​Physical activity levels can be assessed by two main types of measures: objective or subjective measures (Prince et al., 2008). Each measure has its own strengths and weaknesses confirming the need for the examination of their psychometric properties (Prince et al., 2008; Hills et al., 2014). Additionally, researchers/clinical practitioners should make a well-considered choice depending on their measurement goal(s) (Prince et al., 2008; Hills et al., 2014). 

In patients with chronic fatigue syndrome (CFS), the evaluation of their physical activity level is necessary because current treatment strategies include activity management programs to improve patients’ participation in daily activities (Bazelmans et al., 2006). Activity management programs need to be tailored to patients’ coping strategy (Bazelmans et al., 2006), i.e. pervasively passive or relatively active (van der Werf et al., 2000), because each requires a different approach to obtain a successful treatment outcome (Bazelmans et al., 2006; van der Werf et al., 2000; Vos-Vromans et al., 2013). 

Objective measures such as accelerometers are recommended (Bazelmans et al., 2006; Vos-Vromans et al., 2013), but their psychometric properties depend on the device, studied population, type of activity, and more (Hills et al., 2014; Meeus et al., 2011; Plasqui et al., 2007; Prince et al. 2006). Additionally, consensus is lacking on the specifications for data collection, such as cut-points and calibration methods (Duncan et al., 2018; Hills et al., 2014; Prince et al., 2008; Troiano et al., 2014). Current literature mainly shows a good reliability and validity at the group level of healthy persons (Heil et al., 2006; Hills et al., 2014; Plasqui et al., 2007), but their psychometric properties at the individual level of patients with CFS are unclear. Another limitation of accelerometers is that neither the type and context of the performed activity, nor patients’ perception can be registered (Hills et al., 2014; Prince et al., 2006).

Subjective measures are able to capture this additional information and could therefore be complementary to an objective measure when used simultaneously. Unfortunately, a weak relationship between objective and subjective measures has been established (Meeus et al., 2011; Prince et al., 2006; Vercoulen et al., 1997). Various factors have been hypothesized to potentially influence the self-report of physical activity levels, but none have been confirmed. Measures relying on recall are suspected to induce false reporting due to cognitive problems (Cockshell et al., 2010) and measures including subjective interpretations about physical activity could be biased by incorrect cognitions on illness and disability (van der Werf et al., 2000; Vercoulen et al., 1997). Consequently, such measures are not suited for patients with CFS.

Because subjective measures can provide clinical practitioners with valuable information, a subjective measure that is able to accurately assess a patient’s physical activity level would be a significant asset. Therefore, an activity diary was developed that relies on instantaneous, detailed registration of the performed physical activities, eliminating recall bias and subjective interpretation of physical activity. The activity diary was used simultaneously to an accelerometer (Actical) to evaluate its ability to measure the physical activity level of female patients with CFS (Vergauwen et al., 2021). For an acceptable convergent validity, the correlation coefficient should be ≥ 0.50 (Terwee et al., 2010). In case the threshold for convergent validity was not reached, it was also investigated whether illness-related complaints, health-related quality of life (HRQOL) domains or demographic factors were associated with the discrepancy between the activity diary and accelerometer (Vergauwen et al., 2021).

In 66 female patients with CFS, there was a significant, but weak association between the activity diary and accelerometer for weekdays and weekend days. In patients with CFS, the threshold for convergent validity was not reached. In healthy controls (n = 20), there was a significant and moderately strong association for both weekdays and weekend days. These results indicate that female patients with CFS are less capable of assessing their physical activity level through self-report with an activity diary than healthy controls. When examining factors associated with the discrepancy between the self-reported and objective physical activity level, only age was negatively associated with the discrepancy. This result indicates an underestimation of the physical activity level by younger persons and an overestimation by older persons with an activity diary. This association did not differ between healthy controls and patients with CFS (Vergauwen et al., 2021).

Overall, these and previous results indicate that self-report measures for the physical activity level should be used with caution in patients with CFS. Up to now, it is unclear why patients with CFS are less capable of measuring their physical activity level with self-report than healthy controls. Other factors than illness-related complaints, HRQOL domains or demographic factors seem to play a role, but they are yet to be identified. Patients’ activity pattern also seems to have an influence, patients with a pervasively active activity pattern had the highest discrepancy (Vergauwen et al., 2021). This is highly relevant for clinical practice, seeing as activity management programs need to be tailored to patients’ individual activity pattern and valid measures are a requisite. 

In conclusion, these results suggest that the activity diary and accelerometer did not measure the same parameters and are not interchangeable (Prince et al., 2006; van der Werf et al., 2000; Vergauwen et al., 2021). Subjective and objective measures can be used complementary to gain a more comprehensive view of a person’s physical activity level, as long as self-report measures do not fully replace objective measures in patients with CFS. Up to now, no sufficiently valid self-report measure to assess the physical activity level of patients with CFS is available and more insight into factors associated with the discrepancy between objective and subjective measures is necessary (Vergauwen et al., 2021).

Kuni Vergauwen

Kuni Vergauwen is an occupational therapist, lecturer at AP College University and PhD researcher at the University of Antwerp and Maastricht University with a focus on chronic fatigue.

2022 Pain in Motion

References and further reading:  

E. Bazelmans, J. Prins, G. Bleijenberg, Cognitive behavior therapy for relatively active and for passive chronic fatigue syndrome patients, Cogn. Behav. Pract. 13 (2) (2006) 157–166, https://doi.org/10.1016/j.cbpra.2006.02.001. 

S.P. van der Werf, J.B. Prins, J.H. Vercoulen, J.W. van der Meer, G. Bleijenberg, Identifying physical activity patterns in chronic fatigue syndrome using actigraphic assessment, J. Psychosom. Res. 49 (5) (2000) 373–379, https://doi.org/10.1016/ s0022-3999(00)00197-5. 

D.C. Vos-Vromans, I.P. Huijnen, A.J. K¨oke, H.A. Seelen, J.A. Knottnerus, R. J. Smeets, Differences in physical functioning between relatively active and passive patients with chronic fatigue syndrome, J. Psychosom. Res. 75 (3) (2013) 249–254, https://doi.org/10.1016/j.jpsychores.2013.05.001. 

A.P. Hills, N. Mokhtar, N.M. Byrne, Assessment of physical activity and energy expenditure: an overview of objective measures, Front. Nutr. 1 (2014) 5, https:// doi.org/10.3389/fnut.2014.00005. 

M. Meeus, I. van Eupen, J. Willems, D. Kos, J. Nijs, Is the international physical activity questionnaire-short form (IPAQ-SF) valid for assessing physical activity in chronic fatigue syndrome? Disabil. Rehabil. 33 (1) (2011) 9–16, https://doi.org/ 10.3109/09638288.2010.483307. 

G. Plasqui, K.R. Westerterp, Physical activity assessment with accelerometers: an evaluation against doubly labeled water, Obesity (Silver Spring) 15 (10) (2007) 2371–2379, https://doi.org/10.1038/oby.2007.281. 

S.A. Prince, K.B. Adamo, M.E. Hamel, J. Hardt, S.C. Gorber, M. Tremblay, A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review, Int. J. Behav. Nutr. Phys. 5 (2008) 56, https://doi.org/ 10.1186/1479-5868-5-56. 

S. Duncan, T. Stewart, M.B. Schneller, S. Godbole, K. Cain, J. Kerr, Convergent validity of ActiGraph and Actical accelerometers for estimating physical activity in adults, PLoS One 13 (6) (2018), e0198587, https://doi.org/10.1371/journal. pone.0198587. 

R.P. Troiano, J.J. McClain, R.J. Brychta, K.Y. Chen, Evolution of accelerometer methods for physical activity research, Br. J. Sports Med. 48 (13) (2014) 1019–1023, https://doi.org/10.1136/bjsports-2014-093546. 

D.P. Heil, Predicting activity energy expenditure using the Actical® _activity monitor, Res. Q. Exerc. Sport 77 (1) (2006) 64–80. 

J.H. Vercoulen, E. Bazelmans, C.M. Swanink, J.F. Fennis, J.M. Galama, P.J. Jongen, O. Hommes, J.W.M. Van der Meer, G. Bleijenberg, Physical activity in chronic fatigue syndrome: assessment and its role in fatigue, J. Psychiatr. Res. 31 (6) (1997) 661–673, https://doi.org/10.1016/s0022-3956(97)00039-3. 

S.J. Cockshell, J.L. Mathias, Cognitive functioning in chronic fatigue syndrome: a meta-analysis, Psychol. Med. 40 (8) (2010) 1253–1267, https://doi.org/10.1017/ S0033291709992054.

C.B. Terwee, L.B. Mokkink, M.N.M. van Poppel, M.J.M. Chinapaw, W. van Mechelen, H.C.W. de Vet, Qualitative attributes and measurement properties of physical activity questionnaires: a checklist, Sports Med. 40 (2010) 525–537, https://doi.org/10.2165/11531370-000000000-00000

K. Vergauwen, I.P.J. Huijnen, R.J.E.M. Smeets, D. Kos, I. van Eupen, J. Nijs, M. Meeus. An exploratory study of discrepancies between objective and subjective measurement of the physical activity level in female patients with chronic fatigue syndrome, J Psychosom Res. 144 (2021) e110417, https://doi:10.1016/j.jpsychores.2021.110417.