Abstract
Background
Tachycardia after mild activity or during rest is a common complaint among people with post-COVID-19 condition (PCC). Understanding the relationships between heart rate (HR) and physical activity (PA) in this population is crucial for developing appropriate rehabilitation protocols.
Objective
To investigate the associations between HR and PA in individuals with PCC, accounting for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) symptoms.
Design
Observational study.
Subjects
Sixteen adults with PCC (81% females, mean age 51 ± 12 years).
Methods
Participants were instructed to use 2 wearable devices (Garmin smartwatch and ActiGraph accelerometer) during waking hours over 4 days while performing daily activities. Average HR, percentage of time in tachycardia (time with HR > 100 bpm), and daily step count were assessed. The accelerometer counts per minute was used to categorize daily PA as sedentary, light intensity, and moderate-to-vigorous (MVPA).
Results
Participants wore the watches and accelerometers for a mean of 11.36 ± 2.60 and 12.51 ± 1.94 h per day, respectively. Average daily HR increased with increasing PA levels from sedentary to MVPA. However, the percentage of time in tachycardia was significantly lower during periods of MVPA compared with sedentary periods, even after adjusting for ME/CFS symptoms.
Conclusion
Individuals with PCC in our study experienced more tachycardia during periods of minimal physical activity compared with periods categorized as MVPA.
Key words: long COVID, physical activity, heart rate, tachycardia
LAY ABSTRACT
This study explored the associations between heart rate and physical activity in individuals with post-COVID-19 condition accounting for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome symptoms. Tachycardia (elevated heart rate) during mild activity or at rest is common among those with post-COVID-19 condition, and understanding this relationship is important for developing effective rehabilitation strategies. The study involved 16 adults with post-COVID-19 condition (81% females, average age 51 years) who wore a Garmin smartwatch and an ActiGraph accelerometer for 4 days to track their heart rate and daily physical activity. The study found that heart rate increased with higher levels of physical activity, from sedentary to moderate-to-vigorous physical activity. However, the percentage of time spent in tachycardia was significantly lower during moderate-to-vigorous physical activity compared with sedentary periods, even after adjusting for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome symptoms. The results suggest that individuals with post-COVID-19 condition experience more tachycardia when engaging in minimal physical activity, highlighting the importance of understanding heart rate-physical activity dynamics for better managing post-COVID-19 condition rehabilitation.
The Coronavirus disease 2019 (COVID-19) illness can persist beyond the initial stage and lead to post-COVID-19 condition (PCC), which is the continuation or development of symptoms 3 months after the initial infection and lasting at least 2 months (1). PCC is prevalent in about 20% of COVID-19 survivors worldwide, with a higher prevalence identified in female adults and those hospitalized during the acute infection (2). PCC symptoms are multisystemic (3) and may affect physical activity participation, work capacity, and social functioning (4). Therefore, appropriate interventions may be needed to help affected individuals recover and return to optimal functioning.
Individuals with PCC commonly report tachycardia with little to no activity (3), potentially due to autonomic nervous system dysfunction (5), which may be expressed as inappropriate sinus tachycardia or postural orthostatic tachycardia syndrome (POTS) (6). Inappropriate sinus tachycardia is marked by a disproportionate HR increase relative to physiological demand (7), whereas POTS is characterized by tachycardia upon standing (8). Additionally, some individuals with PCC may develop myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) (9), which is the worsening of symptoms due to an atypical response (e.g., an abnormal loss of physical and mental stamina) after physical or mental exertion (10).
Evidence has shown that physical activity (PA) enhances well-being (11) and promotes physical, mental (12, 13), cardiovascular (14), and pulmonary health (15). However, these benefits may not directly apply to individuals with PCC (9, 10). The risk of post-exertional malaise in this population makes the use of established PA guidelines or exercise interventions uncertain, and the presence of dysautonomia may limit the use of heart rate (HR) as a reliable parameter for prescribing, guiding, and adjusting recommended PA. Consequently, the National Institute for Health and Care Excellence and the World Health Organization recommend caution when prescribing exercises for these individuals (16, 17).
The lack of understanding regarding the effects of PA in individuals with PCC, together with the reported cardiorespiratory abnormalities (3), poses a challenge to the use of traditional rehabilitation approaches for managing this condition. Further clarification of the relationships between HR and PA may help inform appropriate rehabilitation strategies for these patients. Therefore, the aim of this study was to examine the associations between HR (average daily HR and percentage of time in tachycardia) and PA (step count and accelerometer-defined PA intensity levels) in individuals with PCC, accounting for the presence of ME/CFS symptoms.
METHODS
Study design and participants
This observational study was approved by the research ethics committee of the University of Manitoba (HS25555) and conducted according to the Declaration of Helsinki. Following public advertising (social media and direct invitation to patients referred from outpatient rehabilitation clinics in Winnipeg, Canada), a convenience sample of 16 adults experiencing persistent symptoms for 3 months or more after acute COVID-19 infection was recruited (1). Those with PCC experiencing mild to severe respiratory symptoms and/or fatigue, and with access to a smart device and internet service at home were included. Exclusion criteria were acute lung or heart conditions, recent lung or cardiac surgery, severe hearing or visual impairments, inability to walk independently, high risk of falling and residence outside of Manitoba, Canada.
Assessments and data collection
A total of 30 people indicated interest in participating in our study and were screened for eligibility via phone call. Twenty participants qualified; however, 1 participant dropped out and 3 were further excluded from the analysis due to incomplete watch data. The study was conducted in 2 phases between November 2022 and April 2023: an in-person assessment of approximately 2 h at the Respirability Lab, followed by home-based data collection. Informed consent was obtained from all participants before the assessment.
Participants self-reported their level of PA as either physically active or not physically active. Anthropometric data (height, weight, and body mass index) were collected at baseline, followed by dyspnoea (Modified Borg Scale, 0–10 points) (18) and fatigue (Fatigue Severity Scale). The latter consists of 9 items rated on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The minimum and maximum scores obtainable are 9 and 63 respectively. The FSS also includes a visual analogue scale ranging from 0 (worst) to 10 (normal) to measure global fatigue (19).
The DePaul Symptom Questionnaire short-form (DSQ-SF) was applied to assess symptoms of ME/CFS by asking participants to rate the frequency and severity of 14 symptoms on a 5-point Likert scale (20). The presence of ME/CFS symptoms was defined according to the Canadian ME/CFS criteria, according to which participants should score at least 2 out of 5 in both severity and frequency across the domains of fatigue, post-exertional malaise, unrefreshing sleep, and neurocognitive impairment, and 1 symptom from at least 2 of the following domains: pain, autonomic, neuroendocrine, and immune (20). The total scores of each individual were averaged and rationalized to 100 points, and higher scores represented higher symptom burden.
Health-related quality of life was assessed via the EuroQol – 5 Dimensions – 5 Levels. This questionnaire consists of 2 parts: a visual analogue scale rated from 0 (worst imaginable health) to 100 (best imaginable health), and a section comprising 5 dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) rated from 1 (best) to 5 (worst). The total score for the VAS is 100, and each dimension is individually scored out of 5 (21).
The 1-minute sit-to-stand test (STST) and 6-minute walking test (6MWT) assessed exercise capacity. During the STST, participants were asked to stand upright and sit as many times as possible at a safe and comfortable pace for 1 min using a standard-height chair without armrests (22). The 6MWT involved the participants walking as far as possible along a 30-m track within 6 min (23). Resting periods were permitted during both tests, and the number of repetitions in the STST and the distance covered in the 6MWT were recorded and presented as absolute and percentage of predicted values (24, 25).
Wearable devices
Participants were provided with a Garmin Venu® SQ smartwatch (Garmin, Olathe, KS, USA) to continuously monitor HR. Garmin devices have high criterion validity and reliability (26). Participants were instructed to wear the smartwatch daily on their non-dominant wrist for 12 h during waking hours and synchronize their watch data in the Garmin Connect® and Labfront apps (Labfront, Boston, MA, USA), which were previously installed on their smartphones during the in-person visit.
A hip-worn ActiGraph wGT3X-BT portable accelerometer (ActiGraph LLC, Pensacola, USA) was also given to the participants to track PA (according to counts per minute [cpm] and number of steps). The GT3X+ has also proven reliable for measuring PA in adults during everyday activities (27). Data were collected at 100 Hz and participants were instructed to wear the accelerometers for at least 12 h each day over 7 days. Wear time was also recorded in an activity log sheet provided to the participants for reference purposes during data processing. Non-wear times were defined as intervals of at least 60 consecutive minutes with zero activity counts. Ideally, a minimum wear time of 10 h/day for 4 days was expected for the accelerometer data to be included in analyses (28).
Data processing and analysis
Accelerometer data were downloaded using ActiLife6 software and used to calculate PA levels according to the following cpm cut-off points: moderate to vigorous PA (MVPA) (> 760 cpm), light intensity PA (101 to 760 cpm), and sedentary (0 to 100 cpm) (29). Step-count was categorized according to the Tudor-Locke and Bassett (2004) classification as follows: sedentary lifestyle index (< 5,000 steps/day); low active (5,000 to 7,499 steps/day); somewhat active (7,500 to 9,999 steps/day); active (≥ 10,000 steps/day); and highly active (> 12,500 steps/day) (30). HR data from the Garmin smartwatches were matched minute by minute to the accelerometer data, and values above 100 bpm were considered tachycardia (31, 32). Average HR and percentage of minutes in tachycardia during sedentary time, light intensity time, and MVPA were calculated based on daily wear time.
Statistical analysis
Data were presented as mean ± standard deviation. Group comparisons of individuals with and without ME/CFS symptoms were performed using unpaired t-test and the Mann–Whitney U test for continuous and ordinal data respectively. HR and PA data were analysed in 4-day clusters for all participants (including 1 participant with 3 days of data collected on the accelerometer). Generalized linear mixed models (fixed effects, linear distribution, and variance component matrix) were used to analyse the associations between PA (step count and cpm intensity levels) and HR (average HR and percentage of minutes in tachycardia). The crude model was adjusted for age, sex, and presence of ME/CFS symptoms (yes and no). Data were analysed using the SPSS software (IBM Corp, Armonk, NY, USA), and significance was set at p < 0.05.
RESULTS
Sixteen individuals (13 females (81%), mean age of 51 ± 12 years) with PCC were included. Three participants (19%) were hospitalized during the acute COVID-19 illness and reported using respiratory equipment while being treated for the COVID-19 infection. All participants reported experiencing fatigue at the time of the initial assessment, while 14 (93%) reported shortness of breath, and 13 self-reported their PA level as not active. Overall, participants performed below the age- and sex-predicted values on the 6MWT (24) and 1-minute STST (25). Notably, the group with ME/CFS symptoms covered 27% less distance and completed 19% fewer repetitions than the group without these symptoms. Additionally, individuals with ME/CFS symptoms experienced greater difficulties in the EuroQoL domains of mobility, self-care, usual activities, and pain/discomfort (Table I).
Table I.
Characteristics of study participants
| Characteristic | All (n = 16) | Canadian ME/CFS criteria | ||
|---|---|---|---|---|
| Yes (n = 9) | No (n = 7) | p-value* | ||
| Age (years) | 50.88 (11.97) | 55.86 (9.44) | 47.00 (12.78) | 0.148 |
| Height (m) | 1.72 (0.94) | 1.79 (0.9) | 1.67 (0.06) | 0.005 |
| Body mass (kg) | 86.60 (18.57) | 87.4 (16.66) | 85.94 (20.93) | 0.883 |
| BMI (kg/m2) | 29.31 (6.52) | 27.11 (3.86) | 31.01 (7.82) | 0.249 |
| Modified Borg Scale (MBS) | 2.03 (1.3) | 2.5 (1.22) | 1.43 (1.27) | 0.110 |
| FSS (total score) | 54.75 (10.98) | 59.22 (6.18) | 49.00 (13.49) | 0.062 |
| FSS (VAS) | 4.54 (2.91) | 5.33 (3.39) | 3.51 (1.94) | 0.228 |
| EuroQoL-5D-5L domains | ||||
| Mobility | 2.19 (0.83) | 2.56 (0.88) | 1.71 (0.49) | 0.040 |
| Self-care | 1.94 (0.93) | 2.44 (0.88) | 1.29 (0.49) | 0.008 |
| Usual activities | 2.94 (1.06) | 3.56 (0.88) | 2.14 (0.69) | 0.004 |
| Pain/discomfort | 2.44 (0.73) | 2.89 (0.6) | 1.86 (0.38) | < 0.001 |
| Anxiety/depression | 2.19 (1.05) | 2.56 (1.13) | 1.71 (0.76) | 0.113 |
| EuroQoL-5D-5L (VAS) | 56.75 (17.39) | 53.44 (16.82) | 61.00 (18.48) | 0.407 |
| 6MWT (metres) | 344.02 (108.82) | 271.56 (84.41) | 437.19 (45.82) | < 0.001 |
| 6MWT (%pred) | 61.67 (19.53) | 49.92 (15.52) | 76.76 (12.64) | 0.002 |
| 1-minute STST (repetitions) | 19.38 (6.49) | 16.56 (6.48) | 23.00 (4.69) | 0.044 |
| 1-minute STST (%pred) | 49.36 (16.81) | 41.63 (16.05) | 60.23 (8.74) | 0.011 |
| Average HR (beats per minute) | 85 (6.22) | 88 (5.80) | 82 (4.50) | 0.464 |
| Percentage of time in tachycardia (%) | 13.59 (5.70) | 15.48 (15.58) | 11.15 (5.99) | 0.219 |
| Step count | 3503.06 (1445.23) | 3402.78 (1544.29) | 3632.00 (1416.85) | 0.479 |
BMI: body mass index, m: metres, kg: kilogram, FSS (fatigue severity scale), 6MWT: 6-minute walk test, STST: Sit-to-stand test, pred: predicted, VAS: Visual Analogue Scale, EuroQol-5D-5L: EuroQol – 5 Dimensions – 5 Levels, MBS ([0 (no breathlessness] to 10 [worst]), FSS (0 [no fatigue] to 63 [worst]), FSS VAS (0 [worst] to 10 [normal]), EuroQol-5D-5L: EuroQol VAS (0 [worst health imaginable] to 100 [best health imaginable]; domains (1 [no problems] to 5 [worst]),
p-value (between individuals with and without ME/CFS symptoms).
Participants wore the watches and accelerometers for a mean of 11.36 ± 2.60 and 12.51 ± 1.94 h per day, respectively. They walked 3,503.06 ± 1,445.23 steps per day, suggesting that they had low levels of PA and were classified as sedentary (30). No significant associations were observed between daily step count and average daily HR or percentage of time in tachycardia before and after adjusting for age, sex, and ME/CFS symptoms (Table II). Although higher PA levels according to the accelerometer data (cpm) were associated with increased average HR, participants displayed a significantly lower percentage of time in tachycardia during the MVPA level compared with the sedentary periods, even after adjusting for age, sex, and ME/CFS symptoms (Table III).
Table II.
Associations between daily step count and HR parameters
| Factor | Average HR | % time in tachycardia | ||||
|---|---|---|---|---|---|---|
| β | CI (95%) | p-value | β | CI (95%) | p-value | |
| Crude model | ||||||
| Step count | –0.001 | –0.001 to 0.001 | 0.813 | 0.001 | 0.000 to 0.001 | 0.860 |
| Age and sex-adjusted | ||||||
| Step count | –0.001 | –0.001 to 0.001 | 0.799 | 0.001 | 0.000 to 0.001 | 0.842 |
| Age | 0.021 | –0.227 to 0.269 | 0.868 | –0.020 | –0.105 to 3.316 | 0.653 |
| Sex | 2.952 | –4.630 to 10.535 | 0.443 | 0.617 | –2.083 to 3.316 | 0.635 |
| Age, sex, and ME/CFS adjusted | ||||||
| Step count | –0.001 | –0.001 to 0.001 | 0.910 | 0.001 | 0.000 to 0.001 | 0.688 |
| Age | 0.089 | –0.169 to 0.346 | 0.497 | –0.009 | –0.102 to 0.085 | 0.850 |
| Sex | –0.495 | –9.209 to 8.218 | 0.911 | 0.001 | –3.292 to 3.294 | 0.999 |
| ME/CFS symptoms (no) | Ref. | |||||
| ME/CFS symptoms (yes) | 5.340 | –1.847 to 12.526 | 0.144 | 0.926 | –1.722 to 3.574 | 0.491 |
Individuals without ME/CFS symptoms were the category of reference. HR: heart rate, CI: confidence interval.
Table III.
Associations between physical activity levels and HR parameters
| Factor | Average HR | % time in tachycardia | ||||
|---|---|---|---|---|---|---|
| β | CI (95%) | p-value | β | CI (95%) | p-value | |
| Crude model | ||||||
| PA levels | ||||||
| Sedentary | Ref. | |||||
| Light PA | 9.117 | 6.946 to 11.288 | < 0.001 | –1.516 | –3.385 to 0.353 | 0.111 |
| Moderate to vigorous PA | 13.450 | 11.279 to 15.621 | < 0.001 | –3.380 | –5.249 to –1.511 | < 0.001 |
| Age and sex adjusted | ||||||
| PA levels | ||||||
| Sedentary | Ref. | |||||
| Light PA | 9.117 | 6.947 to 11.287 | < 0.001 | –1.516 | –3.385 to 0.353 | 0.111 |
| Moderate to vigorous PA | 13.450 | 11.280 to 15.620 | < 0.001 | –3.380 | –5.249 to –1.511 | < 0.001 |
| Age | 0.019 | –0.232 to 0.271 | 0.880 | –0.020 | –0.101 to 0.061 | 0.061 |
| Sex | –3.030 | –10.568 to 4.507 | 0.428 | –0.677 | –3.206 to 1.852 | 0.598 |
| Age, sex, and ME/CFS adjusted | ||||||
| PA levels | ||||||
| Sedentary | Ref. | |||||
| Light PA | 9.117 | 6.948 to 11.287 | < 0.001 | –1.516 | –3.386 to 0.353 | 0.111 |
| Moderate-to-vigorous PA | 13.450 | 11.280 to 15.620 | < 0.001 | –3.380 | –5.249 to –1.510 | < 0.001 |
| Age | 0.091 | –0.166 to 0.348 | 0.487 | –0.009 | –0.098 to 0.079 | 0.837 |
| Sex | 0.454 | –8.026 to 8.934 | 0.916 | –0.180 | –3.167 to 2.807 | 0.905 |
| ME/CFS symptoms (no) | Ref. | |||||
| ME/CFS symptoms (yes) | –5.497 | –12.607 to 1.614 | 0.129 | –0.829 | –3.290 to1.633 | 0.507 |
Sedentary behaviour and individuals without ME/CFS symptoms were used as the category of reference. PA: physical activity, HR: heart rate, CI: confidence interval.
DISCUSSION
To the best of our knowledge, this is the first study that aimed at clarifying the associations between tachycardia (a commonly reported symptom) and PA in individuals with PCC accounting for ME/CFS symptoms. The results of our study suggest that while HR increases with PA intensity level, the time spent in tachycardia is lower at the MVPA level than at the sedentary level. This finding aligns with complaints of “racing HR” commonly reported by PCC patients even during minimal or no activity (3). Our study provides valuable insights into the relationship between HR and PA, potentially guiding development of appropriate rehabilitation strategies for these patients.
The elevated HR experienced during sedentary periods by participants in our cohort can be attributed to autonomic dysfunction, presenting clinically as POTS or inappropriate sinus tachycardia (33). Physiological processes underlying this condition have been identified as downregulation of the renin–angiotensin–aldosterone system (RAAS), hyperadrenergic activity, and direct viral infection of the brainstem (34). This HR response is prevalent (35) and may be explained by episodic disability, which describes fluctuations in health that may lead patients to experience good and bad days related to their illness (36). In this sense, our participants may have chosen to avoid PA on bad days either due to poor health or to prevent the onset or worsening of symptoms and chosen to engage in MVPA on good days (i.e., when they experienced less severe tachycardia and other cardiovascular or respiratory symptoms) (3, 37). In addition, fear-avoidance behaviour, as seen in other post-viral conditions (39), could also have influenced this association (39).
Although both groups of our study participants experienced tachycardia at rest, individuals with PCC experiencing ME/CFS have shown greater fatigue and post-exertional malaise (40), symptoms that arise from physical or mental exertion. As a result, these individuals may avoid or decrease PA (41) to prevent symptom exacerbation. Although fear-avoidance behaviour may be effective in the short term, it may contribute to prolonged symptoms and deconditioning of the individual over time (39). Therefore, it is important to identify and address symptoms commonly experienced by individuals with PCC such as fatigue and dyspnoea (42), as they may contribute to reduce exercise capacity, limit daily activities (42), and hinder physical activity (43).
Similar to previous studies (44, 45), participants in our cohort demonstrated reduced exercise capacity, which can be attributed to skeletal muscle damage and weakness (46), mitochondrial myopathy (47), and deconditioning (48). Low PA was also identified in our participants, with 81.3% reporting themselves as inactive. The average daily step count recorded in our group by the wearable devices reflected a sedentary lifestyle (i.e., < 5,000 steps per day) (30), lower than previously observed in individuals with COPD (49) and interstitial lung diseases (50). Sedentary behaviour and low PA levels may pose long-term health risks because they are independent factors of cardiovascular disease, reduced quality of life, and all-cause mortality (51, 52). Therefore, these factors should be addressed in PCC individuals.
While rehabilitation may help counteract deconditioning and lack of PA, caution is necessary when incorporating exercise into rehabilitation programmes with these patients. Skeletal muscle mitochondria changes, inflammation, and capillary injury may contribute to fatigue (53), and physical activity may further aggravate these processes (54); therefore, a safe and gradual increase in PA should be encouraged to avoid symptom exacerbation. The use of individualized and supervised exercise programmes (55), together with pacing during PA (56), may help mitigate worsening of symptoms. Additionally, HR should be closely monitored during PA, as abnormal HR responses and cardiovascular autonomic dysfunction have been found in this population (33). In our cohort, participants spent significantly more time in tachycardia while sedentary than during MVPA, further supporting this concern. As shown in our study, monitoring can be done using commercial wearable devices, particularly smartwatches, which are accessible tools to achieve this purpose. Wearable HR data can offer meaningful insight into cardiovascular function in this population. Time spent in tachycardia, especially during periods of sedentary activity, may indicate autonomic dysfunction or cardiovascular strain (5). Measures such as resting HR trends, HR variability, and HR recovery after activity may further reflect impaired autonomic regulation or deconditioning (57). Monitoring HR responses during PA also helps identify chronotropic abnormalities (58). Collectively, these indicators support safer exercise prescription, early detection of cardiovascular stress, and individualized rehabilitation planning.
Some limitations should be considered when interpreting our study results as they may constrain the general applicability of our findings. First, the final sample size was smaller than anticipated, which may impact on the generalizability of the findings. Several factors may have contributed to this, including limited symptom recognition among patients and healthcare providers, as well as the lack of a dedicated PCC clinic and centralized referral pathways, which together may have hindered the identification and recruitment of eligible participants. It is also unclear whether the limited sample size was due to participants’ inability or unwillingness to participate. Despite multiple recruitment efforts, only 30 individuals expressed interest, with 20 meeting the inclusion criteria. One participant withdrew due to financial hardship related to illness, and 3 were excluded from analysis due to incomplete data resulting from difficulty adhering to the study protocol. Second, identifying awake/active times during device wear times was not straightforward for all participants due to the variability in individual schedules. However, information from the device and the participants’ logs ensured accurate sorting of the information. Also, HR variability was not assessed, nor was a control group included, which may have better identified unique responses to PA in our PCC cohort.
This study provides valuable insights concerning HR responses to PA in individuals with PCC. Although ME/CFS symptomatic participants in our cohort exhibited poorer performance on physical function tests, potentially reflecting lower physical capacity and possibly contributing to reduced engagement in regular physical activity, this factor did not influence the associations between HR and PA in our study. Furthermore, the use of wearable devices for data collection in this study provided a means for assessing and monitoring outcomes in PCC, which may facilitate patient self-management and rehabilitation in this population. Additional research should examine the relationship between PCC symptoms, tachycardia, and PA while also exploring the impacts of dysautonomia on the PA of individuals with PCC. Future studies should aim for larger and more balanced sample sizes to increase statistical power, enhance generalizability, and enable detailed subgroup analyses. Additionally, collecting 24-h data would allow for a clearer distinction between PA, sedentary behaviour, and sleep, providing a comprehensive view of daily patterns and circadian rhythms.
Conclusion
Individuals with PCC in our study experienced more tachycardia while doing little to no activity than while engaged at higher intensity PA levels independently of the presence of ME/CFS symptoms. This finding is consistent with reports commonly made by PCC patients and provides valuable insights into the association between HR and PA, which may be useful for informing patient care and future studies.
ACKNOWLEDGEMENTS
Ethics
This study was approved by the research ethics committee of the University of Manitoba (HS25555). Informed consent was obtained from all participants.
Funding Statement
Funding This study was funded by the Lung Association, Manitoba operating grant.
Footnotes
The authors have no conflict of interest to declare.
REFERENCES
- 1.World Health Organization. Post COVID-19 condition (long COVID) [Internet] [cited 2025 Dec 10]. Available from: https://www.who.int/news-room/fact-sheets/detail/post-covid-19-condition-(long-covid)
- 2.Taher MK, Salzman T, Banal A, Morissette K, Domingo FR, Cheung AM, et al. Global prevalence of post-COVID-19 condition: a systematic review and meta-analysis of prospective evidence. Health Promot Chronic Dis Prev Can 2025; 45: 112–138. 10.24095/hpcdp.45.6.06 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS, et al. Post-acute COVID-19 syndrome. Nat Med 2021; 4: 601–615. 10.1038/s41591-021-01283-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Twomey R, DeMars J, Franklin K, Culos-Reed SN, Weatherald J, Wrightson JG. Chronic fatigue and post-exertional malaise in people living with long COVID: an observational study. Phys Ther 2022; 4: pzac005. 10.1093/ptj/pzac005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Dani M, Dirksen A, Taraborrelli P, Torocastro M, Panagopoulos D, Sutton R, et al. Autonomic dysfunction in ‘long COVID’: rationale, physiology and management strategies. Clin Med 2021; 21: e63-e67. 10.7861/clinmed.2020-0896 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Park JH, Park S, Kim NH, Lee Y, Chang Y, Song TJ. Postural orthostatic tachycardia syndrome associated with COVID-19: a narrative review. Medicina (Kaunas) 2024; 60: 1325. 10.3390/medicina60081325 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Baruscotti M, Bucchi A, Milanesi R, Paina M, Barbuti A, Gnecchi-Ruscone T, et al. A gain-of-function mutation in the cardiac pacemaker HCN4 channel increasing cAMP sensitivity is associated with familial inappropriate sinus tachycardia. Eur Heart J 2017; 38: 280–288. 10.1093/eurheartj/ehv582 [DOI] [PubMed] [Google Scholar]
- 8.Goldstein DS. The possible association between COVID-19 and postural tachycardia syndrome. Heart Rhythm 2021; 18: 508–509. 10.1016/j.hrthm.2020.12.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Vernon SD, Hartle M, Sullivan K, Bell J, Abbaszadeh S, Unutmaz D, et al. Post-exertional malaise among people with long COVID compared to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Work 2023; 74: 1179–1186. 10.3233/wor-220581 [DOI] [PubMed] [Google Scholar]
- 10.Cotler J, Holtzman C, Dudun C, Jason L. A brief questionnaire to assess post-exertional malaise. Diagnostics (Basel) 2018; 8: 66. 10.3390/diagnostics8030066 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Zhou G, Meyer A, Li S, Chen Z, Lan W, Yang G, et al. Exercise intervention in treatment of neuropsychological diseases: a review. Front Psychol 2020; 11:569206. 10.3389/fpsyg.2020.569206 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Reiner M, Niermann C, Jekauc D, Woll A. Long-term health benefits of physical activity: a systematic review of longitudinal studies. BMC Public Health 2013; 13: 813. 10.1186/1471-2458-13-813 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Callow DD, Arnold-Nedimala NA, Jordan LS, Pena GS, Won J, Woodard JL, et al. The mental health benefits of physical activity in older adults survive the COVID-19 pandemic. Am J Geriatr Psychiatry 2020; 28: 1046–1057. 10.1016/j.jagp.2020.06.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Pinckard K, Baskin K, Stanford K. Effects of exercise to improve cardiovascular health. Front Cardiovasc Med 2019; 4: 69. 10.3389/fcvm.2019.00069 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Spruit MA, Rochester CL, Kenn F, Schols K, Hart AMWJ, Wouters N, et al. Pulmonary rehabilitation, physical activity, respiratory failure and palliative respiratory care. Thorax 2019; 74: 693–699. 10.1136/thoraxjnl-2018-212044 [DOI] [PubMed] [Google Scholar]
- 16.World Health Organization. Clinical management of COVID-19 patients: living guideline 2023 [cited 2025 Dec 10]. Available from: https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2023.2
- 17.Torjesen I. NICE cautions against using graded exercise therapy for patients recovering from covid-19. BMJ 2020; 370: m2912. 10.1136/bmj.m2912 [DOI] [PubMed] [Google Scholar]
- 18.Belman MJ, Brooks LR, Ross DJ, Mohsenifar Z. Variability of breathlessness measurement in patients with chronic obstructive pulmonary disease. Chest 1991; 99: 566–571. 10.1378/chest.99.3.566 [DOI] [PubMed] [Google Scholar]
- 19.Kleinman L, Zodet MW, Hakim Z, Aledort J, Barker C, Chan K, et al. Psychometric evaluation of the fatigue severity scale for use in chronic hepatitis C. Qual Life Res 2000; 9: 499–508. 10.1023/a:1008960710415 [DOI] [PubMed] [Google Scholar]
- 20.Sunnquist M, Lazarus S, Jason LA. The development of a short form of the DePaul Symptom Questionnaire. Rehabil Psychol 2019; 64: 453–462. 10.1037/rep0000285 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Feng YS, Kohlmann T, Janssen MF, Buchholz I. Psychometric properties of the EQ-5D-5L: a systematic review of the literature. Qual Life Res 2021; 30: 647–673. 10.1007/s11136-020-02688-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Bohannon RW, Smith J, Hull D, Palmeri D, Barnhard R. Deficits in lower extremity muscle and gait performance among renal transplant candidates. Arch Phys Med Rehabil 1995; 76: 547–551. 10.1016/s0003-9993(95)80509-5 [DOI] [PubMed] [Google Scholar]
- 23.Guyatt GH, Sullivan MJ, Thompson PJ, Fallen EL, Pugsley SO, Taylor DW, et al. The 6-minute walk: a new measure of exercise capacity in patients with chronic heart failure. Can Med Assoc J 1985; 132: 919–932. https://pubmed.ncbi.nlm.nih.gov/3978515/ [PMC free article] [PubMed] [Google Scholar]
- 24.Enright PL, Sherrill DL. Reference equations for the six-minute walk in healthy adults. Am J Respir Crit Care Med 1998; 158: 1384–1387. 10.1164/ajrccm.158.5.9710086 [DOI] [PubMed] [Google Scholar]
- 25.Strassmann A, Steurer-Stey C, Lana KD, Zoller M, Turk AJ, Suter P, et al. Population-based reference values for the 1-min sit-to-stand test. Int J Public Health 2013; 58: 949–953. 10.1007/s00038-013-0504-z [DOI] [PubMed] [Google Scholar]
- 26.Fuller D, Colwell E, Low J, Orychock K, Ann Tobin M, Simango B, et al. Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR Mhealth Uhealth 2020; 8: e18694. 10.2196/18694 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Ngueleu AM, Barthod C, Best KL, Routhier F, Otis M, Batcho CS. Criterion validity of ActiGraph monitoring devices for step counting and distance measurement in adults and older adults: a systematic review. J Neuroeng Rehabil 2022; 19: 112. 10.1186/s12984-022-01085-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, Mcdowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008; 40: 181–188. 10.1249/mss.0b013e31815a51b3 [DOI] [PubMed] [Google Scholar]
- 29.Freedson PS, Melason E, Sirard. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc 1998; 30: 777–781. 10.1097/00005768-199805000-00021 [DOI] [PubMed] [Google Scholar]
- 30.Tudor-Locke C, Bassett DR. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med 2004; 34: 1–8. 10.2165/00007256-200434010-00001 [DOI] [PubMed] [Google Scholar]
- 31.Page RL, Joglar JA, Caldwell MA, Calkins H, Conti JB, Deal BJ, et al. 2015 ACC/AHA/HRS guideline for the management of adult patients with supraventricular tachycardia: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol 2016; 67: e27–115. 10.1161/cir.0000000000000310 [DOI] [PubMed] [Google Scholar]
- 32.Katritsis DG, Boriani G, Cosio FG, Hindricks G, Jaïs P, Josephson ME, et al. European Heart Rhythm Association (EHRA) consensus document on the management of supraventricular arrhythmias, endorsed by Heart Rhythm Society (HRS), Asia-Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulación Cardiaca y Electrofisiologia (SOLAECE). Europace 2017; 19: 465–511. 10.1093/europace/euw301 [DOI] [PubMed] [Google Scholar]
- 33.Ståhlberg M, Reistam U, Fedorowski A, Villacorta H, Horiuchi Y, Bax J, et al. Post-COVID-19 tachycardia syndrome: a distinct phenotype of post-acute COVID-19 syndrome. Am J Med 2021. 134: 1451–1456. 10.1016/j.amjmed.2021.07.0043 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Park JH, Park S, Kim NH, Lee Y, Chang Y, Song TJ. Postural orthostatic tachycardia syndrome associated with COVID-19: a narrative review. Medicina (Kaunas) 2024; 60: 1325. 10.3390/medicina60081325 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Aranyó J, Bazan V, Lladós G, Dominguez MJ, Bisbal F, Massanella M, et al. Inappropriate sinus tachycardia in post-COVID-19 syndrome. Scie Rep 2022; 12: 298. 10.1038/s41598-021-03831-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.O’Brien KK, Brown DA, McDuff K, St Clair-Sullivan N, Chan Carusone S, Thomson C, et al. Episodic disability framework in the context of Long COVID: findings from a community-engaged international qualitative study. PLoS One 2025; 20: e0305187. 10.1371/journal.pone.0305187 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Leeuw M, Goossens MEJB, Linton SJ, Crombez G, Boersma K, Vlaeyen JWS. The fear-avoidance model of musculoskeletal pain: current state of scientific evidence. J Behav Med 2007; 30: 77–94. 10.1007/s10865-006-9085-0 [DOI] [PubMed] [Google Scholar]
- 38.Geraghty K, Jason L, Sunnquist M, Tuller D, Blease C, Adeniji C. The ‘cognitive behavioural model’ of chronic fatigue syndrome: critique of a flawed model. Health Psychol Open 2019; 6: 2055102919838907. 10.1177/2055102919838907 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Lethem J, Slade PD, Troup JDG, Bentley G. Outline of a fear-avoidance model of exaggerated pain perception—I. Behav Res Ther 1983; 21: 401–408. 10.1016/0005-7967(83)90009-8 [DOI] [PubMed] [Google Scholar]
- 40.Legler F, Meyer-Arndt L, Mödl L, Kedor C, Freitag H, Stein E, et al. Long-term symptom severity and clinical biomarkers in post-COVID-19/chronic fatigue syndrome: results from a prospective observational cohort. EClinicalMedicine 2023; 63: 102146. 10.1016/j.eclinm.2023.102146 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Wright J, Astill SL, Sivan M. The relationship between physical activity and long COVID: a cross-sectional study. Int J Environ Res Public Health 2022; 19: 5093. 10.3390/ijerph19095093 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Fernández-De-las-Peñas C, Palacios-Ceña D, Gómez-Mayordomo V, Palacios-Ceña M, Rodríguez-Jiménez J, de-La-Llave-Rincón AI, et al. Fatigue and dyspnoea as main persistent post-COVID-19 symptoms in previously hospitalized patients: related functional limitations and disability. Respiration 2022; 101: 132–141. 10.1159/000518854 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Rinaldo RF, Mondoni M, Parazzini EM, Baccelli A, Pitari F, Brambilla E, et al. Severity does not impact on exercise capacity in COVID-19 survivors. Respir Med 2021; 187:106577. 10.1016/j.rmed.2021.106577 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Torres-Castro R, Núñez-Cortés R, Larrateguy S, Alsina-Restoy X, Barberà JA, Gimeno-Santos E, et al. Assessment of exercise capacity in post-COVID-19 patients: how is the appropriate test chosen? Life 2023; 13: 621. 10.3390/life13030621 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Neto FR, Marques JM, Brasiliano Da Paz M, Boiteux E, Cavalcanti U, Gomes Costa RR. Sit to stand test and handgrip strength in men and women with post-COVID-19 syndrome without invasive ventilator support: insights from a Brazilian observational study. Monaldi Arch Chest Dis 2023; 94. 10.4081/monaldi.2023.2495 [DOI] [PubMed] [Google Scholar]
- 46.Appelman B, Charlton BT, Goulding RP, Kerkhoff TJ, Breedveld EA, Noort W, et al. Muscle abnormalities worsen after post-exertional malaise in long COVID. Nat Commun 2024; 15: 17. 10.1038/s41467-023-44432-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.de Boer E, Petrache I, Goldstein NM, Olin JT, Keith RC, Modena B, et al. Decreased fatty acid oxidation and altered lactate production during exercise in patients with post-acute COVID-19 syndrome. Am J Respir Crit Care Med 2022; 205: 126–129. 10.1164/rccm.202108-1903le [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Rinaldo RF, Mondoni M, Parazzini EM, Pitari F, Brambilla E, Luraschi S, et al. Deconditioning as main mechanism of impaired exercise response in COVID-19 survivors. Eur Respir J 2021; 58: 2100870. 10.1183/13993003.00870-2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Geidl W, Carl J, Cassar S, Lehbert N, Mino E, Wittmann M, et al. Physical activity and sedentary behaviour patterns in 326 persons with COPD before starting a pulmonary rehabilitation: a cluster analysis. J Clin Med 2019; 8: 1346. 10.3390/jcm8091346 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Iwakura M, Spruit MA, Kawagoshi A, Tamaki A, Oki Y, Oshima Y. Physical activity measurements in individuals with interstitial lung disease: a systematic review and meta-analysis. Eur Respir Rev 2023; 32: 220165. 10.1183/16000617.0165-2022 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Booth FW, Roberts CK, Laye MJ. Lack of exercise is a major cause of chronic diseases. Compr Physiol 2012; 2: 1143-1211. 10.1002/cphy.c110025 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.De Rezende LFM, Lopes MR, Rey-Lo'p ez JP, Matsudo VKR, Luiz ODC. Sedentary behavior and health outcomes: an overview of systematic reviews. PLoS One 2014; 9: 105620. 10.1371/journal.pone.0105620 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Hejbøl EK, Harbo T, Agergaard J, Madsen LB, Pedersen TH, Østergaard LJ, et al. Myopathy as a cause of fatigue in long-term post-COVID-19 symptoms: evidence of skeletal muscle histopathology. Eur J Neurol 2022; 29: 2832–2841. 10.1111/ene.15435 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Haunhorst S, Dudziak D, Scheibenbogen C, Seifert M, Sotzny F, Finke C, et al. Towards an understanding of physical activity-induced post-exertional malaise: insights into microvascular alterations and immunometabolic interactions in post-COVID condition and myalgic encephalomyelitis/chronic fatigue syndrome. Infection 2025; 53: 1–13. 10.1007/s15010-024-02386-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Jimeno-Almazán A, Franco-López F, Buendía-Romero Á, Martínez-Cava A, Sánchez-Agar JA, Sánchez-Alcaraz Martínez BJ, et al. Rehabilitation for post–COVID–19 condition through a supervised exercise intervention: a randomized controlled trial. Scand J Med Sci Sports 2022; 32: 1791–1801. 10.1111/sms.14240 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Ladlow P, Bennett AN, O’Sullivan O. Exercise therapy for post-COVID-19 condition – does no harm. JAMA Netw Open 2024; 7: e246959. 10.1001/jamanetworkopen.2024.6959 [DOI] [PubMed] [Google Scholar]
- 57.Marques KC, Quaresma JAS, Falcão LFM. Cardiovascular autonomic dysfunction in “Long COVID”: pathophysiology, heart rate variability, and inflammatory markers. Front Cardiovasc Med 2023; 10: 1256512. 10.3389/fcvm.2023.1256512 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Durstenfeld MS, Peluso MJ, Kaveti P, Hill C, Li D, Sander E, et al. Reduced exercise capacity, chronotropic incompetence, and early systemic inflammation in cardiopulmonary phenotype Long Coronavirus Disease 2019. J Infect Dis 2023; 228: 542–554. 10.1093/infdis/jiad131 [DOI] [PMC free article] [PubMed] [Google Scholar]