Movement behavior remains stable in stroke survivors within the first two months after returning home.

BACKGROUND AND
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.

Introduction

The majority of stroke survivors are discharged to the home setting immediately after hospital care [1]. Following a stroke, cardiovascular event rates are high. Premature death and disability rates are higher after a recurrent event than after the first stroke [2,3]. Secondary lifestyle interventions are important and have been demonstrated to be effective in reducing systolic blood pressure, one of the strongest risk factors for both first and recurrent stroke [4,5]. An important lifestyle intervention that can favorably influence cardiovascular risk is changing movement behaviors [6]. Movement behaviors during waking hours include sedentary behavior (SB), and physical activity (PA) [7]. Within PA, the intensities of light physical activity (LPA) and moderate to vigorous physical activity (MVPA) can be distinguished. SB is defined as “any waking activity characterized by an energy expenditure of ≤ 1.5 metabolic equivalents (METs) and a sitting or reclining posture” [8], LPA consists of activities between 1.5 and 3.0 METs, and MVPA consists of all activities > 3.0 METs. In general, stroke survivors are highly sedentary and inactive compared to healthy peers [9].

Various movement behavior outcomes have shown associations with health risk and functional decline [10,11]. The composition of movement behavior during waking hours (the relative amounts of SB, LPA and MVPA during waking hours), the continuity of SB (interrupted or prolonged SB), and the continuity of MVPA (bouts ≥10 minutes) are important modifiable risk factors to improve cardiovascular health. High amounts of SB and low amounts of MVPA are independent risk factors for cardiovascular disease incidence and premature mortality. MVPA should occur in bouts of at least ten minutes to contribute to the recommended 150 minutes per week spent in MVPA [12]. Additionally, long uninterrupted sedentary bouts are related to cardiovascular risks [13]. Interrupting SB after 20 minutes has been found to have a positive influence on glucose levels in overweight people [14], and interruption after 30 minutes decreased the systolic blood pressure of stroke survivors [15], thus providing cardiovascular health benefits.

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 [16]. In contrast, another study found an increase in SB [17]. To date, all studies investigating the course of movement behavior outcomes up to the first year after stroke have used averaged group data and found no changes over time [9,16,18,19]. However, recovery after stroke is not a one-size-fits-all principle; it is characterized by individual patterns [20]. Previous studies have demonstrated variation in the trajectories of physical and psychosocial health-related quality of life [21] and functional recovery [22] within the first year after stroke. In healthy populations, SB and MVPA were found to have four to seven subgroup trajectories each [23,24]. Because the stroke recovery process is heterogeneous, different subgroup trajectories of movement behavior outcomes can be expected.

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 [20], and most stroke survivors still receive professional support or rehabilitation during that period [25]. Moreover, during those initial weeks, health care professionals provide information regarding modifiable risk factors, including movement behaviors [26]. Additionally, it is expected during the period shortly after this life event, are especially motivated to improve their lifestyle to prevent recurrent events [27]. Therefore, trajectories of changes in movement behavior outcomes are expected. However, knowledge is currently lacking regarding the course of movement behavior outcomes shortly period after discharge.

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 [28]. This approach will extend our understanding of subgroup trajectories of change in movement behavior outcomes. Once these subgroup trajectories are known, associations will need to be explored. Currently, only a few associations are known with regard to movement behavior outcomes. Lower walking speed and walking capacity, balance problems, presence of depression and poorer quality of life associated with accelerometer activity counts [9]. Additionally, higher age, being a man, higher cardiorespiratory fitness, lower levels of fatigue, higher level of self-efficacy, presence of depression and higher health related quality of life were factors associate with higher levels of PA [29]. Lower walking speed was found to associated with higher amount of sedentary time and long prolonged bouts [30], less functional independence with high amounts of sedentary behavior and prolonged bouts, stroke severity with high amounts of sedentary behavior and age with more prolonged sedentary behavior [19]. Although these studies provide preliminary information deeper understanding of factors related with single movement behavior outcomes is needed.

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.

Method

Design and participants

Data cannot be shared publicly because data contain potentially identifying or sensitive patient information. Data are available from the UMC Utrecht Ethics Committee info@umcutrecht.nl for researchers who meet the criteria for access to confidential data. Eligible participants were recruited between February 2015 and April 2017 from four participating stroke units in the Netherlands. This prospective longitudinal cohort study, carried out after discharge from a hospital directly to the patients’ own home settings, specifically recruited persons who had suffered a clinically confirmed first-ever stroke and who had been independent in ADL before stroke (Barthel index score >18 [31]). Other inclusion criteria were age over eighteen years, ability to sustain a conversation (Utrecht Communication Assessment score > 4 [32]) and at least the ability to walk with supervision after stroke (Functional Ambulation Categories score >2 [33]). People with a subarachnoid hemorrhage were excluded. The written informed consent of the participants was obtained at the stroke unit. All participants gave written informed consent. The study was approved by the Medical Ethics Research Committee of the University Medical Center Utrecht (study number 14/76).

Measurements and procedures

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.

Independent characteristics

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) [34]. Stroke services are a form of integrated care that has been established during the last decade. The aim of stroke services is to improve health outcomes and processes of care by connecting the acute, rehabilitative, and chronic phases of stroke care [35,36]. In a typical Dutch stroke service, the hospital, rehabilitation center (in- or outpatient care), and primary physiotherapy care are represented. Information about physiotherapy care was obtained from medical records and verified by asking the participant. Three options were possible no physiotherapy care, primary physiotherapy care and outpatient multidisciplinary rehabilitation care that included physiotherapy. The Montreal Cognitive Assessment was used to assess cognitive functioning [37,38]. Scores were dichotomized into normal (≥ 26) or impaired (< 26) cognitive function. The Hospital Anxiety and Depression Scale was used to assess the presence of depression and anxiety symptoms. Each subscore was dichotomized into the presence (≥ 8 points) or absence (< 8 points) of depression or anxiety symptoms [39,40]. The Late-Life Function and Disability Instrument Computerized Adaptive Test activity limitations and participation restriction subscales scores were obtained [41]. Physical functioning was measured with the physical functioning subdomain of the Stroke Impact Scale (SIS) 3.0 [42,43] The physical functioning subdomain consists ten questions regarding ADL, eight regarding mobility, and five regarding hand function [42,43]. As recommended, scores were calculated as percentages of the total points, resulting in a range from 0 to 100. Lower scores indicate lower levels of physical functioning. Balance was tested with the Berg Balance Scale [44]. Walking speed was obtained using the five-meter walk test [45]. All outcome measures are valid and reliable in a stroke population.

Accelerometer

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 [46]. The Activ8 has been validated in a community-living stroke population in terms of postures and in a healthy population in terms of energy expenditures [47,48].

Movement behavior outcomes

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 [49]. The mean times spent in SB, LPA, and MVPA in hours per day were computed by summation and divided by the number of wearing days per individual [7]. The mean time of MVPA accumulated in bouts ≥ 10 minutes was calculated. An MVPA bout was defined as 10 or more consecutive minutes of MVPA, with allowance for interruptions of no more than 2 minutes [50]. For each individual, the weighted median sedentary bout length was calculated [49]. The weighted median sedentary bout is the sedentary bout that corresponds to 50% of the total sedentary time [49]. The weighted median sedentary bout length is more sensitive to change than the total time spent in SB [51]. Bouts are ordered from smallest to largest, and for example, if an individual has spent eight hours sedentary, this measure represents the length of the bout that contains the four hour timepoint. If this would be 20 minutes, it means that individuals spend 50% their SB time in bouts ≥ 20 minutes. The lower the weighted median sedentary bout is, the more frequently interrupted the SB.

Data analysis

SPSS version 25.0 [52] was used for descriptive statistics, which are expressed as the means and standard deviations. The multivariate imputation by chained equation procedure was used to impute (multiple) missing values [53]. In our data set, missing data were not dependent on descriptive characteristics; therefore, data were assumed to be missing completely at random, and multivariate imputation by chained equations was therefore indicated to increase statistical power [54]. Multivariate imputation by chained equations was performed by models to predict missing values for a given variable based on all other observed variables. Five imputed data sets were created and combined to create a pooled set using Rubin’s rules [55].

To investigate the average group movement behavior change within the first weeks after discharge to the home setting, generalized estimation equation were employed [56]. Latent class growth analysis was performed with Mplus version 8.1 [57] to identify clinically relevant homogeneous subgroups of stroke survivors that followed different trajectories of movement behavior. For each movement behavior outcome, latent class growth analysis was performed. Latent class growth analysis uses latent variables to estimate differences in mean changes over time in different subgroups, taking into account individual longitudinal trajectories. The trajectories within the subgroups were kept homogenous. The fit of the models was tested by comparing models with two, three, four and five subgroups. Both linear and quadric trajectories were modeled and compared. Statistical considerations for finding the most appropriate model included a Bayesian information criterion, entropy values and the bootstrap likelihood ratio test [58-60]. The lower the Bayesian information criterion score, the better the fit of the model [60]. When bootstrap likelihood ratio test was significant (p<0.05), the model with k-subgroups had a better fit than the model with k-1 trajectory subgroups [28,60]. The entropy statistic was used for the reliability of the subgroup trajectories. Entropy scores above 0.8 are preferred [28]. When less than 5% of the sample was assigned to a subgroup trajectory, a k-1 subgroup trajectory model was chosen [61].

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) [62]. Significantly associated characteristics (p<0.1) were entered into a multiple backward logistic regression analysis.

Results

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 Table 1.

Table 1

Participant characteristics expressed as mean±SD, median [IQR], or n (%).

Characteristics (N = 140)	% or mean±SD
Personal characteristics
Males	66.4
Age (years)	67.1±10.8
Living alone	18.6
Stroke characteristics
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
Psychological characteristics
Cognitive functioning a
Impaired cognitive function (MOCA ≤25)a	39.1
Depression (HADS-D)	13.7
Anxiety (HADS-A)	16.7
Physiotherapy care
Outpatient multidisciplinary rehabilitation, including physiotherapy a	12.8
Primary care physiotherapy a	33.6
No physiotherapy a	53.6
Functional ability
Walking speed (m/s)a	1.03±0.24
Limited community walker (≥0.93 m/s)a	31.4
LFDI-CAT activity limitations a	58.9±10.8
Physical functioning (SIS) a	93.8 [82.3–98.9]
LLFDI-CAT participation restrictions a	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 Table 2.

Table 2

Movement behavior outcomes in the overall group and generalized estimating equations analyses adjusted for wear time.

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%]
Sedentary (hours/day)	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]*
LPA (hours/day)	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]*
MVPA (hours/day)	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]
MVPA accumulated in bouts≥10 minutes (hours/day)	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]
Weighted median sedentary bout (minutes)	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]
Wear time (hours/day)	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 S1 File, for the Bayesian information criterion, entropy and bootstrap likelihood ratio test outcomes for each subgroup trajectory). Although the fit of most models favored a four or five subgroup model, some subgroup trajectories contained too few individuals to be considered clinically relevant (less than 5% of the total sample). Consequently, two subgroup trajectories were determined for SB and LPA. Three subgroups were found for MVPA, MPVA spent in bouts ≥ 10 minutes, and weighted median sedentary bouts. For total SB, LPA and MVPA, quadratic trajectories are presented because lower Bayesian information criterion values and higher entropy values were found. Linear trajectories were presented for weighted median sedentary bouts and MVPA accumulated in bouts ≥ 10 minutes. The Bayesian information criterion, entropy, bootstrap likelihood ratio test, intercepts and slopes are presented in Table 3. All presented subgroup trajectories had entropy scores above 0.8.

Table 3

Model fit indices for the selected subgroup trajectories for each movement behavior outcome, n = 140.

	Subgroups (n)	Intercept for subgroup	Linear slope	Quadric slope	BIC	Entropy	BLRT
Sedentary behavior (hours/day)	Highly sedentary = 90	9.94	0.25	-0.06*	2343.72	0.87	<0.01
	Less sedentary = 50	7.92	-0.37	0.07
LPA (hours/day)	Non-movers = 92	3.17	-0.10	0.03	2192.88	0.82	<0.01
	Movers = 48	5.02	0.33	-0.06
MVPA (hours/day)	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*
MVPA bouts≥10 min (hours/day)	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.
Weighted median sedentary bout length (min)	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 Table 3.

Figs 1–5 show subgroup trajectories of all movement behavior outcomes. A small but significant decrease in sedentary time was found in the subgroup trajectory of highly sedentary people. The inactive subgroup increased slightly in time spent in MVPA, whereas the active subgroup slightly decreased. All other subgroup trajectories of movement behavior outcomes remained stable within the first two months.

Fig 1

Sedentary time in hours per day between three weeks and eight weeks after discharge from the hospital in stroke survivors.

Fig 5

Weighted median sedentary bout in minutes per day between three weeks and eight weeks after discharge from the hospital in stroke survivors.

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) [12]. Additionally, ‘intermediate’ and ‘interrupters’ subgroup trajectories for the weighted median sedentary bout length were merged. Although there are no clear cut-off values available for the interruption of SB, interruption after thirty minutes was been found to have health benefits in people with stroke [63,64]. The distribution of individuals to the different subgroups is presented in Table B in S1 File. The results show that the different movement behavior outcomes reveal distinct trajectories. For example, 53.6% of the population was highly sedentary and classified as nonmovers, and 35.7% was inactive and highly sedentary.

The results of the univariate analyses per movement behavior subgroup are presented in Table 4. The results of the multiple logistic regression analyses are presented in Table 5. No associations were found regarding SB. Factors associated with nonmovers were living with another person and impaired cognitive function. Being male, and younger and having fewer activity limitations were associated with both active groups (MVPA and MVPA spent in bouts ≥10 minutes). Living alone and being a community walker were only associated with the active MVPA group. Factors associated with prolongers were more severe stroke symptoms, cognitive impairment and not being a community walker.

Table 4

Associated factors with highly sedentary, non-movers, active and prolongers using univariate analyses.

	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)
PERSONAL CHARACTERISTICS
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)
STROKE / CARE CHARACTERISTICS
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)
PSYCHOLOGICAL AND COGNITIVE FACTORS
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)
PHYSICAL FUNCTIONING
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

Table 5

Associated characteristics per movement behavior pattern using multiple logistic regression.

	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
INDEPENDENT VARIABLES
PERSONAL CHARACTERISTICS
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)**
STROKE CHARACTERISTICS
LESS SEVERE STROKE SYMPTOMS (NIHSS)					0.87 (0.77–0.99)*
PSYCHOLOGICAL AND COGNITIVE FACTORS
COGNITIVE IMPAIRED (MOCA ≤25)		2.33(1.20–4.51)**			5.02 (1.54–16.37)**
PHYSICAL FUNCTIONING
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

Discussion

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 [65]. In our sample, that the majority of the people were highly sedentary, exceeding 9.5 hours. The relationship between sedentary time and mortality was more pronounced when sedentary periods exceeded 9.5 hours [66]. Therefore, the reduction of SB should be a secondary prevention target for people with stroke. On average, our sample engaged in 42 minutes of MVPA per day, which is high. It is known that the Dutch population is more active than its European peers[67]. In other stroke survivors, the same amount of MVPA was found (44 minutes) [18]. Although, the average amount of MVPA was high, a substantial part of the population was found to be inactive. Particularly in terms of MVPA accumulated in bouts ≥10 minutes.

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 [66]. Therefore, even this small change in LPA and SB are considered relevant. Nevertheless, the absolute amount of SB was still high. A previous study (N = 10) found an increase of forty minutes in absolute activity during the day within the first six weeks after discharge to the home setting [16]. However, this improvement was compared to the absolute activity before discharge. When comparing activity at two weeks after discharge with activity at six weeks after discharge, an increase of only twenty minutes was found. The same increases were observed in another study regarding step count and time spent walking between one and three months after discharge [17]. We also found an LPA increase of twenty minutes. Therefore, it seems that after stroke, people increase their level of LPA in the short term. Regarding SB, conflicting results were found in the literature. In our sample, SB decreased while in another study with a small sample size sitting/reclining time increased [17]. However, in that study, sleep time was included in the sitting/reclining time. Therefore, it remains unknown whether SB, sleep time, or both increase within the first six months after discharge to the home setting [17].

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 [14].

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 [66].

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[30]. It identified only slower walking speed as a significant factor associated with higher amounts of SB. In our sample, people were discharged immediately to the home-setting and had a relatively high walking speed, whereas the study of Hendrickx et al included participants with greater diversity of walking speed. This could explain why walking speed was not identified as a factor associated with SB in our sample. Although living alone was associated with the total MVPA time, it was not associated with MVPA accumulated in bouts ≥10 minutes. It seems that people who live alone spend time in MVPA during their ADLs and devote less leisure time MVPA in such forms as exercise or sports. More severe stroke symptoms, cognitive impairment and not being a community walker were associated with prolongers in our study. These outcomes are in line with previous studies of people with stroke, although those studies found associations with walking speed, more severe stroke symptoms and self-reported ADLs and sedentary bouts [30,68]. The association between cognitive impairment and nonmovers and prolongers is interesting since no associations were found with total sedentary time or MVPA in our sample. A study including older adults found that higher amounts of SB were associated with lower cognitive function when MVPA was not taken into account; however, no association was found after adjustment for MVPA. This indicates the importance of investigating movement behavior patterns and not just single movement behavior outcomes. Additionally, it could be that the health benefits of enough MVPA are counterbalanced by high amounts of SB.

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 [7]. Whether these patterns apply to the stroke population is currently unknown. Using these movement behavior patterns, individuals with an unfavorable patterns of behavior can be identified. Additionally, it will be important to investigate characteristics that help to differentiate among individuals with a favorable and unfavorable movement behavior pattern. This deeper understanding of the clustering patterns could support the development of personalized interventions to improve movement behavior during waking hours [69].

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 [70]. However, improvements capabilities due to functional recovery will automatically improve ADLs, but will not automatically improve daily physical activity [71] or reduce SB. Additionally, as a general practice, all people with stroke in the Netherlands are included in primary care programs in general practice. However, in these programs there is limited attention for secondary prevention after stroke, especially physical activity [72,73]. Additionally, changing movement behavior is a complex process and cannot be triggered by merely providing information [74,75]. Therefore, specific interventions are needed to improve daily physical activity and decrease sedentary time. Particularly since the majority of our sample was sedentary and a substantial part was inactive, improving movement behavior is important and needs to be targeted to counterbalance increased cardiovascular risks. Additionally, the participants in this study had relatively minor stroke symptoms but nonetheless were highly sedentary, proportion of the sample was inactive. Although it is possible to modify daily physical activity and SB, it is not possible at present to suggest the superiority of a particular intervention approach [76].

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)

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1 Oct 2019

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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5. Review Comments to the Author

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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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Reviewer #1: Yes: David Conradsson

Reviewer #2: No

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22 Nov 2019

We provide our response in the 'response to the reviewers'

Submitted filename: 191114_Response_to_the_reviewers.docx

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24 Dec 2019

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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Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #3: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: I Don't Know

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Reviewer #1: Yes

Reviewer #3: No

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6. Review Comments to the Author

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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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Reviewer #1: Yes: David Moulaee Conradsson

Reviewer #3: No

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5 Feb 2020

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11 Feb 2020

Movement behavior remains stable in stroke survivors within the first two months after returning home

PONE-D-19-23463R2

Dear Dr. wondergem,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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With kind regards,

Patrick Bergman

Academic Editor

PLOS ONE

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Reviewers' comments:

13 Mar 2020

PONE-D-19-23463R2

Movement behavior remains stable in stroke survivors within the first two months after returning home

Dear Dr. Wondergem:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Patrick Bergman

Academic Editor

PLOS ONE