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OUTLINE

 1. Abstract
 2. Keywords
 3. Methods
 4. Results
 5. Discussion
 6. Conclusions
 7. Suppliers
 8. References

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ARCHIVES OF REHABILITATION RESEARCH AND CLINICAL TRANSLATION

Volume 5, Issue 1, March 2023, 100250

ORIGINAL RESEARCH
DEPLOYING DIGITAL HEALTH TECHNOLOGIES FOR REMOTE PHYSICAL ACTIVITY MONITORING OF
RURAL POPULATIONS WITH CHRONIC NEUROLOGIC DISEASE

Author links open overlay panelKimberly J. Waddell PhD, MSCI a b c, Mitesh S.
Patel MD, MBA d, Jayne R. Wilkinson MD, MSCE a e, Robert E. Burke MD, MS, FHM a
c f, Dawn M. Bravata MD g h i, Sreelatha Koganti BS a, Stephanie Wood BS a,
James F. Morley MD, PhD a e
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ABSTRACT


OBJECTIVE

The objective of this pilot study was to examine the feasibility of a remote
physical activity monitoring program, quantify baseline activity levels, and
examine predictors of activity among rurally residing adults with Parkinson
disease (PD) or stroke.


DESIGN

Thirty-day observational study. Participants completed standardized assessments,
connected a wearable device, and synced daily step counts via a remote
monitoring platform.


SETTING

Community-based remote monitoring.


PARTICIPANTS

Rurally residing adults with PD or stroke enrolled in the Veterans Health
Administration.


INTERVENTION

N/A.


MAIN OUTCOME MEASURES

Feasibility was evaluated using recruitment data (response rates), study
completion (completed assessments and connected the wearable device), and device
adherence (days recording ≥100 steps). Daily step counts were examined
descriptively. Predictors of daily steps were explored across the full sample,
then by diagnosis, using linear mixed-effects regression analyses.


RESULTS

Forty participants (n=20 PD; n=20 stroke) were included in the analysis with a
mean (SD) age of 72.9 (7.6) years. Participants resided 252.6 (105.6) miles from
the coordinating site. Recruitment response rates were 11% (PD) and 6% (stroke).
Study completion rates were 71% (PD) and 80% (stroke). Device adherence rates
were 97.0% (PD) and 95.2% (stroke). Participants with PD achieved a median
[interquartile range] of 2618 [3896] steps per day and participants with stroke
achieved 4832 [7383] steps. Age was the only significant predictor of daily
steps for the full sample (-265 steps, 95% confidence interval [-407, -123]) and
by diagnosis (PD, -175 steps, [-335, -15]; stroke, -357 steps [-603, -112]).


CONCLUSIONS

A remote physical activity monitoring program for rurally residing individuals
with PD or stroke was feasible. This study establishes a model for a scalable
physical activity program for rural, older populations with neurologic
conditions from a central coordinating site.

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KEYWORDS

Parkinson disease
Rehabilitation
Stroke
Exercise
Rural health
Telemedicine
Wearable electronic devices

Rurally residing populations are older, experience limited access to medical
care, higher rates of morbidity and mortality, and report worse physical health
and health-related quality of life compared with their urban counterparts.1, 2,
3 Individuals with neurologic disease, such as Parkinson disease (PD) or stroke,
are particularly vulnerable to poor outcomes. This is due, in part, to limited
access to specialized care and novel programs to promote disease
self-management.1,3 This restricted access has resulted in a dearth of
information related to healthy behaviors (eg, physical activity) and factors
that may affect daily activity and ongoing disease self-management.

Regular physical activity can improve health, reduce morbidity and mortality,
and mitigate motor and non-motor symptoms of neurologic disease.4,5 Physical
activity is essential to disease self-management but effective, scalable
interventions to increase activity are lacking. Telerehabilitation programs have
expanded to rural populations but are time-limited and require clinical
resources.6,7 Clinical trials designed to increase physical activity for
individuals with PD or stroke are feasible, but resource intensive and often
require in-clinic visits, limiting recruitment to those living near urban
medical centers.8,9 Scalable technology, such as wearable devices and remote
monitoring platforms, affords new opportunities to reach these populations but
are under-studied. The purpose of this study was to examine the feasibility of a
remote physical activity monitoring program for rurally residing adults with PD
or stroke, quantify baseline physical activity levels, and explore predictors of
physical activity.


METHODS

This observational study used the Veterans Affairs (VA) Corporate Data Warehouse
to construct a cohort of Veterans enrolled in the Veterans Health
Administration, the largest health care system in the United States, with an
International Classification of Diseases, Tenth Revision code for PD, ischemic
stroke, or hemorrhagic stroke. Individuals were eligible to participate if they
(1) were able to ambulate outside of the home, without physical assistance; (2)
had a rural or highly rural VA designation (Rural Urban Commuting Area); (3)
primary residence within Veterans Integrated Service Network 4 (all counties in
Pennsylvania, Delaware, and parts of Ohio, New York, New Jersey, and West
Virginia); and (4) had access to a smartphone or tablet. Individuals were
excluded if they (1) had a diagnosis of cerebellar stroke or atypical
parkinsonism; (2) self-reported fall within previous 6 months; or (3) severe
cognitive impairment (mini Montreal Cognitive Assessment [MoCA] score <11
points).10 The VA uses Rural Urban Commuting Area system, based on census data,
to assign all Veterans enrolled in the VA as residing in an urban, rural, or
highly rural area. These designations are available within VA administrative
data, which was used to determine study eligibility based on the criteria listed
above. This study was approved by the Institutional Review Board of the Corporal
Michael J. Crescenz VA Medical Center and all participants provided oral
informed consent.


RECRUITMENT AND STUDY PROCEDURES

Recruitment for this study occurred from February 2021 to September 2021.
Consistent with prior physical activity feasibility studies for adults with PD
or stroke, we established an a priori goal of 40 Veterans (20 PD, 20 stroke) to
be included in our analyses.11, 12, 13 Individuals were mailed a recruitment
letter then contacted the study coordinator, completed a cognitive screen, and
provided oral informed consent via telephone. Enrolled participants were
directed to Way to Health, an online remote monitoring platform used in previous
studies,14, 15, 16 to complete all study surveys. Upon completion of the study
surveys, participants were mailed a wearable device (Fitbit Inspire HR, San
Francisco, CA)a and instructions for connecting their device to the Way to
Health platform. Way to Health is a secure, web-based platform that automates
many research functions such as the administration of standardized assessments
or questionnaires, collecting health data from connected devices, and patient
messaging.

Participants completed a technology questionnaire that quantified pre-enrollment
access and use of different technologies (eg, internet, smartphone)17,18 and
standardized assessments on the Way to Health platform. The standardized
assessments quantified potential predictors of physical activity: exercise
self-efficacy (Exercise Self-Efficacy scale19), fatigue (Facit Fatigue Scale20),
falls efficacy (Falls Efficacy Scale21), depression (Patient Health
Questionnaire, 9-item22), apathy (Apathy Evaluation Scale23), and social support
(Medical Outcomes Study Social Support Survey24). Participants with PD also
completed the Parkinson's Disease Questionnaire (8-item), a measure of the
quality of life.25

Participants wore the Fitbit device during waking hours for 30 days. The 30-day
observational period was selected, in part, to provide sufficient time to
capture current physical activity levels and reduce the potential upward bias of
daily steps that may occur in the first few days of wearing an activity tracker.
The reliability and validity of Fitbit for quantifying daily steps for older
adults with PD and stroke has been established.26, 27, 28 Participants with PD
wore the Fitbit on their hip, eliminating the potential for erroneous step
counts due to hand tremor. Participants in the stroke cohort who used an
assistive device while walking wore the Fitbit on their less affected hip and
those who did not use an assistive device wore the Fitbit on their less affected
wrist. Daily step counts automatically synced to the Way to Health platform via
the Fitbit smartphone application. This eliminated the need for participants to
manually record their daily step count, which reduced participant burden. Data
were considered missing if the daily step count was <100 steps, a validated
daily adherence threshold in adults with stroke.29


OUTCOME MEASURES

Feasibility was evaluated using recruitment data (response rates), study
completion (completed assessments and connected Fitbit device to remote platform
following informed consent), and daily device adherence rates (days recording
≥100 steps).29 The following variables were explored as potential predictors of
daily steps: age (years), disease duration (years), exercise self-efficacy,
fatigue, falls efficacy, depression, apathy, and social support. Quality of life
was also examined as a predictor for the PD cohort using the 8-item Parkinson's
Disease Questionnaire.


STATISTICAL ANALYSIS

Program feasibility and daily step data were assessed descriptively. We also
tested for potential differences between those who were enrolled in the study
and those who were mailed a recruitment letter but did not respond using
independent samples t test for continuous variables and chi-square or Fisher's
Exact tests for categorical variables. This exploratory evaluation was completed
to examine potential bias in our recruitment method to help inform future
recruitment strategies. Next, we explored baseline predictors of daily physical
activity using linear mixed-effects regression analysis across the full sample
adjusting for participant random effect and diagnosis. Because of the small
sample size, predictors were first evaluated for significance individually. All
significant predictors were then added to a final multivariate model. Lastly,
the sample was stratified by diagnosis (PD and stroke) and predictors were first
evaluated individually. All significant predictors were then tested in a final
model for each cohort. Step data were examined at the day level and all models
adjusted for a participant random effect due to repeated observations. Analyses
were completed in R (version 4.0.5)30 using the tidyverse31 and lme432 packages.


RESULTS

We achieved our a priori goal of including 40 rurally residing Veterans with PD
(n=20) or stroke (n=20) in our final analysis. Our recruitment flowchart and
reasons for exclusion are presented in Figure 1. We mailed 695 recruitment
letters (74 contacts) to those with PD and 528 letters (32 contacts) to
individuals with stroke, resulting in an 11% and 6% response rate, respectively.
Among all consented participants, the PD cohort had a slightly lower study
completion rate (20/28, 71.4%) compared with the stroke cohort (20/25, 80.0%).
Across both cohorts, the most common reason for not completing the study was
failing to create a Way to Health account and completing study surveys (Figure
1) after providing informed consent. Four participants with stroke wore the
Fitbit on their belt, while the remaining participants (n=16) wore the device on
their wrist. There were no adverse events reported.

 1. Download : Download high-res image (295KB)
 2. Download : Download full-size image

Fig 1. Recruitment flowchart.

Daily device adherence over the 30-day study period was high (97.0% PD, 95.23%
stroke). The cohorts were demographically similar (Table 1). Overall, the mean
(SD) distance from participants’ residence to the study location was 252.6
(105.6) miles. Nearly all participants had daily access to the internet (95%)
and a smartphone (92.5%), although only a small number had access to a connected
health device (25%). A connected health device is any device that records or
transmits health data such as wearable activity trackers or a Bluetooth-enabled
blood pressure cuff. The PD cohort achieved significantly fewer steps per day
(median [interquartile range], 2618 [3896] steps) compared with the stroke
cohort (median [interquartile range], 4832 [7383] steps, P<.001).

Table 1. Participant demographics

Empty CellPD (n=20)Stroke (n=20)POverall (n=40)SociodemographicAge74.9 (7.5)71.0
(7.4).1072.9 (7.6)Male sex, No. (%)20 (100.0%)19 (95.0%)<.9939
(97.5%)Race/ethnicity, No. (%) White, non-Hispanic20 (100.0%)19 (95.0%)<.9939
(97.5%) Black, non-Hispanic0 (0.0%)1 (5.0%)1 (2.5%)Chronicity (y)5.2 (4.6)10.1
(9.3).047.6 (7.6)Rurality, No. (%) Rural20 (100.0%)20 (100.0%)<.9940
(100.0%) Highly rural0 (0.0%)0 (0.0%)0 (0.0%)Distance from study location249.0
(105.7)256.3 (108.1).82252.6 (105.6)Area Deprivation Index,* median (min,
max)6.5 (3, 9)7 (3, 10).637 (3, 10)Daily technology access,† No. (%) Internet19
(95.0%)19 (95.0%)<.9938 (95.0%) Smartphone18 (90.0%)19 (95.0%)<.9937
(92.5%) Tablet11 (55.0%)14 (70.0%).5125 (62.5%) Connected health device‡4
(20.0%)6 (30.0%).7110 (25.0%)Daily steps, median [IQR]2618 [3896]4832
[7383]<.0013467 [5269]Baseline surveysExercise self-efficacy§17.7 (8.6)20.1
(7.9).3718.9 (8.2)Fatigueǁ34.7 (8.7)34.6 (9.4).9734.6 (8.9)Falls efficacy¶16.7
(11.5)16.4 (12.9).9316.6 (12.1)Social support⁎⁎87.1 (16.4)77.2 (19.6).0982.2
(18.5)Depression (PHQ-9)††5.5 (4.1)5.4 (5.9).925.4 (5.0)Life Space
Mobility‡‡67.7 (24.7)70.5 (31.0).7569.1 (27.7)Apathy§§58.6 (8.5)58.6
(8.6)<.9958.6 (8.4)PDQ-8ǁǁ27.3 (14.5)---



NOTE. Values are means (SD) unless otherwise indicated. Differences in median
daily steps were evaluated using the Mann-Whitney U Test.

Abbreviations: IQR, interquartile range; PDQ-8, Parkinson's Disease
Questionnaire 8-item; PHQ-9, Patient Health Questionnaire-9.



⁎

A validated composite measure of socioeconomic disadvantage by neighborhood.
Values are state decile, ranging from 1 to 10, with higher scores indicating
greater disadvantage.40,41

†

Reported as number of participants reporting they had daily access to each
technology prior to this study.

‡

Any device that records and transmits health data (eg, wearable activity
tracker, electronic pill bottles, Bluetooth enabled blood pressure cuff).

§

Scores range from 9 to 28, higher scores = greater exercise self-efficacy.

ǁ

Scores range from 0 to 52, higher scores = greater quality of life.

¶

Scores range from 0 to 100, scores>70 indicate a fear of falling.

⁎⁎

Scores range from 0 to 100, higher scores = greater social support.

††

Scores range from 0 to 27, score>4 indicates mild depression with higher scores
indicating more severe depression.

‡‡

Scores range from 0 to 120, higher scores = greater mobility across different
environments. §§ Scores range from 18 to 72, higher scores =greater apathy.

ǁǁ

Scores range from 0 to 100, lower scores = better quality of life/health.

Individuals who contacted the study team were demographically similar to those
who did not respond to the recruitment letter (Table 2). For both cohorts,
enrolled participants had a more recent visit to a VA medical center or clinic
for diagnosis-related treatment than those who did not contact the study team.
This was significantly different in the PD cohort (41.9 vs 21.8 weeks, P<.001).

Table 2. Characteristics of responders (contacted, enrolled) and non-responders
(contacted, not enrolled)

Empty CellPD Contacted, Not Enrolled (n=667)PD Contacted, Enrolled (n=28)PStroke
Contacted, Not Enrolled (n=503)Stroke Contacted, Enrolled (n=25)PAge75.2
(7.7)74.7 (6.4).6974.1 (9.1)70.8 (7.5).05Rurality, N (%) Rural663 (99.4%)28
(100.0%)<.99
425 (84.5%)22 (88.0%).79 Highly rural4 (0.6%)0 (0.0%)7 (1.4%)0 (0.0%) Missing0
(0.0%)0 (0.0%)71 (14.1%)3 (12.0%)Race, n (%) Black or African American6 (0.9%)0
(0.0%)<.99
17 (3.3%)2 (8.0%).32 White632 (94.7%)28 (100.0%)473 (94.0%)22 (88.0%) American
Indian or Alaskan Native3 (0.4%)0 (0.0%)1 (0.2%)0 (0.0%) Asian1 (0.1%)0 (0.0%)1
(0.2%)0 (0.0%) Native Hawaiian or Pacific Islander4 (0.6%)0 (0.0%)2 (0.4%)0
(0.0%) Missing21 (3.1%)0 (0.0%)9 (1.8%)1 (4.0%)Ethnicity, n (%) Hispanic or
Latino4 (0.6%)0 (0.0%).563 (0.6%)2 (8.0%).04 Not Hispanic or Latino648 (97.1%)27
(96.4%)495 (98.4%)23 (92.0%) Missing15 (2.2%)1 (3.6%)5 (0.9%)0 (0.0%)Time since
last VA visit (weeks)*41.9 (52.9)21.8 (15.1)<.00177.0 (72.5)58.0 (64.8).16



NOTE. Values are mean (SD) unless otherwise indicated.



⁎

The most recent visit to a VA facility for a PD or stroke related concern
(ICD-10 code for PD or stroke) was used in the electronic medical record.

Results from the regression analysis for the full sample indicated that, after
adjusting for diagnosis, age (in years) was the only significant predictor of
daily steps (-265 steps, 95% confidence interval [CI] [-407, -123], P<.001).
Results from the bivariate regression analysis by cohort demonstrate that age
(in years) was the only significant predictor of daily steps (Figure 2). For
every 1-year increase in age, daily steps significantly decreased for both the
PD cohort (-175 steps, 95% CI [-335, -15], P=.03) and stroke cohort (-357 steps,
95% CI [-603, -112], P=.006).

 1. Download : Download high-res image (552KB)
 2. Download : Download full-size image

Fig 2. Bivariate regression estimates for each predictor of interest for the (A)
PD cohort and (B) stroke cohort. Quality of life (2A) was measured using the
Parkinson's Disease Questionnaire 8-item. Estimates represent associations
between the predictor and daily steps.


DISCUSSION

A remote physical activity monitoring program was feasible among rurally
residing adults with PD or stroke, who were enrolled in the Veterans Health
Administration. Participants demonstrated high device adherence and successfully
interacted with the remote monitoring platform throughout the study duration.
The significant decrease in daily steps among older participants suggests that
older adults with PD or stroke are key populations who may benefit from future
interventions to increase physical activity. These findings provide foundational
knowledge and support toward the development of remote interventions to increase
physical activity among rural populations with PD or stroke. The geographic
reach of this study also speaks to the scalability of such interventions from a
central coordinating center to distant sites.

There has been a significant investment in telehealth and telerehabilitation
resources in the last decade, especially within the Veterans Health
Administration,33, 34, 35, 36 which was accelerated by the COVID-19 pandemic.37
Many tele-based programs are time-limited and require broadband internet access,
which is lacking in many rural areas.38 This can perpetuate existing
disparities.38 While 95% of our sample reported regular internet access, the
physical activity program in this study did not require broadband internet
access, making it especially salient for rural populations with reduced access
to high-speed internet. Participants can access Way to Health if their
smartphone or tablet has a cellular data plan that can provide internet access.
This is an important feature for longer-term remote programs that target
improving physical activity and other healthy behaviors among rurally residing
populations with neurodegenerative disease or complex disability.

Participants with PD or stroke demonstrated high device adherence over the
30-day study period. Over half of our sample had not previously interacted with
a connected health device. The high adherence rates suggest that participants
consistently wore and engaged with the Fitbit device which is an important
finding for future interventions to increase physical activity among older,
medically complex populations. We observed varying levels of daily physical
activity across the sample. Overall, participants with PD completed fewer steps
per day compared with those with stroke, but we observed low levels of physical
activity among participants in both cohorts. These results suggest that rurally
residing individuals with PD or stroke would benefit from a novel, remote
intervention to increase daily physical activity that could potentially improve
long-term disease self-management and health outcomes.


STUDY LIMITATIONS

The limitations of this study include the small cohort size and the racial and
sex homogeneity that affects the generalizability of these findings. Future
studies should explicitly seek to recruit patients from diverse racial
backgrounds given the known racial disparities in access to virtual care.39
Additionally, this study required access to a smartphone or tablet that was
compatible with the Fitbit application. This may have excluded individuals of
lower socioeconomic status. Moving forward, it is important to identify
strategies to improve access to the required technologies to ensure all
individuals can participate in these programs. The remote design prevented the
collection of in-clinic assessments of disease state or impairment (eg, Hoehn
and Yahr Scale). This limited our ability to examine impairment measures (eg,
balance performance) as predictors of daily physical activity.


CONCLUSIONS

In conclusion, this study was a first critical step in reaching a population who
are underserved, understudied, and particularly vulnerable to the pernicious
effects of low physical activity. This “touchless” protocol required no
in-person visits or regular clinician involvement, which expanded our
recruitment radius and is scalable to a larger study to increase physical
activity. Daily physical activity was low, especially among older participants,
indicating a need for novel interventions to increase daily activity. The
evolution of scalable technology platforms has extended the reach of novel
programs to more vulnerable populations.


SUPPLIERS



 * a.
   
   Fitbit. Google LLC Manufacturer.



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CITED BY (0)



The results of this study were presented via poster at the American Society for
Neurorehabilitation Annual Meeting in St. Louis, MO, March 2022.

List of abbreviations: CI, confidence interval; PD, Parkinson disease; VA,
Veterans Affairs

This study was funded by Veterans Integrated Service Network 4 of the Veterans
Health Administration with funds awarded to the Center for Health Equity
Research and Promotion (CHERP).

Disclosures: Dr Patel has financial relationships with Catalyst Health, Humana,
and GlaxoSmithKline, unrelated to the current work. The other authors have
nothing to disclose.

Published by Elsevier Inc. on behalf of American Congress of Rehabilitation
Medicine.


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