Published on 16.04.19 in Vol 7, No 4 (2019): April
Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/11223, first published Jun 05, 2018.
Barriers to and Facilitators of the Use of Mobile Health Apps From a Security Perspective: Mixed-Methods Study
Background: A large number of mobile health (mHealth) apps have been created to help users to manage their health or receive health care services. Many of these mHealth apps have proven to be helpful for maintaining or improving their users’ health. However, many people still choose not to use mHealth apps or only use them for a short period. One of the reasons behind this lack of use is the concern for their health information security and privacy.
Objective: The goal of this study was to determine the relationship between users’ characteristics and their security and privacy concerns and to identify desired security features in mHealth apps, which could reduce these concerns.
Methods: A questionnaire was designed and validated by the research team. This questionnaire was then used to determine mobile app users’ security and privacy concerns regarding personal health data in mHealth apps as well as the security features most users’ desire. A semistructured interview was used to identify barriers to and facilitators of adopting mHealth apps.
Results: In total, 117 randomly selected study participants from a large pool took part in this study and provided responses to the validated questionnaire and the semistructured interview questions. The results indicate that most study participants did have concerns about their privacy when using mHealth apps. They also expressed their preferences regarding several security features in mHealth apps, such as regular password updates, remote wipe, user consent, and access control. An association between their demographic characteristics and their concerns and preferences in security and privacy was identified; however, in most cases, the differences among the different demographic groups were not statistically significant, except for a few very specific aspects. These study participants also indicated that the cost of apps and lack of security features in mHealth apps were barriers for adoption, whereas having free apps, strong but easy-to-use security features, and clear user protection privacy policies might encourage them to use mHealth apps in their health management.
Conclusions: This questionnaire and interview study verified the security and privacy concerns of mHealth app users, identified the desired security and privacy features, and determined specific barriers to and facilitators of users adopting mHealth apps. The results can be used to guide mHealth app developers to create apps that would be welcomed by users.
JMIR Mhealth Uhealth 2019;7(4):e11223
In recent years, both ownership of smartphones and the number of available mobile health (mHealth) apps have increased dramatically. According to one study performed by the Pew Research Center in 2018, 95% of Americans owned a mobile phone, and in 2018, 77% of them were smartphones  as opposed to 2011, when the share of smartphones among American adults was just 35%. In the same period, many mHealth apps were created and published on app stores. Specifically, by October 2017, roughly 325,000 mHealth apps were available on major app stores [ ].
For patients (in a general sense, ie, people who want to maintain or improve their health), mHealth apps can be used to perform tasks such as wellness management, encouraging and monitoring behavior change, health data collection, disease management, self-diagnosis, medication reminders, and rehabilitation schedule management [, ]. A number of research studies have been performed on mHealth apps and their results indicate that well-designed mHealth apps can empower patients, improve medication adherence, and reduce the cost of health care [ - ].
However, the adoption of mHealth apps in personal health care is still limited. The growth rate of mHealth app downloads dropped dramatically from more than 35% in 2015 to roughly 7% in 2016 . Moreover, it has been shown that after smartphone users download mHealth apps, close to half of them stop using mHealth apps for various reasons such as hidden costs, high data entry burden, loss of interest, and security and privacy concerns [ ].
In the context of patient health data, security and privacy are always linked since any unauthorized access to patient health data (security breach) is a violation of patient privacy. Here, security is the state of being protected against the unauthorized use of patient health information, whereas privacy is the freedom from unauthorized intrusion.
There are various types of mHealth apps; some collect health information from patients, whereas others simply provide general guidelines for maintaining a healthy lifestyle and information about certain diseases. If an mHealth app does not handle any patient health data, it typically does not trigger security and privacy concerns; therefore, the mHealth apps discussed in this study are the ones that handle patient health data.
Concern about health data security and privacy is one important reason people choose not to use mHealth apps for their own health care [, - ]. More specifically, users are not certain what type of data are collected and stored by mHealth apps, who can access the self-entered and sensor-collected data, and what purpose data are used for [ , ]. Security and privacy concerns about mHealth apps are greater when the apps are for issues associated with stigma, social isolation, or discrimination such as HIV/AIDS, sexual orientation, and mental disease [ - ]. All of these concerns are not surprising since millions of patients’ health records have been compromised because of hacking or other incidents in recent years in the United States [ ]; many mHealth apps do not have the necessary security features to protect users’ health data [ - ]; at the same time, many smartphone users (including patients and health care providers) do not even use the most basic authentication features (such as a passcode) to prevent access to private data on their phones [ , ]. In addition, a recent study has indicated that in 2015 around 70% of the 600 most commonly used mHealth apps did not provide privacy policies; many current mHealth app developers do not provide privacy policies in their apps either [ , ].
One possible way to reduce users’ concerns about privacy in mHealth apps is to determine the specific concerns of mHealth app users, evaluate the association between these concerns and users’ characteristics (such as demographics, experience with technology, and health care needs), and identify specific mHealth app features that may enhance their trust in the apps so that they will start to use mHealth apps in their own health care and management. In other words, investigating the barriers to and facilitators of using mHealth apps may lead to finding a way to increase users’ adoption of mHealth apps. Before going into the details of this study, a brief review of previous studies on this topic is provided below.
A number of studies have been conducted to identify users’ attitudes toward and perceptions of mHealth apps using focus groups, questionnaires, and interviews [, , - ]. Below is a summary of the findings in a few of these studies.
In 2015, Krebs and Duncan distributed a cross-sectional survey throughout the United States to determine the usage of mHealth apps among mobile phone owners and the reasons behind their choice about whether or not to use mHealth apps . There were 1604 respondents in the study, and more than 40% of these mobile phone users reported that they had chosen not to download mHealth apps. One of the reasons given was security and privacy concerns. The ones who had chosen to download mHealth apps, on the other hand, seemed to trust in the security of the app. Individuals more likely to use health apps tended to be younger, have higher incomes, be more educated, be Latino/Hispanic, and have a body mass index in the obese range.
Atienza et al used a mixed-methods approach (survey and focus group studies) to determine consumer attitudes toward and perceptions of mHealth privacy and security . The conclusion was that user attitudes regarding mHealth privacy and security were highly contextualized. They were related to the type, place, time, purpose, and person accessing the health information. They found that people in similar demographic groups may have quite different opinions on privacy.
Peng et al conducted focus groups and individual interviews with 44 smartphone owners to determine user perceptions of mHealth apps . People in all demographic groups (age, gender, and income) revealed that they did not like to share health data in the app via social networking features. They had concerns about how the information might be exploited by a third party. They were willing to share selected information with a small number of people if necessary. Besides security and privacy concerns, one other major barrier the participants of the study mentioned was the cost of the app. Many people only used free mHealth apps.
Dennison et al conducted 4 focus group studies with students and staff at a university in the United Kingdom to assess the opportunities for and challenges to getting young adults to use smartphone apps in supporting health behavior change . Study results indicated that young, healthy young adults have some interest in apps for behavior change. However, participants expressed concerns about the security of the data in the app. They were afraid that the data might get into the hands of third parties. They also felt it was intrusive when apps use a context-sensing approach to generate reminders or suggestions. They particularly did not like the app using the Global Positioning System to track their locations.
Prasad et al arranged 8 focus groups to identify privacy concerns related to mHealth apps . The participants were young college students (aged between 19 and 30 years), elderly hospital outpatients (aged between 80 and 85 years), and residents of a retirement community (aged between 65 and 100 years). First, elderly participants were more comfortable sharing health information with their doctors than family members. The young participants were willing to share their medication information with their doctors but no other information such as their location and their social interactions with others. Second, some participants were afraid that some information collected by the mobile device might get compromised during transmission and storage. They wanted to have control over the disclosure of their data because they included private information. Young participants did not want the mobile device to collect their information without their consent.
One comprehensive review covered privacy in mobile technology for personal health care and discussed topics such as security regulations, technologies, threats, and possible solutions . One review of privacy and security in mHealth apps briefly summarized the studies on security research in mHealth systems and provided general recommendations for creating secure mHealth apps [ ]. Another study provided more detailed security recommendations for mHealth apps [ ]. These recommendations are theoretical, with a major focus on the sensitivity of information itself. Although these recommendations for mHealth app developers are surely helpful in terms of making mHealth apps more secure, we also need to take end-user concerns into account and consider the usability of the mHealth app [ ]. After all, no matter how secure the app is, if the end-users do not like it and do not use it, it will not contribute to the improvement of users’ health and well-being.
In other words, although there are several studies that have revealed the existence of security and privacy concerns from mobile app users, signiﬁcant diversity in attitudes regarding mHealth privacy/security exists for different demographic groups. Thus, one may need to specifically customize security features for different purposes and different users to address users’ individual concerns regarding mHealth privacy and security in apps.
In this study, we used a questionnaire and held interviews to assess the association between users’ demographic characteristics and their security and privacy concerns, and more importantly, the specific security features they desire to have in mHealth apps and the features or language that may encourage them to use mHealth apps.
The purpose of this questionnaire and interview study is to collect data and answer the following 4 questions:
- What are mobile app users’ opinions or concerns about their personal data security and privacy?
- What are mobile app users’ opinions and concerns about their data security and privacy in mHealth apps?
- What are the security and privacy features they desire to see in mHealth apps?
- What are the barriers to and facilitators of the use of mHealth apps?
Questionnaire and Interview Question Development
Step 1. Literature Search and Review
We used the literature collected in our previous research studies [- ] and performed keyword searches “(security OR privacy)” AND “(questionnaire OR survey OR interview)” for published studies in PubMed, IEEE Xplore, ACMD Digital Library, and INSPEC. We also used the same keywords to perform searches in Google. From the obtained search results, we identified a few hundred statements relevant to information security and privacy.
Step 2. Creating a Draft of the Questionnaire
Each of the research team members went through these identified statements to determine their relevance and clarity in terms of the study purpose on a scale of 1 to 4, where 1 means no relevance or clarity, whereas 4 means high relevance or clarity. If 3 or 4 team members rated the relevance of a statement 1 or 2, it was removed from the questionnaire. If one of the team members rated the clarity of a statement 1 or 2, the wording of the statement was adjusted. The research team had multiple face-to-face meetings to discuss the rating and wording of statements. After this step, 24 statements remained in the questionnaire.
Step 3. Refining the Draft Questionnaire
Step 4. Pilot-Testing the Questionnaire
After we all agreed on the content validity of the statements and interview questions in this study, we then distributed the first version of the questionnaire and interview questions to 14 graduate students in an information security class. These graduate students reviewed this version and provided their comments on some statements and questions. We made changes on the statements and questions according to their suggestions. For instance, almost all the students indicated that before they took the information security class, they had had no idea what role-based access control was or did not know the details about encryption. Almost all of them also indicated that they did not like to use multifactor authentication even though they knew that feature would protect their highly sensitive information. A mobile app with multifactor authentication would simply discourage them from using the app. Therefore, statements corresponding to those security features (role-based access control, encryption, and multifactor authentication) were removed from the questionnaire because if respondents did not understand those features, the results obtained would not be reliable. The final questionnaire had 14 statements.
Step 5. Performing Questionnaire and Interview Studies and Psychometric Analysis
In this step, we recruited a group of study participants to conduct studies using these new questionnaire and interview questions. The obtained data were used to evaluate the reliability and validity of the new questionnaire and answer the research questions. The details of the study and the data analysis are presented in the following sections.
After the study protocol was approved by the institutional review board (IRB) office at the University of Pittsburgh, we recruited study participants with the following criteria: native English speaker, high school or higher education, aged between 18 and 65 years, capable of communicating with others orally and in writing, and has at least a few years of experience in using smart devices such as smartphone, tablet, or smart watch.
Study participants were recruited through flyers distributed in the Greater Pittsburgh area and through the Pitt + Me website at the University of Pittsburgh, which in January 2019 had more than 193,000 potential study participants registered at the site. Potential participants could indicate their interest in this study by sending a message to the research team or clicking on the link of the study on the Pitt + Me website. They were then screened according to the selection criteria. A list of these eligible subjects was stored in an Excel file. We then randomly selected study participants from this list to conduct the questionnaire and interview study.
Each study participant was given the opportunity to read and sign the IRB-approved consent form before the commencement of the study. At the beginning of the study, the investigators explained the purpose of the study, the procedure of the study, and the data to be collected in the study. Study participation was completely voluntary, and participants could stop participating in the study at any time.
All statistical analyses were conducted using SPSS version 24 (IBM). The internal consistency of the questionnaire was evaluated using Cronbach alpha. For research or evaluation, a value of .7 to .8 in Cronbach alpha is considered reliable .
Descriptive statistics were calculated for all the items in the questionnaire and the interview questions. Statistical significance was determined by P<.05. The normality of the data was evaluated with the Shairo-Wilk test. Nonparametric Kruskal-Wallis H (KWH) test was used to determine the significance of differences among multiple categories.
In total, 117 participants were recruited in the Greater Pittsburgh area to undertake the survey and interview study. The demographic information is summarized in.
|Age (years), mean (SD)||31.49 (12.354)|
|Age (years), n (%)|
|Gender, n (%)|
|Race, n (%)|
|African American||16 (13.7)|
|Education, n (%)|
|Below bachelor’s||39 (33.3)|
|Marital status, n (%)|
|Divorced or separated||3 (2.6)|
|Occupation, n (%)|
|Health care provider||9 (7.7)|
|Customer service||19 (16.2)|
|Administrative personnel||14 (12.0)|
|Self-assessed health status, n (%)|
|Very good||49 (41.9)|
|Years of using mobile devices, mean (SD)||6.21 (2.585)|
|Years of using mobile devices, n (%)|
|Used mHealth apps, n (%)|
|Household income, n (%)|
|<US $10,000||20 (17.1)|
|US $10,000-US $75,000||62 (53.0)|
|>US $75,000||22 (18.8)|
|Decline to answer||13 (11.1)|
Responses to the Statements on the Questionnaire
There were 14 statements on this questionnaire, and the study participants were required to select an answer from 1 to 7, corresponding to strongly agree (1), agree (2), somewhat agree (3), neither agree nor disagree (4), somewhat disagree (5), disagree (6), and strongly disagree (7).
The Cronbach alpha of this 14-item questionnaire was .730, which is good for research and exploratory studies. The overall mean for all 14 items was 2.55 (SD 0.658), reflecting that most study participants agreed with these statements to a certain degree. These 14 items were categorized into 3 groups: opinions on personal data, opinions on mHealth apps, and security features that users desire in mHealth apps.
The first category, opinion on personal data security and privacy, had 5 statements: S1, S2, S3, S4, and S5. The Cronbach alpha of this category was .737. The overall mean of the first category was 2.72 (SD 1.06), indicating that the study participants somewhat agreed with the 5 statements about personal information. They had some level of concern about the privacy of their personal data and wanted to have some specific protections on their personal data.
Similarly, the second category, opinion on security and privacy in mHealth apps, had 5 statements as well: S6, S7, S8, S9, and S10. The Cronbach alpha of this category was .785. The overall mean of the second category was 2.78 (SD 1.05), which indicates that the study participants generally accepted using mHealth apps for health care purposes, and most of them also believed that there was some level of privacy protection currently available in mHealth apps.
Relationship Between Demographic Characteristics and Answers to the Statements
The Shairo-Wilk test on the participants’ answers to the 14 statements indicated that the data were not normally distributed (P<.05 in all cases). Therefore, the nonparametric KWH test was used to determine the relationship between study participants’ demographic characteristics and their responses to the statements in the questionnaire. The differences were not statistically significant for people with different education levels (below bachelor’s, bachelor’s, and graduate), health status (excellent, very good, good, and fair), occupations, years of using mobile devices (<3, 3-5, >5), or employment status (employed vs unemployed).
In general, married participants had stronger concerns about information security and privacy and desired to have more stringent security protection. Correspondingly, they also were not very comfortable with their health provider using mHealth apps to manage their health data. However, in most cases, these differences were not statistically significant. A KWH test showed that there was a statistically significant difference in statement S2 between those of different marital status, KWH (1)=4.8, P=.03, with a mean rank score of 61.84 for single participants and 47.28 for married participants. A lower value corresponds to stronger agreement with the statement.
In most statements, the values from males and females were different, but these differences were not statistically significant (P>.05). The difference was statistically significant on S11 (I should have the right to consent to any sharing of my protected health information collected via mHealth apps). There, the mean rank was 67.93 for male and 51.60 for female, P=.001, KWH (1)=11.3, indicating that females have a stronger desire to have the right to consent to their health data collection.
|Statements||Agree (1-3), n (%)||Neutral (4), n (%)||Disagree (5-7), n (%)||Mean (SD)|
|Opinion on personal data (Cronbach alpha=.737)|
|S1. In general, I am concerned about the privacy and security of my personal information in everyday life||97 (82.9)||7 (6.0)||13 (11.1)||2.46 (1.424)|
|S2. I am concerned about the privacy and security of my personal information l when using an mHealth app||82 (70.1)||12 (10.3)||23 (19.6)||2.91 (1.710)|
|S3. I am concerned about submitting personal information on an mHealth app because of what others might do with it||67 (57.2)||16 (13.7)||34 (29.1)||3.38 (1.731)|
|S4. I do not want to store my personal identifiers (such as name, SSN, phone number, email address) in the mHealth apps except for one unique ID number which is only recognizable by authorized personnel||87 (74.4)||19 (16.2)||11 (9.4)||2.65 (1.422)|
|S5. I would like my personal health data to be transferred to a centralized database via a highly secure process||101 (86.3)||11 (9.4)||5 (4.3)||2.19 (1.245)|
|Opinion on mHealth apps (Cronbach alpha=.785)|
|S6. Overall, I am satisfied with the privacy and security of the mHealth apps I am currently using||80 (68.4)||28 (23.9)||9 (7.7)||2.82 (1.317)|
|S7. Health care providers have the necessary security and privacy measures in place. These measures provide a reasonable level of protection for information collected from mHealth apps||79 (67.5)||20 (17.1)||18 (15.4)||3.02 (1.333)|
|S8. I would use mHealth apps for my health care needs||95 (81.2)||10 (8.5)||12 (10.3)||2.50 (1.369)|
|S9. I want my health care providers to use mHealth apps to store and manage my health information||71 (60.7)||27 (23.1)||19 (16.2)||3.05 (1.479)|
|S10. I would feel comfortable if my health information was shared among my doctors and therapists for my health care purpose||94 (80.3)||7 (6.0)||16 (13.7)||2.52 (1.643)|
|Desired features in mHealth apps (Cronbach alpha=.346)|
|S11. I should have the right to consent to any sharing of my protected health information collected via mHealth apps||114 (97.4)||1 (0.9)||2 (1.7)||1.39 (0.861)|
|S12. I would like to know how my health care providers make sure that only the correct personnel have access to the mHealth system I am using||111 (94.9)||6 (5.1)||0 (0)||1.56 (0.814)|
|S13. I read the privacy policies of mHealth apps. The content of the policies influences my decision of whether to use the app||51 (43.6)||15 (12.8)||51 (43.6)||3.89 (1.902)|
|S14. I would like to be able to remotely remove all my health data on my mobile device if it is lost or stolen||114 (97.4)||2 (1.7)||1 (0.9)||1.39 (0.719)|
As shown in the demographic information, three races were represented in this study: African American, white American, and Asian American. In most (13/14, 93%) of these statements, the responses among these three races do not have a statistically significant difference. There is a statistically significant difference between race groups for one statement (S1) as determined by KWH test, P=.01, χ22=8.5. The mean rank was 49.69 for African Americans, 65.42 for white Americans, and 45.55 for Asian Americans. In other words, Asian Americans have a significantly stronger privacy concern than white Americans in general.
The study participants were arranged into 3 age groups: 18 to 28 years, 29 to 50 years, and 50 to 65 years. The general trend of the answers from these age groups was obvious. Participants in the 51 to 65 years age group had the strongest concern about privacy in mHealth apps and were willing to use mHealth apps for health care purposes but expected to have strong security protection in mHealth apps. Participants in the 18 to 28 years age group had the weakest concern about privacy, were somewhat satisfied with current security protection, and believed that health care providers took the necessary security and privacy measures to provide that protection. They were willing to use mHealth apps in their health care but not as strongly willing as those in the other 2 age groups. Therefore, their desire to strengthen security and privacy measures was relatively weaker compared with the other 2 age groups, but they still desired to have those measures strengthened because they did have concerns about privacy. Participants in the 29 to 50 years age group were somewhere in the middle. They had privacy concerns, did not believe the current practice was sufficient, did not have a strong desire to use mHealth apps in health care; therefore, in their response to the statements, they did not show strong desire to strengthen the security measures in mHealth apps. The differences among these age groups in multiple statements (S3, S5, S6, S8, S10, S11, S12, and S14), however, were not statistically significant.
The KWH test showed that there were statistically significant differences in answers to 6 statements (S1, S2, S4, S7, S9, and S13) among the different age groups (AG1: 18-28, AG2: 29-50, AG3: 51-65).shows the test statistics and the mean rank for AG1 and AG3.
Experience Using Mobile Health Apps
In most cases, the means from participants who had used mHealth apps before were smaller, which indicated that these participants had a stronger concern about security and privacy in mHealth apps, but they still wanted to use mHealth apps for their health care needs and desired to have stringent security. Most of these differences were not statistically significant. These 2 groups (participants who had used or had not used mHealth apps before) had a statistically significant difference for one statement: S8 (I would use mHealth apps for my health care needs). The mean rank from the participants who had used mHealth apps before was 54.25, whereas that from the participants who had not used mHealth apps before was 68.87. Here KWH (1)=5.1, P=.02. In other words, participants who had used mHealth apps before still wanted to use mHealth apps for their health care needs, whereas participants who had not used mHealth apps before were still hesitant to commit to such a decision.
Answers to Interview Questions
The first interview question was about password change frequency. Nine (9/117, 7.7%) participants responded they would like to change their passwords every month; 44 (37.6%, 44/117) participants indicated a willingness to change their passwords every 3 months; 33 (28.2%, 33/117) participants accepted changing their passwords every 6 months; 23 (19.7%, 23/117) participants claimed to be willing to change their passwords once a year; 2 participants (1.7%, 2/117) would rather change passwords once every 2 years; and 5 participants (4.3%, 5/117) indicated that they would never change their passwords. One participant stated that he would rather not change it regularly but would like to make the change when he believed necessary.
|Statement||Chi-square (df)||P value||Age groups (mean rank)|
|Age group 1||Age group 3|
|Questions and answers||n (%)|
|Q1. What barriers would prevent the adoption and integration of a mHealth App into your health monitoring and management?|
|Price of mobile apps. I only use free mobile health apps.||78 (66.7)|
|The app sends my data to a remote server without my permission.||73 (62.4)|
|The app asks me to provide my personal information even when I just want to determine whether the app is good for me.||71 (60.7)|
|The app does not encrypt my personal data.||69 (59.0)|
|The app runs slowly even though my mobile device is a recent model.||67 (57.3)|
|The app does not have clear privacy statements about how it handles my personal data.||59 (50.4)|
|The app stores my personal data on my mobile device and makes the data easily accessible to anyone who can access my mobile device.||58 (49.6)|
|The app sends me several alerts each day.||56 (47.9)|
|Name of mobile app. For instance, I do not use an app where the name implies that I have a certain disease.||48 (41.0)|
|Price of mobile app. I only use mobile health apps costing less than $5.||26 (22.2)|
|The app asks me to set up an account with user name and password.||14 (12.0)|
|Other (eg, The app has two-factor authentication. The app requires social network login.)||3 (2.6)|
|Q2. What security measures would give you confidence that an mHealth app would protect the confidentiality of patient data?|
|Explicit encryption on data stored on my mobile device and the data transmitted to a remote server.||96 (82.1)|
|User authentication.||96 (82.1)|
|Remote removal of my personal data on a lost mobile device.||96 (82.1)|
|Access control.||92 (78.6)|
|Data transmission via a secure channel.||74 (63.2)|
|Easily adjustable security settings for different types of data.||74 (63.2)|
|All health care providers’ data access activities are logged and can be audited.||70 (59.8)|
|One unique account for each patient and each health care provider.||67 (57.3)|
|Regular password update.||51 (43.6)|
|Q3. What specific privacy policies of an mHealth app would encourage you to use the app for your own health care purpose?|
|Your data will NOT be shared with any unauthorized personnel.||98 (83.8)|
|Your data will be collected only if you give permission to the app.||94 (80.3)|
|Your data will be removed from the server if you request it.||93 (79.5)|
|You have the right to terminate the permission for data collection at any time.||90 (76.9)|
|Your data will be collected only for health care and/or research purposes.||78 (66.7)|
The second interview question was about whom they were willing to share their medical information with. Participants could mention people from multiple categories. One hundred and nine (109/117, 93.2%) participants indicated that they would share their medical information with their health care providers; 81 (69.2%) participants with their family members; 38 (32.5%, 38/117) participants with friends; 12 (10.3%, 12/117) participants at password-protected personal websites, patient support groups, or password-protected online patient forums for patients with similar conditions; and 4 (3.4%, 4/117) participants claimed that they were not willing to share their medical information with anyone else.
The last 3 interview questions were about barriers to and facilitators of the use of mHealth apps. The answers from the study participants are summarized in.
Cost was a significant barrier among respondents, with a large proportion (78/117, 66.7%) indicating that they would not pay anything for a health app. Other barriers mentioned by more than half of the study participants were lack of encryption and informed consent, poor app performance, and request for personal information during app testing stage. Some study participants also mentioned issues such as unclear policy statements, too many alerts, name of mobile app (for instance, some app names include the name of a disease), and inconvenient user authentication.
In this study, we first confirmed that mobile app users had security and privacy concerns when they used mHealth apps in their daily life and identified the level of these concerns in people with different demographic characteristics such as sex, age, gender, education, income, and experience using mobile devices; some differences were statistically significant, whereas others were not. These results are consistent with the results reported in a number of previous studies [, , - ] and the theory of privacy as contextual integrity [ ]. Specific to mHealth app development, it means mHealth app developers may need to make certain adjustments to mHealth apps’ security features for different user groups in terms of marital status, sex, age, and income.
More importantly, we identified the security features desired in mHealth apps using a questionnaire and interview questions, the encouraging language in privacy policies, and the specific barriers in the mHealth app adoption. The findings can be used to guide the design and development of new mHealth apps. In previous studies [, , - ], the major focus was on eliciting and reporting security and privacy concerns, although the recommendations for mHealth app development were typically brief, and even if they were available, they were not from the research studies themselves but general principles of information security. The frequently mentioned security approaches were requiring user authentication (eg, password), information hiding, and informed consent [ , ]. For previous studies that did provide highly detailed security feature recommendations for mHealth app development [ ], those recommendations were theoretical in terms of information security itself and did not take the user’s characteristics into consideration. The results of this study offer a clearer picture in terms of security and privacy features desired by users with various characteristics in mHealth apps.
One may argue that today’s smartphones already have a number of security features implemented; these include data encryption, device password lock, remote data wipe, remote device locator, and antimalware apps. Moreover, a good use of these security features can provide strong protection to users’ privacy and their sensitive data such as health records; however, a recent questionnaire study involving 458 smartphone users clearly indicated that most smartphone users do not use these security features , and therefore, the task of data protection still falls to mHealth app developers.
According to the results of the study, the vast majority (111/117, 94.9%) of study participants desired to know how health care providers apply access control to their health data. In other words, they wanted to make sure that only authorized personnel could access their health data. The remaining 6 participants (5.1%, 6/117) did not indicate a preference on this issue. This is a topic related to patient education via the mHealth app. In other words, how can an mHealth app convince users that only authorized personnel can access their health data? To address this concern or answer this question, mHealth app developers need to demonstrate to users that corresponding security and privacy features are included in the mHealth apps.
In this study, most of the study participants had good health status even though we did not use health status as one of the selection criteria during screening. We understand that the findings could be significantly different if those with serious health problems and a strong desire to take advantage of the convenience offered by mHealth apps had been part of the population. For instance, patients who have experienced heart failure or who have had a kidney transplant have been shown to welcome mobile app–based home monitoring and reminder systems [, - ]. This is very common in the field of security and privacy. People perform their own risk and benefit assessment when they face a choice. The benefits provided by mHealth apps can be free cost, convenience, real-time health services, saving time, and other monetary incentives [ , ]. If users believe that the benefits outweigh the security and privacy risks, they may choose to sacrifice privacy and enjoy the benefits offered by the service, even though they still have concerns.
The study was performed at the University of Pittsburgh, and the study participants were recruited from the Greater Pittsburgh area. Roughly one-third of the study participants were undergraduate and graduate students, and they were from many different states. Therefore, the opinions reported in this study can also reflect the opinion of people in other states of the country, or at least people in that specific age group.
The sample size of this study was not very big, but it was sufficient for the purpose of this study. In most demographic categories, the number of participants was sufficient for the analysis. We believe the current sample size is big enough for us to obtain reliable results since these participants were randomly selected from a few hundred potential participants who explicitly expressed their interest in this study. On the other hand, a larger sample size would make the results more convincing and increase the generalizability of the results.
Most of the study participants were young and healthy. Therefore, we did not see a significant difference in opinion with respect to privacy among health status groups. As we did not have very sick people included in this study, the results cannot be generalized to that population.
This study was performed in the United States, and some results may not be applicable in other countries, especially in countries with significantly different regulations and culture. For instance, in many Asian countries, a family member’s health information is openly shared with family members. Employees are also typically required to have an annual physical exam, and the results are reported to employers. Therefore, people in this situation typically do not have a strong privacy concern since there is no corresponding protection anyway.
In the future, we will create an mHealth app with those highly desired security and privacy features identified in this study, and we will determine whether that changes app users’ trust in the app. We will also perform analysis on the usage data and their security settings to determine whether they have utilized those security features, whether and how the actual app usage has changed, and which specific security features they commonly choose to disable.
It is also necessary to enhance the security education to mobile app users so that they are well aware of the many readily available security features on their smartphones and can take advantage of these features to protect their data and privacy .
This work was funded in part by grants from the National Science Foundation (NSF) DGE1438809 and the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR) 90RE5018. The contents of this study do not represent the views of the NSF, NIDILRR, or the US government.
Conflicts of Interest
- Pew Research Center. 2018. Mobile Fact Sheet URL: http://www.pewinternet.org/fact-sheet/mobile/ [accessed 2019-03-12] [WebCite Cache]
- Research 2 Guidance. 2017. mHealth App Economics 2017/2018 - Current status and future trends in Mobile Health URL: https://research2guidance.com/product/mhealth-economics-2017-current-status-and-future-trends-in-mobile-health/ [accessed 2019-03-12] [WebCite Cache]
- Kao CK, Liebovitz DM. Consumer mobile health apps: current state, barriers, and future directions. PM R 2017 May;9(5S):S106-S115. [CrossRef] [Medline]
- Roess A. The promise, growth, and reality of mobile health-another data-free zone. N Engl J Med 2017 Nov 23;377(21):2010-2011. [CrossRef] [Medline]
- Parmanto B, Pramana G, Yu DX, Fairman AD, Dicianno BE, McCue MP. iMHere: a novel mHealth system for supporting self-care in management of complex and chronic conditions. JMIR Mhealth Uhealth 2013 Jul 11;1(2):e10 [FREE Full text] [CrossRef] [Medline]
- Fairman AD, Dicianno BE, Datt N, Garver A, Parmanto B, McCue M. Outcomes of clinicians, caregivers, family members and adults with Spina Bifida regarding receptivity to use of the iMHere mHealth solution to promote wellness. Int J Telerehabil 2013 Jun;5(1):3-16. [CrossRef] [Medline]
- Seto E, Leonard KJ, Cafazzo JA, Barnsley J, Masino C, Ross HJ. Perceptions and experiences of heart failure patients and clinicians on the use of mobile phone-based telemonitoring. J Med Internet Res 2012;14(1):e25 [FREE Full text] [CrossRef] [Medline]
- Seto E, Leonard KJ, Cafazzo JA, Barnsley J, Masino C, Ross HJ. Mobile phone-based telemonitoring for heart failure management: a randomized controlled trial. J Med Internet Res 2012;14(1):e31 [FREE Full text] [CrossRef] [Medline]
- Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR Mhealth Uhealth 2015 Nov;3(4):e101 [FREE Full text] [CrossRef] [Medline]
- Atienza AA, Zarcadoolas C, Vaughon W, Hughes P, Patel V, Chou WS, et al. Consumer attitudes and perceptions on mHealth privacy and security: findings from a mixed-methods study. J Health Commun 2015 Apr;20(6):673-679. [CrossRef] [Medline]
- Kotz D, Gunter CA, Kumar S, Weiner JP. Privacy and security in mobile health: a research agenda. Computer 2016 Jun;49(6):22-30 [FREE Full text] [CrossRef] [Medline]
- Wiesner M, Zowalla R, Suleder J, Westers M, Pobiruchin M. Technology adoption, motivational aspects, and privacy concerns of wearables in the German running community: field study. JMIR Mhealth Uhealth 2018 Dec 14;6(12):e201 [FREE Full text] [CrossRef] [Medline]
- Proudfoot J, Parker G, Hadzi PD, Manicavasagar V, Adler E, Whitton A. Community attitudes to the appropriation of mobile phones for monitoring and managing depression, anxiety, and stress. J Med Internet Res 2010 Dec;12(5):e64 [FREE Full text] [CrossRef] [Medline]
- Kenny R, Dooley B, Fitzgerald A. Developing mental health mobile apps: exploring adolescents' perspectives. Health Informatics J 2016 Jun;22(2):265-275. [CrossRef] [Medline]
- Goldenberg T, McDougal SJ, Sullivan PS, Stekler JD, Stephenson R. Preferences for a mobile HIV prevention app for men who have sex with men. JMIR Mhealth Uhealth 2014 Oct 29;2(4):e47 [FREE Full text] [CrossRef] [Medline]
- Goldenberg T, McDougal SJ, Sullivan PS, Stekler JD, Stephenson R. Building a mobile HIV prevention app for men who have sex with men: an iterative and community-driven process. JMIR Public Health Surveill 2015;1(2):e18 [FREE Full text] [CrossRef] [Medline]
- Di Matteo D, Fine A, Fotinos K, Rose J, Katzman M. Patient willingness to consent to mobile phone data collection for mental health apps: structured questionnaire. JMIR Ment Health 2018 Aug 29;5(3):e56 [FREE Full text] [CrossRef] [Medline]
- Office for Civil Rights. US Department of Health & Human Services. 2017. Breach Portal: Notice to the Secretary of HHS Breach of Unsecured Protected Health Information URL: https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf [accessed 2019-03-13] [WebCite Cache]
- Papageorgiou A, Strigkos M, Politou E, Alepis E, Solanas A, Patsakis C. Security and privacy analysis of mobile health applications: the alarming state of practice. IEEE Access 2018;6:9390-9403. [CrossRef]
- Müthing J, Jäschke T, Friedrich CM. Client-focused security assessment of mHealth apps and recommended practices to prevent or mitigate transport security issues. JMIR Mhealth Uhealth 2017 Oct 18;5(10):e147 [FREE Full text] [CrossRef] [Medline]
- Bondaronek P, Alkhaldi G, Slee A, Hamilton FL, Murray E. Quality of publicly available physical activity apps: review and content analysis. JMIR Mhealth Uhealth 2018 Mar 21;6(3):e53 [FREE Full text] [CrossRef] [Medline]
- Smith KA, Zhou L, Watzlaf VJ. User authentication in smartphones for telehealth. Int J Telerehabil 2017 Nov;9(2):3-12 [FREE Full text] [CrossRef] [Medline]
- Mylonas A, Kastania A, Gritzalis D. Delegate the smartphone user? Security awareness in smartphone platforms. Comput Secur 2013 May;34:47-66. [CrossRef]
- Huckvale K, Prieto JT, Tilney M, Benghozi PJ, Car J. Unaddressed privacy risks in accredited health and wellness apps: a cross-sectional systematic assessment. BMC Med 2015;13:214 [FREE Full text] [CrossRef] [Medline]
- Sunyaev A, Dehling T, Taylor PL, Mandl KD. Availability and quality of mobile health app privacy policies. J Am Med Inform Assoc 2015 Apr;22(e1):e28-e33. [CrossRef] [Medline]
- Peng W, Kanthawala S, Yuan S, Hussain SA. A qualitative study of user perceptions of mobile health apps. BMC Public Health 2016 Nov 14;16(1):1158 [FREE Full text] [CrossRef] [Medline]
- Dennison L, Morrison L, Conway G, Yardley L. Opportunities and challenges for smartphone applications in supporting health behavior change: qualitative study. J Med Internet Res 2013 Apr;15(4):e86 [FREE Full text] [CrossRef] [Medline]
- Prasad A, Sorber J, Stablein T, Anthony D, Kotz D. Exposing privacy concerns in mHealth. In: Proceedings of the 2nd USENIX conference on Health security and privacy. 2011 Presented at: the 2nd USENIX conference on Health security and privacy; August 9; San Francisco, CA p. 2-2 URL: https://www.cs.dartmouth.edu/~trdata/reports/TR2012-711.pdf
- Avancha S, Baxi A, Kotz D. Privacy in mobile technology for personal healthcare. ACM Comput Surv 2012 Nov 01;45(1):1-54 [FREE Full text] [CrossRef]
- Martínez-Pérez B, de la Torre-Diez I, López-Coronado M. Privacy and security in mobile health apps: a review and recommendations. J Med Syst 2015 Jan;39(1):181. [CrossRef] [Medline]
- Morera EP, de la Torre Diez I, Garcia-Zapirain B, López-Coronado M, Arambarri J. Security recommendations for mHealth apps: elaboration of a developer's guide. J Med Syst 2016 Jun;40(6):152. [CrossRef] [Medline]
- Tjora A, Tran T, Faxvaag A. Privacy vs usability: a qualitative exploration of patients' experiences with secure Internet communication with their general practitioner. J Med Internet Res 2005 May 31;7(2):e15 [FREE Full text] [CrossRef] [Medline]
- Watzlaf VJ, Zhou L, Dealmeida DR, Hartman LM. A systematic review of research studies examining telehealth privacy and security practices used by healthcare providers. Int J Telerehabil 2017 Nov;9(2):39-59 [FREE Full text] [CrossRef] [Medline]
- Takyi H, Watzlaf V, Matthews JT, Zhou L, Dealmeida D. Privacy and security in multi-user health kiosks. Int J Telerehabil 2017;9(1):3-14 [FREE Full text] [CrossRef] [Medline]
- Watzlaf VJ, Dealmeida DR, Zhou L, Hartman LM. Protocol for a systematic review of telehealth privacy and security research to identify best practices. Int J Telerehabil 2015 Nov;7(2):15-22 [FREE Full text] [CrossRef] [Medline]
- Watzlaf VJ, Moeini S, Matusow L, Firouzan P. VOIP for telerehabilitation: a risk analysis for privacy, security and HIPAA compliance: part II. Int J Telerehabil 2011;3(1):3-10 [FREE Full text] [CrossRef] [Medline]
- Watzlaf VJ, Moeini S, Firouzan P. VOIP for telerehabilitation: a risk analysis for privacy, security, and HIPAA compliance. Int J Telerehabil 2010 Oct;2(2):3-14. [CrossRef] [Medline]
- Zhou L, Parmanto B, Alfikri Z, Bao J. A mobile app for assisting users to make informed selections in security settings for protecting personal health data: development and feasibility study. JMIR Mhealth Uhealth 2018 Dec 11;6(12):e11210 [FREE Full text] [CrossRef] [Medline]
- Landauer TK. Behavioral research methods in human-computer interaction. In: Helander MG, Landauer TK, Prabhu PV, editors. Handbook of Human-Computer Interaction, 2nd edition. Amsterdam, Netherlands: Elsevier; 1997:203-227.
- Powell AC, Singh P, Torous J. The complexity of mental health app privacy policies: a potential barrier to privacy. JMIR Mhealth Uhealth 2018 Jul 30;6(7):e158 [FREE Full text] [CrossRef] [Medline]
- Das G, Cheung C, Nebeker C, Bietz M, Bloss C. Privacy policies for apps targeted toward youth: descriptive analysis of readability. JMIR Mhealth Uhealth 2018 Jan 04;6(1):e3 [FREE Full text] [CrossRef] [Medline]
- Nissenbaum H. Privacy as contexual integrity. Wash Law Rev 2004;79(1):101-139 [FREE Full text]
- Piette JD, Striplin D, Marinec N, Chen J, Aikens JE. A randomized trial of mobile health support for heart failure patients and their informal caregivers: impacts on caregiver-reported outcomes. Med Care 2015 Aug;53(8):692-699 [FREE Full text] [CrossRef] [Medline]
- Browning RB, McGillicuddy JW, Treiber FA, Taber DJ. Kidney transplant recipients' attitudes about using mobile health technology for managing and monitoring medication therapy. J Am Pharm Assoc 2016;56(4):450-454.e1. [CrossRef] [Medline]
- McGillicuddy JW, Weiland AK, Frenzel RM, Mueller M, Brunner-Jackson BM, Taber DJ, et al. Patient attitudes toward mobile phone-based health monitoring: questionnaire study among kidney transplant recipients. J Med Internet Res 2013;15(1):e6 [FREE Full text] [CrossRef] [Medline]
- Ramanathan N, Swendeman D, Comulada WS, Estrin D, Rotheram-Borus MJ. Identifying preferences for mobile health applications for self-monitoring and self-management: focus group findings from HIV-positive persons and young mothers. Int J Med Inform 2013 Apr;82(4):e38-e46. [CrossRef] [Medline]
- Poikela M, Schmidt R, Wechsung I, Moller S. About Your Smartphone Usage - Privacy in Location-Based Mobile Participation. 2015 Presented at: IEEE International Symposium on Technology in Society (ISTAS); Nov 11-12, 2015; Dublin, Ireland URL: https://ieeexplore.ieee.org/document/7439421
|AG: age group|
|IRB: institutional review board|
|KWH: Kruskal-Wallis H|
|mHealth: mobile health|
|NIDILRR: National Institute on Disability, Independent Living, and Rehabilitation Research|
|NSF: National Science Foundation|
Edited by G Eysenbach; submitted 05.06.18; peer-reviewed by P Ware, B Ray; comments to author 06.10.18; revised version received 23.02.19; accepted 04.03.19; published 16.04.19
©Leming Zhou, Jie Bao, Valerie Watzlaf, Bambang Parmanto. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 16.04.2019.
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