Functioning, Disability and Rehabilitation After Mild Infection in Concern to Previous Health Status: A Lithuanian Online Survey Study

Comment No

Reviewers comments

Response with changes in the manuscript and lines

Referee 1

1.

The text in several places loses logical coherence – for example, the sentence “The post-COVID-19 condition, also known as long COVID, defined as the persistence or emergence of new symptoms…” is grammatically incorrect (it lacks a predicate – it should read “is defined as”). Furthermore, the introduction of WHO data, research findings and the definition of the concept is somewhat chaotic and lacks a clear structure (there is no clear transition from the description of the phenomenon to the research objective).

An updated definition is added, please see page 2, lines 42-48.

2.

The authors state that “rehabilitation may be one of the key interventions”, but they do not specify what type of rehabilitation, for which symptoms, or on what evidence this claim is based. Such generality limits the scientific value of the review. Please expand the theoretical section with a concise overview of current research directions in post-COVID-19 rehabilitation – for example, physiotherapy, neurological rehabilitation, psychological interventions – and indicate which aspects remain insufficiently investigated.

Thanks for this comment, please see page 15, lines 448, 450, 453, 455, 457, 461, 464, 466, 469, 471, 487, 496, 500, 502, 505, 509, 511, 516, 521, 528.   

3.

The definition of post-COVID-19 condition is incomplete and linguistically imprecise. It lacks reference to the current WHO definition (2021, updated 2023), which specifies that symptoms must persist for ≥2 months and occur within 3 months after infection, without another explanation. Please cite the latest WHO definition (with year) and indicate differences from earlier definitions to improve the scientific accuracy and timeliness of the text.

Thanks for this comment, we have updated information, please see page 2, lines 42-48.

4.

Although the authors mention “biopsychosocial functions”, they do not describe how rehabilitation affects these domains — for instance, whether it improves quality of life, ability to work, or cognitive function. Please expand this section by referring to theoretical models (e.g. the WHO ICF model) and cite specific studies demonstrating the effectiveness of rehabilitation in long COVID.

Thanks for this comment, we have updated information, please see page 2, lines 68-75.

5.

According to the journal’s guidelines, in-text citation brackets should be square.

 Thank you, we have changed the citation brackets.

6.

Recruitment via open links on social media and local media (Facebook, websites, hospitals) introduces a risk of self-selection bias (10.1016/j.jaad.2021.06.025) — individuals with symptoms are more likely to participate. The text states that the “study encouraged participation independent of the presence or absence of persistent symptoms”, yet no data are provided to confirm that a balance between these groups was actually achieved. Please include information on whether and how the sample composition was controlled (e.g. proportion of participants with/without symptoms, age, sex). It would also be valuable to indicate whether any identity verification was conducted to prevent multiple submissions.

Thanks for this comment, we have updated information, please see page 3, lines 102-111.

7.

The inclusion criterion “known SARS-CoV-2 infection with or without specific diagnostic tests” is overly broad — it allows participants without test confirmation, relying only on self-report. This significantly limits the epidemiological validity of the data. Please clarify whether untested cases were verified (e.g. based on typical symptoms or confirmed contact with an infected person). Consider subgroup analyses: test-confirmed vs. unconfirmed infection

Thanks for this comment. Please see page 4, lines 161-172 for additional information.

8.

The manuscript notes the use of a Google Forms questionnaire, but provides no information about its validation, item sources, or whether it was an original tool or based on existing instruments (e.g. SF-36, WHOQOL, EQ-5D). Please expand the methods section with details on validation — e.g. pilot testing, reliability assessment (Cronbach’s α), or linguistic adaptation. Without this, it is difficult to assess the reliability of the health and wellbeing measures.

To our knowledge there was no validated questionnaire for PCC at the time of pandemics since the nature of the virus and post-virus period was not known.

Since the questionnaire was anonymous we were not able to validate it. We pilot tested the technical solution of it but not validity.

9.

It is unclear how variables such as “physical fitness status”, “psycho-emotional well-being” and “functional capacity” were defined. Please specify whether these were self-rated on numerical, categorical, or descriptive scales, and provide details of the scoring system — e.g. point ranges, data transformation methods, and calculated indices.

Thank you for this comment. We have changed the description of statistics, indicating the analysis of data distribution and analysis of parameters.

10.

The text reports the use of Chi-square, t-test and Mann–Whitney tests, but does not explain which variables were analysed with each test or why. There is no information on normality testing (e.g. Shapiro–Wilk) or corrections for multiple comparisons (e.g. Bonferroni). Please provide a brief justification for the choice of tests and describe how statistical assumptions were verified. Indicate whether multivariate analyses (e.g. logistic regression) were performed to control for confounding factors (age, sex, comorbidities).

We did not perform correlation analysis or multivariate analysis. Other statistics were performed according to normally or non-normally distribution.

11.

Please include effect sizes and their interpretation according to the latest rehabilitation guidelines (10.1016/j.apmr.2025.05.013):
For group differences (Cohen’s d or Hedges’ g), small, medium, and large effects correspond to 0.1, 0.4, and 0.8, respectively. Each statistically significant p-value should be accompanied by the effect size. Results with significant p but small effect sizes should be interpreted as not clinically significant.

Ideally, results should also include 95% confidence intervals (10.1111/trf.13635).

The sample size is large — this should be emphasised as a major strength of the study. Please add that the sample was sufficient to detect even small effect sizes with 90% power, according to the cited guidelines (10.1016/j.apmr.2025.05.013), which represents a notable methodological advantage.

Thank you for your comment. The statistical tables have been adjusted based on your comments. Please review tables 1-5.

12.

Data collection was conducted in two stages (2021 and 2022), yet it is unclear whether comparisons were made between pandemic waves, which differed in vaccine availability and virus variants. Please clarify whether the analysis treated these as one combined sample or as separate subgroups. If data were merged, please justify why they were considered comparable.

Thanks for this comment. Please see page 3-4, lines 102-111,142-156 for additional information.

13.

The authors state that access to rehabilitation was “low” and needs were “significant”, but no specific statistical analyses (effect sizes, confidence intervals, correlations) are presented. Without these, the conclusions sound declarative rather than scientific. Please reintroduce this variable and include quantitative analysis.

Thank you for your comment. The statistical tables have been adjusted based on your comments. Please review tables 1-5.

14.

In several places, the text implies causal relationships (e.g. lack of rehabilitation → slower return to work), although the cross-sectional design only allows for correlation, not causation. Please use more cautious, conditional phrasing.

The statistics indicate weak size effects of the chi-square tests, indicated in result part.

15.

The statement that low rehabilitation availability results from the prioritisation of hospitalised patients is presented merely as “hypothetically”, without reference to system-level data (e.g. number of services, funding limits, waiting lists). Source verification is required.

The statement is our hypothesis, we underlined that in the text. Please see page 12, lines 313-319. We have 3 references supporting our hypothesis.

16.

Several parts of the discussion repeat the same ideas — e.g. the need to prepare for future pandemics, the importance of individualised interventions, and the role of comorbidities. These repetitions weaken the argument and should be condensed; conclusions should be made more specific and aligned with statistically and clinically significant findings.

Discussion revised based on comments. Please see page 12-14.

17.

Please revise the Conclusions so that they explicitly refer to results with statistically significant p-values and clinically meaningful effect sizes.

The text has been adjusted in light of the comments. Please see page 14.

Comment No

Reviewers comments

Response with changes in the manuscript and lines

Referee 2

1.

Do authors have any data before COVID19 which can be used for comparison?

Unfortunately, we do not have access to the data before SARS-CoV-19 infection.

2.

Please list all abbreviations for audience.

We have spelled out these few abbreviations in the text, mainly PCC (post-COVID-19 condition). Others (few) are spelled out.

3.

Table 1: please add participants detailed health status, such as, alcohol and smoking history, BMI/BRI, any metabolic disease, cardiovascular disease. cancer, income level, etc.

We did not collect information on income, BMI, alcohol and smoking in the study.

4.

Table 1: what is the difference between higher non-university vs higher university?

Definition of higher non-university education means non-academic professional education and might award only Professional Bachelor’s degrees. Higher universty means education with traditional research university institutions primarily providing academic education and award Bachelor’s, Master’s, and Doctor’s degrees.

5.

Table 1: in education area, please explain the small sample size for "other"?

“Other” means – participants who did not make a choice corresponding to primary/secondary, higher non-university or higher university? It might include private courses/educational courses.

6.

Please explain why 90% participants are female? Any difficulty recruiting male participants? What is the gender ratio in Lithuana?

 ~ Among inhabitants in Lithuania 47% are men and 53% women.

The less number among male participants in the present study possible depends on the fact that men tend to participate in the questionnaire studies at less extent.  Studies on PCC revealed that women dominate in the group of PCC after mild infection, suggesting a female sex as one of the risk factors.