Maintaining an appropriate gestational weight gain (GWG) is essential for a safe pregnancy and delivery. This study aimed to determine the association between the habit of eating quickly and the risk of excessive GWG.We administered a questionnaire on eating habits to 1246 pregnant Japanese women in their second to third trimesters. We categorized the participants into three groups according to their answers to the question “Do you eat quickly?” Group 1, “always” or “usually”; Group 2, “sometimes”; and Group 3, “rarely” or “never.” We assessed GWG according to the “The Optimal Weight Gain Chart” , and those who exceeded the criteria were considered “excessive.” Logistic regression analysis was performed with the risk of excess GWG as the dependent variable and quick food intake as the independent variable, to obtain relevant odds ratios (ORs) and 95% confidence intervals (CIs). Model 1 was unadjusted, and Model 2 was adjusted for age, prepregnancy body mass index, energy intake, mother's educational attainment, household income, exercise habits, and childbearing experience.p for trend = 0.047). In Model 2, the OR (95% CI) for Groups 2 and 3 were 0.73 (0.55–0.96) and 0.59 (0.40–0.86), respectively (p for trend = 0.003).The OR (95% CI) for Groups 2 and 3 in Model 1, compared with Group 1, was 0.80 (0.62–1.05) and 0.61 (0.43–0.88), respectively (These results suggest that quick food ingestion increases the risk of excessive GWG. Compared with other racial groups, Asian women have a much higher risk of developing GDM3–5 and subsequent type 2 diabetes.6 Excess GWG has also been reported to have an impact after delivery, with an increased risk of obesity lasting 15 years postpartum.7 Therefore, GWG is critical for safe pregnancy and delivery, as well as the general future health of the mother.Maintaining appropriate gestational weight gain (GWG) is essential for safe pregnancy and delivery. Excessive GWG increases the risk of gestational diabetes mellitus (GDM), preeclampsia, macrosomia, and need for cesarean delivery.1, vitamin B2, vitamin B6, vitamin B12, folic acid, vitamin C, magnesium, iron, zinc, copper, and iodine for women during pregnancy.8 The Ministry of Health, Labor, and Welfare of Japan has issued the “Dietary Guidelines for Pre-pregnant, Pregnant and Lactating Women in Japan, 2020 ∼ Get health conscious before your pregnancy ∼” to ensure appropriate dietary habits during pregnancy.9During pregnancy, energy and nutrient requirements increase owing to fetal growth and physiological changes in the mother. The Dietary Reference Intakes for Japanese 2020 set additional amounts of energy, protein, vitamin A, vitamin B10 However, as described earlier, in Japan, although there is an awareness of the nutrients and dietary content that should be consumed during pregnancy, there is little mention of the rate of food intake.The guidelines provide dietary recommendations such as “Achieve your adequate energy intakes through staple foods,” “Side dishes help you take enough amounts of vitamins and minerals,” and “Arrange main dishes so that you can take enough protein.” Furthermore, the “Food Guide for Pregnant and Lactating Women in Japan” indicates the standard amount (No. of servings) of staple foods, main dishes, and side dishes consumed per day during pregnancy.11 It has been suggested that excess energy intake caused by quick food intake increases the risk of obesity.12 Self-reported quick eating rates have also been associated with an increased risk of metabolic diseases. Self-reported quick eating rates have been reported to be associated with increased insulin resistance,13 metabolic syndrome,15 nonalcoholic fatty liver disease,17 and abnormal blood lipid metabolism.18A meta-analysis of the association between obesity and eating speed reported that those who ingested food quickly were more likely to have obesity.19 the eating behavior of chewing food slowly and thoroughly is indicated as behavioral therapy for obesity control. Therefore, eating rate is a vital eating behavior, even during the heightened energy requirements of pregnancy when proper weight control is required in a short period of ∼10 months. However, the relationship between the eating rate and GWG has not yet been reported. This study investigated the association between eating quickly and the risk of excess pregnancy weight gain among pregnant Japanese women.In the Obesity Treatment Guidelines 2016 (Japan),This study used data from a prospective cohort study conducted at the National Center for Child Health and Development  between May 2010 and November 2013. Participants were randomly recruited at the obstetric outpatient clinic between 8 and 15 weeks of gestation at the first visit. A baseline survey was administered at the time of recruitment, followed by a lifestyle and dietary survey during the second survey from the second to the third trimester (26–35 weeks gestation).2.Information on age (years), height (cm), prepregnancy weight (kg), annual household income , and mother's educational level  was obtained at the time of the baseline survey using a self-administered questionnaire. Prepregnancy body mass index (BMI) was calculated using the following formula: prepregnancy weight (kg)/height (m)The lifestyle survey included a question regarding exercise habits: “Do you exercise for at least 15 minutes a week?” with “yes” and “no” response options. The survey also included a question regarding eating speed: “Do you eat quickly?” with five options: “always,” “usually,” “sometimes,” “rarely,” and “never.”21 Spearman's correlation coefficients of nutrients ranged from 0.098 (sodium) to 0.401 (vitamin C), and all of the 36 nutrients were statistically significant. In 27 food groups, the correlation coefficients ranged from −0.015  to 0.572 (yogurt), and 81% were statistically significant.We conducted a dietary survey using the semiquantitative Food Frequency Questionnaire (FFQ), which has reported external validity in Japanese pregnant women, to ascertain habitual energy and nutrient intake for the 2 months preceding survey administration.22 the respondents' daily intakes of energy, protein, fat, and carbohydrate were calculated using the weighted average food composition table prepared for this study. Nutrient intake was also energy-adjusted using the density method, to eliminate the influence of energy intake.Participants responded to the frequency of intake of 165 different foods and the amount consumed per serving for the preceding 2 months. There were nine options for the frequency of intake of each food: (1) less than once a month, (2) 1–3 times per month, (3) 1–2 times per week, (4) 3–4 times per week, (5) 5–6 times per week, (6) once per day, (7) 2–3 times per day, (8) 4–6 times per day, and (9) 7 or more times per day. The amount of food consumed per serving for each food type was divided into three categories: “less ,” “same,” and “more (more than 1.5 times),” compared with the reference amount indicated for each food. Based on the Japanese Standard Tables of Food Composition (2015 edition),23 provided by the Ministry of Health, Labor and Welfare (Japan) at the time the study was conducted (2010 − 2013). According to this chart, the optimal weight gain during pregnancy was 9 − 12 kg for pregnant women with a prepregnancy BMI ≤18.5, 7 − 12 kg for those with a BMI of 18.5 − 25.0, and <5 kg for those with a BMI ≥25.0.The GWG of each participant was calculated by subtracting their self-reported prepregnancy weight from their weight at delivery. We used weight-at-delivery data from the medical records of the delivery center. In addition, we assessed GWG according to the “Optimal Weight Gain Chart During Pregnancy”GWG above the standard values shown in the optimal weight gain chart was rated as “excessive,” weight gain within the standard range was rated as “adequate,” and weight gain below the standard was rated as “insufficient.”n = 94), those with incomplete prepregnancy weights (n = 14), and those with extremely low and high energy intake (1% above and below) (n = 12), 1246 (79.7%) women were eligible for analysis.A total of 1563 women agreed to participate in the study, and 1366 completed the FFQ between 26 and 35 weeks of gestation. After excluding multiple pregnancies  and 95% confidence intervals (CIs) for the risk of excess GWG in Groups 2 and 3 were determined using Group 1, which had the highest frequency of eating quickly, as the reference group.p < 0.05.Model 1 was unadjusted, and Model 2 was adjusted for age (years), prepregnancy BMI , energy intake during the second half of pregnancy , mother's education level , annual household income , exercise habits of >15 minutes per week (yes or no), and previous childbearing experience (yes or no). All statistical analyses were performed using JMP13  with a significance level of Group 1 had 178 (14.3%), Group 2 had 506 (40.6%), and Group 3 had 562 (45.1%) participants. The mean age (standard deviation [SD]) was 35.8 (4.3) years, and the mean BMI (SD) was 20.4 (2.6).Group 1, which had the highest frequency of eating quickly, tended to include older participants, had obesity before pregnancy, and had an annual household income of >10 million yen. Group 1 also had fewer participants with previous childbearing experience and university-level education than Group 3, which had the lowest frequency of eating quickly. Sixty-six participants (37.1%) had excessive GWG in Group 1, 157 (31.0%) in Group 2, and 149 (26.5%) in Group 3.The risk of excessive GWG for the three groups according to their self-reported habits of eating quickly is shown in In this study, pregnant women who self-reported eating quickly were found to be at a higher risk of excess weight gain during pregnancy. The association between frequency of eating quickly and excess GWG remained significant after adjusting for age, prepregnancy BMI, energy intake in the second trimester of pregnancy, education level, annual household income, exercise habits, and previous childbearing experience.12The group with the highest frequency of quick eating had a significantly higher energy intake. A recent meta-analysis that examined the relationship between eating speed and energy intake also reported that eating quickly was associated with increased energy intake.25The association between eating quickly and increased energy intake may be due to increased stomach distention, the release of cholecystokinin, glucagon-like peptide-I, and peptide YY, or elevated blood glucose levels. All of these factors may result in a higher energy intake before the hypothalamic satiety center is stimulated.26 Decreased chewing due to eating quickly may also influence increased energy intake. Chewing activates histamine neurons and suppresses appetite through the satiety center.27 Chewing more than usual has been reported to help people feel satisfied with less food intake.29 Therefore, eating quickly may decrease the amount of chewing, in turn resulting in less satiety, increased energy intake, and excessive GWG.It has been reported that the same amount of the same food, consumed for a longer intake period, results in increased secretion of glucagon-like peptide-I and peptide YY, and a greater sense of satiety.30 In our study, participants in Groups 2 and 3, who self-reported eating quickly, may have consumed foods with higher energy density than those in Group 1. For example, confectioneries and juices are classified as high energy density  foods, whereas vegetables and teas are classified as low energy density foods.30 However, there were no significant differences observed between the three groups in terms of consumption of confectioneries, juices, vegetables, or teas in this study (data not shown).People with high energy intake have been reported to consume diets with high energy density .p = 0.005). Since rice is a relatively soft food that requires less chewing, the consumption of rice (a staple food for Japanese people) may have increased among pregnant women who self-reported eating quickly. Therefore, educating participants who would benefit from spending more time on meals to be aware of the appropriate amount of staple food to consume may help prevent overeating.Carbohydrates contributed significantly to the increase in energy intake observed due to self-reported quick food intake among the participants in this study. A comparison of daily intakes of staple foods such as rice, bread, and noodles among the three groups revealed significant differences only with regard to rice intake. The mean (SD) rice intake (g) for Groups 1–3 were 272 (144), 247 (118), and 231 (108), respectively, with Group 1 having a significantly higher consumption than Group 2 .In this study, the multiparas women were more likely to self-report eating quickly. For example, in Group 3, the group with the lowest frequency of eating quickly, ∼30% were multipara, whereas in Group 1, the group with the highest frequency of eating quickly, ∼60% of participants were multipara. For second and subsequent pregnancies, having to care for another child may not allow mothers enough time for slower meals. When Model 2 was analyzed, stratified by primipara and multipara to eliminate the effect of previous childbirth experience, the ORs were 0.79 (95% CI: 0.60–0.98) for Group 2 and 0.59 (95% CI: 0.42–0.87) for Group 3 among primipara, and 0.69 (95% CI: 0.42–0.97) for Group 2 and 0.54 (95% CI: 0.31–0.95) for Group 3 among multipara, all of which were significantly different  between self-reported and measured weights in women.35 Thus, self-reported weight likely has significant validity. The assessment of quick food intake habits was self-reported rather than objective. However, self-reported eating speeds have been reported to be consistent with those reported by friends, in other studies.32Nevertheless, this study also has several limitations. First, the participants' prepregnancy weights were self-reported. However, a study of Japanese adults (1148 women and 4253 men) reported a very high correlation , will lead to specific intervention strategies for pregnant women.This study showed that eating quickly is an eating behavior that increases energy intake and is a risk factor for excess GWG. In addition to a desirable nutritional balance and dietary content, education about eating rates is also expected to aid weight management during pregnancy.


Nanobubble cavitationis advancing technologies in enhancedwastewatertreatment, cancer therapy and diagnosis, and microfluidic cleaning.Current macroscale models predict that nanobubble oscillations shouldbe isothermal, yet recent studies suggest that they are adiabaticwith an associated increase in natural frequency, which becomes challengingwhen characterizing nanobubble sizes using ultrasound in experiments.We derive a new theoretical model that considers the nonideal natureof the nanobubble’s internal gas phase and nonequilibrium effects,by employing the van der Waals (vdW) equation of state and implementinga temperature jump term at the liquid–gas interface, respectively,finding excellent agreement with molecular dynamics (MD) simulations.Our results reveal how adiabatic behavior could be erroneously interpretedwhen analyzing the thermal response of the gas using the commonlyemployed polytropic process and explain instead how nanobubble oscillationsare physically closer to their isothermal limit. Understanding this thermal/mechanical response has become crucialin experiments, where probing bubble resonant radii using ultrasonicfrequencies is a common way of characterizing their mean size anddistribution.31Cavitationis a complex fluidphenomenon, where bubbles can be induced to grow, oscillate, and collapsein response to external pressure fields. While the high-speed liquidjets that are released during their collapse are often considereddestructive at the macroscale,blessee 1a–c,13,1R is the bubble radius, andχ is the thermal diffusivityof the gas.28 The Péclet number is the ratio of advectivetransport to diffusive heat transport or, in this context, can beconsidered as a nondimensional oscillation frequency: i.e. at lowPe, the rate of heat exchange inside the bubble is sufficient to enableisothermal conditions during its oscillation, while at higher Pe,there is inadequate time for heat transfer, and the bubble interiortends to adiabatic behavior instead.28 More accurate formulations furthered this theory by consideringnonuniform pressure distributions within the bubble and revealed aninterrupted transition to adiabatic expansion, as a function of theMach number Ma, before standing wave interference patterns insteaddominate the radial pressure profile.33 Although suitable atthe macroscale, these classical models fail to predict the apparentadiabatic oscillations that have been recently observed in surfacenanobubbles when driven near their natural frequency.25Early theoretical models assumedan ideal gas anduniform pressurewithin the bubble and proved that the transition from isothermal toadiabatic behavior is dependent on the Péclet numberP, T, and ρ are the gas pressure, temperature, and density, respectively, A and B are empirically fitted constants, Mg is the mass of one gas molecule, and kB is the Boltzmann constant. Additionally, thegas behavior departs from the continuum fluid assumption of localquasi-thermodynamic equilibrium and is found here to manifest as aSmoluchowski temperature jump at the liquid–gas interface,which is implemented as a boundary condition in our model38T0 is theequilibrium gas temperature and β is a dimensionless constant,given byT is the thermal accommodationcoefficient, which typically varies between 0 and 1 for fully specularand diffuse reflections, respectively. Pr is the Prandtl number, givenby Pr  = μg/ρχ, where μg is the gas viscosity, κ is the specificheat ratio of the gas, and Kn is the Knudsen number, given by 38 While Supporting Information (SI) that they providea good estimate for the temperature jump in our high-density nonidealgas case, when modeling heat flux through a planar liquid–gas–liquidslab configuration, with αT = 1. We take some crucialassumptions here by considering 39 is much larger thanthe oscillation period at the nanobubble’s natural frequency∼0.1 ns,29 and in the SI, we show negligible mass transfer in our MD simulations. Second,the liquid temperature is assumed to be constant during nanobubbleoscillations, which is suitable when the thermal conductivity of theliquid is much larger than that of the gas.29 Finally, slipflow regime (10–2 < Kn < 0.1),41 where nonequilibrium behavior can be assumed to be relevant onlyin a thin region next to the liquid–gas interface—theKnudsen layer—while the interior bulk of the bubble is in localquasi-thermodynamic equilibrium.42In this Letter, we investigate the thermal oscillationsof nanobubblesand show how adiabatic behavior could be misinterpreted as a consequenceof (a) nonideal gas behavior, due to the high Laplace pressure, and(b) nonequilibrium effects at the liquid–gas interface. Wepropose a new model that captures these nanoscale phenomena and show,with comparisons to molecular dynamics (MD) simulations, that nanobubblegrowth is in fact physically closer to the isothermal limit for anonideal gas. For nanobubbles, the nonideal gas behavior is foundin this work to be best described by the van der Waals (vdW) equationof stateSI, which describes the nanobubble’s internalpressure and temperature variations when undergoing linear radialoscillations, where ϕ, θ, and ξ (≪ 1) arethe respective nondimensional perturbations, e.g. P = P0(1 + ϕ) and T = T0(1 + θ), with the 0 subscriptsdenoting equilibrium values. The setup of our MD simulations consistsof a nitrogen nanobubble in water with equilibrium radius R0 = 7.56 nm, being subjected to pressure wavesusing a piston,43 oscillating at various frequencies ω= 0.5–125 rad/ns, as shown in 1 For each case,the nanobubble radius, mean gas pressure, and temperature exhibitedsinusoidal oscillations, matching the input piston frequency, as shownin Using T = 300 K, but not the ideal gas law P = ρkBT/Mg. The effects of the interfacial temperaturejump are evident when we look at the local pressure and temperatureinside the gas for a higher Pe case. Following from our derivation,which now includes r and temporal t components, e.g. ϕ = −ξ(t)ϕ̅(r), where both the pressure ϕ̅and temperature θ̅ amplitudes take hyperbolic forms (seethe SI), as shown in SI.Our simulations confirm the presenceof vdW gasbehavior, as shownin W = (ρ/P)(∂P/∂ρ)T and J = (T/P)(∂P/∂T)ρ, obtained from D = χ2ρ/(PR2), which is related to the Mach number by Ma ≈ Pe(D/κ)1/2,28 whilethe nonequilibrium temperature jump is characterized by β in In order to evaluate the limitations of the classicalbubble theories,we first compare our MD results to two benchmark macroscale bubblemodels, employing similar assumptions to those found in refs , and 33.W = J = 1, D → 0, and β= 0. However, our MD simulations in D = 7.2 × 10–3), which correctly identifies a nonuniform pressure,although still underpredicts the MD results. We introduce our newnanobubble (NB) models, by initially considering a vdW gas (withouta temperature jump) by setting W = 1.11, J = 1.27, and D = 5.5 × 10–3 in NB1,2 where we see a small improvement.However, it is only when we also include the temperature jump in modelNB2 with β = 0.12  that we seethe best agreement with our MD simulations. Similarly, MB1 and MB2underpredict temperature in R) > 0, which is only captured by NB2.The first benchmark macrobubble model (MB1) assumesan ideal gasand low Mach number such that the internal pressure is uniform throughoutthe bubble and there is no temperature jump at the liquid–gasinterface, which can be equivalently obtained by setting PRk3 = const,which is necessary to estimate gas pressure in inertial cavitationmodels, such as the Rayleigh–Plesset equation.45 Linearizing the polytropic gas law givest.29 The polytropic exponent k is commonly understoodto vary between 1 and κi, for isothermal and adiabaticgas expansion, respectively, where κi is the specificheat ratio for an ideal gas, and equal to 1.4 when considering diatomicmolecules.33 Alongside k is a corresponding thermal viscosityμth term, which accounts for the energy losses dueto the thermal conduction between the gas and liquid phases and canbe summed with the liquid dynamic viscosity μ for estimatingthe equivalent total bubble damping.29 Determining the thermal viscosity is crucial in applications whereinaccurate estimation of the damping can yield lower than expectedoscillation amplitudes, such as in ultrasound contrast agents.13 The polytropic exponent and thermal viscosity can then be foundby evaluating the pressure amplitude at the interface:After confirming thatour equations accurately predict the internalgas phase’s thermodynamic behavior, we now look to evaluatethe pressure at the liquid–gas interface, such that we canexpress the nanobubble’s expansion equivalently to the polytropicgas law k and nondimensionalthermal viscosity μth/(PR2/χ), respectively, with Péclet number Pe. Our MD results(fitted from k with Pe before reaching a maximumof 1.33 at Pe ≈ 17 (or Ma ≈ 1).29 Neither the MB1 nor MB2 model accurately predicts this behavior;in fact, MB2 suggests that nanobubbles should never achieve adiabaticexpansion and would always appear roughly isothermal (k ≈ 1) for Pe < 20. Instead, our NB2 model completely capturesthis variation. For increasing frequencies, standing waves developwithin the bubble that dominate the pressure profile, resulting inrepeating peak and trough patterns in k.29 We deliberately limited our MD simulations toPéclet numbers below this regime, since taking accurate measurementswith these interference patterns is challenging.k ≈ κi at its maximum, similar to recent observations.25 However, at low Péclet numbers (Pe ≪1), we do not observe the expected limit of k = 1for isothermal expansion in k for isothermal and adiabatic behaviorto W ≈ 1.11 and κW ≈1.77, respectively,46 where κ isthe specific heat ratio of the vdW gas . For simplicity, we havealso neglected the acoustic viscosity in our modeling,another damping mechanism which is equivalent to dissipation fromsound wave radiation within the (compressible) liquid phase, sincethese effects only become relevant for high liquid Mach numbers Mal = ωR/cl ≈ 1 (ω ≈ 200 rad/ns in our case), where cl is the liquid sound speed.48 However, this couldexplain the small discrepancies in our MD μth measurementsfor the higher frequency cases at Pe ≈ 40. The error bars inMD thermal viscosity for Pe < 1 are large, since the thermal noiseand low frequency make it difficult to accurately fit for the ξ̇ term in From our MDresults, it would appear that adiabatic oscillationsof nanobubbles are reasonable, since  gas see 4a. Note R ≈ 10 nm nanobubblein our MD simulations, but what does it predict for a broader rangeof bubble sizes? Using more realistic experimental parameters fora nitrogen bubble in water 49 we plot the variations in β, D, W, and κW in k and thermal viscosity μth in 1 where ρl is the liquid density. We find β and D are roughly constant and at their maximum values for R < 10–6 m, since the Laplace pressuredominates the internal pressure, i.e. P ≈2γ/R, suggesting the effects of the nonequilibriumgas and nonuniform pressure are similar for nearly all nanobubblesizes.42 Conversely, the gas becomes morenonideal as the Laplace pressure increases, so W andκ deviate from their ideal values in this nanobubble range,and we see further changes in the effective polytropic exponent andthermal viscosity, in R = 10 nm nanobubble, k reaches around 1.25, although as discussed previously,this does not necessarily indicate more adiabatic expansion, sincethe limits of isothermal and adiabatic behavior have also increasedat this scale, as shown in the inset in W decreasesbelow unity for some nanobubble sizes R ≈30 nm, which would suggest other nontrivial polytropic behavior ispossible: e.g., isothermal behavior at k < 1.Thermal viscosity in our NB2 model remains significantly larger (∼50%)than that predicted by the classical models, reaching a maximum at R ≈ 100 nm.We have demonstrated that our model accurately describesthe thermaloscillation behavior of a 25 The higher polytropic values observed need to be considered in thereference frame for more physical isothermal and adiabatic limitsappropriate for nanobubbles. In another of our previous works, inwhich we investigated the growth and cavitation threshold of surfacenanobubbles, we fitted a polytropic value of k =1.18, which we now conclude was expanding at its nonideal isothermallimit W.24 Future workon this topic could further explore nonequilibrium behavior with completekinetic modeling of the internal gas phase, particularly in the specialcase of nanobubbles stabilized by reduced surface tension organicshells,54 where higher Knudsen numbers Kn ≳ 0.1 would elicit strongernonequilibrium effects. Mass transfer could also be incorporated intoour model, to capture the evaporation and condensation of gas/vapormolecules, e.g. in plasmonic nanobubbles,57 and the nonideal and nonequilibrium behavior mayexplain other nontrivial adiabatic behavior observed, e.g. k > κi in ref nonideal properties, which we model with the vdW equation of state,and (b) nonequilibrium behavior, which manifests as a temperaturejump at the liquid–gas interface. Our Letter shows compellingevidence that the existence of adiabatic oscillations in nanobubblesreported recently is likely not physical.i in ref .


Task shielding is an important executive control demand in dual-task performance enabling the segregation of stimulus–response translation processes in each task to minimize between-task interference. Although neuroimaging studies have shown activity in left dorsolateral prefrontal cortex (dlPFC) during various multitasking performances, the specific role of dlPFC in task shielding, and whether non-invasive brain stimulation (NIBS) may facilitate task shielding remains unclear. We therefore applied a single-blind, crossover sham-controlled design in which 34 participants performed a dual-task experiment with either anodal transcranial direct current stimulation  or sham tDCS  over left dlPFC. Task shielding was assessed by the backward-crosstalk effect, indicating the extent of between-task interference in dual tasks. Between-task interference was largest at high temporal overlap between tasks, i.e., at short stimulus onset asynchrony (SOA). Most importantly, in these conditions of highest multitasking demands, atDCS compared to sham stimulation significantly reduced between-task interference in error rates. These findings extend previous neuroimaging evidence and support modulation of successful task shielding through a conventional tDCS setup with anodal electrode over the left dlPFC. Moreover, our results demonstrate that NIBS can improve shielding of the prioritized task processing, especially in conditions of highest vulnerability to between-task interference. Because successful multitasking performance relies on a number of executive control functions, such as maintenance, monitoring, inter-task coordination, task segregation, or resource allocation5, attempts to improve multitasking abilities specifically target the optimization of the respective executive control components8.Multitasking is required in a variety of contexts in modern life, ranging from multimedia use to vehicle operation and scheduling of complex task sequences in an emergency room. Yet, performing even two simple cognitive tasks at the same time often results in between-task interference, e.g., when the engagement in an additional task impacts the processing of a prioritized task. Executive control processes, subsumed under the term task shielding serve to reduce this between-task interference by ensuring the correct binding of task-specific stimulus codes to respective response codes in each task to minimize the risk of response reversals and/or confusion15). Stelzel et al.16, for example, reported enhanced functional connectivity between the dlPFC and sensory regions relevant for Task 1 processing when both tasks shared high temporal overlap, suggesting increased task shielding in conditions of highest multitasking demands. Furthermore, neural correlates of training-induced improvements in dual-tasking have been attributed to higher grey matter volumes17, increased efficiency in neural information processing6, or increased separation of task representations in the frontoparietal-subcortical networks, including the left dlPFC as a central network hub18.Recent functional neuroimaging studies confirm a close link between dual-task performance and neural activity in brain regions associated with executive control, such as the dorsolateral prefrontal cortex . tDCS is a NIBS technique that applies a constant, low-amplitude electrical current using two or more electrodes on the scalp. Depending on the polarity of the current, the neural firings can either increase or decrease cortical excitability. In most cases, the beneficial effects on cognitive functions are reported for anodal tDCS (atDCS), which increases cortical excitability through depolarization25. The application of atDCS has revealed behavioral modification and improved cognitive functions such as working memory27 and attention29 in healthy (see30 for a review) and neurological patients (see31 for a review) and thus offers a promising tool to improve task shielding and to reduce between-task interference in dual-task performance.Non-invasive brain stimulation techniques (NIBS), such as transcranial direct current stimulation (tDCS), have been applied to improve dual-task performance . Most studies, however, have focused on comparing dual-task to single-task performance to assess dual-task costs32 or have addressed different executive control functions, such as task order control in dual tasks23. Although their findings generally demonstrate that NIBS over the left lPFC can facilitate dual-task performance, the applied dual-task paradigms, stimulation type, and protocols, as well as the investigated control functions differed greatly between these studies. It, therefore, remains unclear whether applying tDCS over left dlPFC can improve the executive control function of task shielding and thus reduce between-task interference in dual-task performance.Studies focusing on improving executive control in dual tasks with tDCS have supported the claim that the dlPFC plays a central role in dual-task performance, especially when high demands on executive functioning are required  during dual-task performance would modulate the underlying executive control network resulting in a reduced BCE, indicating improved task shielding.In the present study, we aimed to test whether the application of atDCS over the left dlPFC can improve task shielding. We used a dual-task paradigm, in which both tasks were presented in close temporal proximity and shared processing similarities to give rise to crosstalk between the two tasks. Effective task shielding can be inferred from the size of the so-called backward crosstalk effect (BCE) that reflects to which extent Task 1 processing is affected by simultaneous Task 2 processing. Importantly, the smaller this backward crosstalk onto Task 1 performance, the more effective is task shielding and interference controlSD = 2.2 years, range = 19–28 years]). After completion of both sessions, participants received monetary compensation of €20 or course credits. The study was conducted according to the Declaration of Helsinki and was approved by the local Ethics Committee at the University Medicine Greifswald (BB 144/18). Participants had normal or corrected-to-normal vision and gave informed, written consent before data collection. Via pre-screening, participants reported no history of medical, neurological, or psychiatric diseases, no sleep disorders, alcohol or substance abuse, and no treatment with medication acting primarily on the central nervous system.Forty-one participants signed up for participation in the experiment. Data sets of seven participants were not usable for analyses because of technical problems during recording (one participant), not completing both sessions (four participants), difficulties following task instructions, and obvious signs of disinterest in participation (two participants). The final sample consisted of thirty-four healthy participants  in Task 1 and the frequency of a tone (high or low) in Task 2. The high tone corresponded to the letter H, which represents the first letter of the German word Hoch (high), and the low tone corresponds to the letter T, which is the first letter of the German word Tief (low). In addition, the combinations of high tone—H and low tone—T were always mapped onto the same response fingers . As a consequence, compatible responses required both index or both middle fingers. Incompatible responses were based on one index and one middle finger response. The difference between compatible and incompatible trials denotes the BCE. Stimuli of both tasks (S1 and S2) were presented with varying  stimulus onset asynchronies (SOA) to manipulate the temporal overlap between the two tasks, which was meant to emphasize task order and to reduce grouped responses.The dual task consisted of a visual discrimination task (Task 1) and an auditory discrimination task (Task 2), which were performed in close succession. In order to increase processing similarities between the two tasks as the basis for crosstalk39. Responses were collected on a German QWERTZ keyboard.Stimuli were presented in Arial font (font size 30) and in white on a black background with a viewing distance of approximately 60 cm. S1 was responded to with the left middle and index finger pressing the 'a' and 's' keys, respectively. Task 2 consisted of easy to discriminate auditory stimuli (S2), i.e., a high or low tone of 900 and 300 Hz, presented via a loudspeaker. Responses to S2 were provided by pressing the 'k' and 'l' keys using the right index and middle finger, respectively. Half of the participants responded to H and high tones with both index fingers and T and low tones with both middle fingers. The other half of the participants received the reversed assignment. Stimulus presentation and data recording were realized on a Pentium I computer with a Windows 10 platform using E-prime software (Version 3)The participants were instructed to respond first as fast and accurately as possible to S1 and only subsequently as fast and accurately as possible to S2. Each trial began with an 800 ms fixation period, in which a plus sign was presented in the center of the screen, followed by the onset of the visual S1. After a variable stimulus onset asynchrony (SOA) of 40, 130, or 300 ms, the auditory S2 was presented for 150 ms. S1 remained on screen for a maximum of 2500 ms (plus SOA duration) or until responses to both tasks were given. In the case of erroneous responses or wrong order, error feedback  was presented for 500 ms. The next trial started after a blank screen was presented for a random inter-trial-interval (ITI) between 0 and 1000 ms. Figure In each session, the experiment started with 12 dual-task trials to instruct stimulus–response mappings, followed by one practice block and eight experimental blocks consisting of 72 trials each. The 72 trials consisted of 36 compatible and incompatible trials. Of the 36 trials, in each compatibility condition, the three SOAs were presented 12 times each. That is, each combination of 2(S1) × 2(S2) × 3(SOA) was presented six times per block. The practice block was excluded from the analysis. Thus, 576 trials per session were considered for analysis.2, current density = 0.028 mA/cm2 and the right supraorbital region . Figure 21.Stimulation was delivered  using two saline-soaked surface sponge electrodes over the left dlPFC .In the sham tDCS, the stimulation duration was 30 s, including 20 s of ramping47, dependent measures were response times in Task 1 (RT1) and percent error in Task 1 (PE1). RT2 and PE2 are reported for completeness.A 2  × 2  × 3  repeated measures single-blind, crossover, sham-controlled design was applied. Because BCEs take effect in Task 1 performanceThe practice block of each session was not considered for analyses. Before RT analyses, erroneous trials in either Task 1 or Task 2 were removed. Further, all trials with RT1 < 200 ms and RT1 > 2000 ms and RT2 < 200 ms, and RT2 > 2500 ms were considered outliers and were not included in the analysis. This led to a removal of 0.6% of trials. A repeated measures ANOVA with the within-subject factors 2  × 2  × 3  was applied on mean RTs and PEs  as compared to compatible to the Task 1 response , F = 10.29, p = 0.003, ηp2 = 0.24, representing the existence of a BCE. The factor SOA, representing the temporal overlap between the two tasks, was statistically not significant, F = 2.83, p = 0.066, ηp2 = 0.08. Overall, the error rates did not differ between Stimulation condition, F < 1.Participants made more errors in Task 1 when the Task 2 response was incompatible  = 5.50, p = 0.006, ηp2 = 0.14. As illustrated in Fig. ts < 1, but present at the longest SOA, t(33) = − 3.58, p = 0.001. Sham tDCS, however, showed the typical pattern of decreasing BCEs with increasing SOAs , with significant BCEs at the two shortest SOAs, t(33) = − 2.94, p = 0.006 (SOA 40 ms) and t(33) = − 2.21, p = 0.034 (SOA 130 ms), but not at the longest SOA of 300 ms, t(33) = − 1.38, p = 0.176  = 5.96, p = 0.020, ηp2 = 0.15. The difference in BCE between atDCS and sham tDCS was not significant at SOA 130 ms, F = 1.13, p = 0.295, ηp2 = 0.03, and slightly reversed for the longest SOA of 300 ms, F = 6.01, p = 0.020, ηp2 = 0.15. This SOA-dependent pattern of the impact of stimulation on the BCE explains the overall lack of the interaction between Stimulation condition and Compatibility, F = 1.57, p = 0.219, ηp2 = 0.05. No further effects were significant, Fs < 1.57, ps > 0.219.Importantly, the application of atDCS modulated the BCE as compared to sham tDCS across the three SOAs, as indicated by the significant three-way interaction between Stimulation condition, SOA, and Compatibility, st SOA, t3 = − 3.5p’s > 0.066. Finally, an additional factor Stimulation Block (online stimulation vs. offline stimulation), did not further modulate the interaction between Stimulation Condition x SOA x Compatibility, F < 1, suggesting that the effects of atDCS were not restricted to the online stimulation period but extended into the offline stimulation blocks as well.To test for potential effects of stimulation condition order , an analysis with stimulation order as an additional between-subject factor was conducted. However, no significant main effect or interaction with stimulation order was observed, all M = 800 ms, SE = 29) as compared to incompatible trials , confirming a BCE, F = 34.85, p < 0.001, ηp2 = 0.51.Participants responded faster in compatible trials  = 163.40, p < 0.001, ηp2 = 0.83, with increasing RT1 the larger the SOA . This increase was slightly more pronounced for atDCS than for sham tDCS, as revealed by the significant interaction between Stimulation condition and SOA, F = 3.72, p = 0.030, ηp2 = 0.10. However, differences between atDCS and sham tDCS at each SOA condition remained descriptively, as they did not exceed the statistical significance level . Furthermore, RT1s did not generally differ between Stimulation conditions, F = 1.83, p = 0.185, ηp2 = 0.05. Finally, the BCE was modulated by SOA, F = 9.12, p < 0.001, ηp2 = 0.22, with the largest BCEs for the two shortest SOAs  and a somewhat smaller BCE for the SOA 300 ms . However, the three-way interaction between Stimulation condition, SOA, and Compatibility, and other interactions were not significant, Fs < 1.There was also a significant main effect of SOA, F = 22.28, p < 0.001, ηp2 = 0.40, was significant. Participants made more errors in incompatible  as compared to compatible trials . The factor SOA did not reach statistical significance, F = 3.19, p = 0.061, ηp2 = 0.09. The SOA x Compatibility interaction was significant, F = 8.62, p = 0.001, ηp2 = 0.21, with the largest forward-crosstalk at 40 ms , followed by 130 ms  and the lowest at 300 ms . No further effects were significant, Fs < 1.50, ps > 1.0.Mean PE2 is summarized in Table M = 834 ms, SE = 30) than incompatible  trials, F = 40.29, p < 0.001, ηp2 = 0.55. The main effect of SOA was significant, F = 370.02, p < 0.001, ηp2 = 0.92, confirming a decrease in RT2 as the SOA increases . The decrease was slightly more pronounced for sham tDCS  than atDCS , as indicated by the significant Stimulation condition x SOA interaction, F = 3.58, p = 0.033, ηp2 = 0.10. However, RT2 did not differ between sham and atDCS condition at either SOA . The Compatibility x SOA interaction was significant, F = 10.15, p < 0.001, ηp2 = 0.24, with the largest forward crosstalk for the two shortest SOAs , and a smaller forward crosstalk of 68 ms (SE = 44) when the SOA was 300 ms.Mean correct RT2 is summarized in Table p’s > 0.053. Severity ratings were compared using Mann–Whitney-U-Test with exact significance values due to the small sample size  = 3.06, p = 0.217. These guessing rates are similar to other studies that report values of comparable magnitude using a crossover, double-blind design49.The frequency and severity of reported side effects  were assessed for the last experimental session. None of the participants reported metallic taste as a side effect in atDCS or sham tDCS. Two-sided Fisher exact tests revealed no association between the frequency of reported side effects and type of stimulation (atDCS and sham tDCS), all ze Table . FurtherThe present study investigated whether modulation of excitability in the left-lateralized executive control network, as modulated by a prefrontal tDCS setup with anode over left dlPFC, facilitated the executive control function of prioritized task shielding in dual tasking. Results demonstrated that atDCS, as compared to sham tDCS, reduced crosstalk between the two tasks indicating improved task shielding. Importantly, improved task shielding was observed in conditions of highest dual-task demands, i.e., the largest temporal overlap between the two tasks.50). In contrast, the administration of atDCS over left dlPFC completely eliminated the backward crosstalk effect, especially in conditions of highest interference vulnerability, i.e., when both tasks were performed virtually at the same time (short SOA).More precisely, in the condition of sham tDCS, crosstalk was stronger the more simultaneously both tasks were being processed. The close temporal proximity of stimuli at high temporal task overlap allows for more simultaneous task-component processing, which typically increases crosstalk between tasks. With less temporal task overlap, crosstalk typically declines. Thus, our findings in the sham tDCS condition mirror the usual result pattern obtained in dual-task research without any brain stimulation . We could show that modulation of the excitability in the left-lateralized executive control network can improve the cognitive control function of task shielding and, thus, interference control in dual-task processing. The fact that the effect of atDCS is especially pronounced in conditions of highest interference vulnerability is in line with previous behavioral studies demonstrating adaptively increased task shielding when high-interference situations are predicted, such as high frequency of short SOAs34 or high probability of between-task conflict52.The findings of the present interventional approach, therefore, extend previous imaging studies suggesting a significant role of the dlPFC in task shielding . As a consequence, stimulus–response translations for each task would run more efficiently under atDCS, giving less opportunity for crosstalk.Alternatively, tDCS-induced facilitation of task shielding may be directly related to enhanced processing efficiency rather than conflict resolution. That is, neural accounts to explain training effects on cognitive task performance postulate that training can lead to more efficient processing of the already engaged neural circuits59. Frings et al.59, for example, speculated that "better” or “worse” functioning of the dlPFC would not necessarily translate in modulations of overall RTs, but could alter the ratio of trials in which its function of interference control works effectively or fails.Such an assumed benefit in neural efficiency, however, did not show up in the present RT measures, as the effects of improved task shielding under atDCS were found in error rates only. A selective impact of tDCS on error rates is not uncommon and has been reported frequently61. This strategic response deferment was slightly more pronounced for atDCS compared to sham stimulation and also accounts for the weaker decline of RT2 across SOA for atDCS compared to sham stimulation—the more delayed the Task 1 response at SOA 300, the later the response of Task 2. Most importantly, however, this RT pattern does not compromise the findings and their interpretation of atDCS modulating the size of the BCE and the extent of task shielding in Task 1.In contrast to the error rates, RT results showed an increase of RT1 with longer SOAs. In dual-task paradigms with mostly short SOAs, participants tend to execute both responses at the same time, which leads to a typical strategic Task 1 response deferment with increasing SOA22. Further studies should test the duration of tDCS-induced improved task shielding, as previous studies reported that anodal tDCS over left dlPFC can reduce performance costs in dual-task compared to single-task performance up to an hour after stimulation21. Finally, the present study investigated only young adults. Since age-related differences in task shielding have recently been reported63, it remains unclear if other age groups would benefit in the same way from stimulation.While the present results demonstrate that atDCS over the dlPFC improves task shielding in conditions of high between-task interference, further research combining functional neuroimaging and brain stimulation techniques may be informative to which extent facilitated task shielding is a consequence of tDCS-modulated activity and/or functional connectivity in underlying brain networks. In addition, the present effects of tDCS on task shielding were assessed across all experimental blocks, i.e., during and immediately after stimulation23. We are aware that tDCS with this setup over left dlPFC allows for modulation of the activity and/or connectivity in the underlying executive control network. In order to test for a causal role of the involvement of the dlPFC in increased task shielding, targeted stimulation of focal brain regions, e.g., by high-definition (HD) tDCS  would be is required. The use of an active control group allows checking for specificity of tDCS effects66 and to avoid blinding issues arising from perceptual sensation67. It becomes even more important if inefficient blinding leads to increase in demand characteristics, such as arousal or motivation on performance68. Because potential sensory co-stimulation confounds cannot be ruled out with certainty, the use of an active control group, carefully matched for sensation and irritation, is becoming an important necessity and should be considered in future studies69.The present study used a conventional tDCS setup as applied in previous studies that reported successful tDCS-related dual-task performance benefits70 to prevent an experimenter bias. However, to safely exclude the possibility of even unconscious experimenter biases, future research should consider double-blind designs. Because the effects of atDCS on task shielding were obtained in errors, subsequent studies using similar paradigms with typically low error rates23 should ensure a sufficiently high number of errors in the experiment.In addition, to potentially increase blinding efficacy, a double-blind design might be taken into consideration. We carefully implemented certain protocols to standardize testing across all participants, such as written instructions, and minimized interactions with participantsWe investigated the effects of atDCS over left dlPFC on shielding of prioritized task processing in dual tasks. We found that atDCS reduced between-task interference, especially at high temporal overlap between tasks as compared to sham tDCS. These findings extend previous behavioral and neuroimaging studies by demonstrating that stimulating the left-lateralized executive control network can improve dual performance by reducing processing interactions between simultaneously performed tasks. This atDCS-induced improvement of cognitive function was most evident in conditions of highest vulnerability to between-task interference. Given the increasing prevalence of multitasking in modern life, this suggests that atDCS should be explored as an intervention tool in future studies, for example, with repeated training and application of atDCS in individuals in advanced age that are known to be susceptible to impairments in multitasking performance.


In total, 85 children with T1D  were recruited. Of these children, 31.3% showed HbA1c levels of <7.0% and 44.9% had a TIR >70%. A significant positive correlation was found between Hb1Ac and “familiarity” (R: +0.233). Anthropometric measures  showed significant positive correlations with “sensory appeal” and “price”. Parents’ eating behaviors influence the nutritional status of their children with T1D and their glycemic control of the disease.Parents play a key role in what their children eat. The Food Choice Questionnaire (FCQ) has been used elsewhere to assess the dietary motivations of parents of healthy children, but not for parents of children with chronic diseases such as type 1 diabetes (T1D). The aim of our research was to evaluate the associations between parental food choice motivations and the nutritional status and glycemic control of children with T1D. A cross-sectional observational study of children aged 5 to 16 years with T1D attending the Pediatric Endocrinology Unit of Puerta del Mar University Hospital in Cádiz (Spain) was performed. Demographic, anthropometric and clinical data, including glycated hemoglobin, were collected. The FCQ in Spanish was conducted to assess the eating behaviors of the main caregivers of children with T1D. Significance was established at the level of  Type 1 diabetes (T1D) is the most common form of diabetes in young people . The estHowever, childhood adiposity is a serious problem worldwide . HealthyFood preferences continue to change throughout a person’s life, and they are key determinants of food choices and therefore diet quality . ParentsDespite family involvement being a vital component of optimal diabetes management throughout childhood and adolescence , to our The aim of this study was to evaluate the association between parental food choice motives  and the nutritional status and glycemic control of children with T1D.This cross-sectional observational study was based on a survey of the main caregivers of children 5 to 16 years of age, with a diagnosis of T1D with more than 1 year of evolution, treated as outpatients at the Pediatric Endocrinology Unit of Puerta del Mar University Hospital in Cádiz (Spain) from February to May 2022. Patients outside this age range or/and who had been diagnosed with T1D for less than 1 year were excluded. Children without sociodemographic, clinical and biochemical data were also excluded. Parents with a serious nutritional condition were not surveyed and their children were not part of the final sample. As the study involved human participants, it was reviewed and approved by the Ethics Committee of Research of Cadiz. Written informed consent to participate in this study was provided by the participants’ parents.2. Three skinfold measurements were taken using a Holtain caliper (accurate to 0.2 mm) from the following sites: triceps, subscapular and iliac crest. Waist circumference and mid-upper-arm circumference (MUAC) were also measured. Z-scores for age and gender for every anthropometric variable were also registered using Spanish reference values.Face to face interviews were conducted with the participants on the same day as their children’s medical consultation, after an explanations of how to fill in the questionnaire was provided by a trained investigator. Patients’ sociodemographic  and clinical data  were collected from digital clinical records. Biochemical data from most recent last blood test were also included . Anthropometric measurements were recorded for each individual by trained pediatric endocrinology physicians following standard operating procedures. For quality assurance, the same equipment was used to obtain anthropometric data from all the participating subjects. Height was measured using a portable stadiometer (ADE MZ10042) consisting of a vertical stand and an adjustable headpiece. Height was measured to the nearest 0.1 cm. Correct body posture was maintained while recording the standing height. Patient weight was measured with light clothing and without shoes using a Tanita model DC-430 S MA to the nearest 0.2 kg. Body mass index was calculated using the formula BMI = Kg/mUsing bioelectrical impedance (BIA) (Tanita model DC-430 S MA), the following parameters were obtained: fat-free mass, body fat mass and total body water. As recommended by the manufacturer, each participant stood on the scale barefoot in contact with the four electrodes for the feet, dressed in light clothing and with the arms separated from the trunk to prevent contact.The Food Choice Questionnaire (FCQ) , in its t-test was performed to compare continuous variables and χ2 was used when proportions were compared.All analyses were conducted using SPSS statistical software . The descriptive analysis provided means and standard deviations for continuous variables and frequencies and percentages for categorical variables. Student’s Bivariate analysis using simple linear correlation were used to assess the relationships between the parent’s food choice scores (dependent variable) and several independent variables, such as age, HbA1c levels, carbohydrate consumed, TDI, height, weight, BMI, fat mass and percentage, skinfolds and circumferences.p ≤ 0.05.The cut-off point for statistical significance used was Eighty-five patients with T1D were included (48 girls), with ages ranging from 5 to 16 years. The mean age of the children in the sample was 12.04 ± 2.93 years and 18 (22%) were pre-pubertal. The mean age at diabetes diagnosis was 6.44 ± 3.28 years. Fifty-seven of them (67.1%) were receiving multiple daily injection (MDI) therapy and 28 (32.9%) were being treated with a closed-loop system. The clinical and biochemical characteristics of the patients included are summarized in The mean participant HbA1c level was 7.29 ± 0.77%. Among all the study participants, 25 (31.3%) showed HbA1c levels of <7% and 35 (44.9%) showed a value in the range of >70%. The bioimpedanciometry results showed a mean fat-free mass of 33.78 ± 10.42 kg, a mean body fat mass of 14.94 ±9.99 kg and 26.39 ± 11.48% and a mean total body water of 25.58 ± 6.30%.The main caregivers were mostly women (95.3%), and were mothers in the majority of cases (94.1%). The average age of participating parents was 43.9 ± 7.21 years. Of these, 24.7% stated that their highest educational level was middle school or lower, 12.9% stated that they had attended high school, 35.3% reported a technical degree as their highest educational level and 27.1% of the parents were classified as graduates or higher. Further sociodemographic data of the parents are given in A correlation study was performed using the scores from the FCQ food attributes and other independent variables. These data are shown in p = 0.009) and also with children’s pubertal stage . This food attribute also showed a positive correlation with insulin needs .“Sensory appeal” was the food attribute with the highest score (31.8%). A positive correlation was found between “sensory appeal” and the age of children  and venous HbA1c .Weakly positive associations were found between “familiarity” and both levels of HbA1c measured: capillary HbA1c  and the carbohydrate consumed (CH/day) ; (CH/kg) ;  .There were positive correlations between “convenience” and the percentage of prandial insulin , weight , triceps skinfold , subscapular skinfold , iliac skinfold , waist circumference  and MUAC .Further positive correlations were found between “sensory appeal” and anthropometric measures: percentage of body fat mass , weight z-score , BMI z-score , subscapular skinfold , waist circumference , waist circumference z-score , MUAC  and MUAC z-score .Additional positive associations between “weight control” and anthropometric measures were also found: percentage of body fat mass  and kilograms of body fat mass , weight z-score , BMI z-score , triceps skinfold , triceps skinfold z-score , subscapular skinfold , subscapular skinfold z-score , iliac skinfold , waist circumference , MUAC  and MUAC z-score .“Price” showed strongest positive correlations with anthropometric measures: percentage of body fat mass  and between “familiarity” and age .Finally, positive associations between parental food choices and their sociodemographic characteristics were also seen. There was a positive correlation between “convenience” and education level  and questionnaire responses related to food attributes such as “sensory appeal”. Namely, there was an increase in weight gain among children whose parents attached more importance to the taste, smell and texture of the food they consume. Parents, as the children’s role models, are a major environmental factor affecting the formation of their children’s eating habits . A similPrice was another factor positively linked with higher anthropometric parameters in our study. Family affluence, considered a marker of socioeconomic status , has beeIn high-income countries, childhood socioeconomic status is inversely associated with weight among children and adolescents , with paHbA1c levels presented a positive correlation with familiarity. This means that the children of parents who preferred foods similar to those they consumed during their childhood and adolescence had higher HbA1c levels. Previous studies have shown that neophobia (avoidance of unfamiliar food) is familial and is associated with less healthy food choices . Those uFinally, children’s age was another critical parameter in our sample, with a direct correlation with “sensory appeal”. In line with previous research, adolescents have a greater tendency to consume “junk” (energy-dense/low-nutrient) food ,38, and The main strength of this study is that the questionnaire used in the research was validated and reliability tested and has been widely used in other studies. Additionally, the study is particularly important because there is a lack of this type of study on children with T1D.An important limitation of this study involves its cross-sectional design, which complicated causal interpretation of the results. Although the size of the sample included was acceptable, a more representative study sample might have given different results. Another limitation is that none of the items showed a strong correlation with any variable, with 0.365 being the highest R value of all the correlations analyzed (a positive correlation between “price” and the percentage of body fat mass). Therefore, although there were statistically significant positive correlations, their clinical relevance could be limited.In summary, our results suggest that when parental food choices are driven by the considerations of “price” and “sensory appeal”, this seems to have a negative impact on the nutritional status and body composition of their children with T1D, whereas choosing foods based on “convenience” and “familiarity” negatively impacts the children’s glucose profiles in terms of higher prandial insulin requirements and higher HbA1c. Furthermore, this study highlights the importance of educating parents continuously about their role and about the importance of managing their own eating habits to educate their children about healthy eating habits.


Virtual conversational agents  are an intuitive form of data collection. Understanding older adults' experiences with chatbots could help identify their usability needs. This quality improvement study evaluated older adults' experiences with a chatbot for health data collection. A secondary goal was to understand how perceptions differed based on length of chatbot forms.After a demographic survey, participants (≥60 years) completed either a short (21 questions), moderate (30 questions), or long (66 questions) chatbot form. Perceived ease-of-use, usefulness, usability, likelihood to recommend, and cognitive load were measured post-test. Qualitative and quantitative analyses were used.A total of 260 participants reported on usability and satisfaction metrics including perceived ease-of-use (5.8/7), usefulness (4.7/7), usability (5.4/7), and likelihood to recommend (Net Promoter Score = 0). Cognitive load (12.3/100) was low. There was a statistically significant difference in perceived usefulness between groups, with a significantly higher mean perceived usefulness for Group 1 than Group 3. No other group differences were observed. The chatbot was perceived as quick, easy, and pleasant with concerns about technical issues, privacy, and security. Participants provided suggestions to enhance progress tracking, edit responses, improve readability, and have options to ask questions.Older adults found the chatbot to be easy, useful, and usable. The chatbot required low cognitive load demonstrating it could be an enjoyable health data collection tool for older adults. These results will inform the development of a health data collection chatbot technology. CompletWith advancements in healthcare technology and the onset of COVID-19, remote HDC is increasing. Compared to traditional paper-based or in-clinic HDC, remote approaches allow patients to complete health forms at a convenient time and place with easier data input, more flexible corrections, fewer errors, and integration with other health information technologies. In recent years, chatbots have demonstrated better usability and user experience compared to other online HDC tools –5. Patievia chatbots among older adults.Since older adults are often homebound and utilize more healthcare services, it is important that they have access to easy-to-use healthcare services and technologies , 8. Yet,The purpose of this quality improvement (QI) study was to evaluate the workload, usability, and ease-of-use of a chatbot-delivered HDC among older adults. Considering patients are often required to complete long or multiple health forms, we aimed to understand whether the length (measured as numbers of questions) of three chatbot-delivered health forms impacted older adults’ perceptions.2.2.1.In this study, older adults' experiences with data collection were assessed using a chatbot, Dokbotn = 113), Group 2 , and Group 3 . Section Eligible participants for this study included older adults based in the United States aged ≥60 years old. The age range for older adults was chosen based on previous research on assessing older adults' experience with chatbots and health data collection , 15. We 2.2.Step 1. Study information: Participants were informed about the purpose and procedure of this QI study. Participants were ensured that their responses will be kept private and only used for research purposes. If interested, participants clicked a confirmation button to move forward with the study.Step 2. Pre-test questionnaire: Participants completed questions on demographics, experience with health forms, and eHealth literacy : This group contained one form: PRAPARE. PRAPARE is a 21-item questionnaire to assess social drivers of health : Participants in this second group completed two health forms: PRAPARE and PHQ-9. PHQ-9 is a widely used 9-question form in behavioral and physical healthcare to assess mood : Participants in this third group completed three health forms: PRAPARE, PHQ-9, and SF-36. This 36-item form contains a variety of question types and is broadly applicable , the Patient Health Questionnaire (PHQ-9), and the Short Form-36 (SF-36) survey for quality of life –23. We c2.4.2.4.1.We collected information on participant characteristics  and previous experience completing health forms.2.4.2.This is an 8-item eHealth literacy measure to assess combined knowledge, comfort, and perceived skills at finding, evaluating, and applying electronic health information to health problems . Respons2.4.3.This 12-item measure based on the TAM is designed to assess perceived usefulness and ease-of-use, which are two fundamental determinants of user acceptance . Respons2.4.4.The IBM CSUQ is a 19-item questionnaire designed to measure the perception of user experience  and dislikes  regarding the chatbot.Two open-ended questions asked participants about their likes , duplicate Prolific ID (n = 3), and missing/incorrect Prolific ID (n = 2). No significant differences were found in the final sample sizes of the three groups .Of the 334 invited participants, 277 (82.9%) participants started the study and 260 (93.9%) participants completed the study in three groups  = 26.57, p < 0.001. Post-hoc tests revealed a significant difference among all groups (ps < 0.01). The participants were 51.2% female, 93.5% white, and 97.3% non-Hispanic. Participants had a bachelor's degree (36.5%), Master's degree (28.1%), or some college but no degree (14.6%). Participants' mean eHealth literacy score was 33.6 (SD = 4.7). eHealth literacy scores did not vary by group, F = 1.58, p = 0.21. Most (98.8%) participants reported that they had completed health forms before, commonly on paper  and/or online . See The average age of participants was 67 years old, ranging from 60 to 93 years. Age significantly differed by group, 3.2.F = 3.08, p = 0.048, ηp2 = 0.02. Pairwise comparisons revealed that the mean perceived usefulness rating for Group 1 was significantly higher than the mean rating for Group 3 . There was no statistically significant difference among groups in perceived ease-of-use, F = 2.01, p = 0.14, ηp2 = 0.02.Older adults reported a mean perceived ease-of-use score of 5.8 (SD = 1.1) and a mean perceived usefulness score of 4.7  = 2.28, p = 0.10, ηp2 = 0.02. Further, there were no statistically significant differences among groups in scores for system usefulness , information quality , and interface quality .3.4.M = 12.3; SD = 12.1; F = 0.11, p = 0.90, ηp2 = 0.001. There were also no statistically significant differences among groups for the NASA-TLX subscales of mental demand , physical demand , temporal demand , performance , effort , or frustration .Participants’ responses for the NASA-TLX showed low overall mental workload  = 2.17, p = 0.12, ηp2 = 0.02.Groups 1 and 2 reported positive NPS of 5 and 6 respectively . The NPS3.6.ease-of-use had the greatest number of codes (n= 157) with 100% positive codes. Participants commonly referred to ease-of-use in understanding and answering questions, perceiving the chatbot experience as easy (n = 109/157 codes), clear/straightforward (n = 38/157 codes), and simple (n = 25/157 codes). One participant said, “easy to use, quick and convenient.” Several participants noted “simple and straightforward” or “quick and to the point.” With regard to completion time, most participants commented on the quickness of the form, but participants also noted “it takes much longer than doing it myself” and “too slow, needless interaction.” Participants referenced a human-like aspect of interacting with the chatbot, making neutral to positive comments such as “very easy to communicate with, felt like talking with a real person, expressed warmth and empathy” (n = 19/28 codes). On the other hand, some participants felt the chatbot was “impersonal and reactionless” or that it “lacked a personal touvh [sic]” (n = 9/28 codes).The category positive experiences (n = 22 codes) of using the chatbot, saying it was “fun,” “nice,” “pleasant,” and “friendly.” One participant commented, “I thought the questions were brief and understandable. I thought filling in the forms felt almost fun doing them with the dokbot. I felt I could be honest without judgement [sic] with dokbot.” Several participants commented on the friendliness of the chatbot, writing responses such as “I liked that it seemed friendly, like a person.”Participants commonly noted Technology experience had mostly negative  to neutral  comments, citing problems with chatbot functioning, for example, “it didn’t always advance automatically. Sometimes it showed the answers, but the question was obscured by the previous question, so I had to advance it using the scroll bar.” They commented on the experience of using the technology, reporting some technical issues and privacy concerns, and some participants provided actionable suggestions for enhancements to the survey and chatbot interface such as sound, more personalized dialogue, and a way to ask questions or clarify answers.Design had 28% positive responses (n = 14/50 codes) with participants commenting that “it was easy to understand and has a gentle graphic interface” and “I liked the bright colors and the friendly feel of it. It was also very clear and I liked the font.” There were several neutral comments  about design, such as “I wonder how they would respond to my concerns.” Participants expressed that they would like to change “size of the bot and the font” or noted that “the screen was a little too little” .navigation to be pleasant and seamless, commenting that it was “smooth and uncomplicated” and that “it moved along at a good pace.” The chatbot dialogue included intermittent progress updates and positive encouragement such as “Awesome! You are doing great!” Participants shared mixed views about these encouraging messages, with some expressing like and others dislike of the messages. Of the 19 times that it was mentioned, 5 were positive, 12 were negative, and 2 were neutral. Participants commented “I was less thrilled when it wrote things like ‘you are doing really well!’. It seemed a bit condescending” or “I liked how easy it was to answer the questions and how the questions were asked, plus the encouragement such as ‘you’re doing great.”Participants overall found the comparison with other data collection approaches code received 8/19 (42.1%) neutral to positive comments and 57.9% (n = 11/19 codes) negative responses. Participants compared the chatbot to other survey experiences such as talking to a nurse/doctor and paper or online forms. Some participants enjoyed “not having to talk to a person” while others “would have preferred a person.” Further, some noted it was “much easier than filing [sic] out forms by hand and it is rather fun” but “if you had a problem or were confused, it may be easier to talk to a person.”The privacy concerns with providing personal information to the chatbot. This category had the majority of negative comments  with some participants finding the chatbot “a bit intrusive.” One participant commented, “I’m always a little hesitant to provided [sic] personal or medically related information online to someone I don’t know. Prefer in-person or on the phone in those type of matters. Otherwise I had no issues with the dokbot.” Other  participants stated that the chatbot exhibited mannerisms similar to their providers, commenting, “It seemed pleasant. It stated questions clearly, and sensitively. It didn’t rush me.” Another participant mentioned that the chatbot “asked probing questions just like a doctor would.”Participants commented on 3.6.1.n = 117/153) of codes were positive, 7.8% (n = 12/153) were neutral, and 15.7% (n = 24/153) were negative. In Group 2, 68.7% (n = 103/150) of codes were positive, 3.3% (n = 5/150) were neutral, and 28.0% (n = 42/150) were negative. In Group 3, 61.6% (n = 90/146) of codes were positive, 11.0% (n = 16/146) were neutral, and 27.4% (n = 40/146) were negative. Comments specific to the survey questions  were excluded (85 codes) from the positive, negative, and neutral coding categories displayed in Complex coding query was performed to identify group differences in qualitative responses . In Groudesign, actionable suggestions, and navigation categories. Participants commented on the chatbot color scheme, size of the screen, limited chat function, and font. Further, with regard to navigation, participants commented that the movement “was a little ‘jerky’ and unnerving, and could be smoother.” Group 1 had the most positive codes for completion time , navigation (“flowed nicely”), and compare (“it was just like chatting with a human”). Group 2 had the most negative codes for humanness , compare , progress updates , and technology experience (“seemed awkward—like I had to keep scrolling it up to see the questions”).Group 3 had the greatest number of negative codes in 4.4.1.As health technologies are increasingly incorporated into practice, it is important to make designs and user experiences friendly to older adults. In this QI study of experiences with a HDC chatbot (Dokbot), older adults found the chatbot-delivered health forms to be easy, useful, usable, and required low cognitive load. We identified opportunities to reduce concerns regarding design, privacy, and technical issues that would improve overall user experiences.Participants reported a mean perceived ease-of-use score of 5.8/7 with correspondingly positive qualitative feedback. The ease-of-use theme had the greatest number of codes (157/449) with older adults frequently commenting that it was easy to understand and answer the questions using the chatbot. We observed no group differences in perceived ease-of-use, meaning older adults found the chatbot easy to use regardless of length of health forms. It was noted that scores were higher for perceived ease-of-use than perceived usefulness. The conversational design and encouraging statements provided by the chatbot may have felt engaging for shorter forms but became more tedious in longer forms. Although participants often compared the chatbot to talking with a human, older adults mentioned preferring to speak to a real person to ask for clarifications with the forms. However, this also holds true for other remote approaches such as online questionnaires and paper-based forms. In the future, the ability to ask questions to discuss with their providers at a later time could potentially alleviate these concerns. Future research should further examine the timing and structure of motivating statements, comparing generic praise  against more goal-oriented messaging .n = 51/93) providing positive comments for navigation and design. The chatbot was designed to show one question at a time, advancing automatically when the participant answered a question. This design maintained steady progress and may have provided a good user experience, but this automated design and small interface may have been more difficult for some participants to read or review responses across the form. Previous research found that participants praised chatbots for being “quick and easy” despite taking longer to complete  evaluated age differences in perceptions of chatbot-delivered family health history forms using TAM, CSUQ, and NASA-TLX, reporting older adults preferred a chatbot over the standard interface for family health history collection despite taking longer to complete . The stuintrusive” (n = 15 codes). There were several comments relating to the trust, safety, and privacy of using the chatbot for data collection of protected health information. Previous researchers have commented that older adults have concerns about data privacy, which we observed as well with participants expressing concerns about the chatbot invading their privacy, not being connected to a medical authority, and reservations providing medical information to an unfamiliar source  and Group 3 (M = 4.3), meaning older adults' belief that the chatbot enhanced their performance declined as the length increased. We also noticed a decline in NPS to −12 (Group 3) from 5 (Group 1) and 6 (Group 2), reflecting that there were more detractors than promoters in Group 3. The scores were lower in comparison with a previous study with a majority younger (95.1% participants <60 years) population (chatbot NPS = 24). Negative comments also increased with more concerns about design and formatting as the length of the chatbot increased from 6 (Group 1) to 9 (Group 2) to 15 (Group 3). These findings point toward favorable user experiences with shorter chatbots but poorer experiences over longer interaction with the chatbot. Factors such as restricted content space, lack of progress tracking or time estimate, and the constrained presentation of questions one-by-one could hinder a user's experience over a longer form. Future research should further explore the impact of adaptive chatbots that alter variables such as length of forms, types of questions, progress tracking, and speech patterns to personalize user experiences.A secondary goal of this study was to assess participant's experience of completing chatbot-delivered health forms with three different lengths. Results show a significant difference in perceived usefulness between Group 1 . Participants also commented on the inability to edit a previous response. Although undoing previous responses was available in the chatbot, the feature may not have been salient in use. Participants also desired features such as voice capabilities (such as voice-to-text data entry or text-to-voice for reading chatbot questions), viewing a summary of their responses before submission, easy inputs, and ability to ask for clarifications.4.2.Participants were recruited using an online crowd-sourcing platform, Prolific, which may not be representative of the general population. We aimed to recruit participants who were 60 years or older but made no further specifications for race or education. Participants might be more technologically savvy compared to the general population considering their presence on the platform, which may have contributed to high reported eHealth literacy scores –35. A prDue to a technology issue with the chatbot and inability to track time between different technologies, we were unable to collect time data for the study and chatbot-delivered health form. Future studies should look into assessing time taken to complete chatbot health forms and user experience.Individuals may have completed the questionnaires inaccurately or disingenuously considering the remote, unmoderated nature of the study. Individuals could have completed the chatbot-delivered health form and questionnaires in a hurry or at their own convenience and provided careless responses. Although, researchers have reported on the high quality of data collected using Prolific suggesting that data quality may not be a concern . Future Further, biases can occur from paid survey pools for remote, unmoderated studies, specifically at low levels of compensation . Framing5.The study presents findings that chatbots could be a valuable modern HDC approach for older adults. Older adults reported chatbot-delivered health forms to be easy, useful, and usable, and additionally, to require low cognitive load. They reported overall positive experiences and ease-of-use of the chatbot and concerns about technology issues, privacy, and the lack of ability to ask clarifying questions. Many of the participants' responses lead to actionable suggestions such as focusing on design, accessibility, and privacy. As the length of the survey increased, older adults reported a decrease in perceived usefulness, likelihood to recommend, and an increase in negative comments. Improvements in chatbot design and features may make them a useful, interactive data collection tool for health forms of varying length. Findings have broad implications for HDC and chatbot development, warranting continued investigation to establish best practices and design recommendations for the older adult population.


Electrochemical characterization of positively and negatively charged membranes is performed by analyzing membrane potential values on the basis of the Teorell–Meyer–Sievers (TMS) model. This analysis allows the separate estimation of Donnan  and diffusion (differences in ions transport through the membrane) contributions, and it permits the evaluation of the membrane’s effective fixed charge concentration and the transport number of the ions in the membrane. Typical ion-exchange commercial membranes  are analyzed, though other experimental and commercial membranes, which are derived from different materials and have diverse structures , are also considered. Moreover, for some membranes, changes associated with different modifications and other effects  are also analyzed. The presence of fixed charges on the surfaces and bulk structures of membranes used in traditional filtration separation processes  is a significant factor, since it affects the transport of electrolyte solutions and ions/charged particles ,2,3. MorThe presence of charges in a membrane can be associated with existing radicals or charged groups in the membrane matrix, and it could even be acquired after contact with a polar medium. Different kinds of measurements, such as streaming and membrane potentials or impedance spectroscopy, give direct and quantitative information of interest regarding the electrical/electrochemical characteristics of charged membranes, though they can also provide indirect information regarding the membrane’s structure, as well as on surface and/or bulk phase modifications ,20,21,22mbr) values using the Teorell–Mayer–Sievers (TMS) model  − [t−/│z│]), then for 1:1 electrolytes, U = (t+ − t−) = (2t+ − 1). Electrolyte solution diffusion potentials (∆øodif) could be obtained from Equation (3), eliminating all membrane contribution, that is, ∆øodif = (RT/F)[(to+/│z+│) − (−│)]ln(C1/C2), and for 1:1 electrolytes: ∆øodif = (RT/F)[(1 − 21/C2). Diffusion potential:(ii)Donnan potential :(4)∆ΦDonThe following expressions of diffusion and Donnan potentials are considered to assume no pressure gradient and isothermal conditions, while concentrations instead of activities are used for reasons of simplicity :(i)DiffIn this context, we should indicate the impossibility of measuring, via electrochemical techniques, the individual components of membrane potential; consequently, direct measurements of Donnan potential cannot be performed. However, recently, the use of the tender ambient pressure X-ray photoelectron spectroscopy (AP-XPS) method, which is based on the binding energy shift of the membrane equilibrated with salt solutions, has been proposed for use in measuring Donnan potential . Then, according to the TMS model, the membrane potential can be expressed as follows:On the other hand, different effects  could influence the inclusion/transport of electrolyte solutions for membranes with very narrow pores , and, consequently, Equation (5) has to be modified to include particular factors related to those effects ,42.efNafion−117 = −0.23 M and XefRC–CE = −0.016 M, as well as t+Nafion−117 = 0.90 and t+RC–CE = 0.69. For the alumina membranes, the values obtained are as follows: XefALM−1 = + 0.012 M, XefALM−2 = + 0.008 M and XefALM−3 = + 0.003 M, while t−ALM−1 = 0.74, t−ALM−2 = 0.72 and t−ALM−3 = 0.657; the latter value does not differ significantly (6%) from that determined when only considering diffusion potential  due to the high pore radii of the ALM-3 membrane, even for a sample with a relatively compact structure (10% porosity). Permselectivity values of these membranes were determined by using Equations (1) and (2), and the results are as follows: P(−)ALM−1 = 32.5%, P(−)ALM−2 = 27.3%, P(−)ALM−3 = 11.0%, P(+)Nafion−117 = 83.7% and P(+)RC–CE = 49.6%. The fit of the data points shown in f = 10−2 M), NaCl concentrations levels (Cf = 10−3 M and 10−2 M) or the electrolyte solutions (Cf = 10−2 M NaCl and 10−2 M KCl) for the ALM-2 membrane are presented in the mbr values of the KCl solution at high concentrations, which is dominated by diffusion contribution, is associated with the higher transport number of K+ than Na+. On the other hand, differences in membrane potential values due to the use of concentrations instead of activities are also points of interest, and a comparison between the results obtained for Nafion-117 and ALM-1 membranes is shown in the efNafion−117 (actv)= −0.233 M and t+Nafion−117 (actv) = 0.886 , which have fit errors of 4.6%, while practically negligible differences in both fitted values were determined for the ALM-1 membrane. Other factors, such as concentration level, solution stirring rate or the type of electrolyte solution, might affect membrane potential values, and comparisons between these effects  dependence presents two branches (as membrane RC–CE), an expression considers the following issues: (i) the value of the variable concentration in the maximum of the curve (Cv-ext); and (ii) the slope of branch-curve side corresponding to high concentrations (U), which was proposed for the estimation of the membrane’s fixed charge concentration [ef = 2Cv-extU/(1 − U2)1/2 (for 1:1 electrolytes). This analysis provides the following values of the membrane fixed charge concentration and cation transport number for the RC–CE membrane: XefRC–CE = −0.016 M and t+RC–CE = 0.669, that is, a similar Xef value and only a difference of 4% in the transport number of the cation. It should be indicated that different approximations, which are usually valid for weekly charged membranes, have been proposed for the estimation of Xntration , Xef = 2s/Ds), and different expressions that allow the estimation of values of the membrane fixed charge concentration by analyzing Ps/Ds versus feed concentration  have been proposed [ef│ and Ds were estimated using the Filippov et al. model [ef│ALM−1 = 0.013 M and DsALM−1 = 3.6 × 10−10 m2/s, as well as │Xef│RC–CE = 0.015 M and DsRC–CE = 1.3 × 10−10 m2/s. As can be observed, membrane effective fixed charge values are practically similar to those already determined using the membrane potential results, supporting the reliability of the values previously obtained by analyzing ΔΦmbr values, although no information regarding the nature of the electrical character of membranes (positively/negatively charged) is provided via permeability measurement, while the lower value of the NaCl diffusion coefficient in the membrane (around one order of magnitude) compared to the solution [On the other hand, as was previously mentioned, the presence of charges in the membrane matrix affects the values of other characteristic parameters of membranes, such as the permeation/diffusion coefficients , consisting of a copolymer of styrene and divinylbenzene matrix with quaternary ammonium fixed groups .-+(CH3) or –SO3 radicals to provide them positive/negative characters [2SO4 solution, and they were denominated as Ionic(+)/H2SO4 and Ionic(−)/H2SO4, respectively.Two commercial ion-exchange membranes provided by Ionics Iberica ; one membrane was positively charged (AR204-SZRA-412) and the other membrane was negatively charged (CR67-HMR-402). These membranes were prepared using vinyl monomer and acrylic fiber with –Naracters , and the-25H55N+Cl− or commercially, Aliquat 336) in proportions (in mass) of 70% CTA and 30% AliquatCl [A polymer inclusion membrane (PIM) was obtained using cellulose triacetate (CTA) as base-polymer and the ionic liquid AliquatCl , with external and internal  surfaces being coated with an Al2O3 layer via the atomic layer deposition (ALD) method [2O3 coating reduced pore-radii and porosity without modification of the surface nature .A symmetric nanoporous alumina membrane was obtained via the two-step anodization process  using 0.) method ; Al2O3 c-+) through a proton/cation exchange process, maintaining the Nafion membrane during 24 h in a 60% aqueous solution of the IL (C12H25N(CH3)3Cl or DTACl). This membrane was denominated as Nafion-112/DTA+, and it shows higher chemical stability than the Nafion-112 at temperatures higher than 80 °C [A negatively charged commercial membrane Nafion-112 (in protonated form) from Dupont (USA). This membrane, commonly used for fuel cell application, was modified by incorporation of the ionic liquid cation n-dodecyltrimethylammonium  composed of sugars , glucose, mannose and rhamnose) and acyl groups  —and a Ch-A highly hydrophilic regenerated cellulose membrane  modified via inclusion of silver nanoparticles (RC–CE/AgNPs membrane). The Ag NPs were supplied by Prof. M. López-Romero . The RC–CE/AgNPs membrane was obtained by introducing a piece of the RC–CE membrane into an aqueous solution of Ag nanoparticles for 1 h. Ag NPs are commonly included in the structure of polymeric membranes as a way to increase mechanical stability , for different applications have been selected to show the electrochemical information that can be obtained by analyzing membrane potential values. The chosen membranes are as follows:−) connected to a digital voltmeter  in a dead-end test cell with magnetic stirrers to minimize concentration–polarization at the membrane surfaces (stirring rate: 550 rpm). Most of these measurements were carried out with NaCl solutions (at 25 ± 2 °C and pH = 5.9 ± 0.2) by keeping fixed the concentration of the solution at one side of the membrane (Cf = 0.01 M) and gradually changing the concentration of the solution at the other side (0.002 M ≤ Cv ≤ 0.1 M). Prior to measurements, the membranes were maintained overnight in contact with the lower concentration solution. Membrane potential (ΔΦmbr) values were obtained by subtracting the electrode potential (ΔΦelect = −(RT/Fz)ln(Cv/Cf)) from the cell potential of each pair of Cv/Cf concentrations, that is, ΔΦmbr = ΔE − ΔΦelect. Equilibrium ΔE values were recorded until a constant value was reached, which depends on the membranes characteristics’.The equilibrium electrical potential difference  was measured using Ag/AgCl electrodes (reversible to ion Clmbr − ln(Cv/Cf) values obtained for the positively charged samples; in particular, values corresponding to membranes AMX-Sb, Chitosan and ALM-1/Al2O3 are shown in 2SO4 and 70CTA/30AlqCl membranes. Theoretical values of an ideal positively charged membrane (t− = 1) and the solution (NaCl) diffusion potential (ΔΦdifo) are also indicated in Electrochemical characterization of the studied membranes was performed by analyzing the dependence of the membrane potential on the ratio of solution concentrations at both sides of the membrane. − values, anionic permeability for the different membranes was determined via Equation (1), and their values are indicated in 2SO4 solution. It should be indicated that there is not a direct correlation between the Xef value and that corresponding to the ion-exchange capacity of membranes (determined via titration) usually given by suppliers, which is 1.3–1.4 meq/g dry resin for AMX-Sb and 2.1 meq/g dry resin for Ionics(+) membranes [mbr values include a diffusive process [2O3 membrane, although the effective fixed charge concentration value obtained for the alumina membrane is higher than that obtained for the70CTA/30AlqCl membrane (~15%), and the anion transport number and permselectivity are lower due to solution inclusion in the alumina membrane structure (nanopores).Values of the effective concentration of the fixed charge in the membranes and the anion transport numbers were obtained via fitting to Equation (5) the data points shown in embranes ,52, sinc process ,52). The2O3 membranes, as well as the corresponding Donnan and diffusion potential contributions. These results clearly show the higher Donnan contribution exhibited by the 70CTA/30AlqCl membrane . membrane a at low membrane b ranges membrane b shows t2SO4 solution for one year does not seem to affect the ΔΦmbr–ln(Cv/Cf) linear tendency, which only slightly reduces ΔΦmbr values (~2%); the polymeric EPS membrane also shows high cation-exchange behavior for concentrations lower than 0.08-molarity NaCl, though a clear change in the ΔΦmbr–ln(Cv/Cf) tendency is obtained at higher concentrations , which is attributed to partial neutralization of the membrane’s fixed-charge at high solution concentrations, which would favor the transport of co-ions through the structure of this hydrophilic biopolymer, as was previously shown in + membranes (obtained by XPS and contact angle measurements) might explain differences in ΔΦmbr values observed in + sample (19% reduction in contact angle); in fact, the inclusion of ionic liquids cations in Nafion membranes seems to favor water content, reducing the samples’ hydrophobicity [The dependence of membrane potential values on concentration ratios obtained for the negatively charged membranes is shown in hobicity ,54. 2SO4 membranes are slightly higher than those exhibited by the positively charged samples , which could be associated with changes in the polymeric fabric. Xef, t+ and P(−) values of the Nafion-112 membrane are similar to those obtained for Nafion-117  and, more significantly, slightly varies the ΔΦmbr–ln(Cv/Cf) linear tendency to that exhibited by hydrophilic materials, which is in concordance with the lower contact angle of the modified membrane. In this case, the higher Xef value obtained for Ionics(−) membrane than for the Nafion-112 is in agreement with its higher ion-exchange capacity (2.1 meq/g dry resin [v > 0.2 M NaCl) to mask or shield the higher fixed charge on the surface of EPS membrane, though a low Cv value (0.04 M NaCl) is needed in the case of the RC–CE/AgNPs membrane due to its lower fixed charge, high hydrophilicity and swollen degree; the effect of both charge density and water content on salt sorption for hydrogel-type membranes has already been reported [ef and P(+) values of RC–CE/AgNPs and RC–CE membranes shows increases of 14% and 3.5%, respectively, being associated with AgNPs inclusion. On the other hand, the analysis of diffusion measurements proposed by Filippov et al. [s vs. Cfd) gives the following values of the effective membrane fixed charge and electrolyte diffusion coefficient: │XefEPS│ = 0.18 M and DsEPS = 7.0 × 10−11 m2/s, as well as │XefRC–CE/AgNPs│ = 0.02 M and DsRC–CE/AgNPs = 1.8 × 10−10 m2/s. These results show Xef values in rather good agreement with the two kinds of measurements  and analyses performed, while Ds results indicate a more compact structure for the EPS membrane (lower swelling degree) than for the RC–CE/AgNPs membrane.Results in  samples , though fion-117 , though ry resin  and 0.9 reported . A compav et al.  , as well as the differences obtained based on the solution stirring condition (550 rpm or non-stirred for Cf = 10−2 M NaCl). These results show the total likeness of ΔΦmbr values of those corresponding to an almost ideal anion-exchange AMX membrane for the whole range of concentrations studied (even for Cv = 0.5 M NaCl), doing so independently of fixed concentration values due to the almost negligible contribution of the diffusion potential, though the EPS membrane shows a reduction in ΔΦmbr  with the increase in the concentration of both stirred and non-stirred solution conditions.On the other hand, the influence of external factors (concentration level and solution stirring rate) on membrane potential values was considered for some membranes, and ef) and permselectivity (anionic or cationic permselectivity), can be determined by analyzing membrane potential values using the Teorell–Meyer–Sievers model. This analysis also allows the estimation of both diffusion and Donnan  potential contributions, and since individual measurements of Donnan potential for solution(C1)//membrane//solution(C2) systems cannot be performed, this analysis provides information of great interest regarding charged membranes, as well as surface modification, by comparing original and modified membrane results. Basic characteristics parameters of charged membranes, such as membrane fixed charge concentration (Xef and P(i) significantly depend on membrane material and structure, according to the results obtained regarding the different membranes studied. In particular, ion-exchange membranes (both positively and negatively charged) show very high counter-ion permeselectivity (≥90%), with almost total exclusion of co-ions and, consequently, high Donnan potential contributions for a wide range of NaCl solution concentrations due to the high Xef values, as well as their denser structures and/or hydrophobic characters. However, electrolyte inclusion in the membrane structure associated with concentration gradient increase, with a shielding effect on the charged sites caused by the counter ions, seems to be the main reason for the lower permselectivity and higher diffusion potential contribution exhibited by hydrophilic membranes. Low pore-radii, rather than low porosity, seems to control the transport of ions through the nanoporous alumina membranes analyzed.Both, X