The authors have declared that no competing interests exist.
The aim of this study is to investigate changes in movement behaviors, sedentary behavior and physical activity, and to identify potential movement behavior trajectory subgroups within the first two months after discharge from the hospital to the home setting in first-time stroke patients.
A total of 140 participants were included. Within three weeks after discharge, participants received an accelerometer, which they wore continuously for five weeks to objectively measure movement behavior outcomes. The movement behavior outcomes of interest were the mean time spent in sedentary behavior (SB), light physical activity (LPA) and moderate to vigorous physical activity (MVPA); the mean time spent in MVPA bouts ≥ 10 minutes; and the weighted median sedentary bout. Generalized estimation equation analyses were performed to investigate overall changes in movement behavior outcomes. Latent class growth analyses were performed to identify patient subgroups of movement behavior outcome trajectories.
In the first week, the participants spent an average, of 9.22 hours (67.03%) per day in SB, 3.87 hours (27.95%) per day in LPA and 0.70 hours (5.02%) per day in MVPA. Within the entire sample, a small but significant decrease in SB and increase in LPA were found in the first weeks in the home setting. For each movement behavior outcome variable, two or three distinctive subgroup trajectories were found. Although subgroup trajectories for each movement behavior outcome were identified, no relevant changes over time were found.
Overall, the majority of stroke survivors are highly sedentary and a substantial part is inactive in the period immediately after discharge from hospital care. Movement behavior outcomes remain fairly stable during this period, although distinctive subgroup trajectories were found for each movement behavior outcome. Future research should investigate whether movement behavior outcomes cluster in patterns.
The majority of stroke survivors are discharged to the home setting immediately after hospital care [
Various movement behavior outcomes have shown associations with health risk and functional decline [
Few longitudinal studies have investigated changes in movement behavior during waking hours in stroke survivors. Two small longitudinal studies focusing on the first three months after discharge from a rehabilitation hospital stroke unit found significant increases in both LPA and MVPA [
The hypothesis is that a decrease in total sedentary time, increased interruption of SB, and increases in LPA and MVPA will occur in the initial period after discharge. These outcomes might be expected because most functional recovery occurs within the first few weeks after stroke [
Stroke recovery is heterogeneous, and average group data, assumes a one-size-fits-all principle, possible changes in movement behavior outcomes in subgroup trajectories maybe overlooked. Therefore, subgroup trajectories of change in movement behavior outcomes need to be investigated, since they are expected. To identify potential subgroup trajectories, data-driven analyses are needed. Latent class growth analysis is a method whereby participants are assumed to belong to a single class but which class is not known [
Therefore, the aim of the current study is 1) to investigate changes in both the distribution (SB, LPA, and MVPA) and accumulation (bouts) of movement behavior during waking hours for the entire sample, and 2) to detect possible subgroup trajectories within each movement behavior outcome within the first two months after discharge from hospital care to the home setting in first-time stroke patients. Once these subgroup trajectories are known, 3) associated patient characteristics will be explored.
Data cannot be shared publicly because data contain potentially identifying or sensitive patient information. Data are available from the UMC Utrecht Ethics Committee
After discharge from the hospital, participants were visited at home within three weeks after discharge. During this visit, walking speed, balance, and levels of activity and participation were obtained. Participants received an accelerometer to objectively measure movement behavior during waking hours. The participants wore the accelerometer for five consecutive weeks before returning the device by mail.
The personal characteristics obtained were the age and sex of the participants and whether they lived alone. Stroke severity was measured with the National Institute of Health Stroke Scale (NIHSS) (range 0–42, with higher scores indicating more severe stroke symptoms). The NIHSS discerns three subgroups: 1) no stroke symptoms (0 points); 2) minor stroke (1–4 points);and 3) moderate to severe stroke (≥ 5 points) [
Movement behavior during waking hours was objectively measured with an Activ8 accelerometer. The Activ8 is a triaxial accelerometer (30x32x10 mm, 20 grams). Participants were instructed to carry the accelerometer in the front pocket of their pants on the unaffected leg the whole day during waking hours. Only when taking a shower or swimming were participants allowed to remove the device. Clear wearing instructions were given, and participants were asked to record in an activity log the time when they put on the Activ8 in the morning and the time when they removed it. The device can detect SB (combination of reclining and sitting), standing, walking, cycling and running and provide corresponding MET values. The Activ8 measures with a sampling frequency of 12.5 Hz, an epoch of 1 second and a sample interval of 5 seconds. Every 5 minutes, a summary was stored of the different postures and their respective MET values. The device is able to store data for sixty days, and its battery life is at least 30 days [
Individual days were screened, and nonwear time was removed from the data files using starting and stopping times. Using SPSS, the most important and recommended movement behavior outcomes were calculated [
SPSS version 25.0 [
To investigate the average group movement behavior change within the first weeks after discharge to the home setting, generalized estimation equation were employed [
If more than two subgroup trajectories were found based on the latent class growth analysis, trajectories were merged into two clinically relevant subgroups. To determine the characteristics associated with a single subgroup trajectory, logistic regression analyses were performed. Odds ratios were calculated to identify candidate factors using univariate analyses. The related variables were tested for multicollinearity (Pearson’s r < 0.70) and effect modification (variance inflation factor >4) [
In total, 180 people with stroke agreed participation when discharged from the hospital to the home-setting. With twenty persons it was not possible to make an appointment within three weeks, fifteen refused further participation, three were unable to contact, one was to ill and one died before our visit. Resulting in140 participants included in this study. The mean age of the population was 66.4 years, and the majority of the population was male (66.4%). Stroke severity two days after stroke was mild in 63.6% of the population. Other characteristics are presented in
Characteristics (N = 140) | % or mean±SD |
---|---|
Males | 66.4 |
Age (years) | 67.1±10.8 |
Living alone | 18.6 |
Time since stroke (days) | 19.6±5.6 |
Infarction | 91.4 |
Side of stroke | |
Left | 52.9 |
Right | 42.1 |
Both | 2.1 |
Unknown | 2.9 |
Stroke severity day 2 after stroke | |
No symptoms (NIHSS 0) | 15.0 |
Minor stroke symptoms (NIHSS 1 to 4) | 63.6 |
Moderate to severe stroke symptoms (NIHSS ≥5) | 21.4 |
Cognitive functioning |
|
Impaired cognitive function (MOCA ≤25) |
39.1 |
Depression (HADS-D) | 13.7 |
Anxiety (HADS-A) | 16.7 |
Outpatient multidisciplinary rehabilitation, including physiotherapy |
12.8 |
Primary care physiotherapy |
33.6 |
No physiotherapy |
53.6 |
Walking speed (m/s) |
1.03±0.24 |
Limited community walker (≥0.93 m/s) |
31.4 |
LFDI-CAT activity limitations |
58.9±10.8 |
Physical functioning (SIS) |
93.8 [82.3–98.9] |
LLFDI-CAT participation restrictions |
48.9±10.7 |
Balance (BBS) | 55 [52.2–56] |
% = percentage, SD = standard deviation, IQR = interquartile range, NIHSS = National Institutes of Health Stroke Scale, MOCA = Montreal Cognitive Assessment, HADS = Hospital Anxiety and Depression Scale, m/s = meters per second, LLFDI-CAT = Late-Life Function and Disability Instrument Computer Adaptive Testing, SIS = Stroke Impact Scale, BBS = Berg Balance Scale
a Assessments were carried out in the participant’s home setting within three weeks after discharge from inpatient care (hospital or inpatient rehabilitation).
Higher scores indicate better outcomes except for walking speed.
In total, 4.81% of the movement behavior outcomes were missing and imputed. The mean Activ8 wear time in week one was 13.78 hours per day and did not change during the subsequent four assessment weeks. The overall mean sedentary time during the five consecutive weeks was high, with a mean of 9.22 hours in week one, with a significant average decrease of 0.06 hours per week, leading to 8.9 hours in week five. The time spent in LPA was 3.87 in week one, increasing significantly by 0.05 hours per week, leading to 4.08 hours. All other movement behavior outcomes remained stable over time. The mean time spent in MVPA was 0.70 hours in week one, and MVPA accumulated in bouts ≥ 10 minutes in week one was 0.29 hours. A mean weighted median sedentary bout of 21.82 minutes was found in week one. All movement behavior outcomes by week and all generalized estimating equations outcomes can be found in
Movement behavior outcome | Week 1 [95%CI] | Week 2 [95%CI] | Week 3 [95%CI] | Week 4 [95%CI] | Week 5 [95%CI] | B [95%] |
---|---|---|---|---|---|---|
9.22 [8.94–9.46] | 9.18 [8.87–9.49] | 9.25 [8.96–9.54] | 9.00 [8.71–9.30] | 8.99 [8.73–9.26] | -0.06 [-0.11–0.02] |
|
3.87 [3.60–4.13] | 3.98 [3.71–4.25] | 3.93 [3.68–4.18] | 4.06 [3.79–4.33] | 4.08 [3.83–4.33] | 0.05 [0.01–0.09] |
|
0.70 [0.61–0.78] | 0.69 [0.60–0.78] | 0.71 [0.62–0.80] | 0.65 [0.57–0.74] | 0.73 [0.65–0.82] | 0.01 [-0.02–0.02] | |
0.29 [0.22–0.35] | 0.25 [0.20–0.30] | 0.25 [0.20–0.30] | 0.27 [0.22–0.32] | 0.28 [0.23–0.33] | 0.00 [-0.01–0.01] | |
21.82 [19.71–23.93] | 20.71 [18.49–22.92] | 21.85 [19.64–24.06] | 21.53 [19.46–23.60] | 21.16 [19.17–23.16] | -0.05 [-0.44–0.34] | |
13.78 [13.70–13.87] | 13.85 [13.76–13.94] | 13.89 [13.79–13.99] | 13.81 [13.72–13.91] | 13.82 [13.72–13.92] | n.a. |
CI = confidence interval, LPA = light physical activity, MVPA = moderate to vigorous physical activity
*P<0.05.
Different amount of subgroup trajectories were found for movement behavior outcome.(see Table A in
Subgroups (n) | Intercept for subgroup | Linear slope | Quadric slope | BIC | Entropy | BLRT | |
---|---|---|---|---|---|---|---|
Highly sedentary = 90 | 9.94 | 0.25 | -0.06 |
2343.72 | 0.87 | <0.01 | |
Less sedentary = 50 | 7.92 | -0.37 | 0.07 | ||||
Non-movers = 92 | 3.17 | -0.10 | 0.03 | 2192.88 | 0.82 | <0.01 | |
Movers = 48 | 5.02 | 0.33 | -0.06 | ||||
Inactive = 77 | 0.43 | -0.08 |
0.01 |
2192.88 | 0.82 | <0.01 | |
Active = 48 | 1.02 | -0.08 | 0.01 | ||||
Highly active = 15 | 1.43 | 0.21 | -0.04 |
||||
Inactive = 89 | 0.10 | 0.01 | n.a. | -83.89 | 0.93 | <0.01 | |
Active = 42 | 0.40 | 0.01 | n.a. | ||||
Highly active = 9 | 1.05 | -0.01 | n.a. | ||||
Prolongers = 14 | 49.97 | -1.64 | n.a. | 5151.92 | 0.91 | <0.01 | |
Intermediate = 74 | 23.90 | 0.17 | n.a. | ||||
Interrupters = 52 | 11.00 | 0.08 | n.a. |
BIC = Bayesian information criteria, BLRT = bootstrap likelihood ratio test, LPA = light physical activity, MVPA = moderate to vigorous physical activity, min = minutes
*p<0.05.
The stroke survivors allocated to the two subgroup SB trajectories spent a mean of 7.92 and 9.94 hours in SB, respectively. In this manner, 64.3% were classified as ‘highly sedentary’ and 35.7% as ‘less sedentary’. The time spent in LPA varied between 3.17 and 5.02 hours. A total of 65.7% of the participants were classified as ‘nonmovers’, and 34.3% were classified as ‘movers’. Three subgroups were found regarding MVPA and MVPA spent in bouts ≥10 minutes. Only 10.7% were identified as ‘highly active’, while 34.3% were ‘active’, and 55% were ‘inactive’. The results for time spent in MVPA bouts ≥10 minutes was slightly worse. Altogether, 10% of the participants could be classified as ‘prolongers’, 52.8% as ‘intermediate’ and 37.1 as ‘interrupters’, with weighted median sedentary bout lengths of 50 minutes, 24 minutes and 11 minutes, respectively. All outcomes can be found in
Figs
The ‘active’ and ‘highly active’ subgroup trajectories for both MVPA and MVPA spent in bouts ≥10 minutes were merged together since the participants in both subgroups were sufficiently active, since international guidelines recommend at least 150 minutes per week of accumulated moderate to vigorous physical activity (MVPA) [
The results of the univariate analyses per movement behavior subgroup are presented in
HIGHLY SEDENTARY | NON-MOVERS (LPA) | ACTIVE (MVPA) | ACTIVE (MVPA BOUTS> 10 MIN) | PROLONGERS (WMSB) | |
---|---|---|---|---|---|
INDEPENDENT VARIABLES | OR (95%CI) | OR (95%CI) | OR (95%CI) | OR (95%CI) | OR (95%CI) |
MALE | 1.29 (0.61–2.71) | 1.30 (0.63–2.71) | 2.63 (1.25–5.54) |
5.13 (2.09–12.92) |
0.90 (0.28–2.86) |
LOWER AGE (YEARS) | 1.00 (0.97–1.04) | 1.02 (0.98–1.05) | 1.06 (1.02–1.10) |
1.04 (1.01–1.08) |
0.95 (0.9–1.01) |
LIVING TOGETHER | 1.71 (0.73–4.07) | 0.44 (0.19–1.05) |
2.28 (0.95–5.46) |
0.91 (0.37–2.22) | 0.71 (0.15–3.38) |
MORE SEVERE STROKE SYMPTOMS (NIHSS) | 1.12 (1.00-.125) | 0.90 (0.81–1.01) |
1.02 (0.93–1.12) | 1.02 (0.93–1.11) | 0.90 (0.81–0.99) |
INFARCTION | 1.12 (0.32–3.03) | 0.96 (0.27–3.35) | 1.71 (0.49–5.97) | 1.16 (0.33–4.06) | 0.52 (0.10–2.64) |
NO PT CARE | 1.37 (0.68–2.74).68–2.74 | 0.95 (0.47–1.92) | 0.53 (0.27–1.05) |
0.69 (0.34–1.39) | 1.25 (0.41–3.77) |
COGNITIVE IMPAIRED (MOCA ≤25) | 0.83 (0.41–1.67) | 2.09 (1.01–4.33) |
0.97 (0.49–1.89) | 1.38 (0.69–2.77) | 4.68 (1.23–17.84) |
ABSENCE DEPRESSION (<8 HADS-D) | 1.68 (0.57–4.98) | 0.46 (0.14–1.48) | 1.96 (0.70–5.5) | 1.30 (0.46–3.66) | 0.16 (0.05–0.52) |
ABSENCE ANXIETY (<8 HADS-A) | 0.56 (0.23–1.38) | 1.00 (0.39–2.56) | 1.07 (0.44–2.65) | 1.08 (0.42–2.76) | 0.64 (0.16–2.51) |
NONCOMMUNITY WALKER (≥0.93 m/s) | 1.04 (0.05–2.19) | 1.18 (0.55–2.51) | 0.11(0.04–0.28) |
0.14 (0.04–0.38) |
3.33 (1.08–10.29) |
LOWER ACTIVITY LIMITATIONS (SIS) | 0.99 (0.67–1.02) | 1.00 (0.97–1.03) | 0.92 (0.89–0.96) |
0.91(0.87–0.96) |
1.03 (0.98–1.06) |
LOWER FUNCTIONING OF BALANCE (BBS) | 0.96 (0.88–1.05) | 1.01 (0.94–1.09) | 0.77 (0.66–0.90) |
0.64 (0.050–0.81) |
1.03 (0.95–1.13) |
CI = confidence interval, LPA = Light physical activity, MVPA = Moderate to vigorous physical activity, WMSB = Weighted median sedentary bout length, OR = odds ratio, CI = confidence interval, PT = physiotherapy. Age, less severe stroke symptoms, lower activity limitations and balance were analyzed as continues variables. NIHSS = National Institutes of Health Stroke Scale, MOCA = Montreal Cognitive Assessment, HADS-D = Hospital Anxiety and Depression Scale depression subscale, HADS-A = Hospital Anxiety and Depression Scale anxiety subscale, m/s = meters per second, SIS = Stroke Impact Scale, BBS = Berg Balance Scale.
*p<0.1
**p<0.05
***p<0.01
SEDENTARY | NONMOVER (LPA) | ACTIVE (MVPA) | ACTIVE (MVPA BOUTS> 10MIN) | PROLONGER (WEIGHTED MEDIAN SEDENTARY BOUTS) | |
---|---|---|---|---|---|
OR (95%CI), P- value | OR (95%CI)5% | OR (95%CI) | OR (95%CI), P-value | OR (95%CI), P-value | |
MALE | 3.35 (1.39–8.08) |
6.14 (2.37–15.92) |
|||
LOWER AGE (YEARS) | 1.05 (1.02–1.09) |
1.05 (1.02–1.09) |
|||
LIVING ALONE | 0.40 (0.22–0.74) |
8.49 (2.22–32.44) |
|||
LESS SEVERE STROKE SYMPTOMS (NIHSS) | 0.87 (0.77–0.99) |
||||
COGNITIVE IMPAIRED (MOCA ≤25) | 2.33(1.20–4.51) |
5.02 (1.54–16.37) |
|||
NON-COMMUNITY WALKER (≥0.93 M/S) | 0.17 (0.06–0.55) |
3.11(1.15–8.44) |
|||
LOWER ACTIVITY LIMITATIONS (SIS) | 0.94 (0.90–0.98) |
0.96 (0.93–0.99) |
CI = confidence interval, LPA = Light physical activity, MVPA = Moderate to vigorous physical activity, WMSB = Weighted median sedentary bout length, OR = odds ratio, CI = confidence interval
Age, less severe stroke symptoms and lower activity limitations were analyzed as continues variables. NIHSS = National Institutes of Health Stroke Scale, MOCA = Montreal Cognitive Assessment, m/s = meters per second, SIS = Stroke Impact Scale.
*p<0.05
**p<0.01
This study investigated changes in movement behavior outcomes and possible subgroup trajectories using objective and continuous measurement in 140 participants within the first two months after discharge from the hospital to the home setting after a first stroke. Overall, SB decreased very slightly, and LPA showed a small increase in time. Distinct subgroup trajectories were found for all movement behavior outcomes. Small changes within subgroup trajectories for SB and MVPA were found. For all other movement behavior outcomes, the identified subgroup trajectories remained stable. Individuals were distributed into different subgroups according to movement behavior outcomes. Characteristics associated with the different subgroups were explored. No associated characteristics were found regarding SB.
On average, our sample showed SB results comparable to a Dutch older adult population [
Although a significant decrease in SB and an increase in time spent in LPA were found within the first two months after discharge, the changes were small. However, it was recently found that higher levels of physical activity, including light physical activity, and less time spent in SB reduce the risk of premature death in a dose-response manner [
The differences in the distribution and accumulation of movement behavior during the day are interesting. Over 60% of the sample was assigned to a subgroup trajectory with a mean sedentary time per day reaching almost ten hours out of fourteen hours wear time. This indicates high amounts of SB. Prolonged bouts are more difficult to interpret since there is not a given cut-off value available yet. However, the majority of the group had a weighted median bout of over 20 minutes, indicating that over50% of total sedentary time is spent in prolonged bouts. Interruption after 20 minutes of SB has been found to have a positive influence on glucose levels in overweight people [
Additionally, over 90% of the population did not reach sufficient amounts of MVPA accumulated in bouts of at least 10 minutes. Differences in the changes among the subgroup trajectories were found. Participants in the highly sedentary subgroup trajectory decreased their amount of sedentary time, and those in the inactive group increased their MVPA time. Both changes, in theory, can reduce the risk of premature death, although the changes are small [
Remarkably, we found no patient characteristics that were associated with highly sedentary behavior. A recent study, which pooled data from nine studies identifying associations with sedentary time after stroke, found that sedentary time could not be explained by demographic or stroke-related variables[
Trajectories of single movement behavior outcomes overlap; however, they are largely unique. For example, 54% of the people who were highly sedentary were nonmovers but only 36% of the highly sedentary people were inactive. Therefore, the next step in research is to investigate whether movement behaviors cluster in patterns. The emergence of movement behavior patterns will provide insight into individuals’ accumulation and distribution of movement behaviors during the day. Tremblay et al. described four hypothetical movement behavior patterns based on the distribution of movement behavior: 1. active and not sedentary; 2. active and sedentary; 3. inactive and not sedentary; and 4. inactive and sedentary [
Although we expected to observe more changes in movement behavior outcomes based on the efforts of health care professionals, the willingness to change because of having experienced a stroke and the fact that recovery was feasible at the time of the study, only small changes in movement behavior outcomes occurred. In this sample, 46% of the population received physiotherapy care. In general, physiotherapy care focuses on regaining physical function and improving physical fitness [
A limitation of our study was that data regarding movement behavior during the day was obtained within three weeks after discharge. Therefore, it remains unknown whether movement behavioral changes occur within the period immediately after discharge and three weeks later. Additionally, prestroke movement behavior during waking hours was not obtained. Therefore, it remains unknown whether people in this sample changed their movement behavior according to the behavior in the prestroke period. Another limitation was that sleep time during the day was not determined, and therefore, SB may have been overestimated. Last, our study included only participants who were directly discharged to the home-setting. Since, the majority of this population had minor stroke symptoms the results are not generalizable to a more severe stroke population that received inpatient rehabilitation first. However, our findings emphasize the importance of movement behavior changes since our sample had less severe stroke symptoms but still presented high levels of SB and low levels of MVPA.
Overall, the majority of people with stroke are highly sedentary, and a substantial proportion of this population is inactive in the first two months after discharge from hospital care based on continuous objective measurement for five weeks. Furthermore, their movement behavior remains fairly stable in this period. Based on movement behavior outcomes, distinctive subgroup trajectories were found. Although the people in this study had minor stroke symptoms, they were nonetheless highly sedentary, and a substantial portion was inactive. Therefore, changes movement behavior after discharge from the hospital are of paramount interest. Instead of providing information about changing movement behavior, personalized coaching interventions are needed. However, before such interventions take place, insight is needed into whether movement behavior during waking hours may cluster in patterns and which characteristics are related to an unfavorable movement behavior pattern in stroke survivors.
(Table A) Linear slopes and quadrics slopes with outcome values. (Table B) Distribution of individuals to different subgroups per movement behavior outcome expressed in percentages.
(DOCX)
We would like to thank all participants for their contribution to the RISE-study. Furthermore, we would like to thank the staff of Catharina Hospital (Eindhoven), Jeroen Bosch Ziekenhuis (‘s Hertogenbosch), Maxima Medisch Centrum (Veldhoven) and Sint-Jans Gasthuis (Weert) and we would like to thank Thirsa Koebrugge and Joeri Polman who helped with the data collection.
activities of daily living
light physical activity
metabolic equivalents
moderate to vigorous physical activity
sedentary behavior
PONE-D-19-23463
Movement behavior remains stable in stroke survivors within the first two months after returning home
PLOS ONE
Dear Mr. wondergem,
Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.
Apart from the comments made by the reviewers the discussion is lacking a comparison with "healthy" individuals. You state that "the majority of the stroke survivors are inactive and highly sedentary" while report that they spend 0.7 h daily on at least moderate intensity which is approximately equivalent to 42 minutes of MVPA daily. This is around twice the amount compared to a general population. Thus the conclusion seems not supported by your data. Also you should focus on the validity issues raised by the reviewers.
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Reviewer #1: The authors of this manuscript tackle a highly important topic, that is changes in physical activity in the sub-acute phase after stroke. While numerous studies have demonstrated low levels of physical activity in the chronic phase after stroke little is known about the trajectory of physical activity over time. For that reason, as well as the large sample size and prolong follow-up of physical activity using accelerometers, this paper would make an important contribution to this field. There are some concerns though that I would like the authors to consider.
Major comments
1. The term “movement behavior” is broad and vague. This paper has clearly investigated different intensity domains of physical activity – therefore the terminology should be changed from movement behavior to physical activity throughout the manuscript.
2. Please clarify the validity of the Activ8 accelerometer to detect sedentary, light and MVPA in people with stroke.
3. The second aim; detecting possible subgroup trajectories within each outcome (i.e. different intensities of physical activity) using latent class growth analyses. To me, this section is statically driven and I would like the authors to justify this choice. For example, by explaining how detection of sub-groups following certain trajectory in physical activity would inform secondary prevention strategies post-stroke. Personally, I believe it is important to identify personal and functioning predictors of a “positive” vs “negative” (i.e. sedentary) physical activity trajectory post-stroke – and data from this trial seem to be able to contribute to this. Describing and contrasting the characteristics of stroke survivors following different trajectories could inform clinicians and researchers to target those of increased risk of sedentary and worsen cardio-vascular health. Taken together, to increase the influence of this paper, I think the rational and implication of the findings of the second aim should be clarified.
4. Does the results for the physical activity outcomes (sedentary, light and MVPA) reveal unique trajectories or is the results demonstrating a single trajectory of sedentary behavior post-stroke (see comment 11 below). Please clarify.
5. As shown in table 1, functioning and stroke severity data was also collected in this study. Please justify why changes in these outcomes was not considered in relation to changes in physical activity.
Abstract
6. Results; I would suggest to present the percentage of total wear time for each intensity (sedentary, light and MVPA) as it provides an informative summary of the distribution between intensity categories.
7. Result (first sentence): It is unclear whether this sentence refer to the entire time period for data collection or a certain time period.
Introduction
8. The introduction is overall well written, easy to follow, and introducing the reader to the research topic. My only comment is in line with my previous comment regarding aim 2, how would identification of certain subgroup trajectories of physical activity (without identifying who these individuals are) inform secondary stroke prevention?
Method
Recruitment and inclusion criteria are clearly stated.
9. Performance based tests of balance and gait were undertaken in the participants home. Berg Balance Scale and 5MWT requires some space to be performed, was it possible to perform these tests in all subject’s home in a standardized manner?
10. In table 1, data on the provision of physiotherapy care is provided, but I cannot find any information of these variables in the method section. Please a description of these variables including the nature, timing and definitions of these services as they could differ between health care systems.
Statistical analyses are sound and clearly described.
Results
11. Results are overall clearly presented. As pointed out in my previous comment, describing the characteristics of the stroke survivors following different trajectories would be informative. Also, to what extent does the trajectories overlap between the different PA outcomes? For instance, 90 participants were defined as highly sedentary, 92 as non-movers according to light PA and 89 as inactive according to MVPA. The weighted median sedentary bout length also adds up to about 90 if combining the “prolongers” (n=14) and intermediate groups (n=74). Are these PA outcomes revealing a single trajectory of sedentary behavior post-stroke occurring in approximately 65% of this sample? Or do the different PA intensities reveal unique patterns? I am curious about the authors thoughts on this potential overlap and it would be very interesting if data supporting or rejecting such overlap could be presented.
12. Please add information on time since stroke when the assessment of physical activity started – such data would allow comparison with similar longitudinal studies.
13. Figure 1 is blurry – please improve the quality. In addition, I would suggest adding 95% confidence intervals to the estimates.
Discussion
Relevant and clear – linked and contrasted to previous body of research.
14. First sentence (first paragraph): it is stated that the assessment took place “in the first 2 months after discharge” indicating a period of data collection of 10 weeks. Please rephrase.
15. The authors might need to revise the reasoning in the discussion regarding the unique trajectories for different intensity outcomes; e.g. “distinctive subgroup trajectories were found for each movement behavior outcome”, depending on whether they are unique patterns or not.
List of abbreviations
16. As a reader, it is rather demanding to keep track on all the short-forms used in this manuscript (n=16). I would recommend to only use short-forms of the most frequently used terms.
Reviewer #2: This is an interesting study with a relatively large n that examines the time course of activity levels during the first 2 months post stroke
Major:
Why was activity measured only during the time frame from 3 weeks to 2 months post stroke? Recovery post stroke is still likely happening past this time point. Also, were the participants still receiving home or out patient rehabilitation during this time (I see a brief amount of info on this in table 1, but there is no mention of it anywhere else, please elaborate on this, also was there any difference in activity levels based on receiving or not receiving rehab?). Both of these factors could greatly influence activity levels (time in sitting/lying, etc)? Related to this please provide information on exactly when post stroke the participants started wearing the devices and stopped. Just a blanket statement of 3 weeks post discharge from the hospital is not sufficient. This is important information in order to interpret the activity levels.
What are the clinical characteristics of the different subgroups (i.e. GS, BBS, etc.), are there any differences in these between the groups?
This was a very high functioning group in regards to mobility as evidenced by the mean gait speed of 1.03 m/s, this should be at least discussed as a limitation.
The Activ8 device has not been validated to identify energy expenditure in people with stroke, the article cited demonstrates its ability to identify postures (lying/sitting, standing, and walking. The authors use the outcomes time in SB, LP, and MVPA with associated METs. This is misleading as the device has not been validated to do this in people with stroke. Additionally, the method used by the authors to secure the device is not consistent with the study cited. To be more accurate the authors should use time spent in the different postures as their outcomes, not in SB, LP, MVPA.
The authors state that the small decrease in SB and increase in LPA are likely not clinically important. Although they are likely correct in this assessment they should provide some evidence of this.
Minor:
The statement associated with reference 4 in the first paragraph is misleading. Only systolic BP was found to impacted by sedentary lifestyle intervention; cardiovascular event rate mortality, diastolic blood pressure, or total cholesterol were not. Please revise.
The statement related to reference 14 in the second paragraph is also misleading. The article cited is a study protocol, they did not determine that interruption of sedentary bouts with activity reduces BP.
5th paragraph of the discussion, the point about the current approach is not clear and misleading. This was not an intervention study so there was no “approach” to lessen SB and increase activity levels. Also it is not clear how many participants were receiving rehabilitation services.
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Submitted filename:
PONE-D-19-23463R1
Movement behavior during waking hours remains stable in stroke survivors within the first two months after returning home
PLOS ONE
Dear Mr. wondergem,
Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.
As reviewer three points out there are still a few things that you need to adress, most importantly
* The level of physical activity in these patients is high, even compared with healthy adults. The level of sedentary behaviour is also on the same level as healthy adults. Also the proportion of physical activity and sedentary behaviour is similar to that found among healthy adults and children (MVPA ~ 5% and SB ~ 70% of the measured time). The conclusions should be amended accordingly.
* The potential explanation to this may be thet the device used to measure physical activity is not validated in the population, thus there may be missclassifications present. This, however, do not change the conlusions regarding the change in physical activity and sedentary behaviour over time. This should be discussed
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Reviewers' comments:
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The
Reviewer #1: Yes
Reviewer #3: No
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Reviewer #1: The authors have done an excellent job responding to the issues raised and clarifying these in the manuscript. Except for a minor issue with regards to Table 4, I have no further comments or issues that I want to the authors to address.
Table 4 reports the result for the multivariate logistic regression -I would suggest to include the results for the univariate logistic regression as a separate table as well. Also, all independent variables need to be understandable from reading only the table itself (without looping back to the method section) – please clarify the cut-points used for each nominal variables (i.e. similar to Table 1).
Reviewer #3: Review
The line numbers are missing to accurately notify the remarks
The topic of this work is major in the management of stroke patients in the field of physical activity. Evaluating changes in physical activity levels and sedentary behaviour in the home is central to identifying the determinants of these changes in order to propose effective actions. The authors do not elaborate much on this in their introduction and that is a pity.
It is worth noting the authors' effort to have evaluated the participants in such a complete manner and to have carried out such a consistent follow-up on such a large group of patients.
The major limitation of this work is the lack of validation of activ8 for the estimation of energy expenditure or METs in stroke subjects. This is a major limitation because the entire classification process of the study concerns the quality of the device to differentiate the different categories of physical activity intensity performed by the individual stroke at home. Despite a quality work performed by Fanchamps et al showing a validity of physical activity detection, the transposition of METs on activities identified by Activ8 from healthy subject populations is inadequate cf Serra et al 2016, Compagnat et al 2018. In addition, this device uses a uniaxial accelerometer with an algorithm developed on a healthy subject. However, we now know that estimates of this type of device are not reliable in stroke subjects for the same reasons explained below. It therefore seems necessary to explore the validity of a device that has not been validated before being used in clinical research.
If the reader accepts this uncertainty I have developed some remarks about the manuscript.
Abstract results: I would add if possible which subgroups are identified since this is part of the objectives of the study.
Introduction
Your assumptions are contrary to what is reported in the literature, particularly in the literature review of English et a et Rand et al l, which clearly shows that people with stroke tend to be more sedentary. In addition, we observe in the literature and in our clinical experience a strong association between physical activity level and functional abilities, mood disorders, self-efficacy that you do not address at all in your introduction..
Methodology
Design and participant
How can you justify including only stroke survivors with such a high level of autonomy? This is excessively regressive and unrepresentative of strokes treated in rehabilitation unit.
Measurements and procedures :
Specify that the evaluation procedures follow this paragraph, otherwise the reader may be frustrated.
Accelerometer
The absence of validation of activ8 in the stroke subject in terms of energy expenditure is a major limitation for the interpretation of the activity intensities reported by the device. Indeed, these intensities are based on the energy expenditure for the activity which is strongly different between healthy individuals and undivided AVC.
Results :
What was the number of refusals to participate in the study? Wouldn't the patients who agreed to participate in the study be those who consider themselves the least sedentary or inactive? A flow chart seems necessary to inform the reader about these elements.
What was the method used to define the different sedentary classes (low/high)? Is this related to the statistical analysis you describe in the method section? What is the clinical relevance between these 2 separate categories of 2 hours of low-intensity activities when both are sedentary by definition. Same for the activity categories (high, moderate, inactive)...
The authors provide part of the answer in the results paragraph but this is not the place for that. It would be necessary to place this information back in the method section. Moreover, to my knowledge, I do not believe that the clinical relevance of this type of category has been demonstrated in individuals with stroke.
The last paragraph of the results are, in my opinion, extremely interesting and need to be developed because there is a strong stake in knowing what are the associations between sedentary behaviours, autonomy and social participation scores and mood disorders. The assessment of possible changes in sedentary behaviours and physical activity levels associated with these different dimensions that you have assessed (impairments, activity, participation) as well as those that may or may not benefit from physiotherapy would be all the more original.
To be honest, I expected the authors to establish the subgroups on the clinical characteristics of the participants and not on this type of statistically constructed categories. This limits its clinical interest.
Discussion
Is the high value of MVPA in these individuals not related to the particular profile of patients you have included: undivided with very little neuronal sequelae?
Table 1 :
I don't think we can report the SIS and BBS values on average and SD since they are not continuous variables.
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If you choose “no”, your identity will remain anonymous but your review may still be made public.
Reviewer #1: Yes: David Moulaee Conradsson
Reviewer #3: No
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Submitted filename:
Movement behavior remains stable in stroke survivors within the first two months after returning home
PONE-D-19-23463R2
Dear Dr. wondergem,
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Reviewers' comments:
PONE-D-19-23463R2
Movement behavior remains stable in stroke survivors within the first two months after returning home
Dear Dr. Wondergem:
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