The history and future of digital health in the field of behavioral medicine

Arigo, Danielle; Jake‐Schoffman, Danielle E.; Wolin, Kathleen Y.; Beckjord, Ellen Burke; Hekler, Eric B.; Pagoto, Sherry L.

doi:10.1007/s10865-018-9966-z

Cited by 158 publications

(108 citation statements)

References 116 publications

(99 reference statements)

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“…Funding streams which encourage collaboration between health researchers and app developers may facilitate the inclusion of key public health recommendations into new mobile apps. Moreover, academics interested in the development of health apps but lack funding and dissemination expertise, could try to establish industry partnerships in the early stages to take advantage of experienced help in the commercialisation and wide dissemination of apps [33]. As suggested by Arigo et al (2019), behavioural medicine professional organisations can play a vital role in enabling such partnerships and collaborations [33].…”

Section: Discussionmentioning

confidence: 99%

Adherence of popular smoking cessation mobile applications to evidence-based guidelines

Rajani

Weth²,

Mastellos

et al. 2019

BMC Public Health

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Background Smoking remains one of the major preventable causes of chronic diseases. Considering the promising evidence on the effectiveness of mobile technology for health behaviour change, along with the increasing adoption of smartphones, this review aims to systematically assess the adherence of popular mobile apps for smoking cessation to evidence-based guidelines. Methods The United Kingdom Android and iOS markets were searched in February 2018 to identify smoking cessation apps. After screening, 125 Android and 15 iOS apps were tested independently by two reviewers for adherence to the National Institute of Care and Excellence (NICE) Smoking Cessation Guidelines for Self-Help Materials and the Five A Guidelines for Smoking Cessation. Pearson chi square tests were run to examine differences between the two operating systems. Results A majority of apps across both operating systems had low adherence (fulfils 1–2 out of 5 guidelines) to the Five A Guidelines (65.7%) and low adherence (fulfils 1–3 out of 9 guidelines) to the NICE Smoking Cessation Guidelines for Self-Help Materials (63.6%). Only 15% of mobile apps provided information about the benefits of nicotine replacement therapy (NRT), and even fewer provided information regarding types of NRT products (7.1%) or how to use them (2.1%). In addition, only a minority of apps arrange follow-up appointments or provide additional support to help smokers quit. Conclusion Similar to previous mobile app reviews dating back to 2014, our findings show that most mobile apps do not follow existing smoking cessation treatment guidelines, indicating little change regarding the availability of evidence-based mobile apps for smoking cessation in the UK market. Smokers seeking to quit, tobacco control policy makers and software developers need to work together to develop apps that are in line with the latest clinical guidelines and strategies to maximise effectiveness. Electronic supplementary material The online version of this article (10.1186/s12889-019-7084-7) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Adherence of popular smoking cessation mobile applications to evidence-based guidelines

Rajani

Weth²,

Mastellos

et al. 2019

BMC Public Health

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“…With the development of the mobile app, we will next focus on deploying it and the resistance exercise band in real at-home experiments. Although challenges still exist in mHealth’s ability to impact health behavior [ 52 ], monitoring adherence and engagement is the first step. Not all of the insights identified by patients and clinicians could be implemented in this short study and should be added in future versions.…”

Section: Discussionmentioning

confidence: 99%

Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study

Petersen¹,

Halter²,

Kotz³

et al. 2020

JMIR Mhealth Uhealth

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Background Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process. Objective This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis Methods Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models—adjusting for age, sex, subject group (clinician vs patient), and development—to explore the association between sentiment analysis and SUS and USE outcomes. Results The mean age of the 22 participants was 68 (SD 14) years, and 17 (77%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; P=.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics. Conclusions Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.

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“…For example, it is now possible to do passive measurement of: a person’s health behaviors such as physical activity, eating habits [ 35 ], and smoking [ 36–38 ]; a person’s emotions, stress, mood, or depressive state [ 39 , 40 ]; social interactions [ 41–44 ]; colocation of individuals [ 45 ]; driving style [ 46 ]; traffic accidents [ 47 ]; diagnostic tests such as tremors among patients with Parkinson’s [ 48 , 49 ]; inferring heart rate or blood oxygen [ 49 ]; and social status and personality characteristics [ 50–56 ]. These platforms also support delivery of interventions [ 57 ], enabling delivery of support in real-world contexts, when and where it is most needed [ 57 ]. From a technical standpoint, we can measure, intervene, and conduct research within real-world environments using today’s technologies with a great deal of precision.…”

Section: What Has Changed In Technology Methods and Research Framewmentioning

confidence: 99%

Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision

Hekler

Tiro

Hunter

et al. 2020

Annals of Behavioral Medicine

111

107

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Background In 2015, Collins and Varmus articulated a vision for precision medicine emphasizing molecular characterization of illness to identify actionable biomarkers to support individualized treatment. Researchers have argued for a broader conceptualization, precision health. Precision health is an ambitious conceptualization of health, which includes dynamic linkages between research and practice as well as medicine, population health, and public health. The goal is a unified approach to match a full range of promotion, prevention, diagnostic, and treatment interventions to fundamental and actionable determinants of health; to not just address symptoms, but to directly target genetic, biological, environmental, and social and behavioral determinants of health. Purpose The purpose of this paper is to elucidate the role of social and behavioral sciences within precision health. Main body Recent technologies, research frameworks, and methods are enabling new approaches to measure, intervene, and conduct social and behavioral science research. These approaches support three opportunities in precision health that the social and behavioral sciences could colead including: (a) developing interventions that continuously “tune” to each person’s evolving needs; (b) enhancing and accelerating links between research and practice; and (c) studying mechanisms of change in real-world contexts. There are three challenges for precision health: (a) methods of knowledge organization and curation; (b) ethical conduct of research; and (c) equitable implementation of precision health. Conclusions Precision health requires active coleadership from social and behavioral scientists. Prior work and evidence firmly demonstrate why the social and behavioral sciences should colead with regard to three opportunity and three challenge areas.

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The history and future of digital health in the field of behavioral medicine

Cited by 158 publications

References 116 publications

Adherence of popular smoking cessation mobile applications to evidence-based guidelines

Adherence of popular smoking cessation mobile applications to evidence-based guidelines

Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study

Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision

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