A Smartphone App to Improve Physical Activity
Status: | Recruiting |
---|---|
Healthy: | No |
Age Range: | 65 - 84 |
Updated: | 11/10/2018 |
Start Date: | April 23, 2018 |
End Date: | October 2019 |
Contact: | Stacey Schepens Niemiec, PhD, OTR/L |
Email: | schepens@chan.usc.edu |
Phone: | 323-442-2069 |
A Smartphone Application to Improve Physical Activity in Underactive Older Adults
The purpose of this study is to develop, test, and optimize a physical activity (PA)-tracking
smartphone app and specialty features, which are designed to facilitate older adults' PA by
targeting common barriers in this population. For example, one feature sends messages
throughout the day about the good things about growing older to combat negative views about
aging which has been linked to decreased PA. Participants will include older adult smartphone
users who are between the ages of 65 and 84 and are not very physically active. In phase one
of the study, three groups of five older adults will be formed to test the PA-tracking app
and one of three specialty features for a two-week period, followed by a focus group to learn
about the older adults' experiences. In phase two, approximately 100 participants will be
randomly assigned to one of eight groups that include various combinations of specialty
features with the PA tracker, for the purpose of pilot testing the app for a four-month
period. Testing will occur at the beginning and the end of the four-month intervention
period, and will measure PA levels, sedentary activity time, self-reported PA, and functional
mobility.
smartphone app and specialty features, which are designed to facilitate older adults' PA by
targeting common barriers in this population. For example, one feature sends messages
throughout the day about the good things about growing older to combat negative views about
aging which has been linked to decreased PA. Participants will include older adult smartphone
users who are between the ages of 65 and 84 and are not very physically active. In phase one
of the study, three groups of five older adults will be formed to test the PA-tracking app
and one of three specialty features for a two-week period, followed by a focus group to learn
about the older adults' experiences. In phase two, approximately 100 participants will be
randomly assigned to one of eight groups that include various combinations of specialty
features with the PA tracker, for the purpose of pilot testing the app for a four-month
period. Testing will occur at the beginning and the end of the four-month intervention
period, and will measure PA levels, sedentary activity time, self-reported PA, and functional
mobility.
In this study, we will optimize a set of tailored specialty app features designed to be
paired with a physical activity (PA)-tracking app to boost older adults' PA. This package,
termed the MovingUp suite, is distinct from generic fitness apps because it blends a set of
specialized components that reflect empirically supported constructs from social cognitive
and stereotype embodiment theory with evidence-based behavior change techniques (e.g.,
self-regulation) foundational to basic activity monitoring. Specialty features include: (a)
explicit and implicit messaging to promote positive aging views; (b) sedentary activity
monitoring with motivational messaging and peer suggestions; and (c) tailored messaging to
increase the intensity level of everyday activities and overcome barriers. We will utilize a
highly efficient, innovative methodological approach—Multiphase Optimization Strategy
(MOST)—to provide an experimental context for evaluating the viability of each MovingUp
specialty feature.
Aim 1: Assess the feasibility and acceptability of the three MovingUp specialty features. We
will first examine MovingUp's feasibility and acceptability in three groups of five older
adults (aged 65-84 years). A basic PA-tracking app plus one of three specialty features will
be introduced—a different feature per group—at an orientation session. Groups will then test
their assigned specialty feature with the PA tracker for two weeks. This step will involve
real-time user data collection, check-ins via phone, and follow-up focus groups. Feasibility
and acceptability will be determined by analyzing participants' usage patterns, evaluations
of MovingUp features (based on a health technology usability scale and focus group
interviews), and self-reported facilitators and barriers to successful app use. Our team will
review the data and integrate changes as needed, producing an upgraded prototype to be
assessed in Aim 2.
Aim 2: Conduct a pilot test to examine performance characteristics and PA-relevant outcomes
of MovingUp's specialty features. Aim 2 includes the MOST Screening Phase: theory-guided
experimentation to identify viable components within a multifaceted preliminary intervention
plan. Using a factorial design as specified in MOST procedures, 100 underactive older adults
(i.e., accumulating <150 minutes of moderate intensity activity per week) will be randomly
assigned to one of eight conditions which reflect all possible combinations of presence vs.
absence of the three respective specialty features, given usage of a PA tracker app. At the
end of a four-month intervention period, for each specialty feature we will examine changes
from baseline in PA-related outcomes including: objective PA (primary outcome), sedentary
activity time, self-reported PA, and functional mobility. We will also examine the app
components' relationships to theoretically postulated mediating constructs (self-efficacy,
self-regulation, outcome expectation, social support, aging self-perception, and views of
aging). In addition, we will document usage rate, sustained usage, and perceived usefulness
for achieving PA goals for each suite component.
Aim 3: Synthesize information from Aim 2 to design an optimized MovingUp suite to be
evaluated in a future RCT. Our study team will interpret and synthesize the array of
resulting data to derive an optimized MovingUp suite. A set of pre-specified criteria will be
used to guide selection of components in the optimized app. Using preliminary efficacy data,
the stage will be set for a fully powered RCT of MovingUp's beneficial effects in comparison
to alternate technologies such as web-based or mHealth solutions.
This project will help establish a methodological foundation for future attempts to enhance
PA apps via the addition of theoretically based component features. Moreover, it will provide
insights into the theoretical underpinnings of successful PA interventions for older adults,
leading to information that transcends any single technology-based solution.
paired with a physical activity (PA)-tracking app to boost older adults' PA. This package,
termed the MovingUp suite, is distinct from generic fitness apps because it blends a set of
specialized components that reflect empirically supported constructs from social cognitive
and stereotype embodiment theory with evidence-based behavior change techniques (e.g.,
self-regulation) foundational to basic activity monitoring. Specialty features include: (a)
explicit and implicit messaging to promote positive aging views; (b) sedentary activity
monitoring with motivational messaging and peer suggestions; and (c) tailored messaging to
increase the intensity level of everyday activities and overcome barriers. We will utilize a
highly efficient, innovative methodological approach—Multiphase Optimization Strategy
(MOST)—to provide an experimental context for evaluating the viability of each MovingUp
specialty feature.
Aim 1: Assess the feasibility and acceptability of the three MovingUp specialty features. We
will first examine MovingUp's feasibility and acceptability in three groups of five older
adults (aged 65-84 years). A basic PA-tracking app plus one of three specialty features will
be introduced—a different feature per group—at an orientation session. Groups will then test
their assigned specialty feature with the PA tracker for two weeks. This step will involve
real-time user data collection, check-ins via phone, and follow-up focus groups. Feasibility
and acceptability will be determined by analyzing participants' usage patterns, evaluations
of MovingUp features (based on a health technology usability scale and focus group
interviews), and self-reported facilitators and barriers to successful app use. Our team will
review the data and integrate changes as needed, producing an upgraded prototype to be
assessed in Aim 2.
Aim 2: Conduct a pilot test to examine performance characteristics and PA-relevant outcomes
of MovingUp's specialty features. Aim 2 includes the MOST Screening Phase: theory-guided
experimentation to identify viable components within a multifaceted preliminary intervention
plan. Using a factorial design as specified in MOST procedures, 100 underactive older adults
(i.e., accumulating <150 minutes of moderate intensity activity per week) will be randomly
assigned to one of eight conditions which reflect all possible combinations of presence vs.
absence of the three respective specialty features, given usage of a PA tracker app. At the
end of a four-month intervention period, for each specialty feature we will examine changes
from baseline in PA-related outcomes including: objective PA (primary outcome), sedentary
activity time, self-reported PA, and functional mobility. We will also examine the app
components' relationships to theoretically postulated mediating constructs (self-efficacy,
self-regulation, outcome expectation, social support, aging self-perception, and views of
aging). In addition, we will document usage rate, sustained usage, and perceived usefulness
for achieving PA goals for each suite component.
Aim 3: Synthesize information from Aim 2 to design an optimized MovingUp suite to be
evaluated in a future RCT. Our study team will interpret and synthesize the array of
resulting data to derive an optimized MovingUp suite. A set of pre-specified criteria will be
used to guide selection of components in the optimized app. Using preliminary efficacy data,
the stage will be set for a fully powered RCT of MovingUp's beneficial effects in comparison
to alternate technologies such as web-based or mHealth solutions.
This project will help establish a methodological foundation for future attempts to enhance
PA apps via the addition of theoretically based component features. Moreover, it will provide
insights into the theoretical underpinnings of successful PA interventions for older adults,
leading to information that transcends any single technology-based solution.
Inclusion Criteria:
- 65-84 years old
- English speaking
- reside in Los Angeles
- score ≥5 on a 6-item cognitive screener
- report <150 minutes of moderate to vigorous PA/week as per a single-item screener
- ambulatory
- able to safely participate in physical activity as determined by the Revised Physical
Activity Readiness Questionnaire (rPARQ) or proof of medical clearance from a
physician
- smartphone owner for ≥3 months
- observed ability to reliably access and operate a smartphone during orientation.
Exclusion Criteria:
- ≥85 years old, based on limited smartphone ownership and to reduce sample variability
We found this trial at
2
sites
Los Angeles, California 90033
213) 740-2311
Principal Investigator: Stacey Schepens Niemiec, PhD, OTR/L
Phone: 323-442-2069
University of Southern California The University of Southern California is one of the world’s leading...
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800 North Brand Boulevard
Glendale, California 91203
Glendale, California 91203
Phone: 818-254-4274
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