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Anxiety and depression are common in adolescents, but adolescents are often uninterested in formal mental health treatments or are unable to access them. Digital interventions can be delivered at scale to bridge critical gaps in mental health care but must address the needs and preferences of adolescents. This study aims to conduct qualitative research involving adolescents aged 18 years to inform both the design of digital mental health interventions for adolescents broadly and new features and refinements to incorporate in an automated SMS text messaging intervention, Small Steps SMS, that was originally designed for young adults. We recruited non-treatment-engaged older adolescents who were aged 18 years, lived in the United States, and had experienced depression or anxiety. In total, 12 participants were recruited through social media advertising and online self-screeners hosted by Mental Health America, a mental health advocacy organization. For 24 days, participants answered researcher prompts and engaged with one another in an asynchronous online discussion group, with a new discussion prompt released every 3 days. In parallel, partway through the discussion group, participants received interactive messages from Small Steps SMS, an automated SMS text messaging intervention that delivers daily dialogues supporting mental health self-management. Questions in the discussion group pertained to mental health challenges, help-seeking attitudes, perceptions of Small Steps SMS, and ways the program and other digital mental health interventions could meet the needs of older adolescents. A subset of participants (n=4, 33%) also completed interviews to elaborate on their responses. Thematic analysis was applied to transcripts of the discussion group and interviews to characterize user needs and design priorities when making Small Steps SMS and similar interventions available to adolescents. Participants reported factors that contributed to their experience of mental health symptoms, including the transition from adolescence to adulthood, fears that the world is unstable and their futures are uncertain, and ineffective use of social media to cope with symptoms. Participants were proud of their generation's mental health acceptance but also observed a generational divide in mental health stigma and literacy that could impede seeking help from parents and other adults. Participants appreciated that Small Steps SMS allowed them to pursue mental health self-management conveniently and independently. They suggested that the program and similar interventions address adolescent-specific challenges and facilitate intergenerational communication about mental health. They also recommended possible ways to increase engagement through peer-to-peer communication, gamification, and greater explanation of self-management strategies. Major life transitions affected adolescent participants' mental health needs and preferences for digital mental health tools. While interactive automated messaging programs have the potential to support self-management in this population, program content and features should be adapted to adolescents' needs.

Young adults in the United States are experiencing accelerating rates of suicidal thoughts and behaviors but have the lowest rates of formal mental health care. Digital suicide prevention interventions have the potential to increase access to suicide prevention care by circumventing attitudinal and structural barriers that prevent access to formal mental health care. These tools should be designed in collaboration with young adults who have lived experience of suicide-related thoughts and behaviors to optimize acceptability and use. This study aims to identify the needs, preferences, and features for an automated SMS text messaging-based safety planning service to support the self-management of suicide-related thoughts and behaviors among young adults. We enrolled 30 young adults (age 18-24 years) with recent suicide-related thoughts and behaviors to participate in asynchronous remote focus groups via an online private forum. Participants responded to researcher-posted prompts and were encouraged to reply to fellow participants-creating a threaded digital conversation. Researcher-posted prompts centered on participants' experiences with suicide-related thought and behavior-related coping, safety planning, and technologies for suicide-related thought and behavior self-management. Focus group transcripts were analyzed using thematic analysis to extract key needs, preferences, and feature considerations for an automated SMS text messaging-based safety planning tool. Young adult participants indicated that an automated digital SMS text message-based safety planning intervention must meet their needs in 2 ways. First, by empowering them to manage their symptoms on their own and support acquiring and using effective coping skills. Second, by leveraging young adults' existing social connections. Young adult participants also shared 3 key technological needs of an automated intervention: (1) transparency about how the intervention functions, the kinds of actions it does and does not take, the limits of confidentiality, and the role of human oversight within the program; (2) strong privacy practices-data security around how content within the intervention and how private data created by the intervention would be maintained and used was extremely important to young adult participants given the sensitive nature of suicide-related data; and (3) usability, convenience, and accessibility were particularly important to participants-this includes having an approachable and engaging message tone, customizable message delivery options (eg, length, number, content focus), and straightforward menu navigation. Young adult participants also highlighted specific features that could support core coping skill acquisition (eg, self-tracking, coping skill idea generation, reminders). Engaging young adults in the design process of a digital suicide prevention tool revealed critical considerations that must be addressed if the tool is to effectively expand access to evidence-based care to reach young people at risk for suicide-related thoughts and behaviors. Specifically, automated digital safety planning interventions must support building skillfulness to cope effectively with suicidal crises, deepening interpersonal connections, system transparency, and data privacy.

Testimonials from credible sources are an evidence-based strategy for behavior change. Behavioral health interventions have used testimonials to promote health behaviors (eg, physical activity and healthy eating). Integrating testimonials into eating disorder (ED) interventions poses a nuanced challenge because ED testimonials can promote ED behaviors. Testimonials in ED interventions must therefore be designed carefully. Some optimal design elements of testimonials are known, but questions remain about testimonial speakers, messaging, and delivery, especially for ED interventions. We sought to learn how to design and deliver testimonials focused on positive behavior change strategies within our multisession digital binge eating intervention. We applied human-centered design methods to learn users' preferences for testimonial speakers, messaging, and delivery (modalities, over time, and as \"nudges\" for selecting positive behavior change strategies they could practice). We recruited target users of our multisession intervention to complete design sessions. Adults (N=22, 64% self-identified as female; 32% as non-Hispanic Black, 41% as non-Hispanic White, and 27% as Hispanic) with recurrent binge eating and obesity completed individual interviews. Data were analyzed using methods from thematic analysis. Most participants preferred designs with testimonials (vs without) for their motivation and validation of the intervention's efficacy. A few distrusted testimonials for appearing too \"commercial\" or personally irrelevant. For speakers, participants preferred sociodemographically tailored testimonials and were willing to report personal data in the intervention to facilitate tailoring. For messaging, some preferred testimonials with \"how-to\" advice, whereas others preferred \"big picture\" success stories. For delivery interface, participants were interested in text, video, and multimedia testimonials. For delivery over time, participants preferred testimonials from new speakers to promote engagement. When the intervention allowed users to choose between actions (eg, behavioral strategies), participants preferred testimonials to be available across all actions but said that selectively delivering a testimonial with one action could \"nudge\" them to select it. Results indicated that intervention users were interested in testimonials. While participants preferred sociodemographically tailored testimonials, they said different characteristics mattered to them, indicating that interventions should assess users' most pertinent identities and tailor testimonials accordingly. Likewise, users' divided preferences for testimonial messaging (ie, \"big picture\" vs \"how-to\") suggest that optimal messaging may differ by user. To improve the credibility of testimonials, which some participants distrusted, interventions could invite current users to submit testimonials for future integration in the intervention. Aligned with nudge theory, our findings indicate testimonials could be used as \"nudges\" within interventions-a ripe area for further inquiry-though future work should test if delivering a testimonial only with the nudged choice improves its uptake. Further research is needed to validate these design ideas in practice, including evaluating their impact on behavior change toward improving ED behaviors.

Abstract Background Digital interventions can leverage user data to predict their health behavior, which can improve users’ ability to make behavioral changes. Presenting predictions (e.g. how much a user might improve on an outcome) can be nuanced considering their uncertainty. Incorporating predictions raises design-related questions, such as how to present prediction data in a concise and actionable manner. Purpose We conducted co-design sessions with end-users of a digital binge-eating intervention to learn how users would engage with prediction data and inform how to present these data visually. We additionally sought to understand how prediction intervals would help users understand uncertainty in these predictions and how users would perceive their actual progress relative to their prediction. Methods We conducted interviews with 22 adults with recurrent binge eating and obesity. We showed prototypes of hypothetical prediction displays for 5 evidence-based behavior change strategies, with the predicted success of each strategy for reducing binge eating in the week ahead (e.g. selecting to work on self-image this week might lead to 4 fewer binges while mood might lead to 1 fewer). We used thematic analysis to analyze data and generate themes. Results Users welcomed using prediction data, but wanted to maintain their autonomy and minimize negative feelings if they do not achieve their predictions. Although preferences varied, users generally preferred designs that were simple and helped them quickly compare prediction data across strategies. Conclusions Predictions should be presented in efficient, organized layouts and with encouragement. Future studies should empirically validate findings in practice. Clinical Trial information The Clinical Trials Registration #: NCT06349460. Individuals with binge eating and obesity are interested in using predictions from an app to help them lower their binge eating but want these predictions to be encouraging and presented in a way they can understand easily. Lay Summary Digital interventions, like health apps, are promising tools to help people improve their health because they can use data from previous users to make predictions about what strategy someone should try that would help them the most. Showing users these predictions could improve their decision-making and make the app more engaging and useful. However, like any prediction—these could be wrong. Therefore, showing predictions to users can be confusing, decrease their trust in the app, and make them feel bad if they do not meet their predicted level of success. It is also unclear the best way to present the predictions to users in a way that makes sense to most people. We interviewed users who would be interested in using an app to lower their binge eating and manage their weight about using predictions to help them. We showed them how we could present predictions to them and asked for their feedback. We learned that most users were interested in using predictions to help them pick the best strategy to work on. Some were worried that they would feel discouraged and upset if they did not meet their prediction. Users wanted predictions presented in a simple and easy-to-understand formats.

Insects are diverse and sustain essential ecosystem functions, yet remain understudied. Recent reports about declines in insect abundance and diversity have highlighted a pressing need for comprehensive large-scale monitoring. Metabarcoding (high-throughput bulk sequencing of marker gene amplicons) offers a cost-effective and relatively fast method for characterizing insect community samples. However, the methodology applied varies greatly among studies, thus complicating the design of large-scale and repeatable monitoring schemes. Here we describe a non-destructive metabarcoding protocol that is optimized for high-throughput processing of Malaise trap samples and other bulk insect samples. The protocol details the process from obtaining bulk samples up to submitting libraries for sequencing. It is divided into four sections: 1) Laboratory workspace preparation; 2) Sample processing—decanting ethanol, measuring the wet-weight biomass and the concentration of the preservative ethanol, performing non-destructive lysis and preserving the insect material for future work; 3) DNA extraction and purification; and 4) Library preparation and sequencing. The protocol relies on readily available reagents and materials. For steps that require expensive infrastructure, such as the DNA purification robots, we suggest alternative low-cost solutions. The use of this protocol yields a comprehensive assessment of the number of species present in a given sample, their relative read abundances and the overall insect biomass. To date, we have successfully applied the protocol to more than 7000 Malaise trap samples obtained from Sweden and Madagascar. We demonstrate the data yield from the protocol using a small subset of these samples.

The direct infusion of bitter solutions in the gastrointestinal tract can reduce the secretion of orexigenic hormones and influence appetite and food intake. We aimed to explore whether oral ingestion of the bitter tastant hydroxychloroquine sulfate can exert similar effects. Ten lean adult women were included in this double-blind, randomized, two-visit, crossover study. After an overnight fast, each volunteer received film-coated tablets containing 400 mg of hydroxychloroquine sulfate (Plaquenil®) or placebo. Plasma-ghrelin, -motilin, -insulin and blood-glucose concentrations were determined every 10 min before and 30 min after feeding; appetite was scored every 10 min. Hunger scores were investigated with a special interest 50–60 min after the ingestion of hydroxychloroquine sulfate, right before a rewarding chocolate milkshake was offered to drink ad libitum. Compared with the placebo, hydroxychloroquine sulfate tended to reduce hunger at the time of interest (p = 0.10). No effect was found upon subsequent milkshake intake. Motilin plasma concentrations were unaltered, but acyl-ghrelin plasma concentrations decreased after the ingestion of hydroxychloroquine sulfate (t = 40–50; p < 0.05). These data suggest that the oral intake of hydroxychloroquine sulfate tablets reduces subjective hunger via a ghrelin-dependent mechanism but does not affect motilin release, hedonic food intake or insulin levels in healthy women.

Insects are diverse and sustain essential ecosystem functions, yet remain understudied. Recent reports about declines in insect abundance and diversity have highlighted a pressing need for comprehensive large-scale monitoring. Metabarcoding (high-throughput bulk sequencing of marker gene amplicons) offers a cost-effective and relatively fast method for characterizing insect community samples. However, the methodology applied varies greatly among studies, thus complicating the design of large-scale and repeatable monitoring schemes. Here we describe a non-destructive metabarcoding protocol that is optimized for high-throughput processing of Malaise trap samples and other bulk insect samples. The protocol details the process from obtaining bulk samples up to submitting libraries for sequencing. It is divided into four sections: 1) Laboratory workspace preparation; 2) Sample processing-decanting ethanol, measuring the wet-weight biomass and the concentration of the preservative ethanol, performing non-destructive lysis and preserving the insect material for future work; 3) DNA extraction and purification; and 4) Library preparation and sequencing. The protocol relies on readily available reagents and materials. For steps that require expensive infrastructure, such as the DNA purification robots, we suggest alternative low-cost solutions. The use of this protocol yields a comprehensive assessment of the number of species present in a given sample, their relative read abundances and the overall insect biomass. To date, we have successfully applied the protocol to more than 7000 Malaise trap samples obtained from Sweden and Madagascar. We demonstrate the data yield from the protocol using a small subset of these samples.

Rangelands are extensive ecosystems, providing important ecosystem services while undergoing continuous change. As a result, improved monitoring technologies can help better characterize vegetation change. Satellite remote sensing has proven effective in this regard, tracking vegetation dynamics at broad and fine scales. We leveraged the spatial, spectral, and temporal resolution of Sentinel-2 satellites to estimate fractional cover and canopy gap across rangelands of the western United States. We produced annual, 10 m spatial resolution estimates of fractional cover and canopy gap size class for years 2018 to 2024. Fractional cover estimates include that of common plant functional types (annual forb and grass, bareground, littler, perennial forb and grass, shrub, tree) and select genera (including invasive annual grass species, pinyon-juniper species, and sagebrush species); canopy gap size classes include gap sizes 25 to 50, 51 to 100, 101 to 200, and greater than 200 cm. We make these data available as Cloud Optimized GeoTIFFs, organized as 75 × 75 km tiles covering the 17 western states of the United States.

DNA metabarcoding-high-throughput sequencing of barcode regions from bulk samples-has become a key tool for insect biodiversity assessment. Yet, how methodological choices affect the accuracy of metabarcoding data remains insufficiently explored. In this paper, we ask: (1) How does the lysis method (non-destructive lysis vs. destructive homogenization) affect community recovery? (2) How comprehensively does metabarcoding capture species richness? (3) To what extent can spike-ins improve abundance estimates? (4) How accurately can species abundances be estimated?We evaluated the accuracy of insect metabarcoding using 4,749 bulk samples from a large-scale biodiversity survey subjected to mild lysis. Of these samples, 856 were also homogenized, allowing a systematic comparison of the effect of alternative treatments. To potentially improve abundance estimates, we added six biological spike-ins (i.e., foreign insects) to all samples, and two synthetic spike-ins (artificial DNA fragments) to the homogenization treatment. In addition, we established the contents of 15 samples by individually barcoding all specimens, enabling direct assessment of occurrence and abundance estimates.Our results revealed consistent differences between destructive and non-destructive treatments. While both methods reliably detected the majority of species, small and soft-bodied taxa were more often recovered after mild lysis than after homogenization, while the reverse was true for heavily sclerotized, hairy, and large taxa. Using biological spike-ins for calibration reduced the variance in read numbers per specimen considerably, especially in homogenized samples, while synthetic spike-ins were less effective. In a Bayesian analysis, where species data were matched to the best-fitting spike-in calibration curve, accurate abundance estimates (+/-1 individual) were obtained for 72.9% of species occurrences.Our results show that it is possible to obtain reasonably accurate abundance estimates from metabarcoding data, and that mild lysis and homogenization result in different taxon-specific biases in terms of occurrence data, with neither method outperforming the other. Accuracy is improved by homogenization rather than mild lysis of samples, and by the use of biological rather than synthetic spike-ins. Together, these findings provide a major step towards robust, quantitative biodiversity monitoring using DNA-metabarcoding.