Mindi A. Styn

Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial

BACKGROUND Self-monitoring for weight loss has traditionally been performed with paper diaries. Technologic advances could reduce the burden of self-monitoring and provide feedback to enhance adherence. PURPOSE To determine if self-monitoring diet using a PDA only or the PDA with daily tailored feedback (PDA+feedback [FB]), was superior to using a paper diary on weight loss and maintenance. DESIGN The Self-Monitoring and Recording Using Technology (SMART) Trial was a 24-month randomized clinical trial; participants were randomly assigned to one of three self-monitoring groups. SETTING/PARTICIPANTS From 2006 to 2008, a total of 210 overweight/obese adults (84.8% female, 78.1% white) were recruited from the community. Data were analyzed in 2011. INTERVENTION Participants received standard behavioral treatment for weight loss that included dietary and physical activity goals, encouraged the use of self-monitoring, and was delivered in group sessions. MAIN OUTCOME MEASURES Percentage weight change at 24 months, adherence to self-monitoring over time. RESULTS Study retention was 85.6%. The mean percentage weight loss at 24 months was not different among groups (paper diary: -1.94%, 95% CI = -3.88, 0.01; PDA: -1.38%, 95% CI= -3.38, 0.62; PDA+FB: -2.32%, 95% CI= -4.29, -0.35); only the PDA+FB group (p=0.02) demonstrated a significant loss. For adherence to self-monitoring, there was a time-by-treatment group interaction between the combined PDA groups and the paper diary group (p=0.03) but no difference between PDA and PDA+FB groups (p=0.49). Across all groups, weight loss was greater for those who were adherent ≥60% versus <30% of the time (p<0.001). CONCLUSIONS PDA+FB use resulted in a small weight loss at 24 months; PDA use resulted in greater adherence to dietary self-monitoring over time. However, for sustained weight loss, adherence to self-monitoring is more important than the method used to self-monitor. A daily feedback message delivered remotely enhanced adherence and improved weight loss, which suggests that technology can play a role in improving weight loss. TRIAL REGISTRATION This study is registered at clinicaltrials.gov NCT00277771.

The Effect of Electronic Self-Monitoring on Weight Loss and Dietary Intake: A Randomized Behavioral Weight Loss Trial

Technology may improve self‐monitoring adherence and dietary changes in weight loss treatment. Our study aimed to investigate whether using a personal digital assistant (PDA) with dietary and exercise software, with and without a feedback message, compared to using a paper diary/record (PR), results in greater weight loss and improved self‐monitoring adherence. Healthy adults (N = 210) with a mean BMI of 34.01 kg/m2 were randomized to one of three self‐monitoring approaches: PR (n = 72), PDA with self‐monitoring software (n = 68), or PDA with self‐monitoring software and daily feedback messages (PDA+FB, n = 70). All participants received standard behavioral treatment. Self‐monitoring adherence and change in body weight, waist circumference, and diet were assessed at 6 months; retention was 91%. All participants had a significant weight loss (P < 0.01) but weight loss did not differ among groups. A higher proportion of PDA+FB participants (63%) achieved ≥5% weight loss in comparison to the PR group (46%) (P < 0.05) and PDA group (49%) (P = 0.09). Median percent self‐monitoring adherence over the 6 months was higher in the PDA groups (PDA 80%; PDA+FB 90%) than in the PR group (55%) (P < 0.01). Waist circumference decreased more in the PDA groups than the PR group (P = 0.02). Similarly, the PDA groups reduced energy and saturated fat intake more than the PR group (P < 0.05). Self‐monitoring adherence was greater in the PDA groups with the greatest weight change observed in the PDA+FB group.

SMART trial: A randomized clinical trial of self-monitoring in behavioral weight management-design and baseline findings

BACKGROUND The primary form of treatment for obesity today is behavioral therapy. Self-monitoring diet and physical activity plays an important role in interventions targeting behavior and weight change. The SMART weight loss trial examined the impact of replacing the standard paper record used for self-monitoring with a personal digital assistant (PDA). This paper describes the design, methods, intervention, and baseline sample characteristics of the SMART trial. METHODS The SMART trial used a 3-group design to determine the effects of different modes of self-monitoring on short- and long-term weight loss and on adherence to self-monitoring in a 24-month intervention. Participants were randomized to one of three conditions (1) use of a standard paper record (PR); (2) use of a PDA with dietary and physical activity software (PDA); or (3), use of a PDA with the same software plus a customized feedback program (PDA + FB). RESULTS We screened 704 individuals and randomized 210. There were statistically but not clinically significant differences among the three cohorts in age, education, HDL cholesterol, blood glucose and systolic blood pressure. At 24 months, retention rate for the first of three cohorts was 90%. CONCLUSIONS To the best of our knowledge, the SMART trial is the first large study to compare different methods of self-monitoring in a behavioral weight loss intervention and to compare the use of PDAs to conventional paper records. This study has the potential to reveal significant details about self-monitoring patterns and whether technology can improve adherence to this vital intervention component.

Adherence to a behavioral weight loss treatment program enhances weight loss and improvements in biomarkers

Objectives: To describe participants’ adherence to multiple components (attendance, energy intake, fat gram, exercise goals, and self-monitoring eating and exercise behaviors) of a standard behavioral treatment program (SBT) for weight loss and how adherence to these components may influence weight loss and biomarkers (triglycerides, low density lipoproteins [LDL], high density lipoprotein, and insulin) during the intensive and less-intensive intervention phases. Methods: A secondary analysis of a randomized clinical trial consisting of a SBT with either fat-restricted standard or lacto-ovo vegetarian diet. The 12-month intervention was delivered in 33 group sessions. The first six months reflected the intensive phase; the second six months, the less-intensive intervention phase. We conducted the analysis without regard to treatment assignment. Eligible participants included overweight/obese adults (N = 176; mean body mass index = 34.0 kg/m2). The sample was 86.9% female, 70.5% White, and 44.4 ± 8.6 years old. The outcome measures included weight and biomarkers. Results: There was a significant decline in adherence to each treatment component over time (P < 0.0001). In the first six months, adherence to attendance, self-monitoring and the energy goal were significantly associated with greater weight loss (P < 0.05). Adherence to attendance and exercise remained significantly associated with weight loss in the second six months (P < 0.05). Adherence to attendance, self-monitoring and exercise had indirect effects through weight loss on LDL, triglycerides, and insulin (P < 0.05). Conclusions: We observed a decline in adherence to each treatment component as the intervention intensity was reduced. Adherence to multiple treatment components was associated with greater weight loss and improvements in biomarkers. Future research needs to focus on improving and maintaining adherence to all components of the treatment protocol to promote weight loss and maintenance.

Using a Personal Digital Assistant for Self-Monitoring Influences Diet Quality in Comparison to a Standard Paper Record among Overweight/Obese Adults

Self-monitoring has traditionally been done using a paper record, which can be tedious and burdensome. A personal digital assistant (PDA) with dietary software can provide an alternative to a paper record. The study aimed to describe the differences in dietary changes at 6 months between participants randomly assigned to use a paper record or PDA for self-monitoring in a clinical trial of weight-loss treatment. Self-monitoring adherence and changes in weight and diet were assessed between 2006 and 2009. The sample (n=192) was 84% female and 78% white, with a mean age of 49 years and body mass index (calculated as kg/m(2)) of 34.1. At baseline, the groups did not differ in energy intake, percent calories from fat, and number of servings of the examined food groups. At 6 months, both groups had significant reductions in weight, energy intake, and percent calories from total fat and saturated fatty acids (P<0.001); no between-group differences were found. Compared to the paper record group, the PDA group significantly increased consumption of fruit (P=0.02) and vegetables (P=0.04) and decreased consumption of refined grains (P=0.02). Interactions among self-monitoring and the two groups were found in relation to changes in percent calories from total fat (P=0.02), monounsaturated fatty acids (P=0.002), and trans-fatty acids (P=0.04). Frequent self-monitoring was significantly associated with total sugar (P=0.02) and added sugar (P=0.01) intake in both groups. Our findings suggest that use of a PDA for self-monitoring might improve self-awareness of behavior and dietary changes.

Physical Activity Self-Monitoring and Weight Loss: 6-Month Results of the SMART Trial

INTRODUCTION Weight loss has been associated with higher physical activity (PA) levels and frequent dietary self-monitoring. Less is known about how PA self-monitoring affects adherence to PA goals, PA levels, and weight change. METHODS The SMART Trial is a clinical weight loss trial in which 210 overweight adults were randomized equally to one of three arms: 1) paper record (PR), 2) personal digital assistant with self-monitoring software (PDA), and 3) PDA with daily tailored feedback message (PDA + FB). PA self-monitoring and adherence to PA goals were based on entries in weekly submitted diaries. PA levels were measured via self-report by the past 6-month Modifiable Activity Questionnaire at baseline and 6 months. RESULTS Data are presented on 189 participants with complete 6-month PA data (84% female, 77% white, mean age = 47.3 ± 8.8 yr, mean body mass index = 34.1 ± 4.5 kg·m(-2)). Median PA level was 7.96 MET·h·wk(-1) at baseline and 13.4 MET·h·wk(-1) at 6 months, with significant PA increases in all three arms. PDA + FB arm had a higher mean number of weekly self-monitoring entries than the PR arm (3.4 vs 2.4, P = 0.003) and were more likely to maintain high (i.e., 100%) adherence to PA goals over time than the PDA (P = 0.02) or PR arms (P = 0.0003). Both PA self-monitoring and adherence to PA goals were related to higher PA levels at 6 months. A higher mean rate of PA self-monitoring was associated with a greater percentage of weight decrease (ρ = -0.49, P < 0.0001) at 6 months. CONCLUSIONS PA self-monitoring and adherence to PA goals were more likely in participants in the PDA + FB arm and in turn predicted higher PA levels and weight loss.

A randomized clinical trial of a standard versus vegetarian diet for weight loss: the impact of treatment preference

Background:With obesity rampant, methods to achieve sustained weight loss remain elusive.Objective:To compare the long-term weight-loss efficacy of 2 cal and fat-restricted diets, standard (omnivorous) versus lacto–ovo–vegetarian, and to determine the effect of a chosen diet versus an assigned diet.Design, subjects:A randomized clinical trial was conducted with 176 adults who were sedentary and overweight (mean body mass index, 34.0 kg/m2). Participants were first randomly assigned to either receive their preferred diet or be assigned to a diet group and second, were given their diet of preference or randomly assigned to a standard weight-loss diet or a lacto–ovo–vegetarian diet. Participants underwent a university-based weight-control program consisting of daily dietary and exercise goals plus 12 months of behavioral counseling followed by a 6-month maintenance phase.Measurements:Percentage change in body weight, body mass index, waist circumference, low- and high-density lipoprotein, glucose, insulin and macronutrient intake.Results:The program was completed by 132 (75%) of the participants. At 18 months, mean percentage weight loss was greater (P=0.01) in the two groups that were assigned a diet (standard, 8.0% (s.d., 7.8%); vegetarian, 7.9% (s.d., 8.1%)) than in those provided the diet of their choice (standard, 3.9% (s.d., 6.1%); vegetarian, 5.3% (s.d., 6.2%)). No difference was observed in weight loss between the two types of diet. Over the 18-month program, all groups showed significant weight loss.Conclusions:Participants assigned to their dietary preference did not have enhanced treatment outcomes. However, all groups lost weight with losses ranging from 4 to 8% at 18 months.

Smoking Behavior 1 Year after Computed Tomography Screening for Lung Cancer: Effect of Physician Referral for Abnormal CT Findings

Background: Computed tomography (CT) lung cancer screening offers a unique clinical setting in which to promote smoking cessation. Focusing on outcomes related to the reporting of CT abnormality, we examined the natural history of smoking in the Pittsburgh Lung Screening Study. Methods: Pittsburgh Lung Screening Study recruited 50- to 79-year-old current and former cigarette smokers living in the Pittsburgh area. We examined self-reported smoking outcomes 1 year after study entry in a subgroup that contained 2,094 active cigarette smokers without interval lung cancer diagnosis (50.7% women; median age, 57 years; 40-year median duration of cigarette smoking; and 65.2% ≥20 cigarettes/d). Analyses compared efforts to quit in relation to physician referral for abnormal CT. Results: Since study entry, 58.5% [95% confidence interval (95% CI), 56.3-60.6%] reported any quit attempt and 27.2% (95% CI, 25.3-29.1%) reported any quit interval >30 days. One year after study entry, 15.5% (95% CI, 14.0-17.1%) reported not smoking for >30 days. Comparing persons referred because of CT abnormalities creating moderate or high lung cancer suspicion (n = 156; 7.4%) to persons not referred for any reason (n = 1145; 54.7%), propensity score-adjusted fractions with any quit attempt and with any quit interval >30 days increased 18.8% (95% CI, 11.1-26.5%) and 17.7% (95% CI, 9.4-26.0%), respectively. The fraction quit >30 days at 1 year increased 12.2% (95% CI, 4.9-19.5%). Conclusions: Persons who experienced referral because of abnormal CT reported more smoking cessation. (Cancer Epidemiol Biomarkers Prev 2009;18(12):3484–9)

A Randomized Clinical Trial Testing Treatment Preference and Two Dietary Options in Behavioral Weight Management: Preliminary Results of the Impact of Diet at 6 Months—PREFER Study

Objective: The PREFER study objectives were to examine potential differences in weight loss during a standard behavioral intervention between subjects assigned to one of two calorie‐ and fat‐restricted diets [standard behavior treatment (SBT) and lacto‐ovo‐vegetarian ([SBT+LOV)], with or without regard to their preferred dietary treatment. This article reports the differences in outcomes between diet groups after the first 6 months of the intervention.

Factors associated with health-related quality of life among overweight or obese adults

AIMS AND OBJECTIVES To identify factors associated with health-related quality of life among overweight or obese adults. BACKGROUND The obesity epidemic presents a global challenge. Obesity is associated with lower health-related quality of life; however, no study has comprehensively examined correlates of health-related quality of life in this population. DESIGN A cross-sectional design. METHODS The physical component score, mental component score and eight domain scores of the Short Form-36 v2 were used to assess health-related quality of life. We identified 23 possible correlates of health-related quality of life, including age, body mass index, health and weight histories, perceived stress, cholesterol-lowering diet self-efficacy, problem-solving, binge eating, dietary intake and physical activity. Correlational analyses were used to examine the bivariate associations between correlates and health-related quality of life variables. All possible subsets regression was used to develop predictive models of health-related quality of life. RESULTS The sample (n = 210) was predominantly White (84·8%), female (78·1%) and middle-aged (average age = 46·80 years). Age, body mass index, education, having children at home, and being hypertensive were identified as the best predictors of physical component score, explaining about 9% of the variance. Age, marital status, having hyperlipidaemia, perceived stress, problem-solving, self-efficacy, binge eating and barriers to healthy eating predicted mental component score, explaining approximately 48% of the variance. Physical functioning and role physical domains of health-related quality of life had similar sets of predictors, with 15% and 13% of the variance explained, respectively, while similar predictors were identified for bodily pain (6%), general health (26%), vitality (40%), social functioning (32%), role emotional (42%) and mental health (46%) domains. CONCLUSIONS Psychosocial factors were associated with the mental-related quality of life. Further exploration of factors related to physical-related quality of life is warranted in this population. RELEVANCE TO CLINICAL PRACTICE When working with overweight/obese adults who are trying to lose weight, nurses need to consider socio-demographic and psychosocial factors in the development of a treatment plan that will improve health-related quality of life in this population.