected sawtooth pattern with overactivity, Andrews et al. (2014) focused on the fluctuation of activity levels. In Andrews et al. (2015 and 2023), the outcome measures emphasized the concomitant pain increase with overactivity (Figure 3A), rather than focusing on the fluctuation values related to sawtooth pattern. Huijnen et al (2011_1) compared variables related to avoidance and persistence between persons classified as avoider or persister with the POAM-P self-report questionnaire. Persisters were expected to have a longer daily uptime, a higher average activity level and more fluctuations in activity level compared to avoiders. Additionally, it was hypothesized that in persisters, the time series of activity level and pain are associated.

To quantify the level of persistence, Huijnen et al. (2011_2) calculated the daily uptime and a linear composite score of daily uptime, mean total activity score and a fluctuation score. To investigate differences in activity behavior between individuals classified as avoider, persister, mixed performer or healthy performer, Huijnen et al. (2020) used cumulative variables such as overall daily activity level and total sedentary time, as well as variables representing the distribution of activity and rest over time.

In Fanning et al. (2023) the concept of accumulation of rest and physical activity was operationalized using a combination of cumulative measures and breaks within bouts of rest and activity. To investigate the timing of activity and rest as well as the amplitude of activity intensity, Fanning et al. (2024) fitted a 9-basis Fourier-function on each participant’s time series of steps per minute. Subsequently, they performed a functional Principal Component Analysis to identify a set of Fourier functions capturing the most variability.

Neikrug et al. (2017) extracted day to day rhythmic features of activity and rest using cosinor-based techniques and determined correlations with fibromyalgia symptoms. Besides the three parameters in Neikrug et al. (2017), Sarwar et al. (2022) extracted six other rhythmic features with a cosinor model. Some of these parameters and a fitted cosinor model are shown in Figure 3B,C.

Liszka-Hackzell et al. (2004) calculated the cross-correlation of interpolated time series of pain levels with time series of activity counts per minute. Cross-correlation was determined with a time lag of the pain time series of -60, -30, 0, 30 and 60 minutes, with which they investigated whether a pain level increase was ahead of activity level increase, synchronous, or delayed.

Paraschiv et al. derived different time series types of postures and activity intensity from accelerometer data, refining their methods in successive papers (2008, 2012, 2016), based on the method presented in Paraschiv et al. (2004). In the 2004 paper, four different postures (lying, sitting, standing and walking) and the intensity of walking were derived from accelerometer data using discrete wavelet transformation, Savitzky-Golay filters, vector functions and gait analysis parameters. With these methods, Paraschiv et al. (2008) constructed time series, including the sequence of posture allocation (Figure 3D), the sequence of the duration of walking episodes, the timing of transitions from rest to activity and vice versa, and the duration of activity relative to the duration of rest before and after activity, represented as a symbolic sequence. Nonlinear analyses were applied to these time series to investigate the AP. Paraschiv et al. (2012) added intensity to the four activity types based on different acceleration thresholds for each activity type. This resulted in 18 possible physical activity states. Lastly, to explore the ability of a composite score to characterize physical behavior, Paraschiv et al. (2016) performed a factor analysis on outcome variables from these 18 activity states, and assessed the association of pain intensity with activity behavior with multiple regression and discriminant analysis.

Zheng et al. (2023) applied unsupervised learning (Hidden Semi-Markow modelling, HSMM) where ML-algorithms were used to discover a set of hidden states in unlabeled accelerometer data. The modelling identified five hidden states corresponding to five different activity levels.

In summary, AP related research concepts were operationalized in many ways. Figure 3 provides an impression of the variation of methods used. Methods included a measure of fluctuation by subtracting two successive bouts of activity intensity, timing and amplitude of activity intensity quantified by fitted Fourier-functions, parameters derived from a fitted cosine curve, non-linear analyses, complexity metrics and variables that quantify fluctuations and distribution like transition frequency and W-index.

3.3. Measurement Properties and Data Processing

Measurement properties provide information on how data were collected. Data processing refers to the conversion of raw accelerometer data into the outcome measure needed for further analysis (see Table 3).

Measurement properties were heterogeneous (Table 3). Thirteen papers used triaxial accelerometers and two papers used a combination of multiple biaxial and/or uniaxial accelerometers [25,63]. In two studies the accelerometer was worn on the non-dominant arm [62] or wrist [61] and in one study the side of the wrist-worn accelerometer was not specified [67]. In one study the accelerometer was attached to the waist [53], in two studies to the upper midline of the thigh [65,66], and in another study to the front right hip [51]. Five studies used multiple accelerometers on multiple wear locations (chest and both thighs [48], sternum and mediolateral axis of thigh [64], sternum with mediolateral axis of thigh and shank [63] and chest with thigh and shank [25]). In four studies the wear-location was not reported [49,50,52,68].

Measurement frequency ranged from 30 to 128 Hz, with frequencies from 30 to 40 Hz being predominant. Measurement frequency was not reported in seven studies [49,50,53,62,65,66,67]. Sampling duration ranged from 5 to 21 days, with a duration of five days being predominant. Of the studies that sampled five days, two studies included one weekend day [52,68], four studies only included weekdays [25,63,64,67] and four studies didn’t specify which weekdays were covered [48,53,65,66]. Valid data was defined in most studies. Epoch length ranged from one second to one hour, with one minute being predominant.

Four studies sampled pain intensity during the day and used these time series to investigate AP since pain was part of the definition and operationalization of the concept of research [49,52,53,62]. Time series of pain were measured with 11-point visual analogue scale [52,62] or a 7-point likert-scale [49]. The pain measurement instrument was not specified in Andrews et al. 2023. Many other variables were sampled to investigate associations, associations through time and differences between subgroups. These variables include age, sex, self-reported measures of pain intensity, pain duration, pain interference, health-related quality of life, medication intake, approach to activity, depression, anxiety, self-discrepancy type, fatigue, impairment, functioning, mood, sleep and central sensitization symptoms.

Triaxial accelerometry results in time series of acceleration around x-, y- and z- axes, with the number of values per second dependent on sampling frequency. These time series were converted to another parameter in most of the included papers. In general, the description of conversion methods was limited. Some authors only refer to software packages and some to manuals or websites of the accelerometer manufacturer that were no longer available on the web.

For data processing six studies transformed the acceleration time series to activity counts per minute, also named vector counts per minute [49,50,52,53,62,68]. This method operates on the assumption that counts per minute is associated with the energy expenditure of activities and therefore with activity intensity. The vector magnitude of acceleration was calculated from the triaxial acceleration values and to yield counts per minute the number of times per minute of exceedance of a predefined threshold value was counted. The threshold for a count was not specified in the papers. Two studies transformed the acceleration time series to steps per minute and postures of lying, sitting or standing [65,66].

Paraschiv et al. [25,63,64] processed accelerometer data with discrete wavelet transformation, Savitzky-Golay filters and a numerical gradient. Subsequently they constructed time series of activity type with a previously developed algorithm [69]. They used different methods to detect different activities, postures and intensity of walking. Zheng et al. (2023) used raw accelerometer data from which the gravity effects were removed and then computed vector magnitude. Unsupervised learning was applied to these time series which resulted in time series of five activity intensity classes. For comparison of result with a traditional method, they applied the cut-off points approach as well. With this approach the tri-axial acceleration signal was converted to vector magnitude and thresholds were defined for different activity levels.

3.4. Validity of Conceptualization and Operationalization

Content validity was assessed to determine the extent to which the operationalization accurately reflects the definitions of the concepts. Content validity encompasses three aspects: relevance, comprehensiveness and comprehensibility.

The summary of conceptualization and operationalization, as illustrated in Table 2, reveals that content validity is sufficient in some studies, but limited in others. For example, Andrews et al. (2014) operationalize the concept of overactivity using a fluctuation value. However, fluctuating activity levels may be normal or even adaptive. To identify maladaptive fluctuations in the context of overactivity associated with pain, it is essential to include at least the correlation of pain with activity intensity. This was addressed in subsequent studies of Andrews et al. (2015, 2023), although these papers do not quantify the period of incapacity resulting from the rebound effect of excessive activity.

Therefore, these studies do not fully capture all dimensions of overactivity as a maladaptive strategy, as defined by the authors. The comprehensiveness of the operationalization can thus be rated as insufficient, raising concerns about whether the outcome variables truly reflect the construct of overactivity. A similar conclusion applies to the operationalization of persistence in Huijnen et al. (2011_1) that includes a similar fluctuation value and the association of pain with activity intensity. The concept of pacing in Andrews et al. (2023) is directly linked to the operationalization of the concept of overactivity, as it is defined as a reduced frequency of overactivity. Consequently, the content validity of pacing in this study is also insufficient.

In Huijnen et al. (2020) the definitions of avoidance and persistence partly diverged from those used in earlier publications. The definition of avoidance emphasized reduced activity levels due to catastrophizing thoughts and fear of movement. Yet, these motivational aspects – catastrophizing and fear - were not measured, resulting in insufficient comprehensiveness. The concept of persistence was further refined to excessive persistence and defined as doing too much, not respecting one’s physical limits and experiencing a rebound effect of over-activity. However, the successive rebound effect of doing too much was not captured in the outcome variables and pain intensity was not included in the analyses, therewith raising similar concerns on content validity as those identified in previous studies by Huijnen et al. and Andrews et al.

With physics based concepts, achieving comprehensiveness is generally less challenging as the concepts and their definitions are grounded in quantifiable parameters. For example, the extraction of rhythm parameters using cosinor fitting - applied to investigate activity rhythms as demonstrated by Neikrug et al. (2017) and Sarwar et al (2022) - is a clearly valid method. However, the studies by Fanning et al. (2023 and 2024) raise concerns regarding the comprehensiveness of AP profiling. In these studies activity intensity classes are derived from timeseries of stepping frequency, which inherently exclude non-stepping activities such as cycling, swimming and seated activities. Consequently, it is questionable whether the constructed time series adequately capture AP.

Some studies with physics based concepts raise doubts on relevance and interpretability. For example the study of Fanning et al. (2023) counted the number of bouts of certain lengths for different intensity classes and investigated the change of these numbers of bouts after a 12-week behavioral program. Some change values correlated with pain intensity and pain interference. For example an increase of the number of bouts shorter than 5 minutes with light activity intensity correlated significantly with an increased pain intensity, but not with pain interference, while a changed number of 5-minute bouts of very light and moderate intensity did not correlate with pain intensity (see Appendix B). Although the authors provide some interpretation regarding the association between changes in 5-minute bouts of light intensity activity and pain, the overall interpretation and practical relevance of the combined findings remain ambiguous. This is further compounded by the substantial risk of Type I error, given that associations between 22 parameters and pain and pain interference were examined with a relatively small sample size of 41.

The indicators derived from the variables by Paraschiv et al. (2008, 2012 and 2016) are methodologically advanced, yet they may be difficult to interpret by general health care researcher and practitioners, particularly the composite scores. For ensuring their relevance and applicability in daily practice, it is essential to establish the meaning of these indicators by examining their associations with healthcare outcome measures, like state parameters pain, fatigue and mood.

Hypotheses testing may yield further, but indirect, insights into the validity of the measurement instruments by evaluating the consistency of outcome variable scores with predefined hypotheses. This approach assumes that the measurement method validly measures the intended construct. All reviewed studies employed hypotheses testing by examining associations between selected indicators and related behavioral types or clinically relevant outcome parameters, as well as by assessing group differences and intervention-related changes. However, the majority of AP variables showed no significant associations (see Appendix B for a detailed overview of significant and non-significant findings). This lack of associations may reflect inadequacies in the conceptualization and/or operationalization of the concepts, irrelevance of the constructs or outcome variables, or limitations inherent to the comparator instruments – such as insufficient construct overlap or suboptimal clinimetric properties.

Although the number of significant associations between behavioral type and objective AP variables was small (see Figure A in Appendix B), the observed associations appeared plausible. For instance, based on established definitions, it is reasonable to expect that persisters exhibit greater fluctuations in activity [52] and longer days [49] compared to avoiders. Notably, Paraschiv et al. (2016) was among the few studies that consistently identified associations between pain intensity and objective AP parameters. The consistency reported by Paraschiv et al. (2016) may be attributed to the use of more advanced accelerometer data processing techniques and the application of AP composite scores derived through data-driven factor analysis.