Background: Despite the importance of the privacy and confidentiality of patients’ information, mobile health (mHealth) apps can raise the risk of violating users’ privacy and confidentiality. Research has shown that many apps provide an insecure infrastructure and that security is not a priority for developers.
Objective: This study aims to develop and validate a comprehensive tool to be considered by developers for assessing the security and privacy of mHealth apps.
Methods: A literature search was performed to identify papers on app development, and those papers reporting criteria for the security and privacy of mHealth were assessed. The criteria were extracted using content analysis and presented to experts. An expert panel was held for determining the categories and subcategories of the criteria according to meaning, repetition, and overlap; impact scores were also measured. Quantitative and qualitative methods were used for validating the criteria. The validity and reliability of the instrument were calculated to present an assessment instrument.
Conclusions: The proposed comprehensive criteria can be used as a guide for app designers, developers, and even researchers. The criteria and the countermeasures presented in this study can be considered to improve the privacy and security of mHealth apps before releasing the apps into the market. Regulators are recommended to consider an established standard using such criteria for the accreditation process, since the available self-certification of developers is not reliable enough.
More than 5.19 billion people now use mobile phones, which indicates that mobile phones form an important part of daily life worldwide . Mobile phone features, including mobility, instantaneous availability, and direct communication, have changed the provision of health care services. These features introduce mobile health (mHealth). Of about 2 million smartphone apps available in app stores, 318,000 are health apps [ ]. According to a World Health Organization report [ ], the penetration of mHealth, with promising results, in low- and middle-income countries would be even more.
mHealth has improved the patient care status through the provision of health care anytime and anywhere . Even in recent years, the integration of mHealth and wireless technologies has provided clinicians with an opportunity to collect real-time data via wearable sensors [ ]. Health information is deemed sensitive, and its protection is of significance. Nevertheless, smartphones are vulnerable to a wide range of security threats [ ]. Moreover, electronic transmission of information has brought about concerns about its privacy and security. A national survey showed that 1 of the common reasons for people not having downloaded health apps is concern about apps gathering their data [ , ]. The privacy and confidentiality of information, as a human right, have long been considered in law and regulations. Well-known examples are the Health Insurance Portability and Accountability Act (HIPAA) rules, the General Data Protection Regulation (GDPR), and the Common Rule [ - ]. The terms “security,” “privacy” and “confidentiality” are all separate yet connected concepts that need to be addressed. The National Committee for Vital and Health Statistics [ ] defines and distinguishes these concepts as follows:
Health information privacy is an individual’s right to control the acquisition, uses, or disclosures of his or her identifiable health data. Confidentiality, which is closely related, refers to the obligations of those who receive information to respect the privacy interests of those to whom the data relate. Security is altogether different. It refers to physical, technological, or administrative safeguards or tools used to protect identifiable health data from unwarranted access or disclosure.
Despite the importance of the privacy and confidentiality of patients’ information, studies report that mHealth apps may share the information with third parties, which raises the risk of violating patients’ privacy and confidentiality [- ]. Dehling et al [ ] evaluated the information security and privacy of 24,405 health-related apps and revealed that most apps request access to sensitive information. Robillard et al [ ] reported that most of the apps do not include privacy policies and terms of the agreement. Moreover, it has been shown that many apps provide an insecure infrastructure and security is not a priority for the developers [ ]. Similar studies emphasize assessing mHealth apps for the privacy, security, and confidentiality of information to minimize the associated risks [ , , ].
This study was conducted to answer the following question: What security and privacy criteria should be considered when developing or assessing mHealth apps targeting patients based on 3 main phases: item generation, tool development, and tool evaluation? These main phases  were performed based on 4 steps: (1) identifying criteria associated with mHealth apps’ security/privacy according to a literature search (item generation); (2) conducting an expert panel for determining the categories and subcategories according to meaning, repetition, and overlap (tool development); (3) testing the validity of the instrument (tool evaluation); and (4) testing the reliability of the instrument (tool evaluation).
Stage 1: Literature Review
An unstructured literature search was performed to identify papers on app development, assessment, security, or privacy that reported criteria for the security and privacy of mHealth. PubMed, Scopus, Web of Science, and Cochrane were searched for English language papers published until December 15, 2021, without a time limitation. The search strategy () included a combination of 4 keywords: (“mobile device” OR “mobile phone” OR smartphone OR “smart Phone” OR mHealth OR “mobile health”) AND (App OR apps OR application*) AND (security OR privacy OR confidentiality OR cybersecurity) AND (guideline* OR standard* OR criteria OR risk* OR assess* OR evaluat* OR measure).
The HIPAA and GDPR websites were searched for relevant criteria. After removing duplicate papers, the titles and abstracts of the studies were screened for inclusion. The full text of potentially relevant papers was investigated based on study objectives. Studies substantially focusing on security or privacy, not just mentioning them in passing, and stating clear criteria for assessing the privacy/security of mHealth apps were included. Studies evaluating the privacy or security of mHealth apps were also included to specify the criteria used for evaluation. Papers proposing a secure architecture, investigating technical solutions for mHealth apps (eg, access control, authentication approaches, encryption methods), presenting technical solutions for connecting mHealth apps to cloud computing or the internet of things devices or conducted on wearable devices without connecting to a mobile device, and discussing mobile phone access to electronic health records were excluded. Papers focusing on mHealth apps targeting users other than patients, focusing on app quality or determining functional requirements, and examining user experiences were also excluded. The criteria were extracted using content analysis.
Stage 2: Expert Panel
The list of primary criteria extracted through the literature search was presented to a focus group including 2 health information technology (HIT) specialists, 2 medical informatics specialists, and 1 software and IT specialist. The focus group discussion consisted of 4 major steps: designing research, collecting data, analyzing, and reporting results through a moderated interaction . The experts discussed and categorized the criteria and decided over their inclusion or exclusion based on the relevancy, clarity, importance, comprehensiveness, and overlap with other included criteria, and they determined subcategories based on meaning, repetition, and overlap. This method can have a high level of validity due to the interaction among experts that confirms, reinforces, or rejects the individual respondents’ contributions. The criteria extracted through the focus group discussion were used in the next stage.
Stage 3: Testing the Validity of the Instrument
Quantitative and qualitative methods were used for validating the instrument. To validate the instrument based on the qualitative approach, face validity was checked through face-to-face interviews by 8 HIT specialists and 5 software and IT experts. The inclusion criteria for the experts included specialists in HIT, IT, or software, with a master’s degree in science or higher, with at least 1-year work experience in software security, network security, health information security, or mobile app development. The criteria were modified based on the experts’ comments.
To validate the instrument quantitatively, the impact score was calculated for each criterion. The impact score determines inappropriate criteria. Thus, the criteria were evaluated based on a 5-point Likert scale ranging from 5 (very important) to 1 (not at all important). The impact score for each criterion was calculated as follows:
Impact score = Frequency (%) × Importance
Content validity was evaluated by 16 other IT (n=8, 50%) and software (n=8, 50%) experts, of whom 3 (18.8%) experts did not participate. Thus, to make sure the most essential criteria for the study objective were chosen, the content validity ratio (CVR) was measured. The CVR was calculated based on the following formula:
According to the Lawshe table, if the number of experts in the panel is 13, the minimal acceptable CVR is 0.54.
In addition, to ensure the relevancy and clarity of each criterion, the content validity index (CVI) was measured. Thus, the 13 experts also completed a 4-point scale based on relevance, clarity, and simplicity for the criteria. The CVI was calculated using the following formula:
The criteria were included in the final assessment tool if the CVI was ≥0.79 [, ]. If the CVI was between 0.70 and 0.79, it needed to be calculated after the criteria were revised by the experts. Criteria with a CVI of <0.70 were removed.
Stage 4: Testing Reliability
To assess the reliability of the final tool, the hypertensive self-care app developed in our previous study  was selected. The app needs to record a variety of personal information. In total, 30 experts in HIT, medical informatics, IT, and software assessed the reliability of the instrument. The instrument was distributed among these experts twice in a 2-month interval. They were asked to assess the privacy and security of the self-care app using the criteria provided in the checklist. After collecting expert opinions about the self-care app, the data were analyzed using the Cronbach α.
The research was conducted according to the principles stated by the Vice-Chancellorship for Research Affairs of Shiraz University of Medical Science and approved by the Ethics Review Board of the Vice-Chancellorship for Research Affairs of Shiraz University of Medical Science (ethical code IR.SUMS.REC.1397.500).
The search strategy retrieved 10,092 papers, of which 1902 (18.8%) were duplicates. Of the 8190 (81.2%) remaining papers, 8072 (98.6%) were irrelevant. To retrieve the greatest number of possible relevant papers, our search strategy included smartphone or mobile devices as a synonym for mHealth (“mobile device” OR “mobile phone” OR smartphone OR “smart Phone” OR mHealth OR “mobile health”); this resulted in retrieving papers basically irrelevant to the health discipline, in addition to those relevant to the health discipline—for example, studies associated with payment/banking/commercial apps were also retrieved in the primary result. In total, 33 (0.4%) studies were deemed eligible for inclusion in the research (). The characteristics of the included studies [ , , , - , , - ] are presented in .
A total of 218 criteria were extracted based on the literature search; of these, 119 (54.6%) were removed as duplicates (showing the same idea) and 10 (4.6%) were deemed irrelevant to the security or privacy of mHealth apps. The remaining 89 (40.8%) criteria were presented to the expert panel. As shown in, 63 (70.8%) criteria were confirmed at last.
The mean CVR of the total instrument was 0.72, while the mean CVI was 0.86.shows the complete list of removed criteria in the different phases of the study.
Finally, to measure the reliability of the instrument, the experts were asked to assess the hypertensive self-care app using the instrument. When measuring the reliability of the instrument, 18 (28.6%) of the 63 criteria received the lowest and the highest score of the Likert spectrum (“not at all” and “completely”) equally. Since the variance of equal data was 0, these 18 criteria did not automatically enter for calculating the Cronbach α value. Thus, the test was performed with 45 (71.4%) criteria. The Cronbach α value was 0.89.
Final privacy and security assessment criteria.
1. Authentication and authorization
1.1. Is there any registration/log-in available in the app?
1.2. Does the app capture a unique username or “fixed device identifier” used as a user identifier (for both patient and health care provider)?
1.3. Are there procedures to verify that any person or entity claiming access to electronic protected health information complies with its claim?
1.4. Are there any ways to monitor the log and report errors?
1.5. Are there any steps to create, change, and protect the password?
1.6. Are the passwords complex enough (ie, of a minimum length, alphanumeric with upper- and lowercase letters and symbols)?
1.7. Are the passwords updated periodically?
1.8. Is the user’s account locked after a determined number of consecutive unsuccessful log-in attempts?
2. Access management
2.1. Is there patient-centric access control?
2.2. Are there measures taken to access the health information needed in an emergency?
2.3. Is the user allowed to access personal information and to participate in treatment?
2.4. Does the app facilitate the provision of an electronic copy of data?
2.5. Is the app capable of cutting off or blocking a person's access at any time?
2.6. Are users allowed to control the access level of their health information by third parties?
3.1. Does the app use secure connections (Secure Socket Layer [SSL]/Transport Layer Security [TLS])?
3.2. Can the data be remotely controlled if the mobile phone is lost/stolen?
3.3. Does the app use a secure platform for transmitting health data?
3.4. Does the app protect network traffic by strong coding?
3.5. Are default measures present to protect against, identify, and report security incidents/malware?
3.6. Does the app use external devices?
3.7. Does the app use random number generators?
3.8. Are users able to change individual profiles according to the policy of the mobile health (mHealth) app?
3.9. Does the app require interaction with the user while performing a sensitive operation or communicating with an untrusted app?
3.11. Is the security policy transparent and easy to find?
3.12. Are there reminders for periodic system security updates?
3.13. Has anyone been appointed to assume security responsibility?
4. Data storage
4.1. Are data stored locally on the device? If no, are the users notified about using another platform for storing their data?
4.2. Are data centers in a secure condition?
4.3. Are data stored on the mobile phone or to the app company’s own servers?
4.4. Are there any steps to recover lost data or any backup?
5.1. Are there electronic mechanisms to verify that health information is not unauthorized, altered, or destroyed (eg, check-sum verification or digital signatures)?
5.2. Are security measures in place to prevent the unauthorized destruction or tampering of health information that is being exchanged electronically?
6. Encryption and decryption
6.1. Does the app use a strong modern encryption/decryption mechanism?
6.2. Is a proper method of encryption selected and implemented (eg, use encryption through https rather than http)?
6.3. Are the data stored encrypted?
6.4. Are the data transmitted encrypted?
6.5. Is the username/password/keys encrypted?
7.2. Are there any restrictions on the use or disclosure of information contained in the app?
7.3. Are there restrictions on the collection of information?
7.4. Does the app have the ability to disclose information on social media by the user?
7.5. Has the principle of protecting the confidentiality of data been met?
7.6. Does the app state which regulation it complies with and which country the regulation belongs to?
7.7. Does the app ask normal permissions and provide justification for that?
7.8. Is identifiable information anonymized and de-identifiable? If anonymization is not possible, are users informed?
7.9. Have any measures been taken to notify the users of their privacy rights?
7.10. Will the user be informed of any leaks or breaches?
7.11. Does the app have the ability to manage alerts (eg, hide them from the lock screen)?
7.13. Are users able to manipulate or completely delete personal profiles and any data archives?
7.14. Are users informed about any security or privacy measures?
7.15. Does the app prevent disclosure of data about the location or sensor type of the user?
8.1. Is there a time limit for data retention?
8.2. Is the content of the contract with third parties clearly stated?
8.3. Does the app mention the collection of user data and how they are being used?
8.5. Is the data ownership specified?
8.6. Are the administrative details stated (identify data controller or responsible legal entity, legal jurisdiction governing policy, jurisdictions under which transmitted data will be processed, date of policy and next review)?
8.7. Is there an explanation about the retention policy for the health information?
Authentication and Authorization
The criteria in the tool suggest implementing rigorous authentication and authorization techniques. More time and effort should be devoted to preventing unauthorized access to personal health information. The developers are asked to provide a unique master ID and a secret key identity for users to control role-based access and verify users’ activities according to the defined identity and roles. Authentication via a fingerprint or a personal identification number is necessary for internal storage, internal cache, external storage, and databases . Audit trails should be in place to track logs, protect data, and identify which user’s health data was handled and by whom. Each user should be able to create, change, and protect their passwords. The developers should make sure the passwords are strong enough and are changed periodically, because there are tools that produce 1014 guesses in an hour to find the correct password [ ]. There are some strategies to be used by developers to make sure passwords are secure; these include enforcing password complexity; making passwords unviewable, even to the app administrator; and locking a user’s account after a determined number of consecutive unsuccessful log-in attempts. System-generated passwords can be strong, but they do not guarantee memorability. Using Optiwords8 passwords [ ], based on the picture superiority effect on the mobile phone keyboards, guarantees the security of passwords, while keeping them usable and memorable as a result.
mHealth app developers need to define access controls for their team members as well as users. For those apps providing health care provider–patient communication, granting access to specific app functions should be based on predetermined and confirmed roles and attributes. Patients should be users allowed to control the access level of their health information by third parties. Greene et al  proposed the ShareHealth framework, which provides cryptographically enforced access to data. The framework takes advantage of combining a robust cryptographic scheme, hash chains (to control access by data time), and attribute-based encryption (to control access by data type). Rectification, deleting, or blocking of data should be facilitated for users [ ].
Some mHealth apps use connections for several purposes, including fetching mail, sending analytics data, or checking for updates. To protect the authenticity, confidentiality, and integrity of the connection, developers are encouraged to use an up-to-date version of the Transport Layer Security protocol and its predecessor, the Secure Socket Layer (SSL) . SSL protocols provide an encrypted link that connects a server and a client and makes sure the transmitted data remain impossible to read and are kept private; however, if the coding is not strong enough, hackers would be able to interpret health data during transmission [ ]. There should be a functionality of remote control of data to securely transfer, retrieve, or completely erase health information if the mobile phone is stolen/lost [ ]. However, it is safer to store data on users’ own devices rather than on the app company’s servers [ ]. Some apps use external devices, such as cameras, sensors, or payment apps, to improve their functionality, but this endangers users’ confidentiality through attacks, such as external-device misbonding [ ]. Moreover, using cookies can jeopardize user privacy especially those used for data analysis by third parties [ ]. Users should be able to manipulate their profile or delete it completely when they stop using an app [ ].
Encryption and Decryption
Bhanot and Hans  compared various encryption algorithms based on different criteria, such as cryptography type, key management, keys number, and bit numbers used in a key. They found that elliptic-curve cryptography and blowfish encryption algorithms are the best, providing higher security levels as well as faster encryption speeds, which is required for mobile devices due to less power consumption [ ]. Security measures, such as wired equivalent privacy, which is used to provide security to mobile devices, are vulnerable to hackers [ , ]. Thus, developers are required to perform a security risk analysis to determine vulnerabilities at each stage of design and implementation throughout testing and use. Arora et al [ ] suggest using a “red team” for risk analysis. Red team experts are charged with hacking cyber systems in order to detect weaknesses.
Papageorgiou et al  found that although many of the studied apps ask for dangerous permissions (eg, read/write external storage, access camera, location, and contacts), they do not follow well-known regulations, such as HIPAA. Developers are required to collect data as much as they need to provide their services, so they are required to provide reasons for permissions they ask for, the type of data they collect, and how the data will be used by them or third parties, including insurance companies, government institutions, or even research centers [ , ]. Third-party usage of health data can bring about privacy intrusions, such as loss of insurance coverage or higher insurance premiums [ ]. Complying with regulations and which country these regulations belong to is also important because when enforcing privacy rights, the regulations may differ from the users’ own country [ ]. Users’ records should be stored in incognito forms, which are anonymized and unidentifiable; if anonymization is not possible, users should be informed [ ].
In this study, a list of criteria was proposed using published papers. A limitation of this study is conducting an unstructured literature search, due to which we missed some related papers. However, to the best of our knowledge, many of the criteria included in our study overlap those that were not included. Another limitation is the large number of included criteria, which may make it difficult for assessors to consider all of them; however, we tried to limit our criteria to important ones to make them more applicable, and we also used general concepts that cover more specific criteria (eg, user rights) or merged some items into a single one (eg, administrative details). Another limitation is the difficulty in assessing some criteria—for example, app compliance with regulations may not be clearly stated in the app. It is recommended that future studies verify the proposed criteria using mobile apps. However, they should be considered in conjunction with other assessment strategies, such as risk analysis, data leakage detection, and continuous revision accordingly. Moreover, this study focused on the security and privacy challenges of mHealth apps, but there are other important challenges, such as interoperability. Thus, it is recommended that future studies combine both aspects to obtain not only a secure system but also an interoperable one, because mHealth apps communicate with a variety of sources.
With the evolution in the health field through smartphones and mHealth apps, privacy and security challenges need to be addressed. The proposed comprehensive criteria can be used as a quick guide for app designers, developers, regulators, and even researchers. The criteria and the countermeasures presented in this study can be considered to improve the privacy and security of an mHealth app before releasing it into the market. Regulators are recommended to consider an established standard using such criteria for the accreditation process, since the available self-certification of developers is not reliable enough.
We acknowledge all the participants who helped us conduct this research. This paper was extracted from MK’s Master of Science thesis on health information management.
RR, MK, SS, MN, and SZ made substantial contributions to conception and design. SZ and MK made substantial contributions to data collection through a literature search. SZ and RR drafted the manuscript, and all the authors have read it to revise it critically for important intellectual content.
MN was affiliated with the Health Human Resources Research Center at Shiraz University of Medical Sciences at the time of the study and is currently affiliated with the Department of Computer Engineering and Information Technology at Shiraz University of Technology.
Conflicts of Interest
This is the search strategy.DOCX File , 15 KB
Characteristics of the included studies.DOCX File , 61 KB
The complete list of removed criteria.DOCX File , 15 KB
- Kemp S. Digital 2020: global digital overview. DATAREPORTAL. 2020. URL: https://datareportal.com/reports/digital-2020-global-digital-overview [accessed 2023-02-09]
- Szpunar MJ, Parry BL. A systematic review of cortisol, thyroid-stimulating hormone, and prolactin in peripartum women with major depression. Arch Womens Ment Health 2018 Apr;21(2):149-161 [FREE Full text] [CrossRef] [Medline]
- Buranarach M, Chalortham N, Thein YM, Supnithi T. Design and implementation of an ontology-based clinical reminder system to support chronic disease healthcare. IEICE Trans Inf Syst 2011;E94-D(3):432-439. [CrossRef]
- Silva B, Rodrigues J, de la Torre Díez I, López-Coronado M, Saleem K. Mobile-health: a review of current state in 2015. J Biomed Inform 2015 Aug;56:265-272 [FREE Full text] [CrossRef] [Medline]
- Munos B, Baker PC, Bot BM, Crouthamel M, de Vries G, Ferguson I, et al. Mobile health: the power of wearables, sensors, and apps to transform clinical trials. Ann N Y Acad Sci 2016 Jul;1375(1):3-18. [CrossRef] [Medline]
- Zubaydi F, Saleh A, Aloul F, Sagahyroon A. Security of mobile health (mHealth) systems. 2015 Presented at: IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE); November 2-4, 2015; Belgrade, Serbia p. 2-4. [CrossRef]
- Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR Mhealth Uhealth 2015 Nov 04;3(4):e101 [FREE Full text] [CrossRef] [Medline]
- Özkan Ö, Aydin SY, Aydinoğlu AU. Security and privacy concerns regarding genetic data in mobile health record systems: an empirical study from Turkey. bioRxiv 2019. [CrossRef]
- Edemekong P, Annamaraju P, Haydel M. Health Insurance Portability and Accountability Act. Treasure Island, FL: StatPearls Publishing; 2020.
- European Parliament, Council of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data and repealing Directive 95/46. Official J Eur Union 2016;59:1-88 [FREE Full text] [CrossRef]
- Williams ED. Federal protection for human research subjects: an analysis of the common rule and its interactions with FDA regulations and the HIPAA Privacy Rule. Congressional Research Service, Library of Congress. 2005. URL: http://www.fas.org/sgp/crs/misc/RL32909.pdf [accessed 2023-02-09]
- Cohn S. Privacy and confidentiality in the nationwide health information network. National Committee on Vital and Health Statistics. 2006. URL: https://library.ahima.org/doc?oid=75960#.Y-RqgnZBxD8 [accessed 2023-02-09]
- Minen MT, Stieglitz EJ, Sciortino R, Torous J. Privacy issues in smartphone applications: an analysis of headache/migraine applications. Headache 2018 Jul;58(7):1014-1027 [FREE Full text] [CrossRef] [Medline]
- Huckvale K, Torous J, Larsen ME. Assessment of the data sharing and privacy practices of smartphone apps for depression and smoking cessation. JAMA Netw Open 2019 Apr 05;2(4):e192542 [FREE Full text] [CrossRef] [Medline]
- Knorr K, Aspinall D, Wolters M. On the privacy, security and safety of blood pressure and diabetes apps. In: Federrath H, Gollmann D, editors. ICT Systems Security and Privacy Protection. SEC 2015. IFIP Advances in Information and Communication Technology, Vol 455. Cham: Springer; 2015:571-584.
- Dehling T, Gao F, Schneider S, Sunyaev A. Exploring the far side of mobile health: information security and privacy of mobile health apps on iOS and Android. JMIR Mhealth Uhealth 2015 Jan 19;3(1):e8 [FREE Full text] [CrossRef] [Medline]
- Robillard J, Feng T, Sporn A, Lai J, Lo C, Ta M, et al. Availability, readability, and content of privacy policies and terms of agreements of mental health apps. Internet Interv 2019 Sep;17:100243 [FREE Full text] [CrossRef] [Medline]
- Knorr K, Aspinall D. Security testing for Android mHealth apps. 2015 Presented at: 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW); April 13-17, 2015; Graz, Austria p. 13-17. [CrossRef]
- Scott K, Richards D, Adhikari R. A review and comparative analysis of security risks and safety measures of mobile health apps. AJIS 2015 Nov 22;19:1-18. [CrossRef]
- Grindrod K, Boersema J, Waked K, Smith V, Yang J, Gebotys C. Locking it down: the privacy and security of mobile medication apps. Can Pharm J (Ott) 2017;150(1):60-66 [FREE Full text] [CrossRef] [Medline]
- Benjumea J, Ropero J, Rivera-Romero O, Dorronzoro-Zubiete E, Carrasco A. Assessment of the fairness of privacy policies of mobile health apps: scale development and evaluation in cancer apps. JMIR Mhealth Uhealth 2020 Jul 28;8(7):e17134 [FREE Full text] [CrossRef] [Medline]
- Hutton L, Price BA, Kelly R, McCormick C, Bandara AK, Hatzakis T, et al. Assessing the privacy of mHealth apps for self-tracking: heuristic evaluation approach. JMIR Mhealth Uhealth 2018 Oct 22;6(10):e185 [FREE Full text] [CrossRef] [Medline]
- Benjumea J, Ropero J, Rivera-Romero O, Dorronzoro-Zubiete E, Carrasco A. Privacy assessment in mobile health apps: scoping review. JMIR Mhealth Uhealth 2020 Jul 02;8(7):e18868 [FREE Full text] [CrossRef] [Medline]
- Martínez-Pérez B, de la Torre-Díez 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]
- Hinkin TR. A review of scale development practices in the study of organizations. J Manag 2016 Jun 30;21(5):967-988. [CrossRef]
- Morgan D. Focus Groups as Qualitative Research. New York, NY: SAGE Publications; 1996.
- Zamanzadeh V, Ghahramanian A, Rassouli M, Abbaszadeh A, Alavi-Majd H, Nikanfar A. Design and implementation content validity study: development of an instrument for measuring patient-centered communication. J Caring Sci 2015 Jun;4(2):165-178 [FREE Full text] [CrossRef] [Medline]
- Rodrigues IB, Adachi JD, Beattie KA, MacDermid JC. Development and validation of a new tool to measure the facilitators, barriers and preferences to exercise in people with osteoporosis. BMC Musculoskelet Disord 2017 Dec 19;18(1):540 [FREE Full text] [CrossRef] [Medline]
- Zare S, Rezaee R, Aslani A, Shirdeli M, Kojuri J. Moving toward community based telehealth services using mhealth for hypertensive patients. Int J Technol Assess Health Care 2019 Sep 24;35(5):379-383. [CrossRef]
- Jones J, Hook S, Park S, Scott L. Privacy, security and interoperability of mobile health applications. LNCS (subseries LN Artif Intell LN Bioinform) 2011;6767:46-55. [CrossRef]
- Adhikari R, Richards D, Scott K. Security and privacy issues related to the use of mobile health apps. 2014 Presented at: Proceedings of the 25th Australasian Conference on Information Systems (ACIS); December 8-10, 2014; Auckland, New Zealand.
- Carter A, Liddle J, Hall W, Chenery H. Mobile phones in research and treatment: ethical guidelines and future directions. JMIR Mhealth Uhealth 2015 Oct 16;3(4):e95 [FREE Full text] [CrossRef] [Medline]
- Huckvale K, Prieto JT, Tilney M, Benghozi P, Car J. Unaddressed privacy risks in accredited health and wellness apps: a cross-sectional systematic assessment. BMC Med 2015 Sep 07;13:214 [FREE Full text] [CrossRef] [Medline]
- Kramer GM, Kinn JT, Mishkind MC. Legal, regulatory, and risk management issues in the use of technology to deliver mental health care. Cogn Behav Pract 2015 Aug;22(3):258-268. [CrossRef]
- Olff M. Mobile mental health: a challenging research agenda. Eur J Psychotraumatol 2015;6:27882 [FREE Full text] [CrossRef] [Medline]
- Plachkinova M, Andres S, Chatterjee S. A taxonomy of mHealth apps - security and privacy concerns. 2015 Presented at: Proceedings of the Annual Hawaii International Conference on System Sciences; January 5-8, 2015; Hawaii. [CrossRef]
- Scott KM, Gome GA, Richards D, Caldwell PHY. How trustworthy are apps for maternal and child health? Health Technol 2015 Mar 12;4(4):329-336. [CrossRef]
- Bruggemann T, Hansen J, Dehling T, Sunyaev A. An information privacy risk index for mHealth apps. 2016 Presented at: 4th Annual Privacy Forum (APF); September 7-8, 2016; Frankfurt, Germany. [CrossRef]
- Chen C, Osman M, Zaaba Z, Talib A. Managing secure personal mobile health information. Adv Intell Syst Comput 2017:347-356. [CrossRef]
- Jones N, Moffitt M. Ethical guidelines for mobile app development within health and mental health fields. Prof Psychol: Res Pract 2016 Apr;47(2):155-162. [CrossRef]
- Loy JS, Ali EE, Yap KY. Quality assessment of medical apps that target medication-related problems. J Manag Care Spec Pharm 2016 Oct;22(10):1124-1140 [FREE Full text] [CrossRef] [Medline]
- Mense A, Steger S, Sulek M, Jukicsunaric D, Meszaros A. Analyzing privacy risks of mHealth applications. 2016 Presented at: Special Topic Conference (STC) of the European-Federation-for-Medical-Informatics (EFMI) on Transforming Healthcare with the Internet of Things; April 2016; Paris, France p. 17-19.
- Morera EP, de la Torre Díez 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]
- Asaddok N, Ghazali M. Exploring the usability, security and privacy taxonomy for mobile health applications. 2017 Presented at: 5th International Conference on Research and Innovation in Information Systems (ICRIIS); July 16-17, 2017; Langkawi Island, Malaysia. [CrossRef]
- Wu E, Torous J, Hardaway R, Gutheil T. Confidentiality and privacy for smartphone applications in child and adolescent psychiatry: unmet needs and practical solutions. Child Adolesc Psychiatr Clin N Am 2017 Jan;26(1):117-124. [CrossRef] [Medline]
- Gabel A, Schiering I, Müller S, Ertas F. mHealth applications for goal management training - privacy engineering in neuropsychological studies. IFIP Adv Inf Commun Technol 2018:330-345. [CrossRef]
- Hussain M, Al-Haiqi A, Zaidan A, Zaidan B, Kiah M, Iqbal S, et al. A security framework for mHealth apps on Android platform. Comp Security 2018 Jun;75:191-217. [CrossRef]
- 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]
- Scott KM, Richards D, Londos G. Assessment criteria for parents to determine the trustworthiness of maternal and child health apps: a pilot study. Health Technol 2018 Jan 25;8(1-2):63-70. [CrossRef]
- Zelmer J, van Hoof K, Notarianni M, van Mierlo T, Schellenberg M, Tannenbaum C. An assessment framework for e-mental health apps in Canada: results of a modified Delphi process. JMIR Mhealth Uhealth 2018 Jul 09;6(7):e10016 [FREE Full text] [CrossRef] [Medline]
- Al-Sharo YM. Networking issues for security and privacy in mobile health apps. Int J Adv Comp Sci Appl 2019;10(2):186-191. [CrossRef]
- Iwaya LH, Fischer-Hübner S, Åhlfeldt RM, Martucci LA. Mobile health systems for community-based primary care: identifying controls and mitigating privacy threats. JMIR Mhealth Uhealth 2019 Mar 20;7(3):e11642 [FREE Full text] [CrossRef] [Medline]
- Müthing J, Brüngel R, Friedrich CM. Server-focused security assessment of mobile health apps for popular mobile platforms. J Med Internet Res 2019 Jan 23;21(1):e9818 [FREE Full text] [CrossRef] [Medline]
- Nurgalieva L, O'Callaghan D, Doherty G. Security and privacy of mHealth applications: a scoping review. IEEE Access 2020;8:104247-104268. [CrossRef]
- 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 [FREE Full text] [CrossRef] [Medline]
- Srivastava M, Thamilarasu G. MSF: a comprehensive security framework for mHealth applications. 2019 Presented at: 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW); August 26-28, 2019; Istanbul, Turkey p. 26-28. [CrossRef]
- Dev J. Usage of botnets for high speed MD5 hash cracking. 2013 Presented at: Third International Conference on Innovative Computing Technology (INTECH); August 29-31, 2013; London, UK p. 29-31. [CrossRef]
- Guo Y, Zhang Z, Guo Y. Optiwords: a new password policy for creating memorable and strong passwords. Comp Security 2019 Aug;85:423-435 [FREE Full text] [CrossRef]
- Greene E, Proctor P, Kotz D. Secure sharing of mHealth data streams through cryptographically-enforced access control. Smart Health (Amst) 2019 Apr;12:49-65 [FREE Full text] [CrossRef] [Medline]
- Bhanot R, Hans R. A review and comparative analysis of various encryption algorithms. Int J Security Appl 2015 Apr 30;9(4):289-306. [CrossRef]
- Bajwa MI. mHealth security. Pak J Med Sci 2014 Jul 31;30(4):904-907 [FREE Full text] [CrossRef] [Medline]
- Choi CQ. Digital danger. Sci Am 2012 Nov 13;307(6):14-14. [CrossRef]
- Arora S, Yttri J, Nilse W. Privacy and security in mobile health (mHealth) research. Alcohol Res 2014;36(1):143-151 [FREE Full text] [Medline]
- Braghin C, Cimato S, Libera A. Are mHealth apps secure? A case study. 2018 Presented at: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC); July 23-27, 2018; Tokyo, Japan p. 23-27. [CrossRef]
- Tapuria A, Porat T, Kalra D, Dsouza G, Xiaohui S, Curcin V. Impact of patient access to their electronic health record: systematic review. Inform Health Soc Care 2021 Jun 02;46(2):192-204. [CrossRef] [Medline]
|CVI: content validity index|
|CVR: content validity ratio|
|GDPR: General Data Protection Regulation|
|HIPAA: Health Insurance Portability and Accountability Act|
|HIT: health information technology|
|mHealth: mobile health|
Edited by L Buis; submitted 27.04.22; peer-reviewed by J Ropero, KL Mauco; comments to author 25.07.22; revised version received 16.08.22; accepted 21.09.22; published 02.03.23Copyright
©Rita Rezaee, Mahboobeh Khashayar, Saeed Saeedinezhad, Mahdi Nasiri, Sahar Zare. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 02.03.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.