In this paper, we present a real-time algorithm for automatic recognition of not only physical activities, but also, in some cases, their intensities, using five triaxial wireless accelerometers and a wireless heart rate monitor. The algorithm has been evaluated using datasets consisting of 30 physical gymnasium activities collected from a total of 21 people at two different labs. On these activities, we have obtained a recognition accuracy performance of 94.6% using subject-dependent training and 56.3% using subject-independent training. The addition of heart rate data improves subject-dependent recognition accuracy only by 1.2% and subject-independent recognition only by 2.1%. When recognizing activity type without differentiating intensity levels, we obtain a subject-independent performance of 80.6%. We discuss why heart rate data has such little discriminatory power.

The increasing prevalence of overweight and obesity underscores the need for evidence-based, easily disseminable interventions for weight management that can be delivered on a population basis. The Transtheoretical Model (TTM) offers a promising theoretical framework for multiple behavior weight management interventions. Overweight or obese adults (BMI 25-39.9; n=1277) were randomized to no-treatment control or home-based, stage-matched multiple behavior interventions for up to three behaviors related to weight management at 0, 3, 6, and 9 months. All participants were re-assessed at 6, 12, and 24 months. Significant treatment effects were found for healthy eating (47.5% versus 34.3%), exercise (44.90% versus 38.10%), managing emotional distress (49.7% versus 30.30%), and untreated fruit and vegetable intake (48.5% versus 39.0%) progressing to Action/Maintenance at 24 months. The groups differed on weight lost at 24 months. Co-variation of behavior change occurred and was much more pronounced in the treatment group, where individuals progressing to Action/Maintenance for a single behavior were 2.5-5 times more likely to make progress on another behavior. The impact of the multiple behavior intervention was more than three times that of single behavior interventions. This study demonstrates the ability of TTM-based tailored feedback to improve healthy eating, exercise, managing emotional distress, and weight on a population basis. The treatment produced a high level of population impact that future multiple behavior interventions can seek to surpass.

Background. Hypertension is more prevalent and clinically severe among African–Americans than whites. Several health behaviors influence blood pressure

Objective: To evaluate the acceptability and feasibility of a scalable obesity treatment program integrated with pediatric primary care (PC) and delivered using interactive voice technology (IVR) to families from underserved populations.Design and Methods: Fifty parent‐child dyads (child 9‐12 yrs, BMI > 95th percentile) were recruited from a pediatric PC clinic and randomized to either an IVR or a wait‐list control (WLC) group. The majority were lower‐income, African‐American (72%) families. Dyads received IVR calls for 12 weeks. Call content was informed by two evidence‐based interventions. Anthropometric and behavioral variables were assessed at baseline and 3‐month follow‐up.Results: Forty‐three dyads completed the study. IVR parents ate one cup more fruit than WLC (P < 0.05). No other group differences were found. Children classified as high users of the IVR decreased weight, BMI, and BMI z‐score compared to low users ( P < 0.05). Mean number of calls for parents and children were 9.1 (5.2 SD) and 9.0 (5.7 SD), respectively. Of those who made calls, >75% agreed that the calls were useful, made for people like them, credible, and helped them eat healthy foods.Conclusion: An obesity treatment program delivered via IVR may be an acceptable and feasible resource for families from underserved populations.

This study evaluated how well predictions from the transtheoretical model (TTM) generalized from smoking to diet. Longitudinal data were used from a randomized control trial on reducing dietary fat consumption in adults (n =1207) recruited from primary care practices. Predictive power was evaluated by making a priori predictions of the magnitude of change expected in the TTM constructs of temptation, pros and cons, and 10 processes of change when an individual transitions between the stages of change. Generalizability was evaluated by testing predictions based on smoking data. Three sets of predictions were made for each stage: Precontemplation (PC), Contemplation (C) and Preparation (PR) based on stage transition categories of no progress, progress and regression determined by stage at baseline versus stage at the 12-month follow-up. Univariate analysis of variance between stage transition groups was used to calculate the effect size [omega squared (ω²)]. For diet predictions based on diet data, there was a high degree of confirmation: 92%, 95% and 92% for PC, C and PR, respectively. For diet predictions based on smoking data, 77%, 79% and 85% were confirmed, respectively, suggesting a moderate degree of generalizability. This study revised effect size estimates for future theory testing on the TTM applied to dietary fat.