Category: 3. Business

Introduction

Physiological Feedback for Alcohol Use in Young Adults

Wearable fitness technologies (eg, smartwatches and smart rings) are increasingly popular among young adults but may be missing crucial behavioral health data to guide behavior change. Over half (52%) of young adult consumers in the United States own commercial wearables and use them for fitness, stress or weight management, sleep, and other wellness goals []. Their appeal lies in their ability to reliably measure physiological signals (eg, heart rate, blood oxygen, and skin temperature) in a noninvasive, accessible way via biometric sensors []. However, physiological data are provided to users without information about concurrent behaviors that impact physiological states and patterns [], including alcohol use. This leaves users without guidance for how to change risky behaviors to support their wellness goals.

Alcohol use disorder (AUD) onset and rates of heavy drinking peak during young adulthood, but young adults are often more concerned about their general wellness than alcohol use behaviors [-]. However, risky alcohol use can impede wellness goals, like sleep improvement and cardiovascular recovery [,]. Alcohol’s harmful effects across the body are well-established and include effects on cardiovascular health, sleep, and immune function []. Alcohol use can contribute to cardiac arrhythmias [] and poor sleep quality in young adults, and vice versa [,,]. Inadequate sleep may lead to increased, problematic alcohol use in young adults [-] and to relapse for people with AUDs [,]. Furthermore, behavioral interventions for insomnia may reduce alcohol use in adults who drink heavily [], especially digital insomnia programs [] and broader digital sleep interventions []. Therefore, wearable fitness technologies may support sleep and other wellness goals by targeting related risky behaviors, like alcohol use.

Personalized feedback from wearable technologies may help young adults make connections between their alcohol use and their wellness goals, like improved sleep []. In reviews and meta-analyses of clinical trials [,], digital personalized feedback interventions result in small but meaningful reductions in young adults’ drinking. Feedback interventions often involve normalized feedback comparing young adults’ perceptions of peer drinking and actual peer drinking levels, which highlights that young adults’ peers often drink less than they expect []. This feedback then facilitates comparison of their own self-reported drinking to actual norms. Personalized feedback for alcohol reduction is also increasingly integrating multiple personal data streams, enabling comparison between drinking and other facets of young adults’ experiences []. Combining physiological data (eg, alcohol’s effects on heart rate and sleep) and self-reported behavioral data (eg, number of drinks consumed) can provide highly personalized feedback and insights that can increase user engagement in interventions [], which is critical to intervention effectiveness [].

Feedback in digital health interventions tends to be delivered (1) when a behavior is occurring or (2) after a behavior occurs [], which facilitates reflection-in-action or reflection-on-action, respectively, to promote insight []. Retrospective delivery of feedback allows users to think about their behavior in the larger context of related events, feelings, and motivations and to consider relations between their experiences and physiological or behavioral data []. Feedback with a record of experiences over time can facilitate “descriptive reflection” (revisiting behaviors) and “explanatory reflection” (explaining behaviors). Furthermore, feedback showing correlations or causal patterns (eg, associations between alcohol use and poor sleep) can encourage “dialogic reflection” []. Reflecting on causal patterns between alcohol use and hindered wellness goals may boost motivation to change alcohol consumption among young adults who are unconcerned about their alcohol use [].

Popular commercial devices are uniquely positioned to promote reflection and insight among young adults who might not otherwise seek help with risky behaviors, like alcohol use []. Given the potential for wearable technologies to promote wellness and decrease risky behaviors simultaneously [], it is essential to study young adults’ experiences with integrated physiological and behavioral feedback on alcohol use. User engagement is critical to the success of digital health tools [], and intervention designers must understand how to optimize feedback to encourage long-term engagement from young adults.

This Study

The current study is the first randomized controlled trial (RCT) of a wearable, personalized feedback intervention for young adults with risky drinking that combines: (1) physiological metrics of sleep, heart rate variability (HRV), and resting heart rate via wearable photoplethysmography biosensors in the Oura Ring (Oura Health Oy) and (2) behavioral daily diary self-monitoring of sleep and alcohol use.

Our primary evaluation aim was to describe young adults’ perceptions of the acceptability, feasibility, and perceived effectiveness of the Oura Ring wearable, the Oura Ring mobile app, smartphone diary self-monitoring, and personalized feedback and tailored advice reports, with a focus on participants’ experiences in the feedback group. In addition, we also had some exploratory aims. First, we aimed to compare user experiences between the feedback group (full access to the Oura Ring mobile app and feedback reports every 2 weeks) and the assessment group. Second, we aimed to compare user experiences of different intervention components and, finally, explore young adults’ health coaching preferences for personalized feedback.

Methods

Study Design

For this pilot, parallel-group RCT, our goal was to evaluate users’ experiences with a wearable, personalized feedback intervention leveraging physiological data (cardiovascular and sleep) and behavioral data for alcohol reduction. Participants were randomly assigned 1:1 to either the feedback (n=30) or assessment (n=30) group. In 2022, all participants wore the commercial wearable Oura Ring, Gen2 (Oura Health Oy) daily for 6 weeks, completed daily smartphone diaries about their sleep, alcohol or substance use, and health behaviors, and completed follow-ups at weeks 6 and 10. Study staff members gave SMS text reminders to participants to sync Oura Ring data 3‐4 times per week and to complete smartphone diaries (if not yet completed).

The feedback group had full access to the Oura Ring mobile app via smartphone. The app included daily personalized, biometric feedback on sleep (ie, sleep stages, wakefulness, timing, efficiency, latency, and duration), physical activity (ie, calories burned and steps), cardiovascular recovery (HRV and resting heart rate), respiratory rate, and body temperature. Furthermore, the app provided composite health scores in the areas of “sleep,” “activity,” and “readiness” based on proprietary algorithms and in-app sleep tips and activity prompts. The assessment group only had partial access to the Oura Ring mobile app, including general wellness tips, but they did not have access to personalized, biometric feedback in the app (eg, daily sleep and cardiovascular feedback). Based on our previous work [-], we judged this to be the best control as it provides the experience of wearing the ring, having knowledge of being monitored, and using the app.

The feedback group also received personalized feedback and tailored advice reports every 2 weeks, derived separately and delivered by our research team, on integrated physiological Oura Ring data and behavioral smartphone diary data. Personalized feedback included trends of alcohol and other substance use based on self-report (eg, heavy drinking or substance use occasions, drinks per week, and peak blood alcohol level), alongside cardiovascular recovery and sleep data over each 2-week period (refer to the studies by Fucito et al [,] for more details on similar feedback reports in previous research). Reports included data visualization to reveal patterns among sleep, cardiovascular, and alcohol and substance use data streams. Within reports, participants were also given brief, evidence-based advice tailored to their data, such as sleep hygiene, controlled drinking, stress management, and exercise strategies. The assessment group did not receive feedback reports every 2 weeks from the research team, but received 1 delayed feedback report postintervention at week 10. The assessment group participants were unblinded given their knowledge that features of the Oura Ring app were blocked and that they were not receiving feedback reports throughout the intervention period. Furthermore, study team members were unblinded when administering participant appointments and interviews.

Recruitment

Participants were recruited through online advertisements on open-access social media sites (eg, Snapchat [Snap Inc], Instagram [Meta], Facebook [Meta], and Reddit) and offline via community flyers in universities, gyms, and other public spaces. Although advertisements did not explicitly seek out young adults who wanted to reduce their drinking, they did target young adults with heavier drinking levels as central to the study. Interested volunteers were directed to complete an online screener. The study’s affiliation with Yale School of Medicine was apparent in recruitment and screening materials. To be eligible, participants needed to (1) be 18‐25 years old, (2) be fluent in English, (3) report ≥4 heavy drinking occasions (≥5 drinks for men and ≥4 for women) in the past 28 days, (4) be at risk of harmful drinking (Alcohol Use Disorder Identification Test- Consumption [] ≥7 for men or ≥5 for women), and (5) own a smartphone. Potential participants were excluded if they had (1) current (active) alcohol or sleep treatment; (2) clinically severe AUD withdrawal or substance use disorder other than cannabis in the last 12 months as assessed by diagnostic interview; (3) severe symptoms of a mental health disorder (MHD), for example, current psychosis or suicidality; (4) history of sleep disorders; (5) job with a night or rotating shift that prevented a consistent sleep schedule; or (6) travel >2 time zones during study participation or a month before.

Of the 81 participants who were screened online for eligibility, 21 did not meet the inclusion criteria due to insufficient drinking levels (n=15), severe MHDs (n=2), and planned travel (n=1; ). Furthermore, 3 participants were no longer interested. Those 60 participants who met online screening eligibility were invited to attend an intake to confirm eligibility face-to-face. All 60 eligible participants who were enrolled and randomized into groups ultimately completed the treatment, and 59 completed follow-up. Given that this was a pilot trial, neither a power analysis nor other sample size calculations were undertaken. We judged that 60 participants, including 30 in the feedback group, would be sufficient to assess intervention feasibility.

Evaluation Procedure

To assess user experiences, participants completed self-assessed, web-based exit surveys and face-to-face exit interviews designed for this study (). Acceptability was defined as survey ratings of intervention satisfaction and likeability and interview sentiment and descriptions of preference. Feasibility was determined via survey ratings of intervention comfort, schedule workability, life interference, and interview descriptions of understandability. Perceived effectiveness encompassed survey ratings of intervention helpfulness alongside interview descriptions of helpfulness, behavior influence, and behavior change. The timing and some content of assessments varied by group. To assure quality, exit interviews were given face-to-face, and exit surveys were self-assessed by participants under the supervision of a study team member.

Exit Surveys

All participants completed exit surveys at week 6. Participants were emailed a web link to the exit survey, which they could complete via smartphone or computer. Exit surveys included original Likert scale and Likert-type questions written by the study team regarding the acceptability, feasibility, and perceived effectiveness of the overall program, wearing the Oura Ring, and completing the smartphone diaries. These user experience questions were based on validated measures [,] and have been used in a variety of previous online user experience studies [,,]. In the week 6 exit survey, the feedback group participants responded to additional questions about the acceptability, feasibility, and perceived effectiveness of the Oura Ring mobile app and the personalized feedback and tailored advice reports received every 2 weeks. Questions about the perceived effectiveness of the intervention referred to the helpfulness of information and tips for reducing alcohol and improving sleep or cardiovascular health. At week 10, the assessment group participants were not asked questions about the Oura Ring mobile app due to their limited access, but they were asked questions about the delayed feedback report that they received. After trial initiation, additional survey questions were completed by a smaller subset of participants, including their willingness to purchase the Oura Ring (n=51) and feedback reports (n=53), the likelihood of recommending the Oura Ring to others (n=51), the likability and understandability of alcohol or substance information in feedback reports (n=32), and the helpfulness of behavioral tips (such as sleep and alcohol tips) in feedback reports (n=28‐31).

Exit Interviews

Both groups also completed exit interviews with a study team member (MA). The feedback group participants completed exit interviews during week 6 follow-ups. Interview questions focused on changes in overall health, sleep, and alcohol or substance use during the study, comparisons to peers, helpfulness of received intervention components (Oura Ring, smartphone diaries, and personalized feedback reports), interests and preferences for health coaching, and suggestions for future research. The feedback group participants also answered questions about the Oura Ring mobile app and provided comparisons among components. Thematic saturation occurred before interviewing all feedback group participants, but all participants were asked to take part in interviews to ensure all user experiences were captured. Exit interviews were not initially planned for the assessment group but were added after study initiation to their week 10 follow-up to gain a richer understanding of their experiences and reactions to the delayed feedback report. However, this protocol addition resulted in the first assessment group participants (n=4) not being offered interviews. Assessment group participants were not asked questions about the Oura Ring mobile app because they did not have full access, and they were not asked to compare intervention components. Consistent with iterative qualitative research methods [], some interview questions for both groups (eg, exploratory health coaching questions) were adaptively added during the evaluation process in response to users’ experiences.

Data Analysis

We used an innovative convergent mixed methods approach [] incorporating artificial intelligence (AI)–driven natural language processing (NLP) to evaluate this wearable, personalized feedback intervention for young adults with risky drinking. Exit surveys and exit interviews were analyzed in parallel to assess the convergence of findings. For our primary aim, we examined the descriptive results of exit surveys to evaluate the acceptability, feasibility, and perceived effectiveness of the overall program and its intervention components. Then, for our exploratory aims, we assessed (1) predictive results of exit surveys, specifically whether the acceptability, feasibility, or perceived effectiveness of the program varied based on study group, and (2) descriptive results of exit surveys and interviews comparing intervention components and health coach preferences.

Concurrently with exit survey analyses, we also analyzed the content of exit interviews using AI-driven NLP and researcher-coded qualitative analysis. We used NLP first to characterize exit interviews, which included (1) topic modeling analysis with Latent Dirichlet Allocation (LDA; []) and (2) sentiment analysis with the Finn Årup Nielsen (AFINN) lexicon []. With a given number of topics, LDA determines the most likely topics within each document and the most likely terms within each topic []. The number of topics (k) used in our LDA was determined through a preliminary analysis using 3 methods []. Overall topic prevalence, or the topic’s proportion of a given document on average, is characterized by Ɵ_k. Following the LDA, 2 study team members (FJG and OKE) named each topic based on recursive reading of interviews most likely to include each topic. These coders compared the names to ensure trustworthiness. For the sentiment analysis, we used the AFINN lexicon, which assigns values ranging from −3 to 3 based on their negative to positive valence, with 0 indicating neutrality in words. Documents are each given an index score based on the net value of included terms from the AFINN lexicon [].

Based on the scope of the NLP results, study team members then conducted a rapid qualitative analysis on specific exit interview questions to target remaining areas of research interest. In total, 7 study team members (FJG, OKE, SK, MF, LL, and Holly Boyle and Sophia Sniffin) participated in the deductive qualitative coding process informed by the rigorous and accelerated data reduction (RADaR) technique for rapid qualitative analysis []. This technique involves a series of spreadsheets and data tables in which qualitative passages are successively reduced to derive themes []. A randomly selected subset (10/50, 20%) of interviews was independently coded by multiple coders to assess interrater reliability. Prereconciliation meeting Cohen kappa values between coding pairs ranged from 0.72 to 0.82, and postreconciliation meeting kappa values ranged from 0.90 to 0.97. The coding framework was revised based on reconciliation discussions, and remaining interviews were divided among coders who engaged in ongoing consultation and discussion to reduce coder drift and maintain trustworthiness. The researcher-coded qualitative findings from exit interviews were compared with quantitative NLP results from exit interviews using joint display methods, specifically an integrated results matrix []. For interview thematic results, we distinguish between primary aim results, which focus on intervention acceptability, feasibility, and perceived effectiveness, and exploratory aim results, which concentrated on comparisons between trial groups or intervention components and young adults’ health coaching preferences.

Ethical Considerations

This research was approved by the Yale University institutional review board (2000030417). All eligible participants discussed an informed consent form in detail with a study team member before agreeing to take part in the study, and informed consent was obtained from every participant. During the informed consent process, the intervention’s focus on alcohol use and related physiological metrics was made explicit. Participants’ identifying information was kept private and confidential and stored only on a secure university server. All data used for the user experience evaluation were deidentified before analysis. Participants could earn up to US $279 if they completed all study activities (ie, smartphone diaries, study visits, and wearing and returning the Oura Ring).

Results

Sample

A total of 60 participants took part in the RCT and completed the exit survey (). Almost all feedback group participants (n=29) and most assessment group participants (n=21) completed the exit interview. Half of the participants (30/60, 50%) were men, and 81.6% (49/60) were White, with a mean age of 22.02 (SD 1.98) years (). The majority (40/60, 66.6%) were students, and most (41/60, 68.3%) were employed. Over three-fourths (47/60, 78.3%) met criteria for an AUD, 21.7% (13/60) for another substance use disorder, and 15% (9/60) for an MHD. No demographic variable differed significantly between the assessment and feedback group participants.

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Exit Survey

On a 100-point scale, participants in both groups reported high acceptability (mean 84.17, SD 17.81) and perceived effectiveness (eg, promoting hope [mean 70.42, SD 25] and meeting program goals [mean 75.83, SD 21.57]) of the overall program (). Almost all participants (58/60, 96.7%) said that they would recommend the program to a family member. Although participants in both groups found the overall program to be highly feasible (eg, schedule duration [mean 77.92, SD 25.25] and schedule workability [mean 91.67, SD 15.03]), assessment group participants reported some higher aspects of overall program feasibility compared with feedback group participants, including schedule workability (mean difference=8.30, P=.03) and visit comfortability (mean difference=10.00, P=.007) of visits.

Related to their experiences of wearing the Oura Ring, participants in both groups reported high acceptability (eg, likeability [mean 76.25, SD 22.75] and willingness to continue wearing [mean 90.83, SD 17.20]) and feasibility (eg, ring comfortability [mean 68.33, SD 29.06] and no itchiness [mean 93.52, SD 13.77]). Most participants (44/51, 86.3%) said they would recommend the Oura Ring to a family member, and only 33.3% (20/60) noted marks on their skin from wearing it. Feedback group participants with full access to the Oura Ring mobile app also reported high acceptability (27/30, 90% liked the app) and moderate effectiveness (eg, activity prompt helpfulness [mean 52.88, SD 30.27] and readiness tip helpfulness [mean 67.00, SD 29.51]) of in-app recommendations and prompts.

Regarding the smartphone diaries, participants in both groups also reported moderate acceptability (mean 59.17, SD 25.20) and moderately high feasibility (eg, willingness to continue diaries in an app [mean 64.17, SD 28.51] and easiness [mean 82.50, SD 20.74]). Although diaries were highly rated in general, a descriptive comparison of ratings indicates that smartphone diaries may have been less acceptable and feasible than other intervention components (). Furthermore, members of both groups rated information in the feedback report as highly acceptable (eg, layout acceptability [mean 75.00, SD 24.33] and sleep or cardio information likeability [mean 92.67, SD 13.25]), feasible (eg, overall understandability [mean 70.26, SD 20.12] and alcohol or substance use information understandability [mean 84.38, SD 18.78]), and effective (eg, substance use tip helpfulness [mean 69.64, SD 24.87] and sleep or cardio information helpfulness [mean 88.36, SD 17.66]). A descriptive comparison of perceived effectiveness ratings () indicates that the information in feedback reports may have been more effective than that in the Oura Ring app rated by feedback participants.

Feedback group participants had high self-reported adherence to intervention components, including using the app every day on average (mean 83.33, SD 21.71) on a 0‐100 scale from weekly to multiple daily use. The most frequently used aspects of the Oura Ring mobile app were sleep data (28/30, 93.3%), activity data (21/30, 70%), and cardiovascular recovery data (15/30, 50%). Less frequent in-app activities included tagging workouts (12/30, 40%), clicking on personal trend data (7/30, 23.3%), and interacting with story or meditation content (1/30, 3.3%). Most feedback group participants (24/30, 80%) also reported having read all 3 personalized feedback reports, and all feedback group participants (30/30, 100%) read at least 1 report. There was no significant difference between the baseline drinking levels of feedback group participants who read all 3 reports and those who did not (P=.35). Among all participants, they self-reported that they most frequently used sleep tips from the feedback reports (43/60, 71.7%), followed by alcohol or substance use tips (25/60, 41.7%), physical activity tips (19/60, 31.7%), stress management tips (18/60, 30%), and diet tips (14/60, 23.3%).

Exit Interviews

NLP

Among feedback exit interviews (n=29), 6 topics were modeled that were most likely to characterize each interview (). The most common topic was multimodal general change (Ɵ_k=0.18), in which “multimodal” refers to multiple helpful intervention components (Oura Ring, smartphone diaries, and feedback reports) that promoted general wellness in different domains, such as exercise, diet, and overall health and habits. Therefore, on average, 18% of each document was about this topic: general wellness changes related to multiple intervention components. The topic, learning+peer coach interest (Ɵ_k=0.18), or interest in peer coaching about the feedback report, was also prevalent. The next most common topics were multimodal sleep or alcohol change (Ɵ_k=0.17), or multiple helpful program components specifically promoting sleep improvement and alcohol reduction, and mindful sleep strategies (Ɵ_k=0.17), or trying mindfulness to improve sleep. These were followed by awareness before change (Ɵ_k=0.15; or planning health changes based on personalized feedback reports) and multimodal sleep or caffeine insights (Ɵ_k=0.15; or learning about sleep or caffeine from multiple helpful aspects of the program.

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We modeled 5 primary topics from the assessment exit interviews (n=21; ). The most common topic was multimodal insights, good sleep (Ɵ_k=0.22), or learning about good sleep from multiple helpful components of the program. Next most common topics were self-guided report use (Ɵ_k=0.20; or learning from the feedback report without a coach) and report insights, poor sleep (Ɵ_k=0.20; or learning about sleep deficits from the feedback report). These were followed by continued multimodal mobile health use (Ɵ_k=0.19), or finding multiple aspects of the program helpful due to previous use of mobile health, and multimodal sleep strategies (Ɵ_k=0.18), or trying sleep strategies based on multiple helpful components of the program.

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Sentiment analysis showed generally positive perspectives among exit interviews in both the feedback group (mean 14.66, SD 7.53; range –4 to 30) and the assessment group (mean 15.57, SD 9.65; range 1‐32; and ). Positive sentiment scores indicate overall positively valenced words within an exit interview, whereas negative sentiment scores indicate negatively valenced words. Virtually, all participants in the feedback (28/29, 96.6%) and assessment (21/21, 100%) groups had positive sentiment scores (>0). However, on visual inspection, a larger proportion of feedback group participants (25/29, 86.2%) had high positive sentiment (>10) compared with the proportion of assessment group participants (15/21, 71.4%).

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Researcher-Coded Rapid Qualitative Analysis

Study team members conducted a targeted rapid qualitative analysis using the RADaR technique [] (refer to table of themes, definitions, and salience in ). Based on exit interview questions with 50 participants across both groups, we identified 3 thematic categories: helpfulness and comparison of program components, report information and preferences, and program engagement and adherence. As detailed in the , some questions were asked only of feedback group participants (eg, feedback report helpfulness), and some exploratory questions were added iteratively during the study (eg, health coaching preferences).

Furthermore, five themes within helpfulness and comparison of program components included (1) helpfulness of Oura Ring (asked of n=50 participants), (2) helpfulness of smartphone diaries (n=50), (3) helpfulness of feedback report (n=29 feedback group participants), (4) most influential: Oura, diaries, or report (exploratory result; n=24 feedback group participants), and (5) learned more: Oura versus report (exploratory result; n=16 feedback group participants). Most participants in both the feedback and assessment groups discussed helpful aspects of wearing the Oura Ring, and relatively small proportions of the feedback and assessment groups discussed unhelpful aspects or suggestions for improvement. One feedback group participant noted the helpfulness of wearing the Oura Ring and using the mobile app:

I was able to see every morning…how much sleep I got…I was able to…make connections like, ‘Oh…I got six hours of sleep. No wonder why at 3:00 [PM], I’m…exhausted.

Similarly, most participants in the feedback and assessment groups described helpful aspects of the smartphone diaries with comparatively small proportions of the feedback and assessment groups discussing unhelpful aspects of diaries or suggestions for improvement. One assessment group participant said:

[The diary] was helpful. In some days, it helped me keep [my behaviors] in check.

Only feedback group participants were asked about the helpfulness of feedback reports (asked of n=29 participants), and later in the study, as an exploratory question for a subset of participants, to compare program components (n=16‐24). Most found aspects of the feedback reports helpful, whereas very few found aspects of the reports unhelpful. One feedback group participant said of the report:

One thing that was kind of crazy was…the amount of calories I drank [in alcohol]…that was helpful [information] because there was like 3500 calories essentially over the last two weeks.

Similarly, another participant stated:

The one factor [on the report] is…how many calories of alcohol someone drank…during the last two weeks. I think…if you’re not aware of that, that could be…a very helpful thing.

Another stated:

[The report] provided clarification too. It was just very…streamlined…in comparisons.

When comparing different components, almost half of the feedback group participants who were asked this question stated that the Oura Ring was most influential, with about one-third preferring the feedback report, and one-fourth preferring smartphone diaries. One feedback group participant who selected the reports said:

Probably the feedback [report], like the papers that you guys gave me, so that I was able to see…everything at once, rather than…just getting…a one-night thing from…the [Oura] Ring.

Furthermore, the largest group of feedback group participants who were asked to compare what they learned stated that they knew more about their sleep from the Oura Ring app than the report, with one-fourth stating they learned equally from both. One feedback group participant who described learning from the Oura Ring said:

Probably the Oura Ring, because I would look at…the ring every day. I see how I did [with sleep], so I feel like that was the most helpful.

Themes in the report information and preferences category were based on questions added later in the study and asked of subsets of participants, including exploratory questions about health coach preferences. These five themes included (1) report: learned about sleep (asked of n=40 participants), (2) report: information amount (n=31), (3) report: health coach versus self-guided (n=32), (4) report: preferred coach type (n=30), and (5) report: preferred meeting mode (n=23). Among those who were asked what they learned about their sleep from the feedback report, the largest groups of feedback and assessment participants reported learning about their current sleep deficits, including the ways alcohol and other substances impacted their sleep. One feedback group participant reported:

I learned about…the sleeping heart rate…being affected by alcohol…seeing how that’s an indicator of my sleep quality…even if I sleep for a long time, it doesn’t necessarily mean it’s good sleep.

Another stated:

I definitely…noticed like the heart rate and everything…drinking and before sleeping and while sleeping…just actually like realizing…what my BAC [Blood Alcohol Content] can get to…you don’t really think about that, you’re just like out having fun. So, I think you just made me…more mindful of my sleeping and drinking habits.

Most participants who were asked about the amount of information in the report in the feedback and assessment groups thought sections had the right amount of information, whereas some who were asked thought some report sections had too much information. One assessment group participant who appreciated the amount of information in the report stated:

I actually kind of like the lengthy list [of health tips] because you can like pick which [tip] works best for you…I think everything was really well explained and…split up into…different sections that made sense.

As exploratory questions, participants in both groups were asked about their preferences for health coaching based on personalized data in their report. Among those asked, the largest group of participants in the feedback group was interested in health coaching, whereas the largest group among assessment participants preferred to read their report on their own. One feedback group participant said:

I think meeting with someone would probably be better…to walk you through [the report], what it means and…I would be able to adjust and monitor weekly or biweekly.

Regarding coach type, feedback group participants who were asked indicated a slight preference for a clinician over peer coaches, whereas assessment group participants had a slight preference for a peer coach over a clinician. One assessment participant said:

I’m going to have to say [an] educated peer…only because I feel like some people can get the fear of doctors…and get overwhelmed by them.

If they were to meet a health coach, most participants who were asked in the feedback and assessment groups preferred video teleconferencing (eg, Zoom [Zoom Communications]) or other remote methods compared with in-person health coaching.

Themes in the category of program engagement and adherence were based on questions added later in the study and asked of a subset of participants. The three themes were (1) considered dropping out of the program (asked of n=45), (2) motivation to participate in the program (n=44), and (3) motivation to stay engaged in the program (n=40). Almost all feedback and assessment group participants who were asked reported that they never considered dropping out of the program. Beyond financial compensation, the largest group of feedback and assessment participants who were asked reported that they joined the program due to curiosity about personalized health insights, to learn more about their wellness and connections to behaviors, like alcohol use. One assessment group participant said:

I was interested on…my sleep and alcohol and how it’s affected.

Similarly, another assessment group participant said:

I…had questions…about my drinking and everything and wanted to see if it didn’t have…any impact on…everyday things like eating, sleeping and stress.

A feedback group participant stated:

I was curious about sleep for sure…I was very curious what this [Oura] Ring would do.

As to what kept them engaged in the program, the largest group of feedback participants, almost half of those asked, cited SMS text reminders from study staff, and the largest group of assessment participants described that ease of use kept them engaged.

Discussion

Principal Results

Mixed methods evaluation results converged about users’ perceptions of the wearable physiological and behavioral feedback intervention (). Participants described the overall program as having high acceptability, feasibility, and perceived effectiveness in exit surveys and interviews. Wearing the Oura Ring was described as highly acceptable and feasible in the survey and as moderately to highly effective across both the survey and interview. These ratings included the Oura Ring mobile app for feedback group participants. Smartphone daily diaries tracking behavioral data were described as moderately to highly acceptable and feasible in the survey and as highly effective in the interview; therefore, the evaluation of this component had less cross-method convergence. The feedback reports were described as highly or moderately highly feasible and effective in both the exit survey and interviews and highly acceptable in the survey. Both methods used to analyze interviews (NLP and qualitative analysis) also showed that participants reported learning insights about their sleep deficits from the feedback reports.

Comparison of the feedback and assessment groups’ experiences revealed different findings depending on the method. Although the groups did not significantly differ on most exit survey ratings of the program, assessment group participants rated some aspects of program feasibility (comfortability and workability) more highly than feedback group participants. Also, per NLP with exit interviews, feedback group participants may have had higher perceived program effectiveness (reported behavior change).

Our exploratory analysis comparing program components revealed preferences for different aspects of program components. For example, some feedback group participants described the Oura Ring as most effective (influential on behavior) when asked in the exit interview. However, on their exit surveys, feedback group participants tended to rate the personalized information in the feedback reports as more effective (helpful) than the tips and prompts in the Oura Ring app. Also, although participants described the smartphone diaries as similarly effective (helpful) as other components (Oura Ring and feedback reports), they rated the acceptability and feasibility of the smartphone diaries as lower. These findings are consistent with previous research on lower levels of engagement in self-report data []. Actively completing the smartphone diaries (behavioral data) may have been more challenging than passively wearing the Oura Ring (physiological data). However, participants found the integration of both physiological and behavioral data streams in personalized feedback reports to be especially helpful.

Mixed methods analysis of exit interviews also revealed differences in participants’ preferences for health coaching about their personalized feedback. NLP and qualitative analysis of exit interviews indicated that assessment participants preferred to read their feedback reports independently without a health coach. The qualitative analysis revealed additional preferences, including feedback group participants’ interest in clinician health coaching and assessment group participants’ preference for peer coaching. The only discrepancy between evaluation methods was the topic highlighted within the NLP, which noted that feedback group participants are also interested in peer health coaching.

Comparison With Previous Work and Future Directions

This evaluation paralleled previous research on young adults’ greater concern about improved wellness, such as improved sleep [], rather than alcohol use. Study participants were motivated to join the study due to curiosity and interest in their wellness and personalized feedback, as opposed to a desire to reduce their drinking. Curiosity about highly personalized feedback also played a role in maintaining engagement in the intervention after its initiation. This aligns with previous findings that personalized feedback can enhance engagement []. Consistent with previous research, interventions focused on wellness goals may be more accessible and appealing to young adults than those primarily targeting alcohol use []. Despite their explicit focus on fitness and wellness, wearable devices have the potential to contribute to reducing risky behaviors.

Commercial wearable devices, like the Oura Ring, could incorporate more active monitoring of self-reported behaviors, such as alcohol use, to provide highly personalized feedback and foster motivational change in young adults. A key focus of our study was integrating behavioral self-monitoring and feedback, as this is not available in Oura. Although Oura users can make implicit associations by examining and tagging their physiological data, there is no explicit integrated feedback. Whereas it is especially challenging in general to encourage behavioral health app users to maintain engagement [], our findings of high acceptability and feasibility support the integration of behavioral self-report data with passively collected physiological data. In particular, the combination of alcohol-related behavioral data and physiological data related to sleep and cardiovascular recovery could highlight connections between these data streams [,]. Our findings indicated that participants found the experience of active self-monitoring through smartphone diaries to be acceptable, feasible, and perceived as effective. Furthermore, they reported gaining insights from the integration of these data with their passively collected physiological data from the Oura Ring. Some noted they appreciated learning through integrated information and receiving tailored coaching. Insights into contextual factors that influence physiological data, such as sleep deficits, may promote dialogic reflection and enhance motivation to change risky behaviors [,].

Personalized feedback can be optimized to better promote insight and enhance change readiness. In this study, feedback group participants received daily health data and recent trends on the Oura Ring mobile app, along with more retrospective, integrated feedback in written reports every 2 weeks. Participants found both the feedback reports and the Oura Ring mobile app effective. On one hand, they reported preferences for the personalized insights in the integrated feedback reports; whereas, on the other hand, they liked the easy functionality of the Oura Ring and app.

Young adults may be interested in an option that combines the benefits of these intervention components (feedback reports and the Oura ring or app) via highly personalized, integrated in-app feedback. Mobile apps for wearable devices could offer active behavioral monitoring that is flexible according to the amount of time young adults are willing and able to answer self-report questions. Then, apps could offer integrated data reports at different time scales (eg, daily reports and longer trends) to leverage reflection-in-action and reflection-on-action []. Enhanced feedback options could also include opportunities for health coaching via educated peers or clinicians. Participants in both study groups showed some interest in peer coaching, and feedback group participants were more interested in clinician health coaching. Depending on the complexity of some data relationships (eg, HRV after a heavy drinking episode), it may be important to consult a coach to interpret and gain insights from personalized feedback reports.

Our results should be considered in the broader tradition of personalized feedback interventions for alcohol reduction. Normative feedback interventions compare young adults’ own drinking and perceptions of peer levels with actual peer levels, and these interventions may have small but meaningful impacts on alcohol reduction []. These interventions are theorized to address young adults’ social pressure to drink as a mechanism of change; however, highly personalized feedback on physiological and alcohol data may address overall wellness motivations to change. Given that young adults are generally unconcerned about their drinking [], our findings reveal that the integration of personalized feedback on physiological metrics may increase the appeal of personalized alcohol feedback. Accessible, personalized feedback that promotes reflection [] and engagement [] may enhance young adults’ awareness of the connections between their behaviors and aspects of their wellness, like sleep and cardiovascular health. Further, integrated physiological and behavioral feedback has implications for other issues impacted by lifestyle behavior change, such as cardiovascular disease prevention.

Limitations

Although our mixed methods user evaluation approach leveraged AI-driven approaches to enable breadth and depth [], there were limitations in our methodology. Our sample size was relatively small for an RCT, especially for quantitative evaluation analyses. Furthermore, our sample consisted mostly of students from a single geographic location, which may not be representative of other young adults. Additionally, some survey and interview questions were introduced iteratively, limiting them to a subset of participants (eg, health coaching and component comparisons). The prevalence of these themes may have differed if they had been presented to the entire sample from the outset. Finally, as a phase 1 study primarily focused on feasibility, the duration of the intervention was only 6 weeks; however, a longer duration (>8 wk) would have been ideal to fully test the effect of an intervention intended to promote behavior change.

Conclusions

Our results support the inclusion of self-report behavioral data in commercial wearable devices. Participants found the intervention acceptable, feasible, and effective, including the completion of smartphone diary self-monitoring. Many found that personalized feedback reports integrating their physiological and behavioral data were helpful and promoted insights about their sleep and other wellness goals. Wearable devices may lack important functionality by not capturing the behaviors that contribute to wellness goals, such as improved sleep, cardiovascular recovery, or fitness. Additionally, targeting risky and prevalent behaviors, such as alcohol use, through wearable devices could be a more appealing intervention for young adults who are less concerned about heavy drinking than about improving overall wellness.

December 4, 2025

By Jurica Dujmovic

The shortage is a lucrative opportunity – but the window is brief

AI computing workloads could consume around 500 terawatt-hours annually by 2027 – about twice the U.K.’s total electricity consumption in 2023.

Rising infrastructure costs and mounting capital constraints are deflating the AI boom. The hyperscalers can’t solve their computing problems fast enough, and that’s creating a rare arbitrage opportunity.

The solution right now isn’t building data centers. The current investment opportunity lies in the temporary gap between exploding AI demand and the physical constraints of centralized infrastructure expansion. A handful of companies are exploiting this window – which likely will be a 24-to-36- month opportunity. For investors who understand the timing, it’s a compelling hedge against the AI infrastructure bottleneck.

Physical barriers

40% of AI data centers will face power constraints by 2027.

AI’s limiting factor is no longer algorithms or data – it’s the brute-force physics of data-center expansion. Training large models demands tens of thousands of GPUs, dedicated networking and enormous power consumption. Gartner forecasts that 40% of AI data centers will face power constraints by 2027.

The math is brutally simple: AI computing workloads could consume around 500 terawatt-hours annually by 2027 – about twice the U.K.’s total electricity consumption in 2023. This demand spike is already showing up in the grid.

Dominion Energy (D), the biggest utility company in Virginia, nearly doubled its data-center power capacity under contract between July and December 2024, and the trend has persisted.

Even with Microsoft (MSFT), Alphabet (GOOG) (GOOGL), Amazon.com (AMZN) and Meta Platforms (META) spending a combined $370 billion on capex in 2025, they can’t build fast enough. Construction and commissioning typically take 12 to 36 months, but when you include permitting and power-grid build-outs, a full data-center project can stretch to three to six years.

Time and money

The economics are compelling during this shortage window

This time gap is the entire investment thesis.

When essential resources become expensive and concentrated, parallel markets emerge. We saw this with electricity co-ops in the early 20th century, independent oil producers during OPEC’s reign and broadband resellers in the early internet era.

With AI, the scarce resource is GPU computing. Several companies are building marketplaces that aggregate idle capacity – consumer GPUs, academic clusters, enterprise overstock – and resell it at a fraction of centralized data-center costs.

The economics for these companies are compelling during this shortage window:

Cost structure advantage: Alternative networks don’t finance data centers with debt. They pay participants directly for computing capacity through incentive structures, converting spare capacity into productive assets. The cost of scaling shifts from massive capex to distributed incentives.

Speed to market: While hyperscalers wait 18 to 36 months for new facilities, these networks can add capacity node by node, with no billion-dollar commitments up front.

Arbitrage pricing: These companies are capturing demand from the smaller labs, indie studios, emerging markets and others that are priced out of AWS GPU pricing but still need computing.

The catch? The explosive growth window is finite. These networks will remain viable alternatives even after constraints ease – serving cost-sensitive workloads, emerging markets and indie developers – but the opportunity for substantial investment gains compresses as growth normalizes and hyperscalers’ capacity comes online.

Read: AI data centers need juice. The next hot stocks give it.

How to play the computing shortage

Again, this isn’t a moonshot bet. It’s an infrastructure hedge with a defined window. Here are three approaches, ranked by risk profile:

Render Network: Aggregates idle GPU capacity from individuals and studios, reselling to the highest bidder for rendering and AI workloads. Think of it as Airbnb for GPUs – idle capacity that would otherwise sit dormant gets monetized, and users get computing at a fraction of data-center pricing. Rather than operating expensive data centers, Render pays a fraction of that cost to harvest capacity from thousands of computers.

io.net: Focuses on generic GPU computing for AI training and inference. The platform aggregates capacity from data centers, crypto miners and consumer hardware, creating a distributed alternative to centralized cloud providers. Its network is newer and more speculative than Render, but it’s capturing demand from AI startups that can’t afford or access hyperscaler GPU allocations.

Akash Network: Takes the concept broader, offering a marketplace for general cloud computing and storage beyond just GPUs. This positions it as infrastructure for the full stack, not just AI-specific workloads. Akash is a privately held company but it does have a tradeable crypto token, AKT. This is the highest-risk play in this category, but offers the most diversified exposure if decentralized computing extends beyond AI.

These are crypto token plays – not stocks

Before going further, understand what you’re actually buying. All three of these networks operate through native cryptocurrency tokens, not traditional equity. There is no stock ticker, no brokerage-account access and no public-equity wrapper for these businesses.

Direct exposure requires navigating cryptocurrency exchanges:

— Render Network (RENDER) trades on Coinbase, Binance and Kraken.

— is listed on select crypto exchanges such as Binance and Gate.io, with liquidity varying by venue and region.

— Akash Network (AKT) trades on Coinbase, Kraken and similar venues.

This means dealing with crypto custody – whether through exchange accounts or self-custody wallets – and accepting the regulatory uncertainty that comes with token investments. If you’re not comfortable with that infrastructure, this thesis won’t work for you.

For investors who prefer traditional equity exposure, the closest alternatives are second-order beneficiaries of the same capacity constraint:

— Data-center operators: Equinix EQIX, Digital Realty Trust DLR

— Power infrastructure: Dominion Energy, Duke Energy DUK, NextEra Energy NEE

— GPU supply chain: Nvidia NVDA, Broadcom AVGO, Super Micro Computer SMCI

But here’s the critical distinction: These publicly traded companies benefit from the shortage itself – not from the temporary arbitrage window created by aggregating idle distributed capacity. They’ll do well regardless of whether decentralized computing succeeds. What they won’t give you is direct exposure to the specific dislocation that is going on now.

Risk factors

Let’s be clear about what could go wrong with this arbitrage strategy:

Performance and reliability: Distributed GPU networks face inherent challenges with performance variance, latency and quality control. Enterprise customers paying for AI infrastructure demand reliability. If these networks can’t match centralized performance, the arbitrage doesn’t matter – customers won’t switch.

Security and compliance: Regulated industries won’t run sensitive workloads on unknown hardware scattered globally. These networks are limited to specific use cases where data sovereignty and compliance aren’t blockers.

Hyperscaler catch-up timeline: The base case assumes these constraints ease through 2027-’29 as new data centers and power infrastructure come online. If power constraints extend beyond 2029, the high-growth window for these companies stays open.

Regulatory uncertainty: Several of these networks operate in regulatory gray areas. If governments decide to regulate decentralized computing infrastructure, costs increase and flexibility decreases.

Crypto market contagion: These tokens trade on crypto exchanges and correlate with broader crypto markets. A bitcoin crash or crypto regulatory crackdown could affect these assets regardless of fundamentals.

The investment timeline

The window runs from early 2026 through 2027-’28, which is the core 24-to-36-month period. The broader infrastructure constraint lasts longer, but the outsized arbitrage compresses as hyperscalers come online. This aligns with the infrastructure constraint timeline I’ve been tracking, but extends beyond the initial shortage as power-grid limitations persist.

Q1 2026: Begin building positions as the 2027 power constraint window becomes consensus view. Dollar-cost average to smooth volatility.

Q2 2026-Q2 2027: Peak growth opportunity as AI demand continues accelerating while centralized capacity remains severely constrained. These networks capture maximum long-tail demand priced out of hyperscaler infrastructure.

Q3 2027-Q2 2028: Growth continues, but begins normalizing as new data centers come online and power-grid upgrades progress. Monitor hyperscaler capacity announcements closely – each major facility completion incrementally compresses the arbitrage.

Q3 2028-Q4 2029: Maturation phase. These networks settle into specialized roles – emerging markets, cost-sensitive workloads, indie developers. They remain viable businesses but growth normalizes.

It is important to understand that this isn’t a binary “it works until it doesn’t” thesis. It’s a maturation curve where networks transition from high-growth arbitrage plays to steady-state infrastructure alternatives.

The broader implication

If GPU aggregation networks prove they can deliver reliable computing at competitive prices during the 2026-’28 constraint period, they will establish legitimacy. Even if hyperscalers eventually recapture market share, these networks will have carved out niches in emerging markets, indie studios and cost-sensitive workloads.

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12-04-25 1637ET

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December 4, 2025

PRESS RELEASE

Apple announces executive transitions

Jennifer Newstead to join Apple as senior vice president, will become general counsel in March 2026

Kate Adams to retire late next year

Lisa Jackson to retire

CUPERTINO, CALIFORNIA Apple today announced that Jennifer Newstead will become Apple’s general counsel on March 1, 2026, following a transition of duties from Kate Adams, who has served as Apple’s general counsel since 2017. She will join Apple as senior vice president in January, reporting to CEO Tim Cook and serving on Apple’s executive team.

In addition, Lisa Jackson, vice president for Environment, Policy, and Social Initiatives, will retire in late January 2026. The Government Affairs organization will transition to Adams, who will oversee the team until her retirement late next year, after which it will be led by Newstead. Newstead’s title will become senior vice president, General Counsel and Government Affairs, reflecting the combining of the two organizations. The Environment and Social Initiatives teams will report to Apple chief operating officer Sabih Khan.

“Kate has been an integral part of the company for the better part of a decade, having provided critical advice while always advocating on behalf of our customers’ right to privacy and protecting Apple’s right to innovate,” said Tim Cook, Apple’s CEO. “I am incredibly grateful to her for the leadership she has provided, for her remarkable determination across a myriad of highly complex issues, and above all, for her thoughtfulness, her deeply strategic mind, and her sound counsel.”

“I am deeply appreciative of Lisa’s contributions. She has been instrumental in helping us reduce our global greenhouse emissions by more than 60 percent compared to 2015 levels,” said Cook. “She has also been a critical strategic partner in engaging governments around the world, advocating for the best interests of our users on a myriad of topics, as well as advancing our values, from education and accessibility to privacy and security.”

“We couldn’t be more pleased to have Jennifer join our team,” said Cook. “She brings an extraordinary depth of experience and skill to the role, and will advance Apple’s important work all over the world. We are also pleased that Jennifer will be overseeing both the Legal and Government Affairs organizations, given the increasing overlap between the work of both teams and her substantial background in international affairs. I know she will be an excellent leader going forward.”

“I have long admired Apple’s deep focus on innovation and strong commitment to its values, its customers, and to making the world a better place,” said Newstead. “I am honored to join the company and to lead an extraordinary team who are dedicated each and every day to doing what’s in the best interest of Apple’s users.”

“It has been one of the great privileges of my life to be a part of Apple, where our work has always been about standing up for the values that are the foundation of this great company,” said Adams. “I am proud of the good our wonderful team has done over the past eight years, and I am filled with gratitude for the chance to have made a difference. Jennifer is an exceptional talent and I am confident that I am leaving the team in the very best hands, and I’m really looking forward to working more closely with the Government Affairs team.”

“Apple is a remarkable company and it has been a true honor to lead such important work here,” said Jackson. “I have been lucky to work with leaders who understand that reducing our environmental impact is not just good for the environment, but good for business, and that we can do well by doing good. And I am incredibly grateful to the teams I’ve had the privilege to lead at Apple, for the innovations they’ve helped create and inspire, and for the advocacy they’ve led on behalf of our users with governments around the world. I have every confidence that Apple will continue to have a profoundly positive impact on the planet and its people.”

Newstead was most recently chief legal officer at Meta and previously served as the legal adviser of the U.S. Department of State, where she led the legal team responsible for advising the Secretary of State on legal issues affecting the conduct of U.S. foreign relations. She held a range of other positions in government earlier in her career as well, including as general counsel of the White House Office of Management and Budget, as a principal deputy assistant attorney general of the Office of Legal Policy at the Department of Justice, as associate White House counsel, and as a law clerk to Justice Stephen Breyer of the U.S. Supreme Court. She also spent a dozen years as partner at Davis Polk & Wardwell LLP, where she advised global corporations on a wide variety of issues. Newstead holds an AB from Harvard University and a JD from Yale Law School.

About Apple
Apple revolutionized personal technology with the introduction of the Macintosh in 1984. Today, Apple leads the world in innovation with iPhone, iPad, Mac, AirPods, Apple Watch, and Apple Vision Pro. Apple’s six software platforms — iOS, iPadOS, macOS, watchOS, visionOS, and tvOS — provide seamless experiences across all Apple devices and empower people with breakthrough services including the App Store, Apple Music, Apple Pay, iCloud, and Apple TV. Apple’s more than 150,000 employees are dedicated to making the best products on earth and to leaving the world better than we found it.

Press Contacts

Josh Rosenstock

Apple

jrosenstock@apple.com

(408) 862-1142

Apple Media Helpline

media.help@apple.com

WASHINGTON, Dec. 4, 2025 /PRNewswire/ — Vanda Pharmaceuticals Inc. (Nasdaq: VNDA) today announced that the U.S. Food and Drug Administration (FDA) has lifted the partial clinical hold on protocol VP-VLY-686-3403, which until today limited the protocol to a maximum of 90 doses of tradipitant.

The lift followed Vanda’s formal dispute resolution request and an expedited re-review conducted by CDER leadership under the collaborative framework established between Vanda and the FDA in October 2025.

The FDA agreed with Vanda’s position that motion sickness is an acute, self-limiting physiologic response rather than a chronic or chronic-intermittent condition. The Agency therefore concluded that the use of tradipitant in motion sickness represents an acute, event-driven therapy, eliminating the need for an additional six-month dog toxicity study and rendering the partial clinical hold unnecessary.

This decision allows Vanda to extend clinical studies of tradipitant in motion sickness. Separately, the ongoing review of the pending, fully completed New Drug Application for tradipitant for the prevention of vomiting induced by motion remains on track, with a PDUFA target action date of December 30, 2025, positioning tradipitant as potentially the first new pharmacologic treatment for motion sickness in over 40 years.

“The swift and favorable resolution of this issue highlights the effectiveness of our collaborative framework with the FDA,” said Mihael H. Polymeropoulos, M.D., President and CEO of Vanda. “We thank the Agency for its thorough and expedited scientific review and look forward to continued constructive dialogue.”

About Vanda Pharmaceuticals Inc.

Vanda is a leading global biopharmaceutical company focused on the development and commercialization of innovative therapies to address high unmet medical needs and improve the lives of patients. For more on Vanda Pharmaceuticals Inc., please visit www.vandapharma.com and follow us on X @vandapharma.

About Tradipitant

Tradipitant is a neurokinin-1 receptor antagonist licensed by Vanda from Eli Lilly and Company. Tradipitant is currently in clinical development for a variety of indications, including gastroparesis, motion sickness, and the prevention of nausea and vomiting induced by GLP-1 receptor agonists. 

CAUTIONARY NOTE REGARDING FORWARD-LOOKING STATEMENTS

Various statements in this press release, including, but not limited to statements regarding Vanda’s further clinical development plans for tradipitant, Vanda’s pursuit of FDA approval of tradipitant for the prevention of vomiting induced by motion, the potential commercialization of tradipitant for such indication, and Vanda’s future interactions with the FDA are “forward-looking statements” under the securities laws. All statements other than statements of historical fact are statements that could be deemed forward-looking statements. Forward-looking statements are based upon current expectations and assumptions that involve risks, changes in circumstances and uncertainties. Important factors that could cause actual results to differ materially from those reflected in Vanda’s forward-looking statements include, among others, the FDA’s ability to complete its review of the NDA for tradipitant for the prevention of vomiting induced by motion by December 30, 2025, the FDA’s assessment of the evidence supporting the safety and efficacy of tradipitant for the prevention of vomiting induced by motion, and the ability of the FDA and Vanda to continue to work together collaboratively. Therefore, no assurance can be given that the results or developments anticipated by Vanda will be realized, or even if substantially realized, that they will have the expected consequences to, or effects on, Vanda. Forward-looking statements in this press release should be evaluated together with the various risks and uncertainties that affect Vanda’s business and market, particularly those identified in the “Cautionary Note Regarding Forward-Looking Statements”, “Risk Factors” and “Management’s Discussion and Analysis of Financial Condition and Results of Operations” sections of Vanda’s most recent Annual Report on Form 10-K, as updated by Vanda’s subsequent Quarterly Reports on Form 10-Q, Current Reports on Form 8-K and other filings with the U.S. Securities and Exchange Commission, which are available at www.sec.gov.

All written and verbal forward-looking statements attributable to Vanda or any person acting on its behalf are expressly qualified in their entirety by the cautionary statements contained or referred to herein. Vanda cautions investors not to rely too heavily on the forward-looking statements Vanda makes or that are made on its behalf. The information in this press release is provided only as of the date of this press release, and Vanda undertakes no obligation, and specifically declines any obligation, to update or revise publicly any forward-looking statements, whether as a result of new information, future events or otherwise, except as required by law.

Corporate Contact:
Kevin Moran
Senior Vice President, Chief Financial Officer and Treasurer
Vanda Pharmaceuticals Inc.
202-734-3400
[email protected]

Jim Golden / Jack Kelleher / Dan Moore
Collected Strategies
[email protected]

SOURCE Vanda Pharmaceuticals Inc.

This fuel will be used for the Project Pele microreactor being developed through a collaboration among the three parties (alongside the Department of Energy) and represents a major milestone for the project. As Jeff Waksman, Principal Deputy Assistant Secretary of the Army for Installations, Energy and Environment, put it, “This is real nuclear microreactor fuel delivered at its final destination, rather than some letter or memorandum promising to make fuel at a later date.”

More details: According to INL—and despite announcements coming out this week—the 40,000 fuel compacts that made up the shipment were delivered on November 5 (a video of the delivery is available here). BWXT manufactured and shipped the fuel from its facilities in Lynchburg, Va., where the company is also constructing the prototype reactor.

BWXT plans to begin formal system testing as early as 2027 and begin producing electricity at INL as soon as 2028.

While the SCO is leading Project Pele, INL director John Wagner highlighted how instrumental the DOE network has been in facilitating this project, saying, “This milestone reflects years of dedicated effort by the Office of Nuclear Energy’s Advanced Gas Reactor TRISO Fuel Qualification Program to fabricate and qualify TRISO fuel using world-class capabilities at INL’s Advanced Test Reactor and Materials and Fuels Complex, and Oak Ridge National Laboratory—capabilities that exist nowhere else in the world.”

Pele background: In May 2019, the Nuclear Regulatory Commission, DOE, and SCO signed a preliminary MOU on microreactor research laying the groundwork for Project Pele. The original goal of the project was to develop a transportable 1–5 MWe advanced microreactor.

In March 2020, the DOD awarded contracts for three projects under Pele: one to BWXT, one to Westinghouse, and one to X-energy. BWXT emerged as the frontrunner in the project with its 1.5-MWe high-temperature, gas-cooled reactor.

In August 2023 (in a webinar organized by the American Nuclear Society), Waksman said that if all went according to plan, the reactor would be turned on at INL before the end of calendar year 2025. The new operational date of 2028 represents a three-year delay in prior plans but still aims to meet a September 30, 2028, deadline set by President Trump’s Executive Order 14299.

In September 2024, the DOD announced that it had broken ground at INL’s Critical Infrastructure Test Range Complex, where the reactor is set to be tested. In July 2025, BWXT announced that it had started fabrication on the Pele reactor core.

Janus tie-in: Project Pele—of course— is not the only nuclear power–related undertaking of the DOD or the broader federal government. Most directly related is the recently announced Janus Program, which seeks to deploy an operational demonstration microreactor power plant on a U.S. military installation by 2030. The DOD has stated that Janus will build on lessons learned from Pele and that the “laboratory teams which partnered on the technical, legal, and policy aspects of Project Pele will also be working closely on the Janus Program.”

Waksman reiterated this most recently in BWXT’s announcement, saying, “the army’s Janus Program will follow on to deliver affordable, reliable, commercial nuclear power to ensure that our critical infrastructure has power, even if the electric grid is disrupted.” In November, nine sites were selected for possible deployment as part of the program.

Don’t forget ANPI: Project Pele and the Janus Program are running parallel to the DOD’s separate Advanced Nuclear Power for Installations (ANPI) program, which was launched in 2024 to deploy microreactor systems at military sites. At the launch of the program, the DOD aimed to have two microreactors operational on military bases by 2030. In April of this year, the DOD announced eight companies that are eligible to receive Other Transaction Awards for the project.

At this year’s annual conference of the Association of the U.S. Army, Waksman reportedly said that Janus will differ from ANPI by virtue of having different technical requirements, reflecting recent changes in the nuclear power market, including new companies that have emerged since last year.

Russian authorities blocked access to Snapchat and imposed restrictions on Apple’s video calling service FaceTime, the latest step in an effort to tighten control over the internet and communications online, according to state-run news agencies and the country’s communications regulator.

State internet regulator Roskomnadzor alleged in a statement that both apps were being “used to organize and conduct terrorist activities on the territory of the country, to recruit perpetrators (and) commit fraud and other crimes against our citizens.” Apple did not respond to an emailed request for comment, nor did Snap Inc.

The Russian regulator said it took action against Snapchat 10 October, even though it only reported the move on Thursday. The moves follow restrictions against Google’s YouTube, Meta’s WhatsApp and Instagram, and the Telegram messaging service, itself founded by a Russian-born man, that came in the wake of Vladimir Putin’s invasion of Ukraine in 2022.

Under Vladimir Putin, authorities have engaged in deliberate and multi-pronged efforts to rein in the internet. They have adopted restrictive laws and banned websites and platforms that don’t comply. Technology also has been perfected to monitor and manipulate online traffic.

Access to YouTube was disrupted last year in what experts called deliberate throttling of the widely popular site by the authorities. The Kremlin blamed YouTube owner Google for not properly maintaining its hardware in Russia.

While it’s still possible to circumvent some of the restrictions by using virtual private network services, those are routinely blocked, too.

Authorities further restricted internet access this summer with widespread shutdowns of cellphone internet connections. Officials have insisted the measure was needed to thwart Ukrainian drone attacks, but experts argued it was another step to tighten internet control. In dozens of regions, “white lists” of government-approved sites and services that are supposed to function despite a shutdown have been introduced.

The government also has acted against popular messaging platforms. Encrypted messenger Signal and another popular app, Viber, were blocked in 2024. This year, authorities banned calls via WhatsApp, the most popular messaging app in Russia, and Telegram, a close second. Roskomnadzor justified the measure by saying the two apps were being used for criminal activities.

At the same time, authorities actively promoted a “national” messenger app called Max, which critics see as a surveillance tool. The platform, touted by developers and officials as a one-stop shop for messaging, online government services, making payments and more, openly declares it will share user data with authorities upon request. Experts also say it does not use end-to-end encryption.

Earlier this week, the government also said it was blocking Roblox, a popular online game platform, saying the step aimed at protecting children from illicit content and “pedophiles who meet minors directly in the game’s chats and then move on to real life.” Roblox in October was the second most popular game platform in Russia, with nearly 8 million monthly users, according to media monitoring group Mediascope.

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Stanislav Seleznev, cyber security expert and lawyer with the Net Freedom rights group, said that Russian law views any platform where users can message each other as “organizers of dissemination of information”.

This label mandates that platforms have an account with Roskomnadzor so that it could communicate its demands, and give Russia’s security service, the FSB, access to accounts of their users for monitoring; those failing to comply are in violation and can get blocked, Seleznev said.

Seleznev estimated that possibly tens of millions of Russians have been using FaceTime, especially after calls were banned on WhatsApp and Telegram. He called the restrictions against the service “predictable” and warned that other sites failing to cooperate with Roskomnadzor “will be blocked – that’s obvious”.