Background: Smartphone use has increased dramatically and, in parallel, a market for mobile apps, including health apps, has emerged. The business model of targeted mobile app advertisements allows for the collection of personal and potentially sensitive information, often without user knowledge. Older adults comprise a rapidly growing demographic that is potentially vulnerable to exploitation by those accessing data collected via these apps.
The smartphone has created a market for a myriad of mobile apps, including health apps, that play a central in role our daily lives. At present, both Android and Apple offer around 2 million apps for public download . Many of these apps require the collection of user personal data to access services. This information is used to support service delivery, including tailoring algorithms, which produce targeted advertisements customized to app users. It is unlikely that most app users are fully aware of the nature and scope of personal data collected or how those data are used in targeted advertisements and monetization of apps [ ]. By activating an app, users agree to disclose personal information including, for example, demographics, contacts, health, and lifestyle details to access the app services [ ]. The collection of personal and potentially sensitive information may pose unforeseen risks to the app users. Older adults are especially vulnerable to exploitation due to low technology and data literacy. Moreover, prior research has demonstrated that most older adults are unfamiliar with the extent of personal information that they agree to share when using various apps [ , ].
The issue of data sharing is intensified for older adults, a rapidly increasing demographic that has previously demonstrated technology literacy levels far lower than that of younger generations [, ]. In a 2019 paper, Wang et al [ ] discussed how a cohort of seniors living in a retirement community felt uncomfortable working with technology and primarily relied on grandchildren for technological support. Nonetheless, leveraging a younger or more “tech-savvy” family member is not always an accessible resource for the 65 years and older demographic. When asked what future technologies this group of older adults thought would be helpful, some participants suggested features they already had access to but may not have been aware of how to use (eg, universal remote) [ ]. This lack of familiarity with technology can put seniors and other vulnerable populations at risk.
Currently, there is a relatively low threshold for app developers to meet when putting an app into the Apple App Store or Google Play Store . Apps that become available to the public on the Apple App Store or Google Play Store often lack basic security or data protection measures—with prior reports citing as many as 49% of apps fail basic data protection capabilities [ ]. With the vast quantity of apps available for download combined with the lack of safety precautions, it is possible for personal information to be misused and compromise individuals’ privacy by sharing individual data or installing malware through mobile apps [ , ]. This is concerning for older adults, who have varying levels of digital literacy and may be unaware of the data sharing and privacy risks associated with mobile applications [ ]. A study of the 1100 most popular Android apps revealed misuse of personal information [ ]. It found that phone identifiers, which are distinct digit combinations used to distinguish individual devices, could track web-based activity back to an individual person, and in some cases, without permission of the app users. The study also found that advertising and analytic companies collect, buy, and sell data due to insufficient protection of sensitive information by the apps that initially collect these data [ ]. While the researchers found no evidence of harmful malware, the large misuse of information poses potential risks of harm.
Several factors influence behaviors and attitudes surrounding data sharing, including privacy preferences . In general, privacy, transparency, and full disclosure of information sharing are highly important to older adults [ ]. Many seniors, nonetheless, are willing to share their information to delegate control to others or to gain something in return for sharing their data, as long as they know the information that is being disclosed and retain granular control [ ]. This perspective aligns with the business models of apps that offer a service in exchange for the information that they collect. On the other hand, it is unclear whether the user understands the nature and granularity of information being collected when initiating app use, as well as information collected longitudinally while using the app.
As the COVID-19 pandemic led to stay-at-home orders and social distancing, older adults found themselves more isolated. During this time, social technologies became increasingly important in supporting healthy aging. The use of these technologies by older adults requires an awareness of associated benefits as well as possible harms, including risks related to terms of service and privacy protocols . Social isolation and loneliness are important to prevent as they can lead to higher risks of cognitive impairment and can even result in the onset of vascular and neurological diseases [ , ]. A potential solution to mitigate harms related to pandemic quarantine isolation includes access to technology. Daly et al [ ] found that technologies are key in preserving social connectivity among older adults, but they also acknowledged that older adults’ lack of technological literacy presented a barrier to technology effectiveness.
To identify mobile apps targeting older adults, a search was conducted using the terms “apps for older adults” via the Google search engine. Given that only 2%-3% of people who conduct Google searches look past the first page, we limited our data collection to the first 25 links, which was about the first 2.5 pages of Google search results . Each link included upwards of 10 app recommendations, with some listing around 40, which provided a sufficient corpus of data to analyze.
It is important to note that some of the apps recommended by websites had changed their official titles at the time of the search from the time they were recommended. Others no longer existed in the app store. Those unable to be located were not included in the data set. Whether the application required payment was not always clear, as only a few of the apps required money at the first step to download from the Apple App Store. However, there were many instances when an in-app purchase was necessary for the app to be fully used. According to the Apple App Store, keywords, such as “subscription,” denoted a subscription-based fee required to fully use the app, while words such as “pro” or “premium” implied that there was a free, usable, but limited version (often labeled the “lite” version), with the option for a paid ”pro” or “premium” version with full capabilities. Oftentimes, the “free” or “light” versions contained numerous advertisements for the paid versions. Additional research on these apps was required to determine if the free version of the app was comprehensively usable for the app’s function. The features of both the free and paid versions of the app were evaluated to determine whether the app could be used to its full potential without payment.
Investigating free versus paid versions of the specific apps prompted the development of a secondary, more granular classification system for organizing the apps based on the cost (free or paid), option for a trial or “lite” version, and general app category (eg, health, news, education, and entertainment) gleaned from each app’s website description. When reclassifying apps, predefined Apple categories with similarities were combined to create a new classification, while other broad categories were broken down into more specific reclassification categories. Apple had 24 listed categories on the app store that app developers select from to classify their apps. The classification labeled “Games” was a very broad 25th classification that had many subcategories. However, only 2 games appeared in our search results, which may indicate that games are not prioritized for older adults. In total, 26 categories from Apple were identified, with 11 being reclassified for specificity. Once the data were collected and entered into an Excel spreadsheet, descriptive statistics were calculated.
The first 25 links listed within the first 2.5 pages of results to our query of “Apps for Older Adults” comprised our primary data source. Each link contained anywhere from 10 to upwards of 40 apps, totaling 133 different iPhone apps within the first 25 links. Of those applications, 44 (33%) required a fee to operate, with the remaining 89 (67%) usable without any payment ().
|Characteristic||Yes, n (%)||No, n (%)|
|Fee charged||44 (33)||89 (67)|
|Vetting process||43 (32)||90 (68)|
|Ranked within domain||69 (52)||64 (48)|
Most apps (n=19, 14%) were classified as Medical, which included apps like “Pill Monitor” and “iYogi.” The second most common classification with 14 apps (10%) was Utilities, which included apps like “Swiftkey Keyboard” (). displays apps within the Apple categories, comparing the number of apps with privacy policies to the total number of apps within a category. After being reclassified, these 2 categories were still the most common with 22 “Health” apps (16%) and 21 apps in the Utilities category (16%), respectively ( ). None of the apps were classified as the Apple categories of augmented reality apps, kids, magazines and newspapers, or sports.
In addition, some of the categories predetermined by the Apple Store and selected by the app developers to categorize each app did not align fully with the app’s function or purpose. One example is “Flashlight + Magnifying Glass,” which was categorized as a Food and Drink app. Other categories seemed vague and nonspecific to the primary purpose of the apps, such as Lifestyle.
Over the past few decades, and even more recently during the COVID-19 pandemic, use of technology by individuals spanning all age groups has increased. This has resulted in an increasingly expansive market for mobile apps of all subtypes. A growing number of older adults are using smartphones and digital applications, with recent reports suggesting that more than half of people aged 65 years and older own a smartphone .
Comparison With Prior Work
The number of people aged 65 years and older is growing rapidly. Most older adults have relatively low technology literacy as well as data literacy, which makes them vulnerable to scams [, - ]. Future research could focus on technology and data literacy among older adults. Moreover, technologies that can support the review of privacy policies could be useful in today’s digital age. Improving awareness of the nature and granularity of data being collected by mobile apps could assist older adults to make informed choices when considering whether to use a mobile app. Moreover, providing internet safety and security education as well as developing a policy that serves to protect older adults from potential risks embedded in privacy agreements would be important next steps.
Future evaluations of mobile apps should include a data management assessment, including threats to privacy, such as the elements proposed in. To better understand the possible risks of harm associated with app data collection and management practices, a systematic review process is recommended.
Privacy aspects to consider in future evaluations of mobile apps.
Is it clear how data are stored?
Are data shared or sold? If yes, what data?
How is identifiable information protected?
Is bystander data collected?
Is bystander data stored? Shared?
For each of the 19 medical apps, the evaluation might include an assessment to describe the nature and sensitivity of data, identifiability of individual-level data, the purpose of collection of the variable, a description of who would have access to the data (eg, shared with developers, researchers, clinicians, and third parties), and whether there was a statement indicating that a user would give up their rights to file a claim should damages occur. This evaluation of privacy policies could be useful in drawing attention to potential risks as well as making the data-sharing practices more visible to product end users.
Although the first 3 pages in the initial Google search yielded many app results, hundreds of pages were returned as a whole. Additionally, search results are personalized, so different users may have varying results based on individualized returns specific to the digital advertising algorithms of search engines. Our analysis focused only on Apple-specific apps. Including Android apps and predefined categories listed in the Google Play Store may provide additional categorizations. In addition, the Apple apps we investigated did not have a vetting process as to why they were recommended. Future research could focus on these websites to see if there is a related conflict of interest (eg, financial incentives) that explains why these certain websites advertise links while not providing any evidence for the recommendation.
Conflicts of Interest
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Edited by A Mavragani; submitted 05.03.22; peer-reviewed by R Barak Ventura, H Mehdizadeh, Z Wang; comments to author 07.08.22; revised version received 17.02.23; accepted 14.03.23; published 27.04.23Copyright
©Megan Sweeney, William Barton, Camille Nebeker. Originally published in JMIR Formative Research (https://formative.jmir.org), 27.04.2023.
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