Objective We utilized the amyloid imaging ligand Pittsburgh Compound B (PiB) to determine the presence of Alzheimer's disease (AD) pathology in different mild cognitive impairment (MCI) subtypes and to relate increased PiB binding to other markers of early AD and longitudinal outcome. Methods Twenty‐six patients with MCI (13 single‐domain amnestic‐MCI [a‐MCI], 6 multidomain a‐MCI, and 7 nonamnestic MCI) underwent PiB imaging. Twenty‐three had clinical follow‐up (21.2 ± 16.0 [standard deviation] months) subsequent to their PiB scan. Results Using cutoffs established from a control cohort, we found that 14 (54%) patients had increased levels of PiB retention and were considered “amyloid‐positive.” All subtypes were associated with a significant proportion of amyloid‐positive patients (6/13 single‐domain a‐MCI, 5/6 multidomain a‐MCI, 3/7 nonamnestic MCI). There were no obvious differences in the distribution of PiB retention in the nonamnestic MCI group. Predictors of conversion to clinical AD in a‐MCI, including poorer episodic memory, and medial temporal atrophy, were found in the amyloid‐positive relative to amyloid‐negative a‐MCI patients. Longitudinal follow‐up demonstrated 5 of 13 amyloid‐positive patients, but 0 of 10 amyloid‐negative patients, converted to clinical AD. Further, 3 of 10 amyloid‐negative patients “reverted to normal.” Interpretation These data support the notion that amyloid‐positive patients are likely to have early AD, and that the use of amyloid imaging may have an important role in determining which patients are likely to benefit from disease‐specific therapies. In addition, our data are consistent with longitudinal studies that suggest a significant percentage of all MCI subtypes will develop AD. Ann Neurol 2009;65:557–568

The aim of this study was to describe smoking prevalence and smoking behavior in a multiethnic low-income HIV/AIDS population. A cross-sectional survey design was used. The study site was Thomas Street Clinic, an HIV/AIDS care facility serving a medically indigent and ethnically diverse population. Demographic, disease status, behavioral, and psychosocial variables were assessed by participant self-report. Surveys were collected from 348 study participants. Demographic composition of the sample was 78% male, 25% White, 44% Black, and 29% Hispanic. Study participants had a mean age of 40.2 years (SD=7.8). The HIV exposure profile of the sample was diverse: 46% men who have sex with men, 35% heterosexual contact, and 11% injection drug use. Prevalence of current cigarette smoking in the sample was 46.9%. Among participants with a lifetime history of smoking 100 or more cigarettes (62.8%), only 26.6% were currently abstinent, lower than the 48.8% rate seen in the general population. Multiple logistic regression analysis indicated that race/ethnicity, education level, age, and heavy drinking were significantly associated with smoking status. Hispanics were less likely than Whites were to smoke, younger participants were less likely than older participants were to be current smokers, and heavy drinkers were more likely to be current smokers than were those who were not heavy drinkers. As education level increased, the likelihood of smoking decreased and the likelihood of quitting increased. The high smoking prevalence in this HIV/AIDS population demonstrates the need for smoking cessation interventions targeted to the special needs of this patient group.

These results suggest that individuals living with HIV/AIDS are receptive to, and can be helped by, smoking cessation treatment. In addition, smoking cessation treatment tailored to the special needs of individuals living with HIV/AIDS, such as counseling delivered by cellular telephone, can significantly increase smoking abstinence rates over that achieved by usual care.

BackgroundDespite substantial public health progress in reducing the prevalence of smoking in the United States overall, smoking among socioeconomically disadvantaged adults remains high.ObjectiveTo determine the feasibility and preliminary effectiveness of a novel smartphone-based smoking cessation app designed for socioeconomically disadvantaged smokers.MethodsParticipants were recruited from a safety-net hospital smoking cessation clinic in Dallas, Texas, and were followed for 13 weeks. All participants received standard smoking cessation clinic care (ie, group counseling and cessation pharmacotherapy) and a smartphone with a novel smoking cessation app (ie, Smart-T). The Smart-T app prompted 5 daily ecological momentary assessments (EMAs) for 3 weeks (ie, 1 week before cessation and 2 weeks after cessation). During the precessation period, EMAs were followed by messages that focused on planning and preparing for the quit attempt. During the postcessation period, participant responses to EMAs drove an algorithm that tailored messages to the current level of smoking lapse risk and currently present lapse triggers (eg, urge to smoke, stress). Smart-T offered additional intervention features on demand (eg, one-click access to the tobacco cessation quitline; “Quit Tips” on coping with urges to smoke, mood, and stress).ResultsParticipants (N=59) were 52.0 (SD 7.0) years old, 54% (32/59) female, and 53% (31/59) African American, and 70% (40/57) had annual household income less than US $16,000. Participants smoked 20.3 (SD 11.6) cigarettes per day and had been smoking for 31.6 (SD 10.9) years. Twelve weeks after the scheduled quit date, 20% (12/59) of all participants were biochemically confirmed abstinent. Participants responded to 87% of all prompted EMAs and received approximately 102 treatment messages over the 3-week EMA period. Most participants (83%, 49/59) used the on-demand app features. Individuals with greater nicotine dependence and minority race used the Quit Tips feature more than their counterparts. Greater use of the Quit Tips feature was linked to nonabstinence at the 2 (P=.02), 4 (P<.01), and 12 (P=.03) week follow-up visits. Most participants reported that they actually used or implemented the tailored app-generated messages and suggestions (83%, 49/59); the app-generated messages were helpful (97%, 57/59); they would like to use the app in the future if they were to lapse (97%, 57/59); and they would like to refer friends who smoke to use the Smart-T app (85%, 50/59). A minority of participants (15%, 9/59) reported that the number of daily assessments (ie, 5) was “too high.”ConclusionsThis novel just-in-time adaptive intervention delivered an intensive intervention (ie, 102 messages over a 3-week period), was well-liked, and was perceived as helpful and useful by socioeconomically disadvantaged adults who were seeking smoking cessation treatment. Smartphone apps may be used to increase treatment exposure and may ultimately reduce tobacco-related health disparities among socioeconomically disadvant...

Background Smartphone apps for smoking cessation could offer easily accessible, highly tailored, intensive interventions at a fraction of the cost of traditional counseling. Although there are hundreds of publicly available smoking cessation apps, few have been empirically evaluated using a randomized controlled trial (RCT) design. The Smart-Treatment (Smart-T2) app is a just-in-time adaptive intervention that uses ecological momentary assessments (EMAs) to assess the risk for imminent smoking lapse and tailors treatment messages based on the risk of lapse and reported symptoms. Objective This 3-armed pilot RCT aimed to determine the feasibility and preliminary efficacy of an automated smartphone-based smoking cessation intervention (Smart-T2) relative to standard in-person smoking cessation clinic care and the National Cancer Institute’s free smoking cessation app, QuitGuide. Methods Adult smokers who attended a clinic-based tobacco cessation program were randomized into groups and followed for 13 weeks (1 week prequitting through 12 weeks postquitting). All study participants received nicotine patches and gum and were asked to complete EMAs five times a day on study-provided smartphones for 5 weeks. Participants in the Smart-T2 group received tailored treatment messages after the completion of each EMA. Both Smart-T2 and QuitGuide apps offer on-demand smoking cessation treatment. Results Of 81 participants, 41 (50%) were women and 55 (68%) were white. On average, participants were aged 49.6 years and smoked 22.4 cigarettes per day at baseline. A total of 17% (14/81) of participants were biochemically confirmed 7-day point prevalence abstinent at 12 weeks postquitting (Smart-T2: 6/27, 22%, QuitGuide: 4/27, 15%, and usual care: 4/27, 15%), with no significant differences across groups (P>.05). Participants in the Smart-T2 group rated the app positively, with most participants agreeing that they can rely on the app to help them quit smoking, and endorsed the belief that the app would help them stay quit, and these responses were not significantly different from the ratings given by participants in the usual care group. Conclusions Dynamic smartphone apps that tailor intervention content in real time may increase user engagement and exposure to treatment-related materials. The results of this pilot RCT suggest that smartphone-based smoking cessation treatments may be capable of providing similar outcomes to traditional, in-person counseling. Trial Registration ClinicalTrials.gov NCT02930200; https://clinicaltrials.gov/show/NCT02930200