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Insulin resistance and working memory exploring the role of blood glucose levels and lifestyle
(2023)
vIntroduction: Type 2 diabetes mellitus and dementia are among the leading causes for reduced quality of life and life expectancy worldwide and often occur comorbidly. Both diseases are linked by altered insulin signaling. Lifestyle factors and blood glucose monitoring play an essential role in the prevention and treatment of type 2 diabetes. So far, a relationship between blood glucose levels, lifestyle, and cognitive performance – a main symptom of dementia - has mainly been established in laboratory settings which reduces its ecological validity.
Objectives: This study uses ambulatory assessment and continuous glucose monitoring to explore the link between blood glucose levels, lifestyle and working memory in an ecological setting. We hypothesize that glycemic variations affect working memory performance in daily life. Second, we hypothesize that a high variance in blood glucose levels has a higher impact on working memory in insulin resistant participants. With this study, we aim to expand the knowledge on the relationship of insulin resistance and cognitive performance from the laboratory setting to everyday life.
Methods: This prospective, exploratory study will include 80 subjects with insulin resistance and 80 healthy controls. At baseline, blood indicators of insulin resistance will be measured to determine group assignment. Our ambulatory assessment includes smartphone-based sampling and sensor-based assessment. Therefore, cognitive performance will be recorded over three consecutive days using a smartphone. Four times a day, a numerical working memory task is prompted by signal-based alarms on the smartphone. Blood glucose levels are recorded in parallel by continuous glucose monitoring. In addition, lifestyle factors such as diet ad physical activity are examined. Diet is assessed by 24-h dietary protocols and movement acceleration by accelerometery.
Multilevel modelling will be used to map the relationship between blood glucose levels and working memory at the within- and between-person level. Diet and exercise are included in the analyses as additional predictors.
Results: Data collection started in March 2021 and is ongoing. Up to now, 40 insulin resistant participants and 36 healthy controls have been measured. Our preliminary results indicate a positive association between blood glucose levels and working memory performance at the within-person level (estimate = .48, 95% CI [.07, .89], p =0.022). At the between-person level the analysis revealed an inverse association between blood glucose levels and working memory performance (estimate = -.45, 95 % CI [-.86 - -.05], p = 0.029).
Conclusion: Our preliminary results are in line with studies showing that an acute rise in blood glucose levels leads to short-term improvements, while stable glucose profiles are beneficial in the long term. This might expand the understanding of the impact of insulin resistance on working memory and represent a target for early interventions. Our preliminary analysis needs to be repeated in our final dataset to confirm our results.
the benefits of physical activity (PA) and sleep for health, accurate and objective population-based surveillance is important. Monitor-based surveillance has potential, but the main challenge is the need for replicable outcomes from different monitors. This study investigated the agreement of movement behavior outcomes assessed with four research-grade activity monitors (i.e., Movisens Move4, ActiGraph GT3X+, GENEActiv, and Axivity AX3) in adults. Twenty-three participants wore four monitors on the non-dominant wrist simultaneously for seven days. Open-source software (GGIR) was used to estimate the daily time in sedentary, light, moderate-to-vigorous PA (MVPA), and sleep (movement behaviors). The prevalence of participants meeting the PA and sleep recommendations were calculated from each monitor’s data. Outcomes were deemed equivalent between monitors if the absolute standardized difference and its 95% confidence intervals (CI95%) fell within ± 0.2 standard deviations (SD) of the mean of the differences. The participants were mostly men (n = 14, 61%) and aged 36 (SD = 14) years. Pairwise confusion matrices showed that 83–87% of the daily time was equally classified into the movement categories by the different pairs of monitors. The between-monitor difference in MVPA ranged from 1 (CI95%: − 6, 7) to 8 (CI95%: 1, 15) min/day. Most of the PA and sleep metrics could be considered equivalent. The prevalence of participants meeting the PA and the sleep guidelines was 100% consistent across monitors (22 and 5 participants out of the 23, respectively). Our findings indicate that the various research-grade activity monitors investigated show high inter-instrument reliability with respect to sedentary, PA and sleep-related estimates when their raw data are processed in an identical manner. These findings may have important implications for advancement towards monitor-based PA and sleep surveillance systems.
Introduction: The influence of our diet on mental health is of increasing importance in current research. Study results on the gut-brain axis suggest that the gut microbiome can influence mental processes via neuronal, hormonal and immune signaling pathways [1]. The gut microbiome is largely influenced by our diet. Some studies provide evidence that a "Western diet" rich in saturated fat and sugar may promote mental disorders [2]. There is evidence, that dietary behaviour in individuals with Attention Deficit Hyperactivity Disorder (ADHD) is characterized by an increased intake of sugar and saturated fat [3]. So far, it is unclear whether this dietary pattern contributes to ADHD symptoms such as impulsivity. The aim of this study is to investigate the influence of certain macronutrients such as fats and mono/disaccharides on impulsivity in individuals with ADHD. Using our APPetite-mobile-app [4] enabled us to study dietary behaviour and momentary impulsiveness in everyday life of our participants.
Methods: 43 participants with ADHD (mean age 36.0 ± 12.3 years, 21 females) and 186 healthy controls (mean age 28.5 ± 7.7 years, 133 females) without any psychiatric condition were included into the study. Food intake was recorded over a period of three days using the APPetite-mobile-app via a 6 step process: (1) Selection of meal type, (2) Entry of time of meal, (3) Selection of consumed foods and drinks, (4) Specification of consumed amounts, (5) Presentation of reminder for commonly forgotten foods, and (6) Indication of predominant reason for eating. In addition to entering consumed foods in the APPetite-mobile-app, subjects completed an online food log for the last 24 hours (myfood 24) at the beginning of the study. After the data collection period, a detailed analysis of the ingested nutrients was performed for each subject. Trait impulsivity was assessed using the UPPS-P, a self-assessment questionnaire. Momentary impulsiveness was assessed via the mHealth APP by means of the Momentary Impulsiveness scale (MIS). The MIS consists of 4 questions capturing different aspects of impulsivity. The participants were prompted to answer these questions at 8 semi-random times per day between 8 AM and 10 PM. The minimum time between 2 prompts was 1 hour. Thereby participants could not predict the exact time of the next prompt and the assessed situations are a better reflection of the participant’s real life.
Results: ANOVA revealed higher levels of both, trait and momentary impulsivity in individuals with ADHD compared to controls (p < 0,01). After preprocessing of data that was sampled via the mHealth APP is completed, a regression analysis with different macronutrients as predictors and impulsivity as dependent variable will be computed. To assess the association between momentary impulsiveness and dietary intake, generalized linear multilevel modelling will be used. Results of these analyses will be presented.
Exercise interventions in mental disorders have evidenced a mood-enhancing effect. However, the association between physical activity and affect in everyday life has not been investigated in adult individuals with ADHD, despite being important features of this disorder. As physical activity and affect are dynamic processes in nature, assessing those in everyday life with e-diaries and wearables, has become the gold standard. Thus, we used an mHealth approach to prospectively assess physical activity and affect processes in individuals with ADHD and controls aged 14–45 years. Participants wore accelerometers across a four-day period and reported their affect via e-diaries twelve times daily. We used multilevel models to identify the within-subject effects of physical activity on positive and negative affect. We split our sample into three groups: 1. individuals with ADHD who were predominantly inattentive (n = 48), 2. individuals with ADHD having a combined presentation (i.e., being inattentive and hyperactive; n = 95), and 3. controls (n = 42). Our analyses revealed a significant cross-level interaction (F(2, 135.072)=5.733, p = 0.004) of physical activity and group on positive affect. In details, all groups showed a positive association between physical activity and positive affect. Individuals with a combined presentation significantly showed the steepest slope of physical activity on positive affect (slope_inattentive=0.005, p<0.001; slope_combined=0.009, p<0.001; slope_controls=0.004, p = 0.008). Our analyses on negative affect revealed a negative association only in the individuals with a combined presentation (slope=-0.003; p = 0.001). Whether this specifically pronounced association in individuals being more hyperactive might be a mechanism reinforcing hyperactivity needs to be empirically clarified in future studies.
Background: Diet and physical activity (PA) have a major impact on physical and mental health. However, there is a lack of effective strategies for sustaining these health-protective behaviors. A shift to a microtemporal, within-person approach is needed to capture dynamic processes underlying eating behavior and PA, as they change rapidly across minutes or hours and differ among individuals. However, a tool that captures these microtemporal, within-person processes in daily life is currently not present.
Objective: The APPetite-mobile-app is developed for the ecological momentary assessment of microtemporal, within-person processes of complex dietary intake, objectively recorded PA, and related factors. This study aims to evaluate the feasibility and usability of the APPetite-mobile-app and the validity of the incorporated APPetite-food record.
Methods: The APPetite-mobile-app captures dietary intake event-contingently through a food record, captures PA continuously through accelerometers, and captures related factors (eg, stress) signal-contingently through 8 prompts per day. Empirical data on feasibility (n=157), usability (n=84), and validity (n=44) were collected within the Eat2beNICE-APPetite-study. Feasibility and usability were examined in healthy participants and psychiatric patients. The relative validity of the APPetite-food record was assessed with a subgroup of healthy participants by using a counterbalanced crossover design. The reference method was a 24-hour recall. In addition, the energy intake was compared with the total energy expenditure estimated from accelerometry.
Results: Good feasibility, with compliance rates above 80% for prompts and the accelerometer, as well as reasonable average response and recording durations (prompt: 2.04 min; food record per day: 17.66 min) and latencies (prompts: 3.16 min; food record: 58.35 min) were found. Usability was rated as moderate, with a score of 61.9 of 100 on the System Usability Scale. The evaluation of validity identified large differences in energy and macronutrient intake between the two methods at the group and individual levels. The APPetite-food record captured higher dietary intakes, indicating a lower level of underreporting, compared with the 24-hour recall. Energy intake was assessed fairly accurately by the APPetite-food record at the group level on 2 of 3 days when compared with total energy expenditure. The comparison with mean total energy expenditure (2417.8 kcal, SD 410) showed that the 24-hour recall (1909.2 kcal, SD 478.8) underestimated habitual energy intake to a larger degree than the APPetite-food record (2146.4 kcal, SD 574.5).
Conclusions: The APPetite-mobile-app is a promising tool for capturing microtemporal, within-person processes of diet, PA, and related factors in real time or near real time and is, to the best of our knowledge, the first of its kind. First evidence supports the good feasibility and moderate usability of the APPetite-mobile-app and the validity of the APPetite-food record. Future findings in this context will build the foundation for the development of personalized lifestyle modification interventions, such as just-in-time adaptive interventions.
Background: The risk for major depression and obesity is increased in adolescents and adults with attention-deficit / hyperactivity disorder (ADHD) and adolescent ADHD predicts adult depression and obesity. Non-pharmacological interventions to treat and prevent these co-morbidities are urgently needed. Bright light therapy (BLT) improves day–night rhythm and is an emerging therapy for major depression. Exercise intervention (EI) reduces obesity and improves depressive symptoms. To date, no randomized controlled trial (RCT) has been performed to establish feasibility and efficacy of these interventions targeting the prevention of co-morbid depression and obesity in ADHD. We hypothesize that the two manualized interventions in combination with mobile health-based monitoring and reinforcement will result in less depressive symptoms and obesity compared to treatment as usual in adolescents and young adults with ADHD.
Methods: This trial is a prospective, pilot phase-IIa, parallel-group RCT with three arms (two add-on treatment groups [BLT, EI] and one treatment as usual [TAU] control group). The primary outcome variable is change in the Inventory of Depressive Symptomatology total score (observer-blinded assessment) between baseline and ten weeks of intervention. This variable is analyzed with a mixed model for repeated measures approach investigating the treatment effect with respect to all three groups. A total of 330 participants with ADHD, aged 14 – < 30 years, will be screened at the four study centers. To establish effect sizes, the sample size was planned at the liberal significance level of α = 0.10 (two-sided) and the power of 1-β = 80% in order to find medium effects. Secondary outcomes measures including change in obesity, ADHD symptoms, general psychopathology, health-related quality of life, neurocognitive function, chronotype, and physical fitness are explored after the end of the intervention and at the 12-week follow-up.
Discussion: This is the first pilot RCT on the use of BLT and EI in combination with mobile health-based monitoring and reinforcement targeting the prevention of co-morbid depression and obesity in adolescents and young adults with ADHD. If at least medium effects can be established with regard to the prevention of depressive symptoms and obesity, a larger scale confirmatory phase-III trial may be warranted.
Trial registration: German Clinical Trials Register, DRKS00011666. Registered on 9 February 2017. ClinicalTrials.gov, NCT03371810. Registered on 13 December 2017.