By Marli Mesibov
Purpose: This article presents a methodology for using content strategy to promote behavior change in digital interventions. The concept of incorporating motivational theories for achieving health outcomes has been accepted as a valuable element across industries including public health, mental and physical healthcare, financial wellbeing, and education. The author will demonstrate how integrating principles from Self-Determination Theory—a leading theory of human motivation—with content strategy practices can successfully engage and motivate people across digital self-service platforms, focusing specifically on health care.
Method: At Mad*Pow, the design team, content strategy team, and behavior change team have implemented content strategy practices in digital interventions via websites and applications. The creation of a content strategy guides the communication frequency, delivery style, information, and channels/touchpoints in order to best achieve the same goals that behavioral science does in face-to-face interventions. The intervention is then built, tested, and launched, after which the team tracks success metrics.
Results: The Mad*Pow team has since measured results from clients and consumers, and can show how behavior change methodologies—as implemented using a strong content strategy—positively impact outcomes.
Conclusion: Target audiences can be effectively engaged through content strategy in a manner beyond what is available via print, digital design, or other methods.
Keywords: behavioral science, behavior change, content strategy, digital interventions, patient engagement
- Presents content strategy as an effective connector between clinical research and digital interventions for strategists in the academic, clinical, and other high-impact areas
- Provides valuable techniques and insights into devising content strategies to engage and impact populations across self-service industries
- Introduces the field of motivational psychology as an area of study for content strategists to improve engagement
The study of human behavior can broadly be defined as investigations into individual, social, and environmental factors that influence human action. In the roughly 150 years of study, we’ve seen different trends and schools of thought related to which factors weigh more heavily on individual human action from instincts and drives (Freud), to responses to environmental stimuli (Skinner), to more nuanced and reciprocal descriptions of how our internal motivations interact with the broader social environment to produce behavioral intentions and actions.
The leading theory of human motivation, Self-Determination Theory, was developed as a direct rebuttal to the radical behaviorism popularized in the 1950s and ’60s. The theory maintains that motivation develops from within a person, grounded in the basic human needs to develop skills and capacities, to act of one’s own accord, and to connect to others and to their environment. When applied toward health behavior change, the theory has been used to guide interventions that initiate and sustain health behaviors, such as: exercise; healthy diet; smoking cessation; improved mental health; and long-term condition management for conditions including diabetes, hypertension, high-cholesterol, and heart-disease. These interventions have had great success in clinical settings and, only recently, have expanded into digital domains.
Digital health interventions typically employ websites, mobile applications, text messages, email, wearable devices, or sensors to deliver content embedded with techniques designed to change existing patterns of behavior. They may take the form of exercise or meal planners, medication adherence trackers, or personal coaches, all intended to take on some of the work otherwise provided by in-person providers and services. However, where tone and frequency of treatment can be defined by a provider in-person, digital interventions cannot foster adherence in quite the same way. Without a human to reach out to another human, design teams must create and implement behavior change strategies designed to increase the person’s engagement. These strategies create appropriate and effective communication between the device and the patient using features, techniques, content, and tone to best support real-world behavior change.
Content strategy is a discipline which promotes and plans for the design and creation of appropriate and effective content. It has developed as a strategic yet tactical field. The oft-agreed upon definition of the field is some variation on “getting the right content to the right user at the right time through strategic planning of content creation, delivery, and governance” (Content Strategy Alliance, 2014). More detailed definitions include “Content strategy guides the creation, delivery, and governance of useful, usable content. Content strategy means getting the right content, to the right people, in the right place, at the right time. Content strategy is an integrated set of user-centered, goal-driven choices about content throughout its lifecycle” (Halvorson, 2017).
All of these definitions focus on one content objective: to benefit and guide an audience. The last definition from Halvorson also mentions “user-centered, goal-driven choices” (2017, para. 17). In other words, the content strategy is what connects the needs of the audience to the words, images, or other content on the screen. It is a methodology by which one creates engagement.
Content strategy has the opportunity to provide greater impact when it comes to digital intervention design or the implementation of the theories of motivation. In digital intervention design, designers, developers, and content creators build applications or websites to support an individual in positively changing his or her behaviors. As noted, the challenge is to implement a theory of motivation in a manner that best supports the digital constraints and opportunities available, to engage and benefit the patient.
This review will share studies on behavior change and digital interventions to demonstrate the role of content strategy in applying theories of motivation into the digital realm. The first section begins with a review of traditional models of engagement and theories of motivation. The review will then look at studies that display challenges in shifting behavior change strategies to the digital realm and review examples of content strategy in practice, increasing engagement through the use of behavior change methodologies.
Traditional Engagement Methodologies
When healthcare professionals define engagement, they use terms such as meaningful involvement and active participation. Although there is no single agreed-upon definition, all explanations of patient engagement focus on the patient as an active participant in making decisions and taking steps to improve his or her health.
In face-to-face treatment, clinicians and providers use many tactics to promote positive behavior change and improve engagement in patients. Behavior change goals can range from altering dietary patterns, to increasing physical activity, to tracking and improving medication adherence. These sorts of behavioral lifestyle changes can significantly impact a patient’s ability to maintain wellness or improve prognosis. For example, a 2014 study collected evidence that a diabetes-related behavior change could “improv[e] the health behaviors and health outcomes of participants” (Peek et al., 2014, para. 35). Their study showcased numerous behavior change theories and models, including the Theory of Planned Behavior, Social Modeling, and the Ecological Model. In another 2014 study, researchers conducted a qualitative study of chronic obstructive pulmonary disease (COPD) patients and found that successful techniques correlated with motivating interventions in Self-Determination Theory (Langer et al., 2014).
Although there are many theories of motivation used across behavioral health, Self-Determination Theory (SDT) is one macro theory that has seen significant research and a large body of proof emerge since its development in the 1970s. This is the theory most-often utilized by the Mad*Pow behavior change analysts, as it transfers well to digital interventions.
Self-Determination Theory is a theory of motivation originally developed by Edward L. Deci, and refined in the 1990s by Deci and Richard Ryan (Deci and Ryan) as well as scholars internationally. The theory defines motivation as not just a unitary concept—something to have more or less of—but also as a qualitative concept with lower quality motivation stemming from outside the self (such as when we act to receive a reward, avoid punishment, or protect our self-esteem or boost our ego) and higher quality motivation when we act out of personal interest and enjoyment of doing a behavior, or because we believe that behavior to be an important and valuable thing to do. In SDT, the quality of motivation for a given behavior has a greater impact on the likelihood that behavior will be sustained over the long-term. According to SDT, the methods by which higher-quality motivation is achieved center on satisfying what are known as “basic psychological needs” that all people have regardless of age, gender, ethnicity, or cultural background. These three human needs for autonomy, competence, and relatedness form the pillars of self-determination and underpin the change logic in motivational interventions.
Autonomy refers to an individual’s need to experience him or herself as the origin of his or her behavior.
Competence is an individual’s need to feel effective in whatever he or she is doing.
Relatedness is an individual’s need to feel understood and cared for by others.
These three needs represent “psychological nutriments that are essential for ongoing psychological growth, integrity, and well-being” (Deci & Ryan, 2000, p. 229). Support and subsequent satisfaction of these needs provides a higher quality of psychological energy that is predicted to, and has been empirically confirmed to motivate the initiation and long-term maintenance of health behaviors. Content and interactions intended to facilitate behavior change, particularly in the long-term, is best when guided by techniques developed by SDT researchers and practitioners.
Health Care Implications
To engage patients in the health care space, many providers rely on Self-Determination Theory. They employ shared-decision making with patients to provide autonomy. They train patients or offer outside education to ensure competence. And they display empathy, engage family members, and gather patient histories to promote relatedness.
Some models of care are based solely on Self-Determination Theory. In 2008, Richard Ryan, Heather Patrick, Edward Deci, and Geoffrey Williams conducted a review of health interventions based on Self-Determination Theory which indicated positive outcomes. More recent studies, including a 2012 meta-analysis that identified 184 independent data sets from studies using Self-Determination Theory, suggest that models based on the theory correlate to positive behavioral health outcomes (Deci et al., 2012).
Challenges and Opportunities in Health Care
The advent of digital platforms and mobile devices has ushered in a new era of health opportunities. In particular, digital health provides opportunities to combat three challenges the health care world is facing:
- High health care prices
- Poor care coordination
- Chronic condition management (Hixon, 2014)
Digital health provides more opportunities for patients to view coverage and pricing options. It allows for increased care coordination through the use of Electronic Health Records (EHRs), which share patient data with other providers and specialists on the patient’s health care team. And as digital applications are developed, patients with chronic conditions receive new and improving options for care management.
Negative Impacts of Digital Health
However, technology is not a silver bullet. When it does not consider the end-user’s needs, digital health care can do more harm than good. Implementing EHRs and other care coordination formats can be costly for hospitals, and additional diagnostic testing opportunities can lead to higher patient costs. As hospitals and providers learn the new technology, there are unintended consequences, including occasionally lethal miscommunications, some of which are discussed in detail in Robert Wachter’s (2015) book, The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age. Also, patient care is only improved by mobile applications if those applications are designed using evidence-based research and behavior change theories.
In mental health care, for example, there are over 100 apps available in the Apple App Store. However, most are untested, and some can actively harm patients (Anthes, 2016). The American Psychiatric Association warns providers to use caution when recommending apps, as many have “have never actually been studied or evaluated in feasibility or clinical trials” (American Psychiatric Association, para. 2).
Low Digital Engagement
In addition, while devices such as Fitbit claim to “engage individuals” (Fitbit Website, 2018, para. 4), more than half of Fitbit users stop wearing the product—and 33% of those are within the first six months (Patel et al., 2015). Clinical trials, such as an ongoing trial on Monitoring Physical Activity, use Fitbits as tools to gather data but do not rely on the device itself to engage the patient (Rhodes, 2017).
Even Pokémon Go, which engaged over 45 million users at the height of its popularity and increased outdoor walking for those users by over 25% on average (Althoff et al., 2016), lost 15 million users the next month (Sullivan, 2016). This is not surprising. Across the Android and Apple app stores, 40% of health tools are downloaded fewer than 5,000 times (IMS Institute, 2015).
Ultimately, an app alone is not any more engaging than a human. However, an app designed to utilize behavior change methodology has a higher likelihood of improving health care outcomes than an app designed without behavior change expertise.
Low Health Engagement
A 2002 study shows that 71% of engaged individuals maintain a behavior change for one week, and 46% continue to maintain after six months (Norcross et al., 2002). However, in health care, individuals struggle to remain motivated. Improved health outcomes are often not visible in the short-term, which can lead individuals to feel their goals are unachievable.
For example, though nearly 70% of smokers say they want to quit, less than 10% of them are able to each year (World Health Organization, 2018). When it comes to physical activity, up to 30% of individuals express no intention to exercise (Ronda et al., 2001; Rhodes & de Bruijn, 2013). In a 2015 study on completely unmotivated individuals, titled “Motivating the unmotivated: how can health behavior be changed in those unwilling to change?”, the authors concluded from these previous statements that “It is clear, therefore, that a large number of individuals are not motivated to engage in health-promoting behaviors and tend to be those most at risk” (Hardcastle et al., 2015, para. 2)
The challenge is one of an individual’s interest and engagement with their health: When patients are not motivated, a behavior change theory like Self-Determination Theory connects them to their internal values to uncover their motivation. For example, Self-Determination Theory style interventions might:
- Help the unmotivated person shift their priorities, to focus on behaviors they are more motivated to follow-through on
- Provide assistance, structure, and scaffolding to build skills and confidence on smaller tasks, working up to the larger goals
- Rework the initial goals to be more achievable and highlight the smaller elements of progress
- Understand the individual’s perspective and reasons for not wanting change, as well as what he or she values that may encourage him or her in the future
For digital success, this requires a content strategy to develop a plan for appropriate delivery of motivational and behavior change techniques, in a supportive voice and tone, at the right moments and channels throughout an intervention.
Content Strategy as a Means of Engagement
The field of content strategy developed as a means to systematize content and thus improve communication. A 2011 survey of content strategy job ads found that content strategists were expected to have skills including “project management, training, leading and driving teams, client presentations, cross-department liaison and budget management” (Cullinan, 2011, para. 9). These are skills intended to shift principles and concepts into text and other content. In addition, a 2014 survey from the Content Strategy Alliance found that 55.3% of respondents work entirely digitally (Grindlay & Compton, 2014). In health care, this puts content strategists at the forefront of the shift from in-person experiences to digital interactions. Content strategists are designing strategies for interactions including:
- Nurse communication via live chat
- People purchasing health insurance through websites
- Patients receiving provider updates through Web portals
- Patients monitoring health conditions through digital apps
In a 2017 project, Truth Initiative, a non-profit organization dedicated to eradicating smoking in the United States, redesigned their primary smoking cessation tool. They partnered with Mad*Pow Media Inc. to develop a behavior change strategy and content strategy that would bring the value of in-person support groups and smoking cessation interventions to the digital realm.
The previous incarnation of the online tool offered the same print tools that a clinician would offer in person. The clinician would ask probing questions, identify the smoker’s core values (autonomy support), and reinforce his or her ability to maintain cessation (competence), as well as their reasons for not smoking during cravings (autonomy support). On-paper activities asked smokers to list values and interests, and suggested thinking about these things when a craving arrived. However, downloads were low, and without the human touch, site use overall was low.
Constructing a content strategy moved content elements to specific touch points at appropriate moments—such as when a smoker was dealing with a craving. By identifying and using the appropriate digital touch points and crafting language that mirrored provider language but was adapted as necessary for digital delivery, the tool became more usable. Over the course of six months, returning users increased from 35.66% to 37.98%, online community support users increased from 19% to 27%, and users identifying their reasons for quitting increased from 3.13% to 46.98%. All metrics point to increased user autonomy; community use and identifying reasons for quitting also correlate to relatedness.
Similarly, in 2016, corporate health provider New Ocean Health Solutions launched their new employee health and wellness platform covering lifestyle and prevention behaviors, chronic condition management, and mental health treatments. The app was developed with Mad*Pow to deliver up-to-date, evidence-based interventions for self-care and principles from Self-Determination Theory to initiate and sustain behavioral changes linked to outcome guidelines. The user experience was developed with a content strategy, so as to connect scaffolded treatment pathways and relevant content to individual needs. Content was designed to provide:
- Evidence-based guidelines and self-care treatment pathways
- Short- and long-form educational content on a condition, treatment, or recommended behavior, along with easy-to-start scaffolded behavioral skills training to support and build competence
- Barrier identification and problem-solving tips, and activities to further support competence where individuals might face challenges or set-backs
- Multiple goals or ways to achieve a goal that employees could choose from as well as an interface that allows for exploration, discovery, and customization to support autonomy
- Tips and advice for how to talk with an employee’s care team or social network to help maximize his or her support and success (relatedness, autonomy, competence)
After one year, 96% of employees surveyed reported a positive view of the app’s ability to identify their needs (Mad*Pow, 2018). Additional qualitative feedback saw comments including “first [health assessment] in over 30 years in the industry that felt as if it was for me and not my employer” (Mad*Pow, 2018, para. 16).
Table 1. Truth Initiative Engagement Metrics
|Metric||Before Redesign||After Redesign|
|Percent of Users Opt-In to Text Communications||64.51%||76.43%|
|Percent of Users that Set a Smoking Quit Date||64.22%||68.44%|
|Percentage of Users that Designate Reasons for Quitting||3.13%||46.98%|
|Percentage of New Registered Users that Visit the Online Support Community||19%||27%|
|Percent of Users Returning to the Site||35.66%||37.98%|
Methodologies and Tactics
Unlike fields within the sciences, content strategy does not have clinical trials to demonstrate efficacy of methodologies. However, there are recognized methodologies used to tactically align content for engagement.
Journey-Driven Design promotes the need to illustrate the path a user (patient) takes to accomplish a specific task in order to understand the user’s needs (Mesibov and Levin, 2017). The journey designed may be as broad as the path from diagnosis to recovery, or as detailed as the path from scheduling an appointment through to follow up.
Although there are multiple approaches to journey maps (Samadzadeh, 2016), all are useful in identifying touch points where the user will have some connection with an external source. Although this is not necessary for behavior change, it is useful as a reminder or follow-up, in much the same way as a human would reach out in face-to-face interactions. This can be as simple as a call to let the patient know their test results are in, or as complex as a notification recommending a change in treatment, based on self-reported or tracked data.
Creating a journey map for device usage also identifies the types of content appropriate for the patient. For example, if the journey shows patient fear and uncertainty leading up to an appointment, education and reassurance is required. This, in turn, impacts the likely frequency and style of communication between the device and patient. The patient’s journey may require daily app usage or may suggest that weekly is more appropriate.
Personalization as a methodology is well-understood to be a promising means of engagement and increased customer satisfaction among digital users (Kaneko et al., 2018). Tactically, personalization can be developed through a personalization matrix, as well as implementing technical business rules to create more complex personalization through AI. Both options promote creating a series of interactions that react to and complement a user’s individual behaviors.
If personas are available, this can be one method for beginning personalization. Patients exhibiting similar behaviors can be grouped into a behavioral persona, and a personalization matrix will allow the content strategist to connect behaviors to appropriate content, written tone, and pillars of Self-Determination Theory.
Creating a digital voice is a common task for content strategists. The voice, ideally, represents the brand attributes and adds a consistently human aspect to otherwise potentially dry interactions. As health care welcomes personalization, the need for a seamless experience from digital AI to human caregiver increases. By anthropomorphizing digital applications and devices, designers humanize the interactions and increase trust (Waytz et al., 2014; de Visser et al., 2016).
Anthropomorphism can specifically foster relatedness, one of the three pillars of Self Determination Theory. For example,
- Trust: A human-sounding voice increases the feeling of being cared for by others, particularly if the tone provides a genuine sense of caring and warmth, irrespective of the individual’s progress. To that end, the voice should avoid expressions of disapproval, judgment, or blame.
- Personalization: An anthropomorphized health application helps the individual to feel heard. The voice can be used to acknowledge the user’s feelings, use the person’s name, and reflect back other personal data to encourage the user to feel the application has a specific interest in him or her.
- Acknowledgement: An appropriately created voice will also foster relatedness through reflective listening. The voice can ask open-ended questions and summarize or present back information provided by the individual.
When designing to promote behavior change, content strategists craft a voice that echoes this and the other elements of Self-Determination Theory. Between the appropriate overall voice, and specific tones developed for specific scenarios, an anthropomorphized website or application supports autonomy and increases the individual’s sense of competence.
Behavior change theories, such as Self-Determination Theory, can successfully promote motivation and improve health outcomes. In the digital realm, content is the natural connector, taking on the role that providers naturally take in face-to-face situations. Content strategy puts those connections in place.
The examples shown display the impact content strategy can have on behavior change-based digital interventions. If more content strategists begin to study behavior change methodologies and join behavior change specialists and health care professionals, the applications developed for health care and other industries can be more consistently engaging, effective, and long-lasting.
Aitken, M., & Lyle, J. (2015). Patient adoption of mHealth: Use, evidence, and remaining barriers to mainstream acceptance. IMS Institute for Healthcare Informatics Report.
Althoff, T., White, R., & Horvitz, E. (2016). Influence of Pokémon Go on physical activity: Study and implications. Journal of Medical Internet Research, 18(12), 315.
Anthes, E. (2016). Mental health: There’s an app for that. International Weekly Journal of Science, 532(7597), 20–23.
Baers, L. (2018). Digital health 2018: Trends, opportunities and outlook. IDTechEX. Retrieved from https://www.idtechex.com/research/reports/digital-health-2018-trends-opportunities-and-outlook-000590.asp
Baldwin, J. L., Singh, H., Sittig, D.F., & Giardina, T.D. (2017). Patient portals and health apps: Pitfalls, promises, and what one might learn from the other. Healthcare: The Journal of Delivery Science and Innovation, 5(3), 81–85.
Bates, C., & Rowell, A. (2018). Tobacco explained: The truth about the tobacco industry… in its own words. Retrieved from https://www.who.int/tobacco/media/en/TobaccoExplained.pdf
Cassano, C. (2014). The right balance – Technology and patient care. Online Journal of Nursing Informatics, 18(3).
Content Strategy Alliance Charter (2014). Retrieved from https://contentstrategyalliance.com/the-beginnings/csa-charter/
Cullinan, F. (2011). What do employers want? We survey 30 content strategy job adverts to find out. Retrieved from https://firehead.net/2011/09/what-do-employers-want-we-survey-30-content-strategy-job-adverts-to-find-out/
Deci, E. L., & Ryan, R. M. (2002). Handbook of self-determination research. Rochester, NY: University Rochester Press.
de Visser, E. J., Monfort S. S., McKendrick, R., Smith, M. A., McKnight, P. E., Krueger, F., &
Parasuraman, R. (2016). Almost human: Anthropomorphism increases trust resilience in cognitive agents. Journal of Experimental Psychology Applied, 22(3), 331–349.
Grindlay, S., & Compton, N. (2014). 2014 Content Strategy Survey Report. Retrieved from http://contentstrategyalliance.com/wp-content/uploads/2014/03/Revised-Content-Strategy-Report1.pdf
Hallsworth, M., & Halpern, D. (2016). Applying behavioral insights to improve health and health care. NEJM Catalyst. Retrieved from https://catalyst.nejm.org/applying-behavioral-insights-improve-health-care/
Halvorson, K. (2017). What is content strategy? Retrieved from http://braintraffic.com/blog/what-is-content-strategy
Hardcastle, S., Hancox, J., Hattar, A., Maxwell-Smith, C., Thøgersen-Ntoumani, C., & Hagger, M. S. (2015). Motivating the unmotivated: How can health behavior be changed in those unwilling to change. Frontiers in Psychology, 6(835).
Hixon, T. (2014). Why digital health will have huge impact. Forbes.com. Retrieved from https://www.forbes.com/sites/toddhixon/2014/02/03/why-digital-health-will-have-huge-impact/#35e405507693
Kaufman, N., & Khurana, I. (2016). Using digital health technology to prevent and treat diabetes. Diabetes Technology & Therapeutics, 18, 56–68.
Kayyali, B., Kelly, S., & Pawar, M. (2016). Why digital transformation should be a strategic priority for health insurers. McKinsey. Retrieved from https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/why-digital-transformation-should-be-a-strategic-priority-for-health-insurers
Kuneko, K., Kishita, Y., & Yashushi, U. (2018). Toward developing a design method of personalization: Proposal of a personalization procedure. Procedia CIRP, 69, 740–745.
Langer, S., Chew-Graham, C. A., Drinkwater, J., Afzal, C., Keane, K., Hunter, C., Guthrie, E., & Salmon, P. (2014) A motivational intervention for patients with COPD in primary care: Qualitative evaluation of a new practitioner role. BioMed Central Family Practice, 15(164).
Mesibov, M., & Levin, J. (2017). Mobile first is just not good enough: Meet journey-driven design. Retrieved from https://www.smashingmagazine.com/2017/02/mobile-first-is-just-not-good-enough-meet-journey-driven-design/
Moore, S. (2014). Interview with Dustin DiTommaso of Mad*Pow: Explaining “Behavior Change.” MedTech Boston. Retrieved from https://medtechboston.medstro.com/blog/2014/03/03/interview-with-dustin-ditommaso-of-madpow-explaining-behavior-change/
Ng, J. Y., Ntoumanis, N., Thøgersen-Ntoumani, C., Deci, E. L., Ryan, R. M., Duda, J. L., & Williams, G. C. (2012). Self-Determination Theory applied to health contexts. Perspectives on Psychological Science, 7(4), 325–340.
Norcross, J. C., Mrykalo, M. S., & Blagys, M. D. (2002). Auld Lang Syne: Success predictors, change processes, and self-reported outcomes of New Year’s resolvers and nonresolvers. Journal of Clinical Psychology, 58, 397–405.
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers, of health behavior change. JAMA, 315, 459–460.
Peek, M. E., Ferguson, M. J., Roberson, T. P., & Chin, M. H. (2014). Putting theory into practice: A case study of diabetes-related behavioral change interventions on Chicago’s South Side. Health Promotion Practice, 15(2), 40–50.
Rhodes, R. (2018). Monitoring physical activity (Fitbit). Identification No. NCT03248752. Retrieved from https://clinicaltrials.gov/ct2/show/record/NCT03248752
Samadzadeh, S. (2016). Don’t make a journey map: 9 Archetypes of good/bad, and how to decide what to use. Medium. Retrieved from https://medium.com/@shahrsays/dont-make-a-journey-map-9-archetypes-of-good-bad-and-how-to-decide-what-to-use-d65abd30ec6f
Sullivan, T. (2016) Pokémon Go on the decline? That does not bode well for health apps and patient engagement. Health Care IT News. Retrieved from https://www.healthcareitnews.com/news/pokemon-go-decline-does-not-bode-well-health-apps-and-patient-engagement
Torous, J., Luo, J., & Chan, S. R. (2018) Mental health apps: What to tell patients. Current Psychology, 17, 21–25.
Wachter, R. (2015). The digital doctor: Hope, hype, and harm at the dawn of medicine’s computer age. New York, NY: McGraw-Hill.
Waytz, A., Heafner, J., Epley, N. (2016). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 52, 113–117.
Why rate mental health apps? American Psychiatric Association. Retrieved from https://www.psychiatry.org/psychiatrists/practice/mental-health-apps/why-rate-mental-health-apps
About the Author
Marli Mesibov is the VP of Content Strategy at the digital experience design consultancy Mad*Pow. Her work spans strategy and experiences across industries, with a particular interest in healthcare, finance, and education. She is a frequent conference speaker, a former editor of the UX publication UX Booth, and was voted one of MindTouch’s Top 25 Content Strategist Influencers in 2016. Marli is available on Twitter at @marsinthestars, via email at firstname.lastname@example.org, and on her website, http://marli.us.
Manuscript received 30 June 2018, revised 08 January 2019; accepted 19 January 2019.