Mobile applications (apps) to improve health are proliferating, but before healthcare providers or organizations can recommend an app to the patients they serve, they need to be confident the app will be user-friendly and helpful for the target disease or behavior. This paper summarizes seven strategies for evaluating and selecting health-related apps: (1) Review the scientific literature, (2) Search app clearinghouse websites, (3) Search app stores, (4) Review app descriptions, user ratings, and reviews, (5) Conduct a social media query within professional and, if available, patient networks, (6) Pilot the apps, and (7) Elicit feedback from patients. The paper concludes with an illustrative case example. Because of the enormous range of quality among apps, strategies for evaluating them will be necessary for adoption to occur in a way that aligns with core values in healthcare, such as the Hippocratic principles of nonmaleficence and beneficence.

Aims-To examine the influence of risk perception on intentions to quit smoking and posttreatment abstinence. Design-Prospective and longitudinal. Setting-United StatesParticipants-237 adult smokers (M age = 56 years) receiving medical care from home health care nurses. Participants did not have to want to quit smoking to participate, but received cessation counseling within the context of their medical care.

Patients are increasingly using online social networks (ie, social media) to connect with other patients and health care professionals—a trend called peer-to-peer health care. Because online social networks provide a means for health care professionals to communicate with patients, and for patients to communicate with each other, an opportunity exists to use social media as a modality to deliver behavioral interventions. Social media-delivered behavioral interventions have the potential to reduce the expense of behavioral interventions by eliminating visits, as well as increase our access to patients by becoming embedded in their social media feeds. Trials of online social network-delivered behavioral interventions have shown promise, but much is unknown about intervention development and methodology. In this paper, we discuss the process by which investigators can translate behavioral interventions for social media delivery. We present a model that describes the steps and decision points in this process, including the necessary training and reporting requirements. We also discuss issues pertinent to social media-delivered interventions, including cost, scalability, and privacy. Finally, we identify areas of research that are needed to optimize this emerging behavioral intervention modality.

BackgroundLifestyle interventions are efficacious at reducing risk for diabetes and cardiovascular disease but have not had a significant public health impact given high cost and patient and provider burden.ObjectiveOnline social networks may reduce the burden of lifestyle interventions to the extent that they displace in-person visits and may enhance opportunities for social support for weight loss.MethodsWe conducted an iterative series of pilot studies to evaluate the feasibility and acceptability of using online social networks to deliver a lifestyle intervention.ResultsIn Study 1 (n=10), obese participants with depression received lifestyle counseling via 12 weekly group visits and a private group formed using the online social network, Twitter. Mean weight loss was 2.3 pounds (SD 7.7; range -19.2 to 8.2) or 1.2% (SD 3.6) of baseline weight. A total of 67% (6/9) of participants completing exit interviews found the support of the Twitter group at least somewhat useful. In Study 2 (n=11), participants were not depressed and were required to be regular users of social media. Participants lost, on average, 5.6 pounds (SD 6.3; range -15 to 0) or 3.0% (SD 3.4) of baseline weight, and 100% (9/9) completing exit interviews found the support of the Twitter group at least somewhat useful. To explore the feasibility of eliminating in-person visits, in Study 3 (n=12), we delivered a 12-week lifestyle intervention almost entirely via Twitter by limiting the number of group visits to one, while using the same inclusion criteria as that used in Study 2. Participants lost, on average, 5.4 pounds (SD 6.4; range -14.2 to 3.9) or 3.0% (SD 3.1) of baseline weight, and 90% (9/10) completing exit interviews found the support of the Twitter group at least somewhat useful. Findings revealed that a private Twitter weight-loss group was both feasible and acceptable for many patients, particularly among regular users of social media.ConclusionsFuture research should evaluate the efficacy and cost-effectiveness of online social network-delivered lifestyle interventions relative to traditional modalities.

Commercial mobile apps for health behavior change are flourishing in the marketplace, but little evidence exists to support their use. This paper summarizes methods for evaluating the content, usability, and efficacy of commercially available health apps. Content analyses can be used to compare app features with clinical guidelines, evidence-based protocols, and behavior change techniques. Usability testing can establish how well an app functions and serves its intended purpose for a target population. Observational studies can explore the association between use and clinical and behavioral outcomes. Finally, efficacy testing can establish whether a commercial app impacts an outcome of interest via a variety of study designs, including randomized trials, multiphase optimization studies, and N-of-1 studies. Evidence in all these forms would increase adoption of commercial apps in clinical practice, inform the development of the next generation of apps, and ultimately increase the impact of commercial apps.

While reliable detection of illicit drug use is paramount to the field of addiction, current methods involving selfreport and urine drug screens have substantial limitations that hinder their utility. Wearable biosensors may fill a void by providing valuable objective data regarding the timing and contexts of drug use. This is a preliminary observational study of four emergency department patients receiving parenteral opioids and one individual using cocaine in a natural environment. A portable biosensor was placed on the inner wrist of each subject, to continuously measure electrodermal activity (EDA), skin temperature, and acceleration. Data were continuously recorded for at least 5 min prior to drug administration, during administration, and for at least 30 min afterward. Overall trends in biophysiometric parameters were assessed. Injection of opioids and cocaine use were associated with rises in EDA. Cocaine injection was also associated with a decrease in skin temperature. Opioid tolerance appeared to be associated with a blunted physiologic response as measured by the biosensor. Laterality may be an important factor, as magnitude of response varied between dominant and nondominant wrists in a single patient with bilateral wrist measurements. Changes in EDA and skin temperature are temporally associated with intravenous administration of opioids and cocaine; the intensity of response, however, may vary depending on history and extent of prior use.

Given the increased prevalence of non-daily smoking and changes in smoking patterns, particularly among young adults, we examined correlates of smoking level, specifically motives for smoking, and readiness to quit smoking among 2682 college undergraduates who completed an online survey. Overall, 64.7% (n = 1736) were non-smokers, 11.6% (n = 312) smoked 1-5 days, 10.5% (n = 281) smoked 6-29 days and 13.2% (n = 353) were daily smokers. Ordinal regression analyses modeling smoking level indicated that correlates of higher smoking level included having more friends who smoke (β = 0.63, 95% CI 0.57-0.69) and more frequent other tobacco use (β = 0.04, 95% CI 0.02-0.05), drinking (β = 0.04, 95% CI 0.02-0.07) and binge drinking (β = 0.09, 95% CI 0.06-0.13). Bivariate analyses indicated that daily smokers (versus the subgroups of non-daily smokers) were less likely to smoke for social reasons but more likely to smoke for self-confidence, boredom, and affect regulation. Controlling for sociodemographics, correlates of readiness to quit among current smokers included fewer friends who smoke (P = 0.002), less frequent binge drinking (P = 0.03), being a social smoker (P < 0.001), smoking less for self-confidence (P = 0.04), smoking more for boredom (P = 0.03) and less frequent smoking (P = 0.001). Specific motives for smoking and potential barriers to cessation particularly may be relevant to different groups of college student smokers.

Subsequent interventions should incorporate lessons learned from this first randomized controlled trial of a multi-modal longitudinal tobacco treatment curriculum in multiple U.S. medical schools. NIH Trial Registry Number: NCT01905618.