An Integrated Neural Network and Structural Equation Modeling

Approach for Modeling Activity Trackers Use

Ricardo Sol

and Karolina Baras

Exact Sciences and Engineering, University of Madeira, Madeira, Portugal

Keywords: Artificial Neural Networks (ANN), Ubiquitous Systems, Personal Informatics, Personal Data Tracking,

Sports/Exercise, Technology Acceptance Model (TAM), Embodied Interaction.

Abstract: The objective of this study is to enhance a Technology Acceptance Model (TAM) with an Artificial Neural

Network (ANN) approach in order to obtain substantially accurate results when compared to Structural

Equation Modeling (SEM). This study looked at another paper that created a TAM dedicated to activity

trackers (AT) obtained via SEM from a questionnaire to 247 participants. This study uses the constructs of

that paper in an ANN as the input units and the Root Mean Square of Errors to indicate that the ANN method

achieves high prediction accuracy. The results provide conclusive evidence that Perceived Usefulness is the

most significant factor affecting AT acceptance. Perceived Ease of Use and Image affect acceptance, however

their impact is much lower. Hedonic Motivation and Habit were found to have a significant relationship with

TAM while Self-Efficacy showed mixed results. This confirmation can be useful for future designs of activity

trackers.

1 INTRODUCTION

Wearable devices such as activity trackers have

become important in monitoring health behavior, for

recreation, and socialization, and thus are a viable and

significant research topic in Human Computer

Interaction. Confirming this trend is the International

Data Corporation in a press release, stating that

worldwide shipments of wearables grew 9.9%

throughout the third quarter of 2021 reaching 138.4

million units (IDC 2021). The improvement and the

commercialization of activity trackers have helped

many users to reach the recommended goal of ten

thousand steps per day in order to maintain or

improve their health (Akers 2012). However, a study

on the acceptance of a particular activity tracker

device discovered that half of the users stop using the

device after two weeks (Shih 2015).

One possibility, to help solve design issues that

lead to loss of interest or decrease od device usage, is

the use of models. Even though some researchers

think of them as excessively theoretical. In fact,

researchers working with interfaces who had often

been skeptical, started to acknowledge that models

https://orcid.org/0000-0003-4333-7140

https://orcid.org/0000-0002-2050-6565

could be helpful in the design of interfaces (Myers

2000). Since Li et al.’s seminal work, researchers

have been trying to describe the use of trackers

through a model (Li 2010). Li et al. presented a model

with five iterative stages: preparation, collection,

integration, reflection, and action; later the model was

refined by these authors. Also, Epstein et al. looked at

that model and expanded on it by including the lapses

and interruptions of tracking, and highlighting the

intricacy of integration, collection and reflection

(Epstein 2015). Narrowing the scrutiny, Sol and

Baras obtained a model dedicated to activity trackers

use (Sol 2016) that is used in this paper. The most

important advantage of this model is that it is

quantitative. It was obtained by expanding the

Technology Acceptance Model (TAM), with health

oriented, data control, and other constructs. TAM

assumes that user acceptance can be described by two

ideas: Perceived Usefulness (PU), and Perceived Ease

of Use (PEoU) which determine Intention to Use

through Attitude (Davis 1989).

A TAM based model for activity trackers, as most

of the research on technology acceptance models is

done simply with Structural Equation Modeling

Sol, R. and Baras, K.

An Integrated Neural Network and Structural Equation Modeling Approach for Modeling Activity Trackers Use.

DOI: 10.5220/0011526600003323

In Proceedings of the 6th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2022), pages 49-58

ISBN: 978-989-758-609-5; ISSN: 2184-3244

(SEM) methods, or other models, for example, the

Ubiquitous Computing Acceptance Model

(Spiekermann 2007), and the Health Information

Technology Acceptance Model (Kim 2013). SEM is

a sophisticated multivariate technique that can be

used to scrutinize multiple dependence associations

between variables simultaneously. It is useful for

hypothesis specification and testing, can suggest

novel hypotheses that were not considered initially.

Nevertheless, SEM may lead frequently to an

oversimplification of the complexities involved as it

is simply detecting linear relationships (Ringle 2012).

To address this issue, undertaking a second step using

an Artificial Neural Network (ANN) allows for

further scrutiny and examination.

An ANN is “a massively parallel distributed

processor made up of simple processing units, which

have a neural propensity for storing experimental

knowledge and making it available for use” (Haykin

2004). Contrary to SEM an ANN is not suitable for

hypotheses testing, but further to linear relationships

can also deal with non-liner relationships. Moreover,

an ANN has the capability to assess non-

compensatory processes (Svozil 1997). Additionally,

an ANN is more robust and can offer greater

prediction accuracy than linear models (Tan 2014).

This study uses the constructs of the TAM based

model for activity trackers as the input units of an

ANN in order to obtain a more accurate view of the

acceptance and use of these devices.

In the following sections, we contextualize the study

with the literature review, and present the model.

Next, we describe the methodology, and discuss the

results of ANN analysis. Finally, we conclude, and

envision the possibility of future research.

2 LITERATURE REVIEW

In this section we primarily review the literature

related to activity trackers. We also look into

Artificial Neural Networks research to understand its

relations, importance and classifications. The

acceptance and use of activity trackers is due to many

reasons and motives, several of which seem to clash.

Users may begin tracking their activity since they

have a certain goal in mind (Epstein 2015). Still, there

are users who start to use activity trackers with no

goal in mind and use the device to help them set an

objective. This objective becomes clearer as the usage

changes after transitioning from the discovery phase

to the maintenance phase of pondering (Li 2010).

Other users start tracking merely moved by concern

and curiosity of the quantitative data (Lindqvist

2011). Nonetheless, goal seating is just one notion to

help and persuade health-related behavior change.

For example, when the user wants to implement a

habit in daily life, one of the best ways is for the

activity tracker to help implementing routines (Lazar

2015).

An egocentric perspective for these devices

(Elsden 2015) can be looked at as a form of hedonic

motivation, as is individual encouragement (Patel

2015), the acknowledgement of effort (Kim 2016),

and giving credit (Consolvo 2006).

When looking at image one can look into the lifestyle

of the user (Consolvo 2006) or to the aesthetics and

form of the devices (Harrison 2015). Other notions

embrace social comparison (Harrison 2015), social

competition and collaboration (Patel 2015).

Users manage to change their goals, habits, and

devices; however, the applications or dashboards are

ill equipped to allocate this change. For tracking,

users tend to change devices often or even use several

devices in parallel, which leads to complications in

measuring and associating data (Rooksby 2014). This

issue has many impacts as it increases the difficulty

to provide a tailored efficacy evaluation (Klasnja

2011) that is important for the users’ self-efficacy.

When one approaches to data control the

information that activity trackers are gathering can be

extremely sensitive (Lupton 2017) and the risk of

third-party recording is real (Elsden 2015).

When looking at how to improve the usefulness of

activity trackers different researchers produced

several design ideas, one of which was the facilitating

of micro-plans (Gouveia 2018), another was to give

meaningfulness in context (Rooksby 2014), yet

another was to provide a wide variety of adjustable

goals (Clawson 2015), or to appeal to identity (Lazar

2015), and another was the idea of adherence (Tang

2018).

The idea that the devices have to “speak“ the

language of the users (Lazar 2015) because users are

not data scientists (Rooksby 2014), and the need for

devices to remind them (Shih 2015) are ways to

improve the usability of activity trackers.

Nevertheless, these devices are still being used in a

rather limited manner (Didziokaite 2017).

Numerous statistical techniques are parametric,

such as SEM and Multiple Regression Analysis

(MRA), requiring a great statistic background, while

artificial neural networks are non-parametric models,

which can provide higher prediction accuracy (Tan

2014). An ANN uses a considerable interconnection

of simple computing units called neurons or nodes as

inputs, hidden, and outputs layers with connection

values called synaptic weights that are adaptable via

CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications

an iterative process. A classic ANN consists of

several layers: one input layer, one or more hidden

layers and one output layer (Negnevitsky 2011). In

ANNs for technology acceptance typically only one

hidden layer is used (Tan 2014). There are several

types of ANNs, but the most common is feed forward

back propagation multilayer perceptron (BPFF). In

this kind of network, belonging to supervised learning

ANNs, the knowledge stored in the network by

iteratively subjecting it to patterns of known inputs

and outputs (Negnevitsky 2011). The difference

between desired and actual output, is calculated and

propagated back, in order to change the synaptic

weights and by doing so minimize the estimation

error (Haykin 2004).

A node uses a function f defined as a weighted

sum of its inputs based on equation 1 where the w are

the weights, and the x are the inputs, the bias is b and

the output is Z (Haykin 2004).

𝑍=

𝑓

(𝑤



𝑥



+𝑤



𝑥



+𝑏)

(1)

There are many activation functions for the output

layer, however Sigmoid, shown in equation 2, is

generally used in a technology acceptance context

(Tan 2014).

𝑆𝑖𝑔𝑚𝑜𝑖𝑑

(

𝑥

)

1+𝑒



(2)

The root mean square of errors (RMSE) is used to

predict accuracy and is calculated using equation 3

and 4 (Tan 2014), where SSE is the sum of squared

error, and MSE is the mean squared prediction error.

𝑀𝑆𝐸 =

𝑆𝑆𝐸

𝑛

(3)

𝑅𝑀𝑆𝐸 =

√

𝑀𝑆𝐸

(4)

3 MODEL OBTAINED VIA SEM

In order to obtain the model for activity tracking use,

via Structural Equation Modeling, the paper targets a

population of actual activity trackers users. These

users were recruited from social media and on a micro

work site. A survey with 80 questions adapted from

prior research was deployed. The items of the survey

were considered using a seven-point Likert scale,

amid between “Strongly Disagree” and “Strongly

Agree.” Specifically, the strength and significance of

direct effect of nineteen independent variables on

behavioral intention were determined. From a total of

17 tested relationships, 11 were statistically

significant.

There were a total of 247 users, mostly from

Western Europe and North America, that completed

Table 1: Definitions of the constructs present in the model.

Definition

Perceived Susceptibility of

Disease

“The perception of the likelihood of experiencing a condition that would

adversel

affect one's health” (Ja

anti 1998)

Perceived Severity of

Disease

“The beliefs a person holds concerning the effects a given disease or

condition would have on one's state of affairs” (Hochbaum 1952)

Habit “The extent to which people tend to perform behaviors automatically

ecause of learnin

” (Lima

em, 2007)

Health Consciousness “The degree to which health concerns are integrated into a person’s daily

activities” (Ja

anti, 1998)

Hedonic Motivation “The fun or pleasure derived from using a technology, and it has been

shown to play an important role in determining technology acceptance

and use” (Brown, 2005)

Image “The degree to which use of an innovation is perceived to enhance one’s

ima

e or status in one’s social s

stem” (Moore, 1991)

Self-Efficacy “The judgment of one’s ability to use a technology (e.g., computer) to

accomplish a particular

ob or task” (Compeau 1995)

Perceived Data Control “The degree to which a person feels they have control over the use of,

and access to, the data collected” (Lindqvist 2011)

Perceived Usefulness “The degree to which a person believes that using a particular system

would enhance his or her

ob performance” (Davis 1989)

Perceived Ease of Use “The degree to which a person believes that using a particular system

would be free of effort” (Davis 1989)

An Integrated Neural Network and Structural Equation Modeling Approach for Modeling Activity Trackers Use

the survey, from these 144 were male (58.3 percent)

and 103 were female (41.7 percent). The average age

was 33 with a standard deviation of 10.6.

The sample size of 247 has exceeded the

recommended minimum sample size of 111 obtained

from G*Power with an effect size of 0.3, an alpha

level of 0.05 and a power of 0.95 (Faul 2009). In

Table 1 we display the definitions of the constructs

that make up the model.

The previously found model was obtained using

maximum likelihood parameter estimation.

Descriptive statistics, and Exploratory Factor

Analysis were conducted using IBM SPSS version

23. The structural equation model was built-in with

maximum likelihood estimation routines in IBM

SPSS Amos 23.

The Kurtosis analysis found normality issues,

with values higher that 2, in item 2 of the construct

Perceived Usefulness, item 2 of the construct

Perceived Ease of Use, item 1 of the construct

Intention to Use, and in all items of the construct

Behavioral Intention. However, these constructs

passed in the Exploratory Factor Analysis. In Figure

1 we show the path diagram of the activity tracker

acceptance model with the respective path

coefficients.

4 METHODOLOGY

In this work, an Artificial Neural Network is applied

to analyze, complement and verify the SEM approach

and measure the effectiveness of the constructs that

prevailed for the acceptance of activity trackers.

We used a Multilayer Perceptron (MLP) back

propagation feed-forward (BPFF) method. The MLP

is the most used and widespread ANN method

(Liébana-Cabanillas 2017). The ANN contains three

layers: the input layer, the hidden layer, and the

output layer. In this work, the ANN is created using

SPSS 24. The model obtained from SEM is divided

into four ANN models with one output variable.

Model A has the output as the construct Health

Consciousness and has three inputs Perceived

Susceptibility to Disease, Perceived Severity to

Disease, and Habit. Model B has the output as the

construct Perceived Usefulness and has five inputs

Health Consciousness, Hedonic Motivation, Image,

Self-Efficacy, Perceived Ease of Use. Model C has

the output as the construct Perceived Ease of Use and

has three inputs Image, Self-Efficacy, and Perceived

Data Control. Model D has the output as the construct

Behavioral Intention to Use (BIU)/ Acceptance and

has three inputs Perceived Usefulness, Image,

Perceived Ease of Use. The four models ANN are

shown in Figure 2. The nodes (hidden neurons) are

Figure 1: Path Diagram of the Activity Trackers Acceptance Model obtained via SEM with respective path coefficients (Sol

2016).

CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications

automatically generated by SPSS and the activation

function used for both hidden and output layers was

Sigmoid Function. We assigned 90 % of the samples

to the training procedure and the remaining 10% were

used for the testing procedure. To avoid the risk of

over-fitting, we employed a ten-fold cross-validating

process. The root mean square of errors (RMSE) was

used to assure the predictive accuracy of the four

ANNs. The next section analyzes the results of the

ANN.

5 ARTIFICIAL NEURAL

NETWORK RESULTS

An ANN is helpful in discovering both linear and

non-linear relationships without requiring any

distribution assumptions like linearity, normality, or

homoscedasticity as in Structural Equation Modeling

(Leong 2013). By doing so, an ANN can provide

higher prediction accuracy (Tan 2014).

Table 2: RMSE values of ten artificial neural networks.

Model A Model B Model C Model D

Input

Neuron

Perceived

Susceptibility to

Disease (PSusD),

Perceived Severity to

Disease (PSevD), Habi

HC, Hedonic

Motivation (HM),

Image, Self-Efficacy,

Perceived Ease of Use

Image (I), Self-

Efficacy (SE),

Perceived Data

Control (PDC)

Perceived

Usefulness, Image,

Perceived Ease of

Use

Output

Neuron

Health Consciousness

(HC)

Perceived Usefulness

(PU)

Perceived Ease of

Use (PEoU)

Behavioral

Intention to Use

(BIU)/ Acceptance

Trainin

Testin

Trainin

Testin

Trainin

Testin

Trainin

Testin

ANN 1 0.114 0.134 0.072 0.073 0.104 0.096 0.075 0.057

ANN 2 0.117 0.107 0.077 0.077 0.101 0.099 0.072 0.058

ANN 3 0.118 0.116 0.083 0.055 0.100 0.120 0.072 0.067

ANN 4 0.119 0.106 0.076 0.056 0.104 0.094 0.074 0.076

ANN 5 0.119 0.104 0.078 0.077 0.105 0.102 0.087 0.054

ANN 6 0.115 0.129 0.087 0.072 0.102 0.104 0.073 0.071

ANN 7 0.117 0.080 0.080 0.063 0.105 0.159 0.071 0.062

ANN 8 0.116 0.107 0.079 0.049 0.111 0.130 0.079 0.053

ANN 9 0.130 0.108 0.073 0.066 0.100 0.114 0.069 0.083

ANN 10 0.117 0.072 0.069 0.067 0.103 0.079 0.078 0.057

Mean

RMSE

0.118 0.106 0.077 0.066 0.103 0.110 0.075 0.064

Standard

Deviation

0.005 0.019 0.005 0.010 0.003 0.022 0.005 0.010

Table 3: Neural network sensitivity analysis.

Model A Model B Model C Model D

Output Health Consciousness (HC) Perceived Usefulness (PU)

Perceived Ease of Use

(PEoU)

Behavioral Intention to

Use (BIU)/ Acceptance

Relative Importance Relative Importance Relative Importance Relative Importance

ANN PSusD PSevD H HC HM I SE PEoU I SE PDC PU I PEoU

1 0.324 0.240 0.437 0.040 0.671 0.065 0.113 0.112 0.301 0.429 0.270 0.730 0.218 0.052

2 0.326 0.256 0.417 0.070 0.603 0.050 0.123 0.154 0.352 0.356 0.291 0.731 0.166 0.103

3 0.298 0.238 0.464 0.237 0.514 0.025 0.097 0.127 0.358 0.306 0.336 0.690 0.174 0.136

4 0.350 0.161 0.488 0.062 0.603 0.035 0.071 0.229 0.411 0.331 0.258 0.651 0.213 0.136

5 0.298 0.248 0.454 0.091 0.584 0.023 0.052 0.250 0.311 0.423 0.266 0.360 0.188 0.453

6 0.379 0.251 0.370 0.168 0.367 0.033 0.170 0.264 0.324 0.419 0.257 0.694 0.149 0.157

7 0.315 0.217 0.467 0.060 0.567 0.096 0.117 0.161 0.335 0.376 0.289 0.754 0.193 0.053

8 0.311 0.252 0.437 0.039 0.601 0.041 0.029 0.289 0.237 0.503 0.260 0.579 0.174 0.247

9 0.223 0.106 0.671 0.023 0.651 0.018 0.102 0.205 0.304 0.299 0.397 0.723 0.195 0.082

10 0.255 0.275 0.470 0.049 0.604 0.054 0.146 0.146 0.380 0.353 0.267 0.525 0.405 0.070

Average 0.308 0.224 0.468 0.084 0.577 0.044 0.102 0.194 0.331 0.380 0.289 0.644 0.208 0.149

Average 68% 50% 99.8% 16.4% 100% 7.7% 18.9% 35.2% 83.3% 93.4% 72.5% 97.9% 33.3% 25.9%

An Integrated Neural Network and Structural Equation Modeling Approach for Modeling Activity Trackers Use

As shown in Table 2, the RMSE values for the

training data and the testing data are low, representing

a higher predictive accuracy and better data fit.

In Table 3, we show the results of the sensitivity

analysis that assessed the strength of the predictive

power of each of the input neurons. In order to have

the normalized importance of these neurons in

percentage we divided the relative importance by the

maximum importance.

Habit (H) was found to be the key determinant in

predicting Health Consciousness (HC) followed by

Perceived Susceptibility to Disease (PSusD) and

lastly Perceived Severity to Disease (PSevD) in

model A. In model B, the order of importance towards

Perceived Usefulness (PU) in descending order is

Hedonic Motivation (HM), followed by Perceived

Ease of Use (PEoU) and Self-Efficacy (SE) and the

least important were Health Consciousness (HC) and

Image (I). For model C, Self-Efficacy (SE) is the most

prominent predictor for Perceived Ease of Use

Figure 2: Neural Network between Perceived Susceptibility

to Disease, Perceived Severity to Disease, and Habit with

Health Consciousness.

Figure 3: Artificial Neural Network between Health

Consciousness, Hedonic Motivation, Image, Self-Efficacy,

and Perceived Ease of Use with Perceived Usefulness.

(PEoU), followed by Image (I) and lastly Perceived

Data Control (PDC). Finally, Perceived Usefulness

(PU) constituted the most effective in term of

predicting Behavioral Intention to Use (BIU)/

Acceptance, followed by Image (I) and lastly

Perceived Ease of Use (PEoU).

All constructs in all ten ANNs for each model had

at least one non-zero synaptic weight connected to the

hidden neurons which validates the relevance of the

constructs as variables as Figures 2 to 5 show.

Figure 4: Artificial Neural Network between Image, Self-

Efficacy, and Perceived Data Control with Perceived Ease

of Use.

Figure 5: Artificial Neural Network between Perceived

Usefulness, Image, and Perceived Ease of Use with

Behavioral Intention to Use / Acceptance.

6 DISCUSSION

The Technology Acceptance Model (TAM) is based

on many theories and grounded in many studies. In

this work, the determinants of activity trackers use

include TAM constructs and other constructs such as

Image, Hedonic Motivation, Habit and Self-Efficacy.

The results show that the research model studied in

this work is acceptable. Next, we discuss the findings

in more detail.

CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications

6.1 Relationships between Perceived

Susceptibility to Disease, Perceived

Severity to Disease, and Habit with

Health Consciousness

As shown in Table 4, the construct Habit showed a

significant relationship with Health Consciousness

with a path coefficient of 0.451 obtained during the

Structural Equation Modeling and has the highest

normalized importance according to the Model A of

the Artificial Neural Network analysis. While to our

knowledge this relation is not found in the literature,

the fact that both approaches ranked Habit in first

place makes one ponder that a Health Conscious

person has health habits.

The findings in this study also show that the

construct Perceived Severity to Disease with 50%

normalized importance is positively related to Health

Consciousness. Looking at this result one might

consider that if a person is Health Conscious, then

that person should have a high degree of awareness of

disease and related issues. Nevertheless, the construct

Perceived Susceptibility of Disease showed mixed

results within the two approaches.

6.2 Relationships between Health

Consciousness, Hedonic

Motivation, Image, Self-Efficacy,

and Perceived Ease of Use with

Perceived Usefulness

During to the SEM the path coefficient between

Hedonic Motivation and Perceived Usefulness is

0.515, which is a significant positive correlation with

the highest normalized importance given by model B

of the ANN. For many studies, the perception of

Hedonic Motivation has been viewed as egocentric

(Elsden 2015) and an important issue for individual

encouragement (Patel 2015), provide motivation

(Lazar 2015), acknowledgement of effort (Kim 2016)

or giving credit (Consolvo 2016).

The construct Perceived Ease of Use was

promoted by the ANN when compared to SEM

approach, however, the normalized importance was

only 35.2%. This result is in line with previous work

that demanded for reminders to be added to the

devices (Shih 2015).

Table 4: Comparison between SEM and ANN analysis (output: Health Consciousness).

SEM Path

SEM

Ranking

ANN normalized

relative importance

ANN

Ranking

Rank

Matched?

Perceived Susceptibility to

Disease

-0.114 3 68% 2 No

Perceived Severity of

Disease

0.283 2 50% 3 No

Habit 0.451 1 99.8% 1 Yes

Table 5: Comparison between SEM and ANN analysis (output: Perceived Usefulness).

SEM Path

SEM

Ranking

ANN normalized

relative importance

ANN

Ranking

Rank

Matched?

Health Consciousness 0.372 2 16.4% 4 No

Hedonic Motivation 0.515 1 100% 1 Yes

Image -0.013 3 7.7% 5 No

Self-Efficacy -0.060 4 18.9% 3 No

Perceived Ease of Use -0.110 5 35.2% 2 No

Table 6: Comparison between SEM and ANN analysis (output: Perceived Ease of Use).

SEM Path

SEM

Ranking

ANN normalized

relative importance

ANN

Ranking

Rank

Matched?

Image 0.146 1 83.3% 2 No

Self-Efficacy -0.067 2 93.4% 1 No

Perceived Data Control -0.115 3 72.5% 3 Yes

An Integrated Neural Network and Structural Equation Modeling Approach for Modeling Activity Trackers Use

Table 7: Comparison between SEM and ANN analysis (output: Behavioral Intention to Use / Acceptance).

SEM Path SEM

Rankin

ANN normalized

relative importance

ANN

Rankin

Rank

Matched?

Perceived Usefulness 0.492 1 97.9% 1 Yes

Ima

e 0.207 2 33.3% 2 Yes

Perceived Ease of Use -0.115 3 25.9% 3 Yes

Regarding the construct Sell-Efficacy, the weak

influence is corroborated by model B of the ANN. To

some extent, this result is partially contradicted by

earlier studies that ask for a tailored efficacy

evaluation (Klasnja 2011).

The ANN demoted Health Consciousness giving

a low normalized importance that shows a weak

influence in Perceived Usefulness.

Since the normalized importance for Image is less

than 10%, we may conclude that the effect of Image

in Perceived Usefulness is very small in comparison

to Hedonic Motivation. This result seems to

contradict past research (Harrison 2015) however one

should keep in mind that here we are relating Image

to Perceived Usefulness.

6.3 Relationships between Image,

Self-Efficacy, and Perceived Data

Control with Perceived Ease of Use

The construct Image showed a significant

relationship with Perceived Ease of Use obtained

during the SEM and even though it does not have the

highest normalized importance it is a high value

according to Model A of the Artificial Neural

Networks analysis. This finding of this research is

compatible with the findings of existing studies that

state that Image is present as a component of social

tracking (Rooksby 2014) and that it exists as both

social competition and social comparison (Patel

2015).

The ANN came to empower Self-Efficacy as a

relevant construct in its relationship with Perceived

Ease of Use opposing a path coefficient of -0.067 that

SEM found. The ANN result corroborates with

previous research that demanded for good inter-

device reliability (Dontje 2015). Nevertheless, one

has to take into consideration that an ANN measure

with high predictive accuracy has both a linear and

non-linear relationship among variables.

During the SEM the path coefficient between

Perceived Data Control and Perceived Ease of Use is

-0.115, which is in accordance with the lowest

normalized importance ranking but a high value of

72.5% given by model B of the ANN. To some extent,

this result is partially supported by earlier studies

which emphasize that the personal information

collected by self-tracking can be highly sensitive

(Lupton 2017).

One should note that these results are influenced

by the fact that in Model C the Average RMSE value

of the testing is higher than the Average RMSE for

training.

6.4 Relationships between Perceived

Usefulness, Image, and Perceived

Ease of Use with Behavioral

Intention to Use / Acceptance

Perceived Usefulness with the highest normalized

importance (97.9%) given by Model D of the

Artificial Neural Networks approach was found in the

Structural Equation Modeling to have a significant

relationship in predicting Behavioral Intention to Use

/ Acceptance. This finding supports prior research, as

the suggestions for the designers of activity trackers

to facilitate micro-plans (Gouveia 2018), add a wide

variety of adjustable goals (Clawson 2015), and have

adjustable tracking goals (Epstein 2015).

The construct Image shows a significant influence

in predicting Acceptance in both approaches. The

finding of this research is compatible with the

findings of existing studies as the influence of activity

trackers on lifestyle (Consolvo 2006) and the

importance of aesthetics and form (Harrison 2015).

Concerning the SEM, the path coefficient from

Perceived Ease of use to Acceptance is -0.115,

nevertheless the normalized importance given by

model D of the ANN was 25.9%. This result is in line

with previous work which points out that people are

using activity trackers in a rather limited manner

(Didziokaite 2017).

7 CONCLUSIONS

This research aimed to study beliefs and behavioral

variables that impact the acceptance and use of

activity trackers. It looked to an established

technology acceptance model dedicated to activity

trackers that was obtained via Structural Equation

CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications

Modeling. These constructs of the model are used in

an Artificial Neural Network as the input units of four

ANN. The Root Mean Square of Errors with the

highest value of 0.118 indicates that the ANN method

achieves high prediction accuracy.

The constructs of the model were divided in four

ANNs. Model A had as inputs the constructs:

Perceived Susceptibility to Disease, Perceived

Severity to Disease, and Habit, while the output was

the construct Health Consciousness. Model B had as

inputs Hedonic Motivation, Image, Self-Efficacy,

Health Consciousness, and Perceived Ease of Use,

while the output was Perceived Usefulness. Model C

had as inputs Image, Self-Efficacy, and Perceived

Data Control, while the output was Perceived Ease of

Use. Model D had as inputs Perceived Usefulness,

Image, and Perceived Ease of Use, while the output

was Behavioral Intention to Use (BIU) / Acceptance.

When comparing the results of SEM and ANN

analysis, the main disparity lies in the strength of the

effect of the construct Self-Efficacy with regards to

Perceived Ease of Use. The ANN analysis increases

the importance of Self-Efficacy in the Perceived Ease

of Use of activity trackers. Even though with a lower

impact, the ANN also increases the importance of

Perceived Susceptibility of Disease when related to

Health Consciousness. On the other hand, it also

decreases, with a not so high impact the importance

of Health Consciousness in Perceived Usefulness.

The ANN were able to emphasize the strengths

and weaknesses of the model obtained via SEM.

Furthermore, this research shows the relevance of the

two-stage approach integrating SEM and ANN

techniques to fine-tune this technology acceptance

models and to present valuable information that can

be utilized to increase the acceptance and usability of

activity trackers as well as to enhance device designs.

This research is restricted in the sense that it would be

interesting to include control variables such as age

and gender and compare the results. Also, it used a

cross-sectional approach to obtain the responses of

the activity trackers users at one point in time. Hence,

in a future study one may repeat the questionnaire to

the same users in a longitudinal approach to examine

the temporal effects.

REFERENCES

Akers, J. D., Cornett, R. A., Savla, J. S., Davy, K. P., &

Davy, B. M. (2012). Daily self-monitoring of body

weight, step count, fruit/vegetable intake, and water

consumption: a feasible and effective long-term weight

loss maintenance approach. Journal of the Academy of

Nutrition and Dietetics, 112(5), 685-692.

Brown, S. A., & Venkatesh, V. (2005). A model of adoption

of technology in the household: A baseline model test

and extension incorporating household life cycle.

Management Information Systems Quarterly, 29(3), 11.

Clawson, J., Pater, J. A., Miller, A. D., Mynatt, E. D., &

Mamykina, L. (2015, September). No longer wearing

investigating the abandonment of personal health-

tracking technologies on craigslist. In Proceedings of

the 2015 ACM international joint conference on

pervasive and ubiquitous computing (pp. 647-658).

Compeau, D. R., & Higgins, C. A. (1995). Computer self-

efficacy: Development of a measure and initial test.

MIS quarterly, 189-211.

Consolvo, S., Everitt, K., Smith, I., & Landay, J. A. (2006,

April). Design requirements for technologies that

encourage physical activity. In Proceedings of the

SIGCHI conference on Human Factors in computing

systems (pp. 457-466).

Davis, F. D. (1989). Perceived usefulness, perceived ease

of use, and user acceptance of information technology.

MIS quarterly, 319-340.

Didziokaite, G. (2017). Mundane self-tracking: calorie

counting practices with MyFitnessPal (Doctoral

dissertation, Loughborough University).

Dontje, M. L., De Groot, M., Lengton, R. R., Van Der

Schans, C. P., & Krijnen, W. P. (2015). Measuring steps

with the Fitbit activity tracker: an inter-device

reliability study. Journal of medical engineering &

technology, 39(5), 286-290.

Elsden, C., Kirk, D., Selby, M., & Speed, C. (2015, April).

Beyond personal informatics: Designing for

experiences with data. In Proceedings of the 33rd

Annual ACM Conference Extended Abstracts on

Human Factors in Computing Systems (pp. 2341-

2344).

Epstein, D. A., Ping, A., Fogarty, J., & Munson, S. A.

(2015, September). A lived informatics model of

personal informatics. In Proceedings of the 2015 ACM

international joint conference on pervasive and

ubiquitous computing (pp. 731-742).

Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009).

Statistical power analyses using G* Power 3.1: Tests

for correlation and regression analyses. Behavior

research methods, 41(4), 1149-1160.

Gouveia, R., Karapanos, E., & Hassenzahl, M. (2018,

April). Activity tracking in vivo. In Proceedings of the

2018 CHI Conference on Human Factors in Computing

Systems (pp. 1-13).

Harrison, D., Marshall, P., Bianchi-Berthouze, N., & Bird,

J. (2015, September). Activity tracking: barriers,

workarounds and customisation. In Proceedings of the

2015 ACM International Joint Conference on Pervasive

and Ubiquitous Computing (pp. 617-621).

Haykin, S. (2010). Neural networks: a comprehensive

foundation. 1999. Mc Millan, New Jersey, 1-24.

Hochbaum, G., Rosenstock, I., & Kegels, S. (1952). Health

belief model. United states public health service, 1.

An Integrated Neural Network and Structural Equation Modeling Approach for Modeling Activity Trackers Use

IDC. Press release from IDC on December 06, 2021.

Retrieved March 04, 2022, from https://www.idc.com/

getdoc.jsp?containerId=prUS48460121.

Jayanti, R. K., & Burns, A. C. (1998). The antecedents of

preventive health care behavior: An empirical study.

Journal of the academy of marketing science, 26(1), 6-

15.

Kim, D. J., Lee, Y., Rho, S., & Lim, Y. K. (2016, May).

Design opportunities in three stages of relationship

development between users and self-tracking devices.

In Proceedings of the 2016 CHI Conference on Human

Factors in Computing Systems (pp. 699-703).

Kim, J., & Park, H. A. (2012). Development of a health

information technology acceptance model using

consumers’ health behavior intention. Journal of

medical Internet research, 14(5), e133.

Klasnja, P., Consolvo, S., & Pratt, W. (2011, May). How to

evaluate technologies for health behavior change in

HCI research. In Proceedings of the SIGCHI

conference on human factors in computing systems (pp.

3063-3072).

Lazar, A., Koehler, C., Tanenbaum, T. J., & Nguyen, D. H.

(2015, September). Why we use and abandon smart

devices. In Proceedings of the 2015 ACM international

joint conference on pervasive and ubiquitous

computing (pp. 635-646).

Leong, L. Y., Ooi, K. B., Chong, A. Y. L., & Lin, B. (2013).

Modeling the stimulators of the behavioral intention to

use mobile entertainment: does gender really matter?.

Computers in Human Behavior, 29(5), 2109-2121.

Li, I., Dey, A., & Forlizzi, J. (2010, April). A stage-based

model of personal informatics systems. In Proceedings

of the SIGCHI conference on human factors in

computing systems (pp. 557-566).

Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z.

(2017). A SEM-neural network approach for predicting

antecedents of m-commerce acceptance. International

Journal of Information Management, 37(2), 14-24.

Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How

habit limits the predictive power of intention: The case

of information systems continuance. MIS quarterly,

705-737.

Lindqvist, J., Cranshaw, J., Wiese, J., Hong, J., &

Zimmerman, J. (2011, May). I'm the mayor of my

house: examining why people use foursquare-a social-

driven location sharing application. In Proceedings of

the SIGCHI conference on human factors in computing

systems (pp. 2409-2418).

Lupton, D. (2017). Self-tracking, health and medicine.

Health Sociology Review, 26(1), 1-5.

Moore, G. C., & Benbasat, I. (1991). Development of an

instrument to measure the perceptions of adopting an

information technology innovation. Information

systems research, 2(3), 192-222.

Myers, B., Hudson, S. E., & Pausch, R. (2000). Past,

present, and future of user interface software tools.

ACM Transactions on Computer-Human Interaction

(TOCHI), 7(1), 3-28.

Negnevitsky, M. (2005). Artificial intelligence: a guide to

intelligent systems. Pearson education.

Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable

devices as facilitators, not drivers, of health behavior

change. Jama, 313(5), 459-460.

Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012).

Editor's comments: a critical look at the use of PLS-

SEM in" MIS Quarterly". MIS quarterly, iii-xiv.

Rooksby, J., Rost, M., Morrison, A., & Chalmers, M.

(2014, April). Personal tracking as lived informatics. In

Proceedings of the SIGCHI conference on human

factors in computing systems (pp. 1163-1172).

Shih, P. C., Han, K., Poole, E. S., Rosson, M. B., & Carroll,

J. M. (2015). Use and adoption challenges of wearable

activity trackers. IConference 2015 proceedings.

Sol, R., & Baras, K. (2016, September). Assessment of

activity trackers: toward an acceptance model. In

Proceedings of the 2016 ACM International Joint

Conference on Pervasive and Ubiquitous Computing:

Adjunct (pp. 570-575).

Spiekermann, S. (2007). User control in ubiquitous

computing: design alternatives and user acceptance.

Habilitation Humboldt Universität Berlin.

Svozil, D., Kvasnicka, V., & Pospichal, J. (1997).

Introduction to multi-layer feed-forward neural

networks. Chemometrics and intelligent laboratory

systems, 39(1), 43-62.

Tan, G. W. H., Ooi, K. B., Leong, L. Y., & Lin, B. (2014).

Predicting the drivers of behavioral intention to use

mobile learning: A hybrid SEM-Neural Networks

approach. Computers in Human Behavior, 36, 198-213.

Tang, L. M., Meyer, J., Epstein, D. A., Bragg, K., Engelen,

L., Bauman, A., & Kay, J. (2018). Defining adherence:

Making sense of physical activity tracker data.

Proceedings of the ACM on interactive, mobile,

wearable and ubiquitous technologies, 2(1), 1-22.

Temir, E., O'Kane, A. A., Marshall, P., & Blandford, A.

(2016, May). Running: A flexible situated study. In

Proceedings of the 2016 CHI Conference Extended

Abstracts on Human Factors in Computing Systems

(pp. 2906-2914).

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