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.

HypothesisActivity 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

AcknowledgementsAuvil Fellowship, Carolyn Parsey

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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.

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