Background: Due to their ability to collect person-generated health data, digital tools and connected health devices may hold great utility in disease prevention, chronic disease self-monitoring and self-tracking, as well as in tailoring information and educational content to fit individual needs. Facilitators and barriers to the use of digital health technologies vary across demographics, including sex. The “femtech” market is growing rapidly, and women are some of the largest adopters of digital health technologies.
Objective: This paper aims to provide the background and methods for conducting a scoping review on the use of person-generated health data from connected devices in women’s health. The objectives of the scoping review are to identify the various contexts of digital technologies in women’s health and to consolidate women’s views on the usability and acceptability of the devices.
Methods: Searches were conducted in the following databases: Medline, Embase, APA PsycInfo, CINAHL Complete, and Web of Science Core Collection. We included articles from January 2015 to February 2020. Screening of articles was done independently by at least two authors in two stages. Data charting is being conducted in duplicate. Results will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist.
Results: Our search identified 9102 articles after deduplication. As of November 2020, the full-text screening stage is almost complete and data charting is in progress. The scoping review is expected to be completed by Fall 2021.
Conclusions: This scoping review will broadly map the literature regarding the contexts and acceptability of digital health tools for women. The results from this review will be useful in guiding future digital health and women’s health research.
International Registered Report Identifier (IRRID): DERR1-10.2196/26110
Modern-day society is rapidly embracing innovations in connected technologies such as smartphones, text messages, wearables (eg, smartwatches), sensors, the Internet of Things (eg, internet-enabled weight scales) , as well as interactive web applications. When used in the context of health care, these technologies enable their users to collect, store, and reflect on their health data. Person-generated health data (PGHD) are defined as clinically relevant data captured outside traditional care settings [ ] and describe experiences from the everyday lives of individuals. With information derived from this data, users are empowered to take actions toward improving their health [ - ].
The mass adoption of connected technologies and PGHD benefits person-centered care by enabling individuals to play a more active and proactive role in managing their health . Connected health tools provide a platform for information exchange between individuals and their health care providers [ , ] in person and in telemedicine consultations. These tools can also be used asynchronously, at convenient times throughout the day, without requiring health care providers to be online simultaneously with the patient for a real-time conversation. Health outcomes can be measured and monitored weekly, daily, and continuously; these more frequent assessments provide users and health care providers quicker feedback on measures of health status, thus enabling faster medical interventions when necessary [ , ]. Detailed longitudinal PGHD can paint a more complete picture of one’s health, minimizing the risk of recall bias [ ] at health appointments. Using PGHD to monitor health status over time can help detect health concerns early [ ], prevent medical events [ , ], and evaluate patient outcomes during and after medical treatments [ , ].
One of the most promising features of digital health is the ability to personalize and tailor content to address specific health conditions and concerns. With or without consulting their medical team, participants can decide which health metrics are most pertinent to their situation and receive targeted information and feedback based on their personal measurements and symptoms [, ]. Individualized health plans created from PGHD may encourage personal participation and accountability with respect to health and health-related behaviors [ , ]. The impersonal nature of these platforms, which provide the ability to ask questions and track their conditions anonymously, may be especially appreciated in scenarios where participants have reservations about discussing certain health issues in person with health care providers (eg, sexual health [ , ]); independently monitoring these health issues online allows them to be more forthcoming [ ].
Applications specifically targeting women are exploding in what has been coined “femtech.” Historically, women were excluded from health research, which meant that very little was known about female-specific health concerns or diseases that impacted mostly women (eg, menstrual health) [, ]. Considerable advancements have been made in women’s health over the past decades [ ]. Today, health technology is promising to reverse the tide of research in women’s health with women 75% more likely to use digital health tools than men [ ]. It is important that research in new areas such as digital health continues to recognize women’s needs and address their concerns, as sex and gender can influence adoption and acceptability of connected health technologies.
Despite all the benefits of digital health technologies, many known barriers to successful adoption remain. Users of these technologies still have outstanding concerns around privacy and security of health data collected through these various devices. Data tracked by devices can contain large amounts of personal and sensitive information that users care to keep private  and to have more control over who can have access to such data [ - ]. The lack of perceived direct utility of digital health data [ , ] and the lack of applicable insight have slowed down the adoption of digital tools for clinical decision making [ , ]. Various studies have shown that the accuracy of wearable devices is variable and less reliable during dynamic activity outside of a laboratory [ , ]. The lack of adherence to using the technology over extended periods of time constitutes one of the biggest drawbacks from relying on such data. Users may forget or be unwilling to use the devices on a regular basis, and they may abandon self-tracking after a period of time if the perceived value is not realized [ ]. Finally, rates of adoption of digital technologies vary greatly across sociocultural characteristics. Studies have found lower rates of adoption and more negative attitudes among individuals living in rural locations [ , ]. Younger individuals and women are more likely to use health apps and track health information online [ , ]. Women were less willing to share information, in comparison with men who were more confident about protecting their privacy [ , ]. Research has shown that individuals are more accepting of digital health technologies when the health information they deal with is less sensitive [ ], and it is possible that women consider their female-specific health data (eg, menstruation, pregnancy) to be more sensitive than other types of general health data. As the femtech industry continues to grow, these concerns are becoming more prominent [ , ].
Scoping reviews on wearable technologies  and mobile health apps [ ] have not addressed specific areas of women’s digital health. While some have looked at areas such as gestational diabetes [ ], perinatal depression and anxiety [ ], and fertility tracking [ ], no study has broadly mapped the use of PGHD from connected devices for women’s health.
In this scoping review, we aim to explore the different contexts in which digital tools collecting PGHD are being proposed to address women’s health issues. We also want to evaluate women’s opinions with regards to the acceptability of connected health devices in these different contexts. More specifically, our review aims to answer the following research questions:
- What are the different areas of women’s health or health-related behaviors that are being monitored with PGHD from connected health devices?
- What personal metrics are being collected by these technologies?
- What are the facilitators and barriers for women promoting or hindering their use of connected health devices?
The results from this review will allow us to identify gaps and unmet needs in women’s health research to help guide future digital and connected women’s health innovations.
This scoping review protocol has been developed to align with the frameworks developed by Arksey and O’Malley  and Peters et al [ ]. The completed Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) [ ] checklist is provided in .
The search strategy, developed in close collaboration with a reference librarian, was first created in Medline and adapted to Embase, APA PsycInfo, CINAHL Complete, and Web of Science Core Collection.
Initial searches were completed between March 2 and 6, 2020. Search alerts were used to include results added to the databases between March 6 and April 1, 2020. Manual searches were done to ensure that the initial search criteria used were comprehensive. On November 13, 2020, and March 10, 2021, we added additional search terms to broadly encompass possible missed articles. We kept a uniform cut-off date of February 29, 2020, for all included articles.
We focused on keywords and subject headings to ensure a broad coverage of the literature at the intersection of the following four topics: women, health, digital devices, and tracking. The topics of women and health were identified by terms such as “women’s health,” “female,” “mhealth,” and “digital health.” Terms such as “smartphone,” “wearable,” and “Internet of Things” were used to identify digital devices. Tracking was identified by terms including “tracking,” “monitoring,” “self-management,” “ResearchKit,” and “person-generated adj4 data.” The full list of search terms is included in.
Terms referring to telemedicine (consultations with a health care provider in real time) were not included because we are primarily interested in technologies that allow the user to interact with the device on her own time for the collection of PGHD. Searches were limited to articles published in 2015 or later because publications with the keyword “digital health” started to emerge in the literature around that time [, ]. We also excluded conference abstracts, conference reviews, editorials, letters, and comments due to limited feasibility and lack of details in such literature.
Because we wished to broadly map the existing literature on digital data and connected women’s health research, we included a variety of studies: randomized and nonrandomized intervention studies, observational and correlative studies, feasibility and acceptability studies, case studies, reviews, descriptions of prototypes, measurement studies, analytical methods, and viewpoints. Study media releases and user reviews of specific applications were excluded. We only included articles written in English, irrespective of the country or the place of research.
We were interested in technologies and interventions targeting women, so papers were eligible if they specifically targeted women-only health topics (eg, pregnancy) or if they only included female participants. Articles including intersex, transgender, or nonbinary participants were not excluded.
We excluded articles that presented digital health tools designed for health care providers, as we are primarily interested in devices and apps that women can engage with independently outside of a clinical setting. Articles discussing the use of real-time consultations, whether through video, phone, or online chat, were excluded; however, some of the included interventions could involve the use of telemedicine services as long as they included asynchronous use. To maintain the focus of the review on tracking or monitoring one’s data for health, devices must have allowed users to input personal health data; therefore, publications reporting on apps or websites used solely for educational purposes were excluded. Complete inclusion and exclusion criteria are presented inand .
- Published between January 1, 2015, and February 29, 2020
- Refers to a health issue that pertains only to women or consists of only female participants of any age
- Includes the use of connected health tools for tracking or monitoring some aspect of health. This could include smartphones, wearable devices, the Internet of Things (eg, Bluetooth- or internet-enabled glucometers, blood pressure cuffs, and weight scales), and implantable devices
- Involves data collection from the user of the connected health tool (ie, the user either manually inputs data into the device or it is automatically uploaded)
- The user must be able to interact with the app or device on her own at home (outside of a clinical setting)
- Available in English
- Not available in English
- Conference abstracts, conference reviews, editorials, letters, or comments
- Study media releases and user reviews of specific applications
- Research conducted on animals
- Research involving male participants
- Tracking of infants and children, with the exception of tracking breastfeeding (since breastfeeding is directly related to the mother’s health and body)
- Devices or apps that are meant for health care provider use, use in a clinical setting only, or cannot be used independently without a health care provider present
- Digital health tools that are only for educational or informational purposes and do not allow the user to enter or track her own data (ie, no information exchange)
- Telemedicine services (eg, live video consultations with health care providers)
Results from the database searches were imported to the Covidence systematic review software  and deduplicated. Screening of articles occurred in two stages. First, titles and abstracts were independently screened by at least 2 reviewers according to the eligibility criteria defined above. For articles meeting the inclusion and exclusion criteria at the title and abstract level, the full texts were then reviewed independently, also by 2 different reviewers. Conflicts at either stage were discussed and agreed upon between members of the research team.
If we were unable to locate the full text of the article online or through the library, we made a request to the corresponding authors. Articles that remained inaccessible were excluded at this stage, and the count of such articles will be reported in the PRISMA flow diagram in the completed review.
For each article included, data will be charted by 1 reviewer in a spreadsheet and verified for accuracy by a second reviewer. A preliminary list of data charting elements was proposed and finalized () after charting data from a dozen articles. The research team discussed which elements provided the most useful information and which to discontinue, and added important components of the papers if they were not being adequately captured by our preliminary list.
To investigate the different contexts of women’s health that use PGHD from connected health devices, we will record all health area(s) of focus for each article. These areas refer to categories such as maternal health and fetal monitoring, menstruation, gestational diabetes, physical activity, etc. We will also record the year of publication and the country in which the research was conducted. To better understand which health metrics are most collected, we will document the types of connected health technologies discussed in the different studies (eg, wearable), the name of the devices or apps if applicable (eg, Fitbit Charge 2), and the personal metrics collected by the technologies (eg, daily step counts). Finally, to answer the third research question about facilitators and barriers, we will record any comments about the usability and acceptability of the technologies, including features that users liked or disliked. We will not focus on the outcome results of intervention studies, as that would be outside the scope of our review.
|Type of data||Details of charted data|
|Contexts for women’s connected health|
|Digital device details|
|Usability and acceptability|
Presentation of Results
A PRISMA flow diagram will be presented to detail the study selection process. Tables and graphs will be used to report findings on the contexts of the applications, including the various health areas of focus and metrics collected. A thematic analysis will be conducted to identify categories of facilitators and barriers in discussions about the acceptability and usability of the digital health technologies. Exact details of the format of the report will be determined as a team after completing data charting.
The searches from March 2020 returned 11,533 results, and the additional searches run in November 2020 and March 2021 returned 3096 results. There were a total of 9102 articles to screen after deduplication in Covidence. We did not encounter any articles in our search that mentioned the inclusion of intersex, transgender, or nonbinary participants. As of November 2020, the full-text screening stage is nearly complete and reviewers have started data charting. Results are expected to be submitted for publication by Fall 2021 and reported in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines .
Reviews have previously been conducted in specific areas of women’s health and digital health. However, to the authors’ knowledge, no review has been conducted with the broad scope proposed here. This scoping review will provide an extensive report of the literature regarding connected digital health devices for monitoring in all areas of women’s health through the collection of PGHD. This information will be useful for women’s health and digital health researchers to identify new research questions and design their programs according to the identified facilitators and barriers to use.
One limitation of our scoping review is that it does not include conference abstracts, conference reviews, editorials, letters, comments, or gray literature. Although we are including research conducted worldwide, our review does not include non-English articles. Finally, due to the nature of scoping reviews, quality assessments will not be performed on included articles. However, we are not assessing the outcome results of intervention studies, so this is not pertinent to our review.
We would like to thank Shannon Cheng, reference librarian, for her work in developing the search strategy and conducting the database searches. AT is funded by a Michael Smith Foundation for Health Research Scholar award. JLK received funds from the University of Waterloo Mathematics Endowment Fund to support this publication. Both authors conceived the study design, developed the review protocol, and contributed to the writing of the manuscript.
Conflicts of Interest
PRISMA-P checklist.PDF File (Adobe PDF File), 160 KB
Electronic search strategy.PDF File (Adobe PDF File), 198 KB
- Hiremath S, Yang G, Mankodiya K. Wearable Internet of Things: Concept, Architectural Components and Promises for Person-Centered Healthcare. 2014 Presented at: 4th International Conference on Wireless Mobile Communication and Healthcare (MOBIHEALTH 2014); November 3-5; Athens, Greece URL: https://eudl.eu/pdf/10.4108/icst.mobihealth.2014.257440 [CrossRef]
- Shapiro M, Johnston D, Wald J, Mon D. Patient-generated health data. White paper. RTI International. 2012. URL: https://www.rti.org/publication/patient-generated-health-data-white-paper [accessed 2021-05-14]
- Nelson EC, Verhagen T, Noordzij ML. Health empowerment through activity trackers: An empirical smart wristband study. Computers in Human Behavior 2016 Sep;62:364-374. [CrossRef]
- Bond GE, Burr RL, Wolf FM, Feldt K. The effects of a web-based intervention on psychosocial well-being among adults aged 60 and older with diabetes: a randomized trial. Diabetes Educ 2010;36(3):446-456. [CrossRef] [Medline]
- Rossi A, Frechette L, Miller D, Miller E, Friel C, Van Arsdale A, et al. Acceptability and feasibility of a Fitbit physical activity monitor for endometrial cancer survivors. Gynecol Oncol 2018 Jun;149(3):470-475. [CrossRef] [Medline]
- Nguyen NH, Hadgraft NT, Moore MM, Rosenberg DE, Lynch C, Reeves MM, et al. A qualitative evaluation of breast cancer survivors' acceptance of and preferences for consumer wearable technology activity trackers. Support Care Cancer 2017 Nov;25(11):3375-3384. [CrossRef] [Medline]
- Kokts-Porietis RL, Stone CR, Friedenreich CM, Froese A, McDonough M, McNeil J. Breast cancer survivors' perspectives on a home-based physical activity intervention utilizing wearable technology. Support Care Cancer 2019 Aug;27(8):2885-2892. [CrossRef] [Medline]
- Reynolds A. Patient-centered Care. Radiol Technol 2009;81(2):133-147. [Medline]
- Jacob E, Stinson J, Duran J, Gupta A, Gerla M, Ann Lewis M, et al. Usability testing of a Smartphone for accessing a web-based e-diary for self-monitoring of pain and symptoms in sickle cell disease. J Pediatr Hematol Oncol 2012 Jul;34(5):326-335 [FREE Full text] [CrossRef] [Medline]
- Gómez EJ, Cáceres C, López D, Del Pozo F. A web-based self-monitoring system for people living with HIV/AIDS. Comput Methods Programs Biomed 2002 Jul;69(1):75-86. [Medline]
- Jovanov E, Milenkovic A, Otto C, de Groen PC. A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. J Neuroeng Rehabil 2005 Mar 01;2(1):6 [FREE Full text] [CrossRef] [Medline]
- Denis F, Lethrosne C, Pourel N, Molinier O, Pointreau Y, Domont J, et al. Randomized Trial Comparing a Web-Mediated Follow-up With Routine Surveillance in Lung Cancer Patients. J Natl Cancer Inst 2017 Dec 01;109(9):436. [CrossRef] [Medline]
- Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol 2008;4:1-32. [CrossRef] [Medline]
- Steinhubl SR, Waalen J, Edwards AM, Ariniello LM, Mehta RR, Ebner GS, et al. Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial. JAMA 2018 Jul 10;320(2):146-155 [FREE Full text] [CrossRef] [Medline]
- Widmer RJ, Collins NM, Collins CS, West CP, Lerman LO, Lerman A. Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. Mayo Clin Proc 2015 Apr;90(4):469-480 [FREE Full text] [CrossRef] [Medline]
- Dunn J, Runge R, Snyder M. Wearables and the medical revolution. Per Med 2018 Sep;15(5):429-448 [FREE Full text] [CrossRef] [Medline]
- Semple JL, Armstrong KA. Mobile applications for postoperative monitoring after discharge. CMAJ 2017 Jan 09;189(1):E22-E24 [FREE Full text] [CrossRef] [Medline]
- Vix M, Rodriguez M, Ignat M, Marescaux J, Diana M, Mutter D. Postoperative Remote Monitoring with a Transcutaneous Biosensing Patch: Preliminary Evaluation of Data Collection. Surg Innov 2020 Jun 11:1553350620929461. [CrossRef] [Medline]
- Moin T, Ertl K, Schneider J, Vasti E, Makki F, Richardson C, et al. Women veterans' experience with a web-based diabetes prevention program: a qualitative study to inform future practice. J Med Internet Res 2015 May 25;17(5):e127 [FREE Full text] [CrossRef] [Medline]
- Pereira-Salgado A, Westwood JA, Russell L, Ugalde A, Ortlepp B, Seymour JF, et al. Mobile Health Intervention to Increase Oral Cancer Therapy Adherence in Patients With Chronic Myeloid Leukemia (The REMIND System): Clinical Feasibility and Acceptability Assessment. JMIR Mhealth Uhealth 2017 Dec 06;5(12):e184 [FREE Full text] [CrossRef] [Medline]
- Bauer M, Haesler E, Fetherstonhaugh D. Let's talk about sex: older people's views on the recognition of sexuality and sexual health in the health-care setting. Health Expect 2016 Dec;19(6):1237-1250 [FREE Full text] [CrossRef] [Medline]
- Julliard K, Vivar J, Delgado C, Cruz E, Kabak J, Sabers H. What Latina patients don't tell their doctors: a qualitative study. Ann Fam Med 2008;6(6):543-549 [FREE Full text] [CrossRef] [Medline]
- Bradford S, Rickwood D. Young People's Views on Electronic Mental Health Assessment: Prefer to Type than Talk? J Child Fam Stud 2015 Feb;24(5):1213-1221 [FREE Full text] [CrossRef] [Medline]
- Vlassoff C. Gender differences in determinants and consequences of health and illness. J Health Popul Nutr 2007 Mar;25(1):47-61 [FREE Full text] [Medline]
- Westergaard D, Moseley P, Sørup FKH, Baldi P, Brunak S. Population-wide analysis of differences in disease progression patterns in men and women. Nat Commun 2019 Feb 08;10(1):666 [FREE Full text] [CrossRef] [Medline]
- Armstrong P, Pederson A. Women's Health, Second Edition: Intersections of Policy, Research, and Practice. Toronto, ON: Canadian Scholars’ Press; 2015.
- Femtech—Time for a Digital Revolution in the Women’s Health Market. Frost & Sullivan. 2018 Jan 31. URL: https://ww2.frost.com/frost-perspectives/femtechtime-digital-revolution-womens-health-market/ [accessed 2021-05-14]
- 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]
- Rowan M, Dehlinger J. Observed Gender Differences in Privacy Concerns and Behaviors of Mobile Device End Users. Procedia Computer Science 2014;37:340-347. [CrossRef]
- Abelson JS, Kaufman E, Symer M, Peters A, Charlson M, Yeo H. Barriers and benefits to using mobile health technology after operation: A qualitative study. Surgery 2017 Dec;162(3):605-611. [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;20(6):673-679. [CrossRef] [Medline]
- Wyatt JC. How can clinicians, specialty societies and others evaluate and improve the quality of apps for patient use? BMC Med 2018 Dec 03;16(1):225 [FREE Full text] [CrossRef] [Medline]
- Wicks P, Chiauzzi E. 'Trust but verify'--five approaches to ensure safe medical apps. BMC Med 2015;13:205 [FREE Full text] [CrossRef] [Medline]
- Rowland SP, Fitzgerald JE, Holme T, Powell J, McGregor A. What is the clinical value of mHealth for patients? NPJ Digit Med 2020 Jan 13;3(1):4 [FREE Full text] [CrossRef] [Medline]
- Buechi R, Faes L, Bachmann LM, Thiel MA, Bodmer NS, Schmid MK, et al. Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis. BMJ Open 2017 Dec 14;7(12):e018280 [FREE Full text] [CrossRef] [Medline]
- Murakami H, Kawakami R, Nakae S, Yamada Y, Nakata Y, Ohkawara K, et al. Accuracy of 12 Wearable Devices for Estimating Physical Activity Energy Expenditure Using a Metabolic Chamber and the Doubly Labeled Water Method: Validation Study. JMIR Mhealth Uhealth 2019 Aug 02;7(8):e13938 [FREE Full text] [CrossRef] [Medline]
- Gillinov S, Etiwy M, Wang R, Blackburn G, Phelan D, Gillinov AM, et al. Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise. Med Sci Sports Exerc 2017 Aug;49(8):1697-1703. [CrossRef] [Medline]
- Connolly SL, Miller CJ, Koenig CJ, Zamora KA, Wright PB, Stanley RL, et al. Veterans' Attitudes Toward Smartphone App Use for Mental Health Care: Qualitative Study of Rurality and Age Differences. JMIR Mhealth Uhealth 2018 Aug 22;6(8):e10748 [FREE Full text] [CrossRef] [Medline]
- Bhuyan SS, Lu N, Chandak A, Kim H, Wyant D, Bhatt J, et al. Use of Mobile Health Applications for Health-Seeking Behavior Among US Adults. J Med Syst 2016 Jun;40(6):153. [CrossRef] [Medline]
- Carroll JK, Moorhead A, Bond R, LeBlanc WG, Petrella RJ, Fiscella K. Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach. J Med Internet Res 2017 Apr 19;19(4):e125 [FREE Full text] [CrossRef] [Medline]
- Kontos E, Blake KD, Chou WS, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res 2014;16(7):e172 [FREE Full text] [CrossRef] [Medline]
- Frost J, Vermeulen IE, Beekers N. Anonymity versus privacy: selective information sharing in online cancer communities. J Med Internet Res 2014;16(5):e126 [FREE Full text] [CrossRef] [Medline]
- Park YJ. Do men and women differ in privacy? Gendered privacy and (in)equality in the Internet. Computers in Human Behavior 2015 Sep;50:252-258. [CrossRef]
- Jacobson A. The Risks of Pregnancy-Tracking Apps. The Petrie-Flom Center. 2019 Aug 1. URL: https://petrieflom.law.harvard.edu/resources/article/the-risks-of-pregnancy-tracking-apps [accessed 2021-05-17]
- Rosas C. The Future is Femtech: Privacy and Data Security Issues Surrounding Femtech Applications. Hastings Business Law Journal 2019;15(2):319-341 [FREE Full text]
- Loncar-Turukalo T, Zdravevski E, Machado da Silva J, Chouvarda I, Trajkovik V. Literature on Wearable Technology for Connected Health: Scoping Review of Research Trends, Advances, and Barriers. J Med Internet Res 2019 Sep 05;21(9):e14017 [FREE Full text] [CrossRef] [Medline]
- Singh K, Drouin K, Newmark LP, Filkins M, Silvers E, Bain PA, et al. Patient-Facing Mobile Apps to Treat High-Need, High-Cost Populations: A Scoping Review. JMIR Mhealth Uhealth 2016 Dec 19;4(4):e136 [FREE Full text] [CrossRef] [Medline]
- Chen Q, Carbone ET. Functionality, Implementation, Impact, and the Role of Health Literacy in Mobile Phone Apps for Gestational Diabetes: Scoping Review. JMIR Diabetes 2017 Oct 04;2(2):e25 [FREE Full text] [CrossRef] [Medline]
- Hussain-Shamsy N, Shah A, Vigod SN, Zaheer J, Seto E. Mobile Health for Perinatal Depression and Anxiety: Scoping Review. J Med Internet Res 2020 Apr 13;22(4):e17011 [FREE Full text] [CrossRef] [Medline]
- Earle S, Marston HR, Hadley R, Banks D. Use of menstruation and fertility app trackers: a scoping review of the evidence. BMJ Sex Reprod Health 2020 Apr 06:90-101. [CrossRef] [Medline]
- Arksey H, O'Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology 2005 Feb;8(1):19-32. [CrossRef]
- Peters MDJ, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc 2015 Sep;13(3):141-146. [CrossRef] [Medline]
- Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, PRISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 2015 Jan 01;4:1 [FREE Full text] [CrossRef] [Medline]
- Ahmadvand A, Kavanagh D, Clark M, Drennan J, Nissen L. Trends and Visibility of "Digital Health" as a Keyword in Articles by JMIR Publications in the New Millennium: Bibliographic-Bibliometric Analysis. J Med Internet Res 2019 Dec 19;21(12):e10477 [FREE Full text] [CrossRef] [Medline]
- Henriksen A, Haugen MM, Woldaregay AZ, Muzny M, Hartvigsen G, Hopstock LA, et al. Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. J Med Internet Res 2018 Mar 22;20(3):e110 [FREE Full text] [CrossRef] [Medline]
- Covidence Systematic Review Software. URL: https://www.covidence.org/ [accessed 2021-05-14]
- Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 2018 Oct 02;169(7):467-473. [CrossRef] [Medline]
|PGHD: person-generated health data|
|PRISMA-P: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols|
|PRISMA-ScR: PRISMA Extension for Scoping Reviews|
Edited by G Eysenbach; submitted 27.11.20; peer-reviewed by JH Lee, N Hussain-Shamsy, N Brasier; comments to author 22.02.21; revised version received 25.03.21; accepted 04.04.21; published 28.05.21Copyright
©Jalisa Lynn Karim, Aline Talhouk. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 28.05.2021.
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 Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.