Patient-centered communication (PCC) plays a vital role in effective cancer management and care. Patient portals are increasingly available to patients and hold potential as a valuable tool to facilitate PCC. However, whether more frequent use of patient portals is associated with increased perceived PCC and which mechanisms might mediate this relationship have not been fully studied.The goal of this study was to investigate the association between the frequency of access of patient portals and perceived PCC in patients diagnosed with cancer. We further sought to examine whether this association was mediated by patients’ self-efficacy in health information–seeking.We used data from the Health Information National Trend Survey 5 (HINTS 5) cycle 3 (2019) and cycle 4 (2020). This analysis includes 1222 individuals who self-reported having a current or past diagnosis of cancer. Perceived PCC was measured with a 7-item HINTS 5–derived scale and classified as low, medium, or high. Patient portal use was measured by a single item assessing the frequency of use. Self-efficacy about health information–seeking was assessed with a 1-item measure assessing confidence in obtaining health information. We used adjusted multinomial logistic regression models to estimate relative risk ratios (RRRs)/effect sizes of the association between patient portal use and perceived PCC. Mediation by health information self-efficacy was investigated using the Baron and Kenny and Karlson-Holm-Breen methods.P=.06) with perceived PCC in an adjusted multinominal logistic regression model. Patients who accessed their patient portal 10 or more times in the previous 12 months were almost 4 times more likely  to report high perceived PCC. In mediation analysis, the association between patient portal use and perceived PCC was attenuated adjusting for health information–seeking self-efficacy, but those with the most frequent patient portal use (10 or more times in the previous 12 months) were still almost 2.5 times more likely to report high perceived PCC  compared to those with no portal use.A total of 54.5% of the sample reported that they had not accessed their patient portals in the past 12 months, 12.6% accessed it 1 to 2 times, 24.8% accessed it 3 to 9 times, and 8.2% accessed it 10 or more times. Overall, the frequency of accessing the patient portal was marginally associated (Increased frequency of patient portal use was associated with higher PCC, and an individual’s health information–seeking self-efficacy partially mediated this association. These findings emphasize the importance of encouraging patients and providers to use patient portals to assist in patient-centeredness of cancer care. Interventions to promote the adoption and use of patient portals could incorporate strategies to improve health information self-efficacy. Approximately 17 million people in the United States are living with cancer . Living Patient-centered care comprises multiple factors, and patient-centered communication (PCC) is an essential aspect ,8. PCC iMuch research promoting PCC in cancer care has focused on assessing and improving clinicians’ skills and training. Less work, however, has been done on patient-specific characteristics such as a patient’s ability to seek information . One speHealth information self-efficacy is a personal belief that one can take action to get the information if they need it regarding a health concern . PatientThe purpose of this study was to assess the association between the frequency of access to patient portals and perceived PCC in a national sample of individuals who have had a diagnosis of cancer. We further sought to determine whether self-efficacy related to information-seeking mediated the relationship between frequency of access to patient portals and PCC. We hypothesized that greater frequency of portal access would be associated with high PCC and that health information self-efficacy mediates the relationship between portal use frequency and PCC.Data examined for this study were from the Health Information National Trends Survey (HINTS). HINTS is a cross-sectional survey that the National Cancer Institute has regularly administered since 2004. HINTS aims to assess how people access and use health information, how people use information technology to manage health and health information, and the degree to which people are engaged in healthy behaviors . The popThis study combines the third and fourth data collection cycles for HINTS 5. HINTS 5 cycle 3 was conducted from January 22 to April 30, 2019, and it consisted of data from 3500 respondents using a mailed survey. The response rate for the mailed survey was 30.2%. During HINTS 5 cycle 3, a web pilot test was run alongside the self-administered mailed version from January 29 to May 7, 2019. The web pilot comprised 2046 additional respondents. The web-based pilot included an experiment testing the effectiveness of offering a $10 Amazon gift card for responding via the web. Web pilot respondents who were offered the bonus incentive had a slightly higher response rate (31.5%) compared to the control group (29.6%), who did not receive the Amazon gift card . We usedThis study qualified for exempt status from the Committee for the Protection of Human Subjects at the University of Massachusetts Chan Medical School.Use of patient portals was measured by the question: How many times did you access your online medical record in the last 12 months? We categorized this as no use, 1 to 2 times, 3 to 9 times, and 10 or more times during the last 12 months. Online medical records are accessed with the help of patient portal secure log-ins -42, and Perceived PCC was assessed with 7 items. Participants asking about communication with all health professionals were asked to assess the frequency with which their providers engaged in the following behaviors in the past 12 months: Give you the chance to ask all the health-related questions you had? Give the attention you need to your feelings and emotions? Involve you in decisions about your health care as much as you wanted? Make sure you understood the things you needed to do to take care of your health? Explain things in a way you could understand? Spend enough time with you? Help you deal with feelings of uncertainty about your health or health care? All items were measured on a 4-point Likert scale ranging from always (1) to never (4).To create the PCC score, items were reverse coded so that higher numbers reflected higher levels of communication. The mean of all 7 items is transformed to a linear scale ranging from 1 to 100 . The PCCThe mediating variable was health information–seeking self-efficacy. We hypothesized that it mediated the relationship between frequency of portal use and perceived PCC. Self-efficacy in seeking health information was measured using 1 item used in previous studies ,44. In cOur analysis is adjusted for gender , age , race/ethnicity , income level , education level , and health insurance status . Previous research has shown that these variables have an impact on access and use of patient portals ,46,47. IAll analyses used Taylor series variance estimation with HINTS sampling weights to produce nationally representative estimates as suggested in HINTS methodology guides ,39. CharThe analytic sample with complete data responses included 1222 respondents, 661 from HINTS 5 cycle 3 and 561 from HINTS 5 cycle 4. As shown in P=.06) with PCC. Patients who accessed their patient portal only 1 or 2 times were equally as likely to have moderate PCC versus low PCC  than those who did not access it. Those who accessed the patient portal 3 to 9 times had more than twice the odds of moderate versus low PCC  than those who never accessed it. Those who accessed the patient portal 10 or more times were almost 3 times as likely to have moderate PCC versus low PCC  than those who did not access it. With respect to comparisons between respondents with high PCC versus low PCC, those who accessed the patient portal 1 or 2 times were 14% more likely than those who did not access it to have high versus low PCC . Those who accessed it 3 to 9 times had a 67% increase in the odds of high PCC versus low PCC . Last, those who accessed their record 10 or more times were almost 4 times more likely to have high PCC versus low .Results of the multinomial model assessing the association between frequency of portal use and perceived PCC are presented in the middle column of P=.06). Of note, those who accessed their portal 10 or more times (compared to those who did not access it) were more likely to have high PCC versus low PCC . We also found that the frequency of patient portal use was significantly associated with health information–seeking self-efficacy . Health information–seeking self-efficacy was also associated with PCC. Individuals with high self-efficacy were more likely to report high PCC compared to those with low self-efficacy . The association between portal use of 10 or more times (compared to none) was also attenuated but remained statistically significant, with those reporting high use more likely to report high PCC .The 4-step Baron and Kenny method was first used to investigate the role of health information–seeking self-efficacy as a mediator of the association between frequency of patient portal use and PCC . In multThese findings led to a more formal mediation analysis using the Karlson-Holm-Breen method, presented in This study examined the association between the frequency of patient portal use and perceived PCC in patients diagnosed with cancer. We also investigated health information–seeking self-efficacy as a mediator of this association. Our findings indicated that the frequent levels of patient portal use (≥10 times in the past year) may be correlated with high levels of PCC. We also found that this association was partially mediated by health information–seeking self-efficacy.In cancer care delivery, patient portal use has been increasing . PatientThe provider’s role is critical in establishing PCC, and patient portals are intended to enhance, not replace, patient-provider face-to-face interactions ,56. PrioAlthough there was a strong association between high use of patient portals and PCC in this study, only a small proportion of the included sample were frequent users of patient portals, and more than half of the sample reported no patient portal use. The greatest proportion of those with no portal use were females, participants in the 75 years and older age group, non-Hispanic Black participants, in households with <$35,000 per year in income, and participants who reported to have had less than a high school education (76.5%). Our findings are consistent with prior research on these sociodemographic differences except for gender, where males were reported to be less likely to use patient portals in previous studies ,61. A fuOur analysis further confirms that a significant digital divide persists in actively getting patients to engage with patient portals, as previously reported ,63. PatiThis analysis also demonstrated that health information–seeking self-efficacy partially mediates the association between patient portal use and PCC. Hence, our findings suggest that enhancing self-efficacy in portal use is an important intervention target. It is increasingly emphasized to incorporate user perspectives in health information technology designs . NumerouIt is crucial to consider that enhancing portal use is not only dependent on increasing competencies such as knowledge and skills but also on aligning with patient needs and live experiences. To meet these needs, user input is required in designing patient portals in specific populations dealing with distinct health care needs . For exaLimitations of our study include the use of self-reported data and the cross-sectional design. There is the possibility of recall bias in the frequency and use of patient portals, and the design precludes causal inference. Specifically, we cannot infer whether increased portal use causes increased PCC and vice versa . We elecConcerning the generalizability of this study, HINTS weights only reflect certain demographic characteristics of the US population and do not take into consideration other factors that may influence individuals electing to participate in the study, which hypothetically could include factors such as greater motivation related to health and health-related constructs. The study sample includes a mix of patients with recent (<15% diagnosed less than a year ago) and distant (approximately 50% diagnosed ≥11 years ago) cancer diagnoses. Hence our results are not generalizable to more recently diagnosed patients. We also combined non-Hispanic Asians/others as our numbers in each category were too low to keep separate. Hence we could not point toward any differences based on race or ethnicity. Likewise, we were unable to compare our sample to a similar national sample of cancer survivors with respect to sociodemographic profile as these data do not exist. Last, we used the term patient portals in this paper as it is a more widely known term and most online records can be accessed via secure patient portal sign-ins. However, online medical records and patient portals could refer to different types of systems, and we cannot ascertain to which the participants were referring.In summary, PCC is a vital part of quality cancer care. Findings from this national survey suggest that increased frequency of patient portal use is associated with higher PCC and that an individual’s health information–seeking self-efficacy partially mediates this association. While the results of this study need to be replicated in future longitudinal studies, these findings suggest that interventions to encourage the adoption and use of patient portals could incorporate strategies to improve health information self-efficacy and lead to improved PCC.


Sensory input converging on the spinal cord contributes to the control of movement. Although sensory pathways reorganize following spinal cord injury (SCI), the extent to which sensory input from Ia afferents is regulated during voluntary contraction after the injury remains largely unknown. To address this question, the soleus H-reflex and conditioning of the H-reflex by stimulating homonymous  and heteronymous (d), [monosynaptic Ia facilitation of the soleus H-reflex evoked by femoral nerve stimulation (FN facilitation)] nerves were tested at rest, and during tonic voluntary contraction in humans with and without chronic incomplete SCI. The soleus H-reflex size increased in both groups during voluntary contraction compared with rest, but to a lesser extent in SCI participants. Compared with rest, the D1 inhibition decreased during voluntary contraction in controls but it was still present in SCI participants. Further, the FN facilitation increased in controls but remained unchanged in SCI participants during voluntary contraction compared with rest. Changes in the D1 inhibition and FN facilitation were correlated with changes in the H-reflex during voluntary contraction, suggesting an association between outcomes. These findings provide the first demonstration that the regulation of Ia afferent input from homonymous and heteronymous nerves is altered during voluntary contraction in humans with SCI, resulting in lesser facilitatory effect on motor neurons. Anatomical and physiological studies have shown that sensory input to motor neurons is altered following spinal cord injury (SCI) . For exaA receptors, which contributes to reduce branch point failure in Ia afferent fibers (Primary afferent fibers (Ia) are rapidly conducting sensory fibers that originate from muscle spindle primary endings, which constantly monitors the rate at which a muscle stretch changes . Ia affet fibers . GABAergt fibers . Furthert fibers . Thus, Gt fibers .A critical question is how Ia afferent transmission is regulated during voluntary contraction after SCI. In uninjured humans, evidence showed that Ia afferent transmission decreases at the onset of a voluntary contraction  and duriTo test this hypothesis, the soleus H-reflex and conditioning of the H-reflex by stimulating homonymous and heteronymous nerves by measuring the depression of the soleus H-reflex evoked by common peroneal nerve (CPN) stimulation [D1 inhibition] and the monosynaptic Ia facilitation of the soleus H-reflex evoked by femoral nerve (FN) stimulation [FN facilitation] were tested at rest and during 30% of tonic maximal voluntary contraction (MVC). The position of the ankle joint was maintained constant across conditions to standardize the possible effect of other inputs into the testing procedures .1,38=13.7, p=0.001, η2p=0.3; 1,38=5.1, p=0.03, η2p=0.2) but not the maximal H-reflex (H-max)  in the soleus muscle. We also found a group effect on H-max/M-max ratio , CONTRACTION  and in their interaction  on the H-reflex size. Post hoc analysis showed that the H-reflex was larger during 30% of MVC  compared to rest in both groups. Additionally, the H-reflex size increased to a lesser extent in SCI compared to control subjects at 30% of MVC , CONTRACTION , and in their interaction  on the H-reflex size. Post hoc analysis showed that the H-reflex was larger during 30% of MVC  compared to rest in both groups. Note that the increases in H-reflex size were lesser in SCI compared with control at 30% of MVC (p=0.02).We conducted an additional control experiment where we matched absolute EMG level across groups during 30% of MVC by asking control participants to perform the similar EMG activity as SCI participants. Repeated measures ANOVA showed an effect of GROUP , CONTRACTION , and in their interaction  on the D1 inhibition. Post hoc analysis showed that the D1 inhibition decreased during 30% of MVC in controls  but not in SCI . Here, we found that the D1 inhibitionadj decreased during 30% of MVC compared with rest in SCI participants , CONTRACTION , and in their interaction  on the FN facilitation. Post hoc analysis showed that the FN facilitation increased during 30% of MVC in controls  but not in SCI . We found that the FN facilitationadj increased during 30% of MVC and rest in SCI participants  participated in the study. All subjects were provided written consent to experimental procedures, which were approved by the local ethics committee at Northwestern University (IRB protocol #STU00209996). Participants with SCI had a chronic injury (≥1 year) and were classified using the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) as having a C2-T10 SCI. Five out of the 20 subjects were categorized by the American Spinal Cord Injury Impairment Scale (AIS) as AIS C and the remaining 15 subjects were classified as AIS D. Nine individuals with SCI were currently taking anti-spastic medication  calculated from the F-statistic  for the significant GROUP ×CONTRACTION interaction that identified a lesser increase in H-reflex size in SCI compared to controls during voluntary contraction. With a power of 0.95 and α of 0.05, 36 participants were considered sufficient in a repeated measures ANOVA (G*Power 3.1.9.7). Additionally, sample size for the D1 inhibition and FN facilitation was estimated using an effect size  calculated from the significant GROUP ×CONTRACTION interaction; with a power of 0.95 and α of 0.05, 14 and 18 participants (respectively) were considered sufficient in a repeated measures ANOVA (G*Power 3.1.9.7).Twenty individuals with SCI  and 20 control subjects  placed over the belly of the muscle below the gastrocnemius muscles . Note that the FN facilitation have been found to be different between participants with and without spasticity . TherefoDuring all testing procedures, subjects were seated comfortably in a custom armchair with both legs placed on a custom platform with the hip (~120°) and knee (~160°) flexed and the ankle restrained by straps in ~110° of plantarflexion. At the start of the experiment, participants performed three brief MVCs of 3–5 s into plantarflexion, separated by 30 s. The maximal mean EMG activity in the soleus muscle was measure over a period of 1 s on the rectified response generated during each MVC and the highest value of the three trials was used. The soleus H-reflex  at rest The soleus H-reflex was elicited by using electrical stimulation with the cathode positioned over the posterior tibial nerve in the popliteal fossa and the anode positioned above the patella using a constant-current stimulator . The reflex response was measured as the peak-to-peak amplitude of the non-rectified reflex response recorded from the soleus muscle. The stimulus intensity was increased in steps of 0.05 mA starting below H-reflex threshold and increasing up to supramaximal intensity to measure the M-max. To ensure that M-max values were reached, the stimulus intensity was increased until a plateau was observed in the M-max. The size of the H-reflex was kept at 50% of the H-max at rest and the same intensity was used during voluntary contraction. The magnitude of H-reflex during 30% of MVC was expressed as a % of the H-reflex at rest. Twenty reflexes were tested at rest and 20 reflexes were tested during 30% of MVC in a randomized order.adj). This subgroup of subjects were participants that were able to return to complete this testing. Fifteen control H-reflexes and 15 conditioned H-reflexes were tested at rest and during 30% of MVC in a randomized order.The soleus H-reflex (control H-reflex) was conditioned by stimulation of the CPN (conditioned H-reflex). The CPN stimulation elicits a depression of the soleus H-reflex at conditioning-test interval of 8–20 ms  while the control H-reflex was kept at 50% of the H-max. The subgroup of subjects tested with adjusted stimulus intensities was the group of individuals that was able to return for this additional testing. Fifteen control H-reflexes and 15 conditioned H-reflexes were tested at rest and during 30% of MVC in a randomized order.The soleus H-reflex was tested with (conditioned H-reflex) and without (control H-reflex) stimulation of the FN. The FN elicits a facilitation of the soleus H-reflex (FN facilitation), which is thought to reflect the size of the monosynaptic EPSP in soleus motor neurons evoked by activation of Ia afferents from the quadriceps muscle, and changes in its size are considered to indicate changes in Ia afferent transmission . Thus, badj and the FN facilitationadj. One-way ANOVA was used to examine the effect of GROUP on MVCs, H-max, M-max, and the H-max/M-max ratio. Holm-Sidak post hoc analysis was used to test for mean pair wise comparisons. Spearman’s correlation coefficient was used to assess association between the size of H-reflex, D1 inhibition, FN facilitation during 30% of MVC compared with rest. Statistical analysis was conducted using SigmaPlot  and the significance was set at p<0.05. Group data is presented as means ± SDs. Effect sizes were reported as η2p.Normal distribution was tested by the Shapiro-Wilk’s test and homogeneity of variances by the Levene’s test. Sphericity was tested using Mauchly’s test. When sphericity was not met, the Greenhouse-Geisser correction was used. Repeated measures ANOVA was used to examine the effect of GROUP (controls and SCI) and CONTRACTION (rest and 30% of MVC) on background EMG activity and the H-reflex size. Repeated measures ANOVA was also used to examine the effect of GROUP and CONTRACTION on the D1 inhibition and FN facilitation. Similar analysis was also used to examine the effect of CONTRACTION on the D1 inhibition This paper will be of interest to basic and clinical neurophysiologists who are focused on understanding neural mechanisms that influence recovery following spinal cord injury (SCI). The work compares the afferent regulation of motor output to soleus muscle in controls and individuals with SCI. The results indicate differences between groups such that there is less facilitation in the SCI group during muscle contraction. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Christopher Cardozo as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and Mone Zaidi as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Aimee J Nelson (Reviewer #3).Thank you for submitting your article "Altered Regulation of Ia Afferent Input during Voluntary Activity after Human Spinal Cord Injury" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. Please address the concerns of Reviewer 2 regarding the presentation of data in Figure 2.2. Please carefully consider the remaining comments of the reviewers and revise the manuscript to address them or provide an explanation for why such revisions are not possible.Reviewer #1 (Recommendations for the authors):The manuscript would be significantly improved by addressing the following comments.1. It sounds like the final N for controls was 14 subjects but this was not explicitly discussed. Were data for all 20 subjects used in some analyses but only 14 in others? How do the authors think the dropout of those subjects impacted statistical power? This is a minor point but one that should be considered since they provide an a priori sample size estimate of 36 subjects.eLife is not a specialty publication, I recommend the authors expand the introduction to give more information about where Ia fibers arise and what their function is. A bit more introductory background on what the neurophysiologic meaning is of what I understand to be the interaction between Ia signals arising in the femoral nerve with motor neurons for the soleus would also be helpful.2. Given that 3. It would help the reader if the discussion were more clearly delineated when results of literature reporting neurophysiology for the able-bodied are discussed versus findings for persons with SCI.4. Is there a teleological or gait-based way of thinking about the data that provides a model for understanding how gains or loss of effects of Ia inputs on H-reflexes are good or bad after SCI when muscles are already under load? Does this reasoning fit the new data presented?5. Some interpretations of the correlations presented in Figure 6 should be added.6. The key novel aspect as presented by the authors is performing H-reflex texts when a muscle is under load. A brief introduction of what is known of the neurophysiology driving the decision to perform these studies would help the reader understand why they are important. For example, how are inputs from tendons or joints participating in the physiology observed, realizing these were not explicitly measured or manipulated. The discussion could come back to this point more clearly.7. I found that the writing is at times difficult to understand. Careful revision to improve clarity would help the reader.Reviewer #2 (Recommendations for the authors):What is the justification for recording from the leg with the higher MAS?What does the dashed line represent in figure 6C? It doesn't align with the distribution of values.The arbitrary use of ordinate range values makes interpretation more challenging. There is a pattern throughout the manuscript to manipulate the range to make differences seem larger than they are . Additionally, the use of divergent ordinate ranges within the same figure makes it difficult for the reader to compare effects across groups . In the case of figure 3D, it undercuts your argument that H-reflex is attenuated in chronic SCI.Why are the H-wave amplitudes for 30% MVC and the rest similar in Figures4 and 5 (panels A and B)? In Figure 3A, there is a clear difference in amplitude.The yellow color for conditioned H-reflex in figures 4 and 5 is too difficult to see and is confusing with a similar color as the text "spinal cord injury".In the Results section, only panel A of Figures 2 and 3 are referenced in the EMG and Soleus H-reflex paragraphsFigure 2A, Change "Baseline" to "Rest" for consistency.Figure 3A, Why is the gap larger between the Rest and 30% in the SCI group than in the control group?In Figures4D and 5D, can you use color to indicate which individuals in the SCI group are also in the SCI adjusted group? Essential revisions:Reviewer #1 (Recommendations for the authors):The manuscript would be significantly improved by addressing the following comments.1. It sounds like the final N for controls was 14 subjects but this was not explicitly discussed. Were data for all 20 subjects used in some analyses but only 14 in others? How do the authors think the dropout of those subjects impacted statistical power? This is a minor point but one that should be considered since they provide an a priori sample size estimate of 36 subjects.The reviewer raises a good point. We want to clarify that for the main experiment (H-reflex) we tested 20 control and 20 SCI participants. For measurements of D1 inhibition and FN facilitation, we tested 14 control and 20 SCI participants. Six control participants were not able to return for additional testing. Therefore, we recalculated the power for the D1 inhibition and FN facilitation. Note that our results indicate that we had sufficient power with the number of participants that were tested. The following information was added to the manuscript:(page 15),2p=0.7 and η2p=0.6, respectively) calculated from the significant GROUP × CONTRACTION interaction, with a power of 0.95 and α of 0.05, 14 and 18 participants (respectively) were considered sufficient in a repeated measures ANOVA (G*Power 3.1.9.7).”“Additionally, sample size for the D1 inhibition and FN facilitation was estimated using an effect size ,“Primary afferent fibers (Ia) are rapidly conducting sensory fibers that originate from muscle spindle primary endings, which constantly monitors the rate at which a muscle stretch changes . Ia afferent fibers bifurcate on entering the spinal cord and run several segments in both rostral and caudal directions in the dorsal columns and make contact with motor neurons .”3. It would help the reader if the discussion were more clearly delineated when results of literature reporting neurophysiology for the able-bodied are discussed versus findings for persons with SCI.As suggested we now clearly defined in the discussion when results were related to control versus individuals with SCI.4. Is there a teleological or gait-based way of thinking about the data that provides a model for understanding how gains or loss of effects of Ia inputs on H-reflexes are good or bad after SCI when muscles are already under load? Does this reasoning fit the new data presented?The reviewer raises an interesting point. We propose that a lesser facilitatory effect of Ia afferent input on motor neurons at the spinal level in SCI participants might help to control small levels of ongoing voluntary activity as the one performed in our study. Because after SCI prolonged excitatory post-synaptic potentials are observed in motor neurons in response to even brief sensory stimulation , a lesser facilitatory effect of Ia afferent input on motor neurons at the spinal level in SCI participants might be beneficial to control small levels of ongoing voluntary activity as the one performed in our study. It is unclear if this adaptation contributes to the lack of task-dependent modulation of the H-reflex observed in humans with SCI during the gait cycle . H-reflex modulation differs during sitting, standing, and walking in humans with and without SCI  requiring that future studies assess the impact of our results on gait-based and other conditions. This information was added to the manuscript.5. Some interpretations of the correlations presented in Figure 6 should be added.We added the following information to discussion:(page 13),“Another important question is if changes in H-reflex size were related to changes in the D1 inhibition and FN facilitation. We did not find a correlation between these variables in each group. However, when we looked at the groups together, we found a strong positive correlation showing that increases in H-reflex size were associated with lesser D1 inhibition and larger FN facilitation, suggesting a relation between these variable in the overall population.”6. The key novel aspect as presented by the authors is performing H-reflex texts when a muscle is under load. A brief introduction of what is known of the neurophysiology driving the decision to perform these studies would help the reader understand why they are important. For example, how are inputs from tendons or joints participating in the physiology observed, realizing these were not explicitly measured or manipulated. The discussion could come back to this point more clearly.As suggested the following information was added to the introduction:(page 5),“The H-reflex, D1 inhibition, and FN facilitation were studied because these electrophysiological outcomes are modulated in a task-dependent manner during different motor behaviors providing a unique tool to assess the effect of sensory input onto motor neurons during voluntary activity . The position of the ankle joint was maintained constant across conditions to standardize the possible effect of other inputs into the testing procedures.”7. I found that the writing is at times difficult to understand. Careful revision to improve clarity would help the reader.The manuscript has been proofread and carefully revised for clarity.Reviewer #2 (Recommendations for the authors):What is the justification for recording from the leg with the higher MAS?Evidence has shown differences in the regulation of Ia afferent input from heteronymous nerves in people with SCI with and without spasticity . Therefore, for standardization purposes, we recorded data from the more spastic side in each of the SCI participants. This information was added to the manuscript.What does the dashed line represent in figure 6C? It doesn't align with the distribution of values.The dashed line in Figure 6 represent the regression line of all the data points included in the plot. This information was added to the figure legend.The arbitrary use of ordinate range values makes interpretation more challenging. There is a pattern throughout the manuscript to manipulate the range to make differences seem larger than they are . Additionally, the use of divergent ordinate ranges within the same figure makes it difficult for the reader to compare effects across groups . In the case of figure 3D, it undercuts your argument that H-reflex is attenuated in chronic SCI.As suggested, we use the same range of values in the ordinate for all figures.Why are the H-wave amplitudes for 30% MVC and the rest similar in Figures4 and 5 (panels A and B)? In Figure 3A, there is a clear difference in amplitude.In Figures 4 and 5 (panels A and B), we showed the adjusted H-reflex. This is why the same size of the H-reflex is observed at rest and during 30% of MVC. This was clarified in the figure legend.The yellow color for conditioned H-reflex in figures 4 and 5 is too difficult to see and is confusing with a similar color as the text "spinal cord injury".We replaced the yellow color by gray in Figures 4 and 5.In the Results section, only panel A of Figures 2 and 3 are referenced in the EMG and Soleus H-reflex paragraphsWe now referenced all panels in Figures 2 and 3.Figure 2A, Change "Baseline" to "Rest" for consistency.This was changed as suggested.Figure 3A, Why is the gap larger between the Rest and 30% in the SCI group than in the control group?We modified the figure and now the gap is consistent across groups.In Figures4D and 5D, can you use color to indicate which individuals in the SCI group are also in the SCI adjusted group?Now, individual data is color coded in Figures 4 and 5.


After an outbreak of the SARS-CoV-2 Beta variant in the district of Schwaz/Austria, vaccination with Comirnaty vaccine  had been offered to all adult inhabitants (≥ 16 years) in March 2021. This made Schwaz one of the most vaccinated regions in Europe at that time (70% of the adult population took up the offer). In contrast, all other Austrian districts remained with low vaccine coverage.We studied whether this rapid mass vaccination campaign provided indirect protection to unvaccinated individuals such as children (< 16 years) living in the same district.To study the effect of the campaign we used two complementary approaches. We compared infection rates among the population of children (< 16 years) in Schwaz with (i) the child population from similar districts (using the synthetic control method), and (ii) with the child population from municipalities along the border of Schwaz not included in the campaign (using an event study approach).Before the campaign, we observed very similar infection spread across the cohort of children in Schwaz and the control regions. After the campaign, we found a significant reduction of new cases among children of −64.5% (95%-CI: −82.0 to −30.2%) relative to adjacent border municipalities (using the event study model). Employing the synthetic control method, we observed a significant reduction of −42.8% in the same cohort.Our results constitute novel evidence of an indirect protection effect from a group of vaccinated individuals to an unvaccinated group. What did you want to address in this study?Current COVID-19 vaccines have a high efficacy in preventing symptomatic infections, especially for pre-Omicron variants, and severe disease for all variants. We investigated whether a mass vaccination campaign conducted in the adult population in the district Schwaz/Austria in March 2021 had an indirect protective effect on unvaccinated children. What have we learnt from this study?SARS-CoV-2 infections were reduced in the unvaccinated children in the district Schwaz/Austria following the mass vaccination compared with border municipalities in the neighbouring district or with a control region. What are the implications of your findings for public health?Our results demonstrate that high COVID-19 vaccine coverage in the population can provide indirect protection for groups where a vaccine is not yet approved or vaccine-induced immunity may be poor (e.g. old age or underlying conditions).In 2021, many countries still did not have vaccines against coronavirus disease (COVID-19) available for young age cohorts. In addition, some parents were and still are hesitant regarding potential risks and benefits of inoculating their children, meaning that vaccination coverage for this population remains modest . This raTo analyse this indirect protection effect, we studied a unique rapid mass vaccination campaign. In particular, following an outbreak of the Beta variant  lineage designation B.1.351) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the district of Schwaz (Austria), the government of Austria supplied 100,000 extra doses of the Comirnaty vaccine  to rapidly mass-vaccinate the entire adult population (≥ 16 years) of Schwaz ,9. AfterOur study design compared unvaccinated age cohorts (younger than 16 years) in the district of Schwaz with the same age cohort in the control regions before and after the mass vaccination campaign. The study population comprised of two age cohorts: The first age cohort were children under the age of 16 years, who remained unvaccinated. The second cohort were individuals aged 16–50 years, who are likely to represent the population that interacts the most with the cohort of children under 16 years of age . As outcsynthetic control method, which compared the district of Schwaz with a control group of highly similar districts. Secondly, we compared infection dynamics in municipalities along the district border of Schwaz using an event study (difference-in-difference (DID)) design. This research design has already been used in a related study analysing the effect of the mass vaccination campaign on infection rates of the adult population [Our research design rested on two alternative approaches to estimate the effect of the campaign: Firstly, we apply the pulation . For morSupplementary Table S1 provides descriptive statistics of Schwaz and the chosen control regions. Infection rates prior to the mass vaccination campaign were comparable in Schwaz and the control group as we previously published [Supplementary Table S1; the results of the placebo in-space test are shown in Supplementary Figure S1).The synthetic control method is widely applied in causal analysis  and alsoublished . We thenublished , provideSupplementary Table S2 provides municipality-level descriptive statistics of Schwaz and the bordering municipalities included in the study, showing great similarity between the two groups regarding geographical and sociodemographic characteristics. The dependent variable was the number of new infections per 100,000 inhabitants for the age group younger than 16 years. We executed a two-way fixed effects model with an indicator variable for municipalities from the district of Schwaz. We calculated for each week k the DID in the 7-day moving average of new infections per 100,000 inhabitants for children younger than 16 years in the bordering municipalities and Schwaz. The regression equation is given byAs a second approach, we exploited the fine geographical variation the mass vaccination campaign created and compared Schwaz with bordering municipalities that were not included in the campaign. For this analysis we used an event-study model based on a DID design to estimate the effect of the campaign on the incidence among the people younger than 16 years in Schwaz relative to the same age group in the border municipalities [it,wy denotes the 7-day moving average of new infections per 100,000 for children below 16 years from municipality t, which is nested in week w. iδ and wδ represent municipality- and week-fixed effects, respectively. it,wD is a binary variable taking a value of 1 for municipalities in Schwaz and 0 for border municipalities just outside of Schwaz. k in the sum operators stands for the weeks before (first sum) and after (second sum) the vaccination campaign. kβ depicts the difference in outcomes (e.g. incidence among children younger than 16 years) between Schwaz and the border municipalities in any given week relative to the week when the first dose of the vaccination campaign was given  [Where week 10) .Supplementary Figure S2 for the vaccination coverage in children).To illustrate the stark difference in vaccine coverage after the mass vaccination campaign we display the shares of individuals aged 16–50 years who received the first and second dose. Supplementary Table S1 for further details). The age groups from Schwaz and the control region had very similar levels of SARS-CoV-2 infections before the first dose of the mass vaccination campaign. While shortly after the first dose, the control region had somewhat lower daily infections, infection levels diverged with the second dose of the campaign: At the end of the observational period (28 May), we observed a difference of 1,007.2 : 416.1–1,627.1) cumulative daily infections per 100,000 inhabitants for the adults aged 16–50 years . This figure translates into a difference of 57.4%. For children below 16 years, we found a difference of 675.3 avoided infections per 100,000 , a relative difference of 42.8%.In a second approach, we compared Schwaz with border municipalities that were not included in the campaign. In this study, we scrutinised the impact of community-level protection on incidence rates of unvaccinated children. We exploited a unique rapid mass vaccination campaign to estimate this indirect protection effect on unvaccinated children under the age of 16 years. As controls we used the same age cohorts from comparable but untreated districts and border municipalities (i.e. without community-level protection) which followed very similar trends in infection spread before the campaign.We first documented considerable vaccine uptake that raised coverage from 5% to 60% for the population between 16 and 50 years old through the campaign, which is broadly in line with earlier evidence on the vaccination campaign in Schwaz . More imSo far, evidence of this indirect protection effect from mass vaccination against COVID-19 is scarce. To the best of our knowledge, only one study estimated this indirect effect of population-wide mass vaccination coverage , focusinOur study has potential limitations. Firstly, our study was not a randomised clinical trial but an observational study, which may be influenced by confounders such as lockdown policies. Although most non-pharmaceutical interventions (such as school closures or mask mandates) were identical for Schwaz and the control regions, there was an additional requirement to take SARS-CoV-2 test between 11 March and 8 April when crossing the district border . This teSecondly, while our DID design controlled for time-varying general trends over time in infection spread (such as a third wave), we could not account for changing individual behaviour such as vaccinated individuals being less mindful of physical distancing measures. However, a previous analysis of mobility data did not show large differences between Schwaz and the control districts . In factLastly, our study had been conducted in the first half of 2021 when the Alpha variant was the predominant variant in Austria and therefore results cannot be directly transferred to later SARS-CoV-2 variants of concern and the Omicron variant. We assume that the level of indirect protection conferred to unvaccinated children by a high vaccine coverage in adults might be similar for the Delta variant as Comirnaty vaccination provides a similar level of protection against acquiring infection with the Alpha and the Delta variant . HoweverOur study provides evidence of an indirect protection effect from a rapid COVID-19 mass vaccination campaign on an unvaccinated group. Given that the vaccination coverage in Schwaz was very similar to the vaccination coverage in many other countries in 2021 (around 70%), our results may also be relevant for other regions and countries.


The timing of sleep, physical activity, and dietary intake show variation over the week, with different timings in the weekend compared to the weekdays, which may potentially lead to impaired glucose and lipid regulation on Mondays compared to other weekdays.The aim of the study was to investigate differences in glucose metabolism and fasting triglyceride concentrations on Mondays compared to the rest of the week.This cross-sectional study is based on data from the Maastricht Study, including 6067 participants without known diabetes and 1568 previously diagnosed with type 2 diabetes.Confounder-adjusted linear regression analysis was applied to study the associations of day of the week of examination with glucose and insulin responses to an oral glucose tolerance test and fasting triglyceride concentrations.In fully confounder-adjusted models, mean (95% CI) concentrations of fasting glucose, insulin, and triglycerides were slightly higher on Mondays compared with the other weekdays . Interaction analyses revealed that the association of weekday with insulin was only pronounced in men [18% (3-35)], but not in women [1% (−8-10)], whereas the associations with glucose and triglycerides were only apparent for individuals with known type 2 diabetes  compared to the background population .Being examined on a Monday was associated with higher fasting insulin concentrations among men but not women. All participants gave written informed consent.All measurements were performed by trained research staff at the Maastricht Study research center following standardized protocols. The fasting samples and OGTTs were performed on Mondays to Saturdays, with samples from 1443 participants on Mondays, 1568 on Tuesdays, 1601 on Wednesdays, 1445 on Thursdays, 1330 on Fridays, and 248 on Saturdays. Blood sampling was scheduled according to participants’ preference and availability. We dichotomized the weekday variable into Mondays vs the other days of the week combined to ease interpretation and to utilize the full data set. Also, we believe that the effect of weekend behavior is best seen on Mondays as it follows a full weekend.pm the evening prior to blood collection. Individuals with a fasting glucose concentration >11.0 mmol/L measured with a finger prick and individuals on insulin treatment did not undergo an OGTT for safety reasons (https://scicrunch.org/resolver/AB_2819057).A standard 75-g OGTT was performed after an overnight fast, with blood samples at baseline and 15, 30, 45, 60, 90, and 120 minutes after glucose ingestion; not all individuals have full data from the OGTT. Participants were instructed not to eat and drink, except water, after 11  reasons . Fasting0-30min) was calculated as the ratio tAUC0-30min/(fasting concentration × 30 min) and relative total response (rAUC0-120min) as tAUC0-120min/(fasting concentration × 120 min). The rAUC reflects the change in the concentrations relative to the fasting concentration. An advantage of calculating the rAUC instead of the incremental AUC is that rAUC is always positive and can therefore be logarithmically transformed if needed.Early and total responses to the OGTT for plasma glucose and insulin concentrations were calculated as total areas under curves (tAUCs) using the trapezoid rule. Relative early response ] and with the Kruskal-Wallis test for skewed variables [shown with medians ]. Linear regression analysis was used to study the associations of day of the week with concentrations at 0  and 120 minutes (glucose and insulin) as well as with early (rAUC0-30min) and total relative responses (rAUC0-120min) of glucose and insulin. Model 1 was a crude model only including day of the week; Model 2 was adjusted for age, sex, and type 2 diabetes (known type 2 diabetes vs no type 2 diabetes/screen-detected type 2 diabetes); Model 3 was additionally adjusted for body mass index, smoking status, education level, and the Dutch Healthy Eating Index . Differences in characteristics between weekday of examination were compared with a ng Index ; and ModP-value for interaction < 0.05), stratified analyses are shown.In Model 4, we tested for interactions between weekday and (1) known type 2 diabetes, (2) sex, and (3) retirement status. The test for interaction with known type 2 diabetes was performed because the Maastricht Study has been enriched with individuals with type 2 diabetes. The group without known type 2 diabetes consists of individuals without type 2 diabetes and those with screen-detected type 2 diabetes. An interaction analysis with sex was performed because it has become increasingly apparent that sex differences exist in markers of glucose regulation and their associations with diverse outcomes , 19. TheThe outcome variables were, if needed, logarithmically transformed before analysis to obtain normally distributed model residuals. Accordingly, the variables fasting glucose, 120-minute glucose, all insulin, and triglyceride outcomes were transformed with the natural logarithm.We performed a number of sensitivity analyses. First, the 4 models were applied to a data set where Saturdays were excluded, as measurements on Saturdays may partly be influenced by the start of the weekend (Friday afternoon/evening behavior). Second, type 2 diabetes status was exchanged with the participants’ hemoglobin A1c (HbA1c) levels. Third, we performed all analyses with the participants that had information on all variables included in Model 4 for each of the outcomes, as there is a relative large amount of individuals with 1 or more missing variables needed in Model 4. The results of the sensitivity analyses are shown in the supplementary material Table S1-S6 .The statistical analyses were performed in R version 3.6.2 . A 2-sidThe characteristics of the study population stratified by day of the week are shown in The results of the linear regression analyses with estimates of the mean difference (95% CI) between Mondays and the other days of the week for plasma glucose, insulin, and triglyceride concentrations for the full population are shown in Fasting plasma insulin and triglyceride concentrations were respectively 9% to 10% and 3% to 5% higher on Mondays compared to the other days of the week in all models in the full population, whereas post-OGTT glucose and insulin concentrations were not statistically significant different between Mondays and the other days of the week in any of the models in the overall population .We found statistically significant interactions between sex and day of the week for 120-minute glucose and fasting insulin . When st0−120, and triglyceride were observed, and for all outcomes, the associations were more driven by the group of individuals with known type 2 diabetes . We also performed several sensitivity analyses. First, we found comparable results when individuals examined on a Saturday were excluded, which substantiates the robustness of the findings. In the second sensitivity analyses with exchange of known type 2 diabetes status by HbA1c, the estimates were also comparable to those in the main analyses. The last sensitivity analysis where only individuals who had complete data on all covariates for inclusion in Model 4 were included showed that the triglyceride findings in the stratified analyses for individuals with known type 2 diabetes was influenced by those with more data available. As this study is cross-sectional, we are not able to investigate potential differences in glucose, insulin, and triglyceride concentrations within the same individuals during the week. Also, we have no information on reasons for which day of the week participants had their examination, but we have no reason to believe that the day of examination should be related to any specific factors also related to the outcomes. In the Netherlands, a 4-day work week is not uncommon, and a relatively high proportion of women in particular work part-time . TherefoThis study adds knowledge on how potential lifestyle habits during weekdays and on the weekend might influence glucose and lipid metabolism at health examinations. In longitudinal studies or intervention studies, it may therefore be relevant to consider performing health examinations on the same weekday for the same participant when possible to reduce variance in laboratory analysis of blood samples caused by weekday variation. Results from this study can also be used to quantify whether we should be worried about misclassification of glucose tolerance based on fasting glucose samples and OGTTs on different days of the week. Importantly, no weekday differences were seen in the post-OGTT blood samples, and only small differences were observed in fasting plasma glucose concentrations and mainly among people with known type 2 diabetes. These findings suggest that the risk of misclassifying individuals with normal glucose tolerance, impaired fasting glucose, or impaired glucose tolerance due to a weekday effect on glucose concentrations is limited. In the entire population, we found 5% to 9% higher concentrations of fasting insulin and triglycerides on Mondays compared to the other days of the week. It should be noted, however, that a 17% to 18% difference between Mondays and the other weekdays was observed in fasting insulin concentrations among men, which may be of clinical relevance. The differences in fasting glucose and triglycerides were small, and the clinical relevance of these are unclear. However, the results highlight that there seem to be small changes in metabolism during the week, which could be useful for informing interventions aimed at improving metabolic health.In conclusion, we found 17% to 18% higher fasting insulin concentrations among men on Mondays compared to the rest of the week combined, but with similar fasting glucose concentrations. In a subgroup of people with type 2 diabetes, fasting glucose and triglyceride concentrations were also slightly higher on Mondays compared to the other weekdays; however, these findings were less robust and needs confirmation in other studies.