EXERCISE AND QUALITY OF LIFE
Research article
Volume 3, No. 1, 2011, 11-21
UDC 616-057.875:796.035]:004.9
AN AUTOMATED IDENTIFICATION OF INDIVIDUALS AT
HEALTH RISK BASED ON DEMOGRAPHIC
CHARACTERISTICS AND SELF-REPORTED PERCEPTIONS
Dejan Magoc
Department of Health Studies
Eastern Illinois University
Tanja Magoc, Ph.D.
Center for Bioinformatics and Computational Biology
University of Maryland
Joe Tomaka, Ph.D.
Department of Public Health
University of Texas at El Paso
Abstract
The risks of developing diabetes, high blood pressure, and cardiovascular disease could
be reduced by increasing the number of individuals receiving adequate levels of physical activity
(PA). Centers for Diseases Control and Prevention (CDC) has reported that about 30% of
Americans do not engage in any PA and about 40% engage in some levels of PA, but still not
meeting the recommended levels defined by the American College of Sports Medicine (ACSM).
Studies have shown that the greatest declines in PA occur during the transitions from high school
to college and beyond. Thus, it is important to identify students at young age that are at health
risk due to lack of PA, so that specific steps could be taken toward helping these individuals
develop a healthier lifestyle. We used data on 100 college students to develop a preliminary
computer program
(using a backpropagation multilayer neural network approach) to
automatically identify individuals at risk of being not sufficiently physically active. Besides
various types of demographic variables, data included information on the association between
studentsí self-reported levels of PA and Social Cognitive Theory (SCT) constructs (e.g., self-
efficacy, self-regulation, social support, expectations), as predictors of participation in PA. The
results of this study indicated that the backpropagation multilayer neural network identified and
classified individuals at risk of being not sufficiently physically active into right categories (at-
risk individuals or not at-risk individuals) 77% of the time. Collecting additional data points that
contain more at-risk individuals will improve the neural network's prediction of at-risk
individuals.
Keywords: automated identification, physical activity, college students
Corresponding author. Department of Health Studies, Eastern Illinois University, Lantz Charleston, IL 61920, e-
mail: dejanmagoc@yahoo.com
© 2010 Faculty of Sport and Physical Education, University of Novi Sad, Serbia
D. Magoc et al.
Introduction
Lack of physical activity (PA) in general population has become a major public health
concern (Petosa, Suminski, & Hortz, 2003). Even though a relatively large number of people
report participating in some PA, majority of population is not sufficiently physically active to
prevent diseases such as hypertension, diabetes, and cardiovascular disease. According to the
American Heart Association (AHA) and the American College of Sports Medicine (ACSM), at
least 30 minutes of moderate PA (e.g., walking) five days per week or 20 minutes of vigorous
PA (e.g., running) three days per week is required to keep a healthy living style (Haskell et al.,
2007). However, studies show that only about
30% of Americans satisfy the minimum exercise
requirements and another 30% does not exercise at all (CDC, 2005).
Research has also shown that levels of PA dramatrically decrease from high school to
college years and beyond (Rovniak, Eileen, & Winett, 2002). Thus, it is of high importance to
motivate college students to engage in regular physical activity. Unfortunately, many PA events
and promotions on college campuses, such as intramural leagues and sport-specific clubs, tend to
attract students who are already physically active. In order to attract physically inactive or not
sufficiently active students, it is important to identify these individuals quickly and efficiently.
After identifying students at health risk due to physical inactivity, such students could be
approached by targeted interventions.
Studies have shown consistently correlations between PA levels and demographic
characteristics, such as race and gender (Pratt, Macera, & Blanton, 1999; Mouton, Calmbach, &
Dhanda, 2000; Dunn & Wang, 2003). Research has also shown that other factors, such as self-
motivation
, previous physical activity engagement, perception of importance to exercise,
perception of current physical and psychological health and support from friends and family to
exercise are highly correlated to the amount of PA an individual performs (Pratt et al., 1999;
Magoc, Tomaka, & Thompson, 2010). These factors could be used to identify individuals at risk
of being physically inactive at early stage, which could prevent students from becoming
sedentary.
Although such data could be assessed relatively quickly, the review and evaluation
process for each individual would take considerable time and effort to perform. Instead of
manually examining all responses, we propose a system of computerized questionnaires with
automatic and practically instantaneous analysis and presentation of results to immediately
identify students at risk of not being insufficiently physically active to prevent negative health
outcomes.
This paper presents the preliminary results regarding the application of one such tool.
Specfically, using data collected from 100 college students about their weekly physical activities
as well as their demographic characteristics, the machine learning algorithm (see the section on
machine learning) was used to build a model that predict whether an individual is likely to be at
risk of being physically inactive. The predicted output for a particular individual was compared
to the weekly physical activity information entered by that individual to assess the accuracy of
the prediction.
Machine Learning
Machine learning (see Russell & Norvig, 2010; Tan, Steinbach, & Kumar, 2006) is a
branch of computer science that aims at developing computer programs that simulate human
reasoning and can therefore ìreplaceî humans in numerous tasks including data analysis and
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Automated identification of individuals at health risk
decision making. A major focus of machine learning is to automatically learn to recognize
patterns and infer relationships among different variables. Based on the learned relationships,
these computer programs are able to make decisions that are equivalent to decisions that would
be made by humans in a given situation.
A machine learning algorithm consists of two phases: the training phase and the
application phase. In the training phase, the algorithm learns patterns and relationships in a given
data set, while in the application phase, a decision for a new instance (e.g., a new individual) is
made.
The training phase could be performed in a supervised or an unsupervised mode. In either
option, a data set is provided for training. A data set consists of numerous (tens to thousands)
data points. Each data point corresponds to one instance and contains a value for each variable
used to make the final decision. In the supervised learning, the final decision is also provided in
the training sample. Thus, for supervised learning, we need to know the correct decision in all
training data points. The known decisions are used to reduce the error in the machine learning
algorithm by aiding the algorithm to learn patterns and relationships that yield particular
decision. On the other hand, the unsupervised learning does not require knowledge of correct
decisions.
One of the main applications of machine learning is in classification problems. Given an
instance, a machine learning program classifies this instance in one of several possible groups.
An instance that is being classified consists of a value for each variable used to make the
decision, but the correct class is unknown. The classification is based on the patterns and
relationships previously observed in the training phase.
To test the accuracy of a machine learning algorithm, the algorithm is usually trained on
a data set and tested on a different set of data, both of which have known classes for each data
point, to avoid bias that would result in the algorithm performing well only on the data set used
for training. One common method to split available data points in the training and testing data
sets is to randomly split all data points into five groups, and perform 5-fold cross validation. This
method uses all possible combinations of four out of five created sets for training and one set for
testing. Thus, five tests are performed, one for each of five sets to be the testing set, and the
results of the five tests are combined to determine the accuracy of the algorithm.
Neural Network
A neural network (NN) is a type of a machine learning algorithm that is designed to
imitate the actions of the human neural system (see Russell & Norvig, 2010; Tan et al., 2006). A
NN is represented as a directed weighted graph where nodes simulate human neural cells
(neurons), and directed edges simulate the links between neurons (axons). The strength of the
signal transferred between neurons determines the action of a human. This signal strength is
simulated by the weights on the edges in an NN (Figure 1). The basic task of a NN is to learn
these weights in order to yield accurate results when applied to real life data.
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D. Magoc et al.
Figure1. A simple Neural Network with Three Input Nodes (x1,x2,x3) and One Output Node (y)
The simplest NN is called the single-layer NN and consists of only input and output
layers of nodes. The inputs nodes take the values of variables used to make a decision, and the
output node contains the decision. The decision is made by combining the values of input nodes
and the weights on the edges.
Each data point from the training data set is processed by the NN, one by one, and after
each data point is processed, the error is calculated and the necessary adjustments to weights are
made. When all data points are processed, the same process is repeated over and over until a NN,
with a satisfying (i.e., very low) error in classification, is built or until a predefined number of
iterations is reached (usually several hundred iterations).
Single-layer neural networks are good classifiers in simple cases. However, more
complex multilayer networks are much more powerful than neural networks that contain only
input and output layers. Multilayer neural networks contain one or more hidden layers (Figure
2).
Figure 2. A Fully Connected Multilayer Neural Network
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Automated identification of individuals at health risk
Similarly to a single-layer NN, a multi-layer NN takes values of variables of a data point
as inputs (i.e., the input layer), aggregates these values by calculating the weighted sum, and
applies a function, such as sign or sigmoid function, to produce the values of the next level of
nodes (i.e., the hidden layer). Furthermore, the hidden layer of nodes acts as the input layer for
the next level of nodes (i.e., a next hidden layer or the output layer) until the values of the output
nodes are calculated. In a multi-layer network, the links between nodes can go either from a
lower layer to a higher layer (input being the lowest layer and output the highest layer), which is
the case in feed-forward networks, or can be directed from a node to a node at a higher, same, or
lower level, which is the case in recurrent networks.
Similarly to a single-layer NN, a multi-layer NN learning algorithm works by minimizing
the error. The error could be contributed equally to each node or a backpropagation algorithm
could be used to more precisely determine the impact of each node to misclassification and
therefore allow different levels of weights adjustment on edges.
Neural Network to Identify Individuals at Health Risk
Currently available data obtained in numerous studies about PA and the factors that
influence the level of an individual's PA are characterized by still unknown probability
distributions, not precisely known dependencies among different data variables, and unclear
level to which each variable impacts an individual's readiness and commitment to exercise. Due
to all uncertainties about the relationships between PA and features highly correlated with the
level of PA, machine learning is well suited approach to capture important relationships among
features present in the existing data, which are then used to identify individuals at health risk
using only a few demographics and self-reported characteristics of individuals.
Method
Participants and Setting
The participants in this study were 100 part- or full-time currently enrolled male and
female students from a large southwestern university in the U.S. with a large Hispanic
enrollment. All participants were recruited through classroom settings, and all completed the
cross-sectional survey.
Measures
Demographic variables - included self-reported gender, race/ethnicity, class, height and
weight. In addition, participants self-reported perception of their current physical and
psychological health.
International Physical Activity Questionnaire (IPAQ; Booth, 2000) - a self-reporting
measurement of the level of PA the individual performed during the last seven days. It asked
participants to record the number of sessions and the average duration of an exercise session for
vigorous and moderate activities.
Self-Efficacy for Exercise Behavior Scale
(Sallis, Pinski, Grossman, Patterson, &
Nader,1988) - consisted of 12 questions measuring the individual's readiness to overcome
obstacles (e.g., tiredness, large amount of work, not accomplishing set physical activity goals) in
order to exercise. Moreover, the participants reported the importance of setting aside time for
exercise in their schedules and following the set goals.
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D. Magoc et al.
The Family and Friend Support for Exercise Habits Scales (Sallis, Grossman, Pinski,
Patterson, & Nader, 1987) - measured the support and motivation to exercise that participants
received from family and friends, including exercising together with another individual,
receiving reminders from an individual to exercise, or having a discussion about exercising.
Procedures
Participants for this study were largely recruited through regular classroom meetings and
activities, with most receiving extra course credit for participation. All participants completed
informed consent forms prior to completing the questionnaires. It took approximately 20
minutes for participants to complete the questionnaire.
Results
Descriptive Analysis
A slightly more females (59%) participated in the study than males. The majority of
participants were Hispanics
(82%). Because of the high proportion of participants being
Hispanic, for the purpose of the study, all participants were classified either as Hispanic or non-
Hispanic, therefore, not making a distinction among non-Hispanic participants, which included
Caucasians, African Americans, Native Hawaiians, American Indians, and Asians.
Most participants recorded their major being health or sports related studies (73%). The
majority of participants self-reported their physical health to be good or fair (48% and 31%,
respectively) and only 13% self-reported their physical health to be excellent. Majority of the
participants rated their psychological health as good or excellent (54% and 23%, respectively).
Majority of participants recorded at least a medium level for readiness to overcome
obstacles in order to exercise as well as for motivation and support received from friends and
family members. In addition, 21% of the sample self-reported the low importance to exercise.
Almost half of the participants (41%) failed to meet the recommended levels of PA, with
a higher percentage of females being not sufficiently physically active than males (30% and
11%, respectively). In addition, 56% of the sample was overweight, including 26% of those
being classified as obese.
The collected information was used to build an automated predictor of students at risk of
being not sufficiently physically active by applying a machine learning algorithm.
Primary Analysis
Using the information collected from 100 students, we trained a backpropagation NN
with eight input variables, one hidden layer with 13 nodes, and the output layer with two nodes.
The input variables included the following:
Gender: this variable could take two values {male, female}.
Hispanic: this variable could take two values {yes, no} describing whether the individual
is Hispanic or not, respectively. This distinction was made since studies have shown that
Hispanic population exhibits different attitudes towards physical activities from those
exhibited by, for example, Caucasian people (Pratt, 1999). Since not enough data were
available to train the NN on different ethnicities among non-Hispanics, further distinction
among ethnicities was not included.
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Automated identification of individuals at health risk
Major: this variable could take two values
{sport related, not sport related}. This
distinction was made because it is expected that students majoring in sport or health
related studies are more likely to be aware of PA importance and therefore exercise more
than their peers who major in other disciplines.
Physical health: this variable was a self-reported individualís perception, and could take
any of the five values {excellent, good, fair, poor, very poor}.
Psychological health: this variable was a self-reported individualís perception, and could
take any of the five values {excellent, good, fair, poor, very poor}.
Self-efficacy: this variable is a summary of an individual's answers to the 12 questions on
the self-efficacy scale assessment. Since each question allowed participants to express the
level of self-efficacy in the range 1-5 (1 meaning 'low' and 5 meaning 'high'), the values
were averaged, and the score above 4.00 was reported as îreally high'', the score between
3.00 and 4.00 as ìhigh'', etc. The self-efficacy variable could take one of five values
{very high, high, medium, low, very low}.
Importance of exercise: this variable represented how important it was for an individual
to make time for exercise in his/her schedule and to accomplish the scheduled PA goals.
The variable could take one of five values {very high, high, medium, low, very low}.
Support: this variable is a summary of the individual's answers to the exercise habits
scale assessment. It was created similarly to the self-efficacy variable and could take one
of the five values {very high, high, medium, low, very low}.
The output layer of the NN consisted of two nodes {risk, no risk}. Only one of these two
nodes is on as a result of applying the NN to data collected from a new individual. Depending on
which node is on, the person is classified to be or not to be at health risk based on the self-
reported characteristics.
A neural network was trained using free software package weka (Hall, Frank, Holmes,
Pfahringer, Reutemann, & Witten, 2009). Since all collected data was represented as non-
numeric data to weka, each variable that could take more than two values was represented by
multiple input nodes, one for each value the variable could take. For example, the variable
importance of exercise could take one of five values (very high, high, medium, low, and very
low), thus five input nodes were designed for this particular variable. However, even though
some variables could take one of five values, not all five values have shown up in the collected
data sample, and thus, less nodes were used to represent such a variable. For example, no one
reported his/her physical health to be very poor, thus the physical health variable used only four
nodes in the developed NN. Variables with multiple nodes, such as physical health and
importance of exercise, would have only one of their nodes set on in each training or testing
sample.
Furthermore, if a variable could take exactly two values, this variable was represented by
only one node. This node was either on or off, representing two different values that the variable
could take. With this representation, the input layer of the NN developed for the collected data
sample consists of 25 nodes.
The hidden layer was automatically generated by weka. By default, weka generates
(number of attributes + number of classes)/2 nodes in the hidden layer, which resulted in (25
input nodes + 2 output nodes)/2=13.5 for our data sample, so 13 nodes were generated in the
hidden layer.
A fully connected NN (i.e., a NN where each node from a previous layer is connected to
each node of the next layer (see Figure 2) was trained using a backpropagation algorithm.
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D. Magoc et al.
The learning rate in the NN was initially set at 0.2 and decreased throughout training
cycles. Total of 500 iterations were performed to train this NN, which took 0.47 seconds for the
given data set.
To validate the accuracy of the developed NN, we performed 5-fold cross-validation with
80 data points used for training and 20 data points for testing. Combining results of all five runs
in cross-validation, the NN classified correctly 77% of test data. Table 1 shows the classification
of individuals into ìat riskî and ìnot at riskî categories. Out of 41 individuals who should have
been classified ìat riskî, 18 individuals were classified correctly. Similarly, out of 59 individuals
who should have been classified ìnot at riskî, only one individual was classified incorrectly.
Table 1
Classification of Individuals ìat riskî and ìnot at riskî Categories
Individuals classified
Individuals classified
ìat riskî
ìNOT at riskî
Individuals
18
23
ìat riskî
Individuals
1
58
ìNOT at riskî
Discussion
Even though there are two possible types for misclassification (i.e., an at-risk individual
classified as not at-risk, and a not at-risk individual classified as at-risk individual), most
misclassified data points corresponded to individuals that should have been classified as
individuals at risk, but were classified as not being at risk. This is encouraging for two reasons.
First, data in this study were collected from somewhat not traditional group of individuals (more
individuals in this group met the minimum physical activity requirements, which is usually not
the case). Thus, this particular data sample might not contain enough data points to train the NN
to correctly identify all at-risk individuals. We suspect that some training samples in the 5-fold
cross validation did not contain enough at-risk individuals to train the NN. We expect that
collecting additional data points that contain more at-risk individuals will improve the NN's
prediction of at-risk individuals.
Second, the current NN is able to identify almost perfectly the individuals not at risk.
Recall that the main reason for developing this automatic identification of at-risk individuals is
to target at-risk individuals by physical activities on college campuses. While the current NN
does not identify all at-risk individuals and will not contribute to targeting all at-risk individuals,
it will ensure that physically active individuals are not targeted and therefore, no resources will
be ìwasted'' on not at-risk individuals. Here, by resources, we consider items such as gift
certificates for participations in PA studies, individual attention by personal trainers conducting a
research, time spent on providing health and PA seminars, promotional offers through gym
memberships for inactive students, etc., which are often taken by individuals who do not clearly
satisfy the requirements of participation such studies, but rather take the advantage of
promotional offers to continue doing what they have already been doing. Thus, even though not
all inactive students would be reached by the currently developed NN, it is a starting point to
increase PA participation of students at health risk.
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Automated identification of individuals at health risk
Expected Improvements in Neural Network Predictions
Even though the current NN predictions are acceptable and will allow college campuses
to target a large amount of students at health risk due to physical inactivity, further
improvements are possible. We are currently collecting more data from students at different
collegiate institutions. The following factors are expected to contribute towards better NN
training, and therefore an improved prediction rate.
First, a larger amount of data allows easier detection of patterns and relationships among
variables in a NN. Thus, collecting more data will allow for better training of the NN.
Second, a majority of the current data sample is of Hispanic origin. All the other
ethnicities are classified as non-Hispanics even though there are differences in PA attitudes
among, for example, Caucasian and African American individuals. However, currently these two
ethnic groups are considered as the same group. The newly collected data will add to wider
variety of demographic characteristics, which would aid predictions in the subgroups that are not
adequately represented in our current sample.
Third, we believe that students who do not major in health or sport related studies are
more likely to fall into the at-risk category than students majoring in health and sports studies.
Since majority of the current sample contained students that are not expected to be at health risk,
adding data points from students that are at risk will help train NN. The newly collected data will
contain more information from students at risk, and will allow a better training of NN in at risk
cases.
Fourth, a more objective assessment of the physical and psychological health of each
individual is preferable. The current sample relies on self-evaluation of one's health. However,
since not every person has same goals, the reported physical and psychological health might not
be consistent. For example, if a person weighted 220lbs a year ago, and currently weights 200lbs,
this person might feel good about his/her physical health even though this person might still be
overweight. While reports of this type are not expected to happen often and should not
drastically harm machine learning when a large amount of data is available, this type of report
might be harmful in our relatively small sample. A more objective assessment of the PA could be
obtained by combining facts such as the height, weight, and body mass index with the
individual's subjective perception.
Finally, a more objective evaluation of physical activities is needed in order to correctly
determine whether each person satisfies the minimum PA requirements as set by AHA and
ACSM. The current classification is obtained based on an individual's PA within the last seven
days and his/her subjective perception on the intensity of the activity (i.e., whether an exercise is
moderate or vigorous intensity). For example, while running at 6 mph might be considered as
vigorous PA by a person who does not exercise often, it would not be considered vigorous
intensity by an individual who satisfies the minimum PA requirements. Thus, collecting facts
(e.g., the length, time, and incline level of a run) along with a person's perception of the intensity
would provide a more objective evaluation of physical intensity of an activity. Moreover,
collecting the information over at least a few weeks would show the consistency of the exercise
rather than relying on how physically active a person was in only seven days since inactive
persons generally greatly fluctuate in the weekly amount of exercise.
Recommendations for Future Research
The majority of general population is prone to health diseases that could be easily
prevented by regular PA. While most people are aware that PA is important for their health,
many individuals are not aware of how much PA is necessary to keep their bodies in good
health. People live with the idea that some exercise is better than none (and are therefore
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D. Magoc et al.
satisfied by finding time for little exercise), but only a small percent of individuals is aware that
they do not exercise enough to reduce the chance of diseases, such as heart attack and high blood
pressure.
The amount of PA drastically drops from high school to college and beyond. Therefore, it
is of high importance to target the first year college students in promoting PA and spreading
awareness of its importance. Since physically active students are the ones who usually respond to
PA promotions on collegiate campuses, it is important to identify students who are under the risk
of inactivity and target these particular individuals in PA studies and promotion programs.
With larger amount of data, we plan to enhance the classifier to allow three output classes
(sufficiently physically active, not sufficiently physically active, and not physically active at all)
rather than only two classes as presented in this study. The first class would include individuals
that are clearly physically active on regular bases and are reaching the minimum PA
requirements. The second class would include individuals that are physically active to certain
extend, but not enough to meet the minimum PA requirements, and are therefore under the health
risk. The third class would include individuals that are not physically active at all, and are
therefore under a great health risk. Even though the last two groups both contain individuals
under health risk, the risks are at very different levels, and the individuals belonging to these two
classes certainly need different care and motivation to increase their physical activities.
Once more data are collected and more diverse sample of population is reached, we will
train a final NN and develop a web-based tool to administer the questionnaire and immediately
identify individuals at risk of being not physically active enough. This program will be easily
accessible to everyone in order to improve well being of general population.
The developed method predicted accurately 77% of time. Even though the results are not
100% perfect, they show a great potential for quick identification of individuals at health risk
due to physical inactivity. Collecting a larger amount of data to use in the machine learning
approach as well as collecting data from wider variety of collegiate population will improve
already promising results of the proposed approach.
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Automated identification of individuals at health risk
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Submitted 22 April, 2011
Accepted 15 June, 2011
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