Effective gait fundamentally requires spatial and temporal coordination of upper and lower limbs. Individuals with Parkinson’s disease (PD) often exhibit impaired coordination, leading to adverse events such as freezing of gait and falls (Plotnik et al. 2008). Despite their significance, the current literature lacks depth in our understanding of this characteristic, especially their adaptation to changing task demands and symptom laterality. Exploring these relationships may provide new insights into PD gait and facilitate the evaluation of potential treatments. With these objectives in mind, the present study conducted by Hill & Nantel (2024) includes 17 participants with mild to moderate PD and focuses on coordination within and between the more and less affected sides during both single and dual gait tasks. In the study, spatial coordination, assessed by range of motion, range of motion variability, and peak flexion for the shoulder and hip joints, was examined alongside temporal coordination, which was evaluated using the phase coordination index and variability of continuous relative phase.

Their analysis reveals that, due to dual tasking, only the shoulder range of motion and peak flexion decreased within the least affected side, adding to the existing knowledge on arm swing impairments in early-stage PD (Navarro-López et al. 2022). However, no significant difference was observed between the more and less affected sides. Hip range of motion showed dual task-related differences between sides, while lower intralimb phase variability did not. The primary strength of the article lies in its attempt to systematically explore these differences in PD. As the authors pointed out, to interpret the clinical significance of these differences as well as the null findings on temporal coordination, it may be necessary to include a healthy control group or other comparison groups, such as individuals with severe PD. When interpreting these results, readers may also pay attention to the methodological choices, such as the patient-reported most affected side and the choice of dual task. Overall, the study will be of interest to researchers studying intra- and inter-limb coordination during gait in PD.   

References

Plotnik, M., & Hausdorff, J. M. (2008). The role of gait rhythmicity and bilateral coordination of stepping in the pathophysiology of freezing of gait in Parkinson's disease. Movement disorders: official journal of the Movement Disorder Society, 23(S2), S444-S450. https://doi.org/10.1002/mds.21984 

Hill, A., & Nantel, J. (2024). Interlimb coordination in Parkinson’s Disease is minimally affected by a visuospatial dual task. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Health and Movement Science. https://doi.org/10.1101/2022.07.15.500215 

Navarro-Lopez, V., Fernandez-Vazquez, D., Molina-Rueda, F., Cuesta-Gomez, A., Garcia-Prados, P., del-Valle-Gratacos, M., & Carratala-Tejada, M. (2022). Arm-swing kinematics in Parkinson's disease: a systematic review and meta-analysis. Gait & Posture, 98, 85-95. https://doi.org/10.1016/j.gaitpost.2022.08.017 

The study by Barbusse et al. (2024) investigated how motor control of arm movements is affected by reversed gravity. It is commonly assumed that the central nervous system contains an internal gravity model, and that this model is used to optimize movements to minimize effort under the influence of gravity (e.g., Berret et al., 2008). Previously, the effect of decreased and increased gravity was investigated, and it was shown that people were able to adapt to this novel environment in a matter of minutes or days (e.g., Gaveau et al., 2011). Therefore, the authors investigated the effect of inverse gravity on motor control of arm movements.

In this study, an experiment was performed in which participants were placed in an inversion table and asked to perform as many pointing movements with their shoulder as possible in 12 blocks. In each block, the inversion table was placed either in the head-up or head-down position, and the position was switched every 35 seconds, starting from the head-up position. After 4 blocks, a 90 second break was taken. It was found that movement duration and amplitude did not significantly differ between both orientations. An analysis of the difference in time to peak acceleration, time to peak velocity, and time to peak deceleration between upward and downward movements revealed no significant difference for the peak acceleration, while for the peak velocity, the time difference was significantly smaller in the head-down than the head-up position, and for the peak deceleration, the time difference changed in the head-down position with the number of blocks, reaching a value more similar to the head-up (baseline) position.

The time to peak acceleration did not reverse for the head-down position, which showed that the central nervous system is not able to take advantage of gravity when it is placed in a head-down position, since it does not take advantage of the “free” acceleration provided by gravity. A longer exposure to inverse gravity might allow the body to adapt and re-optimize its internal gravity model to the new situation. The time difference was significantly different for the deceleration, but not for acceleration, which indicates that the movement was adapted mainly by feedback control, but that feedforward control remained largely the same. This further supports the conclusion that the central nervous system had not yet adapted its internal gravity model, and that re-optimization starts with adapting feedback control (Izawa et al., 2008). An important limitation is the discomfort that is experienced in the head-down position, which not only changes gravity, but also created negative physiological responses.

References

Denis Barbusse, Sarah Amoura, Jérémie Gaveau, Olivier White (2024) Feedback-driven adaptation of gravity-related sensorimotor control to an upside-down posture. OSF preprints, ver.3 peer-reviewed and recommended by PCI Health & Movement Sciences. https://doi.org/10.17605/OSF.IO/D9JPF.

Berret B, Darlot C, Jean F, Pozzo T, Papaxanthis C, Gauthier JP (2008) The inactivation principle: mathematical solutions minimizing the absolute work and biological implications for the planning of arm movements. PLoS computational biology, 4, e1000194. https://doi.org/10.1371/journal.pcbi.1000194

Gaveau J, Paizis C, Berret B, Pozzo T, Papaxanthis C (2011) Sensorimotor adaptation of point-to-point arm movements after spaceflight: the role of internal representation of gravity force in trajectory planning. Journal of Neurophysiology, 106, 620–629. https://doi.org/10.1152/jn.00081.2011

Izawa J, Rane T, Donchin O, Shadmehr R (2008) Motor adaptation as a process of reoptimization. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 28, 2883–2891. https://doi.org/10.1523/JNEUROSCI.5359-07.2008

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​
[1] Corcoran H. & Steele, J. Cumulative evidence synthesis and consideration of "research waste" using Bayesian methods: An example updating a previous meta-analysis of self-talk interventions for sport/motor performance. SportRxiv, ver.2, peer-reviewed and recommended by PCI Health & Movement Sciences (2024). https://doi.org/10.51224/SRXIV.348​
[​2] ​Bartoš, F., Maier, M., Quintana, D. S. & Wagenmakers, E.-J. Adjusting for publication bias in JASP and R: Selection Models, PET-PEESE, and robust bayesian meta-analysis. Adv. Methods Pract. Psychol. Sci. 5, 25152459221109259 (2022). https://doi.org/10.1177/2515245922110925
[​3] ​Yong, E. Replication studies: Bad copy. Nature 485, 298–300 (2012). https://doi.org/10.1038/485298a.
[​4] ​Hagger, M. S. et al. A multilab preregistered replication of the ego-depletion effect. Perspect. Psychol. Sci. 11, 546–573 (2016). https://doi.org/10.1177/1745691616652873
[​5] ​Scheel, A. M., Schijen, M. R. M. J. & Lakens, D. An excess of positive results: Comparing the standard psychology literature with registered reports. Adv. Methods Pract. Psychol. Sci. 4, 25152459211007467 (2021). https://doi.org/10.1177/2515245921100746
[​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
[​9] ​Open Science Collaboration. Estimating the reproducibility of psychological science. Science 349, aac4716 (2015). https://doi.org/10.1126/science.aac4716
[​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
[​16] ​Soderberg, C. K. et al. Initial evidence of research quality of registered reports compared with the standard publishing model. Nat. Hum. Behav. 5, 990–997 (2021). https://doi.org/10.1038/s41562-021-01142-4
[​17] ​Wunsch, K., Pixa, N. H. & Utesch, K. Open science in German sport psychology. Z. Für Sportpsychol. 30, 156–166 (2023). https://doi.org/10.1026/1612-5010/a000406
[​18] ​Korbmacher, M. et al. The replication crisis has led to positive structural, procedural, and community changes. Commun. Psychol. 1, 1–13 (2023).​ https://doi.org/10.1038/s44271-023-00003-2

Despite public health campaigns, achieving recommended physical activity levels remains challenging. Investigating the factors influencing physical activity is essential for effective promotion. Habit strength is known to correlate with physical activity (Hagger, 2019), making habit formation a key intervention target. Newman et al. (2023) expand current knowledge on physical activity and habit strength. They investigate if habit strength and its association with behavior differ between organized and leisure-time physical activities. Given the broad definition of physical activity and individual differences in preferences, studying habit's influence on varied activities is crucial. The cross-sectional survey, spanning the UK, USA, Australia, and Switzerland, involves 120 young adults (mean age = 25) engaged in organized sports. Although self-report measures are used, excluding commuting and occupational activity, the study yields intriguing results: Authors find significant habit strength differences between organized sports and leisure-time activities, indicating potential distinctions in habit formation drivers. Investigating factors establishing habits in organized sports could inform broader interventions. Remarkably, the impact of habits on behavior is consistent across both activity types, suggesting a universal role of habits. Further analysis reveals stronger habit strength in team sports versus individual ones, with no behavior association difference. Diverse habit strength in organized versus leisure-time activities underscores the need for focused research. Understanding unique aspects of team sports that promote habituation can reshape interventions, aligning leisure activities with organized sports' characteristics.

References

Hagger, M. S. (2019). Habit and physical activity: Theoretical advances, practical implications, and agenda for future research. Psychology of Sport and Exercise, 42, 118–129. https://doi.org/10.1016/j.psychsport.2018.12.007

Newman, K., Forestier, C., Cheval, B., Zenko, Z., De Chanaleilles, M., Gardner, B., & Rebar, A. L. (2023). Comparing habit-behaviour relationships for organised versus leisure time physical activity. OSF Preprints, 1–11, version 4, peer-reviewed and recommended by Peer Community in Health & Movement Sciences. https://doi.org/10.31219/osf.io/x5e9d