H. Gene McFadden

Multiple behavior changes in diet and activity: A randomized controlled trial using mobile technology

BACKGROUND Many patients exhibit multiple chronic disease risk behaviors. Research provides little information about advice that can maximize simultaneous health behavior changes. METHODS To test which combination of diet and activity advice maximizes healthy change, we randomized 204 adults with elevated saturated fat and low fruit and vegetable intake, high sedentary leisure time, and low physical activity to 1 of 4 treatments: increase fruit/vegetable intake and physical activity, decrease fat and sedentary leisure, decrease fat and increase physical activity, and increase fruit/vegetable intake and decrease sedentary leisure. Treatments provided 3 weeks of remote coaching supported by mobile decision support technology and financial incentives. During treatment, incentives were contingent on using the mobile device to self-monitor and attain behavioral targets; during follow-up, incentives were contingent only on recording. The outcome was standardized, composite improvement on the 4 diet and activity behaviors at the end of treatment and at 5-month follow-up. RESULTS Of the 204 individuals randomized, 200 (98.0%) completed follow-up. The increase fruits/vegetables and decrease sedentary leisure treatments improved more than the other 3 treatments (P < .001). Specifically, daily fruit/vegetable intake increased from 1.2 servings to 5.5 servings, sedentary leisure decreased from 219.2 minutes to 89.3 minutes, and saturated fat decreased from 12.0% to 9.5% of calories consumed. Differences between treatment groups were maintained through follow-up. Traditional dieting (decrease fat and increase physical activity) improved less than the other 3 treatments (P < .001). CONCLUSIONS Remote coaching supported by mobile technology and financial incentives holds promise to improve diet and activity. Targeting fruits/vegetables and sedentary leisure together maximizes overall adoption and maintenance of multiple healthy behavior changes.

Integrating Technology Into Standard Weight Loss Treatment: A Randomized Controlled Trial

BACKGROUND A challenge in intensive obesity treatment is making care scalable. Little is known about whether the outcome of physician-directed weight loss treatment can be improved by adding mobile technology. METHODS We conducted a 2-arm, 12-month study (October 1, 2007, through September 31, 2010). Seventy adults (body mass index >25 and ≤40 [calculated as weight in kilograms divided by height in meters squared]) were randomly assigned either to standard-of-care group treatment alone (standard group) or to the standard and connective mobile technology system (+mobile group). Participants attended biweekly weight loss groups held by the Veterans Affairs outpatient clinic. The +mobile group was provided personal digital assistants to self-monitor diet and physical activity; they also received biweekly coaching calls for 6 months. Weight was measured at baseline and at 3-, 6-, 9-, and 12-month follow-up. RESULTS Sixty-nine adults received intervention (mean age, 57.7 years; 85.5% were men). A longitudinal intent-to-treat analysis indicated that the +mobile group lost a mean of 3.9 kg more (representing 3.1% more weight loss relative to the control group; 95% CI, 2.2-5.5 kg) than the standard group at each postbaseline time point. Compared with the standard group, the +mobile group had significantly greater odds of having lost 5% or more of their baseline weight at each postbaseline time point (odds ratio, 6.5; 95% CI, 2.5-18.6). CONCLUSIONS The addition of a personal digital assistant and telephone coaching can enhance short-term weight loss in combination with an existing system of care. Mobile connective technology holds promise as a scalable mechanism for augmenting the effect of physician-directed weight loss treatment. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00371462.

Behavioral intervention to promote smoking cessation and prevent weight gain: A systematic review and meta-analysis

AIMS The prospect of weight gain discourages many cigarette smokers from quitting. Practice guidelines offer varied advice about managing weight gain after quitting smoking, but no systematic review and meta-analysis have been available. We reviewed evidence to determine whether behavioral weight control intervention compromises smoking cessation attempts, and if it offers an effective way to reduce post-cessation weight gain. METHODS We identified randomized controlled trials (RCTs) that compared combined smoking treatment and behavioral weight control to smoking treatment alone for adult smokers. English-language studies were identified through searches of PubMed, Ovid MEDLINE, CINAHL, EMBASE, PsycINFO and Cochrane Central Register of Controlled Trials. Of 779 articles identified and 35 potentially relevant RCTs screened, 10 met the criteria and were included in the meta-analysis. RESULTS Patients who received both smoking treatment and weight treatment showed increased abstinence [odds ratio (OR) = 1.29, 95% confidence interval (CI) = 1.01, 1.64] and reduced weight gain (g = -0.30, 95% CI = -0.57, -0.02) in the short term (<3 months) compared with patients who received smoking treatment alone. Differences in abstinence (OR = 1.23, 95% CI = 0.85, 1.79) and weight control (g = -0.17, 95% CI = -0.42, 0.07) were no longer significant in the long term (>6 months). CONCLUSIONS Findings provide no evidence that combining smoking treatment and behavioral weight control produces any harm and significant evidence of short-term benefit for both abstinence and weight control. However, the absence of long-term enhancement of either smoking cessation or weight control by the time-limited interventions studied to date provides insufficient basis to recommend societal expenditures on weight gain prevention treatment for patients who are quitting smoking.

PDA+: A Personal Digital Assistant for Obesity Treatment - An RCT testing the use of technology to enhance weight loss treatment for veterans

BackgroundObese adults struggle to make the changes necessary to achieve even modest weight loss, though a decrease in weight by as little as 10% can have significant health benefits. Failure to meet weight loss goals may in part be associated with barriers to obesity treatment. Wide-spread dissemination of evidence-based obesity treatment faces multiple challenges including cost, access, and implementing the programmatic characteristics on a large scale.AimsThe PDA+: A Personal Digital Assistant for Obesity Treatment randomized controlled trial (RCT) was designed to test whether a PDA-based behavioral intervention enhances the effectiveness of the existing group weight loss treatment program at VA Medical Centers Managing Overweight/Obese Veterans Everywhere (MOVE!). We also aim to introduce technology as a way to overcome systemic barriers of traditional obesity treatment.Methods/DesignVeterans enrolled in the MOVE! group at the Hines Hospital VAMC with BMI ≥ 25 and ≤ 40 and weigh < 400 pounds, experience chronic pain (≥ 4 on the NRS-I scale for ≥ 6 months prior to enrollment) and are able to participate in a moderate intensity exercise program will be recruited and screened for eligibility. Participants will be randomized to receive either: a) MOVE! treatment alone (Standard Care) or b) Standard Care plus PDA (PDA+). Those randomized to PDA+ will record dietary intake, physical activity, and weight on the PDA. In addition, they will also record mood and pain intensity, and receive biweekly telephone support for the first 6-months of the 12-month study. All participants will attend in-person lab sessions every three months to complete questionnaires and for the collection of anthropomorphic data. Weight loss and decrease in pain level intensity are the primary outcomes.DiscussionThe PDA+ trial represents an important step in understanding ways to improve the use of technology in obesity treatment. The trial will address barriers to obesity care by implementing effective behavioral components of a weight loss intervention and delivering high intensity, low cost obesity treatment. This RCT also tests an intervention approach supported by handheld technology in a population traditionally considered to have lower levels of technology literacy.Trial RegistrationClinicalTrials.gov: NCT00371462

Financial motivation undermines potential enjoyment in an intensive diet and activity intervention.

The use of material incentives in healthy lifestyle interventions is becoming widespread. However, self-determination theory (SDT) posits that when material incentives are perceived as controlling, they undermine intrinsic motivation. We analyzed data from the Make Better Choices trial—a trial testing strategies for improving four risk behaviors: low fruit–vegetable intake, high saturated fat intake, low physical activity, and high sedentary activity. At baseline, participants reported the degree to which financial incentives were an important motivator (financial motivation); self-reported enjoyment of each behavior was assessed before and after the 3-week incentivization phase. Consistent with SDT, after controlling for general motivation and group assignment, lower financial motivation predicted more adaptive changes in enjoyment. Whereas participants low in financial motivation experienced adaptive changes, adaptive changes were suppressed among those high in financial motivation.

Behavioral interventions to promote smoking cessation and prevent weight gain: a reply.

Parsons and colleagues [1] attribute differences between the conclusions of our systematic review [2] and theirs [3] to differences in the way the two reviews grouped studies for meta-analyses. We suggest different explanations for discordant conclusions about the effects of behavioral interventions on smoking cessation and weight gain. First, the two reviews included somewhat different studies. Approximately 25–30% of the clinical trials analyzed in one review fail to overlap those included in the other review. Secondly, the two systematic reviews operationalized the weight gain outcome differently. Parsons et al. [3] analyzed weight gain only in those who abstained from smoking—a very small number of people—which led to comparing extremely small samples for the weight outcome (e.g. one group of three versus another group of seven). In contrast, when data were available, Spring and colleagues [2] analyzed weight gain among all those randomized (i.e. using intent-to-treat analysis per best clinical trials practice). Analyzing abstainers to eliminate a confounding effect of nicotine is appropriate when studying causal mechanisms for weight gain in a pharmacological experiment. When testing clinical practice policy, however, intent-to-treat analysis of all those randomized is necessary. Clinicians do not have the luxury of addressing weight gain among only those who stay abstinent. They are responsible for managing the smoking and weight status of all patients in the practice, many of whom will have gained weight because they quit smoking temporarily, even though they failed to maintain abstinence. For that reason, the correct denominator is the entire population of patients who were offered either treatment or control. A third difference is that Spring and colleagues [2] used more conservative random-effects analysis whereas, for unclear reasons, Parsons et al. [3] used fixed effects. Parsons et al.’s [3] use of fixed effects was surprising given their emphasis on heterogeneity, especially as fixed-effects analysis ignores between-study variability and usually yields narrower confidence intervals and stronger P-values than random-effects analysis. Although many of Parsons et al.’s [3] I tests were non-significant, heterogeneity was non-trivial for some subgroup comparisons, and the test for heterogeneity has low power that is exacerbated by the small sample sizes in most of their comparisons. Because generalization beyond the observed data set can be considered legitimate for random but not fixed-effects analysis, it is hazardous to extrapolate conclusions beyond the several studies in each of Parsons et al.’s [3] comparisons. The danger is amplified because their major grouping of interventions into generic versus individualized interventions appears to have been idiosyncratic and post hoc. Parsons and colleagues [3] grouped together the two studies [4,5] for which abstinence was non-significantly but directionally less in a treatment that addressed smoking only, compared to a treatment that addressed both smoking and weight. They justified that grouping by suggesting that the two studies by Hall and colleagues [4] and Pirie and colleagues [5] were unique in giving participants only generic, non-individualized advice. Then, based upon that rationale, they extrapolated conclusions inappropriately from liberal fixed-effects analyses to draw the alarming conclusion that generic behavioral advice about smoking and weight management undermines abstinence. We disagree strongly with that interpretation and also believe that it mischaracterizes Hall et al.’s [4] and Pirie et al.’s [5] interventions. Rather than giving participants only generic, non-individualized advice, Pirie et al. [4] and Hall et al. [5] provided multiple sessions of group treatment that involved tailoring by both the group leader and participants. Additionally, Pirie and colleagues [5] individualized the prescribed rate of calorie reduction, physical activity increase and smoking reduction for participants, based upon their starting levels. Moreover, the strongest test of whether generic treatment has a worse effect on abstinence than individualized treatment comes from comparisons in which patients are randomized to either condition within a single trial. Two such comparisons exist among the studies included in the meta-analyses, and both fail to show an adverse effect. At 1-year follow-up, Hall and colleagues [4] found abstinence to be nearly identical in their individualized versus non-specific conditions (21% versus 22%). Similarly, Copeland et al. [6] found no difference in abstinence between their individualized versus non-individualized intervention. Parsons et al.’s interpretation [1,3] that individualized intervention suppresses long-term weight gain also seems ill-founded. In addition to the analytical problems already mentioned (use of fixed-effects and abstainers-only analysis), findings were overstated. The authors argue that the weight-suppressing effect of individualized treatment was strengthened at 12 months compared to end of

Effects of an abbreviated obesity intervention supported by mobile technology: The ENGAGED randomized clinical trial

To determine the effects on weight loss of three abbreviated behavioral weight loss interventions with and without coaching and mobile technology.

ACCISS study rationale and design: Activating collaborative cancer information service support for cervical cancer screening

BackgroundHigh-quality cancer information resources are available but underutilized by the public. Despite greater awareness of the National Cancer Institutes Cancer Information Service among low-income African Americans and Hispanics compared with Caucasians, actual Cancer Information Service usage is lower than expected, paralleling excess cancer-related morbidity and mortality for these subgroups. The proposed research examines how to connect the Cancer Information Service to low-income African-American and Hispanic women and their health care providers. The study will examine whether targeted physician mailing to women scheduled for colposcopy to follow up an abnormal Pap test can increase calls to the Cancer Information Service, enhance appropriate medical follow-up, and improve satisfaction with provider-patient communication.Methods/DesignThe study will be conducted in two clinics in ethnically diverse low-income communities in Chicago. During the formative phase, patients and providers will provide input regarding materials planned for use in the experimental phase of the study. The experimental phase will use a two-group prospective randomized controlled trial design. African American and Hispanic women with an abnormal Pap test will be randomized to Usual Care (routine colposcopy reminder letter) or Intervention (reminder plus provider recommendation to call the Cancer Information Service and sample questions to ask). Primary outcomes will be: 1) calls to the Cancer Information Service; 2) timely medical follow-up, operationalized by whether the patient keeps her colposcopy appointment within six months of the abnormal Pap; and 3) patient satisfaction with provider-patient communication at follow-up.DiscussionThe study examines the effectiveness of a feasible, sustainable, and culturally sensitive strategy to increase awareness and use of the Cancer Information Service among an underserved population. The goal of linking a public service (the Cancer Information Service) with real-life settings of practice (the clinics), and considering input from patients, providers, and Cancer Information Service staff, is to ensure that the intervention, if proven effective, can be incorporated into existing care systems and sustained. The approach to study design and planning is aimed at bridging the gap between research and practice/service.Trial RegistrationNCT00873288

Daily and Seasonal Influences on Dietary Self-monitoring Using a Smartphone Application

Objective: To examine within‐person variation in dietary self‐monitoring during a 6‐month technology‐supported weight loss trial as a function of time‐varying factors including time in the study, day of the week, and month of the year. Methods: Smartphone self‐monitoring data were examined from 31 obese adults (aged 18–60 years) who participated in a 6‐month technology‐supported weight loss program. Multilevel regression modeling was used to examine within‐person variation in dietary self‐monitoring. Results: Participants recorded less as time in the study progressed. Fewer foods were reported on the weekends compared with weekdays. More foods were self‐monitored in January compared with October; however, a seasonal effect was not observed. Conclusions and Implications: The amount of time in a study and day of the week were associated with dietary self‐monitoring but not season. Future studies should examine factors that influence variations in self‐monitoring and identify methods to improve technology‐supported dietary self‐monitoring adherence.