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Using ecological momentary assessment to study the impact of social-cognitive factors on paretic hand use after stroke
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Using ecological momentary assessment to study the impact of social-cognitive factors on paretic hand use after stroke
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Content
USING ECOLOGICAL MOMENTARY ASSESSMENT TO STUDY THE IMPACT OF
SOCIAL-COGNITIVE FACTORS ON PARETIC HAND USE AFTER STROKE
by
Yi-An Chen
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOKINESIOLOGY)
August 2017
Copyright 2017 Yi-An Chen
ii
TABLE OF CONTENTS
List of Tables
iii
List of Figures
iv
List of Appendices
v
Abstract
-
Chapter 1 Overview
- References
1
4
Chapter 2 Mobile-Based Ecological Momentary Assessment of Paretic Hand
Use and Related Self-Report Measures after Stroke: Feasibility and
Validity
- References
- Tables
- Figures
- Appendices
6
25
28
29
36
Chapter 3 A Combined Methodology with Objective Accelerometry and Self-
Report Ecological Momentary Assessment for Paretic Hand Use
after Stroke
- References
- Tables
- Figures
44
62
65
67
Chapter 4 Self-Efficacy and Social Interaction Impact Daily Paretic Hand Use
after Stroke: An Ecological Study
- References
- Tables
- Figures
74
94
98
100
Chapter 5 Summary and General Discussion
- References
102
109
iii
LIST OF TABLES
Table 2.1
Characteristics of Participants. 28
Table 3.1
Characteristics of Participants. 65
Table 3.2
Accelerometer Wearing Time and Hand Movement Variables (the
Whole-Day Analysis).
66
Table 4.1
Characteristics of Participants and Outcome Measures of Hand Use
Behavior and Social-Cognitive Factors.
98
Table 4.2
Results of Linear Regression Model Examining the Association
between Hand Use Behavior (Accelerometry Time of the Unimanual
Right/Left and Bimanual Hand Movement) and Social-Cognitive
Factors after Controlling for Motor Capability.
99
iv
LIST OF FIGURES
Figure 2.1
Example Questions of EMA Prompt. 29
Figure 2.2
Overall Response Rate. 30
Figure 2.3
Response Rates by Day and by Time. 31
Figure 2.4
Exit Interview Prompt-Related Questions. 32
Figure 2.5
Correlation Comparisons for Hand Use Behavior between the EMA
Responses and the MAL.
33
Figure 2.6
Correlation Comparisons for Self-Efficacy between the EMA
Responses and (a) the CAHM, (b) the SIS Hand Function, and (c)
the SIS Mobility.
34
Figure 2.7
Correlation Comparisons for Mood between the EMA Responses
and (a) the SIS Emotion, (b) the Positive Affect of PANAS, and (c)
the Negative Affect of PANAS.
35
Figure 3.1
Accelerometer and EMA Smartphone Configuration. 67
Figure 3.2
Flow Chart of Data Availability. 68
Figure 3.3
Consistency (Aim 1) between EMA Hand Use Response and
Accelerometry of Hand Movement.
69
Figure 3.4
Reporting Bias (Aim 2): Accelerometry of Hand Movement between
Unanswered and Answered Scheduled Prompts.
70
Figure 3.5
Reporting Bias (Aim 2): Accelerometry of Hand Movement between
Answered Scheduled and Self-Triggered Prompts.
71
Figure 3.6
Reactivity (Aim 3 Immediate Effect): Accelerometry of Hand
Movement Before and After EMA Prompt.
72
Figure 3.7
Reactivity (Aim 3 Accumulated Effect): Accelerometry of Hand
Movements in Daily Changes.
73
Figure 4.1
Accelerometer and EMA Smartphone Configuration. 100
Figure 4.2 Example Questions of EMA Prompts. 101
v
LIST OF APPENDICES
Appendix 2.1
List of 17 EMA Prompt Questions. 36
Appendix 2.2
Response Rate by Day of Individual Participant. 39
Appendix 2.3
Response Rate by Time of Individual Participant. 40
Appendix 2.4
Hand Use Response for Each Valid EMA Entry of Individual
Participant.
41
Appendix 2.5
Self-Efficacy Score for Each Valid EMA Entry of Individual
Participant.
42
Appendix 2.6
Mood Score for Each Valid EMA Entry of Individual Participant. 43
1
CHAPTER 1
Overview
Limited use of the paretic hand after stroke can severely constrain an individual’s activity
and participation and lead to further functional degradation.
1,2
Clinical practice as well as research
has focused on improving motor capability of the paretic hand to promote spontaneous use in the
natural environment.
3–5
However, evidence has shown that the efforts to enhance recovery in
individuals after stroke may be in vain, in part a result of the non-use phenomenon. The non-use
phenomenon is defined as the discrepancy between recovered motor capability and functional
hand use, whereby the capacity to use the hand is far greater than its actual use.
6,7
This
underscores the fact that motor capability, while a necessary factor, is not the only factor
influencing the use of the paretic hand after stroke.
Recent studies demonstrate that social-cognitive factors, which characterize an
individual’s psychological needs and perceptions, play an essential role in functioning after
stroke.
8–11
For example, greater balance self-efficacy (i.e., confidence) has been shown to be
associated with better balance
8,9
and walking
8,10
improvements in stroke survivors. Positive mood
and positive social interactions with others (e.g., family and friends) can be used to predict
increases in social participation
11
and activities of daily living
12
post-stroke. Nevertheless, there
continues to be a significant knowledge gap in understanding the relationship of self-efficacy,
mood, and social interaction to paretic hand use after stroke.
Therefore, the overall goal of this dissertation work was to investigate the association
between paretic hand use and social-cognitive factors in the natural environment. Our central
2
hypothesis was that these social-cognitive factors (i.e., self-efficacy, mood, and social interaction)
will be shown to have a significant impact on daily paretic hand use after stroke. To examine our
central hypothesis, participants with stroke employed an established methodology, termed
Ecological Momentary Assessment (EMA), to capture self-reported perceptions in daily life.
EMA is a mobile-based prompt methodology, which allows for simultaneous real-time
assessments of behavioral (i.e., paretic hand use) and psychological variables (i.e., social-
cognitive factors) in a natural setting.
13,14
EMA has, to the best of our knowledge, never been used
to gauge paretic hand use and related self-reported measures (e.g., self-efficacy of using the
paretic hand) in individuals recovering from stroke. This is a novel and innovative application of
EMA in the context of stroke rehabilitation. Chapter Two provides the evidence of the feasibility
and validity of using EMA to assess paretic hand use and social-cognitive factors in the daily
context after stroke.
Furthermore, participants were also asked to wear accelerometers on the wrists during
participation to objectively capture hand use in the natural environment. We combined
accelerometry with EMA to have a more individualized and comprehensive understanding of
individuals’ hand use behavior. Combining these two ecological assessments may not only
integrate objective with self-report information but also allow us to obtain different aspects of the
daily hand use behavior in individuals with stroke.
15
In Chapter Three, we established this
combined methodology by demonstrating the consistency between participants’ EMA self-report
hand use behavior and the objective accelerometry hand movement. The potential influences of
the act of EMA responding on the hand use behavior, regarding reporting bias and measurement
reactivity (i.e., potential behavior change due to EMA monitoring)
13,16,17
, were also examined in
Chapter Three.
3
Finally, in Chapter Four, we demonstrated the association between paretic hand use and
social-cognitive factors by using the combined EMA/accelerometry methodology established in
Chapter Two and Chapter Three. The time-based synchronization of accelerometry and EMA
prompts allowed us to conduct a prompt-specific analysis to have a fine-grained level of
understanding in daily paretic hand use. The effect of preceding social-cognitive factors on
subsequent paretic hand use (lagged effect) was also demonstrated in Chapter Four. We provided
evidence that the real-time and repeated measures of EMA exceed the limitations of
laboratory/clinic-based measures and show promise for advancing understanding of paretic hand
use in the day-to-day context.
Chapter Five summarizes the results and discusses the implications, limitations and
significance of this dissertation work.
4
REFERENCES
1. Kleim, J. A. & Jones, T. A. Principles of experience-dependent neural plasticity:
implications for rehabilitation after brain damage. J Speech Lang Hear Res 51, S225-39
(2008).
2. Hidaka, Y., Han, C. E., Wolf, S. L., Winstein, C. J. & Schweighofer, N. Use it and
improve it or lose it: interactions between arm function and use in humans post-stroke.
PLoS Comput Biol 8, e1002343 (2012).
3. Wolf, S. L., Winstein, C. J., Miller, J. P. & Morris, D. Effect of Constraint-Induced
Movement on upper extremity function 3 to 9 months after stroke: the EXCITE
randomized clinical trial. JAMA 296, 2095–2104 (2006).
4. Taub, E. et al. Technique to improve chronic motor deficit after stroke. Arch Phys Med
Rehabil 74, 347–354 (1993).
5. Taub, E., Uswatte, G., Mark, V. W. & Morris, D. M. The learned nonuse phenonmenon:
implications for rehabilitation. Eura Medicophys 42, 241–255 (2006).
6. Sterr, A., Freivogel, S. & Schmalohr, D. Neurobehavioral aspects of recovery: assessment
of the learned nonuse phenomenon in hemiparetic adolescents. Arch Phys Med Rehabil
83, 1726–31 (2002).
7. Stewart, J. C. & Cramer, S. C. Patient-reported measures provide unique insights into
motor function after stroke. Stroke 44, 1111–6 (2013).
8. Hellström, K., Lindmark, B., Wahlberg, B. & Fugl-meyer, A. R. Self-efficacy in relation
to impairments and activities of daily living disability in elderly patients with stroke: a
prospective investigation. J Rehabil Med 35, 202–207 (2003).
9. Pang, M. Y. C., Eng, J. J. & Miller, W. C. Determinants of satisfaction with community
reintegration in older adults with chronic stroke: role of balance self-efficacy. Phys Ther
87, 282–91 (2007).
10. Robinson, C. A., Shumway-Cook, A., Ciol, M. A. & Kartin, D. Participation in
community walking following stroke: subjective versus objective measures and the impact
of personal factors. Phys Ther 91, 1865–76 (2011).
11. Berges, I.-M., Seale, G. S. & Ostir, G. V. The role of positive affect on social participation
following stroke. Disabil Rehabil 34, 2119–23 (2012).
12. Villain, M., Sibon, I., Renou, P., Poli, M. & Swendsen, J. Very early social support
following mild stroke is associated with emotional and behavioral outcomes three months
later. Clin. Rehabil. 31, 135–141 (2016).
13. Dunton, G. F., Atienza, A. a, Castro, C. M. & King, A. C. Using ecological momentary
assessment to examine antecedents and correlates of physical activity bouts in adults age
50+ years: a pilot study. Ann Behav Med 38, 249–55 (2009).
14. Johnson, E. I. et al. Feasibility and validity of computerized ambulatory monitoring in
stroke patients. Neurology 73, 1579–83 (2009).
15. Kayes, N. M. & McPherson, K. M. Measuring what matters: does ‘objectivity’ mean good
science? Disabil. Rehabil. 32, 1011–1019 (2010).
5
16. Scollon, C. N., Chu, K.-P. & Diener, E. Experience sampling: promises and pitfalls,
strengths and weaknesses. J. Happiness Stud. 4, 5–34 (2003).
17. Shiffman, S., Stone, A. A. & Hufford, M. R. Ecological momentary assessment. Annu.
Rev. Clin. Psychol 4, 1–32 (2008).
6
CHAPTER 2
Mobile-Based Ecological Momentary Assessment of Paretic Hand Use and Related Self-
Report Measures after Stroke: Feasibility and Validity
INTRODUCTION
The importance of continued use of the paretic hand after stroke in the day-to-day
environment has been emphasized and fueled by the growing evidence of experience-dependent
neuroplasticity.
1,2
Limited use of the paretic hand can constrain an individual’s activity and
participation and lead to limited recovery and further functional degradation. To maximize
individuals’ motor recovery, beyond what is dictated by motor impairment, there is a need for a
“good” measure of paretic hand use in the daily environment to provide a more comprehensive
understanding of hand use behavior.
Researchers have developed laboratory-based measurements, such as the Motor Activity
Log (MAL)
3,4
and the Actual Arm Use Test (AAUT)
5
, to appraise and quantify people’s daily
paretic hand use. Recently, accelerometry
6–10
has also been used to assess hand movements
outside the laboratory. However, there are limitations of these measurements. For example, the
MAL is a self-report measure that requires the person to think back if the paretic hand was used
for a particular behavior over a period of time (e.g., “Have you used your right [paretic] hand to
turn on a light switch in the past 5 days?”). The responses regarding amount use and quality use
of the paretic hand rely heavily on an individual’s conscious recall of memory.
The information
may also be biased as a result of memory impairments following stroke.
11
The AAUT is a covert
observation tool developed to capture spontaneous use. Yet, it is limited to a single application
and it requires adequate training of examiners to ensure testing validity (i.e., unexperienced
7
examiners may accidently reveal to the participants that they are being tested and thereby
influence the movement).
11
Wrist-worn accelerometers have emerged as an alternative tool that provides an index of
individuals’ hand movement in the natural environment.
6–10
Researchers have developed
different methods, such as spectral analysis
12
, metrics analysis
13,14
, or pattern-recognition
algorithms
15
, to further understand the meaning of the acceleration signals. However, it is still
very challenging to identify activities of daily living (ADLs) that people perform, particularly in
the unsupervised setting. Most importantly, although accelerometry can objectively quantify
hand movement in the real world, it lacks essential information about an individual’s
psychological and contextual perceptions
16
pertaining to hand use behavior and function. Recent
studies have shown that individuals’ psychological needs and perceptions play a critical role in
functioning after stroke.
17–19
For example, people who have higher self-efficacy for walking
showed greater functional mobility
18,19
than those who have similar initial mobility function but
lower self-efficacy. Stroke survivors who had higher levels of positive mood demonstrated
greater levels of social participation than those with lower positive mood.
17
An exclusive use of
accelerometry would miss important psycho-social information, such as how an individual feels
about using the paretic hand to perform ADLs, and result in an incomplete perspective about
post-stroke hand use behavior.
16
To prevail over the shortcomings, we combined accelerometry with an innovative and
well-established application, Ecological Momentary Assessment (EMA), to capture day-to-day
hand use behavior. EMA is a mobile-based (e.g., smartphone) prompt methodology using
electronic questionnaires to capture individuals’ real-time self-reported responses in the natural
environment.
20–23
EMA allows simultaneous assessments of behavioral (i.e., hand use behavior)
8
and psycho-contextual variables (e.g., self-efficacy, mood, physical and social contexts), as well
as the ongoing activities participants were engaged in at that time. The capability of EMA to
measure self-report outcomes may provide an informative supplement to accelerometry data.
Combing these assessments may not only integrate objective with self-report information but
also allow us to obtain a more personalized and comprehensive understanding of individuals’
hand use behavior.
16
Additionally, the real-time measurement feature of EMA minimizes the influence of
retrospective recall biases or memory errors. This overcomes the limitations of the MAL and is
especially critical while collecting data from individuals who may have memory deficits after
stroke. EMA allows for repeated measures over short time intervals, which is expected to capture
dynamic changes of individuals’ behavior and psycho-contextual factors in the daily context.
EMA was initially developed and has been successfully used in the field of behavioral
medicine to understand the psycho-social components of chronic pain,
20,21
and to investigate
physical activity levels in non-disabled older adults
22,23
. Researchers have also utilized EMA to
monitor post-stroke depressive symptoms in the daily environment.
24–26
To the best of our
knowledge, this is the first time that EMA has been employed to assess hand use behavior in
stroke survivors. Our goal was to demonstrate the feasibility and validity of EMA to assess real-
time paretic hand use behavior and related self-report information, including self-efficacy and
mood, in the natural context post-stroke. Given our objective, accelerometry data will not be
included in the present study. It will be included in a subsequent paper that focuses on the
benefits of the combined EMA/accelerometry methodology.
Participants were asked to respond to 6 EMA prompts per day over a 5-day monitoring
period. For feasibility, we hypothesized that EMA is a feasible tool to measure paretic hand use
9
and related self-report information. We expect that (1a) the average response rate from
participants will be greater than 70% when prompted, (1b) no decrease in the response rate will
be observed across days and within day to rule out any novelty or fatigue effects, and (1c)
participants’ feedback and ratings of using EMA will be positive in an exit interview. Regarding
validity, we expect that (2) EMA will be shown to be a valid method for measuring paretic hand
use, self-efficacy, and mood. EMA responses were compared with selected reliable and valid
clinical/research-based measurements to examine construct validity.
METHODS
Participants
Participants were included if they met the following inclusion criteria: (1) pre-morbidly
right-hand dominant as determined by a modified Edinburgh Handedness Questionnaire
27
, (2)
left hemisphere stroke with right-side paresis, (3) minimal or more hand function as measured by
the Upper Extremity Fugl-Meyer Assessment (FM)
28,29
(total motor score ≥ 20, sub-score of the
finger mass flexion ≥ 1), (4) community-dwelling, (5) capability to read and communicate in
English, and (6) capacity to learn and use the EMA smartphone after instruction.
A homogeneous group of participants was selected; we exclusively recruited individuals
with right, dominant-side hemiparesis. Hemiparetic side combined with handedness has been
shown as a critical factor of hand use behavior and motor recovery.
30–32
Participants who have
non-dominant-side stroke demonstrated less paretic hand use
30
, greater impairment
31
, and less
improvement after training
32
than those with dominant-side stroke. To avoid the possible
10
confounding effects of handedness on paretic hand use, hand dominance and side of stroke were
controlled in this study.
Individuals were excluded if they met any of the following exclusion criteria: (1)
moderate to severe cognitive deficits as measured by the Montreal Cognitive Assessment
(MoCA, score < 16)
33
, (2) psychiatric diagnosis (e.g., depression), (3) neglect as measured by the
Albert’s Test
34
, (4) pain or musculoskeletal problems in the paretic limb which affects day-to-
day hand use, (5) any active medical or neurological conditions that would interfere with
participation in this study.
Prior to enrollment, all participants read and signed an Informed Consent form according
to the standard procedures of the University of Southern California (USC) Health
Sciences Institutional Review Board.
Study Design
A 5-day monitoring period was used in which participants were asked to respond to 6
EMA prompts per day. Two visits to the Motor Behavior and Neurorehabilitation laboratory of
the USC Health Sciences Campus, scheduled before and after the monitoring period, were
required for screening and outcome measure acquisition, as well as familiarization with and
return of the EMA smartphone.
Procedure
EMA data were collected through a mobile smartphone (HTC Sensation, AT&T USA
Dallas, TX) installed with custom software, movisensXS (Version 0.6.3658, movisens GmbH,
Karlsruhe, Germany). This software allows one to program prompt schedules, display questions,
11
and save participants’ responses. The mobile phone functions including calls, texting, and
internet browsing capabilities were all blocked and disabled by the software.
During the first lab visit, participants were asked to complete a demographic form and
several screening tests, including the Edinburgh Handedness Questionnaire, the FM, the MoCA,
and the Albert’s Test. The EMA smartphone was provided to the participants with verbal and
written instructions. After practicing at least one prompt with the experimenter, participants were
required to complete a practice prompt independently in order to demonstrate his/her capability
to use the EMA smartphone. A study overview sheet including reminder messages (e.g.,
‘remember to charge the phone every night’) was also given to the participants during the lab
visit. Finally, a customized EMA prompt schedule following a set of general rules was developed
to best accommodate to participants’ daily routine schedule. The general rules were: (1) one
prompt at 10 minutes after breakfast, (2) one at 10 minutes after lunch, (3) one at 10 minutes
after dinner, (4) one between breakfast and lunch, and (5) two between lunch and dinner. There
was an approximate 2-hour interval between prompts.
During the 5-day monitoring period, participants were asked to carry the EMA
smartphone with them during the day (~ 7 am to 9 pm). They were prompted by an auditory
signal 6 times per day (total of 30 prompts during participation) to respond to a set of questions.
Participants commonly started the first prompt between 8 – 10 am in the morning, and finished
the last prompt between 6 – 8 pm in the evening each day. Most of the participants had the same
prompt schedule across days; 5 participants had a varied schedule within the 5 days to
accommodate day-specific schedules.
Upon receiving an EMA prompt, participants were instructed to stop any ongoing
activity, provided it was safe to do so, and then respond to the EMA questions. They had 5
12
minutes to respond to the questions after prompted by the auditory signal. If no response was
made, the phone emitted two additional reminder signals with a 5-min interval. Afterwards, the
prompted EMA questions became inaccessible until the next prompt. Participants were also
allowed to ask for a delay up to 15 minutes. If a prompt occurred during an incompatible activity
(e.g., driving or showering), participants were instructed to ignore the prompt. In spite of the 6
prompts every day, participants were also encouraged to self-initiate an EMA prompt anytime
that they desired (i.e., self-triggered prompt) or when they had missed a scheduled prompt.
During the monitoring period, participants received a phone call on the first evening from
the researcher offering an opportunity to clarify any concerns, answer questions and/or to resolve
any technical issues. A study contact number was also available to the participants to report
problems at any time.
After the 5 days, participants were scheduled for a second lab visit to return the EMA
smartphone and to complete clinical/research-based outcome measures (described below). An
exit interview was included to elicit participants’ experiences using EMA.
Measures
EMA Prompt Questions
Each EMA prompt included questions regarding the ongoing activity, paretic hand use
behavior, and related psychological and contextual information. The questions were developed
from previous EMA studies that investigated physical activity and social-cognitive variables
(e.g., self-efficacy, mood, social support) in non-disabled adults.
22,35
Other measures, such as the
MAL
3,4
, Confidence in Arm and Hand Movements Scale (CAHM)
36
, and Rating of Everyday
Arm-use in the Community and Home (REACH)
37
, were referenced for designing the questions
13
of interests used here. Each prompt included a range of 12 to 17 questions (Appendix 2.1), which
varied depending on participants’ response to the previous question. For example, participants
were asked an additional question about whom they were with if they responded that they were
not alone. Once a prompt was completed, the responses were uploaded to the internet server for
later access. The order of the questions in each EMA prompt varied among days to retain
participants’ attention while answering the questions. All EMA questions were administered in
English.
With the study focus on hand use behavior, participants were first asked what activity
they were doing before responding to a prompt, and then asked which hand they were using for
the reported activity. They could choose from either right/left hand use, bilateral hand use, or
neither-or-none use (e.g., watching TV, relaxing, listening to music) (Figure 2.1a). For the self-
reported variables, self-efficacy was measured by asking participants’ confidence level for using
the paretic hand within the next 2 hours, using a visual analog scale (VAS) ranging from 0 (not
confident at all) to 100 (very confident) (Figure 2.1b). Mood was assessed by asking their
sadness/happiness level with a 0-100 VAS (very sad – very happy) (Figure 2.1c).
Exit Interview Likert-Scale Questions
Three 7-point Likert-scale questions (Figure 2.4) were asked during the exit interview
regarding: (Q1) ease of EMA responding, (Q2) number of prompts per day, and (Q3)
disruptiveness of EMA prompt. Participants were also encouraged to share their experience of
using EMA during the interview (open-ended questions), in terms of technology, question
content, prompt frequency and timing.
14
Clinical/Research-Based Measures
To examine the construct validity of EMA, four self-reported clinical/research-based
measures were administered: the MAL, the Stroke Impact Scale (SIS), the CAHM, and the
Positive Affect and Negative Affect Scale (PANAS).
The MAL
3,4
was used to obtain convergent validity of paretic hand use in the natural
environment. The MAL is a semi-structured interview to assess how much (amount of use
[AOU]) and how well (quality of movement [QOM]) participants use their paretic hand for
accomplishing 28 ADLs (e.g., turn on a light switch, put on socks, brush teeth) over the past 5
days. Validity and reliability of the MAL has been established.
3,4
The modified AOU scale was
used with scores of 0 (no use) or 1 (use). The average score of AOU was calculated by dividing
the number of items scored as ‘1’ (use) by the total number of items scored. The average score of
QOM ranging from 1 (very poor) to 5 (normal) was presented.
The SIS
38–40
(Version 3.0) is a reliable and valid self-report instrument, which contains
eight domains of questions. Each domain includes 4-10 questions; each question was rated in a
5-point Likert scale in terms of the frequency/difficulty level participants experienced (e.g., from
1 [not difficult at all] to 5 [extremely difficult]). The Hand Function and Mobility domains were
used to obtain convergent and discriminant validity of self-efficacy, respectively. The Emotion
domain was used to obtain convergent validity of participants’ mood. The average scores of each
domain were presented in percentage over the full score of each domain.
The CAHM
36
is a 20-item questionnaire that was used to assess participants’ confidence
level for performing a series of functional tasks that involve the paretic hand (e.g., “How certain
are you that you can carry a cafeteria tray full of lunch food and drink from the cashier to a
table?”). Participants were instructed to rate their confidence level over the past 5 days on a scale
15
of 0 (very uncertain) to 100 (very certain). The CAHM was used to obtain the convergent
validity of individuals’ self-efficacy of using the paretic hand in the natural context.
The PANAS
41
was used to measure participants’ mood over the past 5 days. Participants
were asked to rate their feelings on 10 adjectives of positive emotions (e.g., interested,
determined, active) and 10 adjectives of negative emotions (e.g., distressed, irritable, nervous).
Ratings of each item ranging from 1 (very slightly or not at all) to 5 (extremely) were summed to
produce a positive affect and a negative affect scores (range 10–50). A higher value indicates
greater positive/negative affectivity.
Data & Statistical Analysis
Feasibility
The average response rate across participants was used to examine Hypothesis (1a).
Hierarchical linear regression modeling (HLM) was conducted to examine participants’
EMA response rates by day and by time for Hypothesis (1b). HLM, also known as mixed model
or multilevel modeling, is an extension of the standard ordinary least squares regression. It is
used to analyze variance in the outcome variables at varying hierarchical levels (e.g., between-
participant level, within-participant level). HLM allows different numbers of measurements
(unequal cell sizes) when accounting for the variances in each level (e.g., different numbers of
EMA prompts completed among participants, or different numbers of EMA prompts completed
among days for each individual). To accommodate the multilevel structure of our data, a two-
level generalized HLM (between-participant and within-participant levels) was used to test
whether the likelihood of EMA response (completely answered vs. incomplete/unanswered)
varied as a function of day and time.
16
The average ratings of each exit interview question across participants were reported to
verify Hypothesis (1c).
Validity
Construct validity of EMA (Hypothesis 2) was assessed by examining the concordance
between participants’ EMA responses and the self-reported clinical/research-based measures
using Spearman’s rank correlation.
Hand Use Behavior. Prior to the correlation comparison for the validity test in hand use,
the EMA hand use responses were processed because the MAL only included activities that
involve hand use and does not separate the unimanual and the bimanual hand use. The EMA
hand use data with “neither-or-none” responses were excluded since no hand use behavior was
involved. Further, we recoded the EMA responses. If participants selected “left hand use”, the
responses were recoded as 0, which means no right, paretic hand use. If the response was either
“right hand use” or “both hands use”, it was recoded as 1, which indicates the paretic hand was
involved in performing activities. The percentage of paretic hand use from the EMA responses
(%) was then calculated by dividing the number of responses coded as ‘1’ (use) by the total
number of completed EMA entries for each participant. The probability was compared with the
average scores of AOU and QOM to examine convergent validity.
Self-Efficacy & Mood. To compare with the single-time measure of the
clinical/research-based assessments (i.e., CAHM, SIS and PANAS), the repeated measures of
EMA scores for self-efficacy and mood were averaged for each participant, separately.
All statistical analyses were performed using STATA 14.2 (Stata Corporation, College
Station, TX, USA).
17
RESULTS
Participants
Thirty participants with chronic, right dominant-side stroke (average scores of the FM =
47.27) were recruited. The detailed characteristics of the participants are shown in Table 2.1.
Missing Data
Of the total 900 scheduled EMA prompts (30 prompts x 30 participants), 10 prompts
(1.11%) were not provided due to unknown technical problems experienced by 4 participants (6
prompts) and the phone being accidentally turned off by one participant (4 prompts).
Feasibility
Overall Response Rate
Total 754 scheduled prompts were completely answered; 20 prompts were incomplete;
116 prompts were unanswered. The overall response rate (= answered prompts scheduled
prompts) was 84.64 ± 18.51% across participants (range, 26.67 – 100.00%), which represented
an average of 25.10 ± 5.55 completed prompts out of the 30 scheduled prompts (range, 8 – 30
prompts/participant, Figure 2.2 blue bars).
Participants additionally self-triggered a total of 173 prompts during the 5-day
monitoring period. On average, each participant completed an additional 5.23 ± 5.06 prompts
(range, 0 – 17 prompts) during participation (Figure 2.2, red bars). Further analysis showed that
there was a strong negative correlation between the answered scheduled prompts and the self-
triggered prompts (r
s
= - 0.721, p < 0.001). Participants self-initiated fewer prompts when they
completed more scheduled prompts.
18
Out of the total 927 completed EMA entries (= 754 answered scheduled prompts + 173
self-triggered prompts), 17 prompts (from 10 participant) were excluded due to repetitiveness.
The repetitive entry was defined when (1) the time interval between 2 adjacent prompts was less
than 1 minute, and (2) the responses of the 2 prompts were the same on the multiple-choice
questions. One common reason for the repetitive entries was that participants accidentally self-
started one prompt right after completing a scheduled prompt due to the unfamiliarity of the
EMA phone operation on the first day (9 out of 17 prompts).
Overall, a total of 910 valid EMA entries were completed and included in the following
analyses (average, 30.33 ± 3.14 prompts/participant; range, 22 – 35 prompts). The average time
that participants spent to complete one EMA prompt was 3.99 ± 1.72 min (range, 1.80 – 8.94
min) across participants.
Response Rates by Day and by Time
Although there was a trend of decreasing response rate by day (Figure 2.3a), the
generalized HLM analysis revealed that there were no significant effects of day and time on
response rates (p = 0.221 and p = 0.242, respectively). Participants’ response rate did not
significantly decrease as a function of day or time. The group averages of response rates by day
and by time are shown in Figure 2.3a and 2.3b (blue bars). Similarly, the numbers of self-
triggered prompts did not significantly change by day or by time (p = 0.285 and p = 0.081,
respectively; Figure 2.3a and 2.3b, red bars). The response rates by day and by time of each
individual are also shown in Appendix 2.2 and 2.3, respectively.
19
Exit Interview
Participants reported an average score of 6.57 ± 0.63 (range, 5 – 7; Figure 2.4) on exit
interview Q1 for EMA response ease, an average of 4.38 ± 1.17 (range, 1 – 7) on Q2 for prompt
numbers, and an average of 3.00 ± 1.68 (range, 1 – 7) on Q3 for EMA prompt disruptiveness.
Generally, participants felt that responding to the EMA prompts was easy and not disrupting.
The total number of prompts was acceptable to them as well.
Validity
Hand Use Behavior
There were strong positive correlations between paretic hand use measured by EMA and
the AOU (r
s
= 0.745, p < 0.001; Figure 2.5a) and the QOM (r
s
= 0.634, p < 0.001; Figure 2.5b)
of the MAL. The results remained significant when separately examining the scheduled prompts
(r
s
= 0.711, p < 0.001 and r
s
= 0.634, p < 0.001) and self-triggered prompts (r
s
= 0.544, p = 0.004
and r
s
= 0.455, p = 0.020). The hand use response for each valid EMA prompt of individual
participants is presented in Appendix 2.4.
Self-Efficacy & Mood
The average score of the EMA self-efficacy question was strongly correlated with the
CAHM (r
s
= 0.783, p < 0.001; Figure 2.6a) and the average scores of the SIS Hand Function
domain (r
s
= 0.847, p < 0.001; Figure 2.6b). There was no significant relationship between the
EMA self-efficacy question and the SIS Mobility domain (r
s
= 0.233, p = 0.103; Figure 2.6c).
There were significant positive correlations between the EMA mood score and the SIS
Emotion domain score (r
s
= 0.601, p < 0.001; Figure 2.7a) and the PANAS positive affect score
20
(r
s
= 0.493, p = 0.002; Figure 2.7b). A strong negative correlation between the EMA mood score
and the PANAS negative affect score was also observed (r
s
= - 0.612, p < 0.001; Figure 2.7c).
The above results were unchanged when computed separately for the scheduled prompts
and self-triggered prompts. The self-efficacy and mood scores for each valid EMA prompt of
each individual participant are presented in Appendix 2.5 and Appendix 2.6, respectively.
DISCUSSION
The use of EMA to assess stroke survivors’ daily hand use along with informative self-
report information (i.e., self-efficacy and mood) was shown to be feasible. Our results
demonstrated a high average response rate (84.64%), which is similar to the response rates
observed in non-disabled populations
22,23
. The maintained response rate over days and times
indicated that the incomplete/unanswered prompts were not associated with day or time of the
study. Neither were novelty or fatigue effects shown to bias the data obtained. The 30.33 average
valid EMA entries per participant (answered scheduled prompts + self-triggered prompts) was
slightly over the original 30 prompt goal. Half of the participants (n = 15) completed more than
30 prompts during participation (32 – 36 prompts) and only 7 participants finished less than 30
prompts (22 – 29 prompts) (Figure 2.2).
The self-trigger function was the first time used in the stroke population. The original
purpose of employing this function was to increase the possibility of using prompt methodology
to capture hand use. Since hand use behavior/movements are single discrete events that occur in
the daily context, rather than a constant state such as mood, the self-trigger function might be of
21
benefit to capture hand use events, especially in the stroke population who might have a low
level of paretic hand use.
Interestingly, we found that the self-trigger function, instead of capturing more hand use
behavior, was mainly used to compensate for a missed prompt, which unexpectedly alleviated
participants’ burden to respond to the scheduled prompts. In the exit interview, just over 1/3
rd
of
the participants (n = 11) mentioned that they started a prompt themselves when they knew that
they had missed or would have missed one. The negative correlation between the answered
scheduled prompts and the self-triggered prompts also quantified the compensatory role of the
self-triggered prompts. Positive feedback of the self-trigger function revealed that participants
felt less anxious when they missed prompts (e.g., “The [self-triggered] function is a great idea. I
know I can make up some … so I didn’t’ feel too worried when I missed a survey.”). It also
provided flexibility to people who had difficulty following the prompt schedule (e.g., “It is really
hard to follow the [prompt] schedule for an active person like me. … I usually enter a survey
when I have time.”). The high EMA response rate may reflect the fact that the participants feel
comfortable and less stressed when using the prompt technology.
On the other hand, we are aware of the natural differences between the scheduled and the
self-triggered prompts. Participants were externally probed by the experimenters when
responding to the scheduled prompts but actively responded to self-triggered prompts. To avoid
potential confounds of this natural difference, we carefully examined our data and conducted
separate analyses for the scheduled and the self-triggered prompts. We did not find any
difference in responses between the two types of prompts in determining the feasibility and
validity of EMA.
22
The positive feedback from the exit interview further supported our hypothesis of EMA
feasibility. Participants expressed that not only the phone and the EMA software was easy to
operate, but the prompt questions were simple and straightforward to answer. Our participants in
the age of 80 who had no prior experience using a smartphone (n = 2) were able to respond to
EMA prompts successfully. People with more severe stroke (FM score range, 20 – 30; n = 7)
were also able to answer the EMA prompts without problems. Together with 19 participants who
indicated the acceptance of the total prompt number (scored 4 in Q2; Figure 2.4), 8 participants
in fact expressed that they could respond to a few more prompts per day (scored 5 – 7 in Q2).
Over half of the participants (16 out of 30) also reported that the EMA prompts were not
disruptive in their day (scored 1 – 3 in Q3). These results support our EMA design and confirms
the usefulness of EMA in a group of community dwelling stroke survivors with mild to
moderate-to-severe disability.
Furthermore, we established the construct validity of EMA. The correlation comparisons
demonstrated convergence between EMA responses and construct appropriate outcome
measures. Participants who reported more use of the paretic hand, greater self-efficacy, and a
higher level of positive mood (happiness) in EMA expressed: (1) a greater amount and better
quality of paretic hand use in the MAL, (2) higher confidence levels and scores in the CAHM
and the SIS Hand Function domain, and (3) greater positive affect scores and lower negative
affect scores in the PANAS, respectively. The non-significant relationship between the EMA
self-efficacy and SIS Mobility also established discriminant validity, since the SIS Mobility
domain focuses on individuals’ lower extremity mobility (such as standing/walking, climbing
stairs) rather than upper extremity function.
23
Overall, this study provided evidence for a novel means to assess post-stroke hand use
behavior in the natural environment. EMA permits assessments for individuals’ real-time
behavioral and psycho-contextual factors that may be inaccessible to the standard
clinical/research-based measures. The clinical/research-based measures, which produce a single
score at a given time point in a controlled environment (clinic or laboratory), provide general
information that may mask considerable natural fluctuation in an individual’s profile. This study
established the first step for using EMA to capture dynamic paretic hand use behavior in the
stroke population.
Still, there are several limitations. Although participants were instructed to complete the
EMA prompts independently, some of them might receive assistance from partners or caregivers
in responding. The possibility of caregiver assistance, unfortunately, cannot be excluded in the
present study. Second, the fixed-time EMA prompting design could cause reporting bias and
measurement reactivity.
35
Our participants may learn to expect that they will be prompted at
certain times of the day. Further analyses addressing the concerns of reporting bias and reactivity
are necessary. In addition, our participants were exclusively limited to individuals with right,
dominant-side hemiparesis. Due to the confounding effects of handedness on paretic hand use
30–
32
, the generalizability of the feasibility and validity of EMA to people who are pre-morbidly
left-handed may be limited. Lastly, we believe that combining EMA with accelerometry has
potential benefits to provide more insights into people’s daily hand use behavior. Future analyses
that synchronize EMA and accelerometry data will allow a comprehensive understanding in
post-stroke paretic hand use in the daily context.
24
CONCLUSION
Our results established the feasibility and validity of using EMA to assess stroke
survivors’ paretic hand use behavior along with the associated self-reported factors, including
self-efficacy and mood, in the natural environment. EMA is capable of capturing an individuals’
dynamic hand use behavior and the natural fluctuations of self-efficacy and mood. The real-time
and repeated measures feature of EMA exceed the limitations of laboratory/clinic-based hand use
measures and shows promise for advancing our understanding of paretic hand use. Further
analyses addressing the benefits of a combination EMA/accelerometry methodology will be
explored in a subsequent paper.
25
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Table 2.1. Characteristics of Participants.
Mean ± SD (Range)
Sex (n, male: female) 21:9
Age (year) 61.19 ± 13.10 (24.43 – 82.91)
Onset of Stroke (year) 4.68 ± 3.89 (0.68 – 14.80)
MoCA (0 – 30) 24.73 ± 3.42 (19 – 30)
FM (0 – 66) 47.27 ± 14.93 (20 – 66)
Abbreviations: MoCA = Montreal Cognitive Assessment, FM =
Upper Extremity Fugl-Meyer Assessment.
29
Figure 2.1. Example Questions of EMA Prompt. Abbreviation: EMA = Ecological
Momentary Assessment.
(a) Hand Use Behavior (b) Self-Efficacy (c) Mood
30
Figure 2.2. Overall Response Rate. The goal of 30 prompts during participation (6 prompts/day
for 5 days) is indicated by the black horizontal line.
31
Figure 2.3. Response Rates by Day and by Time. The goals for day and time are indicated by the black horizontal line in each
figure. The blue bars represent the group averages of response rates for the scheduled prompts; the red bars are the self-triggered
prompts. The error bars represent the standard deviation of each bar.
32
Figure 2.4. Exit Interview Prompt-Related Questions. Error bars represent standard deviation.
33
Figure 2.5. Correlation Comparisons for Hand Use Behavior between the EMA Responses and the MAL. Abbreviations: EMA
= Ecological Momentary Assessment, MAL = Motor Activity Log.
34
Figure 2.6. Correlation Comparisons for Self-Efficacy between the EMA Responses and (a) the CAHM, (b) the SIS Hand
Function, and (c) the SIS Mobility. Abbreviations: EMA = Ecological Momentary Assessment, CAHM = Confidence in Arm and
Hand Movements Scale, SIS = Stroke Impact Scale.
35
Figure 2.7. Correlation Comparisons for Mood between the EMA Responses and (a) the SIS Emotion, (b) the Positive Affect of
PANAS, and (c) the Negative Affect of PANAS. Abbreviations: EMA = Ecological Momentary Assessment, SIS = Stroke Impact
Scale, PANAS = Positive Affect and Negative Affect Scale.
36
Appendix 2.1. List of 17 EMA Prompt Questions. Questions were displayed on the EMA
phone screen one at a time. Subheadings (e.g., mood, physical location) and question branching
(e.g., “Skip to #9”) were not shown to the participants. Abbreviation: EMA = Ecological
Momentary Assessment.
Mood
In general, over the last 2 hours:
1. How SAD or HAPPY have you been?
• Very sad –––––––––––––––––––––––––––––––––––––––––––– Very happy
2. How ANXIOUS or CALM have you been?
• Very anxious ––––––––––––––––––––––––––––––––––––––––– Very calm
3. How FRUSTRATED have you been?
• Not frustrated at all ––––––––––––––––––––––––––––––––– Very frustrated
4. How much ENJOYMENT have you had?
• Little enjoyment ––––––––––––––––––––––––––– A great deal of enjoyment
Physical Location
5. WHERE were you just before the phone rang?
• Home (indoors) (laundry room, kitchen, bathroom, etc.)
• Home (outdoors) (yard, deck, driveway, etc.)
• Work/Volunteer work places
• Community (park, mall, bank, restaurant, etc.)
• Health facility/Clinic (rehabilitation therapy, etc.)
• Vehicle (car, train, etc.)
• Other
Social Context
6. Were you ALONE just before the phone rang?
• Yes (Skip to #9)
• No (Continue to #7)
7. WHOM were you with just before the phone rang? (Check all that apply)
• Family
• Friends
• Colleagues/Co-workers
• Service providers (therapist, cashier, gardener, waiter…)
• People I don’t know/Strangers
• Other
8. How would you describe your social interaction with the one(s) you were with?
37
• Very stressful –––––––––––––––––––––––––––––– Very positive/uplifting
Activity & Hand Use Behavior
9. Please briefly describe what activity you were doing just before the phone rang.
• Text box (can be skipped)
10. Which arm/hand(s) were you using for the activity?
• Right arm/hand
• Left arm/hand
• Both arms/hands
• Neither or none (Skip to #14)
11. Which of the following(s) can describe the activity you were doing? (Check all that
apply)
• Activity that requires a lot of arm/hand STRENGTH/ENDURANCE
• Activity that requires a high level of hand DEXTERITY/PRECISION
• Activity that needs to COORDINATE both hands working together to accomplish
• Activity that you were under a TIME-PRESSURE to complete
• Activity with a NEGATIVE consequence if you failed
• Activity with a POSITIVE consequence if you succeeded
• None of above
12. Around how long had you been doing the activity?
• Less than 5 min
• 5 – 15 min
• 15 – 30 min
• 30 – 60 min
• Longer than 60 min
13. How DIFFICULT was the activity to you?
• Not difficult at all ––––––––––––––––––––––––––––––––––– Very difficult
14. What was your body position when the phone rang?
• Dynamic (walking, moving around, etc.)
• Standing
• Sitting
• Lying down
Self-efficacy
15. How CONFIDENT are you right now that you can use your RIGHT arm/hand to
accomplish any activity that comes up or that you need to do within the next 2 hours?
• Not confident at all––––––––––––––––––––––––––––––Very confident
16. How CONFIDENT are you right now that you can use your LEFT arm/hand to
accomplish any activity that comes up or that you need to do within the next 2 hours?
38
• Not confident at all––––––––––––––––––––––––––––––Very confident
17. How CONFIDENT are you right now that you can use your BOTH arms/hands to
accomplish any activity that comes up or that you need to do within the next 2 hours?
• Not confident at all––––––––––––––––––––––––––––––Very confident (END)
39
Appendix 2.2. Response Rate by Day of Individual Participant.
40
Appendix 2.3. Response Rate by Time of Individual Participant.
41
Appendix 2.4. Hand Use Response for Each Valid EMA Entry of Individual Participant. Y axis indicates the choices of the EMA
hand use question (R = right hand use, L = left hand use, B = bilateral hand use, and N = neither-or-none use). X axis indicates the
EMA prompt numbers, which are based on the order that prompts were provided or self-triggered (collapsed across days and times).
42
Appendix 2.5. Self-Efficacy Score for Each Valid EMA Entry of Individual Participant. Y axis indicates the EMA self-efficacy
score ranging from 0 (not confident at all) to 100 (very confident). X axis indicates the EMA prompt numbers, which are based on the
order that prompts were provided or self-triggered (collapsed across days and times).
43
Appendix 2.6. Mood Score for Each Valid EMA Entry of Individual Participant. Y axis indicates the EMA mood score ranging
from 0 (very sad) to 100 (very happy). X axis indicates the EMA prompt numbers, which are based on the order that prompts were
provided or self-triggered (collapsed across days and times).
44
CHAPTER 3
A Combined Methodology with Objective Accelerometry and Self-Report Ecological
Momentary Assessment for Paretic Hand Use after Stroke
INTRODUCTION
The use of the paretic hand in the natural environment has been identified as an indicator
of daily function and long-term recovery in individuals with stroke.
1–3
To maximize patients’
motor recovery, researchers have utilized monitoring methodologies (e.g., accelerometry) to
comprehend day-to-day paretic hand use.
e.g.,4–6
The monitoring methods, which observe
participants and collect data in a naturalistic setting, provide ecological assessments and essential
information that clinical/research-based assessments cannot measure, such as what a person
actually does in daily life. Knowing the actual use of the paretic hand is particularly critical in
stroke survivors, as previous studies have demonstrated that there is a discrepancy between what
people can do and what they really do.
7,8
Individuals’ recovered motor capability in the clinical
environment does not translate to increased paretic hand use in the daily context.
9
Hence, the
ecological assessments have become an important measure that allows a deeper understanding of
the real-world paretic hand use to further promote recovery.
As noted above, accelerometry is a commonly-used ecological assessment for post-stroke
hand use behavior. Although we cannot distinguish functional (e.g., reaching to an object) and
non-functional movements (e.g., arm swing while walking) based on the acceleration signals,
accelerometry serves as a useful index of hand use in the uncontrolled, natural environment. It is
a valuable substitute for laboratory-based hand use measures, such as the real-time feature of
45
accelerometry that eliminates recall biases (i.e., a limitation of the Motor Activity Log).
10,11
Studies have employed it to objectively quantify the duration (time) and intensity (magnitude) of
hand movement in the healthy and stroke populations.
e.g.,4–6,12,13
The reliability
14–16
and
validity
13,14,16,17
of accelerometry in assessing hand use behavior outside the laboratory have also
been established.
A mobile-based prompt methodology, named Ecological Momentary Assessment (EMA),
is another well-established ecological measurement
18–21
that can be used to measure daily hand
use behavior. In contrast to the objective measure of accelerometry, EMA allows participants to
self-report their behavior (e.g., what activity they were doing, which hand they were using) and
psychological factors (e.g., self-efficacy, mood, and social interaction), as well as contextual
information (e.g., current physical location) in real-time. It can prompt participants to answer
questions many instances within a short period of time and capture dynamic changes of behavior
and psycho-contextual factors. The feasibility and validity of using EMA to assess hand use
behavior and the related self-report information (i.e., self-efficacy and mood) have been
established in our previous work.
11
The EMA self-report information may provide us a new
perspective of individuals’ hand use behavior in the natural enviroenment.
22
Therefore, we combined both ecological assessments together – with an objective
measure (accelerometry) and a self-report aspect (EMA) – to have a more personalized and
comprehensive understanding of individuals’ day-to-day hand use. Our overall goal was to
determine the validity of this combined methodology in measuring daily hand use in individuals
with stroke. The first aim was to examine the consistency between objective hand movement
(captured by accelerometers) and participants’ self-report hand use behavior (recorded by EMA).
46
A fine-grained prompt-specific analysis that compared EMA response with a before-prompt 10-
min window of accelerometry was conducted to determine the consistency.
Furthermore, we examined the potential influence of the act of EMA responding on hand
use behavior. Studies have suggested that EMA may cause reporting bias and measurement
reactivity.
18,21,23
Reporting bias indicates that there may be situations that participants were less
likely to respond to EMA prompts.
23
Our participants knew that they would be asked about their
hand use behavior. We were interested to know whether they were less likely to answer the EMA
prompts when they used their hand(s) less. Thus, our second aim was to examine the before-
prompt 10-min accelerometry between answered and unanswered prompts to understand whether
the EMA non-response was associated with a low hand movement. Additionally, we encouraged
participants to self-initiate a prompt anytime that they desired (i.e., self-triggered prompt). The
accelerometry was also examined to verify whether participants tended to increase their hand(s)
use before self-triggering an EMA prompt.
Moreover, reactivity is defined as the potential behavioral change induced by the act of
assessing the behavior.
18,21,23
Individuals may put an unusual attention to the monitored behavior
and alter the specific behavior simply due to monitoring alone. Given the EMA question of hand
use behavior, we wanted to understand whether participants changed their hand use behavior due
to reactivity. Our third aim was to compare the 10-min accelerometry of hand movement before
and after each EMA prompt to examine the immediate effect of reactivity. We also investigated
the accumulated effect of reactivity by comparing the whole-day accelerometry data to see
whether the hand movement changed over days.
47
METHODS
Participants
Participants were included if they met the following inclusion criteria: (1) pre-morbidly
right-hand dominant as determined by a modified Edinburgh Handedness Questionnaire
24
, (2)
left hemisphere stroke with right-side paresis, (3) minimal or more hand function as measured by
the Upper Extremity Fugl-Meyer Assessment (FM)
25,26
(total motor score ≥ 20, sub-score of the
finger mass flexion ≥ 1), (4) community-dwelling, (5) capability to read and communicate in
English, and (6) capacity to learn and use the EMA smartphone after instruction.
A homogeneous group of participants was selected; we exclusively recruited individuals
with right, dominant-side hemiparesis. Hemiparetic side combined with handedness has been
shown as a critical factor of hand use behavior and motor recovery.
27–29
Participants who have
non-dominant-side stroke demonstrated less paretic hand use
27
, greater impairment
28
, and less
improvement after training
29
than those with dominant-side stroke. To avoid the possible
confounding effects of handedness on paretic hand use, hand dominance and side of stroke were
controlled in this study.
Individuals were excluded if they met any of the following exclusion criteria: (1)
moderate to severe cognitive deficits as measured by the Montreal Cognitive Assessment
(MoCA, score < 16)
30
, (2) psychiatric diagnosis (e.g., depression), (3) neglect as measured by the
Albert’s Test
31
, (4) pain or musculoskeletal problems in the paretic limb which affects day-to-
day hand use, (5) any active medical or neurological conditions that would interfere with
participation in this study.
48
Prior to enrollment, all participants read and signed an Informed Consent form according
to the standard procedures of the University of Southern California (USC) Health
Sciences Institutional Review Board.
Study Design
A 5-day monitoring period was used in which participants were asked to wear one
accelerometer on each wrist and to respond to 6 EMA prompts per day (Figure 3.1). Two visits
to the Motor Behavior and Neurorehabilitation laboratory of the USC Health Sciences Campus,
scheduled before and after the monitoring period, were required for screening and outcome
measure acquisition, as well as familiarization with and return of devices (i.e., two
accelerometers and one EMA smartphone).
Instruments
The ActiGraph accelerometer (wGT3X-BT) (ActiGraph, Inc. Pensacola, FL) was used to
capture hand movement while participants performed daily activities during the 5-day
monitoring period. Each accelerometer was attached with a Velcro strap for ease of donning for
participants with stroke to wear one on each wrist. The accelerometer with a tri-axial (x-, y-, z-
direction) sensor was set at a sampling rate of 30 Hz.
EMA data were collected through a mobile smartphone (HTC Sensation, AT&T USA
Dallas, TX) installed with custom software, movisensXS (Version 0.6.3658, movisens GmbH,
Karlsruhe, Germany). This software allows one to program prompt schedules, display questions,
and save participants’ responses. The mobile phone functions including calls, texting, and
internet browsing capabilities were all blocked and disabled by the software. The EMA prompts
were time-stamped to synchronize with acceleration data.
49
Procedure
During the first lab visit, participants were asked to complete a demographic form and
several screening tests, including the Edinburgh Handedness Questionnaire, the FM, the MoCA,
and the Albert’s Test. The accelerometers and the EMA smartphone were provided to the
participants with verbal and written instructions. After practicing at least one prompt with the
experimenter, participants were required to complete a practice prompt independently in order to
demonstrate his/her capability to use the EMA smartphone. A study overview sheet including
reminder messages (e.g., ‘remember to charge the phone every night’) was also given to the
participants during the lab visit. Finally, a customized EMA prompt schedule was developed to
best accommodate to participants’ daily routine schedule. There was an approximate 2-hour
interval between prompts.
During the 5-day monitoring period, participants were asked to wear the accelerometers
and to carry the EMA smartphone with them during the day (~ 7 am to 9 pm). Suggestions were
made to take off the accelerometers during activities where the Velcro straps might get wet and
cause discomfort (e.g., showering, swimming), and also while sleeping. They were prompted by
an auditory signal 6 times per day (total of 30 prompts during participation) to respond to EMA
questions. Participants commonly started the first prompt between 8 – 10 am in the morning, and
finished the last prompt between 6 – 8 pm in the evening each day.
Upon receiving an EMA prompt, participants were instructed to stop any ongoing
activity, provided it was safe to do so, and then respond to the EMA questions. They had 5
minutes to respond to the questions after prompted by the auditory signal. If no response was
made, the phone emitted two additional reminder signals with a 5-min interval. Afterwards, the
prompted EMA questions became inaccessible until the next prompt. Participants were also
50
allowed to ask for a delay up to 15 minutes. If a prompt occurred during an incompatible activity
(e.g., driving or showering), participants were instructed to ignore the prompt. In spite of the 6
prompts every day, participants were also encouraged to self-initiate an EMA prompt anytime
that they desired (i.e., self-triggered prompt) or when they had missed a scheduled prompt.
During the monitoring period, participants received a phone call on the first evening from
the researcher offering an opportunity to clarify any concerns, answer questions and/or to resolve
any technical issues. A study contact number was also available to the participants to report
problems at any time. After the 5 days, participants were scheduled for a second lab visit to
return all the devices and to complete outcome measures.
Measures and Data Analysis
Accelerometry of Hand Movement
Raw accelerometry data were first processed using ActiLife 6.0 software (ActiGraph, Inc.
Pensacola, FL). The raw accelerations (m/s
2
) from 3 axes (x, y, z) were filtered by a band-pass
filter (0.25-2.5 Hz) and binned into 2-seccond epochs for each axis. The filtered accelerations
within each epoch (i.e., 60 samples/epoch) were then summed together and converted into
“activity count” values using a proprietary algorithm (e.g., 1 activity count = 0.01664g for an
acceleration produced by a movement with a frequency at 0.75Hz).
32
Further analyses with activity counts were conducted using a custom-written MATLAB
program (version R2015a) (The MathWorks Inc., Natick, MA). Activity counts across three axes
for each epoch were combined into a single resultant value (= x
"
+y
"
+z
"
). To define
participants’ hand movement, we used a threshold of 2 (in activity count) that provides a
reliable
14–16
and valid
13,14,16,17
measure of hand use in daily activities. When the activity count for
51
the epoch was ≥ 2, the hand was considered to have been moved during the 2-second time period
(“movement”). When the activity count was < 2, the hand was marked as “no movement”.
Unimanual right/left hand movement was defined when only the right- or left-hand
accelerometer signal was ≥ 2 (i.e., only one hand was considered as moving during the epoch).
Bimanual hand movement was defined when the accelerometer signals of both right and left
hands were ≥ 2 (i.e., both hands were considered as moving during the epoch).
The time and magnitude of hand movement were the variables of interest. For the time,
seconds of each movement epoch were added up for either unimanual or bimanual hand
movements. The magnitude of unilateral hand movement was indicated by the activity count
value of each movement epoch. The magnitude of bimanual hand movement was the sum of the
activity counts from both hands.
13
The median of the magnitude was used to represent each
participant’s data.
A 10-min window of accelerometer data was created before and after each EMA prompt
for the prompt-specific analysis in each aim. For the whole-day analysis in Aim 3, accelerometer
data were compared among days. Due to the daily time difference of accelerometer wearing, the
time of hand movement in the whole-day analysis was normalized to each person’s everyday
wearing time and presented in percentage.
Any 3-hour segment for which the activity counts in every epoch were < 2 was
considered as the participants did not wear the accelerometer (“no-wearing”).
16
The data were
included in the analyses only when both accelerometers were worn on the wrists.
EMA Question of Hand Use Behavior
Each EMA prompt included a maximal of 17 questions regarding the ongoing activity,
hand use behavior, and related psychological and contextual information (Appendix 2.1).
52
Participants were first asked what activity they were doing before responding to a prompt. They
were then asked, “Which hand/arm were you using for the activity?” They could choose from
either right/left hand use, both hands use, or neither-or-none use (e.g., when watching TV,
relaxing, listening to music).
Statistical Analysis
All the statistical analyses were performed using STATA 14.2 (Stata Corporation,
College Station, TX, USA). A two-level (i.e., between-participant and within-participant levels)
hierarchical linear regression model (HLM) was used to analyze the multilevel repeated
measures (i.e., around 30 repeated measures for each individual participant) to account for
possible correlations of residuals and heteroskedasticity due to nesting effects.
Consistency (Aim 1)
To investigate whether the self-reported hand use behavior was consistent with the
objective accelerometry of hand movement, HLM analysis was conducted with the EMA hand
use response (i.e., right/left hand use, both hands use, and neither-or-none use) as the
independent variable and the before-prompt 10-min accelerometry of hand movement as the
dependent variable (i.e., unimanual right/left hand movement, or bimanual hand movement in
separate HLM analyses).
Reporting Bias (Aim 2)
To examine whether the unanswered EMA prompts were related to participants’
concurrent hand movement, the before-prompt 10-min accelerometry of hand movement before
prompt (dependent variable) was compared between answered and unanswered scheduled
prompts (a dichotomous independent variable).
53
The 10-min accelerometry of hand movement before prompt (dependent variable) was
also compared between the answered scheduled prompts and the self-triggered prompts (a
dichotomous independent variable: prompt type) using HLM to understand whether participants’
hand movement was related to self-trigger behavior.
Reactivity (Aim 3)
To understand the immediate effect of EMA measurement reactivity on hand movement,
the 10-min accelerometry of hand movement before and after EMA prompts were compared
using HLM. Repeated measures analysis of variance (ANOVA) with post-hoc Bonferroni
correction was used to compare the hand movement among days for the accumulated effect of
reactivity. Data were log transformed before statistical analyses if not normal distributed.
RESULTS
Participants
Thirty participants with chronic stroke (average scores of the FM = 47.27 and the Action
Research Arm Test = 36.9) were recruited. The detailed characteristics of the participants are
shown in Table 3.1.
Data Availability
A flow chart of data availability and sources of missing data is presented in Figure 3.2.
No accelerometers and smartphone were damaged, missing or lost. The average wearing time of
accelerometer (as both accelerometers were worn) was 13.65 ± 0.68 hours across 5 days. The
wearing time was slightly decreased over days (Table 3.2), but no significant difference was
found among days in repeated measures ANOVA (F
4, 29
= 1.376, p = 0.259).
54
The average response rate of EMA prompts (= answered prompts scheduled prompts)
was 84.64 ± 18.51% across participants (25.10 ± 5.55 prompts out of 30 scheduled prompts). There
was no significant decrease in the response rate as a function of day of the study to rule out novelty
or fatigue effects. Additionally, each participant self-triggered 5.23 ± 5.06 prompts on average
during participation (see Chapter 2 for complete results of EMA response rate and feasibility).
11
Overall, a total of 837 matched accelerometry data and EMA prompts across 30 participants were included
in the analyses (Figure 3.2).
Consistency (Aim 1)
During the before-prompt 10-min window of accelerometry, the time of the unimanual
right hand movement was significantly longer when participants reported right hand use in EMA
than left hand use (Figure 3.3a; p = 0.017). The time of the unimanual left hand movement was
also significant longer when participants responded left hand use than the other responses
(Figure 3.3b; p = 0.041 [left vs. right], p < 0.001 [left vs. both], p < 0.001 [left vs. neither-or-
none]). Similarly, when participants reported both hands use, the time of the bimanual hand
movement was significant longer than that when reporting the other responses (Figure 3.3c; p =
0.005 [both vs. right], p < 0.001 [both vs. left], p < 0.001 [both vs. neither-or-none]). When
participants responded that they were not using their hand(s), the time of no hand movement (=
10 min – total time of the unimanual and bimanual hand movements) was also significant longer
than that when reporting any hand use behavior (p = 0.005 [neither-or-none vs. right], p = 0.016
[neither-or-none vs. left], p < 0.001 [neither-or-none vs. both]). Generally, participants’ self-
report of hand use behavior in EMA was consistent with the before-prompt 10-min
accelerometry of hand movement in the time measure.
55
Furthermore, the magnitude of the unimanual right hand movement was significantly
greater when participants reported right hand use than left hand use (Figure 3.3d; p = 0.012).
However, there was no significant difference in the magnitude of the unimanual left hand
movement among EMA hand use responses (Figure 3.3e; p > 0.422). When participants reported
both hands use, the magnitude of the bimanual hand movement was significantly greater than
that when reporting left hand use and neither-or-none use, but not right hand use (Figure 3.3f; p
= 0.173 [both vs. right], p = 0.033 [both vs. left], p = 0.039 [both vs. neither-or-none]).
Reporting Bias (Aim 2)
There was no significant difference between answered and unanswered prompts for both
unimanual and bimanual hand movements in time and magnitude (Figure 3.4; time: p = 0.562
[unimanual right], p = 0.220 [unimanual left], and p = 0.130 [bimanual]; magnitude: p = 0.634
[unimanual right], p = 0.491 [unimanual left], and p = 0.073 [bimanual]). The non-response of
EMA prompts was not associated with participants’ concurrent hand movement as measured by
accelerometry.
Likewise, type of prompts was not related to individuals' hand movement. The before-
prompt 10-min hand movements did not differ between the answered scheduled prompts and the
self-triggered prompts for both unimanual and bimanual hand movements in time and magnitude
(Figure 3.5; time: p = 0.130 [unimanual right], p = 0.809 [unimanual left], and p = 0. 066
[bimanual]; magnitude: p = 0.252 [unimanual right], p = 0.990 [unimanual left], and p = 0.639
[bimanual]).
Reactivity (Aim 3)
Immediate Effect
56
There was also no significant difference between the 10-min windows before and after
EMA prompt for both unimanual and bimanual hand movements in time and magnitude (Figure
3.6; time: p = 0.275 [unimanual right], p = 0.169 [unimanual left], p = 0. 293 [bimanual];
magnitude: p = 0.810 [unimanual right], p = 0.935 [unimanual left], p = 0.467 [bimanual]).
Accumulated Effect
Unexpectedly, we found an accumulated effect of reactivity on the unimanual right
(paretic) hand movement in both time and magnitude over days (Figure 3.7, left panel). Repeated
measured ANOVA with post-hoc comparisons demonstrated a significant time increase in the
unimanual right hand movement on Day 2 to Day 5 compared to Day 1 (p < 0.001 ~ p = 0.002),
and on Day 4 compared to Day 3 (p = 0.045). For interpretation and discussion convenience, the
time percentages were converted back to time units (min/hour) using the average accelerometer
wearing time (13.65 hours wearing over the 5 days) and are presented in Table 3.2. Participants’
average unimanual right hand movement significantly increased from 38.49 min on Day 1 to the
highest 52.74 min on Day 4. The magnitude of the unimanual right hand movement was also
significantly greater on Day 4 and Day 5 compared to Day 1 (Figure 3.7, right panel; p = 0.007
and p = 0.002, respectively).
There was a trend of decreasing time and magnitude of the bimanual hand movement
over days (Figure 3.7); however, no significant result was observed (p = 0.197 and p = 0.407,
respectively). There was also no significant change in the unimanual left hand movement for
both time and magnitude (p = 0.578 and p = 0.936, respectively).
57
Discussion
This study established a combined ecological methodology of accelerometry and EMA to
assess stroke survivors’ paretic hand use behavior in daily life. A generally consistent result of
hand use behavior was demonstrated between accelerometry objective measures and EMA self-
responses (Aim 1). Participants’ concurrent hand movement as measured by accelerometry did
not bias them to answer or not answer EMA prompts. The act of EMA self-triggering was also
not associated with any increased hand movement (Aim 2). Participants did not show an
immediate change of hand movement right after responding to EMA prompts. However, we
found a significant accumulated effect of reactivity in the unimanual right (paretic) hand
movement. Participants gradually increased the time and magnitude of the paretic hand
movement over the 5 days during participation (Aim 3).
The 10-min window of accelerometry for prompt-specific analysis was chosen based on
previous studies investigating daily physical activity in non-disabled adults.
e.g.,33–35
The 10-min
window may also be an appropriate choice for post-stroke daily hand use as evidenced by our
results in Aim 1 (consistency). When comparing the two ecological assessments, we found a
more concordant result of the hand use in the accelerometry time measure than in the magnitude
measure. It indicated that participants answered the EMA hand use question mainly based on
“how long” they have been using their hand(s), instead of “how intensively” they used them. The
time duration of the hand use may be easier to notice than the intensity.
Given the validation of EMA questions in our previous work
11
, the self- report EMA data
was further validated in the present study. The self-reported responses that we captured through
EMA were not biased by participants’ behavior. Not only the non-response of the scheduled
prompts but also the initiation of the self-triggered prompts was not associated with participants’
58
hand movement. It suggests that participants missed the scheduled prompts not due to a low use
of the hand(s) which resulted in a situation that they were less likely to respond. In the exit
interview, participants mentioned that they sometimes missed a prompt because they were
performing an incompatible activity (e.g., driving, showering, or on the toilet) (n = 13) or the
phone was temporarily out of reach (e.g., in a bag that was put in the trunk) (n = 4). Participants
then used the self-triggered prompts to compensate for the scheduled prompts that they had
missed.
11
They did not increase their hand use in order to self-trigger or report in the EMA. Due
to the similarity between the scheduled and the self-triggered prompts that we have found in the
previous work
11
and here, we may combine the two types of prompts together in the data
analysis.
The act of EMA responding did not have an immediate effect on participants’ hand
movement. However, the significant increase in both accelerometry time and magnitude of the
unimanual right (paretic) hand over days triggered further questions and valuable discussion.
This was an unexpected finding since our study was simply an observational study. Based on the
previous EMA literatures
18,21,23
, we speculated that EMA may have functioned as a treatment
(i.e., ecological momentary intervention [EMI]) to our participants and caused the increase of the
unimanual paretic hand movement. Although EMA was a monitoring assessment, it has been
considered as “a part of behavior-change treatment, not just assessment”
18
. In the exit interview,
6 participants mentioned that they have noticed that they used their paretic hand more often than
usual (compared to before participation). They may put additional attention to the paretic hand
when they were aware that the hand was being monitored. Some participants also commented
that, when responding to the EMA hand use question, they realized that they did not use the
paretic hand as much as they should have given their motor capability level. Through responding
59
to the question, EMA may act like a reminder to implicitly probe the participants to use the
paretic hand for daily activities. It may also alter participants’ mind-set and subtly but constantly
stimulate the use of the paretic hand. The result of the increased paretic hand movement provided
us an insight of applying EMI in individuals with stroke. We may use the mobile-based EMI to
provide daily intervention and to enhance the use of the paretic hand in the natural environment.
However, the meaningfulness of the increase in the unimanual right (paretic) hand
requires a careful consideration. Although the increase was statistically significant, it is not clear
whether the ~10 min small change over an average of 13.65-hour daily monitoring (i.e., the
average accelerometer wearing time) was meaningful. Additional examinations, such as the
minimal clinically important difference testing, are needed to understand the meaningfulness and
criticalness of the change in accelerometry. Nevertheless, the ~10 min increase was around one
third of the time of the unimanual right hand movement on Day 1 (38.49 min; Table 3.2). The
small amount of change was also a uniform trend that occurred in 23 participants out of the 30
participants. It suggests that, although small, most participants did gradually and surely increase
the use of the paretic hand during participation.
Additionally, we conducted an extra analysis to further examine the significant increase
result. We normalized the time of the unimanual right hand movement to the total time of hand
movement (i.e., time for either hand was moving), instead of the accelerometer wearing time.
This analysis removed the inactivity time (i.e., no hand movement) and showed the same
significant increase in the unimanual right hand movement over days (p < 0.001). It verified that
when participants performed daily activities that required hand use, they did increase the
movement of the unimanual paretic hand over the 5 days.
60
On the other hand, the increase we observed may possibly result from a low start of the
right hand movement on Day 1 due to, for example, the “big brother watching” effect or device
unfamiliarity that decreased participants’ paretic hand use at the beginning. Compared with
previous research, Bailey et al. (2015) reported an average 0.8 hour of the unimanual paretic
hand movement over a 24-hour accelerometer wearing period in individuals with chronic and
moderate stroke (average score of the Action Research Arm Test = 31.1).
2
As in percentage
(0.8/24 = 3.33%), it provided us a sense that our participants may not have a low use of the
paretic hand on Day 1 (4.70%; Table 3.2). However, this one-day study from Bailey et al.
(2015)
2
still cannot help us to rule out the possibility that the increase we observed was the result
of the paretic hand use climbing back to “normal” after Day 1. Other studies either did not report
the time of the unimanual paretic hand movement or have different characteristics of the stroke
population (e.g., an average 0.1 hour of the unimanual paretic hand movement [estimated from
figure] over 24 hours monitoring in a more severe population [average score of the FM = 35.5] in
Michielsen et al. [2012]
12
), which increased the difficulty of comparing the results directly.
Future research with a longer monitoring period is needed to understand the actual unimanual
paretic hand use in stroke survivors after reaching to a stable state.
There were two main limitations in this study. First, due to the goal of natural behavior
observation, we did not include a control group in our study design. Yet, adding a control group
that exclusively wears accelerometers without employing EMA will allow us to better
understand the reactivity effect of EMA and to rule out alternative explanations (e.g., a low start
on Day 1). Second, we were not able to directly interpret the magnitude (count) of hand
movement due to the limited understanding of the “activity count” generated from the
proprietary algorithm. Although we could define hand movement using the count numbers with
61
the valid cut-off threshold
13–17
, the relationship between activity count and raw acceleration is
still not clear. This is a common concern for studies using accelerometers.
22
Future investigation
addressing the issue of activity count will be needed.
CONCLUSION
The combined methodology with accelerometry and EMA is a novel and valuable
application of ecological assessments in assessing hand use behavior in the natural environment
for individuals with stroke. Our finding demonstrated the consistency of hand use behavior
between accelerometry objective measures and EMA self-responses. The sampled EMA data
(i.e., 6 scheduled prompts/day and self-triggered prompts) were not biased by individuals’ hand
movement. In addition, a significant small increase in time and magnitude of the unimanual right
(paretic) hand movement was observed over days. This change may be the result of measurement
reactivity that EMA functioned as EMI (intervention) to implicitly remind participants to use the
paretic hand for performing daily activities. However, due to the small increase (~10 min over an
average of 13.65-hour daily monitoring), the result needs to be interpreted with careful
consideration and further understanding.
62
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assessment of real-world bilateral upper extremity activity. PLoS One 9, e103135 (2014).
14. Uswatte, G. et al. Ambulatory monitoring of arm movement using accelerometry: an
objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch.
Phys. Med. Rehabil. 86, 1498–1501 (2005).
15. Bailey, R. R. & Lang, C. E. Upper-limb activity in adults: referent values using
accelerometry. J. Rehabil. Res. Dev. 50, 1213–22 (2013).
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16. Uswatte, G. et al. Validity of accelerometry for monitoring real-world arm activity in
patients with subacute stroke: evidence from the Extremity Constraint-Induced Therapy
Evaluation trial. Arch. Phys. Med. Rehabil. 87, 1340–1345 (2006).
17. Uswatte, G. et al. Objective measurement of functional upper-extremity movement using
accelerometer recordings transformed with a threshold filter. Stroke 31, 662–667 (2000).
18. Shiffman, S., Stone, A. A. & Hufford, M. R. Ecological momentary assessment. Annu.
Rev. Clin. Psychol 4, 1–32 (2008).
19. Stone, A. A. The science of real-time data capture: self-reports in health research.
(Oxford University Press, 2007).
20. Dunton, G. F., Liao, Y., Kawabata, K. & Intille, S. Momentary assessment of adults’
physical activity and sedentary behavior: feasibility and validity. Front Psychol 3, 260
(2012).
21. Dunton, G. F., Atienza, A. a, Castro, C. M. & King, A. C. Using ecological momentary
assessment to examine antecedents and correlates of physical activity bouts in adults age
50+ years: a pilot study. Ann Behav Med 38, 249–55 (2009).
22. Kayes, N. M. & McPherson, K. M. Measuring what matters: does ‘objectivity’ mean good
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23. Scollon, C. N., Chu, K.-P. & Diener, E. Experience sampling: promises and pitfalls,
strengths and weaknesses. J. Happiness Stud. 4, 5–34 (2003).
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Neuropsychologia 9, 97–113 (1971).
25. Fugl-Meyer, A. R., Jääskö, L., Leyman, I., Olsson, S. & Steglind, S. The post-stroke
hemiplegic patient. 1. a method for evaluation of physical performance. Scand J Rehabil
Med 7, 13–31 (1975).
26. Sullivan, K. J. et al. Fugl-Meyer assessment of sensorimotor function after stroke:
standardized training procedure for clinical practice and clinical trials. Stroke 42, 427–32
(2011).
27. Haaland, K. Y. et al. Relationship between arm usage and instrumental activities of daily
living after unilateral stroke. Arch Phys Med Rehabil 93, 1957–62 (2012).
28. Harris, J. & Eng, J. Individuals with the dominant hand affected following stroke
demonstrate less impairement than those with the nondominant hand affected.
Neurorehabil Neural Repair 20, 380–389 (2006).
29. McCombe Waller, S. & Whitall, J. Hand dominance and side of stroke affect
rehabilitation in chronic stroke. Clin Rehabil 19, 544–51 (2005).
30. Aggarwal, A. & Kean, E. Comparison of the Folstein Mini Mental State Examination
(MMSE) to the Montreal Cognitive Assessment (MoCA) as a cognitive screening tool in
an inpatient rehabilitation setting. Neurosci Med 1, 39–42 (2010).
31. Albert, M. L. A simple test of visual neglect. Neurology 23, 658–64 (1973).
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https://actigraph.desk.com/customer/en/portal/articles/2515580-what-are-counts-.
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33. Kanning, M., Ebner-Priemer, U. & Schlicht, W. Using activity triggered e-diaries to
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association between short periods of everyday life activities and affective states: a
replication study using ambulatory assessment. Front. Psychol. 4, 102 (2013).
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exercise activity: an ambulatory assessment approach using repeated real-time and
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65
Table 3.1: Characteristics of Participants.
Mean ± SD (Range)
Gender (n, male: female) 21:9
Age (year) 61.19 ± 13.10 (24.43 – 82.91)
Onset of Stroke (year) 4.68 ± 3.89 (0.68 – 14.80)
MoCA (0 – 30) 24.73 ± 3.42 (19 – 30)
FM (0 – 66) 47.27 ± 14.93 (20 – 66)
ARAT (0 – 57) 36.90 ± 20.97 (3 – 57)
Abbreviations: MoCA = Montreal Cognitive Assessment, FM =
Upper Extremity Fugl-Meyer Assessment, ARAT = Action
Research Arm Test.
66
Table 3.2: Accelerometer Wearing Time and Hand Movement Variables (the Whole-Day Analysis).
Accelerometry Variables (Mean ± SD) Day 1 Day 2 Day 3 Day 4 Day 5
Wearing time (hour) 14.60 ± 3.20 14.03 ± 3.56 13.50 ± 3.07 13.23 ± 3.38 12.87 ± 4.31
Time (%)
Unimanual right hand movement*
,&
4.70 ± 3.89 5.95 ± 4.56 5.71 ± 4.34 6.44 ± 4.51 6.22 ± 4.47
(38.49 min)
†
(48.73 min) (46.76 min) (52.74 min) (50.94 min)
Unimanual left hand movement 22.74 ± 9.94 21.68 ± 8.83 23.27 ± 9.58 21.81 ± 7.44 23.05 ± 9.79
(3.10 hr) (2.96 hr) (3.18 hr) (2.98 hr) (3.15 hr)
Bimanual hand movement 30.30 ± 13.80 29.51 ± 12.96 30.08 ± 13.28 27.76 ± 13.33 28.20 ± 15.21
(4.14 hr) (4.03 hr) (4.11 hr) (3.79 hr) (3.85 hr)
Magnitude (count)
Unimanual right hand movement
#
33.37 ± 15.93 36.34 ± 20.60 36.99 ± 19.73 42.56 ± 19.17 42.41 ± 19.54
Unimanual left hand movement 90.50 ± 27.80 90.27 ± 23.87 91.16 ± 24.96 91.95 ± 21.43 91.51 ± 25.73
Bimanual hand movement 262.94 ± 49.91 260.01 ± 56.86 261.19 ± 57.89 257.33 ± 51.69 253.39 ± 39.79
Note 1: Due to the positive skewness, the data were log transformed before conducting statistical analyses. We reported the original values here
for interpretation and comparison convenience.
Note 2: Bonferroni post-hoc correction: *Day1 < Day2 (p=0.001), Day3 (p=0.002), Day4 (p<0.001), and Day5 (p<0.001);
&
Day3 < Day4
(p=0.045);
#
Day1 < Day4 (p=0.007) and Day5 (p=0.002).
Note 3:
†
For interpretation and discussion convenience, the time percentages were converted back to time units (min/hour) using the average
accelerometer wearing time (13.65 hours wearing over the 5 days)
67
Figure 3.1: Accelerometer and EMA Smartphone Configuration.
68
x
Total Valid EMA Prompts
with Worn Accelerometers
837 prompts
Initial Enrollment
900 scheduled prompts
(30 participants, 30 prompts/participant)
Scheduled Prompts
Provided EMA Prompts
890 prompts
Provided EMA Prompts
with Worn Accelerometers
796 prompts
Answered EMA Prompts
with Worn Accelerometers
695 prompts
Unknown Technical Problems
6 prompts
Phone Powered Off
4 prompts
Accelerometer Non-Wear
94 prompts
Unanswered Prompts
101 prompts
Self-Triggered EMA Prompts
173 prompts
Accelerometer Non-Wear
15 prompts
Self-Triggered EMA Prompts
with Worn Accelerometers
158 prompts
Repetitive Prompts
16 prompts
Valid Self-Triggered Prompts
with Worn Accelerometers
142 prompts
Self-Triggered Prompts
Figure 3.2: Flow Chart of Data Availability.
#
The repetitive prompt was defined when (1) the
time interval between 2 adjacent prompts was
less than 1 minute, and (2) the responses of the 2
prompts were the same on the multiple-choice
questions.
#
69
Figure 3.3: Consistency (Aim 1) between EMA Hand Use Response and Accelerometry of Hand Movement. Error bars represent
standard errors. Abbreviations: R = right hand use; L = left hand use; B = both hands use; N = neither-or-none use; EMA = ecological
momentary assessment.
70
Figure 3.4: Reporting Bias (Aim 2): Accelerometry of Hand Movement between Unanswered and Answered Scheduled Prompts.
Error bars represent standard errors.
71
Figure 3.5: Reporting Bias (Aim 2): Accelerometry of Hand Movement between Answered Scheduled and Self-Triggered
Prompts. Error bars represent standard errors.
72
Figure 3.6: Reactivity (Aim 3 Immediate Effect): Accelerometry of Hand Movement Before and After EMA Prompt. Error bars
represent standard errors.
73
Figure 3.7: Reactivity (Aim 3 Accumulated Effect): Accelerometry of Hand Movements in Daily Changes. The asterisk (*)
indicates a significant increase of right (paretic) hand movement over days (p < .05). Error bars represent standard errors. Abbreviations:
R = unimanual right hand movement; L = unimanual left hand movement; B = bimanual hand movement.
Accelerometry (Whole-Day Data)
B
B
L
L
R
R
74
CHAPTER 4
Self-Efficacy and Social Interaction Impact Daily Paretic Hand Use after Stroke: An
Ecological Study
INTRODUCTION
The benefit of “use” after stroke has been extensively addressed in rehabilitation, as
fueled by growing evidence of experience-dependent neuroplasticity.
1,2
If use of the paretic hand
is limited, further functional decline is likely to ensue, leading to a vicious, perpetual cycle with
higher levels of disability and limited community reintegration. Clinicians and researchers have
devoted tremendous effort to develop rehabilitative interventions, such as intense task-specific
training (e.g., Constraint-Induced Movement Therapy
3–5
), to promote paretic hand use with the
goal of maximizing motor recovery and breaking the vicious cycle of decline. However, the non-
use phenomenon, signifying the sub-optimal daily use of the paretic hand
6–9
, indicates that the
efforts of rehabilitative interventions may be in vain. The motor improvements that patients
achieve in therapy, where they are “forced” to perform tasks with the paretic hand, are seldom
maintained or translated into the natural environment.
10,11
An early study reported that 52% of patients post-stroke did not incorporate the paretic
hand for performing daily activities at home, even though they had demonstrated the capability
in the rehabilitation unit.
8
A more recent study revealed that individuals only spontaneously used
their paretic hand for 22% of the activities in the Actual Arm Use Test although the motor
capability was sufficient to perform all the activities.
6
Han et al. (2013) provided specific
evidence that this non-use phenomenon cannot be explained by individuals’ motor capability.
75
The difference between what people can do and what people actually do was not correlated with
residual motor capability.
12
These studies underscore the fact that motor capability, while an
essential factor, is not the only factor influencing the use behavior of the paretic hand after
stroke. We believe that there are other factors critically affecting individuals’ paretic hand use in
the natural environment.
Recently, social-cognitive factors, including self-efficacy, mood, and social interaction,
have been found to play an essential role in post-stroke function and behavior.
13–26
Self-efficacy
is “the conviction that one can successfully execute the behavior required to produce [certain]
outcomes”.
27
This task-specific confidence level of one’s capability denotes the importance of an
individual’s perception in functional outcomes. Studies have found that stroke survivors who
have higher self-efficacy for balance/mobility showed better balance improvement
13–15
and
greater functional mobility
16,17
than those who have similar initial mobility function but lower
self-efficacy. An individual’s exercise behavior can also be predicted by his/her self-efficacy.
18,19
People who have greater self-efficacy are more likely to have greater motivation and persistence
in completing exercise training than those with lower self-efficacy.
18,19
In our earlier work, we
found a significant positive correlation between paretic hand use and self-efficacy in a laboratory
reaching task (r = 0.767).
20
Individuals were more likely to use the paretic hand to reach for
targets when they had greater reaching self-efficacy. Self-efficacy was also a significant
predictor of paretic hand use, explaining 84.2% of the variance in the probability of paretic hand
choice for reaching.
20
In addition, mood is a well-known factor that influences an individual’s behavior.
Positive mood enhances individuals’ motivation and leads to greater engagement in activities,
while negative mood increases avoidance behavior.
21–23
For example, a recent study showed that
76
non-disabled older adults demonstrated a higher level of physical activity when they had a
greater positive mood throughout the day.
21
Although there is limited evidence for mood in the
stroke population, the link between positive mood and activity engagement has been
demonstrated. Stroke survivors who have higher levels of positive mood showed greater levels of
social participation 3 months post-discharge than those with lower positive mood.
22
Lastly, a positive social environment has been associated with beneficial outcomes
following stroke.
19,24,25
Individuals with stroke reported a belief that their good recovery would
not have been possible without support from others (e.g., family and friends).
24
Support is
pursued in order to maintain motivation for continued practice with the paretic limbs.
24
Studies
have also found that individuals’ perception of better social support and positive interaction is
associated with better behavioral outcomes.
19,25
People who perceived social support from others
demonstrated persistent determination to participate in a high intensity (3 days per week)
treadmill intervention for 6 months.
19
A recent study, measuring individuals’ momentary social
interaction in daily context, demonstrated that people who perceived a better social interaction
with family and friends showed an increase in activities of daily living 3 months later.
25
Most previous studies pertaining to social-cognitive factors in individuals recovering
from stroke
13–19,24,25,28
have focused on either lower extremity functions (e.g., balance, mobility)
or general behavior (e.g., exercise, activities of daily living). Our early work was the single study
that investigated the association between paretic hand use and social-cognitive factors (i.e., self-
efficacy).
20
Nonetheless, we only used a simple reaching task as a proxy of daily activities and
conducted the study in a laboratory setting. A knowledge gap still exists in understanding the
impact of social-cognitive factors on paretic hand use in the day-to-day context. Therefore, the
77
goal of this study was to determine the contribution of social-cognitive factors to daily paretic
hand use in the natural environment in individuals with stroke.
To capture participants’ natural behavior and response, we used two ecological measures,
accelerometry and Ecological Momentary Assessment (EMA), to appraise the real-time paretic
hand use and social-cognitive factors (i.e., self-efficacy, mood, and social interaction).
Accelerometry provides a valuable index to objectively quantify the duration (time) of hand use
in the natural environment. It is a commonly-used tool to understand paretic hand use behavior
after stroke. The reliability
29–31
and validity
32,29,31,33
of accelerometry in assessing daily hand use
is well established. EMA is a mobile-based prompt methodology that allows participants to self-
report real-time information repeatedly while in the natural environment. Recently, we
demonstrated the feasibility and validity of EMA in assessing self-reported measures (e.g., self-
efficacy and mood) in the stroke population.
34
A combined methodology that uses accelerometry
and EMA was recently established in our previous work.
35
This combined and synchronized
methodology may not only integrate motion sensor data with self-report information, but allows
a more contextualized and comprehensive look at an individuals’ paretic hand use behavior.
36
We approached the study goal from two different levels of analysis: macro and micro
analyses. In the macro analysis (Aim 1), we examined an overall association of paretic hand use
and social-cognitive factors over the 5-day period of study participation. The whole-day hand
movement captured by accelerometry was correlated with the pre-specified social-cognitive
factors captured by the in-laboratory clinical/research-based assessments. These assessments
were acquired once at the end of the study to measure participants’ general perception of social-
cognitive factors. For the micro analysis (Aim 2), we synchronized the accelerometry and the
repeated measures of EMA. The EMA momentary responses pertaining to the social-cognitive
78
factors were compared with a 10-min epoch of accelerometry data prior to each prompt. This
prompt-specific analysis allowed a finer-grained look at the association between hand use
behavior (as measured by accelerometry) and the social-cognitive factors. In addition, we
explored whether there was a time-dependent lag in the effect of social-cognitive factors
acquired at a preceding prompt (t) on hand movement behavior at the subsequent time (t+1). This
analysis allowed us to test for a possible prospective, time-dependent effect on hand use behavior
(Aim 3).
METHODS
Participants
Participants were included if they met the following inclusion criteria: (1) pre-morbidly
right-hand dominant as determined by a modified Edinburgh Handedness Questionnaire
37
, (2)
left hemisphere stroke with right-side paresis, (3) minimal or more hand function as measured by
the Upper Extremity Fugl-Meyer Assessment (FM)
38,39
(total motor score ≥ 20, sub-score of the
finger mass flexion ≥ 1), (4) community-dwelling, (5) capability to read and communicate in
English, and (6) capacity to learn and use the EMA smartphone after instruction.
A homogeneous group of participants was selected; we recruited individuals with right,
dominant-side hemiparesis. Hemiparetic side combined with handedness has been shown as a
critical factor of hand use behavior and motor recovery.
40–42
Participants who have non-
dominant-side stroke demonstrated less paretic hand use
40
, greater impairment
41
, and less
improvement after training
42
than those with dominant-side stroke. To avoid the possible
79
confounding effects of handedness on paretic hand use, hand dominance and side of stroke were
therefore carefully controlled.
Individuals were excluded if they met any of the following exclusion criteria: (1)
moderate to severe cognitive deficits as measured by the Montreal Cognitive Assessment
(MoCA, score < 16)
43
, (2) psychiatric diagnosis (e.g., depression), (3) neglect as measured by the
Albert’s Test
44
, (4) pain or musculoskeletal problems in the paretic limb which affects day-to-
day hand use, (5) any active medical or neurological conditions that would interfere with
participation in this study.
Prior to enrollment, all participants read and signed an Informed Consent form according
to the standard procedures of the University of Southern California (USC) Health
Sciences Institutional Review Board.
Study Design
A 5-day monitoring period was used in which participants were asked to wear one
accelerometer on each wrist and to respond to 6 EMA prompts per day (Figure 4.1). Two visits
to the Motor Behavior and Neurorehabilitation laboratory at the USC Health Sciences Campus,
scheduled before and after the monitoring period, were required for screening and
clinical/research-based outcome assessments, as well as familiarization with and return of the
devices (i.e., two accelerometers and one EMA smartphone).
Instruments
The ActiGraph accelerometer (wGT3X-BT) (ActiGraph, Inc. Pensacola, FL) was used to
capture hand movement while participants performed daily activities during the 5-day
monitoring period. Each accelerometer was attached with a Velcro strap for ease of donning for
80
participants with stroke to wear one on each wrist. The accelerometer with a tri-axial (x-, y-, z-
direction) sensor was set at a sampling rate of 30 Hz.
EMA data were collected through a mobile smartphone (HTC Sensation, AT&T USA
Dallas, TX) installed with custom software, movisensXS (Version 0.6.3658, movisens GmbH,
Karlsruhe, Germany). This software allows one to program prompt schedules, display questions,
and save participants’ responses. The mobile phone functions including calls, texting, and
internet browsing capabilities were all blocked and disabled by the software. The EMA prompts
were time-stamped to synchronize with accelerometry data.
Procedure
During the first lab visit, participants were asked to complete a demographic form and
several screening tests, including the Edinburgh Handedness Questionnaire, the FM, the MoCA,
and the Albert’s Test. The accelerometers and the EMA smartphone were provided to the
participants with verbal and written instructions. After practicing at least one prompt with the
experimenter, participants were required to complete a practice prompt independently in order to
demonstrate his/her capability to use the EMA smartphone. A study overview sheet including
reminder messages (e.g., ‘remember to charge the phone every night’) was given to the
participants during the lab visit. Finally, a customized EMA prompt schedule was developed to
best accommodate to participants’ daily routine schedule. There was an approximate 2-hour
interval between prompts.
During the 5-day monitoring period, participants were asked to wear the accelerometers
and to carry the EMA smartphone with them during the day (~ 7 am to 9 pm). Participants were
advised to take off the accelerometers during activities where the Velcro straps might get wet
and cause discomfort (e.g., showering, swimming), and also while sleeping. They were prompted
81
by an auditory signal 6 times per day (total of 30 prompts during participation) to respond to the
EMA questions. Participants commonly started the first prompt between 8 and 10 am in the
morning, and finished the last prompt between 6 and 8 pm in the evening, each day.
Upon receiving an EMA prompt, participants were instructed to stop any ongoing
activity, provided it was safe to do so, and respond to the EMA questions. They had 5 minutes to
respond to the questions after prompted by the auditory signal. If no response was made, the
phone emitted two additional reminder signals with a 5-min interval. Afterwards, the prompted
EMA questions became inaccessible until the next prompt. Participants were also allowed to ask
for a delay of up to 15 minutes. If a prompt occurred during an incompatible activity (e.g.,
driving or showering), participants were instructed to ignore the prompt. In addition to the 6
prompts every day, participants were encouraged to self-initiate an EMA prompt anytime that
they desired (i.e., self-triggered prompt) or when they had missed a scheduled prompt.
During the monitoring period, participants received a phone call on the first evening from
the researcher offering an opportunity to clarify any concerns, answer questions and/or to resolve
any technical issues. A study contact number was also available to the participants to report, at
any time, any problems they may encounter.
After the 5 days, participants were scheduled for a second lab visit in order to return the
devices and to complete clinical/research-based outcome assessments (described below).
Measures and Data Analysis
Hand Use Behavior (Accelerometry)
Raw accelerometry data were first processed using ActiLife 6.0 (ActiGraph, Inc.
Pensacola, FL). The raw accelerations (m/s
2
) from 3 axes (x, y, z) were filtered using a band-pass
82
filter (0.25-2.5 Hz) and binned into 2-second epochs for each axis. The filtered accelerations
within each epoch (i.e., 60 samples/epoch) were then summed together and converted into
“activity count” values using a proprietary algorithm (e.g., 1 activity count = 0.01664g for an
acceleration produced by a movement with a frequency at 0.75Hz).
45
Further analyses with activity counts were conducted using a custom-written MATLAB
program (version R2015a) (The MathWorks Inc., Natick, MA). Activity counts across three axes
for each epoch were combined into a single resultant value (= x
"
+y
"
+z
"
). To define
participants’ hand movement, we used a threshold of 2 (in activity count units) that provides a
reliable
29–31
and valid
32,29,31,33
measure of hand use in daily activities. When the activity count for
the epoch was ≥ 2, the hand was considered to have moved during the 2-second time period
(“movement”). When the activity count was < 2, the hand was marked as “no movement”.
Unimanual right/left hand movement was defined when only the right- or left-hand
accelerometer signal was ≥ 2 (i.e., only one hand was considered moving during the epoch).
Bimanual hand movement was defined when the accelerometer signals of both right and left
hands were ≥ 2 (i.e., both hands were considered moving during the epoch). Seconds of each
movement epoch were added up for the time of the unimanual right/left and bimanual hand
movements, separately.
Any 3-hour segment for which the activity counts in every epoch were < 2 was defined as
a non-wear period (“no-wearing”).
31
Accelerometry data were included in the analyses only
when both accelerometers were worn on the wrists.
Macro Analysis (Aim 1). In the macro analysis, accelerometry time was calculated for
each day and averaged across 5 days. Due to differences in the daily wear times, the time of the
83
hand movement in the macro analysis was normalized to each person’s daily wearing time and
converted to a percentage of total wear time.
Micro Analysis (Aim 2 & 3). A 10-min window of accelerometer data was created
before each EMA prompt for the micro prompt-specific analysis. The time of the unimanual and
bimanual hand movements during the 10-min window was defined in minutes.
Social-Cognitive Factors
Macro Analysis (Aim 1). Two clinical/research-based measures administered on the
second visit were included in the macro analysis: the Confidence in Arm and Hand Movements
Scale (CAHM) and the positive affect score from the Positive Affect Negative Affect Scale
(PANAS).
The CAHM
46
is a 20-item questionnaire that was used to assess participants’ confidence
level for performing a series of functional tasks that involve the paretic hand (e.g., “How certain
are you that you can carry a cafeteria tray full of lunch food and drink from the cashier to a
table?”). Participants were instructed to rate their confidence level over the past 5 days on a scale
of 0 (very uncertain) to 100 (very certain). The average score across the 20 items was calculated.
The PANAS
47
was used to measure participants’ mood over the past 5 days. Participants
were asked to rate their feelings on 10 adjectives of positive emotions (e.g., interested,
determined, active) and 10 adjectives of negative emotions (e.g., distressed, irritable, nervous).
Ratings of each item ranging from 1 (very slightly or not at all) to 5 (extremely) were summed to
produce a positive affect and a negative affect scores (range 10–50). Only the positive affect
score was used here. A higher value indicates greater positive affectivity.
84
Micro Analysis (Aim 2 & 3). Each EMA prompt included a maximum of 17 questions
regarding ongoing activities, hand use behavior, social-cognitive factors and contextual
information (Appendix 2.1). The order of the questions in each EMA prompt varied among days
to retain participants’ attention while responding to each question.
For self-efficacy, participants were asked to rate their confidence level for using the
paretic hand on a visual analog scale (VAS) ranging from 0 (not confident at all) to 100 (very
confident) (Figure 4.2a). Mood was assessed by asking participants’ sadness/happiness level
with a 0-100 VAS (very sad – very happy) (Figure 4.2b). For social interaction, they were first
asked whether they were alone or not when being prompted. If participants reported that they
were not alone, they were then asked the social interaction level they have felt with the one(s)
that they were with (0 [very stressful] to 100 [very positive/uplifting]) (Figure 4.2c).
Statistical Analysis
Macro Analysis (Aim 1)
To examine the overall association between hand use behavior and social-cognitive
factors during the 5-day period, a simple linear regression was conducted with accelerometry
time as the dependent variable (i.e., time of the unimanual and biannual hand movements in
separate models) and the FM score as the independent variable to verify the contribution of
motor capability in hand use (Model 1). Furthermore, we added the social-cognitive factors (i.e.,
CAHM and PANAS positive affect scores) to the statistical model and used multiple linear
regression to examine whether either or both of the social-cognitive factors can explain a
significant amount of the variance in hand use behavior (Model 2).
Micro Analysis (Aim 2)
85
To determine the momentary relationship between hand use behavior and social-
cognitive factors, a two-level (i.e., between-participant and within-participant level) hierarchical
linear regression model (HLM) was used to analyze the multilevel repeated measures (i.e.,
around 30 repeated measures for each participant) to account for possible correlations of
residuals and heteroskedasticity due to nesting effects.
Model 1 was fitted with the before-prompt 10-min accelerometry time as the dependent
variable (i.e., time of the unimanual and biannual hand movements in separate models) and the
FM score as the independent variable. Day (i.e., Day 1 to 5) was included in the models as a
control variable due to the accumulated and significant effect of EMA reactivity on hand
movement we found in the previous work (i.e., the time of participants’ unimanual right hand
movement increased gradually over 5 days)
35
.
Model 2 was fitted with the three pre-specified factors of interest (i.e., self-efficacy,
mood, and social interaction as measured by EMA). These were added in Model 1 to determine
the effects above and beyond that attributable to participants’ motor capability level.
Micro Predictive Analysis (Aim 3)
HLM analyses was conducted to examine the predictive effects of the social-cognitive
factors reported for a preceding prompt (t) on hand movement acquired during the subsequent
(lagged) time interval (t+1).
All statistical analyses were performed using STATA 14.2 (Stata Corporation, College
Station, TX, USA). All factors of interest were tested for multi-collinearity in the regression
models to avoid unreliable estimates of regression coefficients (the definition of no multi-
collinearity: VIF < 10).
86
RESULTS
Participants
Thirty participants with chronic stroke (average scores of the FM = 47.27) were recruited.
The detailed characteristics of the participants are shown in Table 4.1.
Data Availability
The average wearing time of accelerometers (i.e., both accelerometers were worn at the
same time) was 13.65 ± 0.68 hours per day, across 5 days. No significant difference in wearing
time among days was observed using repeated measures analysis of variance (see Chapter 3,
Table 3.2, for detailed results of accelerometry wearing time)
35
. The summary of participants’
unimanual and bimanual hand movements over the 5-day monitoring period and within the
before-prompt 10-min window is summarized in Table 4.1.
The average scores of the CAHM and the positive affect scores from the PANAS across
participants are summarized in Table 4.1. The average response rate of EMA prompts (= answered
prompts scheduled prompts) was 84.64 ± 18.51% across participants (25.10 ± 5.55 prompts out
of 30 scheduled prompts). There was no significant decrease in the response rate as a function of
day of the study, thus ruling out a novelty or fatigue effect. Additionally, each participant self-
triggered 5.23 ± 5.06 prompts on average during participation (see Chapter 2 for complete results
of EMA response rate and feasibility)
34
. The summary of participants’ social-cognitive factors
measured by EMA is summarized in Table 4.1.
Overall, for these analyses, we included a total of 837 synchronized accelerometry data
and EMA prompt data across 30 participants (see Chapter 3, Figure 3.2, for detailed results of
data availability)
35
.
87
Macro Analysis (Aim 1)
The simple linear regression demonstrated that the FM score significantly predicted
participants’ hand movements (i.e., unimanual right/left and bimanual hand movements) in the
natural environment (Table 4.2; Model 1, Macro Analysis). Right (paretic) hand daily use, with
or without the left (less-paretic) hand, was positively affected by motor capability. In practice, if
participants’ motor capability increased by 1 SD (=14.93; Table 4.1), unimanual right (paretic)
hand movement increased by 24.24 min and bimanual hand movement increased by 65.63 min
over an average 13.65-hour monitoring period (i.e., average accelerometer wearing time per
day). In contrast, daily use of the left (less-paretic) hand was negatively affected by motor
capability. In practice, if participants’ motor capability increased by 1 SD, the unimanual left
hand movement decreased by 50.26 min over the 13.65-hour monitoring period. Overall for the
macro analysis, Model 1, which included only the FM score, explained approximately 40-50% of
the variance in hand movements (see details in Table 4.2).
No significant results were observed after adding the two social-cognitive factors (i.e.,
the CAHM and the positive affect scores) in Models 2 for the unimanual right (paretic) and the
bimanual hand movements (Table 4.2; Model 2, Macro Analysis). However, the positive affect
score from the PANAS positively predicted unimanual left (less-paretic) hand use. This model
explained 60.17% of the variance in unimanual left hand movement (Table 4.2). In practice, if a
participant has a higher level of positive mood by 1 SD (=7.65; Table 4.1), his or her unimanual
left hand movement would be longer by 21.36 min over the 13.65-hour monitoring period.
Micro Analysis (Aim 2)
Similar to the macro analyses, the HLM results demonstrated that the FM score
significantly predicted participants’ use of right (paretic) hand, with or without the left (less-
88
paretic) hand, during the 10-min window before prompts (Table 4.2; Model 1, Micro Analysis).
In practice, if participants’ motor capability increased by 1 SD (=14.93; Table 4.1), the
unimanual right (paretic) hand movement increased by 0.31 min and the bimanual hand
movement by 0.98 min over the 10-min window.
After including the social-cognitive factors in the models (Table 4.2; Model 2, Micro
Analysis), the social interaction scores significantly and positively predicted each of the three
hand movement categories (i.e., unimanual right, unimanual left, and bimanual hand
movements). Participants increased use of the unimanual right hand, unimanual left hand, and
bimanual hands by 0.03 min, 0.09 min, and 0.08 min, respectively, if the social interaction level
they perceived increased (more positive/uplifting) by 1 SD (=12.84; Table 4.1). No significant
result was found in self-efficacy and mood.
The HLM models of the micro analysis generally explained approximately 15-30% of the
variance in unimanual right and the bimanual hand movements (see details in Table 4.2). There
was less than 5% of the variance in unimanual left hand movement that was explained by the
HLM models (Table 4.2).
Micro Predicative Analysis (Aim 3)
In the predictive model, the self-efficacy score at the preceding time interval (t) was
found to have a significant effect on unimanual right (paretic) hand movement at the subsequent
time (t+1) (Table 4.2; Model 2, Micro Predictive Analysis). Participants’ confidence level about
the paretic hand at the present time predicts the use of that hand at the next time period. In
practice, if a participant has a 1 SD (=17.30; Table 4.1) higher self-efficacy at the current
moment, he or she would be more likely to use the right paretic hand independently by 0.05 min
89
longer over a 10-min window lagged by approximately 2-hours later. No other significant results
were observed for the other social-cognitive or hand movement measures.
DISCUSSION
Our results demonstrated that social-cognitive factors (i.e., social interaction and self-
efficacy) play an essential role in post-stroke hand use behavior in the natural environment.
Motor capability, although an important factor, is not the only factor that influences the use of
the paretic hand post-stroke. With the real-time measures of EMA, positive social interaction
was found to be predictive of individuals’ hand movement as measured by accelerometry. Self-
efficacy was also shown to have a lagged effect on unimanual paretic hand movement after
controlling for motor capability. Individuals were more likely to use the paretic hand at a
subsequent time if they had greater self-efficacy of that hand at the present time interval.
As expected, motor capability was shown to be an important factor for daily hand use
post-stroke. Participants with a higher FM score demonstrated longer time of the paretic hand
movement in the natural environment, with or without the less-paretic hand involved. When
examining the overall association between paretic hand use and social-cognitive factors,
participants’ self-efficacy for hand use and positive mood as measured by the one-time
clinical/research-based assessments (i.e., the CAHM and the PANAS) were not significantly
predictive of paretic hand movement. Only the less-paretic hand movement was associated with
positive mood.
In contrast, we found a significant result of social interaction in the instantaneous
association between paretic hand use and social-cognitive factors from the momentary EMA
90
responses. If participants perceived more positive social interaction with the ones that they were
with, the use of the hand(s) (both unimanual right/left and bimanual hand movements) increased
in the daily context. This finding was not exclusive to the paretic or less-paretic hand, suggesting
that participants’ overall hand and arm activity level increased when they perceived a more
positive social environment. People were more motivated to participate in activities with their
hand(s) with the support from others. This is consistent with recent findings, which employed
EMA to measure real-time social interaction in acute stroke survivors specifically, that positive
social interaction with family and friends is associated with an increase in activities of daily
living back to the community 3 months later.
25
Furthermore, participants demonstrated greater use of the paretic hand when they felt
more confidence in using it at the preceding time. This lagged effect of self-efficacy on the
unimanual paretic hand reflects the prospective feature of self-efficacy. When the participants
responded to the EMA self-efficacy question (Figure 4.2a), they denoted their expectation of
their capability to use the paretic hand at a later time point. Some of our participants also
mentioned that they saw the self-efficacy response as a “commitment”. When they responded
that they were confident of using the paretic hand within the next two hours (Figure 4.2a; i.e., the
approximate time interval between prompts), they felt that they had “promised” to use the paretic
hand and set up the goal for themselves to achieve it. Self-efficacy has been recognized as a
motivational determinant of behavior and a facilitator of goal commitment.
27,48–50
High self-
efficacy may motivate and facilitate stroke survivors to achieve success in using the paretic hand.
The significant explanatory effects of social interaction and self-efficacy on paretic hand
behavior provide us fundamental evidence to develop alternative interventions to promote
behavior change. Intervention protocols targeting social interaction and self-efficacy will allow
91
us to approach this problem from the perspective of the person (e.g., how he/she perceives the
surrounding social environment; how he/she feels about using the paretic hand to perform
activities). Clinicians may tailor protocols to provide a more personalized rehabilitation program
to enhance individuals’ paretic hand use in the day-to-day context. Instead of an exclusive focus
on motor capability, a program that takes into account these important social-cognitive factors
may effectively increase spontaneous use of the paretic hand and further change the dynamics of
recovery from a vicious cycle of decline into a virtuous cycle of recovery.
26
Interventions of social support (e.g., support seeking skills
51
or family education
52
) have
been used in public health to improve health outcomes (e.g., quality of life in chronic disease
populations).
53
Researchers have also investigated strategies to enhance self-efficacy and
promote behavioral outcomes (e.g., positive social-comparative feedback for motor skills
learning).
54–58
Based on these previous studies
51–58
, we recommend the development of specific
interventions that include social interaction and self-efficacy for stroke survivors and the
incorporation of these interventions into the post-stroke standard care model to maximize motor
recovery.
The significant explanatory effects of social interaction and self-efficacy also
demonstrated the advantages of using EMA. The repeated measures of EMA allowed us to have a
finer-grained analysis to understand individuals’ hand use behavior in the natural environment.
The clinical/research-based assessments are designed to capture the overall construct of interest
(e.g., confidence, mood) but are insensitive to the time varying context-specific fluctuations. EMA
instead allows us to understand how these ecological real-time nuances can influence people’s
decision-making concerning paretic hand use. The capability of EMA to capture the natural
fluctuations in social-cognitive factors and its momentary impact on hand use behavior grants a
92
more comprehensive understanding of putative modifiers of paretic hand use. It can be a valuable
measurement tool to supplement clinical/research-based assessments of post-stroke hand use
behavior.
Finally, a logical extension of what we have learned from the EM(A) assessment is to
the development of EM(I) interventions. The putative modifiers or the essential factors of hand
use that we found here (i.e., social interaction and self-efficacy) could be targeted using EMI.
Through the mobile-based ecological intervention, we can provide immediate, real-time
application of rehabilitation to individuals’ home and community. As tele-rehabilitation, EMI
can be a beneficial means to deliver home therapy and rehabilitation services through mobile
devices during everyday lives and settings (i.e., in real-time and real world). We may effectively
improve the functional use of the paretic hand and facilitate a more complete return to normal
living for stroke survivors with the assistance of EMI. Due to the near universal use of mobile
devices amongst the older population nowadays
59
, the mobile-based EMA or EMI can become a
useful and handy collaborative tool for both clinicians/researchers and patients to achieve a
better success of one’s recovery after stroke.
One important caveat, we found relatively low R
2
values in the micro analyses (0.0464 –
0.3174) – an indication that approximately 70 – 95% of the variance in momentary hand use
behavior remains unexplained. The low coefficients of determination suggest that there are other
important factors which affect the momentary choice of hand use in the daily environment.
Previous studies have shown that, for example, object orientation
60–62
, object location
12,60,63,64
,
and task complexity
60,61,63
can affect hand selection and strategies. These factors certainly may
impact one’s paretic hand use behavior and covertly reinforce non-use behavior after stroke. Our
previous work demonstrated the measurement reactivity of EMA and its potential to change
93
behavior (i.e., EMI).
35
With the repeated and real-time implication of EMA/EMI to one’s daily
environment, there is potential to increase awareness of hand choice and explicitly motivate use
of the paretic hand (e.g., through interventions of social interaction and self-efficacy) to reverse
non-use behavior.
CONCLUSION
The impact of momentary social interactions and self-efficacy on daily paretic hand use
demonstrates that motor capability alone is not enough to explain paretic hand use in real time
and in the natural environment. These findings help move the field forward by suggesting
intervention strategies that target more than motor capability to maximize recovery potential.
Alternative intervention approaches, targeting social interaction (e.g., support seeking skills) and
self-efficacy (e.g., positive feedback), may foster paretic hand use in daily life. Further
development from EMA to EMI is worthy of future investigations to harness its potential to
optimize stroke survivors’ paretic hand use and to improve motor recovery beyond motor
capability.
94
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Table 4.1: Characteristics of Participants and Outcome Measures of Hand Use Behavior
and Social-Cognitive Factors.
Mean ± SD (Range)
Gender (n, male: female) 21:9
Age (year) 61.19 ± 13.10 (24.43 – 82.91)
Onset of Stroke (year) 4.68 ± 3.89 (0.68 – 14.80)
MoCA (0 – 30) 24.73 ± 3.42 (19 – 30)
FM (0 – 66) 47.27 ± 14.93 (20 – 66)
Hand Use Behavior (Accelerometry Time)
Macro Analysis -- Over the 5-Day Period (%)
Unimanual Right Hand Movement 5.80 ± 4.22 (1.57 – 17.86)
(47.50 min/day)
#
Unimanual Left Hand Movement 22.51 ± 8.53 (7.94 – 43.84)
(3.07 hr/day)
#
Bimanual Hand Movement 29.17 ± 12.47 (6.52 – 55.69)
(3.98 hr/day)
#
Micro Analysis -- Within the Before-Prompt 10-min Window (min)
Unimanual Right Hand Movement 0.59 ± 0.42 (0.11 – 1.56)
Unimanual Left Hand Movement 2.37 ± 0.98 (0.79 – 5.21)
Bimanual Hand Movement 3.34 ± 1.38 (0.84 – 5.85)
Social-Cognitive Factors
Macro Analysis
CAHM (0 – 100) 58.92 ± 27.30 (12.50 – 100.00)
Positive Affect (10 – 50) 36.83 ± 7.65 (23 – 49)
Micro Analysis -- EMA Response
Self-Efficacy (0 – 100) 56.84 ± 17.30 (6.7 – 94.71)
Mood (0 – 100) 69.40 ± 16.63 (38.84 – 93.00)
Social Interaction (0 – 100) 75.11 ± 12.84 (47.43 – 93.00)
#
For interpretation and comparison convenience, the time percentages were converted back to
time unit using the average accelerometer wearing time (i.e., 13.65 hours wearing per day
over the 5 days).
Abbreviations: MoCA = Montreal Cognitive Assessment, FM = Upper Extremity Fugl-Meyer
Assessment, CAHM = Confidence in Arm and Hand Movements Scale, EMA = Ecological
Momentary Assessment.
99
Table 4.2: Results of Linear Regression Model Examining the Association between Hand Use Behavior (Accelerometry Time of the
Unimanual Right/Left and Bimanual Hand Movement) and Social-Cognitive Factors after Controlling for Motor Capability.
b (SE)
Unimanual Right
Hand Movement
Unimanual Left
Hand Movement
Bimanual
Hand Movement
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Macro Analysis
FM -0.198 (0038)*** -0.112 (0.083)*** -0.411 (0.075)*** -0.342 (0.152)*** -0.547 (0.119)*** -0.220 (0.257)***
CAHM -0.054 (0.046)*** -0.049 (0.085)*** -0.206 (0.143)***
Positive Affect -0.034 (0.080)*** -0.341 (0.146)*** -0.122 (0.246)***
R
2
0.4890 0.5143 0.5177 0.6017 0.4284 0.4705
Micro Analysis
FM -0.021 (0.004)*** -0.017 (0.004)*** -0.021 (0.014)*** -0.017 (0.015)*** -0.066 (0.012)*** -0.058 (0.013)***
Self-Efficacy -0.002 (0.001)*** -0.001 (0.005)*** -0.005 (0.005)***
Mood -0.000 (0.001)*** -0.009 (0.006)*** -0.001 (0.005)***
Social Interaction -0.002 (0.001)*** -0.007 (0.003)*** -0.006 (0.002)***
R
2
0.2368 0.2985 0.0174 0.0464 0.1467 0.1671
Micro Predictive Analysis
FM -0.021 (0.003)*** -0.014 (0.003)*** -0.023 (0.014)*** -0.037 (0.015)*** -0.067 (0.012)*** -0.067 (0.014)***
Self-Efficacy -0.003 (0.001)*** -0.004 (0.006)*** -0.001 (0.005)***
Mood -0.001 (0.001)*** -0.002 (0.006)*** -0.002 (0.005)***
Social Interaction -0.000 (0.001)*** -0.002 (0.003)*** -0.002 (0.003)***
R
2
0.2341 0.3174 0.0185 0.0878 0.1442 0.1795
*p<.05; **p<.01; ***p<.001
Abbreviations: FM = Upper Extremity Fugl-Meyer Assessment, CAHM = Confidence in Arm and Hand Movements Scale.
100
Figure 4.1: Accelerometer and EMA Smartphone Configuration.
101
Figure 4.2. Example Questions of EMA Prompts. Abbreviation: EMA = Ecological
Momentary Assessment.
(a) Self-Efficacy (b) Mood (c) Social Interaction
102
CHAPTER 5
Summary and General Discussion
Summary of Main Findings
The overall goal of this dissertation work was to investigate the association between
paretic hand use and social-cognitive factors in the natural environment. Limited use of the
paretic hand after stroke can severely constrain an individual’s daily function and lead to further
degradation.
1–5
The presence of the non-use phenomenon, which is defined as the discrepancy
between recovered motor capability and actual daily hand use
6,7
, underscores the fact that motor
capability, while a necessary factor, may not be the only factor influencing paretic hand use after
stroke.
8,9
Recent studies demonstrate that social-cognitive factors, which characterize an
individual’s psychological needs and perceptions, play an essential role in functioning after
stroke.
10–13
Nevertheless, there continues to be a significant knowledge gap in understanding the
relationship of self-efficacy, mood, and social interaction to post-stroke paretic hand use. This
dissertation work was designed to advance our knowledge in post-stroke hand use behavior and to
minimize the existing knowledge gap.
To capture individuals’ natural behavior and response, we used two ecological measures,
accelerometry and Ecological Momentary Assessment (EMA), to assess the real-time hand
use behavior and social-cognitive factors (i.e., self-efficacy, mood, and social interaction). Since
this is the first time that EMA has been employed to measure hand use in stroke survivors, we
first established the feasibility and validity of using EMA to capture paretic hand use and related
self-report information (e.g., social-cognitive factors) in the natural context (Chapter 2).
103
EMA is a Feasible Tool to Measure Paretic Hand Use and Social-Cognitive Factors in the
Natural Environment Post-Stroke
Our results demonstrated a high average response rate (84.64%) of EMA prompts during
the 5-day monitoring period (6 prompts/day). The maintained response rate over days and times
indicated that neither were novelty nor fatigue effects shown to bias the self-report data that we
obtained from EMA. We also found that the self-trigger function (i.e., participants could trigger a
prompt anytime that they desired) unexpectedly alleviated participants’ burden to respond to
EMA and provided flexibility to people who had difficulty following the prompt schedule.
Participants’ positive feedback (e.g., ease and non-disruptiveness of EMA responding) also
support our EMA design and confirms the usefulness of EMA in a group of community
dwelling, pre-morbidly right-hand dominant stroke survivors with mild to moderate-to-severe
disability.
EMA is a Valid Tool to Measure Paretic Hand Use and Social-Cognitive Factors in the Natural
Environment Post-Stroke
Additionally, we established the construct validity of EMA. The correlation comparisons
demonstrated convergence between EMA responses and construct appropriate outcome
measures. Participants who reported more use of the paretic hand, greater self-efficacy, and a
higher level of positive mood (happiness) in EMA expressed: (1) a greater amount and better
quality of paretic hand use in the Motor Activity Log, (2) higher confidence levels and scores in
the Confidence in Arm and Hand Movements and the Stroke Impact Scale (SIS) Hand Function
subscale, and (3) greater positive affect scores and lower negative affect scores in the Positive
Affect and Negative Affect Scale, respectively. The non-significant relationship between the
EMA self-efficacy and the SIS Mobility also established discriminant validity, since the SIS
104
Mobility subscale focuses on individuals’ lower extremity mobility (such as standing/walking,
climbing stairs) rather than upper extremity function.
Overall, Chapter 2 provided evidence for a novel means to assess post-stroke hand use
behavior in the natural environment. This chapter established a first step toward the use of EMA
to capture paretic hand use behavior in context and in real time.
The Combined Accelerometry/EMA Methodology is a Valuable Method to Measure Paretic
Hand Use Behavior in the Natural Environment Post-Stroke
Furthermore, we synchronized the two ecological assessments (e.g., accelerometry and
EMA). Combing these assessments may not only integrate accelerometer data with self-report
information but also allow us to obtain a more personalized and comprehensive understanding of
individuals’ hand use behavior.
14
In Chapter 3, we further established that this combined
methodology can provide unique information about daily hand use in individuals with stroke
Hand use behavior was generally consistent between the accelerometer data (time of hand
movement) and EMA self-report response about hand use. When participants responded that
they were using, for example, both hands to perform activities, the accelerometry time of
bimanual hand movement was significant longer than that when reporting either right or left
hand use. Participants’ concurrent hand movement (as measured by accelerometry) did not bias
them to answer or not answer EMA prompts. The act of EMA self-initiating (i.e., self-trigger
prompts) was also not associated with any increase in hand movement compared to EMA probe
prompts. These findings indicated that the self-reported responses that we sampled through EMA
(i.e., 6 scheduled prompts/day and self-triggered prompts) were not biased by participants’ hand
movement.
105
Participants did not show a change in hand movement behavior immediately after
responding to EMA prompts. However, we found an unexpected, significant accumulated effect
of reactivity in the unimanual right (paretic) hand movement. Participants gradually increased the
time and magnitude of paretic hand movement over the 5 days during participation. This gradual
accumulated change is suggestive that EMA may have taken on a role similar to an EMI
(intervention) – each probe was an implicit reminder to use the paretic hand for performing daily
activities. Yet, due to the small increase (i.e., ~10 min over an average of 13.65-hour daily
monitoring), the results need to be interpreted with careful consideration and further
understanding.
Social Interaction and Self-Efficacy Play an Essential Role in Paretic Hand Use in the Natural
Environment Post-Stroke
After establishing the feasibility and validity of EMA (Chapter 2) and the usefulness of
the combined methodology (Chapter 3), we then employed the dual methodology paradigm to
examine the association between paretic hand use behavior and three pre-specified social-
cognitive factors (Chapter 4).
We found that two of the three pre-specified social-cognitive factors (i.e., social
interaction and self-efficacy) played an essential role in post-stroke hand use behavior in the
natural environment. Motor capability, although important, is not the only factor that influences
paretic hand use behavior. With the real-time measures of EMA, positive social interaction was
predictive of individuals’ hand movement. People were more motivated to participate in
activities with their hand(s) when they perceived support from others (e.g., family and friends).
We showed that self-efficacy’s effect lagged in time; its impact on unimanual paretic hand
movement was evident after controlling for motor capability. Individuals were more likely to use
106
the paretic hand at a subsequent time (t+1) if they had greater self-efficacy for that hand at the
present time interval (t). The impact of momentary social interactions and self-efficacy on daily
paretic hand use supports our initial hypothesis that motor capability alone is not enough to
explain paretic hand use in real time and in the natural environment. The findings reported in
Chapter 4 provide the foundational evidence to develop alternative and forward-thinking
intervention approaches – those that target social interaction and task-specific self-efficacy – to
foster pre-stroke levels of paretic hand use in daily life.
Limitations
First, our participants were restricted to individuals with right, dominant-side
hemiparesis. We did this to avoid the confounding effects of handedness on paretic hand use
15–17
.
The trade-off is that our findings may not generalize beyond those who are pre-morbidly right-
handed. Second, although participants were instructed to complete the EMA prompts
independently, some may have received assistance from caregivers in responding. We cannot
rule this possibility out in the present study.
Lastly, we were not able to directly interpret the magnitude (in “activity count” unit) of
hand movement measured by accelerometry. We quantified each individuals’ daily hand
movement from the accelerometry data by using a valid and frequently used cut-off threshold
18–
22
; however given that the conversion from raw acceleration to activity count is performed using
a proprietary algorithm held by the software company, the meaning of the magnitude value in
activity count is not known. It turns out that this is a common concern for most studies using
accelerometers.
14
Future research that addresses the meaning of ‘activity count’ is needed for a
better understanding and functional interpretation of these data from accelerometers.
107
Clinical Implications and Future Directions
An Evidence-based, Innovative Intervention – Target Social Interaction and Self-Efficacy – To
Promote Spontaneous Paretic Hand Use Post-Stroke
The impact of social interaction and self-efficacy on paretic hand use provide us
fundamental evidence to develop evidence-based interventions to enhance spontaneous paretic
hand use outside the clinical environment. Intervention protocols which target social interaction
and self-efficacy will enable a personalized approach. Clinicians may tailor the protocols to
provide a more person-centered rehabilitation program to enhance individuals’ paretic hand use
in the day-to-day context. Outcomes of such an approach may impact functional behavior as well
as the dynamics of recovery by converting a vicious cycle of decline into a virtuous cycle of
recovery.
2
Instead of an exclusive focus on improved motor capability, we may incorporate a
more personalized intervention within post-stroke standard treatments to optimize paretic hand
use and to maximize motor recovery for stroke survivors.
EMA can be a Valuable Tool to Supplement Standard Clinical/Research-Based Assessments in
Post-Stroke Hand Use Behavior
The impact of two social-cognitive factors on paretic hand use behavior demonstrated one
advantage EMA methodology. The real-time and repeated measures aspect of EMA provided a
finer-grained analysis to understand an individuals’ hand use behavior in their context. The
clinical/research-based assessments are designed to capture the overall construct of interest (e.g.,
confidence, mood) but are insensitive to the time varying context-specific fluctuations captured by
EMA. EMA instead allows us to understand how these ecological real-time nuances influence
people’s decision-making in paretic hand use. The capability of EMA to capture context-specific
fluctuations in social-cognitive factors and its momentary impact on hand use behavior grants a
108
more comprehensive understanding of putative behavior modifiers. EMA can be a valuable
context-specific assay that can be used to supplement clinical/research-based assessments of post-
stroke hand use behavior.
Future development from EMA to EMI—A promising approach to motivate paretic hand use and
improve motor recovery
A logical extension of EMA, informed by the evidence discussed in this dissertation, is to
EM(I) interventions. Two of the three pre-specified social cognitive factors found to influence
paretic hand use are natural targets for EMI. Through the mobile-based ecological intervention,
we can provide immediate, real-time positive feedback and reward to individuals’ behavior in the
home and community. Similar to tele-rehabilitation, EMI can be a beneficial means to deliver
home therapy and rehabilitation services through mobile devices in the natural setting (i.e., in
real-time and real world). We may effectively improve the functional use of the paretic hand and
facilitate a more complete return to normal living for stroke survivors with the assistance of EMI.
Due to the near universal use of mobile devices amongst the older population nowadays
23
, a
mobile-based EMA or EMI can become a useful and handy collaborative tool for both
clinicians/researchers and patients toward enhanced recovery after stroke.
109
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15. Haaland, K. Y. et al. Relationship between arm usage and instrumental activities of daily
living after unilateral stroke. Arch Phys Med Rehabil 93, 1957–62 (2012).
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16. Harris, J. & Eng, J. Individuals with the dominant hand affected following stroke
demonstrate less impairement than those with the nondominant hand affected.
Neurorehabil Neural Repair 20, 380–389 (2006).
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rehabilitation in chronic stroke. Clin Rehabil 19, 544–51 (2005).
18. Uswatte, G. et al. Ambulatory monitoring of arm movement using accelerometry: an
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Phys. Med. Rehabil. 86, 1498–1501 (2005).
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Abstract (if available)
Abstract
Limited use of the paretic hand after stroke can severely constrain an individual’s activity and participation and lead to further functional degradation. Clinical practice as well as research has focused on improving motor capability of the paretic hand to promote spontaneous use in the natural environment. However, evidence has shown that the efforts to enhance recovery in individuals after stroke may be in vain, in part a result of the non-use phenomenon. The non-use phenomenon is defined as the discrepancy between recovered motor capability and functional hand use, whereby the capacity to use the hand is far greater than its actual use. This underscores the fact that motor capability, while a necessary factor, is not the only factor influencing the use of the paretic hand after stroke. ❧ Recent studies demonstrate that social-cognitive factors, which characterize an individual’s psychological needs and perceptions, play an essential role in functioning after stroke. Nevertheless, there continues to be a significant knowledge gap in understanding the relationship of self-efficacy, mood, and social interaction to paretic hand use after stroke. Therefore, the overall goal of this dissertation work was to investigate the association between paretic hand use and social-cognitive factors in the natural environment. ❧ To capture individuals’ natural behavior and response, we used two ecological measures, accelerometry and Ecological Momentary Assessment (EMA), to assess the real-time hand use behavior and social-cognitive factors (i.e., self-efficacy, mood, and social interaction). Since this is the first time that EMA has been employed to measure hand use in stroke survivors, we first established the feasibility and validity of using EMA to capture paretic hand use and related self-report information (e.g., social-cognitive factors) in the natural context. Our results provided evidence for a novel means to assess post-stroke hand use behavior in the natural environment. EMA is capable of capturing an individuals’ dynamic hand use behavior and the natural fluctuations of self-efficacy and mood. The real-time and repeated measures feature of EMA exceed the limitations of laboratory/clinic-based hand use measures and shows promise for advancing our understanding of paretic hand use. ❧ Furthermore, we synchronized the two ecological assessments (e.g., accelerometry and EMA). Combing these assessments may not only integrate accelerometer data with self-report information but also allow us to obtain a more personalized and comprehensive understanding of individuals’ hand use behavior. Our finding demonstrated the consistency of hand use behavior between accelerometry objective measures and EMA self-responses. In addition, a significant small increase in time and magnitude of the unimanual right (paretic) hand movement was observed over days. This change may be the result of measurement reactivity that EMA functioned as EMI (intervention) to implicitly remind participants to use the paretic hand for performing daily activities. We established a further step toward the use of the combined accelerometry/EMA methodology to capture paretic hand use behavior in the natural context. ❧ After establishing the feasibility and validity of EMA and the usefulness of the combined methodology, we then employed the dual methodology paradigm to examine the association between paretic hand use behavior and three pre-specified social-cognitive factors. We found that two of the three pre-specified social-cognitive factors (i.e., social interaction and self-efficacy) played an essential role in post-stroke hand use behavior in the natural environment. Motor capability, although important, is not the only factor that influences paretic hand use behavior. With the real-time measures of EMA, positive social interaction was predictive of individuals’ hand movement. People were more motivated to participate in activities with their hand(s) when they perceived support from others (e.g., family and friends). We showed that self-efficacy’s effect lagged in time
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Asset Metadata
Creator
Chen, Yi-An
(author)
Core Title
Using ecological momentary assessment to study the impact of social-cognitive factors on paretic hand use after stroke
School
School of Dentistry
Degree
Doctor of Philosophy
Degree Program
Biokinesiology
Publication Date
06/28/2019
Defense Date
05/19/2017
Publisher
University of Southern California
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Tag
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Winstein, Carolee J. (
committee chair
), Fisher, Beth E. (
committee member
), Lewthwaite, Rebecca (
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), Monterosso, John R. (
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yian@usc.edu,yianchenanne@gmail.com
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