Pregnancy has been shown to affect the quality, duration, and pattern of sleep (Paavonen et al., 2017; Reid et al., 2017). These changes have important implications, as insufficient sleep is associated with health problems and complications during labor. In line with studies investigating the general population, a few studies focused on pregnant smokers and have also shown a prevalence of sleep abnormalities (e.g., Danilov et al., 2022; Lange et al., 2018; Paavonen et al., 2017). Studies examining the role of passive smoking on sleep are rare, be it in the general population or in pregnant women. 

The aim of the Ciochon et al. study was to investigate the relationship between active or passive smoking and three types of sleep problems during pregnancy: difficulty falling asleep, difficulty staying asleep, and waking up too early. The authors hypothesized that pregnant women's exposure to smoking (active and passive) would increase their risk of sleep problems during pregnancy.

Participants were part of a larger study: the Corona Mums project, which included 3365 pregnant women from Poland, aged 18 to 43 years. These women completed an online questionnaire during the COVID-19 pandemic, from May 2020 to September 2021. The authors conducted multivariate logistic regressions that included the following control variables: socio-demographic, pregnancy-related variables, and psychological variables.

The results of the study showed that passive smoking is a risk factor for waking up too early, but they showed no evidence suggesting that active or passive smoking was related to any of the other sleep variables. The authors highlighted the roles of control variables included in the models. Specifically, sleep difficulties were related to age, place of residence, education, level of anxiety and depression in pregnant women, and the presence of nausea or vomiting. Further, in all the models, the level of anxiety, depression, and trimester of pregnancy (3rd trimester in comparison to 1st and 2nd) were significantly related to the risk of occurrence of sleep problems.

While only one of the six examined associations showed statistical significance, the findings are still useful in highlighting potential risks associated with passive smoking and sleep disturbances during pregnancy. The study also underscores the need for more comprehensive investigations, including direct measures of sleep quality, such as actigraphy or polysomnography, which are necessary to better understand the underlying mechanisms and to confirm the potential impacts of both active and passive smoking on sleep during pregnancy.

Research is a resource-demanding endeavor that tries to answer questions such as, “Is there an effect?” and “How large or small is this effect?” To answer these questions as precisely as possible, meta-analysis is considered the gold standard. However, the value of meta-analytic conclusions greatly depends on the quality, comprehensiveness, and timeliness of the meta-analyzed studies, while not neglecting older research. Using the established sport psychological intervention strategy of self-talk as an example, Corcoran & Steele demonstrate how Bayesian methods and statistical indicators of questionable research practices can be used to assess these questions [1].

Bayesian methods enable cumulative evidence synthesis by updating prior beliefs (i.e., knowledge from an earlier meta-analysis) with new information (i.e., the studies that have been published on the topic since the earlier meta-analysis had been published) to arrive at a posterior belief - an updated meta-analytic effect size. This approach essentially tells us whether and how much our understanding of an effect has improved as additional evidence has accumulated; as well as the precision with which we are estimating it. Or to put it more bluntly, how much smarter are we now with respect to the effect we are interested in?

Importantly, the credibility of this updated effect depends not only on the newly included studies but also on the reliability of the prior beliefs – that is, the credibility of the effects summarized in the earlier meta-analysis. A set of frequentist and Bayesian statistical approaches have been introduced to assess this (for a tutorial with worked examples, see [2]). For example, methods such as the multilevel precision-effect test (PET) and precision-effect estimate with standard errors (PEESE) [2] can be used to adjust for publication bias in the meta-analyzed studies, providing a more realistic estimation of the effect size for the topic at hand. This would then help to assess the magnitude of the true effect in the absence of any bias favoring the publication of significant results.

Why does it matter for health and movement science?
The replication crisis and evidence of questionable research practices has cast doubts on various findings across disciplines [3–8]. Compared to other disciplines (e.g., psychology [9]), health & movement science has been relatively slow to recognize issues with the potential replicability of findings in the field [10]. Fortunately, this has started to change [10–14]. Research on factors that might negatively affect replicability in health & movement science has revealed evidence for various questionable research practices, such as publication bias [12,13], lack of statistical power [11,13], and indicators of p-hacking [12]. The presence of such practices in original research does not only undermine trustworthiness of individual studies, but also the conclusions drawn from meta-analyses that rely on these studies.

Open Science practices, such as open materials, open data, pre-registration of analyses plans, as well as registered reports are all good steps for improving science in the future [15–17] and might even lead to a ‘credibility revolution’ [18]. However, it is also crucial to evaluate the extent to which an existing body of literature might be affected by questionable research practices and how this might affect conclusions drawn from the research. Using self-talk as an example, Corcoran and Steele demonstrate this approach and provide a primer on how it can be effectively implemented [1]. By adhering to Open Science practices, their materials, data, and analyses are openly accessible. We believe this will facilitate the adoption of Bayesian methods to cumulatively update available evidence, as well as making it easier for fellow researchers to comprehensively and critically assess the literature they want to meta-analyze.      
 
References​
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[​6] ​Perneger, T. V. & Combescure, C. The distribution of P-values in medical research articles suggested selective reporting associated with statistical significance. J. Clin. Epidemiol. 87, 70–77 (2017). https://doi.org/10.1016/j.jclinepi.2017.04.003
[​7] ​Errington, T. M. et al. An open investigation of the reproducibility of cancer biology research. eLife 3, e04333 (2014). https://doi.org/10.7554/eLife.04333
[​8] ​Hoffmann, S. et al. The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines. R. Soc. Open Sci. 8, 201925 (2021). https://doi.org/10.1098/rsos.201925
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[​10] ​Mesquida, C., Murphy, J., Lakens, D. & Warne, J. Replication concerns in sports and exercise science: A narrative review of selected methodological issues in the field. R. Soc. Open Sci. 9, 220946 (2022). https://doi.org/10.1098/rsos.220946
[​11] ​Abt, G. et al. Power, precision, and sample size estimation in sport and exercise science research. J. Sports Sci. 38, 1933–1935 (2020). https://doi.org/10.1080/02640414.2020.1776002
[​12] ​Borg, D. N., Barnett, A. G., Caldwell, A. R., White, N. M. & Stewart, I. B. The bias for statistical significance in sport and exercise medicine. J. Sci. Med. Sport 26, 164–168 (2023). https://doi.org/10.1016/j.jsams.2023.03.002
[​13] ​Mesquida, C., Murphy, J., Lakens, D. & Warne, J. Publication bias, statistical power and reporting practices in the Journal of Sports Sciences: potential barriers to replicability. J. Sports Sci. 41, 1507–1517 (2023). https://doi.org/10.1080/02640414.2023.2269357
[​14] ​Büttner, F., Toomey, E., McClean, S., Roe, M. & Delahunt, E. Are questionable research practices facilitating new discoveries in sport and exercise medicine? The proportion of supported hypotheses is implausibly high. Br. J. Sports Med. 54, 1365–1371 (2020). https://doi.org/10.1136/bjsports-2019-101863
[​15] ​Chambers, C. D. & Tzavella, L. The past, present and future of Registered Reports. Nat. Hum. Behav. 6, 29–42 (2022). https://doi.org/10.1038/s41562-021-01193-7
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Exercise capacity is recognised as a strong predictor of mortality and cardiovascular morbidity in both healthy individuals and patients with coronary heart disease (Novaković et al., 2022). Accordingly, exercise-based cardiac rehabilitation is recommended as an effective secondary preventive intervention (Task Force Members et al., 2016; Anderson et al., 2016). While earlier studies generally focused on changes in exercise capacity during or immediately after rehabilitation (Uddin et al., 2016), recent research has emphasised the importance of physical activity trajectories on mortality in patients with coronary heart disease (Gonzalez-Jaramillo et al., 2022). This highlights the need to understand changes in exercise capacity and physical activity following the rehabilitation phase.

This study specifically explored changes in exercise capacity (assessed using the six-minute walking test) and physical activity (assessed using the International Physical Activity Questionnaire-Short Form) one year after a cardiac rehabilitation programme in patients with coronary heart disease. Additionally, the authors examined changes in motivation for physical activity over the 12 months following rehabilitation.

Within the limitations of its observational and monocentric nature, the study presents important findings that can inform future research, generate hypotheses, and guide the design of targeted trials aimed at improving or maintaining exercise capacity and physical activity levels after rehabilitation. The exploration of potential barriers to physical activity 12 months after rehabilitation could inform strategies to increase participation in physical activity post-rehabilitation, thereby improving survival (Moholdt et al., 2018).

This study is well-conducted and clearly presented. The authors' interpretation is balanced and consistent with the study's design and analysis. As noted by one of the reviewers, retention in cardiac rehabilitation studies is challenging, and the authors have done a commendable job in retaining participants. They have also addressed all the reviewers' concerns properly and accurately. I am pleased to recommend this preprint.

The law of least effort suggests that, certis paribus, people tend to exert as little effort as possible when engaged in a goal-directed task (Cheval & Boisgontier, 2021). At the same time, however, large interindividual differences in the processing of effort have been observed, suggesting that effort per se can sometimes be valued positively (Inzlicht et al., 2018).  However, until the present study by Wolff et al. (2024), all previous studies had largely ignored whether these individual differences in the valuation of effort might depend on the context (mental versus physical), i.e., in layman's terms, we do not know whether people value any effort or whether these preferences are specific to the mental and/or physical domain. The aim of the present study (Wolff et al., 2024) was to answer this question on the basis of two independent studies.

Study 1 (N = 39) used a binary decision task to measure preferences for allocating mental versus physical effort and showed that people differ markedly in their preferred allocation of effort. Crucially, a disposition to value mental effort (as assessed by the Need for Cognition Scale) was associated with a higher preference for mental effort, whereas a disposition to value physical effort (as assessed by the recently developed Value of Physical Effort Scale) was associated with a preference for physical effort.

Study 2 (N = 300 students) confirmed the robustness of the findings and showed that the tendency to value mental effort was associated with better grades in math (but showed no evidence of such an association in sport), whereas the tendency to value physical effort was associated with better grades in sport (but showed no evidence of such an association in math). Furthermore, the study extended these findings by showing that valuing physical effort was associated with less boredom in sports, whereas valuing mental effort was associated with less boredom in math.

In summary, the results of this research provide the first evidence suggesting that the valuation of effort is domain-specific rather than general. This finding paves the way for future research aimed at improving our understanding of the valuation of physical or mental effort. This article makes an important contribution to the knowledge of the key issues surrounding whether effort valuation is domain-specific or general.

Since all reviewers have indicated that they are satisfied with the authors' revision, which accurately and comprehensively addresses the reviewers' and my comments, it is my pleasure to recommend this preprint.