Category: Health Communication

Are Mobile Mental Health Apps User-Friendly?

by Kat Caskey

Currently, only about half of those affected by mental illness in the United States will receive any kind of treatment[1]. In the past few years, however, experts have begun to look to remote healthcare options that could improve access to mental health treatment. Perhaps most promising is the growing consensus that mental health apps, or mHealth apps, “have unprecedented potential for improving quality of life and public health outcomes” for the tens of millions of people affected by mental health conditions in the U.S. each year.[2]

Mobile apps have the unique potential to reduce many of the traditional barriers to mental health treatment. For example, mHealth apps can be significantly less expensive than traditional treatment and may be accessed anytime, including during times of crisis, without an appointment. In addition, apps can reduce cultural barriers to care as they provide a “discrete mobile environment” free from social stigma.[3]

Evidence-based mHealth apps have been proven effective at treating a variety of mental health conditions, including posttraumatic stress disorder[4], anxiety[5], depression[6], obsessive compulsive disorder[7], bipolar disorder, borderline personality disorder, and substance abuse[8]. Unfortunately, however, although patients frequently download any of the myriad of mental health apps available in the App store, many are deleted after only a few uses, and a staggering 26% are used only once. One study that surveyed mental health app users found that among the most common reasons for deleting mental health apps included “not engaging” and “not user friendly,” with “ease of navigation” being the top feature that makes eHealth apps for mental health favorable.[9]

What good are evidence-based mental health apps if people won’t use them? These results indicate trouble in the realm of user experience, which considers “user emotions, affects, motivations, and values” as well as “ease of use, ease of learning and basic subjective satisfaction.”[10] Understanding user experience has been identified as “a key step in realizing the role of mental health apps”[11] and reminds us that it is not enough to understand the clinical basis of new health technologies; equally significant is consideration of the best ways to design and implement apps for people with mental health conditions. Ideally, user experience and usability testing evaluations should involve all relevant stakeholders, including patients and providers.[12]

Especially considering the wide reach of mHealth apps, “even minor efforts to further refine the usability and utility of the app” have the potential to decrease app attrition rates and increase user exposure to evidence-based treatment recommendations.[13] As apps designed to improve mental health continue to proliferate, app designers and researchers should continue to investigate how an emphasis on user experience can improve mHealth tools for mental health.

[1] National Institute of Mental Health.  Accessed February 4, 2018.

[2] Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: using novel data to examine the reach, use, and impact of PTSD coach. JMIR mental health2(1).

[3] Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: using novel data to examine the reach, use, and impact of PTSD coach. JMIR mental health2(1).

[4]Rodriguez-Paras, C., Tippey, K., Brown, E., Sasangohar, F., Creech, S., Kum, H. C., … & Benzer, J. K. (2017). Posttraumatic Stress Disorder and Mobile Health: App Investigation and Scoping Literature Review. JMIR mHealth and uHealth5(10).;
Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: using novel data to examine the reach, use, and impact of PTSD coach. JMIR mental health2(1).

[5] Sucala, M., Cuijpers, P., Muench, F., Cardoș, R., Soflau, R., Dobrean, A., … & David, D. (2017). Anxiety: There is an app for that. A systematic review of anxiety apps. Depression and anxiety.

[6] Lattie, E. G., Schueller, S. M., Sargent, E., Stiles-Shields, C., Tomasino, K. N., Corden, M. E., … & Mohr, D. C. (2016). Uptake and usage of IntelliCare: a publicly available suite of mental health and well-being apps. Internet interventions4, 152-158.

[7] Ameringen, M., Turna, J., Khalesi, Z., Pullia, K., & Patterson, B. (2017). There is an app for that! The current state of mobile applications (apps) for DSM‐5 obsessive‐compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders. Depression and anxiety.

[8] Rizvi, S. L., Dimeff, L. A., Skutch, J., Carroll, D., & Linehan, M. M. (2011). A pilot study of the DBT coach: an interactive mobile phone application for individuals with borderline personality disorder and substance use disorder. Behavior therapy42(4), 589-600.

[9] Smith, D. Motivating Patients to use Smartphone Health Apps. Consumer Health Information Corporation. Published 2014. Accessed February 4, 2018.

[10] Abrahão, S., Bordeleau, F., Cheng, B., Kokaly, S., Paige, R. F., Störrle, H., & Whittle, J. (2017, September). User Experience for Model-Driven Engineering: Challenges and Future Directions. In 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS) (pp. 229-236). IEEE.

[11] Lemon, Christopher. “The User Experience: A Key Step in Realizing the Role of Mental Health Apps.” Psychiatric Times, 7 Feb. 2018,

[12] Price, M., Yuen, E. K., Goetter, E. M., Herbert, J. D., Forman, E. M., Acierno, R., & Ruggiero, K. J. (2014). mHealth: a mechanism to deliver more accessible, more effective mental health care. Clinical psychology & psychotherapy21(5), 427-436.

[13] Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: using novel data to examine the reach, use, and impact of PTSD coach. JMIR mental health2(1).

STOP Act: Implementation and Effects on the Opioid Epidemic in North Carolina

The rise of the opioid epidemic nationwide has led to an increase of attention from both media and policy makers. Here in North Carolina, a recently passed policy is the Strengthen Opioid Misuse Prevention, or STOP Act, which aims to reduce the amount of Opioids prescribed a one approach to tackle the epidemic. The STOP Act was signed into law by Governor Roy Cooper on June 29, 2017, and since then its four stage implementation has been put into effect, which will continue until 2020.

The first step of implementation occurred almost immediately after the law’s passage, on July 1st 2017, requiring Physician Assistants (PAs) and Nurse Practitioners (NPs) to personally consult with a supervising physician. This applied to Pas and NPs at facilities that primarily engage in treating pain, and the prescription will, or is expected to, last longer than 30 days. Additionally, PAs and NPs have to consult with a supervising physician every 90 days for patients for are continuously prescribed opioids.  Providers are also required to provide information on the disposal of controlled substances, both written and orally, when a patient concludes a course of treatment. The second aspect, implemented on September 1st, 2017, requires that pharmacies report targeted prescriptions to the North Carolina Controlled Substance Reporting System within a day of the prescription is dispensed.

The most recent aspect of the STOP Act was implemented on January 1st, 2018, and limits the amount of opioids prescribed for acute pain. Practitioners are not able to prescribe more than five days’ worth of any Schedule II or III Opioid or Narcotic, with an exception to things like pain after surgery, where the prescription cannot for longer than seven days. The final part of the law will be implemented on January 1st, 2020, and will require practitioners to electronically prescribed targeted controlled substances, with a few exceptions.

While it is still unclear what impact the law will have on overdose deaths in the state, it appears that the State government is attempting to address this issue. While more resources could be devoted to mental health services, naloxone access and syringe exchanges, and more programs geared toward injecting drug users rather than only those who use prescription drugs, it’s commendable that a joint effort was reached to combat this ongoing epidemic.



New! Summary of NC’s new opioids law, the STOP Act: North Carolina Medical Board –

FAQs: The STOP Act of 2017: North Carolina Medical Board –

STOP Act Provision Takes Effect Jan. 1, Will Limit Opioid Prescriptions: NC Governor Roy Cooper –

STOP Act Bill Summary: North Carolina Medical Board –


Just Trust Me (Part II)

“Dr. X, or whoever she was, she must have been experimenting on me…she left a big scar on my neck … I don’t want that lady to ever touch me again. I don’t like her and I don’t trust her.

Last week, I introduced the issue of trust in the medical setting and how it may vary across scenarios and patient characteristics. But to truly understand why some patients don’t trust the healthcare system, we absolutely cannot ignore the history of their oppression by its hands.

The most well-known medical violation under the guise of research in the U.S. is the Tuskegee “study,” a 40-year-long theft of human rights that, brilliantly and viciously, utilized both government and community-level networks to recruit black men in Macon County, AL. Most had syphilis. The participants, many of whom had never seen a physician, were not made aware of the dangers, causes, and treatment options for their disease. They were not offered informed consent, nor the option to leave the study, and many died.

But Tuskegee is only the tip of the iceberg. Henrietta Lacks’ cells were cultured without her consent during her battle with cervical cancer in 1951 and are still widely used today. Gynecologist J. Marion Sims ran “practice runs” of his procedures on enslaved women. A common belief in the 20th century was that those who could not pay for medical care, many of them poor minorities and immigrants, “owed their bodies” to science. Harry Laughlin performed forced sterilizations on “socially inadequate” Puerto Rican women until the 1970s. The list could go on.

Today, this unfathomable history is manifested in mistrust in healthcare and scientific research. Evidence suggests that black patients are less likely to trust physicians, are more worried about medical privacy and experimentation than are white patients, and are less likely to participate in clinical trials. Some patients are not fully aware of the history to their mistrust – it is a cultural feeling that has been passed down through generations. This is called historical trauma. In addition, racism is still rampant in the healthcare system, both intentional and subconscious, which I will delve into in Part III. These features make this issue even more difficult to address.

Read Part III here.

Just Trust Me (Part I)

Well I’m not trained medically, so I’m taking a lot of what they say on faith.”

This was the response of a 47-year-old man, whose interview was part of a study on patients’ trust in hospitals.

There is no universal definition of trust that will apply to every scenario. Physician training, patient’s racial and cultural background, personalities, and expectations all come into play as their relationship evolves. One recurring theme in the study cited above was “sensing that you are in good hands.” Some mentioned that their trust developed from knowing the sheer amount of training required to be a medical provider. Other patients pointed out that being desperate, or having few other options, accelerates the formation of trust.

The Trust Project at Northwestern emphasizes the role that vulnerability plays in forming trust. Generally, once we come to trust someone, we open up to them; we expose vulnerability. In the healthcare system, it works backwards: being sick, worried, or simply confused by jargon (this is called information asymmetry), the patient often begins her relationship with her provider in a state of vulnerability.

Trust can also vary in different facets of the healthcare system. When we say that a patient has mistrust in the healthcare system, are we referring to his relationship with his provider, institutions like his hospital and insurance company, or the notion of Western medicine to begin with? One study suggests that repeated interactions are a key to building trust, and that patients do not see their providers as interchangeable. These findings suggest that we should enhance continuity, not just access.

Patients with low health literacy may reveal trust in a number of ways. One extreme is blind faith in the expertise of the provider, and another is mistrust and suspicion. One study found that blind trust in physicians was stronger in patients who were older, perceived their prognosis to be uncertain, or sometimes of low SES. Trust in the healthcare system tends to be lower among racial minorities, due to a history of unethical treatment. Could race moderate the relationship between SES and trust? Can these two extremes be reconciled, or even coexist in a single patient?

Read Part II here.

Is the Use of Strava’s Data for City Planning Racist?

By Margot Schein

Strava blends social media and fitness tracking to provide cyclists the ability to track, post, and receive feedback from other users on their activities. Strava Metro aggregates these data on a city level, and for $20,000/year cities can purchase them.1 Seeing the potential to utilize such data, cities around the US have begun to purchase these data to inform their efforts to build better and more bike lanes and paths.

Overall, this kind of data seems promising, and to my knowledge, big data hasn’t been used in this way to provide an evidence base for the creation of bike lanes. In the best case, data-driven development could lead to more people commuting using bikes and more accessible bike paths for those looking for a workout.

But, I have ethical concerns about using big data to guide city planning decisions. Namely, whether data reflect the needs of a city in general, or the needs of a particular subset of a city. In this case, there are good reasons to question how representative of a city Strava users are: wealthier, white males are overrepresented in the app2, and in fact, 77% of users overall are male.3

Cities are thus spending taxpayer money to access data that not only reflects a small portion of ridership2, but also does not reflect the demographics of that city. I wonder: who is being left out? Making bike lanes more accessible to white, upper class men and not to lower-income members of the community (who are disproportionately people of color) could serve to create a larger disparity in terms of both income and health. If bike lanes are not also made available to those who don’t already bike due to safety concerns or distance to a work place, lower-income individuals are essentially excluded from any benefits gained by the purchase of big data. Meanwhile, those who do benefit, gain even more access to work via their bikes and more available paths for workout purposes.

Cities need to be judicious about their use of big data, especially when such data are likely non-representative, accessed using taxpayer dollars, and provide benefits to white people in a city, and not black and brown people. If equity and disparities aren’t considered, well intentioned plans may serve to exacerbate problems. Strava Metro cannot be the only source or data for city planning.


  1. Davies A. Strava’s Cycling App Is Helping Cities Build Better Bike Lanes | WIRED. Wired. Published 2014. Accessed January 21, 2018.
  2. Flahive P. Could big data unlock safer commutes for cyclists? Marketplace. Published 2018. Accessed January 21, 2018.
  3. Jestico B, Nelson T, Winters M. Mapping ridership using crowdsourced cycling data. J Transp Geogr. 2016;52:90-97. doi:10.1016/J.JTRANGEO.2016.03.006.

Should There Be an App for That? Regulating the App Store

By Nikhil Sanon

There is mixed evidence that mHealth smartphone applications, commonly referred to as “apps,” are effective in achieving their intended outcome. Furthermore, a quarter of all app downloads are used only once, and consumers fail to return to apps that they do not find engaging. While some postulate that employing a User-Centered Design approach in the development of mHealth apps is necessary to stimulate engagement with mHealth apps, I believe that a more fundamental problem needs to be addressed: Any developer can publish a mHealth app to the Apple App Store or Google Play Store with no vetting for empirical evidence in support of the app’s effectiveness.

Both the Apple App Store and the Google Play Store have published guidelines for app developers that describe criteria that will result in an app failing to be uploaded to their respective stores. However, most of the criteria focus on the appropriateness of the content on the app, not the validity of the app itself. This is particularly problematic when considering that mHealth apps are uniquely positioned to provide healthcare services to their users. The guidelines for the Apple App Store state that medical apps “must clearly disclose data and methodology to support accuracy claims relating to health measurements.” However, no additional disclosures need to be made for medical apps with a scope beyond collecting or providing health measurements.

Stricter regulation of mHealth apps uploaded to the Apple App Store and the Google Play Store is necessary, such that apps with empirical support for their effectiveness are clearly labeled and marketed as such. This will not inherently solve the issue of poor consumer engagement with mHealth apps, but it will ensure that the mHealth apps available to consumers are safe to use.



[1] Tara McCurdie, Svetlena Taneva, Mark Casselman, Melanie Yeung, Cassie McDaniel, Wayne Ho, and Joseph Cafazzo (2012mHealth Consumer Apps: The Case for User-Centered Design. Biomedical Instrumentation & Technology: Mobile Health, Vol. 46, No. s2, pp. 49-56.

[2] App Review. Accessed 01/25/2018

[3] Developer Policy Center!?modal_active=none. Accessed 01/25/2018

Image: Betta, Christiano. “App Store.” 26 Sept 2008. Online image licensed under a Creative Commons Attribution 2.0 Generic (CC-BY2.0). Accessed 26 Jan 2018.

What is Information Poverty?

The theory of information poverty was originally introduced by Elfreda Chatman (E.A. Chatman, 1991, 1996, 1999). The definition and use of information poverty has developed since Chatman’s original conception, and several scholars have attempted to trace this lineage (Haider & Bawden, 2007; Yu, 2006, 2011). However, Britz (2004) combines the connectivity, content, and human approaches to information poverty to outline seven important elements of information poverty. Using these elements, he provides the following definition: “Information poverty is that situation in which individuals and communities, within a given context, do not have the requisite skills, abilities or material means to obtain efficient access to information, interpret it and apply it appropriately. It is further characterized by a lack of essential information and a poorly developed information infrastructure” (Britz, 2004, p. 204). Lor and Britz (2010) also remind us to question what it means to have access to knowledge, specifically introducing access as an epistemological dimension that looks at information and knowledge socially and to understanding knowledge as a process, an idea derived from constructivist approaches to education.

For example, Lingel and boyd (2013) approached the extreme body modification community to understand information poverty experienced by its members and to examine the information world. Ultimately, they found that the community itself was highly knowledgeable, but stigma contributed to a security culture and hiding of information from outsiders. Further, Savolainen (2016) proposes six socio-cultural barriers to information seeking, which are “barriers due to language problems, barriers related to social stigma and cultural taboo, small-world related barriers, institutional barriers, organizational barriers, and barriers due to the lack of social and economic capital.”

Britz, J. J. (2004). To Know or not to Know: A Moral Reflection on Information Poverty. Journal of Information Science, 30(3), 192-204. doi:10.1177/0165551504044666

Chatman, E. A. (1991). Life in a Small World: Applicability of Gratification Theory to Information-Seeking Behavior. Journal of the American Society for Information Science (1986-1998), 42(6), 438.

Chatman, E. A. (1996). The Impoverished Life-World of Outsiders. Journal of the American Society for Information Science, 47(3), 193.

Chatman, E. A. (1999). A Theory of Life in the Round. Journal of the American Society for Information Science, 50(3), 207.

Haider, J., & Bawden, D. (2007). Conceptions of “information poverty” in LIS: A discourse analysis. Journal of Documentation, 63(4), 534-557. doi:10.1108/00220410710759002

Lor, P. J., & Britz, J. (2010). To access is not to know: A critical reflection on A2K and the role of libraries with special reference to sub-Saharan Africa. Journal of Information Science, 36(5), 655-667. doi:10.1177/0165551510382071

Savolainen, R. (2016). Approaches to socio-cultural barriers to information seeking. Library & Information Science Research, 38(1), 52-59. doi:10.1016/j.lisr.2016.01.007

Yu, L. (2006). Understanding information inequality: Making sense of the literature of the information and digital divides. Journal of Librarianship and Information Science, 38(4), 229-252. doi:10.1177/0961000606070600

Yu, L. (2011). The divided views of the information and digital divides: A call for integrative theories of information inequality. Journal of Information Science, 37(6), 660-679. doi:10.1177/0165551511426246

UNC alumnus writes about journalism’s role in stopping stigma against obesity

Chioma Ihekweazu is a recent doctoral graduate from our very own School of Media and Journalism here at UNC. Not only was I thrilled to see a kind peer’s work showcased in my newsfeed, I was also drawn in by her accurate criticism of how we talk about weight–obesity in particular.

She makes the very important point that while it’s not likely to hear patients who are suffering from cancer referred to as “cancerous” or “diseased”, it is quite common, even among respected news sources, to see the descriptor “obese people”. Chioma advises us to avoid playing into shaming language and “put the person before the condition”.

Please read her article here, though a few key takeaways are outlined below:

  • Avoid headless imagery (this is a form of shaming)–if needed, use non-stigmatizing stock photos
  • Recognize that weight loss is influenced by many factors–such as location, time, and access to food/physical activity
  • Do not use value-laden language; use “classes”, based on BMI, defined by CDC and NIH to talk about obesity
  • Have an appropriate headline
  • Report on facts

Chioma also provides some great examples and resources in her article, to not only help writers and reporters change their words, but also to recognize the flaws in our perspective.



Cigarette package

Federal Courts Take on Big Tobacco

It’s been a big week in terms of wins for public health and tobacco control. On November 26, 2017, tobacco companies such as Phillip Morris USA and RJ Reynolds were mandated by the US federal court to place full page corrective statements about the negative health effects of tobacco products. These ads will be placed in newspapers, magazines and television ads. The corrective statements are black and white ads that detail effects of smoking, addictiveness of smoking and lack of significant health benefit of switching to low tar and light from regular cigarettes. The tobacco companies have been advertising false information for years about their products which lead to these mandated statements.

These mandated statements came out of lawsuit that began in 1999 where the Justice department sued these major companies of civil fraud and racketeering violations. For those of us (myself included) not familiar with the legal jargon it means lying (or misrepresenting) information to the public and when organizations run illegal businesses. Now eighteen years later, these companies are required to make up for these actions.

As a current health communication student though my initial thoughts are these basic ads enough? It seems very intentional by these companies who have developed colorful and intricate ads to sell their products are using plain black and white ads for their corrective statements. It definitely is a step in the right direction but will be interesting to see how these simple ads impact attitudes towards their products.



Could a lack of communication between older Americans and their healthcare providers increase the likelihood of a bad interaction? And by “bad interaction,” I don’t just mean interpersonally. The University of Michigan conducted a national poll of 1,690 Americans ages 50 to 80 and found that only 35% of those taking multiple medications had discussed possible drug interactions with a health professional in the past two years.

This lack of open-dialogue may be due to the transient nature of where we get our medication. Of the sample, 20% had used more than one pharmacy in the past two years. And even so, only 36% reported that their pharmacist definitely knew of all the medications they were taking. Alcohol, supplements, and certain foods can affect how the body responds to medication as can other medications.

Older adults especially may also be under the care of many different doctors and specialists, with 60% seeing more than one doctor. Addressing medication interactions can be challenging even when all the information is presented but when doctors don’t have the whole picture of which medications are at play, they very well could miss something. Electronic records and medical computer systems may be of assistance in flagging potential interactions, but a complete list of a patient’s medications is still necessary.

Patient-provider communication in recent years has been supplemented with patient portals and electronic paper trails, and I wonder if this older age group is slipping through the gap between interpersonal and electronic communication.