1. Relationship between Physical Activity and Well-Being1Katherine Ladwig, 2Maureen Schmitter-Edgecombe, 1,3David Lin
1Voiland School of Chemical Engineering and Bioengineering, 2Department of Psychology,
3Department of Integrative Physiology and Neuroscience
Washington State University, Pullman, WA USA
Introduction
• Higher activity levels generally correspond to higher
levels of positive affect in the young population [1].
• Wristwatch-like accelerometers offer a non-invasive
method to monitor daily activity levels and patterns.
• People generally report the most accurate self
assessments in the moment, making data collection by
phone a desirable option [2].
Goals
• Find correlations between physical activity and self-
assessments of well-being in healthy and unhealthy
older adults.
• Long term: Increase awareness of physical and mental
health as well as assist in diagnosis and treatment.
Hypothesis
Activity data can be used to monitor the well-being of
older adults in their home environment, which can
ultimately help improve quality of life.
Methods and Results
Data Collection
• Participants wore an accelerometer (Mini Motion
Logger, Ambulatory Monitoring, Inc.) for one week.
• Automated phone interviews were conducted four
times a day and responses to 12 questions about
mood and activity were entered on a Likert scale.
Population Summary
• 50 adults between 50 and 90 years old.
• Most cognitively healthy, some had varying degrees of
cognitive deficit.
Data Analysis
• MATLAB and Microsoft Excel were used to view the
raw data and perform the analysis.
• Questions analyzed (Responses: 1 = very poor / not at all /
none, 5 = very good / very much):
• Q3: Your general mood is currently…?
• Q8: In the past two hours, how much social contact have
you had?
• Q9: In the past two hours, how physically active have
you been?
• Q10: In the past two hours, how mentally engaged have
you been?
• Only participants with enough responses and variability to
establish correlations were included in further analysis (Q3:
n = 9, Q8: n = 18, Q9: n = 16, Q10: n = 18).
• The average activity level during a fixed time interval (Q3:
30 min, Q8, 9, & 10: 120 min) before and after each
response was calculated.
• The slope (activity counts per Likert response level) and
correlation coefficient were calculated for each question
and participant.
• To test whether cognitive health had an effect of the
relationship between activity and well-being, the
correlation coefficients between activity and well-being
assessment were compared for two groups based upon
cognitive health with an unpaired t-test.
• Only question 3 had a significant difference between the
two groups. (Q3: P = .034, Q8, 9, & 10: P > .25)
Discussion
• One major challenge was finding sufficient data due to a
lack of variability within each participant.
• As predicted, mood, social contact, and cognitive
engagement were all positively correlated with activity,
but due to the variability in the population the statistical
significance was often not met.
• We expected participants who were cognitively healthy to
have a stronger correlation between mood and activity,
but this was only statistically significant for question 3.
Future Work
• With more participants, the effects cognitive deficits on
correlations between mood and activity could be further
explored. This could help identify when a person is
transitioning from cognitively healthy to unhealthy.
• The effects of sleep length and quality could be included.
• Data collected by accelerometers worn on the wrist (the
standard for activity data collection) can be compared to
data collected by a smart home to verify results.
References
[1] Schwerdtfeger et al., J Sport & Exer Psych, 2010
[2] Shiffman et al. Ann Rev Clin Psych, 2008
Acknowledgements
Auvil Fellowship, Carolyn Parsey
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Participant 75 Responses to Question 10
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4 Participant 75 Accelerometer Data
time (minutes)
Counts/Minute(30MinurteFrames)
Figure 2: Raw mood data.
The histogram shows all of the
phone responses that
participant 75 made to
question 10. Negative 1
indicates that the participant
skipped that question, which
was one challenge that we
faced when analyzing the
data.
Figure 3: Correlation
between activity level and
cognitive engagement
(Q10). Most participants
had higher activity levels
when they were reporting
more cognitive engagement.
Each blue point represents
the average activity counts
per minute over the 120
minutes before they
answered the question. The
green stars show the
average of the individual
points for each Likert
response level.
Figure 1: Raw activity
data. Activity data were
recorded as counts/min
and were filtered by a
30 minute moving
average to make the
trends more visible.
The sleep-wake cycles
are clearly visible over
the seven day
collection period.
Figure 4: Comparison
between healthy and
unhealthy individuals.
We hypothesized that
healthy individuals
would have stronger
correlations between
activity and well-being.
The average correlation
coefficients for each
were statistically
significant for Q3.
-1 0 1 2 3 4 5
Likert Response Level (-1 indicates Skipped)
Frequency
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5
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1
0