Consumer Perception and Attitude towards the Usage of M-Health Applications

 

Dr. S. Selvabaskar¹, Dr. K. G. Prasanna Sivagami², Ms. S. Aishwarya³

¹Associate Professor, School of Management, SASTRA University, Thanjavur- 613401, Tamilnadu, India

²Assistant Professor, Department of Management Studies, Shrimati Indira Gandhi College, Tiruchirapalli, Tamilnadu, India

³MBA Class of 2015-17, School of Management, SASTRA University, Thanjavur- 613401, Tamilnadu, India

 

ABSTRACT:

This study focuses on determining the perception and attitudes of the consumers towards the usage of mHealth apps with the various attributes like simplicity, trustworthy, accuracy, factors considered while downloading mHealth applications and which factors resist the user from downloading such apps are also examined. In addition, the study tries to identify their familiarity with mHealth and their willingness to use their mobile devices in health -related functions. The duration of this study was between the months of February-march of 2017. For this study, the data was collected from various respondents using a well-structured questionnaire. Those who were using mHealth application in various cities across Tamilnadu were included for this study. Judgment sampling method is adopted and 40 respondents were included for this study. The collected data was analyzed by using frequency analysis, factor analysis, chi square analysis and Regression analysis with the help of SPSS package. The result revealed that the consumer perception has a positive impact on the attributes that build confidence in using mHealth applications. It has been found that the factors like relevance, rating and popularity plays a very important role in influencing the consumer to download the mHealth applications. The study found that there is an association between the skincare app category and gender i.e. more skincare apps are downloaded by the female respondents and also it found that other remaining categories of mHealth applications don’t have any association with the gender. Risk of privacy and inaccurate information, app dependency, are some of the factors which cause fear in the respondents’ mind and it resist them from downloading such apps.

 

 

 

 

INTRODUCTION:

Now a day’s Mobile phones are highly used for the Internet access due to low cost and easy availability. The mobile technology is now being incorporated in healthcare sector which is known as mHealth.

 

Usage of the mHealth applications is gaining momentum as it provides handy solution for the several categories like fitness, diet, health reference, sleep, health monitor, weight tracker, psychological health and brain exercise. With growing work culture and pressure individual don’t have the time to take of themselves.

 

Even approaching the doctor for consultation and counseling becomes very hard with tight schedule flowing them. Due to this, the mHealth apps have been introduced and it has very positive response. Attitudes influence the human evaluations of a stimulus by judging it in either a positive or negative way and the attitude also determine the intentions, which in turn it could predict the behaviors. The importance of attitudes extends to the field of healthcare as well. The perceptions of consumers towards these types of applications are very important for the correct and ease of usage. This made this research to study on the consumer perception and attitude towards the usage of mHealth applications.

 

STATEMENT OF THE PROBLEM:

In developing countries like India, mHealth is relatively in its early stage. To maximize the usage of mHealth applications, it is important to identify the perception and attitudes of the consumer regarding mHealth applications. Perceptions may provide ideas how end users would respond to mobile health applications and it also allow the mobile developers to determine their motivations for staying healthy.

 

It would also be advantageous to explore the variables in the health attitude-behavior connection. By doing so, it will provide insights regarding the process on how consumer attitudes influence and shape the behaviors of consumer in the health domain. Therefore, this study has been conducted to find the consumer perception and attitudes towards the usage of mHealth applications.

 

OBJECTIVES OF THE STUDY:

·         To ascertain the consumer perception and attitudes towards the usage of mobile applications.

·         To determine the top three mHealth applications.

·         To identify the important factors which influences consumer to download the mHealth mobile applications?

·          To study association between demographic variables and different attributes of the mHealth mobile applications.

 

RESEARCH METHEDOLOGY:

Research methodology is a way to solve the research problem systematically. Questionnaire was constructed to study the consumer perception and attitude towards the usage of mHealth applications.

 

The data was obtained through the use of structured questionnaire. The questionnaire was administered to 40 respondents who are using mHealth applications. Judgment sampling method was adopted for collection of information and the collected data were analyzed with the percentage analysis, chi-square tests, factor analysis and regression analysis.

REVIEW OF LITERATURE:

(Vanessa Jung Tirman, 2016) The study focused on the usage of m-Health applications in assessment, treatment and delivery of healthcare services. Healthcare practitioners and consumers demand mHealth developers that apps are transparent and share the information gathered which violates the privacy of individuals.

 

(Cook, 2016) This study evaluates the impacts of technology in the form of a mobile application in the assessment and documentation of sleep health apps in a primary care setting.

 

(Alkhushayni, 2016) The study aims at exploring the usability problems with the user interface of mHealth apps. The usability test helps to identify the satisfaction level of the mHealth app users.

 

(Subramanian, 2015) The study found that the majority of apps did not follow any guidelines of national health agencies and also found that there were no health professionals were involved during the app design and development process.

 

(Chaudhry, 2015) The study found that there is a lack of understanding about how to design mobile applications for people with low socio-economic status.

(Deng, 2014).

 

The study reveals that, to predict the intension of the user to use mobile health services it is necessary to access the perceived value, attitude and behavior and it also stated that perceived value and attitude has positive effect on behavior.

 

(Martin, 2014) The study found that the certain attributes like incentives play an important role in willingness to pay more for mobile apps related to the healthcare delivery.

 

(LeRouge, 2013) In this study the user profiles are studied to inform the design and development of healthcare devices for an aging population to enhance the design of the healthcare devices.

 

(Loo, 2009) The study reveals that there are different factors which influence the consumer’s adoption decisions like consideration of their health status, environment, personality and usefulness of the service and it also reveals that most of the consumers were interested in using mobile health services. 

 

RESULTS AND DISCUSSION:

Table No.1 Socio Demographic Profile of The Respondents

Demographic Variables	Frequency (%)
Gender	Male (52.5%)		Female (47.5%)
Age	21-30 (65%)	31-40 (27.5%)		>41 (7.5%)
Marital Status	Single (60%)		Married (50%)
Education	UG (20%)	PG (27.5%)		Professional (52.5%)
Occupation	Student (37.5%)	Professional (27.5%)	Salaried (private) (25%)	Home maker (10%)
Monthly Household Income	31,000 to 40,000(7.5%)	41,000 to 50,000 (40%)		>51,000(52.5%)

Source: Primary data

 

The table depict the socio demographic profile analysis of respondents ,which reveals that respondents who are using mHealth apps were men with the percentage level of 52.5% as compared to women, the age of respondents who are using mHealth apps mostly falls in between the age of 21-30 with the percentage level of 65% and the respondents are mostly unmarried with the percentage level of 60%, out of 40 respondents the education level of most of them falls under professional level with the percentage of 52.5% and mostly the students are using mHealth apps when compared to the other occupation category with the percentage level of 37.5%,the income level of the respondents mostly falls above 51,000 monthly with the percentage level 52.2%.

 

 

Table No.2 M-Health App Usage Behavior

Frequency (%)

Device using

Mobile (92.5%)

Tab (7.5%)

No of mHealth apps using

Only 1 app (67.5%)

2 apps (20%)

3 apps (12.5%)

Categories of mHealth apps *

Workout/ Exercise (65%)

Food/diet (30%)

Health monitors (2.5%)

Weight tracker (10%)

Sleep apps (2.5%)

Brain exercise (17.5%)

Skin care (15%)

Top 3 apps

run keeper

Calorie counter

Lumosity

Frequency of using mHealth apps

More than once per day (35%)

1 time per day (55%)

1-6 times per week (7.5%)

1-3 times per month (2.5)

Source

Searching the app store (60%)

Friends or family (37.5)

Web searches (2.5%)

Reasons for using mHealth apps *

Track how much activity I get (60%)

Help me watch what I eat (27.5%)

Weight loss (22.5%)

Teach me exercise (35%)

Track a Health measure 2.5%)

Track how much sleep I get (2.5%)

Help me relax (20%)

*Based on the individual responses (more than one response was allowed) Source: Primary data

 

The above table explains the mHealth app usage behavior, which reveals that  92.5 % of the respondents are using mHealth apps through mobile devices and most of the respondents were using only one mHealth apps currently, the top three downloaded program involves exercise (run keeper), diet(calorie counter)  and brain exercise (Lumosity), about 65% of the respondents were using workout/exercise apps when compared to the other categories of mHealth apps, out of 40 respondents 60% of the respondents were came to know about the mHealth apps through by searching the app store, most of the respondents were using the mHealth apps only one time per day with the percentage level of 55%.

 

Table No.3 Factors Considered While Downloading M-Health Apps

Source: Primary data

From table no 3, the weighted average for the factors that considered while downloading mHealth apps were calculated and based on the weighted average score the ranks were given to the factors, relevance is the most important factor considered while downloading the mHealth apps followed by rating, popularity, free download, features, user-friendliness and uniqueness, here the uniqueness is not that much considered as the important factor while downloading mHealth apps.

 

CHI SQUARE TESTS:

The association between the categories of mHealth app usage and gender using chi-square test with the following hypothesis:

H₀: There is no significant association between the categories of mHealth app and gender.

 

H₁: There is no significant association between the categories of mHealth app and gender.

 

Table No.4 Categories Of M-Health Apps Downloaded* Gender of The Respondents

S. No	Variable	Pearson Chi–Square Test Value	Df	Asymp. Sig. (2-sided)	Status
1	Workout/exercise/running apps and gender.	.803	1	.370	Accepted
2	Food/nutrient/diet apps and gender.	.043	1	.836	Accepted
3	Health’s monitor apps and gender.	.928	1	.335	Accepted
4	Weight tracker apps and gender.	1.348	1	.246	Accepted
5	Sleep apps and gender	.928	1	.335	Accepted
6	Brain exercise app and gender	1.219	1	.270	Accepted
7	Skin care app and gender	7.802	1	.005	Rejected

Source: Primary data

 

The Table no 4 shows that the chi-square test results statistically, which illustrates the significant association exists between the dependant variables and independent variables. The significance value is less than .005 so reject null hypothesis and there is some relationship between the variable namely skincare mHealth apps category usage and gender, were there is no relationship between the other categories of mHealth apps usage and the gender.

 

FACTOR ANALYSIS:

To factorize the important variable of perception about mHealth app, the 17 variables were subjected to factor analysis using principle component method and Varimax rotation.

Kmo and Bartlett's Test:

Table no.5 kmo and Bartlett`s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.817
Bartlett's Test of Sphericity	Approx. Chi-Square	712.725
	Df	136
	Sig.	.000

Source: Primary data

 

The Kaiser-Meyer-Olkin measure of sampling adequacy value is 0.817. The significance value is less than 0.05 which indicates that there is a relationship among the variable and this data is suitable for factor analysis.

 

 

Table no.6 factors, variable loadings and % of variance.

Factors	Loaded Variables	Factor Loading values	Cronbach`s alpha	% of variance of factors
Factor 1 (Functionality)	mHealth apps are easy to track health related information’s	.742	.958
	mHealth apps are simple and user-friendly	.739
	mHealth apps provides health awareness	.777
	mHealth apps assist in frequent health monitoring	.681
	mHealth apps make me set health goals	.763		36.535
	mHealth apps makes personal health care better	.841
	mHealth apps aids preventive healthcare	.809
	mHealth apps facilitates healthy living	.829
	It becomes habitual to use mHealth apps	.709
Factor 2 (Benefits of usage)	mHealth apps are accurate and reliable	.745	.895
z	Iam Optimistic about the benefits and usefulness	.790
	It is harmless to use mHealth apps	.799		58.527
	mHealth apps help users to communicate potential medical conditions to health care providers	.742
Factor 3 (perceived risk)	mHealth apps have chances of inaccurate information	.885	.889
	mHealth apps may leads to app dependency /addiction	.861
	mHealth apps Discourages visiting medical professionals	.763		77.929
	There is always a risk of privacy while using mHealth apps	.852

Source: Primary data

 

Extraction method:

principal component analysis.

 

Rotation method:

Varimax with Kaiser Normalization. a. Rotation converged in 5 iterations

 

From the above table, the variance for the factor solution were explained. The first factor explained 36.535%; the second factor explained 58.527%; the third factor explained 77.929%. Overall the three factors have explained 77.929 %. The Cronbach`s alpha value for each factor were calculated (factor1 - 0.958, factor 2 - 0.895, factor 3 - 0.889) and it shows that these set of factors are more reliable for this study. During the factor analysis, the seventeen variables were reduced to three factors. The first nine variables are combined and form the first factor, the next set of 4 variables form the second factor and final set of 4 variables form the third factor which are shown in the above table. The factors were named as:

1      Functionality;

2      Benefits of usage;

3      Perceived risk.

 

REGRESSION ANALYSIS:

Table No.7 -Overall Satisfaction-Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.708a	.501	.460	.55078

a. Predictors: (Constant), factor 3, factor 2, factor 1

Source: Primary data

 

From the above table, R (0.708) value is observed and it shows that there is a correlation between the variables (functionality, benefits of usage, perceived risk) and Overall satisfaction with the usage of mHealth applications.

 

Table No.8 –Overall Satisfaction-Anova

Model		Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	10.979	3	3.660	12.064	.000a
	Residual	10.921	36	.303
	Total	21.900	39

Predictors: (Constant), factor 3, factor 2, factor 1

Source: Primary data

 

a.         Dependent Variable: Overall satisfaction with the usage of mHealth apps

 

In table, no 8 the significance of F being less than 0.05 which signifies that this model is a good fit for the variables.

 

Table No.9 Coefficients for Estimation of Overall Satisfaction with The Usage of mHealth Apps

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	95% Confidence Interval for B
		B	Std. Error	Beta			Lower Bound	Upper Bound
1	(Constant)	.111	.607		.183	.856	-1.120	1.342
	factor 1	.324	.270	.040	1.198	.239	-.224	.872
	factor 2	.636	.245	.491	2.597	.014	.139	1.132
	factor 3	-.022	.113	-.027	-.191	.849	-.251	.208

a. Dependent Variable: Overall satisfaction with the usage of mHealth apps Source: Primary data

 

Overall opinion about the satisfaction with the usage of mHealth apps=a+b1x1+b2x2+b3x4

 

Table no 9 shows that the Functionality (factor 1) and benefits of usage (factor 2) are significant while estimating overall opinion about the satisfaction with the usage of mHealth apps, as the significance of t is less than 0.05.

 

Since the significant of t is more than 0.05 for perceived risk (factor 3) is insignificant in estimating satisfaction towards the usage of mHealth applications.

 

IMPLICATIONS:

1.     The researcher identifies that the most of the respondents across various cities who are using mHealth applications have their education up to professional level, so that there is no need for the organization to take much effort to educate the consumer about the usage of mHealth apps.

2.     The age of respondents who were using mHealth applications falls in-between the age of 21 to 30, so that the organization needs to focus those who fall on this age category than the other. It is inferred that there is significant association between gender and the skin care category of m-Health apps, while other categories does not have any association with respect to gender so that the developers did not face any difficulties while developing the apps.

3.     The study also found that the overall satisfaction depends on several attributes like functionality, benefits of usage of mHealth apps it shows that the organization needs to incorporate these attributes while developing the mHealth apps to improve the satisfaction among consumers.

4.     In addition, the study reveals that the top three mHealth applications (run keeper, calorie counter, Lumosity) which are used by the respondents, it shows that there is a lack of awareness on the other category of apps so the organization has to take necessary steps to bring awareness among the consumers mind. Based on the three factors such as functionality, benefits of usage, perceived risk, the consumer make perceptions about the mHealth apps.

 

CONCLUSION:

The result reveals that the consumer perception has a positive impact on the attributes which build the confidence among consumers about the usage of mHealth apps. The mHealth apps are having some perceived risk so it should be clarified and bring positive attitude among the consumers mind. mHealth apps are relatively in its early stage so that the organization needs to create awareness on its usage among the consumers mind. This study provides the useful insights to the app developers and organization about the consumer perception and attitude to the acceptance of mobile apps in the health care delivery, which contribute to the promotion and modification of improved delivery of mHealth apps to the end consumers.

 

REFERENCES:

1.     Alkhushayni, S. (2016). m-Health technology: Towards a new persuasive mobile application for caregivers that addresses motivation and usability (Doctoral dissertation, The University of Wisconsin-Milwaukee).

2.     Tirman, V. J. (2016). The current state of m-Health applications and the need for improved regulatory guidelines to protect the privacy of patient health information (Doctoral dissertation, Alliant International University).

3.     Cook, S. (2016). MySleep101©: An educational mobile medical application for sleep health in primary care (Doctoral dissertation, University of South Carolina).

4.     Subramanian, R. (2015). Diet, exercise, and Smartphone’s-A content analysis of mobile applications for weight loss. Southern Illinois University at Carbondale.

5.     Connelly, K., Siek, K. A., Chaudry, B., Jones, J., Astroth, K., and Welch, J. L. (2012). An offline mobile nutrition monitoring intervention for varying-literacy patients receiving hemodialysis: a pilot study examining usage and usability. Journal of the American Medical Informatics Association, 19(5), 705-712.

6.     Deng, Z., Mo, X., and Liu, S. (2014). Comparison of the middle-aged and older users’ adoption of mobile health services in China. International journal of medical informatics, 83(3), 210-224.

7.     Martin, T. R. (2014). Applications of contingent valuation and conjoint analysis in mHealth: understanding the willingness to pay for healthcare smartphone applications (Doctoral dissertation, University of Delaware).

8.     LeRouge, C., Ma, J., Sneha, S., and Tolle, K. (2013). User profiles and personas in the design and development of consumer health technologies. International journal of medical informatics, 82(11), e251-e268.

9.     Loo, J. L. (2009). Acceptance of health services on mobile phones (Doctoral dissertation, University of North Carolina at Chapel Hill).

 

 

 

 

Accepted on 03.07.2017            © RJPT All right reserved