The factors affecting the acceptance of mobile obesity-management applications (apps) by the public were analyzed using a mobile healthcare system (MHS) technology acceptance model (TAM).
The subjects who participated in this study were Android smartphone users who had an intent to manage their weight. They used the obesity-management app for two weeks, and then completed an 18-item survey designed to determine the factors influencing the acceptance of the app. Three questions were asked pertaining to each of the following six factors: compatibility, self-efficacy, technical support and training, perceived usefulness, perceived ease of use, and behavior regarding intention to use. Cronbach's alpha was used to assess the reliability of the scales. Pathway analysis was also performed to evaluate the MHS acceptance model.
A total of 94 subjects participated in this study. The results indicate that compatibility, perceived usefulness, and perceived ease of use significantly affected the behavioral intention to use the mobile obesity-management app. Technical support and training also significantly affected the perceived ease of use; however, the hypotheses that self-efficacy affects perceived usefulness and perceived ease of use were not supported in this study.
This is the first attempt to analyze the factors influencing mobile obesity-management app acceptance using a TAM. Further studies should cover not only obesity but also other chronic diseases and should analyze the factors affecting the acceptance of apps among healthcare consumers in general.
With the recent popularization of wireless devices, such as tablet PCs, PDAs, and smartphones, mobile healthcare (mHealth) has also become widespread [
Several models have been introduced to explain the acceptance of technological products and services, including mobile products and services. Technology acceptance models (TAMs) and their extensions, such as TAM2 and the unified theory of acceptance and use of technology (UTAUT), are the most commonly used models for examining users' acceptance of mHealth products and services. For example, Wu et al. [
Obesity is one of the most pressing issues in healthcare for the general public, including in the Republic of Korea. According to the regional health statistics data of the Korean Ministry of Health and Welfare, the prevalence of obesity in Korea was 23.3% in 2011, which represents a 0.8% increase from the previous year [
The market size of mobile apps in the healthcare field, including those for obesity management, has increased by 44% annually from US $500 million in 2010, with the industry's sales profit increasing more than tenfold in 2012 [
This study analyzed the factors influencing the acceptance of an obesity-management app using the MHS acceptance model (
Wu et al. [
BI, extracted from TAM2, is defined as the individual's interest in using an IS for future work, PEOU is defined as the degree to which a person believes that using a particular IS would be free from effort, and PU is defined as the degree to which a person believes that using a particular IS would enhance his or her job performance. BI is directly influenced by PU and PEOU [
Compatibility, a concept extracted from IDT, refers to the degree to which a technological innovation is consistent with the values, experiences, and needs of the potential users. Compatibility was found to be a factor that directly influences BI, such that a technology that is more compatible with the user's previous experience is more likely to be accepted [
According to Compeau and Higgins [
Technical support and training, a concept presented by Igbaria et al. [
The relationships among the various concepts included in the MHS acceptance model were examined in this study using a mobile obesity-management app. From the hypothesized model, each pathway becomes a hypothesis of the study.
The research hypotheses for the dependent variable, BI, were the following:
H1: Compatibility has a direct effect on the BI to use the mobile obesity-management app.
H2: Perceived usefulness has a direct effect on the BI to use the mobile obesity-management app.
H3: Perceived ease of use has a direct effect on the BI to use the mobile obesity-management app.
The research hypotheses for the mediating variable, PU, were the following:
H4: Compatibility has a direct effect on the PU of the mobile obesity-management app.
H5: Self-efficacy has a direct effect on the PU of the mobile obesity-management app.
H6: Technical support and training have a direct effect on the PU of the mobile obesity-management app.
H7: PEOU has a direct effect on the PU of the mobile obesity-management app.
The research hypotheses for the mediating variable, PEOU, were the following:
H8: Compatibility has a direct effect on the PEOU of the mobile obesity-management app.
H9: Self-efficacy has a direct effect on the PEOU of the mobile obesity-management app.
H10: Technical support and training have a direct effect on the PEOU of the mobile obesity-management app.
The research hypotheses for the mediating variable, selfefficacy, were the following:
H11: Compatibility has a direct effect on the self-efficacy of using the mobile obesity-management app.
H12: Technical support and training have a direct effect on the self-efficacy of using the mobile obesity-management app. II. Methods
This was a survey study designed to examine the factors involved in the adoption of a mobile obesity-management app using the MHS acceptance model.
The subjects for this study were 110 adult Android smartphone users who had an intent to manage their weight. The number of subjects required for the study was calculated based on the recommendation for the structural equation model: at least 15 subjects per observed variable [
A convenience sample of the study subjects was recruited from the College of Medicine, College of Dentistry, College of Nursing of Seoul National University Yongon Campus, and Seoul National University Hospital through public notices posted on bulletin boards or websites at these institutions.
Subjects responding to the recruitment notice received a URL via SMS from which the obesity-management app could be downloaded. On first execution of the app, the subject was briefed about the research and asked to provide informed consent to participate. If the subject signed the consent form, the form was stored and an image file of the form was sent to the researcher. The subjects who agreed to participate in the study then received an instructional video on how to use the app. After receiving the instructions, each subject was asked to use the app for two weeks and then to complete a survey provided as a pop-up window. The survey collected demographical data and data on obesity-management app acceptance factors. The completed survey data, along with the data entered over the two-week study period (diet records, exercise records, body mass index (BMI), body weight, weight-loss goal, and weight-loss period) and usage logs of the app (access time) were sent to the researcher. The researcher analyzed the collected data using descriptive statistics and structural equation modeling. The study was conducted between November 12 and December 2, 2013.
The factors affecting mobile obesity-management app acceptance were assessed using 18 questions, comprising three questions for each of the six factors [
The survey instrument was translated from English into Korean by the second author, and the translated questionnaire was back-translated into English by an independent professional translator [
The subjects' demographic information, length of smartphone use, prior experience of obesity management, and prior experience of using an MHS app were analyzed for descriptive statistics using SPSS ver. 20.0 software (IBM SPSS, Armonk, NY, USA).
Descriptive statistics of the observed variables for mobile obesity-management app acceptance factors were analyzed for frequency, average, and standard deviation using SPSS ver. 20.0, and the reliability of the instrument was assessed using Cronbach's alpha. The correlations between the six acceptance factors were analyzed using Pearson correlation coefficients.
The MHS acceptance model was analyzed by path analysis using AMOS ver. 20.0 (IBM SPSS). The goodness of fit of a hypothetical model constructed based on the TAM was evaluated with the various goodness-of-fit indices including chi-square (χ2) statistics, the goodness-of-fit index (GFI), and the normed fit index (NFI). The cutoff for statistical significance was set at
A total of 110 subjects participated in this study, of whom 95 completed the survey (response rate, 86.4%). One of these subjects returned an incomplete response and was therefore excluded; thus, the data of 94 subjects were used in the further analysis.
Of the 94 participants, 80 returned their usage record. For the 56 female participants who returned their data, the bodyweight was 55.3 ± 6.56 kg (mean ± SD), and the BMI was 21.2 ± 2.35 kg/m2. For the 24 male participants, the bodyweight was 77.3 ± 10.83 kg, and the BMI was 25.0 ± 3.57 kg/m2 (
The usage logs (access and recording frequencies) of the 80 subjects who returned their data via the obesity-management app are summarized in
The internal consistency of the instrument used in this study was evaluated by calculating Cronbach's alpha (
Among the observed variables of the MHS acceptance model, the mean values for compatibility, self-efficacy, and technical support and training were 3.17, 2.57, and 3.98, respectively. The mean values for PEOU and PU were 3.44 and 2.95, respectively, and the mean BI for the mobile obesity-management app was 3.20. Since the Z scores for skewness and kurtosis for all variables measured in this study did not exceed the critical value (±1.96) at the statistical significance level of 0.05 set by Hair et al. [
The correlation coefficients among the MHS acceptance model variables are listed in
The model included six latent constructs: technical support and training, compatibility, self-efficacy, PU, PEOU, and BI.
The ratio of χ2 to its degrees of freedom, 3.24, was greater than the recommended less than 3.0, and the root mean square error of approximation of 0.16 was not within the acceptable range of 0.05-0.08 set by Hair et al. [
Compatibility had a direct effect on BI for the mobile obesity-management app (H1: β = 0.21,
The factors influencing the general public's acceptance of an obesity-management app were analyzed in this study using the MHS acceptance model, a composite model of TAM with and compatibility, self-efficacy, and technical support and training from the study of Wu et al. [
Descriptive statistics revealed that the age of the subjects who used the obesity-management app was 27.0 ± 8.46 years, most of whom (89.3%) fell into the age group of 20 to 39 years. This result is in line with that of Wu et al. [
The results of the path analysis demonstrated that self-efficacy does not significantly affect PU or PEOU, which is the direct opposite of what Wu et al. [
We also found that technical support and training had a significant effect on PEOU but not on PU. These findings contradict those of Wu et al. [
The factors influencing the acceptance of an obesity-management mobile app, developed as a part of the Health Avatar project, were analyzed with a view to encouraging adoption of the app. Various mobile apps acting as health avatars were developed in the Health Avatar project. Mobile apps for the management of chronic diseases, such as diabetes, hypertension, and hyperlipidemia, are currently under development. These mobile apps will provide various healthcare interventions that are free from temporal and spatial limitations. Health avatars utilizing various health and disease management technologies are enabled by a variety of features, such as life-logging, self-tracking, and qualified-self. In fact, webbased programs and other smartphone apps that track, analyze, and provide feedback based on a person's diet, exercise, sleeping pattern, and activity are already popularly in use [
This study was subject to two main limitations. First, we did not analyze the relationship between obesity-management app usage (access frequency and diet/exercise recording frequency) and acceptance factors. This was due to the obesity management being used for a very short period of time and the low frequencies of access and data recording. Adding an alarm or reminder function to the app, as the users requested, would improve access and data recording frequencies, thus making it possible to study the relationship between app usage and acceptance factors. Second, snowball and convenient sampling methods, and a self-report survey were used in this study, which could have resulted in both selection bias and social desirability bias.
Notwithstanding these limitations, this study represents the first attempt to analyze mobile obesity-management app acceptance factors based on a TAM. Further studies are recommended to examine the effectiveness of the obesity-management app in terms of clinical outcomes, such as weight and BMI, and to confirm the findings of this study by repeating it with different study subjects using other mHealth apps or services for the management of various diseases.
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2010-0028631).
*Correlation is significant at the 0.05 level (two-tailed). **Correlation is significant at the 0.01 level (two-tailed).
BI: behavioral intention, PU: perceived usefulness, PEOU: perceived ease of use.