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Haptic Nudges Increase Affected Upper Limb Movement During Inpatient Stroke Rehabilitation: A Multiple-Period Randomized Crossover Study

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Background: As many as 80% of stroke survivors experience upper limb (UL) disability. The strong relationships between disability, lost productivity, and ongoing healthcare costs mean reducing disability after stroke is critical at both individual and society levels. Unfortunately, the amount of UL-focused rehabilitation received by people with stroke is extremely low. Activity monitoring and promotion using wearable devices offers a potential technology-based solution to address this gap. Commonly, wearable devices are used to deliver a haptic nudge to the wearer with the aim of promoting a particular behavior. However, little is known about the effectiveness of haptic nudging in promoting behaviors in patient populations. Objective: To estimate the effect of haptic nudging delivered via a wrist worn wearable device on UL movement in people with upper limb disability following stroke undertaking inpatient rehabilitation. Methods: A multiple-period randomized crossover design was used to measure the association of UL movement with the occurrence of haptic nudge reminders to move the affected upper limb in 20 people with stroke undertaking inpatient rehabilitation. UL movement was observed and classified using movement taxonomy across 72 one-minute observation periods from 07:00 h to 19:00 h on a single weekday. On 36 occasions a haptic nudge to move the affected UL was provided just before the observation period. On the other 36 occasions, no haptic nudge was given. The timing of the haptic nudge was randomized across the observation period for each participant. Statistical analysis was performed using mixed logistic regression. The effect of a haptic nudge was evaluated from the intention-to-treat dataset as the ratio of the odds of affected UL movement during the observation period following a “Planned Nudge” to the odds of affected limb movement during the observation period following “No Nudge.” Results: The primary intention-to-treat analysis showed the odds ratio (OR) of affected UL movement following a haptic nudge was 1.44 (95% confidence interval [CI]: 1.28, 1.63; P<.0001). The secondary analysis revealed the increased odds of affected UL movement following a Planned Nudge was predominantly due to increased odds of spontaneous affected UL movement (OR 2.03, 95% CI: 1.65 2.51; P<.0001) rather than affected UL movement in conjunction with unaffected UL movement (OR 1.13, 95% CI: 0.99, 1.29; P=.07). Conclusions: Haptic nudging delivered via a wrist-worn wearable device increases affected UL movement in people with UL disability following stroke undertaking inpatient rehabilitation. The promoted movement appears to be specific to the instructions given. Clinical Trial: ACTRN12616000654459.
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Original Paper
Haptic Nudges Increase Affected Upper Limb Movement During
Inpatient Stroke Rehabilitation:Multiple-Period Randomized
Crossover Study
Nada Elizabeth June Signal1, BHSc, MHSc, PhD; Ruth McLaren1, BHSc, MHPrac; Usman Rashid1, BEng, MSc,
PhD; Alain Vandal2, MA, PhD; Marcus King3, BE; Faisal Almesfer4, BE, ME; Jeanette Henderson5, BHSc; Denise
Taylor1, BPhys, MSc, PhD
1Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
2Department of Statistics, University of Auckland, Auckland, New Zealand
3Callaghan Innovation, Christchurch, New Zealand
4Exsurgo Rehabilitation, Auckland, New Zealand
5Assessment, Treatment and Rehabilitation Department, Waitakere Hospital, Waitemata District Health Board, Auckland, New Zealand
Corresponding Author:
Nada Elizabeth June Signal, BHSc, MHSc, PhD
Health and Rehabilitation Research Institute
Auckland University of Technology
90 Akoranga Drive
Northcote
Auckland, 0627
New Zealand
Phone: 64 21929144
Email: nada.signal@aut.ac.nz
Abstract
Background: As many as 80% of stroke survivors experience upper limb (UL) disability. The strong relationships between
disability, lost productivity, and ongoing health care costs mean reducing disability after stroke is critical at both individual and
society levels. Unfortunately, the amount of UL-focused rehabilitation received by people with stroke is extremely low. Activity
monitoring and promotion using wearable devices offer a potential technology-based solution to address this gap. Commonly,
wearable devices are used to deliver a haptic nudge to the wearer with the aim of promoting a particular behavior. However, little
is known about the effectiveness of haptic nudging in promoting behaviors in patient populations.
Objective: This study aimed to estimate the effect of haptic nudging delivered via a wrist-worn wearable device on UL movement
in people with UL disability following stroke undertaking inpatient rehabilitation.
Methods: A multiple-period randomized crossover design was used to measure the association of UL movement with the
occurrence of haptic nudge reminders to move the affected UL in 20 people with stroke undertaking inpatient rehabilitation. UL
movement was observed and classified using movement taxonomy across 72 one-minute observation periods from 7:00 AM to
7:00 PM on a single weekday. On 36 occasions, a haptic nudge to move the affected UL was provided just before the observation
period. On the other 36 occasions, no haptic nudge was given. The timing of the haptic nudge was randomized across the
observation period for each participant. Statistical analysis was performed using mixed logistic regression. The effect of a haptic
nudge was evaluated from the intention-to-treat dataset as the ratio of the odds of affected UL movement during the observation
period following a “Planned Nudge” to the odds of affected limb movement during the observation period following “No Nudge.
Results: The primary intention-to-treat analysis showed the odds ratio (OR) of affected UL movement following a haptic nudge
was 1.44 (95% CI 1.28-1.63, P<.001). The secondary analysis revealed an increased odds of affected UL movement following
a Planned Nudge was predominantly due to increased odds of spontaneous affected UL movement (OR 2.03, 95% CI 1.65-2.51,
P<.001) rather than affected UL movement in conjunction with unaffected UL movement (OR 1.13, 95% CI 0.99-1.29, P=.07).
Conclusions: Haptic nudging delivered via a wrist-worn wearable device increases affected UL movement in people with UL
disability following stroke undertaking inpatient rehabilitation. The promoted movement appears to be specific to the instructions
given.
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Trial Registration: Australia New Zealand Clinical Trials Registry 12616000654459;
https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370687&isReview=true
(JMIR Mhealth Uhealth 2020;8(7):e17036) doi: 10.2196/17036
KEYWORDS
stroke; rehabilitation; physical activity; movement; disability; technology; upper limb; wearable; haptic; nudge
Introduction
Although the incidence of stroke has reduced, its burden
continues to grow as more people are surviving after stroke and
living with disability [1]. Direct and indirect health care costs
following stroke are strongly correlated with stroke disability,
with greater disability associated with greater costs [2,3].
Around 80% of stroke survivors experience upper limb (UL)
disability, with only 5%-20% achieving full recovery of UL
function [4-6]. UL disability has subsequent impacts on
independence in activities of daily living, discharge destination,
return to work, quality of life, and mood [7-10].
Effective rehabilitation involves high-dose, intensive,
task-specific activity [11]. Meta-analyses of randomized
controlled trials suggest there is a dose-response relationship,
with higher doses of rehabilitation resulting in better outcomes
[12-14]. However, studies describing usual stroke care illustrate
that the dose of UL rehabilitation received by people with stroke
is extremely low, with as little as 4-6 minutes in physiotherapy
sessions and 11-17 minutes in occupational therapy sessions
[15]. Movement of the affected UL outside formal therapy
sessions during inpatient rehabilitation is also low [16].
Consequently, affected UL movement dose, both within formal
therapy sessions and across the rehabilitation day, is currently
insufficient to reduce UL disability following stroke.
Rehabilitation technologies have been proposed as potential
solutions to the limited dose of rehabilitation this population
receives [17]. A number of technological solutions have been
developed for use in UL stroke rehabilitation, including virtual
reality, gaming, and robotics [17-19]. Despite indications of
effectiveness [20,21], the use of rehabilitation technologies is
not yet pervasive in stroke care [18]. Therapists have identified
several barriers to adopting rehabilitation technologies, including
concerns about patient safety, whether the technology effectively
addresses a clinical need, and the feasibility of technologies
from time, space, and cost perspectives [22,23]. The poor uptake
of rehabilitation technologies is inconsistent with research
involving people with stroke that indicates rehabilitation
technologies can support engagement and interest in performing
repetitive rehabilitation activities and offer a means of social
support [19,24,25]. Activity monitoring and promotion using
wearable devices is a potential low-cost and feasible
rehabilitation technology. Research investigating the effect of
wearable devices on outcomes following stroke is in its infancy.
Preliminary indications suggest that wearable devices may
increase the amount and intensity of physical activity undertaken
during rehabilitation [26-28] and potentially contribute to
improved functional outcomes [29]. However, the effect of
wearable devices on UL rehabilitation and outcomes has been
less well studied [30,31], with much of the research to date
focusing on the accuracy and validity of accelerometry
measurement of real-world UL movement [32-34].
Wearable devices that deliver haptic nudges have been used to
promote physical activity in both healthy and patient populations
[35-39]. Commonly, a haptic nudge reminder is delivered via
a small motor embedded inside a wearable device. Wearers are
encouraged to respond to a haptic reminder by performing a
particular behavior. For example, a haptic nudge might be used
to remind the wearer to stand up and move after an extended
period of sitting or to undertake rehabilitation exercises.
However, despite the pervasiveness of haptic nudging in
consumer wearable devices, there remains much to learn about
the effectiveness of haptic nudging in promoting behaviors in
patient populations. Haptic nudges have been used to effectively
promote behaviors in people with autism spectrum disorder [40]
and traumatic brain injury engaged in a rehabilitation task [41].
Research also suggests that wearable devices are feasible and
well tolerated in people with stroke [30,42,43], with preliminary
data indicating haptic nudging via wearable devices may
promote affected UL movement [31]. The aim of this study was
to estimate the effect of haptic nudging delivered via a
wrist-worn wearable device on UL movement in people with
UL disability following stroke undertaking inpatient
rehabilitation.
Methods
Study Design
A multiple-period randomized crossover design was used to
measure the association of UL movement with the occurrence
of haptic nudge reminders to move the affected UL in 20 people
with stroke undertaking inpatient rehabilitation. UL movement
was observed and classified using movement taxonomy across
72 one-minute observation periods from 7:00 am to 7:00 pm
on a single weekday. On 36 occasions, a haptic nudge (Nudge)
to move the affected UL was provided just before the
observation period, and on the other 36 occasions, no haptic
nudge was given (No Nudge). The timing of the haptic nudge
was randomized. Approval for this study was obtained from the
New Zealand Health and Disability Ethics Committee
(16/NTA/74).
Participants
All people with stroke admitted to the rehabilitation service
from July 2018 through December 2018 were considered for
inclusion in this study. Participants were included if they had
a confirmed diagnosis of stroke based on the Oxford
classification system [44], presented with UL deficit as a result
of stroke as determined by their rehabilitation therapist, were
deemed medically stable and fit for rehabilitation by their
medical consultant, and provided written informed consent.
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Participants were excluded if they had cognitive, behavioral, or
communication impairments that, in the opinion of the research
team (RM, DT, NS), would limit their ability to participate in
the research (for example, if the person was unable to follow a
2-step verbal command or recall the details of the research
study); were within 3 days of planned discharge from inpatient
rehabilitation; or reported shoulder pain.
Procedure
Potential participants were identified and referred to the research
team by their rehabilitation therapist. They were then informed
about the study and screened against the inclusion and exclusion
criteria by a trained research assistant. Eligible participants
provided written informed consent and identified a mutually
agreeable day for data collection. Demographic, clinical, and
medical information was gathered from the medical record of
consenting participants by the rehabilitation therapist. Collected
data included age, sex, ethnicity, date of stroke, type of stroke,
side of body most affected, dominant hand prior to stroke, date
of admission to the rehabilitation ward, estimated date of
discharge, comorbidities, and medications.
On the day of data collection, the participant was fitted with a
BuzzNudge wearable device on the wrist of the affected UL.
The BuzzNudge is a Bluetooth-enabled wearable device with
a 2.3 V coin vibration motor (Precision Microdrives Ltd, Model
310-103), which provided 3 consecutive vibratory stimuli of
0.3 seconds duration at 150 Hz within 1.5 seconds, representing
a similar magnitude of stimulus to the vibration of a phone. The
researcher explained the value of moving the affected UL after
stroke and instructed the participant to “move, try and move,
or visualize moving their (affected) arm” following a nudge.
The researcher emphasized that the participant should do
whatever movement they felt they could manage. If sensation
was impaired in the affected UL such that the participant could
not feel the haptic nudge, the device was worn on the less
affected UL, but the participant was still instructed to use the
haptic nudge as a reminder to move the affected UL.
During data collection, participants were followed discreetly
out of their field of view around the rehabilitation ward, therapy
areas, and hospital facilities by a trained researcher (where
feasible). The researcher manually recorded UL movement for
1 minute every 10 minutes [45]. Each minute of observation
was broken into 6 epochs of 10 seconds using a silent interval
timer. UL movement was classified according to a previously
defined taxonomy: (1) unilateral affected UL movement; (2)
unilateral unaffected UL movement; (3) bimanual movement,
where movement of both ULs was observed to achieve a
common task or purpose; (4) bilateral limb movement, where
movement of both ULs was observed to achieve independent
or unrelated tasks; and (5) no movement [16]. When patients
were not able to be directly observed (ie, because curtains were
drawn or when in showers or toilets), activity was recorded after
conferring with the participant, staff, or family members, as
appropriate. In circumstances where the activity could not be
estimated (eg, during 4 randomly scheduled observer breaks),
activity was coded as unobserved [16,46].
Haptic nudge reminders were triggered by the researcher via
Bluetooth immediately before movement observation according
to a planned randomization schedule. For half of the observation
periods, a haptic nudge was to be provided, and for half, a haptic
nudge was not to be provided. Further details regarding the
randomization schedule are presented in Multimedia Appendix
1. Haptic nudges were not triggered if the participant was not
visible, the participant was asleep, or a nudge was considered
inappropriate (eg, if the participant was drinking a hot beverage
or undertaking an assessment procedure). Any scheduled nudge
that was not given was recorded as a “Missed Nudge.
Statistical Analysis
Coded data were entered into a Microsoft Excel spreadsheet.
Descriptive analysis was used to examine the amount and type
of UL movement. If a participant withdrew from the study, their
movement observations were coded as missing values, and their
scheduled nudges were coded as Missed Nudges. Statistical
analysis was performed using mixed logistic regression. Total
affected UL movement was collated based on all observations
in which the affected UL was moved: total affected UL
movement = unilateral affected UL movement + bilateral limb
movement + bimanual movement.
In the primary analysis using the intention-to-treat dataset,
nudges were represented as a fixed effect factor with two levels:
Planned Nudge (ie, Nudge + Missed Nudge) and No Nudge,
meaning the analysis considered whether a nudge was planned
or not, rather than delivered. The effect of Planned Nudges was
evaluated as the ratio of the odds of affected UL movement
during the observation period following a Planned Nudge to
the odds of affected UL movement during the observation period
following No Nudge. More formally, the primary null hypothesis
tested with the model was: H0: ORPlanned Nudge/No Nudge = 1.
The sensitivity of the effect of the Planned Nudge to missing
values was tested with the pooled effect of 10 worst-case random
imputations at the level of the participant with the worst
outcomes.
In the secondary analysis using the intention-to-treat dataset,
two additional models were used to evaluate the effect of a
Planned Nudge on unilateral affected UL movement and the
sum of bilateral limb movement + bimanual movement,
respectively. A post-hoc exploratory analysis with an
instrumental variable approach was used to evaluate the local
average treatment effect (also known as complier average causal
effect) of the haptic nudge reminder. This analysis considered
the effect of the haptic nudge when delivered (Nudged)
compared with no haptic nudge (Not Nudged) irrespective of
the schedule on total affected UL movement. To explain the
variation in UL movement across the day, all models fitted the
data with smooth natural splines that had 1 degree of freedom
per hour. To account for correlated repeated measures, the
models included hierarchical random effects per participant and
per hour within participant. Statistical analyses were performed
using R version 3.5.1 (R Foundation for Statistical Computing,
Vienna, Austria) with lme4 version 1.1-21 [47], splines version
3.5.1 [48], and emmeans version 1.3.4 [39]. The threshold of
statistical significance was set at .05. A detailed statistical
analysis report containing code snippets and additional graphical
representations of data is available in Multimedia Appendix 1.
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Results
Participant Characteristics
In total, 20 people consented to participate in this study (Table
1). Participants’ median age was 76 years (IQR 68-83 years),
and the median time since stroke was 23.5 days (IQR 8.25-38.25
years); 9 participants had left hemiparesis, 10 had right
hemiparesis, and 1 participant had bilateral symptoms with the
left UL more affected than the right. Five participants had total
anterior circulation syndrome, 10 had partial anterior circulation
syndrome, 4 had lacunar circulation syndrome, and 1 had
posterior circulation syndrome. Of the participating patients, 4
had hemorrhagic stroke, and 16 had ischemic stroke. Participant
2 was withdrawn from the study when 6 nudges in a row were
unable to be delivered due to a technical error. Participant 6
asked to withdraw 20 minutes into data collection due to
experiencing anxiety associated with wearing the device.
Table 1. Participant demographics.
Device worn on AULAUL = dominant ULb
AULa
Days since strokeStroke classificationGenderAge range (years)Participant
YesNoLeft9LACS-Ic
Male70-791
YesNoLeft39TACS-Ie
Female80-892d
YesNoLeft59TACS-IFemale70-793
YesYesRight8LACS-Hf
Female40-494
YesYesRight5PACS-Ig
Female60-695
YesNoLeft34PACS-IMale80-896d
YesYesRight27TACS-IMale70-797
YesNoLeft7PACS-IFemale80-898
YesYesRight67TACS-IMale80-899
YesYesRight36PACS-IMale60-6910
YesYesLeft25TACS-IFemale50-5911
NoNoLeft33LACS-HMale70-7912
YesNoLeft12PACS-IMale80-8913
NoYesRight3LACS-IMale60-6914
YesYesRight40PACS-Hh
Male70-7915
YesYesRight22PACS-IFemale80-8916
YesNoLeft6PACS-IFemale60-6917
YesYes
Bilater-
al10POCS-Ii
Male60-6918
YesYesRight9PACS-IMale80-8919
YesYesRight160PACS-HMale80-8920
aAUL: affected upper limb.
bUL: upper limb.
cLACS-I: lacunar circulation syndrome ischemic.
dParticipant withdrawn.
eTACS-I: total anterior circulation syndrome ischemic.
fLACS-H: lacunar circulation syndrome hemorrhagic.
gPACS-I: partial anterior circulation syndrome ischemic.
hPACS-H: partial anterior circulation syndrome hemorrhagic.
iPOCS-I: posterior circulation syndrome ischemic.
Data Completeness
In total, 7517 of a possible 8640 observations of movement in
10-second time intervals were recorded across the 20
participants (median 414 observations/participant, IQR 402-420
observations/participant, range 12-432 observations/participant),
representing data completeness of 87.0%. Data loss was due to
the 2 participants who withdrew (769/8640 observations, 8.9%)
and the remaining participants being away from the service for
appointments or involved in private personal hygiene activities
(354/8640 observations, 4.1%).
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Of the 720 Planned Nudges, 32.6% (235 Nudges) were not
delivered (Missed Nudge), with 8.7% (63/720 Nudges) ascribed
to the 2 participants that withdrew. For the remaining Missed
Nudges, participants were sleeping for 12.6% (90/720) of the
Planned Nudges; Nudges could not be directly observed for
6.7% (48/720) of the Planned Nudges; a nudge was deemed
inappropriate for 2.8% (20/720) of the Planned Nudges; a
technical error prevented nudging for 1.4% (10/720) of the
Planned Nudges; and the reason was not stated for 0.4% (3/720)
of the Planned Nudges.
UL Movement
During observations without a nudge scheduled (No Nudge),
the affected UL moved 19.2% of the time; 15.6% of movement
occurred in conjunction with the unaffected UL, and only 3.6%
of the time the movement was of the affected UL by itself. The
unaffected UL moved 39.2% of the time, with half of this
movement (23.6%) being movement of the unaffected UL by
itself. Participants used one or both ULs for 42.8% of the
observation time.
Haptic Nudge Effect
The results of the statistical analyses are presented in Table 2.
The treatment effect of the intervention is represented by the
odds ratio (OR) for Planned Nudge versus No Nudge. This OR
indicated that the odds of moving the affected UL either
independently (unilateral affected UL movement) or in concert
with the unaffected limb (bimanual movement or bilateral limb
movement) was 1.44 times greater following a Planned Nudge
than following No Nudge. The proportions estimated by the
model for the affected UL movement (unilateral affected UL
movement + bilateral limb movement + bimanual movement)
recorded during the observation periods following a Planned
Nudge and No Nudge were 26.7% (95% CI 15.4%-42.2%) and
20.2% (95% CI 11.2%-33.6%), respectively. Therefore, the
average absolute increase in the proportion of affected UL
movement with the intervention was 6.5% (95% CI 4.2%-8.6%),
representing an increase of 32.2% in average activity. The
sensitivity analysis showed that the effect of the Planned Nudge
on unilateral affected UL movement + bilateral limb movement
+ bimanual movement was robust to missing values (P<.001).
The proportion of observation periods with affected UL
movement following Planned Nudges and No Nudges by
participants is represented in Table 3.
The secondary analysis revealed that the odds of moving the
affected UL independently (unilateral affected UL movement)
was 2.03 times greater following a Planned Nudge than
following No Nudge. However, the OR for either bilateral or
bimanual movement (bilateral limb movement + bimanual
movement) was only 1.13 times greater. The exploratory
analysis revealed that the OR for the effect of the haptic nudge
when delivered (Nudged) compared with no haptic nudge (Not
Nudged) irrespective of the schedule was 1.64.
Table 2. Odds of an affected upper limb (UL) movement recorded during the observation periods.
PvalueZ valueStandard error95% CIOdds ratioEstimate
Planned nudge/no nudge
<.0015.860.091.28-1.631.44
Primary analysis: AUa+BiLb+BiMc
<.0014.40.081.16-1.461.30Sensitivity analysis: AU+BiL+BiM
<.0016.650.221.65-2.512.03Secondary analysis: AU
.071.800.080.99-1.291.13Secondary analysis: BiL+BiM
Nudged/not nudged
<.0016.770.121.42-1.891.64Exploratory analysis: AU+BiL+BiM
aAU: unilateral affected upper limb movement.
bBiL: bilateral limb movement.
cBiM: bimanual movement.
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Table 3. Proportion of observations with affected upper limb movment (unilateral affected upper limb movement + bilateral limb movement + bimanual
movement) following No Nudge and Planned Nudge.
Planned Nudge (%)No Nudge (%)Participant
11.117.411
0.001.85
2a
30.0926.393
13.893.244
52.3146.305
0.003.70
6a
12.9618.067
38.8935.198
5.561.399
9.2616.6710
7.875.5611
0.936.4812
37.9630.0913
42.1315.7414
39.3520.8315
43.9832.8716
37.9623.6117
24.5438.8918
43.0625.9319
16.6723.6120
aParticipant withdrawn.
Discussion
Principal Findings
This multiple-period randomized crossover study explored the
effect of haptic nudging on UL movement in people with stroke.
Haptic nudging increased the likelihood that a person with stroke
moved their affected UL by 1.44 times (P<.001) during the
subacute rehabilitation phase. Haptic nudging resulted in a
relative increase in the proportion of affected UL movement of
32.2%. The actual amount and type of UL movement observed
without nudging in our study was comparable with our
previously published research [16] and other observational
studies [33,46]. This strengthens the assertion that haptic nudges
influence the amount of affected UL movement in people with
stroke. This study is the first to specifically investigate whether
haptic nudges delivered by a wrist-worn wearable device
influence the amount of UL movement undertaken during
rehabilitation following stroke. Given the limited amount of
spontaneous UL movement following stroke [16,46,49] and the
challenges associated with increasing the dose of UL
rehabilitation [15,50], these research findings indicate that haptic
nudging represents a potentially powerful stroke rehabilitation
tool that could be easily implemented in clinical practice.
Our secondary analysis clarified the type of UL movement
promoted by haptic nudging. Participants were 2.03 times more
likely to move their affected UL in isolation (unilateral affected
UL movement) following a haptic nudge compared with moving
the affected UL in concert with the unaffected UL (bimanual
movement + bilateral limb movement), which was just 1.13
times more likely. Given that participants were instructed to
move their affected UL rather than both limbs together, the
instructions related to haptic nudging may be important in
determining exactly which movements are promoted. This
specificity in effect has been noted in other studies in which
people with stroke altered their behavior in response to feedback.
Dobkin and Plummer-D'Amato [51] gave daily feedback in
relation to gait speed to people with stroke undertaking
rehabilitation. They reported the feedback group had
significantly increased gait speed but did not change walking
endurance or independence compared with the control group
(usual care). Our study contributes to the growing body of
research suggesting that drawing attention to specific aspects
of movement and physical activity throughout the rehabilitation
day can influence patient behavior during stroke rehabilitation
[26-31].
The greater odds of moving the affected UL in the minute
following a planned nudge resulted in a 32.2% increase in the
average amount of movement. Another small-scale
proof-of-concept study involving people with stroke (n=7) [30]
indicated an increase of 19.7% in the average amount of affected
UL movement in the hour following a haptic nudge reminder
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to undertake exercises. In that study, participants were instructed
to perform up to 80 repetitions of task-specific training in
response to a haptic nudge, but only received a median of 4
nudges across the rehabilitation day. In contrast, participants in
our study received an average of 27 haptic nudges and were
instructed to move, try to move, or think about moving their
affected UL following a nudge. It is not yet known how the
frequency of nudges, burden of the required behavioral response,
and capacity to integrate that response into everyday activities
influence the magnitude and duration of the haptic nudge effect
in people with stroke.
Results for individual participants illustrated that there was
considerable variability in response to haptic nudging. For
example, 13 participants increased the amount of movement of
their affected UL in response to nudging, with 8 exhibiting large
relative increases. In contrast, 5 participants had a reduction in
the amount of affected UL movement in response to haptic
nudging. There appeared to be no relationship between
individual response and participant age, stroke severity,
hemiplegic side, or whether the hemiplegic side was the
dominant hand. Although the researchers checked that the
participant understood the instruction to move the affected UL
in response to haptic nudging at the beginning of data collection,
it remains unclear whether cognitive, communication,
perceptual, or sensory deficits influenced participants’ ability
to attend and respond to the nudge. Other nudging modalities
(eg, auditory tones, lights, and text messaging) may be effective
and more appropriate than haptic vibration for some people
with stroke [52]. In addition, we relied on observation of
movement, and it was not possible to determine whether
participants who were more severely affected were attempting
to or thinking about moving their affected UL. This could be
addressed in future research by using alternative data collection
methods such as ecological momentary assessment [53] or
electroencephalography to determine movement intention [54].
One participant who had moved less in response to haptic
nudging had a cerebellar stroke that influenced UL movement
bilaterally; advocating increased movement of the more affected
limb might have been inappropriate in that case. The relationship
between haptic nudge efficacy and clinical and demographic
factors requires further investigation to ensure that this type of
technology is used appropriately.
The magnitude of effect in response to haptic nudging might
have been underestimated in our study given that we included
observations in which planned nudges were not delivered
(Missed Nudges), for example, when a participant was asleep
or not visible to the researcher. This assertion was supported
by the exploratory analysis that revealed the OR for the effect
of the scheduled nudge when actually delivered compared with
no haptic nudge was 1.64. It is also noteworthy that our
participants were advised of the value of moving the affected
UL after stroke on a single occasion. The effect of haptic
nudging may be enhanced by providing regular positively
framed information on the consequences of nudged behavior
(eg, “more movement promotes recovery”), encouraging explicit
action planning (eg, “I will move my affected arm by…when I
feel the nudge”), repeated practice of the desired behavioral
response to the haptic nudge, and tracking and reporting the
desired behavior [35,55,56]. In commercial wearable devices,
haptic nudging is commonly coupled with other behavior change
and persuasive strategies including education, gamification,
social support via social network services, and reward systems
[56,57]. It is likely that the magnitude of effect of a
comprehensive rehabilitation wearable technology that
incorporates haptic nudging with other behavioral change and
persuasive strategies would have a larger effect than the effect
of haptic nudging alone, as estimated in this study [35,58,59].
This study sought to investigate the effect of haptic nudging on
UL movement across an inpatient rehabilitation day; we did not
explore the effect on UL movement over a longer timeframe or
in a community setting. It is possible that people with stroke
habituate or become less responsive to haptic nudging with
everyday use. Conversely, they may learn to respond to haptic
nudging more effectively over time. Understanding the effect
over time is important, as increasing the dose of upper limb
movement to a therapeutic level through continued engagement
over a matter of weeks to months is likely required to promote
functional gains. In the subacute phase following stroke,
adherence to wearable devices has been reported as high [31,42],
although use appears to dwindle over time [42]. This is
consistent with studies in healthy community-dwelling people,
where half to two-thirds of purchasers continued to use wearable
devices 6 months after purchase [35,36]. In healthy populations,
uptake and ongoing use of such devices are influenced by
personal characteristics, including age, computer self-efficacy,
ease of use, usual levels of physical activity, internalization of
intention to change, and personality [36,60]. Although wearable
devices have been found to be acceptable to people with stroke
[61], one of our participants withdrew at the beginning of data
collection because wearing the device made him feel anxious.
Previous research indicates that the use of wearable technologies
may increase anxiety in clinical populations [62,63]. When
developing wearable devices to promote physical activity and
movement in people with stroke it may be important to consider
the personal and clinical characteristics of the intended users,
when and where in the continuum of care the device will be
used and for how long, and how users’ engagement and
adherence can be supported.
Limitations
A key limitation of this study was that the researcher observing
and recording movement was also responsible for triggering
the nudge and therefore not blinded to the intervention. While
the randomization schedule was designed to address lag, where
the effect of a nudge influences subsequent movement
observation periods (refer to Multimedia Appendix 1), the
duration of the nudge effect was unknown and might have
influenced subsequent observations. While participants were
blinded to the study hypothesis, it is possible that the research
protocol, particularly being observed by a researcher, may have
influenced the likelihood they moved in response to the haptic
nudge. Documentation of the number of potential participants
screened and the reasons for exclusion from the study would
have helped to interpret the external validity of the study
findings. A more detailed evaluation of the included participants’
sensorimotor, perceptual, cognitive, and communication
impairments along with measurement of their UL functional
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abilities may have allowed for a more nuanced interpretation
of the effect of haptic nudging in people with different clinical
presentations of stroke.
Conclusions
Haptic nudging increased the likelihood that a person with stroke
moved their affected UL by 1.44 times. This equated to an
increase of 32% in the average amount of affected UL
movement. Participants were twice (OR 2.03) as likely to move
their affected UL in isolation (unilateral affected UL movement)
in response to haptic nudging, compared with movement in
conjunction with the unaffected UL (OR 1.13), indicating that
the effect of haptic nudging was specific to the behavioral
instructions given. Given the limited amount of spontaneous
UL movement following stroke and the challenges associated
with increasing the dose of UL rehabilitation, haptic nudging
as part of a comprehensive wearable device aimed at increasing
the dose of UL movement represents a potentially powerful
stroke rehabilitation tool.
Acknowledgments
This research was funded by a grant from the New Zealand Medical Technologies Centre of Research Excellence. The authors
are grateful to the Waitemata District Health Board and the staff of the rehabilitation service at Waitakere Hospital who supported
this research. Simon Tse, Rohil Chauhan, Shikha Chaudhary, Abdallah Abou El Ela, and Nicole Wallen contributed to data
collection.
Authors' Contributions
NS, DT, and MK conceptualized the study and acquired funding. DT, AV, RM, and NS designed the methodology, and RM, DT,
and NS performed project administration. RM and JH collected the data, and MK and FA were responsible for the technology.
Data analysis was performed by UR and AV, and UR created the visualizations. Supervision was performed by NS and DT. The
original draft was written by NS, RM, and UR, and all authors reviewed and edited the manuscript.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Statistical analysis for the haptic nudge study.
[PDF File (Adobe PDF File), 738 KB-Multimedia Appendix 1]
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Abbreviations
AU: unilateral affected upper limb movement.
AUL: affected upper limb.
BiL: bilateral limb movement.
BiM: bimanual movement.
LACS-H: lacunar circulation syndrome hemorrhagic.
LACS-I: lacunar circulation syndrome ischemic.
OR: odds ratio.
PACS-H: partial anterior circulation syndrome hemorrhagic.
PACS-I: partial anterior circulation syndrome ischemic.
POCS-I: posterior circulation syndrome ischemic.
TACS-I: total anterior circulation syndrome ischemic.
UL: upper limb.
Edited by G Eysenbach; submitted 12.11.19; peer-reviewed by M Whelan, K Ng, N Cecilia; comments to author 10.01.20; revised
version received 15.03.20; accepted 13.05.20; published 29.07.20
Please cite as:
Signal NEJ, McLaren R, Rashid U, Vandal A, King M, Almesfer F, Henderson J, Taylor D
Haptic Nudges Increase Affected Upper Limb Movement During Inpatient Stroke Rehabilitation: Multiple-Period Randomized
Crossover Study
JMIR Mhealth Uhealth 2020;8(7):e17036
URL: https://mhealth.jmir.org/2020/7/e17036
doi: 10.2196/17036
PMID:
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©Nada Elizabeth June Signal, Ruth McLaren, Usman Rashid, Alain Vandal, Marcus King, Faisal Almesfer, Jeanette Henderson,
Denise Taylor. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 29.07.2020. This is an open-access
article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/),
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... We relied on participants to visually check their Manumeter displays without providing reminders. There is some indication that providing movement reminders through tactile (i.e., vibratory) inputs to the wrist can increase the amount of UE activity, and that this increase may have at least a small therapeutic benefit [12,[30][31][32]. Adding voice capability to the watch could also potentially improve the saliency of the feedback and could be especially valuable for persons with visual impairments. ...
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After stroke, many people substantially reduce use of their impaired hand in daily life, even if they retain even a moderate level of functional hand ability. Here, we tested whether providing real-time, wearable feedback on the number of achieved hand movements, along with a daily goal, can help people increase hand use intensity. Twenty participants with chronic stroke wore the Manumeter, a novel magnetic wristwatch/ring system that counts finger and wrist movements. We randomized them to wear the device for three weeks with (feedback group) or without (control group) real-time hand count feedback and a daily goal. Participants in the control group used the device as a wristwatch, but it still counted hand movements. We found that the feedback group wore the Manumeter significantly longer (11.2 ± 1.3 h/day) compared to the control group (10.1 ± 1.1 h/day). The feedback group also significantly increased their hand counts over time (p = 0.012, slope = 9.0 hand counts/hour per day, which amounted to ~2000 additional counts per day by study end), while the control group did not (p-value = 0.059; slope = 4.87 hand counts/hour per day). There were no significant differences between groups in any clinical measures of hand movement ability that we measured before and after the feedback period, although several of these measures improved over time. Finally, we confirmed that the previously reported threshold relationship between hand functional capacity and daily use was stable over three weeks, even in the presence of feedback, and established the minimal detectable change for hand count intensity, which is about 30% of average daily intensity. These results suggest that disuse of the hand after stroke is temporarily modifiable with wearable feedback, but do not support that a 3-week intervention of wearable hand count feedback provides enduring therapeutic gains.
Chapter
In this chapter, we provide a review of the current applications of wearable sensors in the field of stroke rehabilitation. Four key points are discussed in this review. First, wearable sensors are a viable solution for monitoring movement during rehabilitation exercises and clinical assessments, but more work needs to be done to derive clinically relevant information from sensor data collected during unstructured activities. Second, wearable technologies provide critical information related to the performance of activities in daily life, information that is not necessarily captured during in-clinic assessments. Third, wearable technologies can provide feedback and motivation to increase movement in the home and community settings. Finally, technologies are rapidly emerging that can complement “traditional” wearable sensors and sometimes replace them as they provide less obtrusive means of monitoring motor function in stroke survivors. These developing technologies, as well as readily available wearable sensors, are transforming stroke rehabilitation, their development is progressing at a fast pace, and their use so far has allowed us to gather important information, that we would have not been able to collect otherwise, which has tremendous potential to further advance stroke rehabilitation.
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Background: The course of spontaneous biological recovery indicates that no essential improvements in upper limb (UL) capacity should be expected 3 months after stroke. Likewise, UL performance as assessed with accelerometers does not seem to increase. However, this plateau may not apply to all patients with stroke. Objectives: This study aimed to investigate the changes in UL capacity and performance from 3 to 6 months post-stroke, and the association between patients' UL capacity and actual UL performance. Methods: This study was a secondary analysis of a prospective longitudinal cohort study. Patients with UL impairment and first or recurrent stroke were included. Their UL capacity was assessed at 3 and 6 months with the Action Research Arm Test (ARAT) and UL performance was examined with accelerometry and expressed as a use ratio. The association between ARAT and use ratio was examined with multiple regression analyses. Results: Data from 67 patients were analyzed. It was shown that UL capacity as assessed with ARAT still improved from 3 to 6 months. A clinically meaningful improvement (≥ 6 points on ARAT) was found in 16 (46%) of the 35 patients whose scores allowed for such an increase. Improvements were mainly observed for patients with ARAT scores in the range of 15-51 at 3 months. Conversely, UL performance did not change. Three and 6 months after stroke respectively 69% and 64% of the variation in use ratio was explained by ARAT. Conclusion: While a substantial part of patients improved their UL capacity, UL performance did not change from 3 to 6 months post-stroke. Strategies to remind patients of including their affected UL may encourage the transfer from better capacity to increased performance.
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Background There is growing interest in the use of wearable devices that track upper limb activity after stroke to help determine and motivate the optimal dose of upper limb practice. The purpose of this study was to explore clinicians’ perceptions of a prospective wearable device that captures upper limb activity to assist in the design of devices for use in rehabilitation practice. Methods Four focus groups with 18 clinicians (occupational and physical therapists with stroke practice experience from a hospital or private practice setting) were conducted. Data were analyzed thematically. Results Our analysis revealed three themes: (1) “Quantity and quality is ideal” emphasized how an ideal device would capture both quantity and quality of movement; (2) “Most useful outside therapy sessions” described how therapists foresaw using the device outside of therapy sessions to monitor homework adherence, provide self-monitoring of use, motivate greater use and provide biofeedback on movement quality; (3) “User-friendly please” advocated for the creation of a device that was easy to use and customizable, which reflected the client-centered nature of their treatment. Conclusions Findings from this study suggest that clinicians support the development of wearable devices that capture upper limb activity outside of therapy for individuals with some reach to grasp ability. Devices that are easy to use and capture both quality and quantity may result in greater uptake in the clinical setting. Future studies examining acceptability of wearable devices for tracking upper limb activity from the perspective of individuals with stroke are needed.
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Background Digitally enabled rehabilitation may lead to better outcomes but has not been tested in large pragmatic trials. We aimed to evaluate a tailored prescription of affordable digital devices in addition to usual care for people with mobility limitations admitted to aged care and neurological rehabilitation. Methods and findings We conducted a pragmatic, outcome-assessor-blinded, parallel-group randomised trial in 3 Australian hospitals in Sydney and Adelaide recruiting adults 18 to 101 years old with mobility limitations undertaking aged care and neurological inpatient rehabilitation. Both the intervention and control groups received usual multidisciplinary inpatient and post-hospital rehabilitation care as determined by the treating rehabilitation clinicians. In addition to usual care, the intervention group used devices to target mobility and physical activity problems, individually prescribed by a physiotherapist according to an intervention protocol, including virtual reality video games, activity monitors, and handheld computer devices for 6 months in hospital and at home. Co-primary outcomes were mobility (performance-based Short Physical Performance Battery [SPPB]; continuous version; range 0 to 3; higher score indicates better mobility) and upright time as a proxy measure of physical activity (proportion of the day upright measured with activPAL) at 6 months. The dataset was analysed using intention-to-treat principles. The trial was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12614000936628). Between 22 September 2014 and 10 November 2016, 300 patients (mean age 74 years, SD 14; 50% female; 54% neurological condition causing activity limitation) were randomly assigned to intervention (n = 149) or control (n = 151) using a secure online database (REDCap) to achieve allocation concealment. Six-month assessments were completed by 258 participants (129 intervention, 129 control). Intervention participants received on average 12 (SD 11) supervised inpatient sessions using 4 (SD 1) different devices and 15 (SD 5) physiotherapy contacts supporting device use after hospital discharge. Changes in mobility scores were higher in the intervention group compared to the control group from baseline (SPPB [continuous, 0–3] mean [SD]: intervention group, 1.5 [0.7]; control group, 1.5 [0.8]) to 6 months (SPPB [continuous, 0–3] mean [SD]: intervention group, 2.3 [0.6]; control group, 2.1 [0.8]; mean between-group difference 0.2 points, 95% CI 0.1 to 0.3; p = 0.006). However, there was no evidence of a difference between groups for upright time at 6 months (mean [SD] proportion of the day spent upright at 6 months: intervention group, 18.2 [9.8]; control group, 18.4 [10.2]; mean between-group difference −0.2, 95% CI −2.7 to 2.3; p = 0.87). Scores were higher in the intervention group compared to the control group across most secondary mobility outcomes, but there was no evidence of a difference between groups for most other secondary outcomes including self-reported balance confidence and quality of life. No adverse events were reported in the intervention group. Thirteen participants died while in the trial (intervention group: 9; control group: 4) due to unrelated causes, and there was no evidence of a difference between groups in fall rates (unadjusted incidence rate ratio 1.19, 95% CI 0.78 to 1.83; p = 0.43). Study limitations include 15%–19% loss to follow-up at 6 months on the co-primary outcomes, as anticipated; the number of secondary outcome measures in our trial, which may increase the risk of a type I error; and potential low statistical power to demonstrate significant between-group differences on important secondary patient-reported outcomes. Conclusions In this study, we observed improved mobility in people with a wide range of health conditions making use of digitally enabled rehabilitation, whereas time spent upright was not impacted. Trial registration The trial was prospectively registered with the Australian New Zealand Clinical Trials Register; ACTRN12614000936628
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Background: Physical activity (PA) is a key health behavior in people with stroke including risk reduction of recurrent stroke. Despite the beneficial effects of PA, many community-dwelling stroke survivors are physically inactive. Information and communication technologies are emerging as a possible method to promote adherence to PA. Objective: The aim of this study is to investigate the effectiveness of a mobile-health (mHealth) App in improving levels of PA. Methods: Forty-one chronic stroke survivors were randomized into an intervention group (IG) n=24 and a control group (CG) n=17. Participants in the IG were engaged in the Multimodal Rehabilitation Program (MMRP) that consisted on supervising adherence to PA through a mHealth app, participating in an 8-week rehabilitation program that included: aerobic, task-oriented, balance and stretching exercises. Participants also performed an ambulation program at home. The CG received a conventional rehabilitation program. Outcome variables were: adherence to PA, (walking and sitting time/day), walking speed (10MWT); walking endurance (6MWT); risk of falling (TUG); ADLs (Barthel); QoL (Eq-5D5L) and participant’s satisfaction. Results: At the end of the intervention, community ambulation increased more in IG (38.95 min; SD: 20.37) than in the CG (9.47 min; SD: 12.11) (p≤.05). Sitting time was reduced by 2.96 (SD 2.0) hours/day in the IG and by 0.53 (SD 0.24) hours in the CG (p≤.05). Conclusions: The results suggest that mHealth technology provides a novel way to promote adherence to home exercise programs post stroke. However, frequent support and guidance of caregiver is required to ensure the use of mobile devices.
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Objectives To study trends in stroke mortality rates, event rates, and case fatality, and to explain the extent to which the reduction in stroke mortality rates was influenced by changes in stroke event rates or case fatality. Design Population based study. Setting Person linked routine hospital and mortality data, England. Participants 795 869 adults aged 20 and older who were admitted to hospital with acute stroke or died from stroke. Main outcome measures Stroke mortality rates, stroke event rates (stroke admission or stroke death without admission), and case fatality within 30 days after stroke. Results Between 2001 and 2010 stroke mortality rates decreased by 55%, stroke event rates by 20%, and case fatality by 40%. The study population included 358 599 (45%) men and 437 270 (55%) women. Average annual change in mortality rate was −6.0% (95% confidence interval −6.2% to −5.8%) in men and −6.1% (−6.3% to −6.0%) in women, in stroke event rate was −1.3% (−1.4% to −1.2%) in men and −2.1% (−2.2 to −2.0) in women, and in case fatality was −4.7% (−4.9% to −4.5%) in men and −4.4% (−4.5% to −4.2%) in women. Mortality and case fatality but not event rate declined in all age groups: the stroke event rate decreased in older people but increased by 2% each year in adults aged 35 to 54 years. Of the total decline in mortality rates, 71% was attributed to the decline in case fatality (78% in men and 66% in women) and the remainder to the reduction in stroke event rates. The contribution of the two factors varied between age groups. Whereas the reduction in mortality rates in people younger than 55 years was due to the reduction in case fatality, in the oldest age group (≥85 years) reductions in case fatality and event rates contributed nearly equally. Conclusions Declines in case fatality, probably driven by improvements in stroke care, contributed more than declines in event rates to the overall reduction in stroke mortality. Mortality reduction in men and women younger than 55 was solely a result of a decrease in case fatality, whereas stroke event rates increased in the age group 35 to 54 years. The increase in stroke event rates in young adults is a concern. This suggests that stroke prevention needs to be strengthened to reduce the occurrence of stroke in people younger than 55 years.
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Objective: To observe upper limb activity patterns of people with stroke during sub-acute rehabilitation to inform the development of treatment strategies for upper limb rehabilitation. Design: Observational study of upper limb activity. Methods: Twenty participants admitted for sub-acute rehabilitation following stroke were observed during a week day for 1 minute every 10 min between 7 am and 7 pm. Upper limb activity was recorded and categorized into five types of movement. Results: Participants used either one or both upper limbs for 45.8% of the observation time. The affected arm moved 26.4% of the time, with most movement occurring in conjunction with the unaffected arm (18.9% of the time) and only 7.5% of the time being movement of the affected arm by itself. The largest proportion of upper limb activity was observed during mealtimes. Conclusions: Recognition of the need to improve upper limb outcomes after stroke has not yet translated into changes in the amount of upper limb activity undertaken during sub-acute rehabilitation. Opportunities to rehabilitate the hemiplegic upper limb are not fully realized. The dominance of bilateral movement in the early stages after stroke may provide scope for interventions that maximize this aspect of motor control. • IMPLICATIONS FOR REHABILITATION • Despite advances in rehabilitation, time spent in upper limb activity following stroke is very low, particularly in the affected arm. • Most movement of the affected arm occurs in conjunction with the unaffected arm. • There is an urgent need to redress this low level of movement, given the importance of upper limb recovery to quality of life for people following stroke.
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Purpose: Information and communication technology devices have become a ubiquitous part of everyday life and a primary means of communication. The aim of this study was to describe the experience of information and communication technology and to explore the barriers and motivators to its use following stroke. Materials and methods: This observational study used semi-structured individual interviews and video observation of information and communication technology device use with six people, four men, and two women age 60–82 years with upper limb disability following stroke. They were analyzed using thematic analysis. Results: Three themes were identified that relate to barriers: (i) Sensory and motor impairments; (ii) Limited vision and impaired speech; and (iii) Device-specific limitations. Six themes were identified as motivators: (i) Connect with others; (ii) Provide safety; (iii) Facilitate reintegration; (iv) Reinforce technology adoption; (v) Leisure activities; and (vi) Contribute to the rehabilitation process. Conclusion: All participants used some form of information and communication technology daily to promote safety, enable daily activities, and social interaction, and to a lesser extent engage in leisure and rehabilitation activities. Barriers to information and communication technology use were primarily related to stroke related impairments and device-specific requirements, which limited use, particularly of smartphones. These barriers should be addressed to facilitate the use of information and communication technology devices. • Implications for rehabilitation • This research suggests that; • People with stroke are highly motivated to use information and communication technology devices in daily activities • Stroke-specific and age-related impairments limit the use and functionality of information and communication technology devices for people with stroke • Information and communication technology devices do not appear to be promoted or used in the rehabilitation or as assistive technologies
Article
Objective: To test if pedometers, as a motivational tool, could affect mobility outcomes in inpatient rehabilitation. Design: Randomized controlled clinical trial. Setting: Subacute hospital rehabilitation unit in Australia. Participants: A total of 78 participants with reduced mobility and clinician-determined capacity to improve. Interventions: Both groups received usual care. For the intervention group, a pedometer was worn on the hip with the step count visible to participant and recorded daily on an exercise log. For the control group, a pedometer fixed shut was worn on the hip and they recorded estimated distances walked on an exercise log. Main measures: Primary outcome was functional mobility - De Morton Mobility Index. Secondary outcome measures were walking velocity, functional independence measure, time spent upright and daily step count. Results: Significant improvements over time (P < 0.001) in functional mobility, comfortable walking velocity and functional independence measure were not influenced by the intervention. The daily average upright time (hours) in the first week of intervention was different (P = 0.004) between the intervention group (median, interquartile range (IQR): 1.67, 1.77) compared to the control group (median, IQR: 1.12, 0.82). Conclusion: Pedometers as a motivational tool without targets do not improve functional mobility in this population. Pedometers may improve daily upright time in this setting.
Article
Objectives The objectives of the study are to characterize paretic upper limb (UL) use in people with different levels of impairment 4 weeks poststroke and to compare accelerometry and direct observational approaches. Methods Twelve stroke inpatients (five mild, three moderate, and four severe UL impairment) were recruited from a rehabilitation hospital. UL use was measured using accelerometry (24 hr) and direct observation (12 hr of behavioural mapping). Accelerometry variables included duration of use, use ratio, magnitude ratio, bilateral magnitude, and variation ratio. Direct observation recorded the duration of use and type of UL movement (e.g., functional vs. non‐functional). Results From accelerometry data, people with mild, moderate, and severe UL impairments used their paretic UL 59%, 45%, and 22% of a 24 hr‐day, respectively. People with severe UL impairment had the lowest paretic UL use duration (median 1.49 hr/day), magnitude ratio, and variation ratio compared with people with mild and moderate UL impairment. From 12 hr of observational data, people with mild impairment were using their UL for 37.8% of the observed time, whereas the people with moderate and severe impairment were using their UL 15.8% and 4.9%, respectively. UL movements for the mild cohort were mainly functional, whereas UL movements of the moderate and severe cohorts were mainly non‐functional. UL movements were predominantly active for the mild and moderate cohorts but passive for the severe cohort. Duration of paretic UL use from accelerometry and observation data were highly correlated (ICC > 0.8), but the absolute percentage error between methods ranged from 34.2% to 42.7%. Conclusions Paretic UL use within the first 4 weeks poststroke differs across levels of impairment in this exploratory study. Accelerometry and observation findings of paretic UL use were correlated and may be needed in different situations as they capture different information.
Article
Objective: To evaluate the feasibility of a multicentre, observer-blind, pilot randomized controlled trial (RCT) of a wristband accelerometer with activity-dependent vibration alerts to prompt impaired arm use after stroke. Design: Parallel-group pilot RCT. Setting: Four English stroke services. Participants: Patients 0-3 months post stroke with a new arm deficit. Intervention: Participants were randomized to wear a prompting or 'sham' wristband during a four-week self-directed therapy programme with twice-weekly therapy review. Main outcomes: Recruitment, retention and adherence rates, safety and completion of assessments were reported. Arm recovery was measured by Action Research Arm Test (ARAT) and Motor Activity Log (MAL) without statistical comparison. Results: In total, 33 patients were recruited (0.6 per month/site; median time post stroke: 26 days (interquartile range (IQR):15.5-45)). Baseline, four-week and eight-week median (IQR) ARAT for the control group (n = 19) were 15 (2-35), 35 (15-26) and 31 (21-55) and those for the intervention group (n = 14) were 37 (16-45), 57 (29-57) and 57 (37-57), respectively; for MAL Amount of Use, the corresponding values in the control group were 0.2 (0.0-1.2), 1.1 (0.3-2.9) and 1.2 (0.7-2.9) and in the intervention group were 1.4 (0.5-2.6), 3.8 (1.9-4.5) and 3.7 (2.1-4.3). Four participants withdrew from the study. Wristbands were worn for 79% of the recommended time. The intervention and control group participants received a median of 6.0 (IQR: 4.3-8.0) and 7.5 (IQR: 6.8-8.0) therapy reviews. A median of 8 (IQR: 6-10) prompts were delivered per intervention participant/day. Research assessments were completed for 28/29 and 25/28 patients at four and eight weeks. Eight serious adverse events were reported, all unrelated to the intervention. Conclusion: A multicentre RCT of wristband accelerometers to prompt arm activity early after stroke is feasible. A total sample of 108 participants would be required.
Article
Objective: To evaluate the effectiveness of wearable device interventions (eg, Fitbit) to improve physical activity (PA) outcomes (eg, steps/day, moderate to vigorous physical activity [MVPA]) in populations diagnosed with cardiometabolic chronic disease. Data source: Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses, an electronic search of 5 databases (Medline, PsychINFO, Scopus, Web of Science, and PubMed) was conducted. Study inclusion and exclusion criteria: Randomized controlled trials (RCTs) published between January 2000 and May 2018 that used a wearable device for the full intervention in adults (18+) diagnosed with a cardiometabolic chronic disease were included. Excluded trials included studies that used devices at pre-post only, devices that administered medication, and interventions with no prospective control group comparison. Data extraction: Thirty-five studies examining 4528 participants met the inclusion criteria. Study quality and RCT risk of bias were assessed using the Cochrane Collaboration Tool. Data synthesis: Meta-analyses to compute PA (eg, steps/day) and selected physical dispersion and summary effects were conducted using the raw unstandardized pooled mean difference (MD). Sensitivity analyses were examined. Results: Statistically significant increases in PA steps/day (MD = 2592 steps/day; 95% confidence interval [CI]: 1689-3496) and MVPA min/wk (MD = 36.31 min/wk; 95% CI: 18.33-54.29) were found for the intervention condition. Conclusion: Wearable devices positively impact physical health in clinical populations with cardiometabolic diseases. Future research using the most current technologies (eg, Fitbit) will serve to amplify these findings.