Intraday relations between physical activity and energy intake among behavioral weight loss participants

OBJECTIVE

Weight loss results from a negative energy balance, when energy intake (EI) is less than energy expended, e.g., from physical activity (PA). However, PA may impact energy balance beyond energy expenditure alone, through indirect effects on eating behavior. Yet, no research has examined if engaging in PA-a central component of most weight loss programs-is associated with same-day EI among individuals with overweight/obesity pursuing weight loss.

METHOD

Adults (N = 101) with overweight/obesity in a weight loss program were prescribed a reduced-calorie diet and PA regimen (250 min of moderate-to-vigorous PA at midtreatment). For 3 weeks at midtreatment, PA and EI were measured via an accelerometer and self-monitoring app, respectively. Multilevel models examined within-person relations between PA and EI preceding PA ("pre-PA"), acutely following PA ("acute post-PA," the 2 hr following PA), in the time following the acute post-PA period ("remaining time in day"), and across entire PA days ("full-day"), relative to non-PA matched time periods.

RESULTS

EI was higher in the pre-PA and acute post-PA periods. There were no reliable differences in EI during the remaining time in day nor across the full-day on PA days versus within-subject matched non-PA days. There also was insufficient evidence to suggest EI pre-PA, post-PA, or across entire PA days, relative to non-PA matched time periods, was associated with percent weight change.

CONCLUSIONS

Findings suggest that engaging in PA was associated with different within-person EI patterns compared to non-PA days, though there was little evidence to support that these patterns relate to weight change. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Application of machine learning to discover interactions predictive of dietary lapses

The purpose of this study it to build a machine learning model to predict dietary lapses with comparable accuracy, sensitivity, and specificity to previous literature while recovering predictor interactions. The sample for the current study consisted of merged data from two separate studies of individuals with obesity/overweight (total N = 87). Participants completed six ecological momentary assessment surveys per day where they were asked about 16 risk factors of lapse and if they had lapsed from their dietary prescriptions since the previous survey. Alcohol consumption and self-efficacy were the most prevalent in the top 10 stable interactions. Alcohol consumption decreased the protective effect of self-efficacy, motivation, and planning. Higher planning predicted higher risk for lapse only when consuming alcohol. Low motivation, hunger, cravings, and lack of healthy food availability increased the protective effect of self-efficacy. Higher self-efficacy increased risk effect of positive mood and having recently eaten a meal on lapse. For individuals with lower levels of self-efficacy, planning increased the risk of lapse. Alcohol intake and self-efficacy interact with several variables to predict dietary lapses, and these interactions should be targeted in just-in-time adaptive interventions that deliver interventions for lapses.

Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse

Ecological momentary assessment (EMA; brief self-report surveys) of dietary lapse risk factors (e.g., cravings) has shown promise in predicting and preventing dietary lapse (nonadherence to a dietary prescription), which can improve weight loss interventions. Passive sensors also can measure lapse risk factors and may offer advantages over EMA (e.g., objective, automatic, semicontinuous data collection), but currently can measure only a few lapse predictors, a notable limitation. This study preliminarily compared the burden and accuracy of commercially available sensors versus established EMA in lapse prediction. N = 23 adults with overweight/obesity completed a 6-week commercial app-based weight loss program. Participants wore a Fitbit, enabled GPS tracking, completed EMA, and reported on EMA and sensor burden poststudy via a 5-point Likert scale. Sensed risk factors were physical activity and sleep (accelerometer), geolocation (GPS), and time, from which 233 features (measurable characteristics of sensor signals) were extracted. EMA measured 19 risk factors, lapse, and categorized GPS into meaningful geolocations. Two supervised binary classification models (LASSO) were created: the sensor model predicted lapse with 63% sensitivity (true prediction rate of lapse) and 60% specificity (true prediction rate of non-lapse) and EMA model with 59% sensitivity and 72% specificity. EMA model accuracy was higher, but self-reported EMA burden (M = 2.96, SD = 1.02) also was higher (M = 1.50, SD = 0.94). EMA model accuracy was superior, but EMA burden was higher than sensor burden. Findings highlight the promise of sensors in contributing to lapse prediction, and future research may use EMA, sensors, or both depending on prioritization of accuracy versus participant burden.

Gender differences in the effect of gamification on weight loss during a daily, neurocognitive training program

A major contributor to the obesity epidemic is the overconsumption of high-calorie foods, which is partly governed by inhibitory control, that is, the ability to override pre-prepotent impulses and drives. Computerized inhibitory control trainings (ICTs) have demonstrated qualified success at affecting real-world health behaviors, and at improving weight loss, particularly when repeated frequently over an extended duration. It has been proposed that gamification (i.e., incorporating game-like elements such as a storyline, sounds, graphics, and rewards) might enhance participant interest and thus training compliance. Previous findings from a mostly female sample did support this hypothesis; however, it might be expected that the effects of gamification differ by gender such that men, who appear more motivated by gaming elements, stand to benefit more from gamification. The present study evaluated whether gender moderated the effect of a gamified ICT on weight loss. Seventy-six overweight individuals received a no-sugar-added dietary prescription and were randomized to 42 daily and 2 weekly ICTs focused on sweet foods that were either gamified or nongamified. Results supported the hypothesis that gamification elements had a positive effect on weight loss for men and not women (p = .03). However, mechanistic hypotheses for the moderating effect (in terms of enjoyment, compliance, and improvements in inhibitory control) were generally not supported (p's > .20). These results suggest that gamification of ICTs may boost weight loss outcomes for men and not women, but further research is needed to determine the specific mechanisms driving this effect and to arrive at gamification elements that enhance effects for both men and women.

Attentional bias to diabetes cues mediates disease management improvements in a pilot randomized controlled trial for adolescents with type 1 diabetes

For type 1 diabetes management, the role of attentional bias remains unclear. This secondary analysis examined type 1 diabetes attentional bias and adolescent type 1 diabetes management prior to and during a cognitive and behavioral intervention. Youth with type 1 diabetes and above target glycemic control were assigned to intervention or usual care control. Participants completed baseline and follow-up type 1 diabetes Stroop tasks, HbA1c tests, and blood glucose meter downloads. Intervention was associated with greater reductions in type 1 diabetes attentional bias than control, and these reductions partially mediated the effect of treatment on diabetes management behaviors. Type 1 diabetes attentional bias is a potential target to improve type 1 diabetes management.

Is physical activity a risk or protective factor for subsequent dietary lapses among behavioral weight loss participants?

OBJECTIVE

Dietary lapses drive weight loss failure, and specific factors influence risk of lapse. Physical activity (PA) may be one such risk factor, though whether PA increases or decreases appetite, and thus risk of lapse, is unclear. In fact, most studies examining the relation between PA and energy intake are limited by use of laboratory-based settings, intensive PA manipulations, and healthy-weight samples. This study aimed to maximize ecological validity by examining the extent to which free-living PA of various intensities prospectively predicts same-day dietary lapses among individuals enrolled in a weight loss program.

METHOD

Participants were 130 adults with overweight/obesity in a behavioral weight loss treatment instructed to follow a PA and dietary prescription. At midtreatment, moderate-to-vigorous PA (MVPA) and light PA were measured using hip-worn Actigraph GT3X+ accelerometers (Actigraph, Pensacola, FL). Lapses were assessed using ecological momentary assessment. Within-subject total PA (b = -0.012, SE = 0.005, p = .01) and light PA (b = -0.014, SE = 0.006, p = .01) negatively predicted lapse. MVPA followed the same pattern, but the effect was not statistically significant (b = -0.013, SE = 0.009, p = .12).

CONCLUSION

This study was the first to investigate if objectively measured PA prospectively predicts lapse from a weight loss program. Results suggested that for every additional 10 min of total PA one engaged in, the risk of lapse decreased by 1%. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss

This randomized trial demonstrated qualified support for the ability of a machine learning-powered, smartphone-based just-in-time, adaptive intervention to enhance weight loss over and above a commercial weight loss program.

Long-Term Follow-up of the Mind Your Health Project: Acceptance-Based versus Standard Behavioral Treatment for Obesity

OBJECTIVE

In the Mind Your Health Trial, acceptance-based behavioral treatment (ABT) for obesity outperformed standard behavioral treatment (SBT) at posttreatment. This trial compared effects over 2 years of follow-up.

METHODS

Participants with overweight or obesity (n = 190) were randomized to 25 sessions of SBT or ABT over 1 year and assessed at months 12 (i.e., posttreatment), 24 (1 year posttreatment), and 36 (2 years posttreatment).

RESULTS

Weight-loss differences previously observed at 12 months attenuated by follow-up, though a large difference was observed in the proportion of treatment completers who maintained 10% weight loss at 36 months (SBT = 17.1% vs. ABT = 31.6%; P = 0.04; intent-to-treat: SBT = 14.4% vs. ABT = 25.0%; P = 0.07). The amount of regain between posttreatment and follow-up did not differ between groups. ABT produced higher quality of life at 24 and 36 months. Autonomous motivation and psychological acceptance of food-related urges mediated the effect of condition on weight. No moderator effects were identified.

CONCLUSIONS

Overall, results suggest that infusing SBT for weight loss with acceptance-based strategies enhances weight loss initially, but these effects fade in the years following the withdrawal of treatment. Even so, those receiving ABT were about twice as likely to maintain 10% weight loss at 36 months, and they reported considerably higher quality of life.

Executive function and somatic problems in adolescents with above target glycemic control

Adolescents with type 1 diabetes may be at elevated risk for somatic problems. This study used cross-sectional, baseline data from an intervention to examine if problems with executive function (EF) were associated with greater somatic problems independent of poor adherence and disease severity in adolescents with type 1 diabetes and above target glycemic control. In addition, it examined whether certain types of EF skills, that is, metacognitive and behavior regulation, accounted for variance in somatic problems. Ninety-three adolescents completed a glycated hemoglobin (HbA1c) blood test and parents completed adherence, somatic problems, and EF questionnaires, which measured metacognitive, behavior regulation, and global EF. Greater somatic problems had significant bivariate associations with greater global (r = 0.42, P < 0.01), metacognitive (r = 0.43, P < 0.01), and behavior regulation EF problems (r = 0.31, P < 0.01), worse adherence (r = -0.39, P < 0.01), and poorer metabolic control (r = 0.26, P < 0.05). However, when adherence, metabolic control, and EF subscales were examined together in the same model, only greater global EF problems (b = 0.15, P < 0.01) and metacognitive EF problems (b = 0.16, P < 0.01) were independently associated with greater somatic problems; behavior regulation EF problems were not independently associated with greater somatic problems when controlling for adherence. Metacognitive EF problems may predict somatic problems in adolescents with above target glycemic control above and beyond physical symptoms related to disease management, underscoring the importance of proper assessment and treatment of these distinct somatic problems.

Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?

Behavioral weight loss (WL) trials show that, on average, participants regain lost weight unless provided long-term, intensive-and thus costly-intervention. Optimization solutions have shown mixed success. The artificial intelligence principle of "reinforcement learning" (RL) offers a new and more sophisticated form of optimization in which the intensity of each individual's intervention is continuously adjusted depending on patterns of response. In this pilot, we evaluated the feasibility and acceptability of a RL-based WL intervention, and whether optimization would achieve equivalent benefit at a reduced cost compared to a non-optimized intensive intervention. Participants (n = 52) completed a 1-month, group-based in-person behavioral WL intervention and then (in Phase II) were randomly assigned to receive 3 months of twice-weekly remote interventions that were non-optimized (NO; 10-min phone calls) or optimized (a combination of phone calls, text exchanges, and automated messages selected by an algorithm). The Individually-Optimized (IO) and Group-Optimized (GO) algorithms selected interventions based on past performance of each intervention for each participant, and for each group member that fit into a fixed amount of time (e.g., 1 h), respectively. Results indicated that the system was feasible to deploy and acceptable to participants and coaches. As hypothesized, we were able to achieve equivalent Phase II weight losses (NO = 4.42%, IO = 4.56%, GO = 4.39%) at roughly one-third the cost (1.73 and 1.77 coaching hours/participant for IO and GO, versus 4.38 for NO), indicating strong promise for a RL system approach to weight loss and maintenance.

A multimodal investigation of impulsivity as a moderator of the relation between momentary elevations in negative internal states and subsequent dietary lapses

Suboptimal outcomes from behavioral weight loss (BWL) treatments are partially attributable to accumulated instances of non-adherence to dietary prescriptions (i.e., dietary lapses). Results identifying negative internal triggers for dietary lapses are inconsistent, potentially due to individual differences that impact how individuals respond to cues. Impulsivity is one factor that likely influences reactivity to internal states. We examined three dimensions of impulsivity (delay discounting, inhibitory control, and negative urgency) as moderators of the relation between affective and physical states and subsequent dietary lapses at the beginning of BWL. Overweight/obese adults (n = 189) completed behavioral and self-reported measures of impulsivity at baseline of BWL and an ecological momentary assessment (EMA) protocol across the first two weeks of treatment to report on affective/physical states and instances of dietary lapses. Results indicated that baseline negative urgency, but not delay discounting or inhibitory control, was positively associated with overall lapse risk. Moderation analyses indicated that poorer inhibitory control strengthened the relation between momentary increases in stress and subsequent dietary lapse, and higher negative urgency strengthened the relation between increases in loneliness and dietary lapse. Negative urgency also moderated the impact of momentary hunger on subsequent dietary lapse risk in an unexpected direction, such that higher negative urgency weakened the relation between hunger and subsequent lapse. Results lend partial and tentative support for the moderating role of impulsivity on the relation between internal states and lapse likelihood. With replication, the development and testing of personalized treatment components based on baseline impulsivity level may be warranted.