Child wasting and concurrent stunting in low- and middle-income countries.

Sustainable Development Goal 2.2-to end malnutrition by 2030-includes the elimination of child wasting, defined as a weight-for-length z-score that is more than two standard deviations below the median of the World Health Organization standards for child growth1. Prevailing methods to measure wasting rely on cross-sectional surveys that cannot measure onset, recovery and persistence-key features that inform preventive interventions and estimates of disease burden. Here we analyse 21 longitudinal cohorts and show that wasting is a highly dynamic process of onset and recovery, with incidence peaking between birth and 3 months. Many more children experience an episode of wasting at some point during their first 24 months than prevalent cases at a single point in time suggest. For example, at the age of 24 months, 5.6% of children were wasted, but by the same age (24 months), 29.2% of children had experienced at least one wasting episode and 10.0% had experienced two or more episodes. Children who were wasted before the age of 6 months had a faster recovery and shorter episodes than did children who were wasted at older ages; however, early wasting increased the risk of later growth faltering, including concurrent wasting and stunting (low length-for-age z-score), and thus increased the risk of mortality. In diverse populations with high seasonal rainfall, the population average weight-for-length z-score varied substantially (more than 0.5 z in some cohorts), with the lowest mean z-scores occurring during the rainiest months; this indicates that seasonally targeted interventions could be considered. Our results show the importance of establishing interventions to prevent wasting from birth to the age of 6 months, probably through improved maternal nutrition, to complement current programmes that focus on children aged 6-59 months.

Early-childhood linear growth faltering in low- and middle-income countries.

Globally, 149 million children under 5 years of age are estimated to be stunted (length more than 2 standard deviations below international growth standards)1,2. Stunting, a form of linear growth faltering, increases the risk of illness, impaired cognitive development and mortality. Global stunting estimates rely on cross-sectional surveys, which cannot provide direct information about the timing of onset or persistence of growth faltering-a key consideration for defining critical windows to deliver preventive interventions. Here we completed a pooled analysis of longitudinal studies in low- and middle-income countries (n = 32 cohorts, 52,640 children, ages 0-24 months), allowing us to identify the typical age of onset of linear growth faltering and to investigate recurrent faltering in early life. The highest incidence of stunting onset occurred from birth to the age of 3 months, with substantially higher stunting at birth in South Asia. From 0 to 15 months, stunting reversal was rare; children who reversed their stunting status frequently relapsed, and relapse rates were substantially higher among children born stunted. Early onset and low reversal rates suggest that improving children's linear growth will require life course interventions for women of childbearing age and a greater emphasis on interventions for children under 6 months of age.

Causes and consequences of child growth faltering in low-resource settings.

Growth faltering in children (low length for age or low weight for length) during the first 1,000 days of life (from conception to 2 years of age) influences short-term and long-term health and survival1,2. Interventions such as nutritional supplementation during pregnancy and the postnatal period could help prevent growth faltering, but programmatic action has been insufficient to eliminate the high burden of stunting and wasting in low- and middle-income countries. Identification of age windows and population subgroups on which to focus will benefit future preventive efforts. Here we use a population intervention effects analysis of 33 longitudinal cohorts (83,671 children, 662,763 measurements) and 30 separate exposures to show that improving maternal anthropometry and child condition at birth accounted for population increases in length-for-age z-scores of up to 0.40 and weight-for-length z-scores of up to 0.15 by 24 months of age. Boys had consistently higher risk of all forms of growth faltering than girls. Early postnatal growth faltering predisposed children to subsequent and persistent growth faltering. Children with multiple growth deficits exhibited higher mortality rates from birth to 2 years of age than children without growth deficits (hazard ratios 1.9 to 8.7). The importance of prenatal causes and severe consequences for children who experienced early growth faltering support a focus on pre-conception and pregnancy as a key opportunity for new preventive interventions.

Adaptive sequential surveillance with network and temporal dependence.

Strategic test allocation is important for control of both emerging and existing pandemics (eg, COVID-19, HIV). It supports effective epidemic control by (1) reducing transmission via identifying cases and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest (positive infection status) is often a latent variable. In addition, presence of both network and temporal dependence reduces data to a single observation. In this work, we study an adaptive sequential design, which allows for unspecified dependence among individuals and across time. Our causal parameter is the mean latent outcome we would have obtained, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. The key strength of the method is that we do not have to model network and time dependence: a short-term performance Online Super Learner is used to select among dependence models and randomization schemes. The proposed strategy learns the optimal choice of testing over time while adapting to the current state of the outbreak and learning across samples, through time, or both. We demonstrate the superior performance of the proposed strategy in an agent-based simulation modeling a residential university environment during the COVID-19 pandemic.

Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross-validation (cross-validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross-validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross-validation helps in avoiding unintentional over-fitting of nuisance parameter functionals and leads to more robust inferences.

Similarities in Maternal Weight and Birth Weight Across Pregnancies and Across Sisters.

Objectives The current study examined how prepregnancy body mass index (BMI), gestational weight gain, and birth weight cluster between births within women and between women who are sisters. Methods Using data from the National Longitudinal Survey of Youth 1979 cohort, we utilized nested, multivariable hierarchical linear models to examine the correlation of these three outcomes between births (n = 6006) to women (n = 3605) and sisters (n = 3170) so that we can quantify the clustering by sibship and by woman for these three pregnancy-related outcomes. Results After controlling for confounding covariates, prepregnancy BMI (intraclass correlation (ICC) 0.24, 95% CI 0.16, 0.32), gestational weight gain (ICC 0.23, 95% CI 0.16, 0.31), and infant's birthweight (ICC 0.07, 95% CI 0.003, 0.13) were correlated between sisters. Additionally, all three outcomes were significantly correlated between births for each sister, suggesting that prepregnancy BMI (ICC 0.82, 95% CI 0.81, 0.83), gestational weight gain (ICC 0.45, 95% CI 0.42, 0.49), and birth weight (ICC 0.31, 95% CI 0.28, 0.35) track between pregnancies in the same woman. Conclusions for Practice The observed clustering both within women and between sisters suggests that shared genetic and environmental factors among sisters play a role in pregnancy outcomes above and beyond that of women's own genetic and environmental factors. Findings suggest that asking a woman about her sisters' pregnancy outcomes could provide insight into the possible outcomes for her current pregnancy. Future research should test if collecting such a family history and providing tailored clinical recommendations accordingly would be useful.

Excessive Gestational Weight Gain and Subsequent Maternal Obesity at Age 40: A Hypothetical Intervention.

OBJECTIVES: To model the hypothetical impact of preventing excessive gestational weight gain on midlife obesity and compare the estimated reduction with the US Healthy People 2020 goal of a 10% reduction of obesity prevalence in adults. METHODS: We analyzed 3917 women with 1 to 3 pregnancies in the prospective US National Longitudinal Survey of Youth, from 1979 to 2012. We compared the estimated obesity prevalence between 2 scenarios: gestational weight gain as reported and under the scenario of a hypothetical intervention that all women with excessive gestational weight gain instead gained as recommended by the Institute of Medicine (2009). RESULTS: A hypothetical intervention was associated with a significantly reduced estimated prevalence of obesity for first (3.3 percentage points; 95% confidence interval [CI] = 1.0, 5.6) and second (3.0 percentage points; 95% CI = 0.7, 5.2) births, and twice as high in Black as in White mothers, but not significant in Hispanics. The population attributable fraction was 10.7% (95% CI = 3.3%, 18.1%) in first and 9.3% (95% CI = 2.2%, 16.5%) in second births. CONCLUSIONS: Development of effective weight-management interventions for childbearing women could lead to meaningful reductions in long-term obesity.

Measuring and Increasing Adoption Rates of Cookstoves in a Humanitarian Crisis.

Traditional smoky cooking fires are one of today's greatest environmental threats to human life. These fires, used by 40% of the global population, cause 3.9 million annual premature deaths. "Clean cookstoves" have potential to improve this situation; however, most cookstove programs do not employ objective measurement of adoption to inform design, marketing, subsidies, finance, or dissemination practices. Lack of data prevents insights and may contribute to consistently low adoption rates. In this study, we used sensors and surveys to measure objective versus self-reported adoption of freely-distributed cookstoves in an internally displaced persons camp in Darfur, Sudan. Our data insights demonstrate how to effectively measure and promote adoption, especially in a humanitarian crisis. With sensors, we measured that 71% of participants were cookstove "users" compared to 95% of respondents reporting the improved cookstove was their "primary cookstove." No line of survey questioning, whether direct or indirect, predicted sensor-measured usage. For participants who rarely or never used their cookstoves after initial dissemination ("non-users"), we found significant increases in adoption after a simple followup survey (p = 0.001). The followup converted 83% of prior "non-users" to "users" with average daily adoption of 1.7 cooking hours over 2.2 meals. This increased adoption, which we posit resulted from cookstove familiarization and social conformity, was sustained for a 2-week observation period post intervention.

Pregnancy and post-delivery maternal weight changes and overweight in preschool children.

OBJECTIVES: High maternal weight before and during pregnancy contributes to child obesity. To assess the additional role of weight change after delivery, we examined associations between pre- and post-pregnancy weight changes and preschooler overweight. SAMPLE: 4359 children from the Children and Young Adults of the 1979 National Longitudinal Survey of Youth (NLSY) born to 2816 NLSY mothers between 1979 and 2006 and followed to age 4-5years old. EXPOSURES: gestational weight gain (GWG) and post-delivery maternal weight change (PDWC). OUTCOME: child overweight (body mass index (BMI) ≥85th percentile). RESULTS: Adjusted models suggested that both increased GWG (OR: 1.08 per 5kg GWG, 95% CI: 1.01, 1.16) and excessive GWG (OR: 1.29 versus adequate GWG, 95% CI: 1.06, 1.56) were associated with preschooler overweight. Maternal weight change after delivery was also independently associated with child overweight (OR: 1.12 per 5kg PDWC, 95% CI: 1.04, 1.21). Associations were stronger among children with overweight or obese mothers. CONCLUSIONS: Increased maternal weight gain both during and after pregnancy predicted overweight in preschool children. Our results suggest that healthy post-pregnancy weight may join normal pre-pregnancy BMI and adequate GWG as a potentially modifiable risk factor for child overweight.

Treatment Heterogeneity of Water, Sanitation, Hygiene, and Nutrition Interventions on Child Growth by Environmental Enteric Dysfunction and Pathogen Status for Young Children in Bangladesh.

Background: Water, sanitation, hygiene (WSH), nutrition (N), and combined (N+WSH) interventions are often implemented by global health organizations, but WSH interventions may insufficiently reduce pathogen exposure, and nutrition interventions may be modified by environmental enteric dysfunction (EED), a condition of increased intestinal permeability and inflammation. This study investigated the heterogeneity of these treatments' effects based on individual pathogen and EED biomarker status with respect to child linear growth. Methods: We applied cross-validated targeted maximum likelihood estimation and super learner ensemble machine learning to assess the conditional treatment effects in subgroups defined by biomarker and pathogen status. We analyzed treatment (N+WSH, WSH, N, or control) randomly assigned in-utero, child pathogen and EED data at 14 months of age, and child LAZ at 28 months of age. We estimated the difference in mean child length for age Z-score (LAZ) under the treatment rule and the difference in stratified treatment effect (treatment effect difference) comparing children with high versus low pathogen/biomarker status while controlling for baseline covariates. Results: We analyzed data from 1,522 children, who had median LAZ of -1.56. We found that myeloperoxidase (N+WSH treatment effect difference 0.0007 LAZ, WSH treatment effect difference 0.1032 LAZ, N treatment effect difference 0.0037 LAZ) and Campylobacter infection (N+WSH treatment effect difference 0.0011 LAZ, WSH difference 0.0119 LAZ, N difference 0.0255 LAZ) were associated with greater effect of all interventions on growth. In other words, children with high myeloperoxidase or Campylobacter infection experienced a greater impact of the interventions on growth. We found that a treatment rule that assigned the N+WSH (LAZ difference 0.23, 95% CI (0.05, 0.41)) and WSH (LAZ difference 0.17, 95% CI (0.04, 0.30)) interventions based on EED biomarkers and pathogens increased predicted child growth compared to the randomly allocated intervention. Conclusions: These findings indicate that EED biomarker and pathogen status, particularly Campylobacter and myeloperoxidase (a measure of gut inflammation), may be related to impact of N+WSH, WSH, and N interventions on child linear growth.

The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions.

The optimal dynamic treatment rule (ODTR) framework offers an approach for understanding which kinds of patients respond best to specific treatments - in other words, treatment effect heterogeneity. Recently, there has been a proliferation of methods for estimating the ODTR. One such method is an extension of the SuperLearner algorithm - an ensemble method to optimally combine candidate algorithms extensively used in prediction problems - to ODTRs. Following the ``causal roadmap," we causally and statistically define the ODTR and provide an introduction to estimating it using the ODTR SuperLearner. Additionally, we highlight practical choices when implementing the algorithm, including choice of candidate algorithms, metalearners to combine the candidates, and risk functions to select the best combination of algorithms. Using simulations, we illustrate how estimating the ODTR using this SuperLearner approach can uncover treatment effect heterogeneity more effectively than traditional approaches based on fitting a parametric regression of the outcome on the treatment, covariates and treatment-covariate interactions. We investigate the implications of choices in implementing an ODTR SuperLearner at various sample sizes. Our results show the advantages of: (1) including a combination of both flexible machine learning algorithms and simple parametric estimators in the library of candidate algorithms; (2) using an ensemble metalearner to combine candidates rather than selecting only the best-performing candidate; (3) using the mean outcome under the rule as a risk function. Finally, we apply the ODTR SuperLearner to the ``Interventions" study, an ongoing randomized controlled trial, to identify which justice-involved adults with mental illness benefit most from cognitive behavioral therapy to reduce criminal re-offending.

Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action.

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 km2. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June-September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.

A method to quantify illicit intake of drugs from urine: methamphetamine.

Qualitative urinalysis can verify abstinence of drug misuse but cannot detect changes in drug intake. For drugs with slow elimination, such as methamphetamine (MA), a single episode of abuse can result in up to 5 days of positive urine drug screens. Thus, interventions that produce substantial decreases in drug use but do not achieve almost complete abstinence are classified as ineffective. Using nonpharmacologic doses of deuterium-labeled l-methamphetamine (l-MA-d(3)) we have developed a simple, robust method that reliably estimates changes in MA intake. Twelve subjects were dosed with 5 mg of l-MA-d(3) daily and challenged with 15, 30, and 45 mg of nonlabeled d-MA (d-MA-d(0)) after reaching plasma steady status of l-MA-d(3). Urinary concentration ratios of d-MA-d(0) to l-MA-d(3) provided clear separation of the administered doses with as little as 15-mg dose increments. Administered doses could not be resolved using d-MA-d(0) concentrations alone. In conclusion, the urinary [d-MA-d(0)]:[l-MA-d(3)] provides a quantitative, continuous measure of illicit MA exposure. The method reliably detects small, clinically relevant changes in illicit MA intake from random urine specimens, is amenable to deployment in clinical trials, and can be used to quantify patterns of MA abuse.

A contribution to drought resilience in East Africa through groundwater pump monitoring informed by in-situ instrumentation, remote sensing and ensemble machine learning.

The prevalence of drought in the Horn of Africa has continued to threaten access to safe and affordable water for millions of people. In order to improve monitoring of water pump functionality, telemetry-connected sensors have been installed on 480 electrical groundwater pumps in arid regions of Kenya and Ethiopia, designed to improve monitoring and support operation and maintenance of these water supplies. In this paper, we describe the development and validation of two classification systems designed to identify the functionality and non-functionality of these electrical pumps, one an expert-informed conditional classifier and the other leveraging machine learning. Given a known relationship between surface water availability and groundwater pump use, the classifiers combine in-situ sensor data with remote sensing indicators for rainfall and surface water. Our validation indicates a overall pump status sensitivity (true positive rate) of 82% for the expert classifier and 84% for the machine learner. When the pump is being used, both classifiers have a 100% true positive rate performance. When a pump is not being used, the specificity (true negative rate) is about 50% for the expert classifier and over 65% for the machine learner. If these detection capabilities were integrated into a repair service, the typical uptime of pumps during drought periods in this region could potentially, if budget resources and institutional incentives for pump repairs were provided, result in a drought-period uptime improvement from 60% to nearly of 85% - a 40% reduction in the relative risk of pump downtime.

Dynamic impact of transfusion ratios on outcomes in severely injured patients: Targeted machine learning analysis of the Pragmatic, Randomized Optimal Platelet and Plasma Ratios randomized clinical trial.

BACKGROUND: Massive transfusion protocols to treat postinjury hemorrhage are based on predefined blood product transfusion ratios followed by goal-directed transfusion based on patient's clinical evolution. However, it remains unclear how these transfusion ratios impact patient outcomes over time from injury. METHODS: The Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) is a phase 3, randomized controlled trial, across 12 Level I trauma centers in North America. From 2012 to 2013, 680 severely injured patients required massive transfusion. We used semiparametric machine learning techniques and causal inference methods to augment the intent-to-treat analysis of PROPPR, estimating the dynamic relationship between transfusion ratios and outcomes: mortality and hemostasis at different timepoints during the first 24 hours after admission. RESULTS: In the intention-to-treat analysis, the 1:1:1 group tended to have decreased mortality, but with no statistical significance. For patients in whom hemostasis took longer than 2 hours, the 1:1:1 ratio was associated with a higher probability of hemostasis, statistically significant from the 4 hour on. In the per-protocol, actual-transfusion-ratios-received analysis, during four successive time intervals, no significant association was found between the actual ratios and mortality. When comparing patient groups who received both high plasma/PRBC and high platelet/PRBC ratios to the group of low ratios in both, the relative risk of achieving hemostasis was 2.49 (95% confidence interval, 1.19-5.22) during the third hour after admission, suggesting a significant beneficial impact of higher transfusion ratios of plasma and platelets on hemostasis. CONCLUSION: Our results suggest that the impact of transfusion ratios on hemostasis is dynamic. Overall, the transfusion ratios had no significant impact on mortality over time. However, receiving higher ratios of platelets and plasma relative to red blood cells hastens hemostasis in subjects who have yet to achieve hemostasis within 3 hours after hospital admission. LEVEL OF EVIDENCE: Therapeutic IV.

Effects of the Psychedelic Amphetamine MDA (3,4-Methylenedioxyamphetamine) in Healthy Volunteers.

Entactogens such as 3,4-Methylenedioxymethamphetamine (MDMA, "molly", "ecstasy") appear to have unusual, potentially therapeutic, emotional effects. Understanding their mechanisms can benefit from clinical experiments with related drugs. Yet the first known drug with such properties, 3,4-Methylenedioxyamphetamine (MDA), remains poorly studied and its pharmacokinetics in humans are unknown. We conducted a within-subjects, double-blind, placebo-controlled study of 1.4 mg/kg oral racemic MDA and compared results to those from our prior similar studies with 1.5 mg/kg oral racemic MDMA. MDA was well-tolerated by participants. MDA induced robust increases in heart rate and blood pressure and increased cortisol and prolactin to a similar degree as MDMA. MDA self-report effects shared features with MDMA as well as with classical psychedelics. MDA self-report effects lasted longer than those of MDMA, with MDA effects remaining elevated at 8 h while MDMA effects resolved by 6 h. Cmax and AUC0-∞ for MDA were 229 ± 39 (mean ± SD) and 3636 ± 958 µg/L for MDA and 92 ± 61 and 1544 ± 741 µg/L for the metabolite 4-hydroxy-3-methoxyamphetamine (HMA). There was considerable between-subject variation in MDA/HMA ratios. The similarity of MDA and MDMA pharmacokinetics suggests that the greater duration of MDA effects is due to pharmacodynamics rather than pharmacokinetics.

Ensemble machine learning and forecasting can achieve 99% uptime for rural handpumps.

Broken water pumps continue to impede efforts to deliver clean and economically-viable water to the global poor. The literature has demonstrated that customers' health benefits and willingness to pay for clean water are best realized when clean water infrastructure performs extremely well (>99% uptime). In this paper, we used sensor data from 42 Afridev-brand handpumps observed for 14 months in western Kenya to demonstrate how sensors and supervised ensemble machine learning could be used to increase total fleet uptime from a best-practices baseline of about 70% to >99%. We accomplish this increase in uptime by forecasting pump failures and identifying existing failures very quickly. Comparing the costs of operating the pump per functional year over a lifetime of 10 years, we estimate that implementing this algorithm would save 7% on the levelized cost of water relative to a sensor-less scheduled maintenance program. Combined with a rigorous system for dispatching maintenance personnel, implementing this algorithm in a real-world program could significantly improve health outcomes and customers' willingness to pay for water services.

Development and Testing of a Smartphone-Based Cognitive/Neuropsychological Evaluation System for Substance Abusers.

INTRODUCTION: In methamphetamine (MA) users, drug-induced neurocognitive deficits may help to determine treatment, monitor adherence, and predict relapse. To measure these relationships, we developed an iPhone app (Neurophone) to compare lab and field performance of N-Back, Stop Signal, and Stroop tasks that are sensitive to MA-induced deficits. METHODS: Twenty healthy controls and 16 MA-dependent participants performed the tasks in-lab using a validated computerized platform and the Neurophone before taking the latter home and performing the tasks twice daily for two weeks. RESULTS: N-Back task: there were no clear differences in performance between computer-based vs. phone-based in-lab tests and phone-based in-lab vs. phone-based in-field tests. Stop-Signal task: difference in parameters prevented comparison of computer-based and phone-based versions. There was significant difference in phone performance between field and lab. Stroop task: response time measured by the speech recognition engine lacked precision to yield quantifiable results. There was no learning effect over time. On an average, each participant completed 84.3% of the in-field NBack tasks and 90.4% of the in-field Stop Signal tasks (MA-dependent participants: 74.8% and 84.3%; healthy controls: 91.4% and 95.0%, respectively). Participants rated Neurophone easy to use. CONCLUSION: Cognitive tasks performed in-field using Neurophone have the potential to yield results comparable to those obtained in a laboratory setting. Tasks need to be modified for use as the app's voice recognition system is not yet adequate for timed tests.