Rashelle B. Hayes

Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations

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.

Risk perception and smoking behavior in medically ill smokers: a prospective study

AIMS To examine the influence of risk perception on intentions to quit smoking and post-treatment abstinence. DESIGN Prospective and longitudinal. SETTING United States. PARTICIPANTS A total of 237 adult smokers (mean 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. MEASUREMENTS Three measures of risk perception were given pre- and post-treatment: perceived vulnerability, optimistic bias and precaution effectiveness. Smoking status was verified biochemically at end of treatment and at 2, 6 and 12 months later. FINDINGS Principal components analysis supported the theoretical discriminability of the risk perception measures, and intercorrelations provided evidence for concurrent and predictive validity. Elevated risk perception was associated with a variety of socio-demographic and psychosocial characteristics. Optimistic bias was associated significantly with older age and ethnic minority status. Smokers in pre-contemplation had lower perceived vulnerability and precaution effectiveness and greater optimistic bias than those in contemplation and preparation. Smokers in preparation had higher perceived vulnerability and lower optimistic bias than those in earlier stages. Change in perceived vulnerability predicted smoking cessation at follow-up. Optimistic bias predicted a lower likelihood of cessation and precaution effectiveness predicted a greater likelihood of smoking cessation, but only among those with a smoking-related illness. CONCLUSIONS In patients receiving medical care from home health-care nurses, change in perceived vulnerability to smoking-related disease is predictive of smoking cessation. In those with smoking-related illnesses, optimistic bias predicts continued smoking while precaution effectiveness predicts cessation.

Smoking frequency among current college student smokers: distinguishing characteristics and factors related to readiness to quit smoking

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.

Adapting Behavioral Interventions for Social Media Delivery

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.

Tobacco Use, Quitting Behavior, and Health Characteristics Among Current Electronic Cigarette Users in a National Tri-Ethnic Adult Stable Smoker Sample

INTRODUCTION The present study characterizes the tobacco use, quitting behaviors, and health characteristics of cigarette smokers who did not change their smoking pattern over the past 6 months and have used electronic cigarettes (ECs) in the past 30 days. This is an important subpopulation to characterize if EC dual use with cigarettes continues to grow. METHODS Participants (N = 2,376) from a research survey panel completed an online cross-sectional survey between June and August 2012. Sampling was stratified to recruit equal numbers of cigarette smoking participants by race/ethnicity (Black, Hispanic, and Caucasian) and smoking frequency (nondaily and daily). All displayed a stable rate of smoking for the past 6 months and were not currently in treatment. Bivariate and multivariate analyses were used to examine correlates of current EC use (any use within the past 30 days). RESULTS Current EC use was reported by 9.2% (n = 219) of the total sample. Of current EC users, 44% reported having used ECs as a quit method. Bivariate and multivariate analyses showed that current EC use was significantly associated with greater nicotine dependence, concurrent poly-tobacco use, more past-year quit attempts, past use of multiple cessation methods, and more depressive symptoms. No demographic variables were significantly associated with current EC use. CONCLUSIONS This study suggests that stable smokers who currently use ECs possess characteristics that are associated with difficulty in achieving smoking cessation. These characteristics should be considered when examining the effectiveness of ECs on cessation and in designing future cessation trials using ECs.

Smoking Status, Physical Health–Related Quality of Life, and Mortality in Middle-Aged and Older Women

INTRODUCTION Women who smoke, particularly older women, have been relatively neglected in smoking research. There is a lack of knowledge concerning the relation of level of smoking to quality of life and mortality among middle-aged and older women smokers. METHODS This study examined the relation of smoking status to physical health-related quality of life (PHRQL) and total mortality in women in the Womens Health Initiative (WHI) Observational Study. Participants were 90,849 postmenopausal women, who were an average age of 63.6 years at baseline. Analyses used multiple linear and Cox proportional hazards regression and controlled for age, educational level, and ethnicity. Never-smokers were the reference group. RESULTS We found that smoking status was significantly related to PHRQL cross-sectionally at baseline and prospectively at a 3-year follow-up, with those who smoked having lower PHRQL. Heavier smokers showed large, clinically meaningful associations with PHRQL and light smokers showed small associations. In addition, we found that the smoking status at baseline was significantly related to 10-year total mortality. Both light and heavier smoking at baseline significantly correlated with higher mortality risk; however, the relationship of smoking to mortality was dose dependent. Among former smokers, those who had smoked longer showed significantly lower PHRQL and significantly increased mortality risk. CONCLUSIONS Findings suggest that the risks of smoking may not be evident to light smokers and that educational interventions targeted to middle-aged and older women stressing the consequences of light smoking may be particularly beneficial.

Social influences on smoking in middle-aged and older women

The purpose of this study was to examine the role of 2 types of social influence--general social support and living with a smoker--on smoking behavior among middle-aged and older women in the Womens Health Initiative (WHI) Observational Study. Participants were postmenopausal women who reported smoking at some time in their lives (N=37,027), who were an average age of 63.3 years at baseline. Analyses used multiple logistic regression and controlled for age, educational level, and ethnicity. In cross-sectional analyses, social support was associated with a lower likelihood and living with a smoker was associated with a higher likelihood of being a current smoker and, among smokers, of being a heavier smoker. Moreover, in prospective analyses among baseline smokers, social support predicted a higher likelihood and living with a smoker predicted a lower likelihood of smoking cessation 1-year later. Further, in prospective analyses among former smokers who were not smoking at baseline, social support predicted a lower likelihood and living with a smoker predicted a higher likelihood of smoking relapse 1-year later. Overall, the present results indicate that social influences are important correlates of smoking status, smoking level, smoking cessation, and smoking relapse among middle-aged and older women.

Twitter-Delivered Behavioral Weight-Loss Interventions: A Pilot Series

Background Lifestyle 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. Objective Online 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. Methods We conducted an iterative series of pilot studies to evaluate the feasibility and acceptability of using online social networks to deliver a lifestyle intervention. Results In 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. Conclusions Future research should evaluate the efficacy and cost-effectiveness of online social network-delivered lifestyle interventions relative to traditional modalities.

Cotinine and tobacco-specific carcinogen exposure among nondaily smokers in a multiethnic sample

BACKGROUND Nondaily smoking has increased among current U.S. smokers during the past decade and is practiced by a significant percentage of smokers. Although research in nondaily smoking has grown, little is known about levels of exposure to tobacco toxicants among nondaily smokers and their variation across ethnic groups. METHODS We examined urinary levels of cotinine and a tobacco-specific nitrosamine (NNAL) in community participants. Associations between the biomarker data and smoking characteristics were evaluated with Spearmans correlation analysis. RESULTS Participants included 28 Blacks, 4 Latinos, and 25 Whites who smoked at least 1 cigarette on 4-24 days in the past 30 days. Participants averaged 3.3 (SD = 2.1) cigarettes per day (cpd) on days smoked, they smoked an average of 13.0 (SD = 5.4) days in the past month, and they smoked nondaily for 10.5 (SD = 10.5) years. Median levels of creatinine-normalized cotinine and NNAL were 490.9 ng/mg and 140.7 pg/mg, respectively. NNAL and cotinine were highly correlated (r = .84); NNAL and cotinine were modestly correlated with cpd (r = .39 and r = .34; all p values <.05). The number of days smoked per month was not associated with any biomarker levels. CONCLUSIONS Our findings demonstrate that nondaily smokers are, on average, exposed to significant levels of nicotine and carcinogenic nitrosamines, with exposures of 40%-50% of those seen in daily smokers. This level of exposure suggests a significant health risk. Nicotine and carcinogen exposure is most closely related to number of cigarettes smoked per day but not to number of days per month of smoking.

Differences in smoking behavior and attitudes among Puerto Rican, Dominican, and non-Latino white caregivers of children with asthma

Purpose. No studies have examined the differences in smoking attitudes and behavior between Dominicans (DRs) and Puerto Ricans (PRs). Identification of pretreatment differences is important for cultural adaptation of evidenced-based smoking cessation treatments. Design. Secondary analysis. Setting/Intervention. Three home visits for asthma education and smoking cessation. Subjects. Caregivers who smoke and have a child with asthma: DRs (n = 30), PRs (n = 67), and non-Latino whites (n = 128; NLWs). Measures. Baseline assessment of psychosocial variables. Analyses. Controlled for age, education, and acculturation. Results. Compared with DRs, PRs were more acculturated, more nicotine dependent, less motivated and confident to quit, and identified more pros of smoking (all p < .05). Compared with NLWs, PRs were less likely to be employed, smoked fewer cigarettes per day, and had lower education, greater depressed mood, greater pros and cons of smoking, less social support, and higher child asthma morbidity (all p < .05). Compared with NLWs, DRs were less nicotine dependent, more confident to quit, and less likely to live with a smoker; reported greater cons of smoking and greater stress; and were more likely to have a household smoking ban (DRs 60% vs. NLWs 33.6%). Only 3.3% of DRs were precontemplators vs. 16.4% (PRs) and 10.9% (NLWs). Conclusions. PRs appear to have more factors associated with risk of smoking treatment failure; DRs appear to have more protective factors. Examination of the role of these smoking attitudes as potential moderators and mediators of smoking behavior are needed to guide the cultural adaptation of evidenced-based treatments. (Am J Health Promot 2011;25[5 Supplement]:S91—S95.)