Physical activity based on daily step-count in inpatient setting in stroke and traumatic brain injury patients in subacute stage: A cross-sectional observational study

Abstract

BACKGROUND:

Daily step-count is important post-insult in the subacute phase to influence neuroplasticity, functional recovery and as a predictive factor for activity level one-year post event.

OBJECTIVE:

Measure daily step-count in subacute patients follow-ing brain injury in an inpatient neurorehabilitation setting and compare these to evi-dence-based recommendations.

METHODS:

30 participants measured of daily step-count over a seven-day period, throughout the day to assess when and how activity varied. Step-counts were analyzed in sub-groups based on walking ability using the Functional Ambulation Categories (FAC). Correlations between steps-count and FAC level, walking speed, light touch, joint position sense, cognition, and fear of falling were calculated.

RESULTS:

Median (IQR) daily steps for all patients was 2512 (568.5,4070.5). Not independently walkers took 336 (5–705), the value is below the recommendation. Participants walking with assistance took 700 (31–3080), significantly below recommended value (p = 0.002), independent walkers took 4093 (2327–5868) daily steps, significantly below recommended value (p = < 0.001). Step-count showed moderate to high and statistically-significant correlations: positive for walking speed, joint position sense, negative for fear of falling, and number of medications.

CONCLUSIONS:

Only 10% of all participants reached the recommended daily steps. Interdisciplinary team-work and strategies to increase daily activity between therapies may be crucial to achieve recommended step-levels in subacute inpatient settings.

Keywords

Neurorehabilitation stroke traumatic brain injury steps stepcount

1 Background

Following acquired brain injury, improving walking ability is one of the most important goals during rehabilitation from both the patients’ and therapists’ point of view (Eng and Tang, 2007). Affected individuals often need assistance in daily life activities for months after stroke (Lloyd-Jones et al., 2010). Between 15% and 30% of these people are permanently disabled and 20% needs institutional care at 3 months post injury (Asplund K. et al., 2010). Studies indicate that these functional limitations are due to hemiparesis in 50% of individuals and by limited ability to walk in 30% of cases after stroke (Kelly-Hayes et al., 2003). Further studies demonstrate that neural deficits, such as cognitive, motor and sensory symptoms negatively affect walking ability followingbrain injury (Mansfield et al., 2018; Stuart et al., 2020).

In order to recover crucial motor skills, neuroplasticity as the substrate for motor-learning must occur. The most efficacious phase to influence plasticity following stroke, is within the first 3 months (Langhorne et al., 2011). Two decisive factors influencing plasticity during motor learning are task specificity and number of repetitions of movement. Clark (2015) Animal studies have shown that 400–600 repetitions of a specific movement per day are necessary to induce neuroplastic changes such as axonal growth and an increase in synaptic density (Kleim et al., 2002; Nudo & Milliken, 1996). During gait rehabilitation, the task-specific repetitions measured as number of daily steps taken are pivotal to promote neuroplasticity (Forrester, 2008). A proven measurement tool to measure the number of repetitions throughout the day are sensors that record daily steps. The StepsWatch™ foot sensor is a reliable and valid method in persons taking slow and short steps (Toth et al., 2018).

In humans, recommendations for daily-step counts are derived from the existing literature and indicate the number of steps required to influence plasticity and walking ability in acute and subacute brain injured patients. For patients who are non-functional walkers or who require continuous manual support, classified as Functional Ambulation Category 1 (FAC 1), 500–1000 steps per day should be taken to improve walking ability (Pohl et al. 2007; Peurala 2009; Dohle et al., 2015). For patients who can walk with intermittent physical assistance or supervision (FAC 2–3) there is actually no recommandation. 6,000 to 10,000 steps, depending on age, is recommended for patients who can walk independently (FAC 4–5) (Dohle et al., 2015).

Despite these recommendations, observational studies indicate that the actual number of steps taken are significantly lower. Values between 288 (SD 242) (Scrivener et al., 2012), 357 (296–418) (Lang et al., 2009) and 1516 steps per day (Hornby et al., 2015), have been measured. To date the number of steps taken during intensive, interprofessional, in-patient neurorehabilitation in a Swiss setting have not been measured.

Research suggests that intensive neurorehabilitation should be started in the first month’s post injury and that high dosage rehabilitation with more training sessions produces better functional outcomes (Veerbeek et al., 2014). Further, it is important to identify patients at risk of an inadequately active lifestyle as early as possible after brain injury. Daily steps outside the clinical setting are thought to provide unique insight into physical activity and walking behavior (Eng & Tang, 2007; Shaughnessy et al., 2005). It appears that the number of daily steps may even predict future health outcomes. Reducing secondary disease events is of great importance, as nearly one in four patients will suffer a recurrent stroke (Pennlert et al., 2014). Ambulatory function at discharge was found to be an independent predictor of stroke recurrence within one year of first stroke (Kono et al., 2015). Improving daily physical activity, as measured by steps, early after stroke appears to be important in increasing physical activity one year after stroke. One study shows that daily step count and balance ability two months after stroke are the strongest predictors of future steps-counts (Handlery et al., 2021). In addition, mobility level has been shown to inversely correlate with health care costs (Rajsic et al.,2019).

For these reasons, this study aimed to investigate the number of steps taken in the subacute phase (< 3 months) following traumatic brain injury or stroke in a clinical neurorehabilitation setting in Switzerland and to evaluate the number of patients reaching the recommended threshold necessary to influence neuroplasticity and function. Further aims were to investigate where the steps were taken and the influence of clinical parameters such as walking ability, walking speed, sensory function, cognition, fear of falling, number of medications and the Charlson Comorbidity Index (CCI) on the number of steps taken.

2 Methods

2.1 Participants

2.1.1 Inclusion/exclusion

All participants who suffered a first-time traumatic brain injury (TBI) or a first-time stroke within the last three months had a Functional Ambulatory Categories (FAC) score of one or more (able to walk at least 10 meters with two assistants) and remained as an inpatient over the weekend were included in the study. Cognitive capacity to understand and follow instructions was required.

The exclusion criteria were previous neurological disease, an FAC score of zero, orthopedic injuries that limited walking, hospital absence (home stay) over the weekend (Table 1).

Table 1
Participants characteristics

Participants Age (years) Sex (male:female) Time after event (days) CCI Medication (n) FAC

Stroke(n = 18)

3 50 male 38 0 12 3

4 37 male 66 1 2 4

5 41 male 44 0 11 1

6 46 male 61 0 13 2

7 56 male 88 1 14 2

8 58 male 83 0 12 2

9 67 female 46 1 7 4

11 32 male 37 0 5 3

12 59 male 19 1 4 4

13 64 female 34 0 2 4

15 72 female 21 0 7 3

16 62 male 44 3 7 4

19 58 female 58 0 12 3

20 75 male 65 0 6 2

21 57 female 51 0 8 5

23 21 male 47 1 15 2

24 26 male 12 0 2 4

29 59 male 12 1 3 5

TBI(n = 12)

1 69 female 80 2 9 2

2 63 female 34 0 9 4

10 75 male 76 0 2 5

14 72 male 45 2 10 5

17 51 male 81 0 15 1

18 64 male 46 3 13 4

22 54 male 63 1 15 1

25 52 female 32 2 1 4

26 48 male 62 0 4 4

27 58 male 38 2 7 5

28 47 man 24 3 6 2

30 60 male 58 0 12 1

Mean 55.1(13.6) 48.8(21.1) 0.8(1.0) 8.2(4.5)

Median 58.0 46.0 0.0 7.5

IQR 48.5, 63.8 34.8, 62.8 0.0, 1.0 4.3, 12.0

Range 21.0–75.0 12.0–88.0 0.0–3.0 1.0–15.0

Ratio 22:8

Participants	Age (years)	Sex (male:female)	Time after event (days)	CCI	Medication (n)	FAC
Stroke(n = 18)
3	50	male	38	0	12	3
4	37	male	66	1	2	4
5	41	male	44	0	11	1
6	46	male	61	0	13	2
7	56	male	88	1	14	2
8	58	male	83	0	12	2
9	67	female	46	1	7	4
11	32	male	37	0	5	3
12	59	male	19	1	4	4
13	64	female	34	0	2	4
15	72	female	21	0	7	3
16	62	male	44	3	7	4
19	58	female	58	0	12	3
20	75	male	65	0	6	2
21	57	female	51	0	8	5
23	21	male	47	1	15	2
24	26	male	12	0	2	4
29	59	male	12	1	3	5
TBI(n = 12)
1	69	female	80	2	9	2
2	63	female	34	0	9	4
10	75	male	76	0	2	5
14	72	male	45	2	10	5
17	51	male	81	0	15	1
18	64	male	46	3	13	4
22	54	male	63	1	15	1
25	52	female	32	2	1	4
26	48	male	62	0	4	4
27	58	male	38	2	7	5
28	47	man	24	3	6	2
30	60	male	58	0	12	1
Mean	55.1(13.6)		48.8(21.1)	0.8(1.0)	8.2(4.5)
Median	58.0		46.0	0.0	7.5
IQR	48.5, 63.8		34.8, 62.8	0.0, 1.0	4.3, 12.0
Range	21.0–75.0		12.0–88.0	0.0–3.0	1.0–15.0
Ratio		22:8

Notes: CCI = Charlson Comobidity Index max. possible score 37; FAC = functional ambulation categories scale 0–5; TBI = traumatic brain injury; n = number; IQR = interquartile range IQ1-IQ3.

2.1.2 Recruitment

For this cross-sectional observational study, all individuals admitted to REHAB Basel, a Swiss neurorehabilitation center, were screened weekly via an electronic internal file system by the principal investigator. Screening took place over a seven-month period between June 21 and January 22. Those fulfilling inclusion and exclusion criteria were asked to participate and signed the written informed consent form.

2.2 Outcome measures

2.2.1 Demographics baseline

Data such as age, sex, time after event, location of injury, number of medications and CCI were collected from the electronic database.

Subsequently, baseline measurements were performed in all patients to determine the extent of neurological deficits and to more accurately describe the included participants. Measurements included the 10meter walk test (10 MWT) a validated test for stroke (Bowden et al., 2008) and TBI (Moseley et al., 2004) to assess walking speed. Additionally, the FAC a validated test for stroke and TBI patients was used to assess walking ability. Categories are: continuous physical assistance by one or two persons (FAC1), intermittent or light physical assistance (FAC2), verbal or standby help, without physical contact (FAC3), iindependently on level ground, help on stairs, slopes and uneven surfaces (FAC4), independently in all environments (FAC5) (Mehrholz et al., 2007).

Using the sub score “sensory functioning” from the Fugl-Meyer Assessment of the lower extremity, light touch and joint position sense were evaluated. The Fugl-Meyer Assessment is a validated and reliable assessment for hemiplegic patients (Fugl-Meyer et al., 1975).

The Montreal Cognitive Assessment (MoCA) was measured. This is a tool to assess various aspects of cognition (Nasreddine et al., 2005). It has been validated for different neurological conditions, including stroke (Toglia et al., 2011).

The Tinetti Falls Efficacy Scale (FES) was completed to assess fear of falling. The accurate definition of this fear is low perceived self-efficacy in avoiding falls in essential, non-hazardous activities of daily living (Tinetti et al., 1990). The FES is a reliable and valid method for measuring fear of falling in stroke patients as well as in patients with traumatic brain injury (Hellström & Lindmark, 1999; Medley et al., 2006).

2.2.2 Primary outcomes

The primary outcome of this study was the number of steps taken daily over seven days using the StepWatch™ sensor, a validated measurement tool to measure daily activity (Toth et al., 2018). They use a proprietary mechanical measurement system instead of the three-axis accelerometer used in almost all commercially available activity meters (Comstock, 2014). The StepWatch™ mode has been scientifically peer-reviewed and publications have shown that this ankle sensor is the most sensitive and reliable monitor for gait analysis. Especially for uneven, slow and small steps, the StepWatch™ proved to be very valid (Treacy et al., 2017). For these participants after brain injury this sensor is well suited.

Recommended step counts were based on the literature and varied depending on Functional Ambulatory Categories (FAC), a scale that assesses independence in walking within 10 meters is scored from 0–5 (Mehrholz et al., 2007). For this study the following steps-counts are recommended: FAC 1, should reach at least 500 (Pohl et al. 2007; Peurala 2009; Dohle et al., 2015), FAC 2–3 at least 3000 and FAC 4–5 at least 6000 daily steps (Dohle et al., 2015). To define a target value for FAC 2–3, we used the predictive value from Handlery et al., 2021 with 1632 steps within 2 months for stroke patients. Since we had a broader patient group and included patients within 3 months, we defined for this group a target value of 3000 steps per day.

2.2.3 Secondary outcomes

Daily schedules and therapy plans copied from the electronic system for the days when the sensors were worn. Thus, changes in step counts during the day could be analyzed to assess when and during which therapies or activities steps were taken. This enabled activity and step count to be compared. Schedules were divided into the following categories: physiotherapy, occupational therapy, group therapy (e.g. sport groups, balance groups, co-ordination groups etc.), treadmill, Lokomat (robotic gait training for lower FAC levels), gym (weight training, stationary bicycles etc.) and steps outside therapy. Other therapies such as speech and language therapy, psychology, recreational therapy etc. take place in sitting and therefore do not generate significant numbers of steps.

2.3 Measurement procedure

The sensors were attached to the more affected ankle of the participants in bed before getting up in the morning and taken off in the evening shortly before going to bed (Fig. 1). The sensors were worn all day and were only removed briefly when showering. This procedure was repeated for seven days(5 weekdays, Saturday and Sunday) within a maximum of two weeks. Date, time of application and removal as well as initials of staff member were documented in a standardized form.

Fig. 1

Demonstration of wearing the sensor.

Since the sensors were applied and removed by the nursing staff or occupational therapists, a 45-minute training session involving a presentation and demonstration was held for these professions in each of the two participating brain injury wards before data collection. During this session, the main investigator demonstrated the handling and procedure of the application and removal of the sensors, as well as the necessary documentation. Written illustratedinstructions were also provided. This ensured consistent and systematic application and documentation of sensors in all participants.

Following inclusion in the study, the baseline measurements were performed by the main investigator. Following baseline measurements sensors were individually calibrated and left directly in the patient’s room, as preparation for wearing and step-count measurement begin the following day.

The literature have been shown that step monitors increase motivation to walk (Danks et al., 2016), which could have been a source of bias. It was therefore explained to staff members and the participants that the sensor was measuring muscle activity rather than step count.

As soon as a patient was included in the study, the principle investigator reminded the responsible nursing and occupational therapy staff about the handling, the end procedure of the sensors and the documentation. In addition, illustrated instructions were placed in the participants’ room.

2.4 Step-count data capture and evaluation

On completion of seven day data collection, the principle investigator read in the sensors via the SW4 RE App and exported them as an Excel file (Fig. 2). The sensor data were measured per minute and the corresponding time of day was recorded. The therapy units were read from the daily schedules and the number of steps in the corresponding minutes was summed from the sensor data.

Fig. 2

^*View via Research (RE) App, ^**View after downloading the data in the Excel file.

2.5 Data analysis

A power calculation using G^*Power 3.1.9.7 was performed to determine the sample size of this study to achieve the effect size of 0.6 on a significance level of 0.05 and a power of 80%. Since no standard deviation was known, the effect size d from the study by (Lang et al., 2009) with 357 (296–418) steps were taken. The calculation resulted - for the two-sided sample t test - in a required sample size of 24 participants and with dropouts of 20%, a target of 30 subjects was set. 30 participants were included with no dropouts.

Using the RStudio Desktop (2022.02.2 + 485) statistics program a distribution plot was used to determine the normal distribution of steps per day. Since the levels of this population were not normally distributed, the median and the inter-quartile range (IQR) were primarily used for the descriptive statistics. However, in one calculation, the mean was used. The evidence-based recommended daily number of steps is given as a mean value. To compare whether the recommended number of steps was achieved for the different functional categories, the mean values of this study over seven days were used. To showif the groups deviate from their recommended value, the Wilcoxon signed rank test was used. However, the individual subgroups are too small to achieve sufficient statistical significance.

Since the therapies with different FAC scores but also within the same FAC score were determined differently, certain subjects could not participate in some forms of therapy. In Table 3, only those therapies were counted that were actually attended. Thus, participants who did not receive any Lokomat, treadmill, gym or group therapies were not included in this calculation. With this view it can be shown how many steps can be achieved in which therapy with which FAC, if the therapy can be carried out. However, all participants were included to obtain a complete overview of all FAC and their therapy distribution. If they were not able to perform the corresponding form of therapy, this was calculated with0 steps.

To test the correlation between number of steps and neurological deficits of the baseline measurement, the rho value was calculated for each variable with the non-parametric spearman’s rank correlation rho. Values of Spearman’s correlation were interpreted as follows: very strong (0.90–1.00), strong (0.70–0.89), moderate (0.40.0.69) and weak correlation (0.10–0.39) (Schober et al., 2018).

2.6 Ethical approval

All participants or their closest relatives were informed in writing and verbally and were required to sign an informed consent form. The study received ethical approval from the Ethics Committee Northwestern Switzerland (May 25, 2021, ID number: 2021-00955) and follows the Declaration of Helsinki.

3 Results

3.1 Participants

57 subjects were screened, of which 36 fulfilled the inclusion and exclusion criteria and were asked to participate (Fig. 3). Of these a further six subjects were excluded due to: going home over the weekend, change of hospital, no interest. Finally, 30 participants were included and 30 completed the full study.

Fig. 3

Flow diagram of participants.

Of the 30 participants in Table 1, 60% were post-stroke and 40% were after traumatic brain injury, of these 26.7% were women and 73.3% were men. By FAC Score, FAC 1 (n = 4), FAC 2 (n = 7), FAC 3 (n = 4), FAC 4 (n = 10), FAC 5 (n = 5) were represented.

3.2 Baseline functional characteristics

In Table 2 the functional characteristics were presented in their walking ability.

Table 2
Functional characteristics split in FAC score 1–5

1 (n = 4) 2 (n = 7) 3 (n = 4) 4 (n = 10) 5 (n = 5) Total (n = 30)

10 MWT(m/s)

Mean(SD) 0.2(0.1) 0.3(0.1) 0.7(0.2) 1.1(0.3) 1.3(0.2) 0.8(0.5)

Median 0.2 0.3 0.7 1.1 1.3 0.8

IQR 0.2, 0.2 0.2, 0.4 0.6, 0.7 0.9, 1.4 1.3, 1.4 0.4, 1.1

Range 0.2 –0.4 0.2 –0.5 0.5 –0.9 0.8 –1.7 0.9 –1.4 0.2 –1.7

Light Touch Sen.

Mean(SD) 2.0(0.8) 3.0(1.5) 3.5(1.0) 3.4(0.7) 3.6(0.9) 3.2(1.1)

Median 2.0 4.0 4.0 3.5 4.0 4.0

IQR 1.8, 2.3 2.5, 4.0 3.5, 4.0 3.0, 4.0 4.0, 4.0 2.3, 4.0

Range 1.0 –3.0 0.0 –4.0 2.0 –4.0 2.0 –4.0 2.0 –4.0 0.0 –4.0

Joint Pos. Sense

Mean(SD) 4.8(2.8) 5.9(2.5) 6.5(1.9) 7.5(0.9) 7.6(0.6) 6.6(1.9)

Median 4.5 6.0 7.0 8.0 8.0 8.0

IQR 2.8, 6.5 4.5, 8.0 5.5, 8.0 7.3, 8.0 7.0, 8.0 6.0, 8.0

Range 2.0 –8.0 2.0 –8.0 4.0 –8.0 6.0 –8.0 7.0 –8.0 2.0 –8.0

FES

Mean(SD) 68.8(17.8) 67.9(22.9) 36.3(18.5) 31.1(15.4) 11.4(2.0) 42.1(26.8)

Median 62.0 67.0 41.0 35.0 10.0 41.0

IQR 59.8, 71.0 62.0, 80.0 31.8, 45.5 15.3, 41.5 10.0, 13.0 13.3, 60.5

Range 56.0 –95.0 26.0 –98.0 10.0 –53.0 11.0 –52.0 10.0 –14.0 10.0 –98.0

MoCA Total

Mean(SD) 15.3(9.8) 13.1(7.0) 19.0(9.4) 22.0(6.7) 14.6(10.8) 17.4(8.6)

Median 16.5 17.0 21.0 25.0 16.0 19.0

IQR 9.8, 22.0 7.0, 18.0 15.8, 24.3 16.5, 26.5 4.0, 24.0 11.3, 25.0

Range 3.0 –25.0 4.0 –21.0 6.0 –28.0 11.0 –30.0 3.0 –26.0 3.0 –30.0

	1 (n = 4)	2 (n = 7)	3 (n = 4)	4 (n = 10)	5 (n = 5)	Total (n = 30)
10 MWT(m/s)
Mean(SD)	0.2(0.1)	0.3(0.1)	0.7(0.2)	1.1(0.3)	1.3(0.2)	0.8(0.5)
Median	0.2	0.3	0.7	1.1	1.3	0.8
IQR	0.2, 0.2	0.2, 0.4	0.6, 0.7	0.9, 1.4	1.3, 1.4	0.4, 1.1
Range	0.2 –0.4	0.2 –0.5	0.5 –0.9	0.8 –1.7	0.9 –1.4	0.2 –1.7
Light Touch Sen.
Mean(SD)	2.0(0.8)	3.0(1.5)	3.5(1.0)	3.4(0.7)	3.6(0.9)	3.2(1.1)
Median	2.0	4.0	4.0	3.5	4.0	4.0
IQR	1.8, 2.3	2.5, 4.0	3.5, 4.0	3.0, 4.0	4.0, 4.0	2.3, 4.0
Range	1.0 –3.0	0.0 –4.0	2.0 –4.0	2.0 –4.0	2.0 –4.0	0.0 –4.0
Joint Pos. Sense
Mean(SD)	4.8(2.8)	5.9(2.5)	6.5(1.9)	7.5(0.9)	7.6(0.6)	6.6(1.9)
Median	4.5	6.0	7.0	8.0	8.0	8.0
IQR	2.8, 6.5	4.5, 8.0	5.5, 8.0	7.3, 8.0	7.0, 8.0	6.0, 8.0
Range	2.0 –8.0	2.0 –8.0	4.0 –8.0	6.0 –8.0	7.0 –8.0	2.0 –8.0
FES
Mean(SD)	68.8(17.8)	67.9(22.9)	36.3(18.5)	31.1(15.4)	11.4(2.0)	42.1(26.8)
Median	62.0	67.0	41.0	35.0	10.0	41.0
IQR	59.8, 71.0	62.0, 80.0	31.8, 45.5	15.3, 41.5	10.0, 13.0	13.3, 60.5
Range	56.0 –95.0	26.0 –98.0	10.0 –53.0	11.0 –52.0	10.0 –14.0	10.0 –98.0
MoCA Total
Mean(SD)	15.3(9.8)	13.1(7.0)	19.0(9.4)	22.0(6.7)	14.6(10.8)	17.4(8.6)
Median	16.5	17.0	21.0	25.0	16.0	19.0
IQR	9.8, 22.0	7.0, 18.0	15.8, 24.3	16.5, 26.5	4.0, 24.0	11.3, 25.0
Range	3.0 –25.0	4.0 –21.0	6.0 –28.0	11.0 –30.0	3.0 –26.0	3.0 –30.0

Notes: 10 MWT(m/s) = 10meter walk test in meters per second; Light touch sensation (score 0–4) and joint position sense (score 0–8) measured by subitems of the Fugl-Meyer assessment; FES = Falls Efficacy Scale (score 0–100); MoCA = Montreal cognitive assessment, total score (score 0–30); IQR = interquartile range IQ1-IQ3; n = number.

With increasing FAC, higher values were measured for walking speed, light touch sensation and joint position sense. A lower score in the Falls Efficacy Scale indicates higher self-efficacy regarding falls. Those with lower the FAC score show higher fear of falling. In the area of cognition, measured by the MoCA total score, the value decreased at FAC 5. Thus, although cognition improved with increasing FAC at FAC 1–4, those with FAC 5 exhibited lower levels of cognition.

3.3 Primary endpoint

Overall participants achieved over seven days (mo-su) a median (IQR) of 2512 (568.5,4070.5), on weekdays 2747 (717.5,4417.2) and on weekends 1818 (75.9,3090.3) steps per day. If the weekdays and the weekend are separated, they took significantly fewer steps p = < 0.001 on weekend days compared to weekdays.

If participants are divided into FAC categories of walking ability and mean step-count over the week and on the weekend are compared with recommended values, we see significant short falls in all three groups. Figure 4 shows that none of the groups reached the target value at the weekend (sa-su). When only the weekdays (mo-fr) are considered, 4 out of 30 subjects reached the target recommended step-count for their group. If all 7 days including the weekend (mo-su) are considered, only 3 subjects reached their target value.

Fig. 4

Primary endpoint comparison with recommended values.

The group with the lowest walking ability FAC 1, achieved a mean(SD) of 346(324.8) steps per day (mo-su) over seven days which does not significantly deviate (p = 0.625) from the recommended value. Thus, 50% of this group achieves the goal of 500 steps per day. Looking at weekdays (mo-fr), the group achieved 484(454.7) and on weekends 0(0.0).

9.1% of the group with walking ability of FAC 2 and 3 reached the recommended value of 3000 steps per day. Over seven days (mo-su) they achieved 948(1056.0) steps per day which deviates significantly p = 0.002 from the recommended value. On weekdays (mo-fr) the group reached 1072(1191.8) and on weekends 639(741.2) steps per day.

Only one of 15 in the third group with FAC 4 and 5 reached the minimum recommended value of 6000 steps per day. The mean group value of 3997(978.6) steps per day over seven days (mo-su) deviated significantly p = < 0.001 from the recommended value. On weekdays (mo-fr) the group achieved 4371(1077.4) and on weekends 3063(1073.9) steps per day.

3.4 Secondary endpoints

The Table 3 shows the step-counts achieved in each setting and type of therapy during weekdays for each FAC category. It is also possible to see which interventions were not used for which FAC.

Table 3
Number of steps per day were taken by setting and type of therapy (mo-fr)

Type of therapy median (IQR) FAC 1 FAC 2 FAC 3 FAC 4 FAC 5

Individual physiotherapy 99 (60.9;119.3) 43 (29.4;104.6) 433 (368.7;554.3) 198 (181.2;371.6) 480 (331.6;502.2)

Individual occupational therapy 0 (0.0;0.0) 10 (0.0;44.1) 351 (127.7;598.4) 181 (132.2;379.6) 235 (213.8;296.6)

Outside of therapy 0 (0.0;0.0) 71 (0.0;219.3) 1392 (1176.5;1641.6) 3076 (2539.1;3874.2) 3258 (3133.2;3689.6)

Lokomat robotic gait 372 (69.4;772.3) – – – –

Treadmill – 67 (54.0;80.0) 149 (148.6;148.6) 271 (161.6;380.7) 145 (144.8;144.8)

Group sport therapies – 38 (26.6;48.0) – 235 (118.4;551.0) 323 (107.9;718.1)

Gym strength training – – 161 (93.4;228.1) 233 (161.8;361.0) 636 (635.0;635.6)

Type of therapy median (IQR)	FAC 1	FAC 2	FAC 3	FAC 4	FAC 5
Individual physiotherapy	99 (60.9;119.3)	43 (29.4;104.6)	433 (368.7;554.3)	198 (181.2;371.6)	480 (331.6;502.2)
Individual occupational therapy	0 (0.0;0.0)	10 (0.0;44.1)	351 (127.7;598.4)	181 (132.2;379.6)	235 (213.8;296.6)
Outside of therapy	0 (0.0;0.0)	71 (0.0;219.3)	1392 (1176.5;1641.6)	3076 (2539.1;3874.2)	3258 (3133.2;3689.6)
Lokomat robotic gait	372 (69.4;772.3)	–	–	–	–
Treadmill	–	67 (54.0;80.0)	149 (148.6;148.6)	271 (161.6;380.7)	145 (144.8;144.8)
Group sport therapies	–	38 (26.6;48.0)	–	235 (118.4;551.0)	323 (107.9;718.1)
Gym strength training	–	–	161 (93.4;228.1)	233 (161.8;361.0)	636 (635.0;635.6)

Notes: Therapies that were not performed in this group are marked with “–“; IQR = interquartile range IQ1-IQ3; mo-fr = Monday till Friday.

Figure 5 shows where how many steps were taken during which activity on weekdays and weekend days for all participants.

Fig. 5

Setting and type of therapy of steps were taken.

The group with FAC 1 achieved the most steps during robotic gait training on the Lokomat® and in individual physiotherapy during the week. No further steps were taken in or outside of therapies or on weekends.

The steps of the group with a FAC 2 were achieved primarily outside of therapy and in individual physiotherapy. Few steps were taken in occupational therapy and on the treadmill. Comparing steps on weekends, to week they reached on weekends 29.2% less steps per day.

In the group with FAC 3, the most steps were taken outside of therapies. Compared to other FAC categories, the physiotherapy and occupational therapy achieved the most steps in this group. Few steps were performed on the treadmill. Comparing the steps during the week, to the steps at the weekend the numbers reduced to 42.9% of stepsper day.

The group with FAC 4 also achieved the most steps outside of therapies, followed by during physiotherapy and occupational therapy. Fewer steps are taken in the gym, in group therapies and on the treadmill. Comparing steps over the week, to weekend this group performed 34.3% fewer stepsper day.

The most independent ambulatory group, FAC 5, took the most steps outside of therapies compared with the other groups, followed by physiotherapy and occupational therapy. The group therapies, the gym and the treadmill resulted in fewer steps. Also in this group, they achieved 21.8% fewer steps per day on weekends than over the week.

At weekends, 100% of all steps were taken outside the therapies. For all groups apart from the most dependent, the majority of steps are generated outside of therapies.

3.4.1 Correlation

Table 4 shows the correlations between daily step count and the clinical parameters measured at baseline.

Table 4
Correlation of steps taken with demographic and clinical variables

Independent variable Number of daily steps Mo-Su

r coefficient CI p-value

Age 0.2333 –0.1;0.5 0.215

Sex –0.0871 –0.4;0.2 0.647

Diagnosis –0.1415 –0.5;0.3 0.456

Time after event –0.4406 –0.7;–0.1 0.015

CCI 0.0590 –0.3;–0.4 0.757

Medication –0.6676 –0.9;–0.4 < 0.001

FAC 0.8612 0.7;0.9 < 0.001

10MWT 0.8817 0.7;1.0 < 0.001

Light touch sensation 0.3870 0.0;0.7 0.035

Joint position sense 0.4989 0.2;0.7 0.005

FES –0.7945 –0.9;–0.6 < 0.001

MoCA total score 0.2597 –0.1;0.6 0.165

Independent variable	Number of daily steps Mo-Su
	r coefficient	CI	p-value
Age	0.2333	–0.1;0.5	0.215
Sex	–0.0871	–0.4;0.2	0.647
Diagnosis	–0.1415	–0.5;0.3	0.456
Time after event	–0.4406	–0.7;–0.1	0.015
CCI	0.0590	–0.3;–0.4	0.757
Medication	–0.6676	–0.9;–0.4	< 0.001
FAC	0.8612	0.7;0.9	< 0.001
10MWT	0.8817	0.7;1.0	< 0.001
Light touch sensation	0.3870	0.0;0.7	0.035
Joint position sense	0.4989	0.2;0.7	0.005
FES	–0.7945	–0.9;–0.6	< 0.001
MoCA total score	0.2597	–0.1;0.6	0.165

Notes: CI = 95% confidence interval; CCI = Charlson comorbidy Index; FAC = Funtcional ambulatory Category; 10MWT=10meter Walk Test; FES = Fall efficacy scale; MoCA = Montreal cognitive Assessment; Mo-Su=Monday till Sunday.

The 10MWT, the FAC and the FES had a strong and significant correlation. The number of medications taken, the joint position, and the time after event showed a moderate and significant correlation. Light touch sensation shows a weak but significant correlation. All others such as factors like age, gender, diagnosis, comorbidity, and cognition show weak, non-significant correlations or no association.

4 Discussion

The aim of this study was to investigate the number of steps taken in the subacute phase following traumatic brain injury and stroke in a clinical neurorehabilitation setting in Switzerland. Further aims were to investigate where the steps were taken and the influence of clinical parameters on the number of steps taken. In this study we used a cross-sectional design to measure number of steps taken during waking hours over a seven-day period.

The entire population, including all gait abilities and both diagnoses, achieved a median of 2512 steps per day. Compared to existing studies, this value appears quite good. The highest previously reported value was a median of 1516 steps per day (Hornby et al., 2015). This study only measured steps from 7:30 AM to 5:00 PM, it might be that some steps were missed. Further the study has already included 80 percent of the subjects within 30 days. In our study, only 5 of 30 subjects were included before day 30 after event. It could be that subjects are still somewhat worse motor-wise within the first 30 days than after the first 30 days due to additional acute symptoms such as cognition, circulation and well-being.

The days post-event can be very decisive due to the fact that in the first phase poststroke the best motor recovery takes place (Langhorne et al., 2011). The recovery curve of neuroplasticity also increases steadily within the first 3 months, so it is likely that subjects will have recovered more motor skills and achieve more steps by the time they are included in the study.

However, in comparison to the recommended number of steps, none of the FAC groups reached the mean recommended value required to influence neuroplasticity.

Two of the four participants who met the target step-count were in the FAC 1 group. The gait roboticsystem Lokomat^® (Hocoma, 2022), an individually adjustable exoskeleton, in combination with the patented dynamic body weight support, ensures a physiological gait pattern, in our study the high step counts can be achieved with dependent persons.

To achieve a sufficient number of daily steps to influence plasticity and functional recovery, we recommend that patients who cannot walk independently be offered daily robotic gait training, such as the Lokomat^®, in addition to usual physiotherapy.

In the group of participants with walking ability of FAC 2 and 3, only one participant achieved the recommended value measured over seven days. There are large differences in walking ability within FAC 2 and 3 which could account for the range of deviations from the recommended value Additional assessments would be needed to subdivide the walking ability more precisely. For people with FAC 2 who are less able to walk, the Lokomat^® was no longer used and the treadmill training did not lead to high step-counts. For better walkers in FAC 2 group, more treadmill training should take place.

With one exception, it can be said that the better the FAC value, i.e. the better their walking ability, the more steps are taken. The exception is that participants with FAC 2 take fewer steps than participants with a FAC 1. The reason for this could be that patients can already walk a few steps on the floor and therapies are more focusing on quality of walking or increasing balance skills rather than quantity.

In the group with FAC 3, most steps are taken outside of therapies although step-counts achieved in physiotherapy (433 steps/day) and occupational therapy (351 steps/day) are also reasonably high. Relatively few steps are achieved on treadmill and in group therapies. It may be that for patients who are already able to walk (FAC 3–5), the treadmill or a walking group should supplement the therapies on a daily basis, rather than the two to three times weekly in current practice. For the ambulatory with assistance, more step-generating group therapies could be offered. For example, a walking group or circuit training with walking activities could be established.

In the third group with FAC 4 and 5, no one achieved the recommended value measured over seven days. They achieved the majority of stepsoutside of therapies (FAC4:3076; FAC5:3258 steps/day), followed by physiotherapy (FAC4:198; FAC5:480 steps/day) and occupational therapy (FAC4:181; FAC5 235 steps/day). Those able to walk independently could also be brought closer to target with more frequent of with higher intensity group therapy and treadmill sessions.

The fact that more steps were achieved during physiotherapy in the groups that need assistance to walk (FAC3) than in groups that are able to walk independently (FAC 4/5) could be explained by considering the emphasis during individual therapy sessions at different stages of gait recovery. The focus in the earlier stages may be on increasing walking distance and independence. At later stages other issues such as high-level balance deficits, strength and endurance training or pain, may be emphasized leading to the generation of fewer steps.

Despite rehabilitation in a well-equipped and well-staffed rehabilitation specialized clinic, only three out of 30 subjects reached the target, recommended step count. This may imply that the treatment dosage in terms of repetitions within the first months after the event is too low to significantly influence neuroplasticity beyond spontaneous recovery. The results of several studies show strong to moderate correlations between steps per day and improvements in motor and non-motor outcomes, including walking ability (FAC), discharge location and mobility impairment in chronic and subacute patients after stroke (Holleran et al., 2014; Hornby et al., 2015). Low step counts could therefore lead to worse functional outcomes at discharge, lower chances to return home and higher dependence on other persons. Previous studies have shown that increasing physical activity in people with stroke has a positive effect on fatigue, mood, participation in society, and quality of life (Holleran et al., 2014; Hornby et al., 2015). Higher physical activity and cardiorespiratory fitness correlate with lower risk of stroke and lower Mortality (Kokkinos & Myers, 2019).

Practical recommendations for inpatient neurorehabilitation

This study confirms previous findings which suggest that too few steps are generated during physiotherapy and occupational therapy (Scrivener et al., 2012; Lang et al., 2009). The low step counts measured in this and other studies suggest that r strategies in the interprofessional team, between therapy, may be decisive to enable supported and independent walking. Active collaboration between staff and across professions is needed to develop local solutions. These may include earlier and closer partnership between nursing and therapy staff to develop rehabilitation plans which identify barriers and facilitators to walking for individual patients.

Simple interventions such as walking to the dining room, giving patients tasks like watering flowers, making agreements with patients regarding when wheelchairs will and will not be used can be effective. Increasing confidence and up-skilling handling skills across all team members in order to facilitate walking in patients who need support, may be a useful strategy.

As all participants took significantly fewer steps on weekends, when physiotherapy and occupational therapy did not take place. It may be that increasing the number of groups or activities available at the weekend or in the evenings may be helpful.

Another important resource is the motivation of patients to move more in the inpatient but also in the outpatient setting. Visual feedback to reach their daily goal via sensors indicating step-count could motivate patients to walk more (Danks et al., 2016) because Step-count monitoring increases steps in short and long-term (Chaudhry et al. 2020). In another study, patients with stroke wore sensors around their ankles each day of the week during inpatient rehabilitation. The intervention group received daily feedback on walking activity, while the control group received no feedback. Initially, there were no significant differences in physical activity scores between the two groups. At follow-up however, the intervention group’s scores were higher than those of the control group, suggesting that feedback on daily steps could motivate patients (Mansfield et al., 2015).

These interventions could also increase patients self-efficacy regarding independent gait and daily mobility. As Self-Efficacy significantly predicts participation and step watch activity in stroke patients, clinicians should measure patients’ self-efficacy and try to promote it, for example, with activity diaries (French et al. 2016).

The influence of joint position sense on walking has not been fully elucidated. Cho et al. 2021 demonstrated that ankle proprioception is a predictive factor for balance problems in chronic stroke patients (Cho & Kim, 2021). Somatosensory impairments of the legs have been shown to negatively impact balance and gait, with affected individuals exhibiting lower balance scores and greater sway in standing (Parsons et al., 2016). Somatosensory training has been shown to be effective in improving somatosensory impairments and balance. However, studies to date indicate that it does not affect gait (Chia et al., 2019). In our Study proprioception seems to have an influence in daily steps and therefore confirm the importance of proprioception on gait function. Further research is required to investigate effective treatments.

It seems that the number of medications taken has a negative impact on the number of steps per day. Several studies in the general population have shown that polypharmacy leads to greater impairment in mobility and daily functioning, and thus has a negative impact on gait (Gnjidic et al., 2012; Langeard et al., 2016). Polypharmacy is reported to be associated with poor gait performance and with gait deterioration. There is an association between gait dysfunction and medications may influence the incidence of falls (Montero-Odasso et al., 2019).

These factors should be kept in mind and positively influenced if possible. Time after event has a negative moderate significant correlation. This is due to the fact that the groups were of different sizes and the group walking without assistance (FAC4-5) represented the largest group with 14 of 30 participants. They were included early in the study and could explain the negative correlation.

Therefore, it is important to consider the patient’s medications and the proprioception, because, in addition to the subjective fear of falling, walking speed, and walking ability they seem to have a positive influence on the number of steps taken every day.

Strengths

Measurement with the StepWatch™ sensors enabled simple and valid measurement of daily step-count with little risk of measurement errors. One potential source of error was in wheelchair-bound individuals, who often use leg movements to propel the wheelchair while seated. These leg movements would have been counted as steps by the sensor. Through a pilot project, this error was recognized and corrected before the start of the study. The sensor was attached to the more affected leg and this was positioned on a footrest so that propelling the wheelchair with this leg was impossible. Through extensive education of the nursing and other professions, the attachment and detachment of the sensors worked flawlessly. There were no dropouts due to the thorough education of the nursing staff and the participants.

Limitations

To make a statement about the differences in each group in relation to their walking ability, more subjects are needed in each group. Thus, only tendencies can be determined. It was difficult to blind the participants, also for ethical reasons total blinding could not take place in this study. Although they were not told that the foot sensor measured number of steps taken, it may be that some participants were aware that a measure of activity was taking place and were consequently motivated to increase levels (Danks et al., 2016). Based on the exclusion criteria that participants who had homestays over the weekend were excluded, it could be that more severely affected participants with higher cognitive and motor deficits were included.

Reliable data regarding the long-term effects of low physical activity during in-patient rehabilitation following acquired brain injury are lacking. This knowledge gap is critical and should beexplored.

Strengths and weaknesses of the article

Significant methodological strengths of this study were the clear instructions provided to involved staff members and participants and consequently the systematic method of data collection. In order to ensure optimal accuracy in data collection, special attention was paid to the instruction and information provided. In collaboration with nursing staff a procedure was defined to ensure that sensor application occurred at the correct times and was not overlooked. Participants were also informed and encouraged to notify staff if mistakes occurred. This lead to full-day data collection, complete data sets and prevented drop-outs. Another strong point was that the measurements on the participants were always performed by the same assessor. Therefore, the reliability and consequently validity of the measurements wereimproved.

However, some methodological points could have been improved. We used the Research (RE) Mobile App for StepWatch™, which has been validated for research purposes in this patient group. Some inaccuracies may nevertheless occur when used in a clinical setting. For wheelchair users who take small steps to propel the wheelchair, leg movements on the floor can be counted as steps. To prevent this error we attached footrests to the wheelchairs for the study duration and asked participants to refrain from the activity. Furthermore, it was difficult for participants with impairments to apply the sensors. However, the sensor should be more practical and allow direct feedback. The blindness of participants was hardly possible because the sensor was applied to the leg and even if the participants did not inform about steps, it was certainly clear to some what the study was about.

It is clear that the sample number of participants in this study is too small to provide generalisable results. Also, the FAC categories could be better compared if the each category group was larger and included approximately the same number of subjects Unfortunately, there is little information on the target value of daily steps for FAC group 2–3 at the moment. Therefore, it is questionable whether the FAC group 2–3 target value is valid and if the comparison can be reasonably be made. It is also questionable to combine the FAC categories 2 + 3 into one group. There can be large differences in their walking ability and therefore it could make more sense to separate these into two groups and evaluate themseparately.

5 Conclusions

The participants in the subacute phase in an inpatient setting completed median 2511 daily steps. Only 3 of 30 participants reached the recommended number of steps regardless of walking ability. The walking ability (FAC and gait speed) joint position sense and fear of falling (FES) seems to have moderate to large effects on the number of daily steps. For patients and rehabilitation clinics, it seems advisable to increase the number of steps in the inpatient setting in the subacute state after stroke and TBI. Possible interventions could include devices which give patient feedback such as step counters or commonly used electronic activity monitors. Therapists could set the target value individually depending on the walking ability thus supporting patients to participate in goal-oriented, individually tailored rehabilitation patient diaries could be used to describe activity and document the achievement of therapy goals. Dairies can support self-efficacy and enable improvement to be seen over months. Peer-support and group-interaction could also be harnessed e.g. via within-patients competitions using white boards showing step number on the ward or through regular walking groups. These strategies are simple and low-cost ways to incorporate increased walking activity into the daily rehabilitation routine. They are feasible for most clinics and can be well integrated between therapies. Further research should evaluate the influence on gait recovery and long-term activity levels when the recommended number of steps are achieved in the clinical setting and continue to investigate effective strategies to increase daily step count during in-patient rehabilitation.

Regarding predictive factors of daily step-count in practice, tools are needed to provide visual feedback to achieve more steps in the inpatient setting.

Conflict of interest

The authors declare that they have no conflicts of interest.

Data sharing

The raw data is available from: https://access.olos.swiss/portal/home.

Footnotes

Acknowledgments

First and foremost, I would like to express my sincere thanks to all the people who participated in the sensor study and made the project possible. Further, important stakeholders of the study supported me, despite multiple hurdles in the planning as well as evaluation of the study. The work and realization of the project was an incredible enrichment for my education and also for me personally. There remain many positive memories regarding additional working hours for data collection and the cooperation and constant communication in the interprofessional setting of the REHAB Basel.

In particular, I would like to thank Dr. Clare Maguire, who helped to set up the project. As the head of physiotherapy at REHAB Basel, I have been given a great deal of trust and responsibility. The individual care and motivating support have made my work much easier. Further thanks go to Prof. Roger Hilfiker, physiotherapist PhD and specialist in the field of statistics. Thanks to his help, the analysis and presentation of the data were optimized and simplified. I would also like to thank all the staff of the REHAB Basel. Especially to all the nursing staff who put the sensors on and took them off the respective test persons. Their voluntary and committed help made the project possible. A big thank you goes to the REHAB Basel, as sponsor of the sensors the project could only be realized and implemented.

I would also like to thank all those involved in the private sphere for their support in various areas and for proofreading the work.

References

Asplund

, Stegmayr

, & Peltonen

(2010). Heart Disease and Stroke Statistics–2010 Update. https://doi.org/10.1161/CIRCULATIONAHA.109.192667

Bowden,

M. G.

, Balasubramanian,

C. K.

, Behrman,

A. L.

, & Kautz,

S. A.

(2008) Validation of a speed-based classification system using quantitative measures of walking performance poststroke. Neurorehabilitation and Neural Repair, 22(6), 672–675. https://doi.org/10.1177/1545968308318837

Chia

F. S.

, Kuys

, & Low Choy

(2019) Sensory retraining of the leg after stroke: Systematic review and meta-analysis. Clinical Rehabilitation, 33(6), 964–979. https://doi.org/10.1177/0269215519836461

Cho,

J.-E.

, & Kim,

(2021) Ankle Proprioception Deficit Is the Strongest Factor Predicting Balance Impairment in Patients With Chronic Stroke. Archives of Rehabilitation Research and Clinical Translation, 3(4), 100165. https://doi.org/10.1016/j.arrct.2021.100165

Comstock

(2014, July 9). Modus health wants to build a more clinical activity tracker. MobiHealthNews. https://www.mobihealthnews.com/34726/modus-health-wants-to-build-a-more-clinical-activity-tracker

Danks,

K. A.

, Pohlig,

, & Reisman,

D. S.

(2016) Combining fast walking training and a step activity monitoring program to improve daily walking activity after stroke: A preliminary study. Archives of Physical Medicine and Rehabilitation, 97(9 Suppl), S185–S193. https://doi.org/10.1016/j.apmr.2016.01.039

Dohle,

, Quintern,

, Saal,

, Stephan,

K. M.

, Tholen,

, & Wittenberg,

(2015) S2e-Leitlinie ≫Rehabilitation der Mobilität nach Schlaganfall (ReMoS)≪Kurzfassung der Konsensusversion 6.

Duncan, F., Lewis, S. J., Greig, C. A., Dennis, M. S., Sharpe, M., MacLullich, A. M. J., & Mead, G. E. (2015) Exploratorylongitudinal cohort study of associations of fatigue after stroke. Stroke, 46(4), 1052–1058. https://doi.org/10.1161/STROKEAHA.114.008079

Duncan,

P. W.

, Sullivan,

K. J.

, Behrman,

A. L.

, Azen,

S. P.

, Wu,

S. S.

, Nadeau,

S. E.

, Dobkin,

B. H.

, Rose,

D. K.

, Tilson,

J. K.

, Cen,

, Hayden,

S. K.

, & LEAPS Investigative Team. (2011) Body-weight-supported treadmill rehabilitation after stroke. The New England Journal of Medicine, 364(21), 2026–2036. https://doi.org/10.1056/NEJMoa1010790

10.

Eng,

J. J.

, & Tang,

P. F.

(2007) Gait training strategies to optimize walking ability in people with stroke: A synthesis of the evidence. Expert Review of Neurotherapeutics, 7(10), 1417–1436. https://doi.org/10.1586/14737175.7.10.1417

11.

Forrester,

L. W.

(2008) Exercise-mediated locomotor recovery and lower-limb neuroplasticity after stroke. The Journal of Rehabilitation Research and Development, 45(2), 205–220. https://doi.org/10.1682/JRRD.2007.02.0034

12.

Fugl-Meyer,

A. R.

, Jääskö,

, Leyman,

, Olsson,

, & Steglind,

(1975) The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scandinavian Journal of Rehabilitation Medicine, 7(1), 13–31.

13.

Gnjidic,

, Hilmer,

S. N.

, Blyth,

F. M.

, Naganathan,

, Waite,

, Seibel,

M. J.

, McLachlan,

A. J.

, Cumming,

R. G.

, Handelsman,

D. J.

, & Le Couteur,

D. G.

(2012) Polypharmacy cutoff and outcomes: Five or more medicines were used to identify community-dwelling older men at risk of different adverse outcomes. Journal of Clinical Epidemiology, 65(9), 989–995. https://doi.org/10.1016/j.jclinepi.2012.02.018

14.

Graven,

, Brock,

, Hill,

, & Joubert,

(2011) Are rehabilitation and/or care co-ordination interventions delivered in the community effective in reducing depression, facilitating participation and improving quality of life after stroke? Disability and Rehabilitation, 33(17–18), 1501–1520. https://doi.org/10.3109/09638288.2010.542874

15.

Handlery,

, Regan,

E. W.

, Stewart,

J. C.

, Pellegrini,

, Monroe,

, Hainline,

, Handlery,

, & Fritz,

S. L.

(2021) Predictors of Daily Steps at 1-Year Poststroke. Stroke, 52(5), 1768–1777. https://doi.org/10.1161/STROKEAHA.121.034249

16.

Hellström,

, & Lindmark,

(1999) Fear of falling in patients with stroke: A reliability study. Clinical Rehabilitation, 13(6), 509–517. https://doi.org/10.1191/026921599677784567

17.

Holleran,

C. L.

, Straube,

D. D.

, Kinnaird,

C. R.

, Leddy,

A. L.

, & Hornby,

T. G.

(2014) Feasibility and potential efficacy of high-intensity stepping training in variable contexts in subacute and chronic stroke. Neurorehabilitation and Neural Repair, 28(7), 643–651. https://doi.org/10.1177/1545968314521001

18.

Hornby,

T. G.

, Holleran,

C. L.

, Leddy,

A. L.

, Hennessy,

, Leech,

K. A.

, Connolly,

, Moore,

J. L.

, Straube,

, Lovell,

, & Roth,

(2015) Feasibility of Focused Stepping Practice During Inpatient Rehabilitation Poststroke and Potential Contributions to Mobility Outcomes. Neurorehabilitation and Neural Repair, 29(10), 923–932. https://doi.org/10.1177/1545968315572390

19.

Jørgensen

H. S.

, Nakayama,

, Raaschou,

H. O.

, & Olsen,

T. S.

(1995) Recovery of walking function in Copenhagen stroke patients: The Stroke Study. Archives of Physical Medicine and Rehabilitation, 76(1), 27–32. https://doi.org/10.1016/s0003-9459993(95)80038-7

20.

Kelly-Hayes,

, Beiser,

, Kase,

C. S.

, Scaramucci,

, D’Agostino,

R. B.

, & Wolf,

P. A.

(2003) The influence of gender and age on disability following ischemic stroke: The Framingham study. Journal of Stroke and Cerebrovascular Diseases: The Official Journal of National Stroke Association, 12(3), 119–126. https://doi.org/10.1016/S1052-9523057(03)00042-9

21.

Kleim,

J. A.

, Barbay,

, Cooper,

N. R.

, Hogg,

T. M.

, Reidel,

C. N.

, Remple,

M. S.

, & Nudo,

R. J.

(2002) Motor learning-dependent synaptogenesis is localized to functionally reorganized motor cortex. Neurobiology of Learning and Memory, 77(1), 63–77. https://doi.org/10.1006/nlme.2000.4004

22.

Kokkinos,

, & Myers,

(2019). Physical Activity, Cardiores piratory Fitness, and Health: A Historical Perspective. In P. Kokkinos & P. Narayan (Eds.), Cardiorespiratory Fitness in Cardiometabolic Diseases: Prevention and Management in Clinical Practice (pp. 1–9). Springer International Publishing. https://doi.org/10.1007/978-3-030-04816-7_1

23.

Kono,

, Kawajiri,

, Kamisaka,

, Kamiya,

, Akao,

, Asai,

, Inuzuka,

, & Yamada,

(2015) Predictive impact of daily physical activity on new vascular events in patients with mild ischemic stroke. International Journal of Stroke: Official Journal of the International Stroke Society, 10(2), 219–223. https://doi.org/10.1111/ijs.12392

24.

Lang,

C. E.

, Macdonald,

J. R.

, Reisman,

D. S.

, Boyd,

, Jacobson Kimberley,

, Schindler-Ivens,

S. M.

, Hornby,

T. G.

, Ross,

S. A.

, & Scheets,

P. L.

(2009) Observation of amounts of movement practice provided during stroke rehabilitation. Archives of Physical Medicine and Rehabilitation, 90(10), 1692–1698. https://doi.org/10.1016/j.apmr.2009.04.005

25.

Langeard,

, Pothier,

, Morello,

, Lelong-Boulouard,

, Lescure,

, Bocca,

M.-L.

, Marcelli,

, Descatoire,

, & Chavoix,

(2016) Polypharmacy Cut-Off for Gait and Cognitive Impairments. Frontiers in Pharmacology, 7, 296. https://doi.org/10.3389/fphar.2016.00296

26.

Langhorne,

, Bernhardt,

, & Kwakkel,

(2011) Stroke rehabilitation. The Lancet, 377(9778), 1693–1702. https://doi.org/10.1016/S0140-6736(11)60325-5

27.

Lloyd-Jones,

, Adams,

R. J.

, Brown,

T. M.

, Carnethon,

, Dai,

, De Simone,

, Ferguson,

T. B.

, Ford,

, Furie,

, Gillespie,

, Go,

, Greenlund,

, Haase,

, Hailpern,

, Ho,

P. M.

, Howard,

, Kissela,

, Kittner,

, & Lackland,

, American Heart Association Statistics Committee and Stroke Statistics Subcommittee (2010) Executive summary: Heart disease and stroke statistics–2010 update: a report from the American Heart Association. Circulation, 121(7), 948–954. https://doi.org/10.1161/CIRCULATIONAHA.109.993192666

28.

Mansfield,

, Inness,

E. L.

, & Mcilroy,

W. E.

(2018). Chapter 13–Stroke. In B. L. Day & S. R. Lord (Eds.), Handbook of Clinical Neurology (Vol. 159, pp. 205–228). Elsevier. https://doi.org/10.1016/B978-0-444-63916-5.00013-6

29.

Mansfield,

, Wong,

J. S.

, Bryce,

, Brunton,

, Inness,

E. L.

, Knorr,

, Jones,

, Taati,

, & McIlroy,

W. E.

(2015) Use ofAccelerometer-Based Feedback of Walking Activity for AppraisingProgress With Walking-Related Goals in Inpatient StrokeRehabilitation: A Randomized Controlled Trial. Neurorehabilitation and Neural Repair, 29(9), 847–857. https://doi.org/10.1177/1545968314567968

30.

Medley,

, Thompson,

, & French,

(2006) Predicting theprobability of falls in community dwelling persons with braininjury: A pilot study. Brain Injury, 20(13–14), 1403–1408. https://doi.org/10.1080/02699050601082057

31.

Mehrholz,

, Wagner,

, Rutte,

, Meißner

, & Pohl,

(2007) Predictive Validity and Responsiveness of the Functional Ambulation Category in Hemiparetic Patients After Stroke. Archives of Physical Medicine and Rehabilitation, 88(10), 1314–1319. https://doi.org/10.1016/j.apmr.2007.06.764

32.

Montero-Odasso,

, Sarquis-Adamson,

, Song,

H. Y.

, Bray,

N. W.

, Pieruccini-Faria,

, & Speechley,

(2019) Polypharmacy, Gait Performance, and Falls in Community-Dwelling Older Adults. Results from the Gait and Brain Study. Journal of the American Geriatrics Society, 67(6), 1182–1188. https://doi.org/10.1111/jgs.15774

33.

Moseley,

A. M.

, Lanzarone,

, Bosman,

J. M.

, van Loo,

M. A.

, de Bie,

R. A.

, Hassett,

, & Caplan,

(2004) Ecological validity of walking speed assessment after traumatic brain injury: A pilot study. The Journal of Head Trauma Rehabilitation, 19(4), 341–348. https://doi.org/10.1097/00001199-1026200407000-0000

34.

Nasreddine,

Z. S.

, Phillips,

N. A.

, Bédirian,

, Charbonneau,

, Whitehead,

, Collin,

, Cummings,

J. L.

, & Chertkow,

(2005) The Montreal Cognitive Assessment, MoCA: A brief screeningtool for mild cognitive impairment. Journal of the AmericanGeriatrics Society, 53(4), 695–699. https://doi.org/10.1111/j.1532-5415.2005.53221.x

35.

Nudo,

R. J.

, & Milliken,

G. W.

(1996) Reorganization of movement representations in primary motor cortex following focal ischemic infarcts in adult squirrel monkeys. Journal of Neurophysiology, 75(5), 2144–2149. https://doi.org/10.1152/jn.1996.75.5.2144

36.

Parsons,

S. L.

, Mansfield,

, Inness,

E. L.

, & Patterson,

K. K.

(2016) The relationship of plantar cutaneous sensation and standing balance post-stroke. Topics in Stroke Rehabilitation, 23(5), 326–332. https://doi.org/10.1080/10749357.2016.1162396

37.

Pennlert,

, Eriksson,

, Carlberg,

, & Wiklund,

P.-G.

(2014) Long-term risk and predictors of recurrent stroke beyond the acute phase. Stroke, 45(6), 1839–1841. https://doi.org/10.1161/STROKEAHA.114.005060

38.

Rajsic,

, Gothe,

, Borba,

H. H.

, Sroczynski,

, Vujicic,

, Toell,

, & Siebert,

(2019) Economic burden of stroke: A systematic review on post-stroke care. The European Journal of Health Economics: HEPAC: Health Economics in Prevention and Care, 20(1), 107–134. https://doi.org/10.1007/s10198-1052018-0984-0

39.

Schober,

, Boer,

, & Schwarte,

L. A.

(2018) Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia and Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864

40.

Scrivener,

, Sherrington,

, & Schurr,

(2012) Exercise dose and mobility outcome in a comprehensive stroke unit: Description and prediction from a prospective cohort study. Journal of Rehabilitation Medicine, 44(10), 824–829. https://doi.org/10.2340/16501977-1028

41.

Shaughnessy,

, Michael,

K. M.

, Sorkin,

J. D.

, & Macko,

R. F.

(2005) Steps after stroke: Capturing ambulatory recovery. Stroke, 36(6), 1305–1307. https://doi.org/10.1161/01.STR.0000166202.00669.d2

42.

Stuart,

, Parrington,

, Morris,

, Martini,

D. N.

, Fino,

P. C.

, & King,

L. A.

(2020) Gait measurement in chronic mild traumatic brain injury: A model approach. Human Movement Science, 69, 102557. https://doi.org/10.1016/j.humov.2019.102557

43.

Tinetti,

M. E.

, Richman,

, & Powell,

(1990) Falls efficacy as a measure of fear of falling. Journal of Gerontology, 45(6), P239–243. https://doi.org/10.1093/geronj/45.6.p239

44.

Toglia,

, Fitzgerald,

K. A.

, O’Dell,

M. W.

, Mastrogiovanni,

A. R.

, & Lin,

C. D.

(2011) The Mini-Mental State Examination and Montreal Cognitive Assessment in persons with mild subacute stroke: Relationship to functional outcome. Archives of Physical Medicine and Rehabilitation, 92(5), 792–798. https://doi.org/10.1016/j.apmr.2010.12.034

45.

Toth,

L. P.

, Park,

, Springer,

C. M.

, Feyerabend,

M. D.

, Steeves,

J. A.

, & Bassett,

D. R.

(2018) Video-Recorded Validation of Wearable Step Counters under Free-living Conditions. Medicine and Science in Sports and Exercise, 50(6), 1315–1322. https://doi.org/10.1249/MSS.0000000000001569

46.

Treacy,

, Hassett,

, Schurr,

, Chagpar,

, Paul,

S. S.

, & Sherrington,

(2017) Validity of Different Activity Monitors to Count Steps in an Inpatient Rehabilitation Setting. Physical Therapy, 97(5), 581–588. https://doi.org/10.1093/ptj/pzx010

47.

Veerbeek, J. M., van Wegen,

, van Peppen,

, van der Wees,

P. J.

, Hendriks,

, Rietberg,

, & Kwakkel,

(2014) What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PloS One, 9(2), e87987. https://doi.org/10.1371/journal.pone.0087987